CN113392185B - Public opinion early warning method, device, equipment and storage medium - Google Patents
Public opinion early warning method, device, equipment and storage medium Download PDFInfo
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Abstract
The application provides a public opinion early warning method, a public opinion early warning device, public opinion early warning equipment and a storage medium, wherein the method acquires telephone traffic data, performs data preprocessing on the telephone traffic data, and acquires effective telephone traffic data; according to a preset intention recognition model, carrying out intention recognition on the effective telephone traffic data to obtain intention telephone traffic data; normalizing the intended traffic data according to a preset predicted traffic regression model to obtain first normalized intended data, wherein the preset predicted traffic regression model is obtained by training historical traffic data and historical public opinion data; if the first normalized intention data is larger than the preset intention threshold, determining that the public opinion is abnormal, generating public opinion early warning information, and improving the accuracy of public opinion early warning.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a public opinion warning method, device, equipment and storage medium.
Background
With the rapid development of the internet and informatization technology, especially new media such as network platforms, network media and the like, the novel media have great influence on politics, economy, culture and social life due to the characteristics of rich forms, high coverage rate and the like, and the consequent network public opinion problem also becomes an important factor affecting social stability, so that the public opinion needs to be monitored, analyzed and early warned.
Currently, keywords are usually captured through natural language processing technology, and if the keyword is too frequent, it is determined that an emergency occurs, and public opinion warning is needed.
However, in the prior art, the condition of misjudgment of public opinion is caused in a special period, an accurate public opinion early warning result cannot be obtained, and the accuracy of public opinion early warning is poor.
Disclosure of Invention
The application provides a public opinion early warning method, device, equipment and storage medium, thereby solving the technical problems that the prior art can cause the condition of public opinion misjudgment in a special period, an accurate public opinion early warning result can not be obtained and the accuracy of public opinion early warning is poor.
In a first aspect, an embodiment of the present application provides a public opinion early warning method, including:
acquiring telephone traffic data, and performing data preprocessing on the telephone traffic data to obtain effective telephone traffic data;
according to a preset intention recognition model, carrying out intention recognition on the effective telephone traffic data to obtain intention telephone traffic data;
normalizing the intended traffic data according to a preset predicted traffic regression model to obtain first normalized intended data, wherein the preset predicted traffic regression model is obtained by training historical traffic data and historical public opinion data;
if the first normalized intention data is larger than a preset intention threshold, determining that the public opinion is abnormal, and generating public opinion early warning information.
In the embodiment of the application, firstly, data preprocessing is performed on telephone traffic data, effective telephone traffic data is screened, only the effective data is reserved for facilitating subsequent analysis, in order to reduce the maintenance cost of a keyword library, the word frequency is not used as a characteristic, the intention recognition can be performed on the effective telephone traffic data through a preset intention recognition model, the intention telephone traffic data is obtained, and then the normalization processing is performed on the intention telephone traffic data through a preset predicted telephone traffic regression model.
Optionally, before the intention recognition is performed on the effective traffic data according to the preset intention recognition model to obtain the intention traffic data, the method further includes:
establishing an intention labeling training database;
and training the intention labeling training data in the intention labeling training database by using a natural language processing model to obtain a preset intention recognition model.
Here, the embodiment of the application does not use word frequency as a feature to perform intention recognition, so that the cost of keyword library maintenance is reduced, and a preset intention recognition model can be built before intention recognition by building intention labeling training data pre-labeled in an intention labeling training database, so that intention recognition is performed according to the model, the cost of public opinion analysis and early warning is reduced, and the efficiency is improved.
Optionally, before the normalizing the intended traffic data according to the preset predicted traffic regression model to obtain the first normalized intended data, the method further includes:
acquiring historical telephone traffic data and historical public opinion data;
and establishing a preset prediction telephone traffic regression model according to the historical telephone traffic data and the historical public opinion data.
Here, in order to normalize the intended traffic data, the embodiment of the application pre-establishes a preset predicted traffic regression model, and the establishment of the model combines the historical traffic data and the historical public opinion data, so that the traffic increased by the emergency can be effectively removed, thereby obtaining an accurate traffic regression model matched with the actual traffic, and further improving the accuracy of public opinion early warning.
Optionally, before determining that the public opinion is abnormal if the normalized intention data is greater than the preset intention threshold and generating public opinion warning information, the method further includes:
acquiring newly added service traffic data;
establishing a new business regression model according to the new business traffic data;
performing second normalization processing on the first normalization intention data according to the newly added business regression model to obtain second normalization intention data;
correspondingly, if the first normalized intention data is greater than a preset intention threshold, determining that the public opinion is abnormal, and generating public opinion warning information includes:
if the second normalized intention data is larger than a preset intention threshold, determining that the public opinion is abnormal, and generating public opinion early warning information.
Here, the embodiment of the application can also establish a newly added service input system, embed the newly added service input system in the public opinion early warning process, record the time point of the newly added service, namely, the newly added service traffic data, and the data can be used for establishing a newly added service regression model, thereby realizing the secondary normalization of the first normalization intention data, eliminating the influence of public opinion analysis caused by the newly added service data, and further improving the accuracy of public opinion early warning.
Optionally, the data preprocessing of the traffic data includes:
and classifying and screening invalid data for the telephone traffic data through a natural language processing technology.
Optionally, after the generating of the public opinion warning information, the method further includes:
and outputting a public opinion report.
Here, after determining that there is an abnormal public opinion, the embodiment of the present application may output a public opinion report, so as to facilitate grasping public opinion information, and perform service adjustment and traffic prediction according to the public opinion information.
In a second aspect, an embodiment of the present application provides a public opinion warning device, including:
the first acquisition module is used for acquiring telephone traffic data, and carrying out data preprocessing on the telephone traffic data to obtain effective telephone traffic data;
the first processing module is used for carrying out intention recognition on the effective telephone traffic data according to a preset intention recognition model to obtain intention telephone traffic data;
the second processing module is used for carrying out normalization processing on the intention traffic data according to a preset prediction traffic regression model to obtain first normalized intention data, wherein the preset prediction traffic regression model is obtained by training historical traffic data and historical public opinion data; the method comprises the steps of carrying out a first treatment on the surface of the
And the early warning module is used for determining that the public opinion is abnormal and generating public opinion early warning information if the first normalized intention data is larger than a preset intention threshold value.
Optionally, before the first processing module performs intention recognition on the effective traffic data according to a preset intention recognition model to obtain intention traffic data, the apparatus further includes:
the first establishing module is used for establishing an intention labeling training database;
the training module is used for carrying out natural language processing model training on the intention annotation training data in the intention annotation training database to obtain a preset intention recognition model.
Optionally, before the second processing module normalizes the intended traffic data according to a preset predicted traffic regression model to obtain first normalized intended data, the apparatus further includes:
the second acquisition module is used for acquiring historical telephone traffic data and historical public opinion data;
and the second building module is used for building a preset prediction telephone traffic regression model according to the historical telephone traffic data and the historical public opinion data.
Optionally, before the early warning module determines that the public opinion is abnormal if the normalized intent data is greater than a preset intent threshold, and generates public opinion early warning information, the apparatus further includes:
the third acquisition module is used for acquiring the traffic data of the newly added service;
the third building module is used for building a new business regression model according to the new business traffic data;
the third processing module is used for carrying out second normalization processing on the first normalization intention data according to the newly added business regression model to obtain second normalization intention data;
correspondingly, the early warning module is specifically configured to:
if the second normalized intention data is larger than a preset intention threshold, determining that the public opinion is abnormal, and generating public opinion early warning information.
Optionally, the first obtaining module is specifically configured to:
and classifying and screening invalid data for the telephone traffic data through a natural language processing technology.
Optionally, after the early warning module generates the public opinion early warning information, the method further includes:
and the generating module is used for generating public opinion report forms.
In a third aspect, an embodiment of the present application provides a public opinion warning device, including: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executes computer-executable instructions stored in the memory, such that the at least one processor performs the public opinion warning method as described above in the first aspect and the various possible designs of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer readable storage medium, where computer executable instructions are stored, and when executed by a processor, implement the public opinion warning method according to the first aspect and the various possible designs of the first aspect.
In a fifth aspect, embodiments of the present invention provide a computer program product, comprising a computer program, which when executed by a processor, implements the public opinion warning method according to the first aspect and the various possible designs of the first aspect.
According to the public opinion warning method, device and equipment and storage medium, data preprocessing is performed on telephone traffic data, effective telephone traffic data are screened out, only effective data are reserved for facilitating subsequent analysis, in order to reduce maintenance cost of a keyword bank, word frequency is not used as a characteristic, intention recognition can be performed on the effective telephone traffic data through a preset intention recognition model, the intention telephone traffic data are obtained, normalization processing is performed on the intention telephone traffic data through a preset prediction telephone traffic regression model, and because the preset prediction telephone traffic regression model is obtained according to historical telephone traffic data and historical public opinion data training, influence caused by phenomena of rapid increase of telephone traffic in a special period is eliminated, influence of telephone traffic increased by an emergency on public opinion data analysis is eliminated, and accuracy of public opinion warning is improved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive faculty for a person skilled in the art.
Fig. 1 is a schematic diagram of a public opinion warning system architecture according to an embodiment of the present application;
fig. 2 is a flow chart of a public opinion warning method according to an embodiment of the present application;
fig. 3 is an actual traffic data graph of a preset predicted traffic regression model according to an embodiment of the present application;
FIG. 4 is a graph of data for traffic intended for use in accordance with an embodiment of the present application;
FIG. 5 is a graph of first normalized intent data after normalization processing;
fig. 6 is a flow chart of another public opinion early warning method according to an embodiment of the present application;
FIG. 7 is a graph of 5 month traffic provided in an embodiment of the present application;
FIG. 8 is a graph of 6 month traffic provided in an embodiment of the present application;
FIG. 9 is a graph of 6 months traffic after correction provided in an embodiment of the present application;
FIG. 10 is a graph of traffic volume without additional traffic provided in an embodiment of the present application;
fig. 11 is a graph of traffic after an addition according to an embodiment of the present application;
fig. 12 is a schematic structural diagram of a public opinion warning device according to an embodiment of the present application;
fig. 13 is a schematic structural diagram of a public opinion warning device according to an embodiment of the present application.
Specific embodiments of the present disclosure have been shown by way of the above drawings and will be described in more detail below. These drawings and the written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the disclosed concepts to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.
The terms "first," "second," "third," and "fourth" and the like in the description and in the claims of this application and in the above-described figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The public opinion early warning in the communication industry is mainly used for monitoring emergency events such as network abnormality, system abnormality, group complaint events and the like, and adopts a common method, wherein the recall rate of the emergency event early warning can reach 99 percent, but the accuracy rate is less than 10 percent, and the main reason is that the telephone operation content in the communication industry is related to time factors, such as the general account period at the end of a month, the telephone fee problem can be increased sharply, the flow using problem can be increased sharply in the month, and the problems all belong to normal phenomena.
The prior art has the technical problems that the public opinion misjudgment can be caused in a special period, an accurate public opinion early warning result can not be obtained, and the accuracy of public opinion early warning is poor.
In order to solve the above problems, embodiments of the present application provide a public opinion warning method, apparatus, device and storage medium, where the method can identify intent by using natural language technology, count intent frequency, and construct regression model by using big data, so as to eliminate the influence of phenomena of intent rapid increase caused by different periods in industry.
Optionally, fig. 1 is a schematic diagram of a public opinion warning system architecture according to an embodiment of the present application. In fig. 1, the above architecture includes an operator server 101 and an information supervision server 102.
The information supervision server 102 may collect traffic data through the operator server 101, and perform public opinion warning according to the traffic data.
It can be understood that the structure illustrated in the embodiments of the present application does not constitute a specific limitation on the architecture of the public opinion early warning system. In other possible embodiments of the present application, the architecture may include more or fewer components than those illustrated, or some components may be combined, some components may be separated, or different component arrangements may be specifically determined according to the actual application scenario, and the present application is not limited herein. The components shown in fig. 1 may be implemented in hardware, software, or a combination of software and hardware.
It should be understood that the above-described processor may be implemented by a processor that reads instructions in a memory and executes the instructions, or may be implemented by a chip circuit.
In addition, the network architecture and the service scenario described in the embodiments of the present application are for more clearly describing the technical solution of the embodiments of the present application, and do not constitute a limitation on the technical solution provided in the embodiments of the present application, and as a person of ordinary skill in the art can know, with evolution of the network architecture and appearance of a new service scenario, the technical solution provided in the embodiments of the present application is also applicable to similar technical problems.
The following describes the technical scheme of the present application in detail with reference to specific embodiments:
optionally, fig. 2 is a schematic flow chart of a public opinion warning method provided in the embodiment of the present application. The execution subject of the embodiment of the present application may be the information supervision server 101 in fig. 1, and the specific execution subject may be determined according to an actual application scenario. As shown in fig. 2, the method comprises the steps of:
s201: and obtaining traffic data, and performing data preprocessing on the traffic data to obtain effective traffic data.
Optionally, performing data preprocessing on the traffic data includes:
and classifying and screening invalid data on the traffic data through natural language processing technology.
Optionally, preprocessing of traffic data may be implemented by one or more of crawler technology, chinese word segmentation technology, and natural language processing technology.
S202: and carrying out intention recognition on the effective telephone traffic data according to a preset intention recognition model to obtain the intention telephone traffic data.
Optionally, before performing intention recognition on the effective traffic data according to the preset intention recognition model to obtain the intention traffic data, the method further includes:
establishing an intention labeling training database; and training the intention labeling training data in the intention labeling training database by using a natural language processing model to obtain a preset intention recognition model.
The method can be used for intention recognition of the effective data after natural language processing (Natural Language Processing, NLP) model training by manually marking a large amount of content-intention training data.
Here, the embodiment of the application does not use word frequency as a feature to perform intention recognition, so that the cost of keyword library maintenance is reduced, and a preset intention recognition model can be built before intention recognition by building intention labeling training data pre-labeled in an intention labeling training database, so that intention recognition is performed according to the model, the cost of public opinion analysis and early warning is reduced, and the efficiency is improved.
Alternatively, the intention recognition may be performed based on word frequency.
S203: and carrying out normalization processing on the intended traffic data according to a preset predicted traffic regression model to obtain first normalized intended data.
The preset predictive telephone traffic regression model is obtained through training according to historical telephone traffic data and historical public opinion data.
Exemplary, fig. 3 is an actual traffic data graph of a preset predicted traffic regression model provided by an embodiment of the present application, fig. 4 is an intended traffic data graph provided by an embodiment of the present application, and fig. 5 is a normalized first normalized intended data graph, where public opinion warning may be performed according to fig. 5.
S204: if the first normalized intention data is larger than the preset intention threshold, determining that the public opinion is abnormal, and generating public opinion early warning information.
It will be appreciated that the preset intent threshold value herein may be determined according to practical situations, and the comparison of the embodiments of the present application is not particularly limited.
Optionally, after generating the public opinion warning information, the method further includes:
and outputting a public opinion report.
Here, after determining that there is an abnormal public opinion, the embodiment of the present application may output a public opinion report, so as to facilitate grasping public opinion information, and perform service adjustment and traffic prediction according to the public opinion information.
According to the method, firstly, data preprocessing is carried out on telephone traffic data, effective telephone traffic data are screened out, only the effective data are reserved, subsequent analysis is convenient, in order to reduce maintenance cost of a keyword library, word frequency is not used as a characteristic, intention recognition can be carried out on the effective telephone traffic data through a preset intention recognition model, the intention telephone traffic data are obtained, normalization processing is carried out on the intention telephone traffic data through a preset predicted telephone traffic regression model, and because the preset predicted telephone traffic regression model is obtained according to historical telephone traffic data and historical public opinion data training, influences caused by the phenomenon that telephone traffic increased sharply in a special period are eliminated, influences on public opinion data analysis caused by the fact that telephone traffic increased in an emergency is removed are eliminated, and accuracy of public opinion early warning is improved.
In an optional implementation manner, the embodiment of the present application may further pre-establish a preset prediction traffic regression model and a new service regression model, and perform twice normalization processing on the intended traffic data through the above models, so as to effectively eliminate the influence of special time and the new service on public opinion early warning, and correspondingly, fig. 6 is a schematic flow diagram of another public opinion early warning method provided by the embodiment of the present application, as shown in fig. 6, where the method includes:
s601: and obtaining traffic data, and performing data preprocessing on the traffic data to obtain effective traffic data.
S602: and carrying out intention recognition on the effective telephone traffic data according to a preset intention recognition model to obtain the intention telephone traffic data.
The implementation of steps S601-S602 is similar to that of steps S201-S202, and will not be described here.
S603: and acquiring historical telephone traffic data and historical public opinion data.
S604: and establishing a preset predictive telephone traffic regression model according to the historical telephone traffic data and the historical public opinion data.
The preset predictive traffic regression model needs to remove the traffic increased by the emergency, and for example, if network failure occurs in a place for 6 months and 5 days, the regression model cannot be built by adopting the data of 6 months and 5 days, and the normal data of 5 months and 5 or 7 months and 5 needs to be replaced by the data of 6 months and 5 days.
Exemplary, fig. 7 is a 5 month traffic graph provided by the embodiment of the present application, fig. 8 is a 6 month traffic graph provided by the embodiment of the present application, in which, when the traffic is abnormal in 6 months and 5 days, data replacement is performed according to normal data of 5 months and 5 or 7 months and 5, so as to obtain a corrected curve, and fig. 9 is a corrected 6 month traffic graph provided by the embodiment of the present application.
In order to normalize the intended telephone traffic data, the embodiment of the application establishes a preset predicted telephone traffic regression model in advance, combines the historical telephone traffic data and the historical public opinion data, and can effectively remove the telephone traffic increased by the emergency, thereby obtaining an accurate telephone traffic regression model matched with the actual telephone traffic, and further improving the accuracy of public opinion early warning.
S605: and carrying out normalization processing on the intended traffic data according to a preset predicted traffic regression model to obtain first normalized intended data.
S606: and acquiring the data of the newly added service traffic.
S607: and establishing a new business regression model according to the new business traffic data.
Exemplary, fig. 10 is a graph of traffic without new addition provided in the embodiment of the present application, and fig. 11 is a graph of traffic after new addition provided in the embodiment of the present application.
S608: and carrying out second normalization processing on the first normalization intention data according to the newly added business regression model to obtain second normalization intention data.
S609: if the second normalized intention data is larger than the preset intention threshold, determining that the public opinion is abnormal, and generating public opinion early warning information.
The embodiment of the application can also establish a new service input system, embed the new service input system in the public opinion early warning process, record the time point of the new service, namely the new service telephone traffic data, and the data can be used for establishing a new service regression model, thereby realizing the secondary normalization of the first normalization intention data, eliminating the influence of public opinion analysis caused by the new service data and further improving the accuracy of public opinion early warning.
Fig. 12 is a schematic structural diagram of a public opinion warning device provided in an embodiment of the present application, and as shown in fig. 12, the device in the embodiment of the present application includes: a first acquisition module 1201, a first processing module 1202, a second processing module 1203, and an early warning module 1204. The public opinion warning device may be the information monitoring server 102 itself, or a chip or an integrated circuit for realizing the functions of the information monitoring server 102. Here, the division of the first acquisition module 1201, the first processing module 1202, the second processing module 1203, and the early warning module 1204 is just a division of a logic function, and both may be integrated or independent physically.
The first acquisition module is used for acquiring telephone traffic data, and carrying out data preprocessing on the telephone traffic data to obtain effective telephone traffic data;
the first processing module is used for carrying out intention recognition on the effective telephone traffic data according to a preset intention recognition model to obtain intention telephone traffic data;
the second processing module is used for carrying out normalization processing on the intention traffic data according to a preset prediction traffic regression model to obtain first normalized intention data, wherein the preset prediction traffic regression model is obtained by training historical traffic data and historical public opinion data; the method comprises the steps of carrying out a first treatment on the surface of the
And the early warning module is used for determining that the public opinion is abnormal and generating public opinion early warning information if the first normalized intention data is larger than a preset intention threshold value.
Optionally, before the first processing module performs intention recognition on the effective traffic data according to the preset intention recognition model to obtain the intention traffic data, the apparatus further includes:
the first establishing module is used for establishing an intention labeling training database;
the training module is used for carrying out natural language processing model training on the intention labeling training data in the intention labeling training database to obtain a preset intention recognition model.
Optionally, before the second processing module normalizes the intended traffic data according to the preset predicted traffic regression model to obtain the first normalized intended data, the apparatus further includes:
the second acquisition module is used for acquiring historical telephone traffic data and historical public opinion data;
and the second building module is used for building a preset predictive telephone traffic regression model according to the historical telephone traffic data and the historical public opinion data.
Optionally, before the early warning module determines that the public opinion is abnormal if the normalized intention data is greater than the preset intention threshold value and generates public opinion early warning information, the apparatus further includes:
the third acquisition module is used for acquiring the traffic data of the newly added service;
the third building module is used for building a new business regression model according to the new business traffic data;
the third processing module is used for carrying out second normalization processing on the first normalization intention data according to the newly added business regression model to obtain second normalization intention data;
correspondingly, the early warning module is specifically used for:
if the second normalized intention data is larger than the preset intention threshold, determining that the public opinion is abnormal, and generating public opinion early warning information.
Optionally, the first obtaining module is specifically configured to:
and classifying and screening invalid data on the traffic data through natural language processing technology.
Optionally, after the early warning module generates the public opinion early warning information, the method further includes:
and the generating module is used for generating public opinion report forms.
Fig. 13 is a schematic structural diagram of a public opinion warning device according to an embodiment of the present application, where the public opinion warning device may be the information monitoring server 102. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not limiting of the implementations of the application described and/or claimed herein.
As shown in fig. 13, the public opinion warning apparatus includes: processor 1301 and memory 1302, the various components are interconnected using different buses, and may be mounted on a common motherboard or in other manners as appropriate. The processor 1301 may process instructions executed within the public opinion alert device, including instructions stored in or on memory to display graphical information on an external input/output device, such as a display device coupled to an interface. In other embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. One processor 1301 is illustrated in fig. 13.
The memory 1302 is used as a non-transitory computer readable storage medium, and is used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules (e.g., the first acquisition module 1201, the first processing module 1202, the second processing module 1203, and the early warning module 1204 shown in fig. 12) corresponding to the method of public opinion warning device in the embodiments of the present application. The processor 1301 executes various functional applications of the authentication platform and data processing, that is, a method of implementing the public opinion warning device in the above-described method embodiment, by running non-transitory software programs, instructions, and modules stored in the memory 1302.
The public opinion warning device may further include: an input device 1303 and an output device 1304. The processor 1301, memory 1302, input device 1303, and output device 1304 may be connected by a bus or other means, for example in fig. 13.
The input device 1303 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the public opinion warning device, such as a touch screen, a keypad, a mouse, or a plurality of mouse buttons, a trackball, a joystick, etc. The output device 1304 may be an output device such as a display device of a public opinion warning device. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device may be a touch screen.
The public opinion warning device of the embodiment of the present application may be used to execute the technical schemes of the embodiments of the methods of the present application, and its implementation principle and technical effects are similar, and are not repeated here.
The embodiment of the application also provides a computer readable storage medium, wherein computer execution instructions are stored in the computer readable storage medium, and the computer execution instructions are used for realizing the public opinion warning method according to any one of the above when being executed by a processor.
The embodiment of the application also provides a computer program product, which comprises a computer program, wherein the computer program is used for realizing the public opinion early warning method according to any one of the above when being executed by a processor.
In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (8)
1. The public opinion early warning method is characterized by comprising the following steps of:
acquiring telephone traffic data, and performing data preprocessing on the telephone traffic data to obtain effective telephone traffic data;
according to a preset intention recognition model, carrying out intention recognition on the effective telephone traffic data to obtain intention telephone traffic data;
normalizing the intended traffic data according to a preset predicted traffic regression model to obtain first normalized intended data, wherein the preset predicted traffic regression model is obtained by training historical traffic data and historical public opinion data;
acquiring newly added service traffic data;
establishing a new business regression model according to the new business traffic data;
performing second normalization processing on the first normalization intention data according to the newly added business regression model to obtain second normalization intention data;
if the second normalized intention data is larger than a preset intention threshold, determining that the public opinion is abnormal, and generating public opinion early warning information.
2. The method of claim 1, further comprising, prior to said intention recognition of said valid traffic data according to a preset intention recognition model, obtaining intention traffic data:
establishing an intention labeling training database;
and training the intention labeling training data in the intention labeling training database by using a natural language processing model to obtain a preset intention recognition model.
3. The method of claim 1, further comprising, prior to normalizing the intended traffic data according to the preset predicted traffic regression model to obtain first normalized intended data:
acquiring historical telephone traffic data and historical public opinion data;
and establishing a preset predictive telephone traffic regression model according to the historical telephone traffic data and the historical public opinion data.
4. A method according to any one of claims 1 to 3, wherein said data preprocessing said traffic data comprises:
and classifying and screening invalid data for the telephone traffic data through a natural language processing technology.
5. A method according to any one of claims 1 to 3, further comprising, after said generating public opinion warning information:
and generating a public opinion report.
6. The utility model provides a public opinion early warning device which characterized in that includes:
the first acquisition module is used for acquiring telephone traffic data, and carrying out data preprocessing on the telephone traffic data to obtain effective telephone traffic data;
the first processing module is used for carrying out intention recognition on the effective telephone traffic data according to a preset intention recognition model to obtain intention telephone traffic data;
the second processing module is used for carrying out normalization processing on the intention traffic data according to a preset prediction traffic regression model to obtain first normalized intention data, wherein the preset prediction traffic regression model is obtained by training historical traffic data and historical public opinion data;
the third acquisition module is used for acquiring the traffic data of the newly added service;
the third building module is used for building a new business regression model according to the new business traffic data;
the third processing module is used for carrying out second normalization processing on the first normalization intention data according to the newly added business regression model to obtain second normalization intention data;
and the early warning module is used for determining that the public opinion is abnormal and generating public opinion early warning information if the second normalized intention data is larger than a preset intention threshold value.
7. Public opinion early warning equipment, characterized by, include:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the public opinion warning method of any of claims 1-5.
8. A computer readable storage medium having stored therein computer executable instructions for implementing the public opinion warning method of any one of claims 1 to 5 when executed by a processor.
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