CN114328907A - Natural language processing method for early warning risk upgrade event - Google Patents
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Abstract
The invention provides a natural language processing method for early warning of risk upgrade events, which comprises the following steps: s1, establishing a data warehouse, and accessing a data source to the data warehouse; s2, based on natural language processing, extracting and processing event information in the data source through an event analysis related model; and S3, inputting the result value of the event analysis related model into the early warning analysis model, and carrying out risk value analysis and early warning of risk upgrading event upgrading. According to the method, various elements in the event are scientifically and efficiently analyzed through a natural language processing technology, a risk analysis model is obtained through massive event data training, the risk of converting a certain event into a high-risk case can be accurately obtained, and related workers can intervene in the event in advance, resolve contradiction disputes and prevent the high-risk case from occurring.
Description
Technical Field
The invention belongs to the technical field of early warning risk upgrading events, and particularly relates to a natural language processing method for early warning risk upgrading events.
Background
Currently, with the development of social economy, civil cases caused by family marital disputes, neighborhood disputes, economic compensation disputes, labor and resources disputes, life trivia disputes and the like in daily work and life are increasingly frequent. If not correctly guided, along with the accumulation of contradictions, high-risk cases are often caused, and huge losses are brought to the lives and properties of people. In the past, no intelligent and efficient method is available for analyzing the cases of disputes and civil affairs to obtain the risk of upgrading the events into high-risk cases.
The natural language processing integrates linguistics, computer science and mathematics, and can perform entity extraction, automatic summarization, text classification and the like on texts. In a civil event, the event can be classified through natural language processing, factors such as related personnel, personnel property loss, personnel emotion and the like of the event are extracted, whether the event is further upgraded can be scientifically and accurately analyzed, and meanwhile, an algorithm model is perfected by tracking the whole life cycle of the event. In the prior art, a method for analyzing disputes and civil cases and obtaining the risk of upgrading events to high-risk cases by a natural language processing method does not exist.
Disclosure of Invention
The invention aims to solve the problems, provides a natural language processing method for early warning of risk upgrading events, can accurately obtain the risk of converting a certain event into a high-risk case, is convenient for resolving contradiction disputes, and prevents the occurrence of the high-risk case.
In order to achieve the purpose, the invention adopts the following technical scheme:
the natural language processing method for early warning of risk escalation events comprises the following steps:
s1, establishing a data warehouse, and accessing a data source to the data warehouse;
s2, based on natural language processing, extracting and processing event information in the data source through an event analysis related model;
and S3, inputting the result value of the event analysis related model into the early warning analysis model, and carrying out risk value analysis and early warning of risk upgrading event upgrading. According to the invention, various elements in the event are scientifically and efficiently analyzed by the natural language processing method, and a risk analysis model obtained by mass event data training is combined, so that the risk upgrading event can be early warned, and related personnel can intervene the event in advance, resolve contradiction disputes and prevent and reduce high-risk cases.
Further, S1 specifically includes:
s101, accessing a multi-channel data source;
and S102, cleaning the data according to the uniform format to generate uniform structured data, and writing the uniform structured data into a database. According to the method, event data information is acquired from each channel, a risk analysis model can be obtained by combining mass event data training, and the generated structured data is stored in the same database or is distributed in a plurality of databases.
Further, the data source includes event data in each field, and specifically includes: the system comprises citizen hot lines, hot spot events, non-police affair warning conditions, various help hot lines, citizen regulation data and contradiction regulation center data. The data resource library established by the invention is accessed to the event data of each field, and provides data basis and basis for subsequent event classification and analysis.
Further, the event analysis related model comprises an event classification model capable of dynamically configuring event types according to business needs, an entity extraction model for extracting event places, organizations and personnel, a property loss model for calculating property loss, a casualty model for calculating personnel and a civil emotion model for calculating the emotional intensity of the civil.
Further, S2 includes:
s201, classifying events according to event information, and dividing the events into a plurality of major classes through an event classification model, wherein a plurality of minor classes are arranged under each major class;
s202, sending the classified results into an entity extraction model for entity extraction, and extracting the characteristics of texts and categories which are in accordance with the social comprehensive treatment;
s203, performing preset keyword collision according to different entity extraction result contents, calculating a score through a property loss model and a casualty model, enabling a full text to enter a people emotion model, and obtaining the people emotion violence degree of a risk upgrading event through analysis and calculation;
and transmitting corresponding model calculation results downwards among the models in an interface mode.
Further, event classification classifies events by a hybrid algorithm combining a K-means algorithm and a bayesian network: training sample clustering through a K-means algorithm, providing an artificial intervention interface for artificial dynamic intervention to configure a training result, and classifying events by using a Bayesian network according to the training result. The invention can intervene the training result dynamically.
Further, prior to step S202, corpus feeding is performed on the entity extraction model in advance: and (3) arranging and summarizing a large number of events related to the social comprehensive treatment, then training, marking and naming each entity in the training process, and correcting according to the obtained result to obtain an entity extraction model capable of extracting the characteristics of texts and categories which are in accordance with the social comprehensive treatment.
Further, risk value calculation is carried out on the risk value analysis of the risk upgrading event according to a risk value algorithm, and the citizen turning risk value is calculated by combining the event extraction result.
Further, step S3 is followed by adjusting the weight of each element to the risk value through the historical data to optimize the risk value algorithm.
Further, the risk value early warning of the risk upgrade event provides early warning of the risk value of the event upgrade by establishing a risk upgrade event early warning trigger mechanism: and setting a corresponding early warning risk value threshold value for the event, and sending early warning information to related personnel when the risk upgrading event upgrading risk value exceeds the risk threshold value. According to the invention, when the risk value of the risk upgrade event exceeds the risk threshold value, related personnel are notified to process the risk value in a convenient and fast manner.
Compared with the prior art, the invention has the advantages that:
the natural language processing method for the early warning risk upgrade event, disclosed by the invention, scientifically and efficiently analyzes various elements in the event through a natural language processing technology, and can accurately obtain the risk of converting a certain event into a high-risk case by combining a risk analysis model obtained by mass event data training, so that related workers can intervene in the event in advance, resolve contradiction disputes and prevent the high-risk case.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
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FIG. 1 is a flow chart of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, the natural language processing method for warning a risk upgrade event according to the present embodiment, which warns a risk upgrade event through the natural language processing method, includes the following steps:
s1, establishing a data warehouse, and accessing a data source to the data warehouse;
s2, based on natural language processing, extracting and processing event information in the data source through an event analysis related model;
and S3, inputting the result value of the event analysis related model algorithm into the early warning analysis model, and carrying out risk value analysis and early warning of risk upgrading event upgrading. The data source in this embodiment includes event data. According to the method, various elements in the event are scientifically and efficiently analyzed through a natural language processing method, and a risk analysis model obtained through mass event data training is combined, so that the risk upgrading event can be used for early warning, relevant personnel can intervene in the event in advance according to the risk upgrading event, conflict disputes are resolved, and high-risk cases are prevented from happening.
This embodiment S1 specifically includes:
s101, accessing a multi-channel data source: in the embodiment, access event information is acquired through sources such as a large data center in the whole city, each operator, a hot line data center, each basic management platform and the like;
and S102, cleaning the data according to the uniform format to generate uniform structured data, and writing the uniform structured data into a plurality of databases.
The data repository, namely the data warehouse, established in the embodiment can be accessed to event data of each field, and the event data of each field comprises event data obtained from citizen hotlines, hot spot events, non-police affairs, various help hotlines, people mediation data, spear mediation center and the like of the whole city. The data resource library established in the embodiment is accessed to event data in each field, and can provide data basis and basis for subsequent classification and analysis.
In this embodiment, an event analysis correlation model is established based on natural language processing, and the event analysis correlation model of this embodiment includes: 1. an event classification model, namely, according to business needs, the event type can be dynamically configured; 2. an entity extraction model, which extracts event locations, organizations, personnel and the like; 3. a property loss model; 4. a casualty model; 5. the people emotion model obtains the emotional bias and the violence degree of characters or voice according to an algorithm. And inputting the result values of the 5 model algorithms into an early warning analysis model.
The present embodiment S2 includes:
s201, event classification is carried out according to event information, events are divided into a plurality of major classes through an event classification model, a plurality of minor classes are arranged under each major class, and the events are classified according to a mixed algorithm combining a K-means algorithm and a Bayesian network in the embodiment: training sample clustering through a K-means algorithm, and classifying events by using an improved Bayesian network according to a training result; the embodiment can dynamically configure the training result through automatic intervention or manual intervention of a result machine;
s202, sending the classified result into an entity extraction model for entity extraction, and extracting the text and the class characteristics which are fit for the social comprehensive treatment, wherein the text and the class characteristics are fit for the social comprehensive treatment, namely the text and the class characteristics are fit for the social comprehensive treatment;
s203, performing preset keyword collision according to different entity extraction result contents, calculating to obtain a score through a property loss model and a casualty model, enabling a full text to enter a civil emotion model, and performing analytic operations such as lexical analysis, Chinese word vector representation, word meaning similarity, Chinese DNN language model, dependency syntactic analysis, short text similarity and the like on the civil emotion model to obtain the emotional intensity of the event;
and the calculation results of each model are transmitted downwards in an interface mode. That is, in this embodiment, the model calculation result is transmitted downward through the interface between the aforementioned models.
The score in this embodiment S203 is a configuration rule that can be customized, such as whether there is casualty to obtain different scores, whether there is property loss and how much loss to obtain different scores. Full text refers to all data, the entirety of data. The calculation rule of the emotional intensity is that the excitation composition is divided into three parts according to a negative emotion machine learning algorithm, and the three parts are specifically as follows: 1-33 were mild, 34-67 were moderate, and 68-100 were severe.
In this embodiment, nlp is adopted as the method for extracting the text and category features in accordance with the social comprehensive treatment, and before step S202, the corpus is fed in advance: a large number of events related to the social comprehensive treatment are sorted and summarized, then training is carried out, labeling and naming are carried out on each entity (entity) in the training process, and the obtained result is corrected, so that a text and category feature extraction method which is fit for the social comprehensive treatment is designed.
According to the risk value analysis and early warning of the risk upgrading event, the event is converted into the high-risk case risk value through calculation by combining result information extracted by the event according to a specific algorithm model. The risk value calculation of the embodiment calculates the civil turning penalty risk value according to a risk value algorithm and by combining result information extracted by an event. The event extraction result comprises a large category, a small category, a characteristic entity, a difference rate of preset keywords and the like.
Meanwhile, the risk value early warning of the risk upgrade event of the embodiment provides the early warning of the risk value of the event upgrade by establishing a risk upgrade event early warning trigger mechanism: the early warning is carried out according to early warning risk value thresholds set by various events, and when the risk value reaches a certain value, namely the risk value is upgraded to exceed the risk threshold in a risk upgrading event, the early warning is provided for relevant departments. The embodiment can inform related personnel to process through convenient forms such as short messages, mails and the like. The early warning risk value threshold of each kind of event is set up in advance, and the technical personnel in the field can set up according to actual conditions is nimble.
In addition, in the long-term calculation process, the risk value algorithm can be optimized according to the historical data, and the weight of each element to the risk value can be adjusted. And the weight of each element to the risk value is adjusted through historical data to optimize a risk value algorithm, and each element is a result extracted from the event used as the basis for computation of the civil transfer criminal risk value and comprises each intermediate product.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (10)
1. A natural language processing method for early warning of risk escalation events is characterized by comprising the following steps:
s1, establishing a data warehouse, and accessing a data source to the data warehouse;
s2, based on natural language processing, extracting and processing event information in the data source through an event analysis related model;
and S3, inputting the result value of the event analysis related model into the early warning analysis model, and carrying out risk value analysis and early warning of risk upgrading event upgrading.
2. The natural language processing method for early warning of a risk escalation event according to claim 1, wherein the S1 specifically includes:
s101, accessing a multi-channel data source;
and S102, cleaning the data according to the uniform format to generate uniform structured data, and writing the uniform structured data into a database.
3. The natural language processing method for early warning risk escalation event according to claim 1, wherein the data source includes event data of each field, specifically including: the system comprises citizen hot lines, hot spot events, non-police affair warning conditions, various help hot lines, citizen regulation data and contradiction regulation center data.
4. The natural language processing method for early warning of risk escalation events according to claim 1, wherein the event analysis correlation models include an event classification model for dynamically configurable event types according to business needs, an entity extraction model for extracting event locations, organizations, and people, a property loss model for calculating property losses, a casualty model for calculating people, and a people emotion model for calculating the emotional intensity of people.
5. The natural language processing method for early warning of a risk escalation event according to claim 4, wherein the S2 includes:
s201, classifying events according to event information, and dividing the events into a plurality of major classes through an event classification model, wherein a plurality of minor classes are arranged under each major class;
s202, sending the classified results into an entity extraction model for entity extraction, and extracting the characteristics of texts and categories which are in accordance with the social comprehensive treatment;
s203, performing preset keyword collision according to different entity extraction result contents, calculating a score through a property loss model and a casualty model, enabling a full text to enter a people emotion model, and obtaining the people emotion violence degree of a risk upgrading event through analysis and calculation;
and transmitting corresponding model calculation results downwards among the models in an interface mode.
6. The natural language processing method for early warning of risk escalation events according to claim 5, wherein the event classification classifies events by a hybrid algorithm combining K-means algorithm and Bayesian network:
training sample clustering through a K-means algorithm, providing an artificial intervention interface for artificial dynamic intervention to configure a training result, and classifying events by using a Bayesian network according to the training result.
7. The natural language processing method for early warning of risk escalation event according to claim 5, characterized in that, before step S202, the entity extraction model is corpus-fed in advance: and (3) arranging and summarizing a large number of events related to the social comprehensive treatment, then training, marking and naming each entity in the training process, and correcting according to the obtained result to obtain an entity extraction model capable of extracting the characteristics of texts and categories which are in accordance with the social comprehensive treatment.
8. The natural language processing method for early warning risk escalation events according to claim 1, wherein the risk escalation event escalation risk value analysis performs risk value calculation according to a risk value algorithm, and calculates a civil diversion risk value by combining the event extracted result.
9. The natural language processing method for early warning of a risk escalation event according to claim 8, further comprising, after step S3: and adjusting the weight of each element on the risk value through historical data to optimize the risk value algorithm.
10. The natural language processing method for early warning of a risk escalation event according to claim 1, wherein the risk escalation event escalation risk value early warning provides early warning of an event escalation risk value by establishing a risk escalation event early warning trigger mechanism:
and setting a corresponding early warning risk value threshold value for the event, and sending early warning information to related personnel when the risk upgrading event upgrading risk value exceeds the risk threshold value.
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