CN114386429A - Coal mine disaster risk prediction method and system based on semantic recognition - Google Patents
Coal mine disaster risk prediction method and system based on semantic recognition Download PDFInfo
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Abstract
The application provides a coal mine disaster risk prediction method and a system based on semantic recognition, wherein the method comprises the following steps: extracting coal mine characteristic data in historical coal mine accident data and coal mine non-accident data according to a pre-constructed coal mine characteristic extraction model to serve as a coal mine risk prediction training set; inputting the coal mine risk prediction training set into a logistic regression model for training to obtain a coal mine risk prediction model; extracting coal mine characteristic vectors in the coal mine hidden danger text information and the measuring point data according to a coal mine characteristic extraction model to serve as coal mine risk evaluation data; and calling a coal mine risk prediction model to perform risk prediction on the extracted coal mine risk evaluation data. The method and the device fully consider the fusion of the coal mine text data and the measuring point data, establish the dynamic incidence relation between the accident risk and the hidden danger, track the hidden danger risk in the coal mine in real time, improve the safety management level of the coal mine and the hidden danger disposal efficiency, block key links of accident occurrence, and reduce the accident risk of the coal mine disaster.
Description
Technical Field
The application relates to the technical field of coal mine safety risk evaluation, in particular to a coal mine disaster risk prediction method and system based on semantic recognition.
Background
In recent years, along with the enhanced management of coal mines in the aspect of standardized construction, the popularization and application of a coal mine 'three-in-one' (integrated management on coal mine risks, hidden dangers and safety standardization) system changes the traditional hidden danger management mechanism and process. Along with the promotion of colliery underground communication network, explosion-proof mobile terminal's use, the safety control personnel on-the-spot inspection risk hidden danger information, accessible explosion-proof mobile terminal inputs "three in one" system, and the system is automatic with hidden danger information transmission to the safety control department, and each department carries out hidden danger reorganization and supervise and does, realizes the closed loop of hidden danger information and deals with the management. With the popularization and application of the enterprise 'three-in-one' system, a large amount of structured data and unstructured information are uploaded and summarized, and powerful support is provided for a coal mine to deeply mine the incidence relation between related accident potential hazards by applying a natural language processing technology.
However, the existing coal mine disaster risk evaluation and management method has the following technical problems:
firstly, the analysis data is single, the analysis is mainly performed on coal mine measuring point data, and the fusion of coal mine text data and measuring point data is not fully considered. The analysis of coal mine text data is more complex than that of measuring point data, most results can be obtained by statistical analysis of the measuring point data, and the coal mine text data needs to be analyzed by combining expert experience, manual labeling, a natural language processing method and a model.
Secondly, at the present stage, the relevance between the coal mine hidden danger and the accident is not high. At present, hidden danger troubleshooting work is carried out on coal mines, but no correlation analysis model is established among various hidden dangers and accidents, the logic relation of dynamic correlation of accident risks and hidden dangers cannot be well realized, and the influence degree of the hidden dangers on the corresponding accident risks cannot be dynamically and visually embodied when the hidden dangers are troubleshot.
Therefore, how to fully consider the fusion of coal mine text data and measuring point data, establish the dynamic incidence relation between accident risk and hidden danger, improve the coal mine safety management level and hidden danger disposal efficiency, block the key link of accident occurrence, and reduce coal mine disaster accident risk is a technical problem which needs to be solved urgently at present.
Disclosure of Invention
The application aims to provide a coal mine disaster risk prediction method and system based on semantic recognition, fusion of coal mine text data and measuring point data is fully considered, a dynamic incidence relation between accident risk and hidden danger is established, the coal mine safety management level and hidden danger disposal efficiency are improved, key accident links are blocked, and coal mine disaster accident risk is reduced.
In order to achieve the purpose, the application provides a coal mine disaster risk prediction method based on semantic recognition, and the method comprises the following steps:
acquiring historical coal mine accident data and coal mine non-accident data;
extracting coal mine characteristic data in historical coal mine accident data and coal mine non-accident data according to a pre-constructed coal mine characteristic extraction model to serve as a coal mine risk prediction training set;
inputting the coal mine risk prediction training set into a logistic regression model for training to obtain a coal mine risk prediction model;
acquiring coal mine hidden danger text data and measuring point real-time data;
extracting coal mine characteristic vectors in the coal mine hidden danger text data and the measuring point real-time data according to a coal mine characteristic extraction model to serve as coal mine risk evaluation data;
and calling a coal mine risk prediction model, and performing risk prediction on the extracted coal mine risk evaluation data to obtain a coal mine risk prediction evaluation result.
The method for constructing the coal mine feature extraction model in advance comprises the following sub-steps:
acquiring a hidden danger text vector training set;
calling a preset classification model, and training a hidden danger text vector training set to obtain a text hidden danger vector corresponding to the hidden danger text;
and acquiring a measuring point hidden danger vector, and combining the measuring point hidden danger vector and the text hidden danger vector to obtain a coal mine characteristic extraction vector.
The coal mine characteristic extraction model extracts coal mine characteristic vectors, and the coal mine characteristic vectors comprise measuring point hidden danger vectors and text hidden danger vectors.
As above, the method for obtaining the hidden danger text vector training set includes the following sub-steps:
acquiring a hidden danger text training set labeled manually;
preprocessing a hidden danger text training set;
and vectorizing the preprocessed hidden danger text training set to obtain a hidden danger text vector training set.
As above, the method for acquiring the hidden danger vector of the measuring point comprises the following steps:
acquiring real-time data of coal mine measuring points;
and judging the real-time data of the measuring points, and taking the category corresponding to the risk hidden danger of the measuring points as a measuring point hidden danger vector.
The method for acquiring the coal mine risk prediction training set comprises the following steps:
acquiring historical coal mine accident data corresponding to each accident category; acquiring coal mine non-accident data;
extracting coal mine characteristic data in historical coal mine accident data and coal mine non-accident data according to a coal mine characteristic extraction model to serve as a coal mine risk prediction training set;
wherein the accident category includes: gas accidents, water damage accidents, coal dust accidents, and roof accidents.
As above, among others, the coal mine accident information includes: the method comprises coal mine hidden danger text data and measuring point hidden danger data, wherein the coal mine hidden danger text data are used for extracting text vectors, and the measuring point hidden danger data are used for extracting measuring point vectors.
As above, the weights of the coal mine feature vectors are learned by using a logistic regression model, and the weights of the coal mine feature vectors are optimized.
The method comprises the steps of inputting coal mine risk evaluation data into a coal mine risk prediction model; and outputting the predicted coal mine accident category by the coal mine risk prediction model.
The application also provides a coal mine disaster risk prediction system based on semantic recognition, and the system comprises:
the training data acquisition module is used for acquiring historical coal mine accident data and coal mine non-accident data; extracting coal mine characteristic data in historical coal mine accident data and coal mine non-accident data according to a pre-constructed coal mine characteristic extraction model to serve as a coal mine risk prediction training set;
the model construction module is used for inputting the coal mine risk prediction training set into the logistic regression model for training to obtain a coal mine risk prediction model;
the real-time data acquisition module is used for acquiring coal mine hidden danger text data and measuring point real-time data;
the risk evaluation data acquisition module is used for extracting coal mine characteristic vectors in coal mine hidden danger text data and measuring point real-time data according to a coal mine characteristic extraction model to serve as coal mine risk evaluation data;
the risk prediction module is used for calling the coal mine risk prediction model, performing risk prediction on the extracted coal mine risk evaluation data and acquiring a coal mine risk prediction evaluation result
The beneficial effect that this application realized is as follows:
(1) the coal mine information data that this application make full use of was collected contains: the hidden danger text data and the measuring point real-time data are obtained by adopting a semantic recognition method to obtain a coal mine text hidden danger vector, a statistical method is adopted to obtain a coal mine measuring point hidden danger vector, the two types of vectors are fused to obtain a coal mine characteristic vector, the fusion of the coal mine text data and the measuring point data is fully considered, the coal mine risk prediction accuracy is improved, and the occurrence probability of coal mine safety production accidents is reduced.
(2) According to the method and the device, the incidence relation is established between the hidden danger data and the accident data, and after the incidence relation is established, the dynamic risk distribution and the hidden danger incidence relation of the coal mine accident can be better analyzed and excavated, so that the accident risk is reduced for a coal mine, major risks are timely dealt with, and data support is provided. Specifically, this application make full use of the accident data that collect, the accident data contains: the method has the advantages that gas accidents, water damage accidents, coal dust accidents, roof accidents and the like are combined, a logistic regression model is adopted to predict coal mine accident risks in real time by combining coal mine characteristic vectors, prediction results are obtained, and the method has great significance for coal mine risk prevention.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art according to the drawings.
Fig. 1 is a flowchart of a coal mine disaster risk prediction method based on semantic recognition according to an embodiment of the present application.
Fig. 2 is a flowchart of a method for pre-constructing a coal mine feature extraction model according to an embodiment of the present application.
Fig. 3 is a flowchart of a method for obtaining a hidden danger text vector training set according to an embodiment of the present application.
Fig. 4 is a schematic diagram of a method for obtaining a coal mine feature vector according to an embodiment of the present application.
Fig. 5 is a schematic diagram of a coal mine risk prediction method according to an embodiment of the present application.
Fig. 6 is a schematic structural diagram of a coal mine disaster risk prediction system based on semantic recognition according to an embodiment of the present application.
Reference numerals: 10-a training data acquisition module; 20-a model building module; 30-a real-time data acquisition module; 50-a risk prediction module; 100-coal mine disaster risk prediction system.
Detailed Description
The technical solutions in the embodiments of the present application are clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Example one
As shown in fig. 1, the present application provides a coal mine disaster risk prediction method based on semantic recognition, which includes the following steps:
and step S1, constructing a coal mine characteristic extraction model in advance.
As shown in fig. 2, step S1 includes the following sub-steps:
and step S110, acquiring a hidden danger text vector training set.
As shown in fig. 3, step S110 includes the following sub-steps:
and step S111, acquiring a hidden danger text training set labeled manually.
Specifically, the method for acquiring the hidden danger text training set of the coal mine comprises the following steps: and acquiring the text information of the hidden danger of the coal mine in a near time period (for example, 60 days) and carrying out manual category marking.
The categories are easily caused coal mine disasters and are divided by experts according to experience, and include: detonating the fire source, the amount of wind is not enough, the mine gushes water unusually, the sump is not cleared up, the water pump trouble, the pipeline trouble, the dust fall is failed, produce the coal dust, strut untimely, strut work is not normal, strut work inefficacy or effect reduce, and remaining hidden danger description etc. for each classification settlement classification label, classification label can replace with digit etc. for example: 0. 1, 2, 3, etc.
And step S112, preprocessing the hidden danger text training set.
Specifically, the method for preprocessing the hidden danger text training set comprises the following steps: and performing word segmentation and word stop removal processing on the hidden danger text training set.
And step S113, vectorizing the preprocessed hidden danger text training set to obtain a hidden danger text vector training set.
The method for vectorizing the preprocessed hidden danger text training set comprises the following steps: and calculating the text type data in the hidden danger text training set by adopting a TF-IDF (text vectorization) calculation method, and realizing the vectorization processing of the text type data in the hidden danger text training set.
Specifically, the TF-IDF calculation method comprises the following steps:
TF-IDF＝TF*IDF；
wherein, TF represents the word frequency and represents the frequency of the occurrence of the entry t in the document; IDF denotes the inverse text frequency index, the main idea of IDF is: if the number of documents containing the entry t is less, namely the number n of times of occurrence of the keyword in the hidden danger text is smaller, and the IDF is larger, the entry t is proved to have good category distinguishing capability, tfi,jRepresenting the word frequency of the ith word in the jth document; n isi,jRepresenting the number of times the ith word appears in the jth document; sigmaknk,jRepresenting the sum of the times of all the words appearing in the jth document; idfiAn inverse text frequency index representing an ith word; d represents a document; | D | represents the total number of documents; i { j, t |)i∈DiMeans to indicate the inclusion of the word tiThe number of files.
And step S120, calling a preset classification model, and training the hidden danger text vector training set to obtain a text hidden danger vector corresponding to the hidden danger text.
Specifically, the hidden danger text vector training set is divided into a training set and a test set. Training by adopting a training set to obtain a text hidden danger classification model, testing the text hidden danger classification model by adopting a testing set, optimizing a text hidden danger vector, and obtaining the optimized text hidden danger vector. The text hidden danger vector comprises hidden danger characteristics of a plurality of categories.
Preferably, the feature vectors and the class labels (labels) in the hidden danger text vector training set are divided into a training set and a testing set according to the ratio of 8: 2.
The training method of the text hidden danger classification model comprises the following steps: the classification is carried out by adopting an SVM two-classifier, a text hidden danger classification model is trained, when the problem of multi-classification is met, a one-to-many strategy can be adopted, each class is classified into one class, other classes are classified into another class in sequence, and U SVM two-classifiers are required to be constructed.
The optimization problem of solving the optimal classification hyperplane is transformed into the following minimization problem using a kernel function.
The minimization problem is:
wherein K represents a kernel function; x represents a training feature; c represents a penalty factor to prevent overfitting; l represents the total number of training features; n represents the number of times of occurrence of the keywords in the hidden danger text; y represents a category label; t represents transposition; s.t. indicates that the condition is satisfied; p, q and k are parameters; alpha is alphapAnd alphaqRepresents a parameter selected in the range of 0-C; y ispRepresents the p-th category label; y isqRepresents the qth category label; xpRepresenting the p-th training feature; xqRepresenting the qth training feature; alpha ═ alpha1，α2，......,αl]。
Wherein, the conversion from the low-dimensional space to the high-dimensional space is realized through a kernel function. Commonly used kernel functions include: RBF kernel, linear kernel, polynomial kernel and Sigmoid kernel.
And S130, acquiring the potential hazard vector of the measuring point, and combining the potential hazard vector of the measuring point and the potential hazard vector of the text to obtain a coal mine characteristic extraction model.
Specifically, the method for acquiring the potential risk vector of the measuring point comprises the following steps:
acquiring real-time data of coal mine measuring points, wherein the real-time data of the coal mine measuring points comprises the following steps: and (4) respectively carrying out statistical judgment on the real-time data of the measuring points by using gas, oxygen, coal dust, wind speed, mine pressure and the like, and judging whether the real-time data of the measuring points correspond to high-risk risks. And if the real-time data of the measuring points correspond to high-risk risks, namely the real-time data of the measuring points belong to risk hidden dangers, taking the category corresponding to the real-time data of the measuring points as a vector of the potential hidden dangers of the measuring points, or else, taking the category corresponding to the real-time data of the measuring points not as the vector of the potential hidden dangers of the measuring points.
Specifically, the potential hazard vectors of the measuring points and the potential hazard vectors of the texts are combined to obtain a coal mine characteristic extraction model for extracting coal mine characteristic vectors.
The coal mine characteristic extraction model extracts coal mine characteristic vectors, namely extracting measuring point hidden danger vectors and text hidden danger vectors. Specifically, when data consistent with a measuring point hidden danger vector or a text hidden danger vector in the coal mine feature extraction model exists in the extracted data, the consistent data is extracted and used as a coal mine feature vector.
Fig. 4 is a flow chart of a method for obtaining a coal mine feature vector. The method for acquiring the coal mine feature vector comprises the following steps: and acquiring a text hidden danger vector and a measuring point hidden danger vector, and combining the text hidden danger vector and the measuring point hidden danger vector into a coal mine characteristic vector.
As shown in fig. 4, the method for acquiring a text hidden danger vector includes: acquiring a hidden danger text training set labeled manually, performing word segmentation and word removal processing on the acquired hidden danger text training set to obtain a preprocessed hidden danger text training set, and performing vectorization processing on the hidden danger text training set to obtain a hidden danger text vector training set; training an SVM classification model according to the hidden danger text vector training set, and obtaining a file hidden danger vector according to the hidden danger text test set and the trained SVM classification model.
The method for acquiring the potential risk vector of the measuring point comprises the following steps: and (4) carrying out statistical analysis on real-time data (gas, oxygen, coal dust, wind speed, mine pressure and the like) of the measuring points to obtain measuring point hidden danger vectors which are easy to generate high-risk risks.
And step S2, acquiring historical coal mine accident data and coal mine non-accident data, and extracting coal mine characteristic data in the historical coal mine accident data and the coal mine non-accident data according to a coal mine characteristic extraction model to serve as a coal mine risk prediction training set.
Specifically, historical coal mine accident information corresponding to each accident category is obtained, coal mine non-accident data are obtained, and coal mine characteristic data in the historical coal mine accident data and the coal mine non-accident data are extracted according to a coal mine characteristic extraction model to serve as a coal mine risk prediction training set.
Wherein the accident category includes: gas accidents, water damage accidents, coal dust accidents, roof accidents, etc.
Step S2 includes the following sub-steps:
step S210, coal mine accident information in a period of time (for example, in the last half year) is acquired aiming at each accident category.
The coal mine accident information comprises: the method comprises coal mine hidden danger text data and measuring point hidden danger data, wherein the coal mine hidden danger text data are used for extracting a text hidden danger category, and the measuring point hidden danger data are used for extracting a measuring point hidden danger category.
And S220, extracting coal mine non-accident data from the historical coal mine data in proportion.
And randomly extracting negative examples of the coal mine non-accident data according to a positive-negative ratio of 1:5, wherein the positive examples represent the coal mine accident data, and the negative examples represent the coal mine non-accident data. In other words, according to the proportion of coal mine accident data to coal mine non-accident data being 1: and 5, extracting coal mine non-accident data according to the data volume.
And step S230, extracting coal mine accident data and coal mine characteristic data in the coal mine non-accident data through a coal mine characteristic extraction module, and obtaining a coal mine risk prediction training set according to the coal mine characteristic data.
The coal mine characteristic data comprises a text hidden danger category and a measuring point hidden danger category. And dividing the coal mine characteristic data in the extracted coal mine accident data and coal mine non-accident data into a coal mine risk prediction training set and a coal mine risk prediction testing set. The coal mine risk prediction training set is used for training to obtain a coal mine risk prediction model, and the coal mine risk prediction testing set is used for testing and optimizing the coal mine risk prediction model.
And step S3, inputting the coal mine risk prediction training set into a logistic regression model for training to obtain a coal mine risk prediction model.
The logistic regression base model is a linear regression model normalized by a Sigmoid function (logistic equation), and is a machine learning method for solving the binary problem.
The assumed functional form of the logistic regression model is as follows:
wherein h isθ(x) Representing a hypothesis function; x is input, and theta is a parameter required to be obtained; e is 2.718.
The assumptions made by the logistic regression model are as follows:
wherein g () represents a logical function; p (y 1| x; theta) represents the possibility of calculating the output variable y equal to 1 according to the selected parameter theta; t denotes transposition.
The decision function corresponding to the logistic regression model is:
y*＝1,ifP(y＝1|x)＞0.5；
wherein, y*Representing a decision function; if indicates if; x is input; y is an output variable.
In logistic regression models, the most common cost function is cross entropy; in optimizing the parameters, the most common method is gradient descent.
And learning the weight of the coal mine feature vector by adopting a logistic regression model, optimizing the weight of the coal mine feature vector, and obtaining a coal mine risk prediction model, thereby achieving a better prediction effect.
Specifically, the weight of the coal mine feature vector is used as a parameter theta, the coal mine feature vector is used as input, and the parameter theta meeting the decision function is obtained and used as the weight of the optimized coal mine feature vector. And taking the weight of the optimized coal mine feature vector as a weight parameter in a coal mine risk prediction model, and better predicting the coal mine risk.
And step S4, acquiring coal mine hidden danger text data and measuring point real-time data.
And S5, extracting coal mine characteristic vectors in the coal mine hidden danger text data and the measuring point real-time data according to the coal mine characteristic extraction model to serve as coal mine risk evaluation data.
In other words, the potential hazard vector of the measuring point and the potential hazard vector of the text are extracted to serve as coal mine risk evaluation data.
And step S6, calling a coal mine risk prediction model, performing risk prediction on the extracted coal mine risk evaluation data, and obtaining a coal mine risk prediction result.
Step S6 includes the following sub-steps:
and step S610, inputting coal mine risk evaluation data into a coal mine risk prediction model.
And S620, outputting the predicted coal mine accident category by the coal mine risk prediction model. Therefore, corresponding preventive measures are taken according to the predicted coal mine accident category.
As shown in fig. 5, the method for obtaining the coal mine risk prediction result includes: and obtaining a coal mine risk prediction evaluation result according to the trained LR logistic regression model and the coal mine test set.
As shown in fig. 5, the training method of the LR logistic regression model is: the method comprises the steps of obtaining coal mine accident data and coal mine non-accident data, extracting coal mine feature vectors in the coal mine accident data to serve as a coal mine risk prediction training set, and obtaining an LR logistic regression model according to the coal mine risk prediction training set, wherein the LR logistic regression model is used for predicting coal mine risks.
Example two
As shown in fig. 6, the present application provides a coal mine disaster risk prediction system 100 based on semantic recognition, which includes:
the training data acquisition module 10 is used for acquiring historical coal mine accident data and coal mine non-accident data; extracting coal mine characteristic data in historical coal mine accident data and coal mine non-accident data according to a pre-constructed coal mine characteristic extraction model to serve as a coal mine risk prediction training set;
the model construction module 20 is used for inputting the coal mine risk prediction training set into the logistic regression model for training to obtain a coal mine risk prediction model;
the real-time data acquisition module 30 is used for acquiring coal mine hidden danger text data and measuring point real-time data;
the risk evaluation data acquisition module 40 is used for extracting coal mine characteristic vectors in coal mine hidden danger text data and measuring point real-time data according to a coal mine characteristic extraction model to serve as coal mine risk evaluation data;
and the risk prediction module 50 is used for calling the coal mine risk prediction model, performing risk prediction on the extracted coal mine risk evaluation data and obtaining a coal mine risk prediction evaluation result.
The beneficial effect that this application realized is as follows:
(1) the coal mine information data that this application make full use of was collected contains: the hidden danger text data and the measuring point real-time data are obtained by adopting a semantic recognition method to obtain a coal mine text hidden danger vector, a statistical method is adopted to obtain a coal mine measuring point hidden danger vector, the two types of vectors are fused to obtain a coal mine characteristic vector, the fusion of the coal mine text data and the measuring point data is fully considered, the coal mine risk prediction accuracy is improved, and the occurrence probability of coal mine safety production accidents is reduced.
(2) According to the method and the device, the incidence relation is established between the hidden danger data and the accident data, and after the incidence relation is established, the dynamic risk distribution and the hidden danger incidence relation of the coal mine accident can be better analyzed and excavated, so that the accident risk is reduced for a coal mine, major risks are timely dealt with, and data support is provided. Specifically, this application make full use of the accident data that collect, the accident data contains: the method has the advantages that gas accidents, water damage accidents, coal dust accidents, roof accidents and the like are combined, a logistic regression model is adopted to predict coal mine accident risks in real time by combining coal mine characteristic vectors, prediction results are obtained, and the method has great significance for coal mine risk prevention.
The above description is only an embodiment of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement or the like made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (10)
1. A coal mine disaster risk prediction method based on semantic recognition is characterized by comprising the following steps:
acquiring historical coal mine accident data and coal mine non-accident data;
extracting coal mine characteristic data in historical coal mine accident data and coal mine non-accident data according to a pre-constructed coal mine characteristic extraction model to serve as a coal mine risk prediction training set;
inputting the coal mine risk prediction training set into a logistic regression model for training to obtain a coal mine risk prediction model;
acquiring coal mine hidden danger text data and measuring point real-time data;
extracting coal mine characteristic vectors in the coal mine hidden danger text data and the measuring point real-time data according to a coal mine characteristic extraction model to serve as coal mine risk evaluation data;
and calling a coal mine risk prediction model, and performing risk prediction on the extracted coal mine risk evaluation data to obtain a coal mine risk prediction evaluation result.
2. A coal mine disaster risk prediction method based on semantic recognition as claimed in claim 1, wherein the method for pre-constructing a coal mine feature extraction model comprises the following sub-steps:
acquiring a hidden danger text vector training set;
calling a preset classification model, and training a hidden danger text vector training set to obtain a text hidden danger vector corresponding to the hidden danger text;
and acquiring a measuring point hidden danger vector, and combining the measuring point hidden danger vector and the text hidden danger vector to obtain a coal mine characteristic vector.
3. The coal mine disaster risk prediction method based on semantic recognition as recited in claim 2, wherein the coal mine feature extraction model extracts coal mine feature vectors, and the coal mine feature vectors comprise measuring point hidden danger vectors and text hidden danger vectors.
4. The coal mine disaster risk prediction method based on semantic recognition as recited in claim 2, wherein the method for obtaining the text hidden danger vector training set comprises the following sub-steps:
acquiring a hidden danger text training set labeled manually;
preprocessing a hidden danger text training set;
and vectorizing the preprocessed hidden danger text training set to obtain a hidden danger text vector training set.
5. The coal mine disaster risk prediction method based on semantic recognition as recited in claim 2, wherein the method for obtaining the hidden danger vector of the measuring point is as follows:
acquiring real-time data of coal mine measuring points;
and judging the real-time data of the measuring points, and taking the category corresponding to the risk hidden danger of the measuring points as a measuring point hidden danger vector.
6. A coal mine disaster risk prediction method based on semantic recognition as claimed in claim 1, wherein the method for obtaining coal mine risk prediction training set comprises the following steps:
acquiring historical coal mine accident data corresponding to each accident category; acquiring coal mine non-accident data;
extracting coal mine characteristic data in historical coal mine accident data and coal mine non-accident data according to a coal mine characteristic extraction model to serve as a coal mine risk prediction training set;
wherein the accident category includes: gas accidents, water damage accidents, coal dust accidents, and roof accidents.
7. A coal mine disaster risk prediction method based on semantic recognition as claimed in claim 1 wherein the coal mine accident data comprises: the method comprises the following steps of coal mine text hidden danger data and measuring point hidden danger data, wherein the coal mine text hidden danger data are used for extracting text hidden danger vectors, and the measuring point hidden danger data are used for extracting measuring point hidden danger vectors.
8. The coal mine disaster risk prediction method based on semantic recognition as recited in claim 1, wherein the weights of the coal mine feature vectors are learned by using a logistic regression model to optimize the weights of the coal mine feature vectors.
9. A coal mine disaster risk prediction method based on semantic recognition according to claim 1, characterized in that coal mine risk evaluation data is inputted into a coal mine risk prediction model; and outputting the predicted coal mine accident category by the coal mine risk prediction model.
10. A coal mine disaster risk prediction system based on semantic recognition is characterized by comprising:
the training data acquisition module is used for acquiring historical coal mine accident data and coal mine non-accident data; extracting coal mine characteristic data in historical coal mine accident data and coal mine non-accident data according to a pre-constructed coal mine characteristic extraction model to serve as a coal mine risk prediction training set;
the model construction module is used for inputting the coal mine risk prediction training set into the logistic regression model for training to obtain a coal mine risk prediction model;
the real-time data acquisition module is used for acquiring coal mine hidden danger text data and measuring point real-time data;
the risk evaluation data acquisition module is used for extracting coal mine characteristic vectors in coal mine hidden danger text data and measuring point real-time data according to a coal mine characteristic extraction model to serve as coal mine risk evaluation data;
and the risk prediction module is used for calling the coal mine risk prediction model, performing risk prediction on the extracted coal mine risk evaluation data and acquiring a coal mine risk prediction evaluation result.
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