CN112115263A - NLP-based social management big data monitoring and early warning method - Google Patents
NLP-based social management big data monitoring and early warning method Download PDFInfo
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Abstract
The invention discloses a method for monitoring and early warning social treatment big data based on NLP, which classifies events and carries out event early warning on the operation of a social treatment system and comprises the following steps of S1: inputting an event and classifying the input event through an NLP technology to extract an entity keyword of the input event; step S2: performing word segmentation and classification according to the entity keywords of the extracted input event; step S3: and performing word segmentation early warning according to the entity keywords of the extracted input event. The invention discloses a method for monitoring and early warning of social treatment big data based on NLP, which is characterized in that on the basis of NLP word segmentation, a method for classifying event keywords according to the similarity is provided, classification under the condition of differential description of similar events is realized, a statistical technology is adopted, abnormal hot words in a normal state are found out, an early warning function in the field of social treatment is realized, and a basis is provided for a social manager to make a decision and dispose.
Description
Technical Field
The invention belongs to big data event classification and event early warning, and particularly relates to a social management big data monitoring and early warning method based on NLP.
Background
With the advance of smart city construction, the requirement of the social governance field on intellectualization is also improved. The intelligent social management is to use means such as big data analysis, cloud computing and the Internet of things to promote the more precise and accurate social management. The social governance relates to the field of each side of a city, and the gathered data naturally covers each side and is quite huge in quantity. At present, a plurality of intelligent systems provide services in the field of social governance and help city managers to improve the effectiveness of the social governance.
People are the main bodies of urban life, and people-related matters are the most important concerns for social governance. The core business of most social governance systems is the reporting and processing of events occurring in urban society. However, due to the differences of different human leaders in the event reporting process, the social management system has many defects in identifying and extracting effective key events: (1) events of the same kind cannot be classified due to different description habits of different people; (2) the early warning information cannot be generated according to the existing events.
Disclosure of Invention
The invention mainly aims to provide a method for monitoring and early warning of social management big data based on NLP (Natural Language analysis). A method for classifying event keywords according to the similarity is provided on the basis of NLP (Natural Language Processing) word segmentation, classification of similar events under the condition of differential description is realized, a statistical technology is adopted, abnormal hot words in a normal state are found out, an early warning function in the field of social management is realized, and a basis is provided for a social manager to decide disposal.
The invention also aims to provide a social governance big data monitoring and early warning method based on NLP, which can continuously improve the accuracy of analysis and early warning.
In order to achieve the above purpose, the invention provides a method for monitoring and early warning of social treatment big data based on NLP, which classifies events and carries out event early warning on the operation of a social treatment system, and comprises the following steps:
step S1: inputting an event and classifying the input event through an NLP technology to extract an entity keyword of the input event;
step S2: performing word segmentation and classification according to the entity keywords of the extracted input event;
step S3: the word segmentation warning is performed according to the entity keyword of the extracted input event (steps S2 and S3 are two independent steps on the basis of step S1, and not step S3 is performed on the basis of step S2).
As a further preferable embodiment of the above technical means, step S2 is specifically implemented as the following steps:
step S2.1: judging whether the input event has the type according to the entity key words so as to determine the field of the input event;
step S2.2: archiving the input event;
step S2.3: classification is completed.
As a further preferred embodiment of the above technical solution, step S2.1 is specifically implemented as the following steps:
step S2.1.1: when the input event has the type, executing step S2.2;
step S2.1.2: when the input event does not have a type present, the input event is added to the new added category and step S2.2 is performed.
As a further preferable embodiment of the above technical means, step S3 is specifically implemented as the following steps:
step S3.1: judging whether the entity keywords of the input event accord with preset exclusion words or not;
step S3.2: judging whether the entity keywords conforming to the preset excluded words reach effective lengths or not;
step S3.3: carrying out phrase combination on the stored effective word segmentation;
step S3.4: and matching the phrase combinations with the approximation degree through an NLP technology, and obtaining a classification list according to a preset approximation degree rule.
As a further preferred embodiment of the above technical solution, step S3.1 is specifically implemented as the following steps:
step S3.1.1: when the entity key words of the input event accord with the preset exclusion words, executing the step S3.2;
step S3.1.2: and when the entity key words of the input events do not accord with the preset exclusion words, discarding the entity key words.
As a further preferred embodiment of the above technical solution, step S3.2 is specifically implemented as the following steps:
step S3.2.1: when the entity key words which accord with the preset exclusion word reach the effective length, effective word segmentation is formed and stored, and the step S3.3 is executed;
step S3.2.2: and when the entity key words conforming to the preset exclusion words do not reach the effective length, discarding the entity key words.
As a further preferable technical means of the above technical means, after the step S3.4, there are further provided:
step S3.5: counting the number of similar phrases in a period according to the classification list, and judging whether the number of similar phrases in the period exceeds a threshold value;
step S3.6: and if the number of the similar phrases in the period exceeds the threshold value, sending an early warning message, and otherwise, executing the step S1.
As a further preferable technical means of the above technical means, after the step S3.4, there are further provided:
step T3.5: carrying out periodic statistics on the combined phrases according to the classification list to obtain statistical results, and sequencing the statistical results to obtain social event hot words in the current period;
step T3.6: comparing the social event hot words in the current period with the social event hot words in the conventional period to obtain abnormal hot words;
step T3.7: and sending early warning information according to the abnormal hot words, otherwise executing the step S1.
The invention further provides an electronic device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the steps of the method for monitoring and early warning social governance big data based on NLP when executing the program.
The present invention also provides a non-transitory computer readable storage medium storing thereon a computer program which, when executed by a processor, implements the steps of a method for NLP-based social abatement big data monitoring and early warning.
Drawings
Fig. 1 is a time word segmentation classification flow chart of the method for monitoring and early warning of social governance big data based on NLP of the present invention.
Fig. 2 is a flowchart of event segmentation early warning of a first embodiment of the method for monitoring and early warning of social governance big data based on NLP in the present invention.
Detailed Description
The following description is presented to disclose the invention so as to enable any person skilled in the art to practice the invention. The preferred embodiments in the following description are given by way of example only, and other obvious variations will occur to those skilled in the art. The basic principles of the invention, as defined in the following description, may be applied to other embodiments, variations, modifications, equivalents, and other technical solutions without departing from the spirit and scope of the invention.
Referring to fig. 1 of the drawings, fig. 1 is a time segmentation classification flow chart of a method for monitoring and early warning of NLP-based social governance big data according to the present invention, and fig. 2 is an event segmentation early warning flow chart of a first embodiment of the method for monitoring and early warning of NLP-based social governance big data according to the present invention.
In a preferred embodiment of the present invention, those skilled in the art should note that the electronic device, the non-transitory computer-readable storage medium, the NLP technology, and the like, to which the present invention relates may be regarded as prior art.
A first embodiment.
The invention discloses a method for monitoring and early warning social treatment big data based on NLP, which classifies events and performs event early warning on the operation of a social treatment system and comprises the following steps:
step S1: inputting an event and classifying the input event through an NLP technology to extract an entity keyword of the input event;
step S2: performing word segmentation and classification according to the entity keywords of the extracted input event;
step S3: the word segmentation warning is performed according to the entity keyword of the extracted input event (steps S2 and S3 are two independent steps on the basis of step S1, and not step S3 is performed on the basis of step S2).
Specifically, step S2 is implemented as the following steps:
step S2.1: judging whether the input event has the type according to the entity key words so as to determine the field of the input event;
step S2.2: archiving the input event;
step S2.3: classification is completed.
More specifically, step S2.1 is embodied as the following steps:
step S2.1.1: when the input event has the type, executing step S2.2;
step S2.1.2: when the input event does not have a type present, the input event is added to the new added category and step S2.2 is performed.
Further, step S3 is specifically implemented as the following steps:
step S3.1: judging whether the entity keywords of the input event accord with preset exclusion words or not;
step S3.2: judging whether the entity keywords conforming to the preset excluded words reach effective lengths or not;
step S3.3: carrying out phrase combination on the stored effective word segmentation;
step S3.4: and matching the phrase combinations with the approximation degree through an NLP technology, and obtaining a classification list according to a preset approximation degree rule.
Preferably, phrase combination and similarity matching are a recursive process, each existing classification is used for selecting a typical word, new words are sequentially matched with the typical words, classification is carried out according to a set threshold value, the type reselects the typical words, and classification is newly established if no attribution exists.
Further, step S3.1 is embodied as the following steps:
step S3.1.1: when the entity key words of the input event accord with the preset exclusion words, executing the step S3.2;
step S3.1.2: and when the entity key words of the input events do not accord with the preset exclusion words, discarding the entity key words.
Preferably, step S3.2 is embodied as the following steps:
step S3.2.1: when the entity key words which accord with the preset exclusion word reach the effective length, effective word segmentation is formed and stored, and the step S3.3 is executed;
step S3.2.2: and when the entity key words conforming to the preset exclusion words do not reach the effective length, discarding the entity key words.
Preferably, step S3.4 is further followed by:
step S3.5: counting the number of similar phrases in a period according to the classification list, and judging whether the number of similar phrases in the period exceeds a threshold value;
step S3.6: and if the number of the similar phrases in the period exceeds the threshold value, sending an early warning message, and otherwise, executing the step S1.
The invention also discloses an electronic device which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the steps of the social governance big data monitoring and early warning method based on NLP when executing the program.
The invention also discloses a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of a method for social governance big data monitoring and early warning based on NLP.
Preferably, in order to improve the accuracy of word segmentation recognition, the invention also designs training functions, including dictionary training and word segmentation training, and the method comprises the following steps:
(1) and (3) dictionary training: and finding out entity vocabularies which are not accurately analyzed or extracted, classifying and filing the entity vocabularies, editing the entity vocabularies into different dictionary files, and periodically adding the dictionary files into a dictionary library of the NLP.
(2) Word segmentation training: and performing lexical analysis on the events, finding out the events with errors in analysis according to the analysis result, submitting the events to online training after correct modification, adding the events to a model source file, and periodically generating a new model according to the model source file.
Preferably, the machine with training function is limited by existing logic and data, so that the limited dictionary amount requires continuous vocabulary collection and dictionary database feeding and updating in real time. The word segmentation training needs to judge whether the word segmentation and the part-of-speech definition in the result are correct or not on the basis of the segmented words, if the word segmentation and part-of-speech definition are wrong, the word segmentation and part-of-speech definition are corrected, then the model is generated through reverse training, the professional word segmentation model in the social management field is slowly improved, and the accuracy of classification and early warning in the future is continuously improved.
A second embodiment.
The invention discloses a method for monitoring and early warning social treatment big data based on NLP, which classifies events and performs event early warning on the operation of a social treatment system and comprises the following steps:
step S1: inputting an event and classifying the input event through an NLP technology to extract an entity keyword of the input event;
step S2: performing word segmentation and classification according to the entity keywords of the extracted input event;
step S3: the word segmentation warning is performed according to the entity keyword of the extracted input event (steps S2 and S3 are two independent steps on the basis of step S1, and not step S3 is performed on the basis of step S2).
Specifically, step S2 is implemented as the following steps:
step S2.1: judging whether the input event has the type according to the entity key words so as to determine the field of the input event;
step S2.2: archiving the input event;
step S2.3: classification is completed.
More specifically, step S2.1 is embodied as the following steps:
step S2.1.1: when the input event has the type, executing step S2.2;
step S2.1.2: when the input event does not have a type present, the input event is added to the new added category and step S2.2 is performed.
Further, step S3 is specifically implemented as the following steps:
step S3.1: judging whether the entity keywords of the input event accord with preset exclusion words or not;
step S3.2: judging whether the entity keywords conforming to the preset excluded words reach effective lengths or not;
step S3.3: carrying out phrase combination on the stored effective word segmentation;
step S3.4: and matching the phrase combinations with the approximation degree through an NLP technology, and obtaining a classification list according to a preset approximation degree rule.
Preferably, phrase combination and similarity matching are a recursive process, each existing classification is used for selecting a typical word, new words are sequentially matched with the typical words, classification is carried out according to a set threshold value, the type reselects the typical words, and classification is newly established if no attribution exists.
Further, step S3.1 is embodied as the following steps:
step S3.1.1: when the entity key words of the input event accord with the preset exclusion words, executing the step S3.2;
step S3.1.2: and when the entity key words of the input events do not accord with the preset exclusion words, discarding the entity key words.
Preferably, step S3.2 is embodied as the following steps:
step S3.2.1: when the entity key words which accord with the preset exclusion word reach the effective length, effective word segmentation is formed and stored, and the step S3.3 is executed;
step S3.2.2: and when the entity key words conforming to the preset exclusion words do not reach the effective length, discarding the entity key words.
Preferably, step S3.4 is further followed by:
step T3.5: carrying out periodic statistics on the combined phrases according to the classification list to obtain statistical results, and sequencing the statistical results to obtain social event hot words in the current period;
step T3.6: comparing the social event hot words in the current period with the social event hot words in the conventional period to obtain abnormal hot words;
step T3.7: and sending early warning information according to the abnormal hot words, otherwise executing the step S1.
The invention also discloses an electronic device which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the steps of the social governance big data monitoring and early warning method based on NLP when executing the program.
The invention also discloses a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of a method for social governance big data monitoring and early warning based on NLP.
Preferably, in order to improve the accuracy of word segmentation recognition, the invention also designs training functions, including dictionary training and word segmentation training, and the method comprises the following steps:
(1) and (3) dictionary training: and finding out entity vocabularies which are not accurately analyzed or extracted, classifying and filing the entity vocabularies, editing the entity vocabularies into different dictionary files, and periodically adding the dictionary files into a dictionary library of the NLP.
(2) Word segmentation training: and performing lexical analysis on the events, finding out the events with errors in analysis according to the analysis result, submitting the events to online training after correct modification, adding the events to a model source file, and periodically generating a new model according to the model source file.
Preferably, the machine with training function is limited by existing logic and data, so that the limited dictionary amount requires continuous vocabulary collection and dictionary database feeding and updating in real time. The word segmentation training needs to judge whether the word segmentation and the part-of-speech definition in the result are correct or not on the basis of the segmented words, if the word segmentation and part-of-speech definition are wrong, the word segmentation and part-of-speech definition are corrected, then the model is generated through reverse training, the professional word segmentation model in the social management field is slowly improved, and the accuracy of classification and early warning in the future is continuously improved.
It should be noted that the technical features of the electronic device, the non-transitory computer readable storage medium, the NLP technology, etc. related to the present patent application should be regarded as the prior art, and the specific structure, the operation principle, the control mode and the spatial arrangement mode of the technical features may be selected conventionally in the field, and should not be regarded as the invention point of the present patent, and the present patent is not further specifically described in detail.
It will be apparent to those skilled in the art that modifications and equivalents may be made in the embodiments and/or portions thereof without departing from the spirit and scope of the present invention.
Claims (10)
1. A method for monitoring and early warning of social treatment big data based on NLP is used for carrying out event classification and event early warning on the operation of a social treatment system, and is characterized by comprising the following steps:
step S1: inputting an event and classifying the input event through an NLP technology to extract an entity keyword of the input event;
step S2: performing word segmentation and classification according to the entity keywords of the extracted input event;
step S3: and performing word segmentation early warning according to the entity keywords of the extracted input event.
2. The NLP-based social governance big data monitoring and early warning method according to claim 1, wherein the step S2 is implemented as the following steps:
step S2.1: judging whether the input event has the type according to the entity key words so as to determine the field of the input event;
step S2.2: archiving the input event;
step S2.3: classification is completed.
3. The NLP-based social governance big data monitoring and early warning method according to claim 2, wherein the step S2.1 is implemented as the following steps:
step S2.1.1: when the input event has the type, executing step S2.2;
step S2.1.2: when the input event does not have a type present, the input event is added to the new added category and step S2.2 is performed.
4. The NLP-based social governance big data monitoring and early warning method according to claim 3, wherein the step S3 is implemented as the following steps:
step S3.1: judging whether the entity keywords of the input event accord with preset exclusion words or not;
step S3.2: judging whether the entity keywords conforming to the preset excluded words reach effective lengths or not;
step S3.3: carrying out phrase combination on the stored effective word segmentation;
step S3.4: and matching the phrase combinations with the approximation degree through an NLP technology, and obtaining a classification list according to a preset approximation degree rule.
5. The NLP-based social governance big data monitoring and early warning method according to claim 4, wherein step S3.1 is implemented as the following steps:
step S3.1.1: when the entity key words of the input event accord with the preset exclusion words, executing the step S3.2;
step S3.1.2: and when the entity key words of the input events do not accord with the preset exclusion words, discarding the entity key words.
6. The NLP-based social governance big data monitoring and early warning method according to claim 5, wherein step S3.2 is implemented as the following steps:
step S3.2.1: when the entity key words which accord with the preset exclusion word reach the effective length, effective word segmentation is formed and stored, and the step S3.3 is executed;
step S3.2.2: and when the entity key words conforming to the preset exclusion words do not reach the effective length, discarding the entity key words.
7. The NLP-based social governance big data monitoring and early warning method according to claim 6, further comprising the following steps after step S3.4:
step S3.5: counting the number of similar phrases in a period according to the classification list, and judging whether the number of similar phrases in the period exceeds a threshold value;
step S3.6: and if the number of the similar phrases in the period exceeds the threshold value, sending an early warning message, and otherwise, executing the step S1.
8. The NLP-based social governance big data monitoring and early warning method according to claim 6, further comprising the following steps after step S3.4:
step T3.5: carrying out periodic statistics on the combined phrases according to the classification list to obtain statistical results, and sequencing the statistical results to obtain social event hot words in the current period;
step T3.6: comparing the social event hot words in the current period with the social event hot words in the conventional period to obtain abnormal hot words;
step T3.7: and sending early warning information according to the abnormal hot words, otherwise executing the step S1.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method for NLP-based big data monitoring and forewarning according to any one of claims 1 to 8.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of a method of NLP based social abatement big data monitoring pre-warning as claimed in any one of claims 1 to 8.
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