CN113111165A - Deep learning model-based alarm receiving warning condition category determination method and device - Google Patents
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Abstract
The embodiment of the disclosure discloses a method and a device for determining alarm receiving and warning condition categories based on a deep learning model. One embodiment of the method comprises: performing word segmentation on the alarm receiving text to be classified to obtain a corresponding word segmentation sequence; determining a text feature vector of the alarm receiving text to be classified based on the obtained word segmentation sequence; for each alarm receiving situation category in a preset alarm receiving situation category set, inputting the text feature vector into a classification model corresponding to the alarm receiving situation category to obtain a classification result for indicating whether the alarm receiving text to be classified belongs to the alarm receiving situation category, wherein the classification model corresponding to each alarm receiving situation category is obtained based on deep learning model training; and generating an alarm receiving condition category set corresponding to the alarm receiving text to be classified by using each target alarm receiving condition category in the preset alarm receiving condition category set, wherein the classification result indicates that the alarm receiving text to be classified belongs to the target alarm receiving condition category. The implementation mode realizes automatic alarm receiving and alarm condition classification of the alarm receiving text.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of computers, in particular to a method and a device for determining alarm receiving and warning condition categories based on a deep learning model.
Background
At present, 110 police-receiving personnel in a public security organization can input corresponding police-receiving texts when receiving police, and manually give out the warning classification of the warning recorded in the police-receiving texts. Subsequently, the processing by the corresponding alarm handler can be determined according to the given alert classification. Therefore, classifying the alert text for alert is very important in the process of receiving and processing the alert.
Disclosure of Invention
The embodiment of the disclosure provides a method and a device for determining alarm receiving and warning condition categories based on a deep learning model.
In a first aspect, an embodiment of the present disclosure provides a method for determining an alarm receiving alert category based on a deep learning model, where the method includes: performing word segmentation on the alarm receiving text to be classified to obtain a corresponding word segmentation sequence; determining a text feature vector of the alarm receiving text to be classified based on the obtained word segmentation sequence; for each alarm receiving situation category in a preset alarm receiving situation category set, inputting the text feature vector into a classification model corresponding to the alarm receiving situation category to obtain a classification result for indicating whether the alarm receiving text to be classified belongs to the alarm receiving situation category, wherein the classification model corresponding to each alarm receiving situation category is obtained based on deep learning model training; and generating an alarm receiving condition category set corresponding to the alarm receiving text to be classified by using each target alarm receiving condition category in the preset alarm receiving condition category set, wherein the classification result indicates that the alarm receiving text to be classified belongs to the target alarm receiving condition category.
In some embodiments, the classification model corresponding to each alarm receiving class in the preset alarm receiving class set is obtained by pre-training through the following training steps: acquiring a training sample set, wherein the training sample comprises a historical alarm receiving text and a corresponding labeled alarm condition category set; for each alarm receiving alarm condition category in the preset alarm receiving alarm condition category set, executing the following classification model training operation: determining a text feature vector corresponding to a historical alarm receiving text in each training sample of which the corresponding labeled alarm category set in the training sample set comprises the alarm receiving alarm category as a positive sample set corresponding to the alarm receiving alarm category; determining a text feature vector corresponding to a historical alarm receiving text in each training sample of which the corresponding labeled alarm category set does not comprise the alarm receiving alarm category in the training sample set as a negative sample set corresponding to the alarm receiving alarm category; and training a deep learning model-based classification model corresponding to the alarm receiving alarm condition category based on the positive sample set and the negative sample set corresponding to the alarm receiving alarm condition category.
In some embodiments, determining a text feature vector of the alarm receiving text to be classified based on the obtained word segmentation sequence comprises: for each word segmentation in the word segmentation sequence, calculating a word frequency-inverse text frequency index TF-IDF of the word segmentation, and setting a component corresponding to the word segmentation in a text feature vector of the alarm receiving text to be classified as the calculated TF-IDF of the word segmentation, wherein each component in the text feature vector of the alarm receiving text to be classified corresponds to each word in a preset dictionary one by one; and setting each unassigned component in the text feature vector of the alarm receiving text to be classified as a preset numerical value, wherein the unassigned component is a component corresponding to a word which belongs to a preset dictionary but does not belong to a word segmentation sequence.
In some embodiments, the preset alarm receiving alert category set comprises at least one of: a labor dispute category, an economic dispute category, and a civil dispute category.
In a second aspect, an embodiment of the present disclosure provides an alarm receiving alert category determination apparatus based on a deep learning model, including: the word segmentation unit is configured to segment words of the alarm receiving texts to be classified to obtain corresponding word segmentation sequences; the characteristic determining unit is configured to determine a text characteristic vector of the alarm receiving text to be classified based on the obtained word segmentation sequence; the classification unit is configured to input the text feature vector into a classification model corresponding to each alarm receiving class in a preset alarm receiving class set to obtain a classification result for indicating whether the alarm receiving text to be classified belongs to the alarm receiving class, wherein the classification model corresponding to each alarm receiving class is obtained based on deep learning model training; and the generation unit is configured to generate an alarm receiving condition category set corresponding to the alarm receiving text to be classified by using each target alarm receiving condition category in the preset alarm receiving condition category set, wherein the classification result indicates that the alarm receiving text to be classified belongs to the target alarm receiving condition category.
In some embodiments, the classification model corresponding to each alarm receiving class in the preset alarm receiving class set is obtained by pre-training through the following training steps: acquiring a training sample set, wherein the training sample comprises a historical alarm receiving text and a corresponding labeled alarm condition category set; for each alarm receiving alarm condition category in the preset alarm receiving alarm condition category set, executing the following classification model training operation: determining a text feature vector corresponding to a historical alarm receiving text in each training sample of which the corresponding labeled alarm category set in the training sample set comprises the alarm receiving alarm category as a positive sample set corresponding to the alarm receiving alarm category; determining a text feature vector corresponding to a historical alarm receiving text in each training sample of which the corresponding labeled alarm category set does not comprise the alarm receiving alarm category in the training sample set as a negative sample set corresponding to the alarm receiving alarm category; and training a deep learning model-based classification model corresponding to the alarm receiving alarm condition category based on the positive sample set and the negative sample set corresponding to the alarm receiving alarm condition category.
In some embodiments, the feature determination unit comprises: the first assignment module is configured to calculate a word frequency-inverse text frequency index (TF-IDF) of each participle in a participle sequence, and set a component corresponding to the participle in a text feature vector of the alarm receiving text to be classified as the calculated TF-IDF of the participle, wherein each component in the text feature vector of the alarm receiving text to be classified corresponds to each word in a preset dictionary one by one; and the second assignment module is configured to set each unassigned component in the text feature vector of the alarm receiving text to be classified as a preset numerical value, wherein the unassigned component is a component corresponding to a word which belongs to a preset dictionary but does not belong to a word segmentation sequence.
In some embodiments, the preset alarm receiving alert category set comprises at least one of: a labor dispute category, an economic dispute category, and a civil dispute category.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including: one or more processors; a storage device, on which one or more programs are stored, which, when executed by the one or more processors, cause the one or more processors to implement the method as described in any implementation manner of the first aspect.
In a fourth aspect, the present disclosure provides a computer-readable storage medium on which a computer program is stored, wherein the computer program, when executed by one or more processors, implements the method as described in any implementation manner of the first aspect.
In the prior art, alarm condition classification is generally carried out on an alarm receiving text manually by an alarm receiving person, and the following problems may exist: (1) a large amount of alarm receiving texts which are not classified are left in history, and an alarm receiver can input a large amount of new alarm receiving texts every day along with the lapse of time, so that the amount of data to be classified of the alarm receiving texts is too large, and the labor and time cost required by manual classification is too high; (2) the alarm receiving texts are mostly described by natural language, the expression mode is seriously spoken and irregular, and the manual classification difficulty is higher; (3) the number of alarm receiving and warning situation categories is high, and the artificial experience is relied on, namely the learning cost in the artificial classification process is high.
According to the method for determining the alarm receiving class based on the deep learning model, the text feature vectors corresponding to the alarm receiving texts to be classified are input into the classification models corresponding to the alarm receiving classes in the preset alarm receiving class set and based on the deep learning model, and the alarm receiving class set corresponding to the alarm receiving texts to be classified is finally obtained, so that the classification models corresponding to the alarm receiving classes and based on the deep learning model are effectively utilized, automatic alarm classification of the alarm receiving texts is realized, manual operation is not needed, the cost of performing alarm classification on the alarm receiving texts is reduced, and the classification speed of performing alarm classification on the alarm receiving texts is improved.
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Other features, objects and advantages of the disclosure will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram in which one embodiment of the present disclosure may be applied;
FIG. 2 is a flow diagram of one embodiment of a deep learning model based alert receiving alert category determination method according to the present disclosure;
FIG. 3 is a flow diagram for one embodiment of determining text feature vectors for the alarm receiving text to be classified based on the resulting sequence of word segments in accordance with the present disclosure;
FIG. 4 is a flow chart of one embodiment of training steps according to the present disclosure;
fig. 5 is a schematic structural diagram of an embodiment of an alarm receiving alert category determination apparatus based on a deep learning model according to the present disclosure;
FIG. 6 is a schematic block diagram of a computer system suitable for use in implementing an electronic device of an embodiment of the present disclosure.
Detailed Description
The present disclosure is described in further detail below with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 illustrates an exemplary system architecture 100 to which an embodiment of the deep learning model-based alarm receiving class determination method or the deep learning model-based alarm receiving class determination apparatus of the present disclosure may be applied.
As shown in fig. 1, system architecture 100 may include terminal device 101, network 102, and server 103. Network 102 is the medium used to provide communication links between terminal devices 101 and server 103. Network 102 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
A user may use terminal device 101 to interact with server 103 over network 102 to receive or send messages and the like. Various communication client applications, such as an alarm receiving record application, an alarm receiving text alarm classification application, a web browser application, etc., may be installed on the terminal device 101.
The terminal apparatus 101 may be hardware or software. When the terminal device 101 is hardware, it may be various electronic devices having a display screen and supporting text input, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like. When the terminal apparatus 101 is software, it can be installed in the electronic apparatuses listed above. It may be implemented as multiple software or software modules (e.g., to provide an alarm receiving text alert classification service) or as a single software or software module. And is not particularly limited herein.
The server 103 may be a server that provides various services, such as a background server that provides alert classification for the alert receiving text sent by the terminal device 101. The background server can analyze and process the received alarm receiving text, and feed back the processing result (such as the alarm receiving alarm condition category set) to the terminal device.
In some cases, the method for determining the alarm receiving alert category based on the deep learning model provided by the embodiment of the present disclosure may be performed by the terminal device 101 and the server 103 together, for example, the step of "obtaining the alarm receiving text to be classified" may be performed by the terminal device 101, and the rest steps may be performed by the server 103. The present disclosure is not limited thereto. Accordingly, the alarm receiving alert category determination device based on the deep learning model may also be respectively disposed in the terminal device 101 and the server 103.
In some cases, the method for determining an alarm receiving class based on a deep learning model provided by the embodiment of the present disclosure may be executed by the server 103, and accordingly, an alarm receiving class determining apparatus based on a deep learning model may also be disposed in the server 103, in which case, the system architecture 100 may also not include the terminal device 101.
In some cases, the method for determining the alarm receiving class based on the deep learning model provided by the embodiment of the present disclosure may be executed by the terminal device 101, and accordingly, the device for determining the alarm receiving class based on the deep learning model may also be disposed in the terminal device 101, in which case, the system architecture 100 may not include the server 103.
The server 103 may be hardware or software. When the server 103 is hardware, it may be implemented as a distributed server cluster composed of a plurality of servers, or may be implemented as a single server. When the server 103 is software, it may be implemented as a plurality of software or software modules (for example, for providing the alarm receiving text alert classification service), or may be implemented as a single software or software module. And is not particularly limited herein.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to fig. 2, a flow 200 of one embodiment of a deep learning model-based alarm receiving alert category determination method in accordance with the present disclosure is illustrated. The method for determining the alarm receiving alert condition category based on the deep learning model comprises the following steps:
In this embodiment, an executing agent (e.g., the server shown in fig. 1) of the deep learning model-based alarm receiving alert category determination method may first obtain the alarm receiving text to be classified locally or remotely from other electronic devices (e.g., the terminal devices shown in fig. 1) connected to the executing agent via a network.
Here, the alarm receiving text to be classified may be text data that the alarm receiver sorts according to the contents of the alarm receiving phone. The alarm receiving text to be classified can also be an alarm text which is received from the terminal device and is input by a user in an alarm application installed on the terminal device or a webpage with an alarm function.
Then, the execution main body can cut words of the alarm receiving text to be classified to obtain a corresponding word segmentation sequence. It should be noted that how to cut words of text is the prior art of extensive research and application in this field, and will not be described herein. For example, a word segmentation method based on string matching, a word segmentation method based on understanding, or a word segmentation method based on statistics, etc. may be employed. For example, tokenizing the alert text "zhang san alert says that company owes its payroll with thirty-thousand dollars without payment" may result in a sequence of tokenization "zhang san/alert/call/company/owe/its payroll/thirty-ten thousand dollars/no payment".
And step 202, determining a text feature vector of the alarm receiving text to be classified based on the obtained word segmentation sequence.
In this embodiment, the execution subject (e.g., the server shown in fig. 1) may employ various implementations to determine the text feature vector of the alarm receiving text to be classified based on the obtained word segmentation sequence. For example, the text feature vector of the alarm receiving text to be classified may include N-dimensional components, where N is a positive integer, and each dimension of the N-dimensional components corresponds to each word of a preset dictionary, respectively, and in the process of determining the text feature vector of the alarm receiving text to be classified based on the obtained word segmentation sequence, a component corresponding to a word appearing in the word segmentation sequence in each component of the text feature vector of the alarm receiving text to be classified may be set as a first preset value (e.g., 1); and setting components corresponding to the terms which do not exist in the segmentation sequence in the components of the text feature vector of the alarm receiving text to be classified as a second preset numerical value (for example, 0).
In some alternative implementations, step 202 may include steps 301 and 302 as shown in fig. 3. Referring to fig. 3, fig. 3 shows a flow 300 of an embodiment of determining a text feature vector of a text for a police dispatch receiving text to be classified based on a resulting word segmentation sequence according to the present disclosure, where the flow 300 includes the following steps:
And the unassigned components are components corresponding to words which belong to the preset dictionary but do not belong to the word segmentation sequence.
For ease of understanding, the following is exemplified.
Taking the word segmentation sequence of "zhang san/alarm/call/company/delinquent/its/wage/san/ten thousand/yuan/none/pay" as an example, assuming that the preset dictionary has 20 words, the text feature vector of the alarm receiving text to be classified also has 20 dimensions, wherein each dimension corresponds to 20 words in the preset dictionary one to one. The word Frequency-Inverse text Frequency indexes (TF-IDF, Term Frequency-Inverse Document Frequency) corresponding to the 12 segmented words included in the segmented word sequence "zhang san/alarm/call/company/delinquent/payroll/san/ten thousand/yuan/non/pay" calculated in step 301 are respectively: 0.27/0.48/0.25/0.57/0.46/0.51/0.96/0.34/0.52/0.19/0.43/0.11, and the 12 participles correspond to the 1 st, 2 nd, 3 rd, 5 th, 8 th, 9 th, 11 th, 12 th, 13 th, 14 th, 19 th and 20 th dimensions in the 20-dimensional vector respectively, and assuming that the preset value is 0, the text feature vector of the text to be classified as the alarm receiving text can be obtained through steps 301 and 302 as follows: (0.27,0.48,0.25,0,0.57,0,0,0.46,0.51,0,0.96,0.34,0.52,0.19,0,0,0,0,0.43,0.11).
The text feature vector determined according to the optional implementation manner can reflect the occurrence frequency of the alarm receiving text to be classified in the current text and the corpus, and further can express the text features of the alarm receiving text to be classified.
And 203, inputting the text feature vector into a classification model corresponding to each alarm receiving class in the preset alarm receiving class set to obtain a classification result for indicating whether the alarm receiving text to be classified belongs to the alarm receiving class.
In this embodiment, the executing entity may input the text feature vector obtained in step 202 into a classification model corresponding to the alarm receiving class for each alarm receiving class in the preset alarm receiving class set, so as to obtain a classification result indicating whether the alarm receiving text to be classified belongs to the alarm receiving class.
Here, the classification model corresponding to each alert receiving category is used to represent the correspondence between the text feature vector and the classification result. And the classification model corresponding to each alarm receiving warning situation category is obtained based on deep learning model training.
In some optional implementations, the deep learning model may include at least one of: convolutional neural networks, cyclic neural networks, long-short term memory networks, conditional random fields.
In some alternative implementations, the set of preset alarm receiving alert categories may be manually established by an expert familiar with the alarm receiving alert categories. As an example, the preset alarm receiving alert category set may include at least one of: a labor dispute category, an economic dispute category, and a civil dispute category.
In some alternative implementations, the classification model corresponding to each alarm receiving alert category in the preset alarm receiving alert category set may be obtained by pre-training through a training step as shown in fig. 4. Referring to fig. 4, fig. 4 shows a flow 400 of one embodiment of training steps according to the present disclosure. The training step comprises the following steps:
Here, the execution subject of the training step may be the same as that of the above-described alarm receiving alert category determination method based on the deep learning model. In this way, the executing body in the training step may store, after the classification model corresponding to each alarm receiving class in the preset alarm receiving class set is obtained through training, the model parameter of the classification model corresponding to each alarm receiving class in the preset alarm receiving class set in the executing body locally, and read the model parameter of the classification model corresponding to each alarm receiving class in the preset alarm receiving class set obtained through training in the process of executing the deep learning model-based alarm receiving class determination method.
Here, the execution subject of the training step may be different from the execution subject of the alarm receiving alert category determination method based on the deep learning model. In this way, the executing body of the training step may send the model parameters of the classification model corresponding to each alarm receiving class in the preset alarm receiving class set to the executing body of the deep learning model-based alarm receiving class determination method after the classification model corresponding to each alarm receiving class in the preset alarm receiving class set is obtained through training. In this way, the executing agent of the method for determining alarm receiving class based on the deep learning model may read the model parameters of the classification model corresponding to each alarm receiving class in the preset alarm receiving class set received from the executing agent of the training step in the process of executing the method for determining alarm receiving class based on the deep learning model.
Here, the performing subject of the training step may first obtain a set of training samples. Wherein, the training sample can comprise historical alarm receiving texts and corresponding labeled alarm category sets.
It should be noted that, in practice, the alarm conditions involved in the alarm receiving process of the alarm receiver are often complex and often include more than one type of alarm receiving conditions. Accordingly, the alarm described in the alarm receiving text may belong to more than one alarm category, and thus, the labeled alarm category set included in the training sample may also belong to more than one alarm category.
Here, the labeled warning situation category set in the training sample may be obtained by manually labeling the corresponding historical warning receiving text.
In practice, in order to improve the accuracy of classifying the alarm receiving class by the classification model corresponding to each alarm receiving class in the preset alarm receiving class set obtained by training, the historical alarm receiving text in the training sample obtained here may not include an invalid alarm receiving text. For example, some alarm receiving texts do not actually describe the real alarm receiving situation, and do not have the classification value of the real alarm receiving situation, such alarm receiving texts can be considered as invalid alarm receiving texts.
In some optional implementations, for each alarm receiving condition category in the preset alarm receiving condition category set, a ratio of the number of positive samples to the number of negative samples corresponding to the alarm receiving condition category in the training sample set may be within a first preset proportion range, that is, too many positive samples and too few negative samples may not be available, or too many negative samples and too few positive samples may not be available. As an example, the first preset proportion range may be between 0.6 or more and 1.6 or less. Wherein, the positive sample corresponding to the alarm receiving alert category in the training sample set is the training sample corresponding to the labeling alert category set in the training sample set, and the negative sample corresponding to the alarm receiving alert category is the training sample corresponding to the labeling alert category set in the training sample set, but not the training sample corresponding to the alarm receiving alert category set.
And 402, executing a classification model training operation for each alarm receiving class in a preset alarm receiving class set.
Here, the classification model training operation may include steps 4021 to 4023:
For convenience of understanding of step 4021 and step 4022, it is assumed that the preset alarm receiving and warning situation category set is { "labor and resource dispute category", "economic dispute category", "civil dispute category" }, and the training sample set is as shown in table 1:
the positive sample set corresponding to the "labor dispute category" in the training sample set may include: the text feature vectors corresponding to the training sample 2, the training sample 3, the training sample 5, the training sample 6, and the training sample 7, and the negative sample set corresponding to the "labor dispute category" may include: and the text feature vectors corresponding to the training sample 1, the training sample 4, the training sample 8, the training sample 9 and the training sample 10.
The positive sample set corresponding to the "economic dispute category" in the training sample set may include text feature vectors corresponding to the training sample 1, the training sample 3, the training sample 4, the training sample 6, the training sample 8, and the training sample 10, and the negative sample set corresponding to the "economic dispute category" in the training sample set may include text feature vectors corresponding to the training sample 2, the training sample 5, the training sample 7, and the training sample 9.
The positive sample set corresponding to the "civil dispute category" in the training sample set may include: the text feature vectors corresponding to the training samples 3, 4, 5 and 9, and the negative sample set corresponding to the "civil dispute category" in the training sample set may include: and the text feature vectors corresponding to the training sample 1, the training sample 2, the training sample 6, the training sample 7, the training sample 8 and the training sample 10.
TABLE 1
And step 4023, training a deep learning model-based classification model corresponding to the alarm receiving alarm condition category based on the positive sample set and the negative sample set corresponding to the alarm receiving alarm condition category.
With the positive sample set and the negative sample set corresponding to the alarm receiving alert category, the executing agent of the training step may train the deep learning model-based classification model corresponding to the alarm receiving alert category based on the positive sample set and the negative sample set corresponding to the alarm receiving alert category. Specifically, the following can be performed:
first, a model structure of a deep learning model-based classification model corresponding to the alarm receiving alert category may be determined.
For example, if the deep learning model-based classification model corresponding to the alarm receiving alert category is determined to be a convolutional neural network, it may be determined which layers the convolutional neural network specifically includes, such as which convolutional layers, pooling layers, fully-connected layers, and the precedence connection relationship between layers. If convolutional layers are included, the size of the convolutional kernel of the convolutional layer, the convolution step size, can be determined. If a pooling layer is included, a pooling method may be determined.
Secondly, the initial value of the model parameter included in the classification model corresponding to the alarm receiving alert category can be determined.
For example, if the deep learning model-based classification model corresponding to the alarm receiving alert category is determined to be a convolutional neural network, the convolutional kernel parameters of convolutional layers possibly included in the convolutional neural network may be initialized, the connection parameters of the full connection layers may be initialized, and the like.
Finally, the following parameter adjustment operations may be performed on the positive sample in the positive sample set corresponding to the alarm receiving alert condition category and the negative sample in the negative sample set corresponding to the alarm receiving alert condition category until a preset training end condition is satisfied, where the parameter adjustment operations include: inputting the positive sample/negative sample into the deep learning model-based classification model corresponding to the alarm receiving alert category to obtain a corresponding actual output result, calculating the difference between the actual output result obtained and the classification result used for indicating that the positive sample/negative sample belongs to/does not belong to the alarm receiving alert category, and adjusting the model parameters of the deep learning model-based classification model corresponding to the alarm receiving alert category based on the obtained difference. Here, the training end condition may include, for example, at least one of: the number of times of executing parameter adjustment operation reaches the preset maximum training number, and the calculated difference is smaller than the preset difference threshold value.
Through the parameter adjustment operation, the model parameters of the deep learning model-based classification model corresponding to the alarm receiving alert category are optimized, and finally the deep learning model-based classification model corresponding to the alarm receiving alert category is obtained. It should be noted that how to adjust and optimize the model parameters of the classification model based on the deep learning model based on the difference obtained by calculation is the prior art widely studied and applied in the field, and is not described herein again. For example, a gradient descent method may be employed.
By using the training steps shown in the above-mentioned process 400, the classification model corresponding to each alarm receiving alert category in the preset alarm receiving alert category set can be automatically generated, and the labor cost for generating the deep learning model-based classification model corresponding to each alarm receiving alert category is reduced. Over time, the expression mode of people changes, the reaction also changes in the alarm receiving text, and false judgment can occur if the alarm receiving text is classified in an inherent mode. At this time, the latest training sample set can be obtained, and the classification model based on the deep learning model corresponding to each alarm receiving situation category is retrained by adopting the training step, so as to meet the latest expression requirement of the current alarm receiving text.
And 204, generating an alarm receiving alert category set corresponding to the alarm receiving text to be classified by using each target alarm receiving alert category in the preset alarm receiving alert category set.
Here, the classification result obtained by inputting the alarm receiving text to be classified into the classification model corresponding to the target alarm receiving alert category in step 202 indicates that the alarm receiving text to be classified belongs to the target alarm receiving alert category. Therefore, the alarm receiving class set corresponding to the alarm receiving text to be classified can be generated by using each target alarm receiving class in the preset alarm receiving class set.
It should be noted that, when the text to be classified is input into the classification model corresponding to each alert receiving situation category in the preset alert receiving situation category set, there may be a classification result corresponding to at least one classification model corresponding to an alert receiving situation category in the preset alert receiving situation categories, and the text to be classified belongs to the alert receiving situation category. At this time, the alarm receiving alert category set corresponding to the alarm receiving text to be classified may include at least one alarm receiving alert category. Of course, the classification result obtained by inputting the text to be classified into the classification model corresponding to each of the preset alarm receiving categories indicates that the text to be classified does not belong to any of the preset alarm receiving categories, and at this time, the set of the alarm receiving categories corresponding to the text to be classified may be empty.
According to the method for determining the alarm receiving class based on the deep learning model, the text feature vectors corresponding to the alarm receiving texts to be classified are input into the classification models corresponding to the alarm receiving classes in the preset alarm receiving class set and based on the deep learning model, and the alarm receiving class set corresponding to the alarm receiving texts to be classified is finally obtained, so that the automatic alarm classification of the alarm receiving texts is realized, manual operation is not needed, the cost for performing the alarm classification of the alarm receiving texts is reduced, and the classification speed for performing the alarm classification of the alarm receiving texts is improved.
With further reference to fig. 5, as an implementation of the methods shown in the above-mentioned figures, the present disclosure provides an embodiment of an alarm receiving alert category determining apparatus based on a deep learning model, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be applied to various electronic devices.
As shown in fig. 5, the deep learning model-based alarm receiving class determination apparatus 500 of the present embodiment includes: a word segmentation unit 501, a feature determination unit 502, a classification unit 503, and a generation unit 504. The word segmentation unit 501 is configured to segment words of the alarm receiving text to be classified to obtain a corresponding word segmentation sequence; a feature determining unit 502 configured to determine a text feature vector of the alarm receiving text to be classified based on the obtained word segmentation sequence; a classifying unit 503 configured to, for each alarm receiving situation category in a preset alarm receiving situation category set, input the text feature vector into a classification model corresponding to the alarm receiving situation category to obtain a classification result indicating whether the alarm receiving text to be classified belongs to the alarm receiving situation category, where the classification model corresponding to each alarm receiving situation category is obtained based on deep learning model training; and the generating unit 504 is configured to generate an alarm receiving category set corresponding to the alarm receiving text to be classified by using each target alarm receiving category in the preset alarm receiving category set, wherein the classification result indicates that the alarm receiving text to be classified belongs to the target alarm receiving category.
In this embodiment, the detailed processing and the technical effects brought by the word segmentation unit 501, the feature determination unit 502, the classification unit 503 and the generation unit 504 of the alarm receiving alert category determination apparatus 500 based on the deep learning model may refer to the related descriptions of step 201, step 202, step 203 and step 204 in the corresponding embodiment of fig. 2, and are not repeated herein.
In some optional implementation manners of this embodiment, the classification model corresponding to each alarm receiving class in the preset alarm receiving class set may be obtained by pre-training through the following training steps: acquiring a training sample set, wherein the training sample comprises a historical alarm receiving text and a corresponding labeled alarm condition category set; for each alarm receiving alarm condition category in the preset alarm receiving alarm condition category set, executing the following classification model training operation: determining a text feature vector corresponding to a historical alarm receiving text in each training sample of which the corresponding labeled alarm category set in the training sample set comprises the alarm receiving alarm category as a positive sample set corresponding to the alarm receiving alarm category; determining the text characteristic vector corresponding to the historical alarm receiving text in each training sample of which the corresponding labeled alarm category set does not comprise the alarm receiving alarm category in the training sample set as a negative sample set corresponding to the alarm receiving alarm category; and training a deep learning model-based classification model corresponding to the alarm receiving alarm condition category based on the positive sample set and the negative sample set corresponding to the alarm receiving alarm condition category.
In some optional implementations of this embodiment, the feature determining unit 502 may include: the first assignment module 5021 is configured to calculate the word frequency-inverse text frequency index (TF-IDF) of each participle in the participle sequence, and set a component, corresponding to the participle, in a text feature vector of the alarm receiving text to be classified as the calculated TF-IDF of the participle, wherein each component in the text feature vector of the alarm receiving text to be classified corresponds to each word in a preset dictionary one by one; the second assigning module 5022 is configured to set each unassigned component in the text feature vector of the alarm receiving text to be classified as a preset numerical value, where the unassigned component is a component corresponding to a word belonging to the preset dictionary but not belonging to the word segmentation sequence.
In some optional implementations of this embodiment, the preset alarm receiving and warning condition category set may include at least one of the following items: a labor dispute category, an economic dispute category, and a civil dispute category.
It should be noted that, for details of implementation and technical effects of each unit in the alarm receiving situation category determination device based on the deep learning model provided in the embodiment of the present disclosure, reference may be made to descriptions of other embodiments in the present disclosure, and details are not described herein again.
Referring now to FIG. 6, shown is a block diagram of a computer system 600 suitable for use in implementing the electronic devices of embodiments of the present disclosure. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An Input/Output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input section 606 including a touch screen, a tablet, a keyboard, a mouse, or the like; an output section 607 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a Network interface card such as a LAN (Local Area Network) card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication section 609. The above-described functions defined in the method of the present disclosure are performed when the computer program is executed by a Central Processing Unit (CPU) 601. It should be noted that the computer readable medium in the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, Python, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a word segmentation unit, a feature determination unit, a classification unit, and a generation unit. The names of the units do not form a limitation on the units themselves in some cases, for example, the word segmentation unit can also be described as a unit for segmenting the alarm receiving text to be classified into corresponding word segmentation sequences.
As another aspect, the present disclosure also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be present separately and not assembled into the device. The computer readable medium carries one or more programs which, when executed by the apparatus, cause the apparatus to: performing word segmentation on the alarm receiving text to be classified to obtain a corresponding word segmentation sequence; determining a text feature vector of the alarm receiving text to be classified based on the obtained word segmentation sequence; for each alarm receiving situation category in a preset alarm receiving situation category set, inputting the text feature vector into a classification model corresponding to the alarm receiving situation category to obtain a classification result for indicating whether the alarm receiving text to be classified belongs to the alarm receiving situation category, wherein the classification model corresponding to each alarm receiving situation category is obtained based on deep learning model training; and generating an alarm receiving condition category set corresponding to the alarm receiving text to be classified by using each target alarm receiving condition category in the preset alarm receiving condition category set, wherein the classification result indicates that the alarm receiving text to be classified belongs to the target alarm receiving condition category.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is possible without departing from the inventive concept as defined above. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Claims (10)
1. A deep learning model-based alarm receiving and warning condition category determination method comprises the following steps:
performing word segmentation on the alarm receiving text to be classified to obtain a corresponding word segmentation sequence;
determining a text feature vector of the alarm receiving text to be classified based on the obtained word segmentation sequence;
for each alarm receiving class in a preset alarm receiving class set, inputting the text feature vector into a classification model corresponding to the alarm receiving class to obtain a classification result for indicating whether the alarm receiving text to be classified belongs to the alarm receiving class, wherein the classification model corresponding to each alarm receiving class is obtained based on deep learning model training;
and generating an alarm receiving condition category set corresponding to the alarm receiving text to be classified by using each target alarm receiving condition category in the preset alarm receiving condition category set, wherein the classification result indicates that the alarm receiving text to be classified belongs to the target alarm receiving condition category.
2. The method according to claim 1, wherein the classification model corresponding to each alarm receiving class in the preset alarm receiving class set is obtained by pre-training through the following training steps:
acquiring a training sample set, wherein the training sample comprises a historical alarm receiving text and a corresponding labeled alarm condition category set;
and executing the following classification model training operation for each alarm receiving alarm condition category in the preset alarm receiving alarm condition category set: determining a text feature vector corresponding to a historical alarm receiving text in each training sample of which the corresponding labeled alarm category set in the training sample set comprises the alarm receiving alarm category as a positive sample set corresponding to the alarm receiving alarm category; determining a text feature vector corresponding to a historical alarm receiving text in each training sample of which the corresponding labeled alarm category set does not comprise the alarm receiving alarm category in the training sample set as a negative sample set corresponding to the alarm receiving alarm category; and training a deep learning model-based classification model corresponding to the alarm receiving alarm condition category based on the positive sample set and the negative sample set corresponding to the alarm receiving alarm condition category.
3. The method of claim 2, wherein the determining a text feature vector of the alarm receiving text to be classified based on the obtained word segmentation sequence comprises:
for each word segmentation in the word segmentation sequence, calculating a word frequency-inverse text frequency index (TF-IDF) of the word segmentation, and setting a component corresponding to the word segmentation in a text feature vector of the alarm receiving text to be classified as the calculated TF-IDF of the word segmentation, wherein each component in the text feature vector of the alarm receiving text to be classified corresponds to each word in a preset dictionary one by one;
and setting each unassigned component in the text feature vector of the alarm receiving text to be classified as a preset numerical value, wherein the unassigned component is a component corresponding to a word which belongs to the preset dictionary but does not belong to the word segmentation sequence.
4. The method according to any of claims 1-3, wherein the set of preset alarm receiving alert categories comprises at least one of: a labor dispute category, an economic dispute category, and a civil dispute category.
5. An alarm receiving and warning condition category determination device based on a deep learning model comprises:
the word segmentation unit is configured to segment words of the alarm receiving texts to be classified to obtain corresponding word segmentation sequences;
a feature determination unit configured to determine a text feature vector of the alarm receiving text to be classified based on the obtained word segmentation sequence;
the classification unit is configured to input the text feature vector into a classification model corresponding to each alarm receiving class in a preset alarm receiving class set to obtain a classification result for indicating whether the alarm receiving text to be classified belongs to the alarm receiving class, wherein the classification model corresponding to each alarm receiving class is obtained based on deep learning model training;
and the generating unit is configured to generate an alarm receiving situation category set corresponding to the alarm receiving text to be classified by using each target alarm receiving situation category in the preset alarm receiving situation category set, wherein the classification result indicates that the alarm receiving text to be classified belongs to the target alarm receiving situation category.
6. The apparatus of claim 5, wherein the classification model corresponding to each alarm receiving class in the preset alarm receiving class set is obtained by pre-training through the following training steps:
acquiring a training sample set, wherein the training sample comprises a historical alarm receiving text and a corresponding labeled alarm condition category set;
and executing the following classification model training operation for each alarm receiving alarm condition category in the preset alarm receiving alarm condition category set: determining a text feature vector corresponding to a historical alarm receiving text in each training sample of which the corresponding labeled alarm category set in the training sample set comprises the alarm receiving alarm category as a positive sample set corresponding to the alarm receiving alarm category; determining a text feature vector corresponding to a historical alarm receiving text in each training sample of which the corresponding labeled alarm category set does not comprise the alarm receiving alarm category in the training sample set as a negative sample set corresponding to the alarm receiving alarm category; and training a deep learning model-based classification model corresponding to the alarm receiving alarm condition category based on the positive sample set and the negative sample set corresponding to the alarm receiving alarm condition category.
7. The apparatus of claim 6, wherein the feature determination unit comprises:
the first assignment module is configured to calculate a word frequency-inverse text frequency index (TF-IDF) of each participle in the participle sequence, and set a component, corresponding to the participle, in a text feature vector of the alarm receiving text to be classified as the calculated TF-IDF of the participle, wherein each component in the text feature vector of the alarm receiving text to be classified corresponds to each word in a preset dictionary one by one;
and the second assignment module is configured to set each unassigned component in the text feature vector of the alarm receiving text to be classified as a preset numerical value, wherein the unassigned component is a component corresponding to a word which belongs to the preset dictionary but does not belong to the word segmentation sequence.
8. The apparatus according to any of claims 5-7, wherein the set of preset alarm receiving alert categories comprises at least one of: a labor dispute category, an economic dispute category, and a civil dispute category.
9. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method recited in any of claims 1-4.
10. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-4.
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