CN114418115A - Method, device, equipment and storage medium for training sympathy meeting of psychological consultant - Google Patents
Method, device, equipment and storage medium for training sympathy meeting of psychological consultant Download PDFInfo
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Abstract
The application relates to the technical field of artificial intelligence, in particular to a method, a device, equipment and a storage medium for training shared interviews of psychological consultants, wherein the method comprises the following steps: asking questions of the psychological consultants to obtain a situation-sharing response text of the psychological consultants; performing text processing on the common situation response text, and performing vectorization processing to generate vectorized text characteristics; inputting the vectorized text features into a pre-trained machine learning model for exploratory technology classification and feedback to obtain the co-situation technology category and similarity, determining the current co-situation level of the psychological consultant based on the co-situation technology category and the similarity, and continuing the co-situation conversation training according to the training target of the psychological consultant or ending the co-situation conversation training. Therefore, the problems that the training mode of a psychological consultant in the related technology depends on expert supervision seriously, the training level is reduced, and the consultation skills are restricted to master the shared situation conversation skills by the consultant are solved.
Description
Technical Field
The application relates to the technical field of natural language processing, in particular to a method, a device, equipment and a storage medium for training shared interviews of psychological consultants.
Background
The situation sharing refers to the process of experiencing the emotion or thinking of other people and actively placing the people in the world perceived by other people, the meaning of faithfully seeing life and life events through eyes of other people is utilized, the consultation and treatment relationship has the treatment function, the situation sharing is one of three sufficient necessary conditions for establishing good consultation relationship, and the situation sharing is widely accepted and adopted as an important factor in psychological consultation.
As a conversation technique, the sympathy is trained. Training is carried out to improve the co-situation level of novice consultants, and the training of the psychological consultants and the improvement of the psychological consultant effect are very necessary.
Innovative experiential intervention as a common-emotion training mode which breaks through the routine and bypasses the language-technology training and directly acts on the emotional experience of trainees is concerned in recent years, but is limited by training conditions, and the training of psychological consultants is still the mainstream of the traditional micro-technology training method.
Although the trainees systematically criticize the microtechnology training means, it is undeniable that this training method is most widely used and has proven effective by a large number of empirical studies, especially in terms of basic meeting skills. The assistive technology three-stage model, such as Hill, integrates the interpersonal interaction training model of Carkhuff at an early stage, the micro-counseling model of Ivey, and the interpersonal process review model of Kagan. The assistant technology in the exploration phase is based on the principal center theory of Rogers and deeply embodies the principle of sharing the emotion.
However, the training mode of the psychological consultant in the related art depends on expert supervision seriously, the training level is not high, the mastering of the psychological consultant on the shared emotion conversation skills is restricted, the training effect is reduced, and the training efficiency is low.
Disclosure of Invention
The application provides a psychological consultant's meeting-sharing training method, device, equipment and storage medium to solve among the relevant art and rely on expert supervision seriously to psychological consultant's training mode, the level of the deliberate exercise of training reduces, has restricted the consultant to the grasp of meeting-sharing skill, and the training effect is relatively poor, training efficiency lower scheduling problem.
The embodiment of the first aspect of the application provides a method for training a sympathy meeting of a psychological consultant, which comprises the following steps: asking questions of a psychological consultant, and acquiring a co-emotional response text of the psychological consultant; performing text processing on the co-situation response text, and performing vectorization processing to generate vectorized text features; inputting the vectorized text features into a pre-trained exploratory technology classification and feedback machine learning model to obtain a co-situation technology category and similarity, determining the current co-situation level of the psychological consultant based on the co-situation technology category and the similarity, and continuing the co-situation conversation training or ending the co-situation conversation training according to the training target of the psychological consultant.
Further, before inputting the text features into a pre-trained machine learning model for exploratory technical classification and feedback, the method further comprises: acquiring a training sample in a real conversation by using a common situation opportunity defined by a common situation exchange coding system; training a machine learning model of the exploratory technology classification and feedback based on the training samples, wherein the co-estrus opportunities include direct expressions of emotion, challenge, and progress.
Further, the questioning of the psychological consultant and the obtaining of the sympathy response text of the psychological consultant include: and acquiring the co-emotional response of the psychological consultant, and converting the co-emotional response into a co-emotional response text in a preset mode.
Further, the performing text processing on the common situation response text, performing vectorization processing, and generating a vectorized text feature includes: preprocessing the common situation response text, and performing word segmentation processing on the preprocessed common situation response text to obtain the text characteristics; and carrying out word frequency and inverse file frequency vectorization on the text features to generate the vectorized text features.
Further, the inputting the vectorized text features into a pre-trained machine learning model for exploratory technology classification and feedback to obtain a co-situation technology category and similarity includes: obtaining the actual technical classification of the shared situation response text according to a machine learning algorithm; the determining a current co-estrus level of the psychological consultant based on the co-estrus technology category and the similarity comprises: and obtaining the shared situation level responded by the psychological consultant according to the similarity between the shared situation responding text of the psychological consultant and a preset text.
In a second aspect of the present application, an embodiment provides a device for training a social meeting of a psychological consultant, including: the system comprises an acquisition module, a query module and a query module, wherein the acquisition module is used for asking a psychological consultant to acquire a situation-sharing response text of the psychological consultant; the processing module is used for extracting text features from the co-situation response text, carrying out vectorization processing and generating vectorized text features; and the training module is used for inputting the vectorized text features into a pre-trained machine learning model for exploratory technology classification and feedback to obtain the co-situation technology category and the similarity, determining the current co-situation level of the psychological consultant based on the co-situation technology category and the similarity, and continuing the co-situation conversation training according to the training target of the psychological consultant or ending the co-situation conversation training.
Further, still include: the model construction module is used for acquiring training samples in real conversations by using the common-case opportunities defined by the common-case exchange coding system before inputting the text features into a pre-trained exploratory technology classification and feedback machine learning model; training a machine learning model of the exploratory technology classification and feedback based on the training samples, wherein the co-estrus opportunities include direct expressions of emotion, challenge, and progress.
Further, the acquisition module is used for acquiring the co-emotion response of the psychological consultant and converting the co-emotion response into a co-emotion response text in a preset mode; the processing module is used for preprocessing the common situation response text, performing word segmentation processing on the preprocessed common situation response text to obtain the text characteristics, and performing word frequency and inverse file frequency vectorization on the text characteristics to generate the vectorized text characteristics; the training module is used for obtaining the actual technical classification of the shared situation response text according to a machine learning algorithm and obtaining the shared situation level responded by the psychological consultant according to the similarity between the shared situation response text of the psychological consultant and a preset text.
An embodiment of a third aspect of the present application provides an electronic device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method for mental consultant's sympathy training as described in the above embodiments.
A fourth aspect of the present application provides a computer-readable storage medium, having a computer program stored thereon, where the computer program is executed by a processor, to implement the method for training a psychological consultant's sympathy and meeting as described in the previous embodiments.
Therefore, the application has at least the following beneficial effects:
by programming and designing a machine model for automatically classifying the technology and scoring the shared emotion level for the user input, the category of the shared emotion technology in consultation session can be effectively predicted, and the shared emotion level responded by the user can be reasonably reflected according to the similarity of the user response and the expert response, so that the expert is not required to supervise the session skill of a psychological consultant to be automatically and effectively trained, the reaction shared emotion level in the consultation session is improved, and the training effect and efficiency are effectively improved. Therefore, the problems that the training mode of a psychological consultant in the correlation technique depends on expert supervision seriously, the training level is reduced, the mastering of the consultant on the shared-emotion meeting skills is restricted, the training effect is poor, the training efficiency is low and the like are solved.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a flow chart illustrating a flow chart of a method for training a sympathy meeting of a psychological consultant according to an embodiment of the present application;
FIG. 2 is a flow diagram of machine learning model development for heuristic technical classification and feedback provided in accordance with an embodiment of the present application;
FIG. 3 is a diagram of an example of an application of a machine learning model for heuristic technical classification and feedback provided in accordance with an embodiment of the present application;
FIG. 4 is a block diagram of an exemplary apparatus for training a social interview between psychological consultants according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
The sympathy can be trained, the training is carried out to improve the level of the sympathy of the novice consultant, and the training of the psychological consultant and the improvement of the psychological consultant effect are very necessary. Psychological consultants are still mainly trained by traditional microtechnology training methods, which are most widely used and proved to be effective by a large number of empirical studies, especially in terms of basic meeting skills. For example, the three-stage model of the humanity-assisting technology of Hill integrates an early consultation technical model, wherein the humanity-assisting technology of the exploration stage deeply embodies the principle of co-situation on the basis of the principal center theory.
NLP (Natural Language Processing) aims at understanding human Language, and is applied to the field of machine translation at first, and the flow of NLP includes: obtaining corpora, preprocessing the corpora (cleaning data, segmenting words, removing stop words and the like), selecting characteristics, and training models such as SVM and model evaluation. Machine learning can be simply divided into supervised learning and unsupervised learning algorithms depending on whether the original data need to be artificially labeled, and is gradually applied to the fields of clinical psychology and psychiatry in recent years. In the psychological consultation field, machine learning is combined with natural language processing, and a method is provided for researchers to automatically and deeply analyze consultation session texts.
The common emotion interaction in the medical scene communication can be automatically detected by utilizing the voice characteristics and the vocabulary characteristics, and the expressive common emotion is further coded into different levels of three reactions, namely strong/weak explanation, strong/weak exploration and strong/weak emotion reflection, and the common emotion reflection and the expression mechanism after reflection are recognized on a large-scale mental health support platform. Motivational interviews are a consulting mode widely used for substance abuse therapy, and in MI, the counselor co-estrus level can be subjected to detailed behavior coding, and the counselor co-estrus perceived by visitors in MI is predicted by taking words inspired by psycholinguistic (LIWC features, n-grams and the like) as features; or consultant consensus level for observer scoring predicted by using SVM (Support Vector Machine).
However, the training mode of the modern psychology consultants depends on the supervision of experts seriously, the level of the training careless training is not high, and the mastering of the modern consultants on the shared emotion conversation skills is restricted; the machine learning method for predicting the co-emotional level is not applied in the Chinese clinical consultation situation.
In order to solve the above problems, the short board of micro-technology training is supplemented and developed by combining the machine learning method, and the consultant is further cultivated by using the efficient method, the application proposes a new solution: an ECCS (encoding system) introduced into the medical field selects samples in real conversations, specifically focuses on five exploratory technologies, constructs a machine learning model for classification and feedback of the exploratory technologies, is used for training the conversation skills of a novice consultant, and improves the reaction co-situation level in consultation conversations.
Hereinafter, a method, an apparatus, a device, and a storage medium for training a social interview by a psychological consultant according to an embodiment of the present application will be described with reference to the accompanying drawings. Aiming at the problems that the training mode of a psychological consultant in the related technology mentioned in the background technology depends on expert supervision seriously, the training level of careful practice is reduced, the mastering of the consulting expert on the shared situation consultation skill is restricted, the training effect is poor and the training efficiency is low, the application provides the shared situation consultation training method of the psychological consultant. Therefore, the problems that the training mode of a psychological consultant in the correlation technique depends on expert supervision seriously, the training level is reduced, the mastering of the consultant on the shared-emotion meeting skills is restricted, the training effect is poor, the training efficiency is low and the like are solved.
Specifically, fig. 1 is a schematic flow chart of a method for training a meeting with a psychological consultant according to an embodiment of the present disclosure.
As shown in fig. 1, the method for training a sympathy meeting of a psychological counselor comprises the following steps:
in step S101, the psychological consultant is asked questions to obtain a sympathy response text of the psychological consultant.
In this embodiment, the co-emotional response of the psychological consultant is obtained, and the co emotional response is converted into a co emotional response text in a preset manner.
The preset manner may include a voice conversion manner, and the like, which is not particularly limited.
It can be understood that the text response result of the psychological consultant to the sample can be obtained in various ways, for example, the text is converted from the voice response of the psychological consultant; for example, the text reply input by the psychological consultant through the input device is directly obtained, which is not particularly limited.
In step S102, the common situation response text is subjected to text processing and vectorization processing, and a vectorized text feature is generated.
In this embodiment, the text processing and vectorization processing are performed on the common-situation response text, and the generating of the vectorized text features includes: preprocessing the common situation response text, and performing word segmentation processing on the preprocessed common situation response text to obtain text characteristics; and performing word frequency and inverse file frequency vectorization on the text features to generate vectorized text features.
The preprocessing can include processing of removing duplication, null and other characters except Chinese and English characters and numbers of the shared-case response text.
It can be understood that, in the embodiment of the present application, after the text is preprocessed, a word segmentation tool, such as a chinese word segmentation tool Jieba, is used to perform word segmentation on the preprocessed co-emotion response text, and TF-IDF (term frequency-inverse document frequency) vectorization is performed on the word segmented text, so as to convert the text into numbers that can be processed by a computer.
In step S103, the vectorized text features are input into a pre-trained machine learning model for exploratory technology classification and feedback to obtain a situation sharing technology category and similarity, a current situation sharing level of the psychological consultant is determined based on the situation sharing technology category and similarity, and the situation sharing session training is continued or ended according to a training target of the psychological consultant.
The training target can be specifically set according to training requirements of the psychological consultant so as to be used for determining that the consultant continues to repeatedly practice or finish training.
It can be understood that the machine model for automatically classifying the user input and scoring the co-emotional level can be designed through programming in the embodiment of the application, and the machine learning algorithm can effectively predict the category of the co-emotional technology in the consultation session; the similarity calculation mode of the user response and the expert response can reasonably reflect the shared situation level of the user response; therefore, the consultant can repeatedly practice at any time to improve the sharing level. It should be noted that other machine learning models, including deep learning models, may also be used in the embodiments of the present application to classify exploratory technologies.
In this embodiment, inputting the vectorized text features into a pre-trained machine learning model for heuristic technology classification and feedback to obtain a co-situation technology category and similarity, including: obtaining the actual technical classification of the co-emotion response text according to a machine learning algorithm; determining a current co-estrus level of a psychological consultant based on the co-estrus technology category and the similarity, comprising: and obtaining the shared feeling level responded by the psychological consultant according to the similarity between the shared feeling responding text of the psychological consultant and the preset text.
The preset text features may be features obtained based on expert response texts.
It can be understood that the embodiment of the application can apply a Chinese natural language processing technology, and provides an approximate way for automatically evaluating the co-situation level of the user input text by adopting a way of calculating the similarity between the user input and the expert input, so that the co-situation level evaluation does not depend on the work of high-level experts in the field any more, and the operation is more convenient.
In this embodiment, before inputting the text features into the machine learning model of the heuristic technical classification and feedback trained in advance, the method further includes: acquiring a training sample in a real conversation by using a common situation opportunity defined by a common situation exchange coding system; training a machine learning model for exploratory technology classification and feedback based on training samples, wherein the co-situational opportunities include direct expression of emotion, direct expression of challenge, and direct expression of progress.
It can be understood that, in the embodiment of the present application, aiming at the blank of the co-emotional training program based on the machine learning method in the field of chinese psychological counseling, a machine learning model construction method of exploratory technology classification and feedback as shown in fig. 2 is provided for training and improving the co emotional level of novice consultants. Specifically, the machine learning model is constructed as follows:
step one, marking the opportunity of sharing the situation: the embodiment of the application introduces an ECCS (electronic Communication Code System) in the medical field, and adopts a common-case opportunity marking mode to extract samples from the first hand data of consultation session, namely word-by-word manuscript. ECCS defines three co-estrus opportunities, direct expression of emotion, challenge and progression respectively. Training psychological researchers, marking the original conversations by character drafts and by conversation rounds after the marking consistency meets the requirements, and selecting conversations with high consistency for discussion to reach the negotiation consistency; then, the chance of sharing the situation is rewritten, so that the excessive colloquial expression is removed under the condition of ensuring independent semantics and smooth expression; and selecting the chance of sharing the case that the rewritten word number is about the preset word number, such as 90 words, as the research sample.
It should be noted that the co-emotional communication coding system is a coding system for measuring co emotional communication behaviors in communication between doctors and patients, and includes two parts: identifying the chance of sharing the case created by the patient and coding the reaction of the medical staff to the chance of sharing the case. Co-estrus opportunities are operatively defined in ECCS as direct expression of emotion, progression and challenge by patients. Although there are still more potential co-emotional opportunities for providing indirect clues in the consultation session, it is necessary to focus on the well-defined "direct" co emotional opportunities, considering that identifying the party "say nothing" is not a simple task for the expert consultant, and one of the model design objectives of the embodiments of the present application is to train the novice consultant. The definitions and examples of the three co-estrus opportunities are shown in table 1.
TABLE 1
Step two, composing a co-emotional reaction: the main techniques for the exploration phase in the Help technology three-phase model of Hill are selected, namely restatement, open questions for ideas, emotional reflection, emotional exposure, and open questions for emotions. Doctor students who are clinically and consulted with psychology specialty are trained and asked to use the five technologies to perform text response on the samples. The Chinese psychology society registers the supervising teacher to check the quality of responses.
It should be noted that, in the embodiment of the present application, a machine learning method in the field of artificial intelligence may be adopted to automatically classify heuristic technologies in an HSS (help users System, help people technology model), so that a tedious manual coding manner is omitted, and a research method for a consultation session text is improved; other interview techniques besides exploratory techniques in the assistive technology model, such as insight-promoting techniques and action-promoting techniques, may also be used for the response training.
In particular, the helpers technical model exploration phase is based on the theory of human-based treatment, with the aim of understanding the consulting objectives of the parties, wherein restatement and open questions for ideas can help the parties explore ideas, while emotion reflection, emotion exposure and open questions for emotion can encourage the parties to experience and express emotion. The increase of the application ratio of exploratory skills in consultation sessions is also taken as a target for technical training of psychological counseling helpers. The definition, examples, and general intentions of the consultant of the five exploratory techniques are shown in table 2, according to the definition of the assistive technology system.
TABLE 2
Step three, data preprocessing: python 3.9.6 programming can be used to perform preliminary data processing on the sympathy responses (including expert and user sympathy responses) including deduplication, nulling, and removing characters other than chinese and english characters and numbers; and performing word segmentation on the preliminarily processed common-case response text by adopting a common Chinese word segmentation tool Jieba for word segmentation.
Step four, text vectorization: and carrying out word frequency-inverse file frequency vectorization on the text after word segmentation, and converting the text into numbers which can be processed by a computer.
Step five, similarity calculation: and calculating the cosine distance between the text of the user response and the expert response by using a similarity function in the generic module, wherein the similarity between the user response and the expert response is used as the similarity of the user response and the expert response.
Step six, machine learning classification: the method comprises the steps of programming by Python 3.9.6, carrying out similar preliminary data processing on samples, carrying out TF-IDF vectorization on the text after word segmentation to obtain text characteristics, carrying out model training by adopting common machine learning models such as a polynomial naive Bayes classifier, a logistic regression classifier, a random forest classifier and a support vector linear classifier, and verifying the classification accuracy of the model on five exploratory technologies.
Step seven, embedding the small program: the applet is developed based on a traditional developer tool, a cloud server is adopted, and a Mysql database is configured on the cloud server to realize data storage and extraction. In addition, the small program is integrated with a voice recognition function, and the voice input of a user can be converted into character output. The cloud server is built by taking a python language as an environment, the applet can transmit a text into the server by a POST method, the text is processed by a python algorithm, an operation result is returned in a json format, and the operation result is displayed in the applet after being processed.
In a specific application, as shown in fig. 3, when the user inputs a co-emotion response, the speech input of the user is transcribed into text in the background, compared with the existing expert responses, and the model outputs the similarity between the user response and the expert responses and the co-emotion technology classification of automatic prediction. The purpose of improving the shared situation level of novice consultants is achieved by the deliberate practice of a specific exploratory technology.
According to the common-situation conversation training method for the psychological consultants, provided by the embodiment of the application, a machine model for automatically classifying the user input by technology and scoring the common-situation level is designed through programming, the category of the common-situation technology in the psychological consultants can be effectively predicted, and the common-situation level responded by the user can be reasonably reflected according to the similarity between the user response and the expert response, so that the expert is not required to supervise and guide the conversation skill of the psychological consultants to be automatically and effectively trained, the reaction common-situation level in the psychological consultants is improved, and the training effect and efficiency are effectively improved.
Next, a sympathy session training apparatus for psychological consultants according to an embodiment of the present application will be described with reference to the accompanying drawings.
Fig. 4 is a block diagram of a device for training a social interview between psychological consultants in accordance with an embodiment of the present application.
As shown in fig. 4, the apparatus 10 for training a sympathy meeting of a psychological counselor includes: an acquisition module 100, a processing module 200, and a training module 300.
The obtaining module 100 is configured to ask a question of a psychological consultant and obtain a situation-sharing response text of the psychological consultant; the processing module 200 is configured to perform text processing on the common situation response text, perform vectorization processing, and generate vectorized text features; the training module 300 is configured to input the vectorized text features into a pre-trained machine learning model for exploratory technology classification and feedback to obtain a situation-sharing technology category and similarity, determine a current situation-sharing level of the psychological consultant based on the situation-sharing technology category and similarity, and continue to perform the situation-sharing conversation training according to a training target of the psychological consultant or end the situation-sharing conversation training.
Further, the apparatus 10 of the embodiment of the present application further includes: and a model building module. The model construction module is used for acquiring training samples in real conversations by using the common-case opportunities defined by the common-case exchange coding system before inputting text features into a pre-trained exploratory technology classification and feedback machine learning model; training a machine learning model for exploratory technology classification and feedback based on training samples, wherein the co-situational opportunities include direct expression of emotion, direct expression of challenge, and direct expression of progress.
Further, the obtaining module 100 is configured to obtain a co-emotion response of the psychological consultant, and convert the co-emotion response into a co-emotion response text in a preset manner; the processing module 200 is configured to pre-process the common situation response text, perform word segmentation on the pre-processed common situation response text to obtain text features, and perform word frequency and inverse file frequency vectorization on the text features to generate vectorized text features; the training module 300 obtains the actual technical classification of the co-emotional response text according to the machine learning algorithm, and obtains the co-emotional level responded by the psychological consultant according to the similarity between the co-emotional response text of the psychological consultant and the preset text.
It should be noted that the explanation of the embodiment of the method for training a sympathy session of a psychological consultant is also applicable to the apparatus for training a sympathy session of a psychological consultant of the embodiment, and is not repeated herein.
According to the common-situation conversation training device for the psychological consultants, which is provided by the embodiment of the application, a machine model for automatically classifying the technology and scoring the common-situation level of the user input is designed through programming, the category of the common-situation technology in the psychological consultants can be effectively predicted, and the common-situation level of the user response is reasonably embodied according to the similarity of the user response and the expert response, so that the expert is not required to supervise the conversation skill of the psychological consultants to be automatically and effectively trained, the reaction common-situation level in the psychological consultants is improved, and the training effect and efficiency are effectively improved.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device may include:
a memory 501, a processor 502, and a computer program stored on the memory 501 and executable on the processor 502.
The processor 502, when executing the program, implements the sympathy session training method for psychological consultants provided in the above-described embodiments.
Further, the electronic device further includes:
a communication interface 503 for communication between the memory 501 and the processor 502.
A memory 501 for storing computer programs that can be run on the processor 502.
The memory 501 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 5, but this is not intended to represent only one bus or type of bus.
Optionally, in a specific implementation, if the memory 501, the processor 502, and the communication interface 503 are integrated on a chip, the memory 501, the processor 502, and the communication interface 503 may complete communication with each other through an internal interface.
The processor 502 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement embodiments of the present Application.
Embodiments of the present application also provide a computer-readable storage medium having a computer program stored thereon, where the computer program is executed by a processor to implement the above method for training a social interview by a psychological consultant.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more N executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of implementing the embodiments of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. A method for training a sympathy meeting of a psychological consultant is characterized by comprising the following steps:
asking questions of a psychological consultant, and acquiring a co-emotional response text of the psychological consultant;
performing text processing on the co-situation response text, and performing vectorization processing to generate vectorized text features; and
inputting the vectorized text features into a pre-trained machine learning model for exploratory technology classification and feedback to obtain the co-situation technology category and the similarity, determining the current co-situation level of the psychological consultant based on the co-situation technology category and the similarity, and continuing the co-situation conversation training according to the training target of the psychological consultant or ending the co-situation conversation training.
2. The method of claim 1, further comprising, prior to inputting the textual features to a pre-trained exploratory technology classification and fed-back machine learning model:
acquiring a training sample in a real conversation by using a common situation opportunity defined by a common situation exchange coding system;
training a machine learning model of the exploratory technology classification and feedback based on the training samples, wherein the co-estrus opportunities include direct expressions of emotion, challenge, and progress.
3. The method of claim 1, wherein said questioning a psychological consultant and obtaining a sympathy response text of said psychological consultant comprises:
and acquiring the co-emotional response of the psychological consultant, and converting the co-emotional response into a co-emotional response text in a preset mode.
4. The method of claim 1, wherein the text processing the co-emotion response text and performing vectorization processing to generate vectorized text features comprises:
preprocessing the common situation response text, and performing word segmentation processing on the preprocessed common situation response text to obtain the text characteristics;
and carrying out word frequency and inverse file frequency vectorization on the text features to generate the vectorized text features.
5. The method of claim 1,
inputting the vectorized text features into a pre-trained machine learning model for heuristic technology classification and feedback to obtain a co-situation technology category and similarity, comprising: obtaining the actual technical classification of the shared situation response text according to a machine learning algorithm;
the determining a current co-estrus level of the psychological consultant based on the co-estrus technology category and the similarity comprises: and obtaining the shared situation level responded by the psychological consultant according to the similarity between the shared situation responding text of the psychological consultant and a preset text.
6. A sympathy session training apparatus for psychological consultants, comprising:
the system comprises an acquisition module, a query module and a query module, wherein the acquisition module is used for asking a psychological consultant to acquire a situation-sharing response text of the psychological consultant;
the processing module is used for performing text processing on the common situation response text, performing vectorization processing and generating vectorized text characteristics; and
and the training module is used for inputting the vectorized text features into a pre-trained machine learning model for exploratory technology classification and feedback to obtain the co-situation technology category and the similarity, determining the current co-situation level of the psychological consultant based on the co-situation technology category and the similarity, and continuing the co-situation conversation training according to the training target of the psychological consultant or ending the co-situation conversation training.
7. The apparatus of claim 6, further comprising:
the model construction module is used for acquiring training samples in real conversations by using the common-case opportunities defined by the common-case exchange coding system before inputting the text features into a pre-trained exploratory technology classification and feedback machine learning model; training a machine learning model of the exploratory technology classification and feedback based on the training samples; wherein the co-estrus opportunities include direct expression of emotion, direct expression of challenge, and direct expression of progression.
8. The apparatus of claim 6,
the acquisition module is used for acquiring the co-emotion responses of the psychological consultants and converting the co-emotion responses into co-emotion response texts in a preset mode;
the processing module is used for preprocessing the common situation response text, performing word segmentation processing on the preprocessed common situation response text to obtain the text characteristics, and performing word frequency and inverse file frequency vectorization on the text characteristics to generate the vectorized text characteristics;
the training module is used for obtaining the actual technical classification of the shared situation response text according to a machine learning algorithm and obtaining the shared situation level responded by the psychological consultant according to the similarity between the shared situation response text of the psychological consultant and a preset text.
9. An electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method of mental consultant's sympathy training of any of claims 1-5.
10. A computer-readable storage medium, on which a computer program is stored, the program being executable by a processor for implementing the method for mental consultant's sympathy session training according to any one of claims 1 to 5.
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