A semantic analysis method, a referral processing method and a device

Abstract

The present application provides a semantic analysis method, a referral processing method and a device, which relate to the field of electronic digital data processing technology, and in particular to the field of semantic analysis in processing natural language data, wherein the semantic analysis method includes: obtaining element information of the interaction between a target user and a first device end; performing semantic analysis on the element information to match a target cluster; switching the target user from a communication connection with the first device end to a communication connection with a second device end in the target cluster. The above scheme solves the problems of low query efficiency and low data utilization in the existing interactive switching, and achieves the technical effect of effectively improving the interactive switching efficiency and data utilization.

Description

Technical Field

[0001] The present application belongs to the field of electronic digital data processing technology, in particular to the field of semantic analysis in natural language data processing, and more particularly to a semantic analysis method, a referral processing method and a device. Background Art

[0002] Currently, in the interaction field of different types of subjects, for example, the interaction between patients and doctors, and the interaction between patients and pharmacists, the two cannot communicate with each other because of the different interaction subjects. As a result, if the pharmacist cannot give an effective response during the interaction with the pharmacist and needs a response from the doctor, the patient needs to exit the scene of interaction with the pharmacist and then initiate a request to interact with the doctor. In other words, the pharmacist and the doctor cannot communicate with each other.

[0003] This results in a waste of data resources. For example, the patient has already explained his or her condition to the pharmacist, but when switching to the doctor, he or she needs to describe it again, resulting in low inquiry efficiency and ineffective use of data.

[0004] Currently, no effective solution has been proposed to the above problems. Summary of the invention

[0005] The purpose of this application is to provide a semantic analysis method, a referral processing method and a device, which can solve the existing problems of low inquiry efficiency and low data utilization.

[0006] The present application provides a semantic analysis method, a referral processing method and a device which are implemented as follows:

[0007] A semantic analysis method, comprising:

[0008] Acquire element information of the interaction between the target user and the first device end;

[0009] Performing semantic analysis on the element information to match and obtain a target cluster;

[0010] The target user is switched from a communication connection with the first device end to a communication connection with a second device end in the target cluster.

[0011] A referral approach comprising:

[0012] The pharmacist receives medication consultation requests from target users;

[0013] In response to the medication consultation request, communicating with the target user;

[0014] During the doctor-patient communication process, the target user is matched with a department through the above-mentioned semantic analysis method, and the target user is referred to the matched department.

[0015] A semantic analysis device, comprising:

[0016] An acquisition module, used to acquire element information of the interaction between the target user and the first device end;

[0017] An analysis module, used for performing semantic analysis on the element information to match and obtain a target cluster;

[0018] The switching module is used to switch the target user from a communication connection with the first device end to a communication connection with a second device end in the target cluster.

[0019] An electronic device includes a processor and a memory for storing instructions executable by the processor, wherein when the processor executes the instructions, the steps of the following method are implemented:

[0020] Acquire element information of the interaction between the target user and the first device end;

[0021] Performing semantic analysis on the element information to match and obtain a target cluster;

[0022] The target user is switched from a communication connection with the first device end to a communication connection with a second device end in the target cluster.

[0023] A computer-readable storage medium having computer instructions stored thereon, wherein the instructions, when executed, implement the steps of the following method:

[0024] Acquire element information of the interaction between the target user and the first device end;

[0025] Performing semantic analysis on the element information to match and obtain a target cluster;

[0026] The target user is switched from a communication connection with the first device end to a communication connection with a second device end in the target cluster.

[0027] The semantic analysis method provided by the present application obtains the element information of the interaction when the target user interacts with the first device end, and then matches the target user to the target department based on the element information, thereby realizing direct and efficient switching from the first device end to the second device end. For example, regardless of whether the user is currently facing a nurse, a pharmacist, or a drug seller, etc., they can efficiently switch to the second device end of the target cluster. The above solution solves the problems of low inquiry efficiency and low data utilization in the existing interactive switching, and achieves the technical effect of effectively improving the interactive switching efficiency and data utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0029] Figure 1 It is an architectural diagram of the semantic analysis system provided by this application;

[0030] Figure 2 is a method flow chart of an embodiment of the semantic analysis method provided by the present application;

[0031] Figure 3 is a method flow chart of an embodiment of the referral processing method provided by the present application;

[0032] Figure 4 This is a schematic diagram of the referral process from pharmacists to doctors provided by this application;

[0033] Figure 5 It is a schematic diagram of the principle of obtaining the full word set and word frequency from information provided by this application;

[0034] Figure 6 It is a schematic diagram of the principle of vectorized similarity matching provided by this application;

[0035] Figure 7 It is a hardware structure block diagram of an electronic device of a semantic analysis method provided by the present application;

[0036] Figure 8 It is a structural block diagram of an embodiment of the semantic analysis method provided by the present application. DETAILED DESCRIPTION

[0037] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0038] Considering that after the existing target user interacts with the first device, if the first device cannot meet his requirements, he needs to switch to the second device for interaction. In this way, the user needs to exit the interaction with the first device and then trigger the interaction with the second device, which means there is a breakpoint. To this end, in this example, a semantic analysis system in the interaction process is provided, such as Figure 1As shown, it may include: a target user, a first device end, and a second device end.

[0039] Based on the semantic analysis system, when the first target user interacts with the first device, if the first device determines that the target user needs to switch to the interaction with the second device, or the target user actively triggers the interaction to switch to the second device. Then, the target user can be matched with a cluster through the element information of the interaction between the target user and the first device, and then the target user is switched from the interaction with the first device to the interaction with the second device. These processing operations, i.e., semantic analysis and switching operations, can be implemented by a server independent of the device, or integrated in the first device. The specific setting method can be determined according to actual needs, and this application does not limit this.

[0040] To this end, a semantic analysis method is provided in this example, such as Figure 2 As shown, the following steps may be included:

[0041] Step 201: Obtaining element information of the interaction between the target user and the first device end;

[0042] Among them, assuming that the current scenario is medication consultation, the above-mentioned element information may include but is not limited to the following two or more: the target user's chief complaint data, the interaction data between the target user and the first device end, and the symptom data associated with the target user's inquiry about the drug. That is, if the user is consulting on medication, the element information may include: drugs and symptoms associated with the drugs. Furthermore, in order to achieve accurate cluster matching, it may include the interaction content between the user and the first device end, as well as the target user or the target user's chief complaint data generated by the first device end.

[0043] Step 202: performing semantic analysis on the element information to match and obtain a target cluster;

[0044] Step 203: Switch the target user's communication connection from the first device end to the communication connection with the second device end in the target cluster.

[0045] In order to perform cluster matching based on element information, a word segmentation mode may be adopted. For example, the element information may be segmented to obtain a full word set; word frequency statistics may be performed on each word in the full word set; and cluster matching may be performed based on the word frequency statistics results.

[0046] For example, the element information is segmented to obtain: word 1, word 2, word 1, word 3, word 3, etc., and then the frequency of each word is counted, that is, the number of times each word appears in the result of the word segmentation is determined. For example, the statistics obtain word 1 (5 times), word 2 (6 times), word 3 (8 times), etc. After obtaining the word frequency statistics results, the consultation tendency of the target user can be characterized based on the word frequency statistics results, so as to perform cluster allocation.

[0047] After the word frequency statistics are performed, cluster matching can be performed based on the word frequency statistics results using one of the following matching logics, but not limited to:

[0048] Method 1: The word with the highest frequency of occurrence is used as the target word; cluster matching is performed using the target word.

[0049] This processing method is to directly select the word with the highest frequency as the target word, which is relatively simple to implement and has the lowest cost.

[0050] Method 2: Perform word frequency vectorization processing on the word frequency statistics results to obtain a first vector; perform word frequency statistics and word frequency vectorization processing on the main treatment items of each cluster to obtain multiple second vectors; calculate the similarity between the first vector and each second vector; and use the cluster corresponding to the second vector with the highest similarity as the matched target cluster.

[0051] This processing method is to perform cluster matching through vectorization, that is, calculate the vector obtained based on the element information and the vector obtained based on the cluster's main treatment term, and then match them by calculating the similarity between the vectors to obtain the target cluster. Through vectorization and vector similarity, more accurate similarity matching can be achieved, and the results can be matched from similar dimensions.

[0052] Method 3: Set a weight value for each word according to the word frequency statistics; determine the element information type of each word and obtain the weight coefficient of each element information type; multiply the weight value of each word by the weight coefficient of the element information type to obtain the target weight of each word; select the word with the highest target weight as the target word for cluster matching.

[0053] This processing method is to set a weight coefficient, that is, set a weight for each word in the word frequency statistics results, and set a weight for the element type to which each word belongs in the element information. The two weights are combined to obtain the word with the highest weight as the target word. By setting the weight, different weight coefficients can be set based on actual conditions and needs, so as to determine the target word with emphasis. Relatively speaking, the accuracy and adaptability are higher, and the results obtained are more consistent with the actual situation.

[0054] In order to reduce the information input by the user during the interaction between the second device and the user, so that the second device can get the user's request as soon as possible, in this example, after the target user is switched from the communication connection with the first device to the communication connection with the second device in the target department, the element information of the interaction between the target user and the first device can be transmitted to the second device. By transmitting the interactive content between the user and the first device to the second device, the data utilization rate can be effectively improved, so that the second device can quickly obtain the required information.

[0055] The above-mentioned semantic analysis method and system can be applied in multiple scenarios, such as switching between e-commerce sellers and pharmacists, switching between e-commerce sellers and doctors, switching between pharmacists and doctors, or other scenarios where there is a need to switch between interactive objects with application isolation.

[0056] Based on this, taking the interactive switching between pharmacists and physicians as an example, a referral processing method is provided in this case, such as Figure 3 As shown, the following steps may be included:

[0057] Step 301: The pharmacist receives a medication consultation request from a target user;

[0058] Step 302: In response to the medication consultation request, conduct doctor-patient communication with the target user;

[0059] Step 303: During the doctor-patient communication process, department matching is performed for the target user through the above-mentioned semantic analysis method, and the target user is referred to the matched department.

[0060] That is, when a user consults a pharmacist about medication, if the pharmacist cannot answer the user's question, then the user can be triggered to switch to the doctor's side for medication consultation. In order to achieve simple and efficient consultation switching, the department can be matched based on the doctor-patient communication data between the user and the pharmacist, so that the user can be divided into the appropriate department.

[0061] To this end, the pharmacist can directly switch to the target department, or consult the user to determine whether the user wishes to make a referral. If the user agrees to the referral, the pharmacist will switch to the target department and match or select a doctor for further medication consultation.

[0062] In order to reduce the workload of the doctor after the consultation, the pharmacist can generate a work order, record the user's chief complaint data in the work order, and then the doctor can know the problems and situations that the user wants to consult as soon as possible by browsing the chief complaint data, or the pharmacist can directly push the communication data with the user to the doctor, so that the doctor can quickly know the communication request to understand the problems and situations that the user wants to consult. That is, before matching the department for the target user, the pharmacist can generate a work order, and the work order carries the chief complaint data of the target user. By generating a work order and recording the user's chief complaint data in it, the viewer can quickly obtain the user's information. When implemented, after the target user is referred to the matched department, the medical communication data between the pharmacist and the target user can be transmitted to the matched department; and / or the work order is transmitted to the matched department.

[0063] The following is explained in conjunction with a specific scenario. However, it is worth noting that the specific scenario is only an exemplary description and can be extended to other similar scenarios. This application does not limit this.

[0064] In this scenario, we consider that many users have a vague understanding of the roles of online pharmacists and doctors. During the drug purchase process, the questions asked to the pharmacist may be too biased towards diagnosis and treatment, and the pharmacist may find it difficult to give an accurate answer. In this case, the user needs to log in to the medical application and consult the doctor, which is bound to result in a breakpoint. Therefore, in this example, if the pharmacist cannot answer the question, the user can be directly given an entrance to the doctor's consultation service to guide the user into the process of consulting the doctor. This can avoid the occurrence of breakpoints, reduce user operations, and improve user experience.

[0065] Furthermore, when the pharmacist needs to transfer the service to the doctor after finishing the service, the pharmacist can summarize the record of this service and give the user's main complaint information as the basis for the patient's triage to the doctor's service, and provide it to the doctor. In this way, the user does not need to describe the condition or personal situation, thereby avoiding multiple descriptions of the condition, reducing the user's operating costs, and opening up the service node from pharmacist to doctor.

[0066] Therefore, the overall process can be as follows Figure 4 As shown in the figure, the user initiates an inquiry, and the pharmacist workstation determines whether the problem can be solved and replies to the problem that can be solved. If the pharmacist workstation cannot solve the problem, a service record is created and the inquiry service is transferred to the doctor. The triage system can perform intelligent triage based on the service record information, and then manually verify and push it to the user for selection. The user can choose the doctor service, and then the doctor accesses and starts the consultation. After the consultation is completed, a prescription can be prescribed, and the user clicks on the prescription to purchase the medicine.

[0067] In actual implementation, a referral trigger button can be set on the display interface of the pharmacist side. When the pharmacist feels that referral is necessary, the button can be triggered to match the department for the user, or to inquire the user whether to refer the patient.

[0068] For the triage system, when performing triage, the target department can be determined according to the above semantic analysis method. Specifically, the target department can be determined based on the pharmacist's service record or the communication data between the pharmacist and the user, as well as the disease information of the drug, etc.

[0069] Specifically, you can Figure 5 As shown, the disease information of the drug, the communication messages between the pharmacist and the patient, and the main complaint content filled in by the pharmacist are obtained, and then the three types of information are segmented to obtain the segmentation result set of each type of information, and merged into a large full word set. A weight coefficient can be set for each type of information.

[0070] For example, sets are derived from three types of information respectively and combined into a large full-word set.

[0071] 1. Drug symptom information (weight coefficient n), segment the drug symptom information to obtain a keyword set (keyword 1, keyword 2, keyword 3);

[0072] 2. Communication messages between pharmacists and patients (weight coefficient m), analyze the communication messages and obtain a set of keywords (keyword 4, keyword 5, keyword 6);

[0073] 3. The content of the chief complaint filled in by the pharmacist (weight coefficient k), the chief complaint information generated by the pharmacist is segmented to obtain a keyword set (keyword 7, keyword 8, keyword 9).

[0074] Then, the frequency of each word in the full word set is calculated, and the recommended departments are matched through the full word set to obtain the recommended departments.

[0075] Specifically, matching the recommended departments through the full word set and obtaining the recommended departments may include but is not limited to one of the following methods:

[0076] Method 1: Perform word frequency statistics on the entire word set to obtain the word with the highest frequency, and then match the department using the word with the highest frequency.

[0077] Method 2: Sort each word in the full word set according to word frequency, and set a continuous default weight value for each word based on the sorting result, multiply the default weight value of the word by the weight coefficient in the type it belongs to (if the word is in two types, select the largest weight coefficient), and select the word with the highest weight value for department matching based on the sorting result.

[0078] Method 3: Based on the full word set and word frequency, perform word frequency vectorization (for example, the information content and the department's main treatment can be vectorized), and then calculate the similarity between the two vectors, and use the department with the highest similarity as the recommended department.

[0079] For example: Figure 6 As shown, in the information content, the word frequency of word 1 is 2, so the corresponding vector value is x1=2, the word frequency of word 2 is 1, so the corresponding vector value is x2=1, the word frequency of word 3 is 3, so the corresponding vector value is x3=3; in the department's attending set, the word frequency of word 1 is 1, so the corresponding vector value is y1=1, the word frequency of word 2 is 1, so the corresponding vector value is y2=1, the word frequency of word 3 is 1, so the corresponding vector value is y3=1.

[0080] The vector corresponding to the information content is (x1, x2, x3), and the vector corresponding to the department's main treatment is (y1, y2, y3).

[0081] When finding similar values ​​between the two, you can use the following formula to find them:

[0082]

[0083] That is, cos(θ) represents the degree of similarity, and the closer the department similarity is to 1, the higher the similarity is, because the department with the similarity value closest to 1 can be used as the target recommended department.

[0084] However, it is worth noting that the application scenario given in the above example and the specific calculation method adopted are only exemplary expressions. In actual implementation, they can be applied to other scenarios, and other methods based on word frequency statistics can be used for department matching.

[0085] The method embodiments provided in the above embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on an electronic device as an example, Figure 7 : is a hardware structure block diagram of an electronic device of a semantic analysis method provided by this application. Figure 7As shown, the electronic device 10 may include one or more (only one is shown in the figure) processors 02 (the processor 02 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 04 for storing data, and a transmission module 06 for communication functions. It can be understood by those skilled in the art that Figure 7 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 7 More or fewer components as shown, or with Figure 7 Different configurations shown.

[0086] The memory 04 can be used to store software programs and modules of application software, such as program instructions / modules corresponding to the semantic analysis method in the embodiment of the present application. The processor 02 executes various functional applications and data processing by running the software programs and modules stored in the memory 04, that is, implements the semantic analysis method of the above-mentioned application. The memory 04 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 04 may further include a memory remotely arranged relative to the processor 02, and these remote memories can be connected to the electronic device 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0087] The transmission module 06 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the electronic device 10. In one example, the transmission module 06 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission module 06 can be a radio frequency (Radio Frequency, RF) module, which is used to communicate with the Internet wirelessly.

[0088] At the software level, the above devices can be Figure 8 As shown, it may include:

[0089] The acquisition module 801 is used to acquire the element information of the interaction between the target user and the first device end;

[0090] An analysis module 802 is used to perform semantic analysis on the element information to match the target department;

[0091] The switching module 803 is used to switch the target user from a communication connection with the first device end to a communication connection with the second device end in the target department.

[0092] In one implementation, the analysis module 802 may specifically include: performing word segmentation processing on the element information to obtain a full word set; performing word frequency statistics on each word in the full word set; and performing department matching based on the word frequency statistics results.

[0093] In one embodiment, performing department matching according to word frequency statistics may include: taking the word with the highest frequency of occurrence as the target word; and performing department matching using the target word.

[0094] In one embodiment, department matching based on word frequency statistics results may include: performing word frequency vectorization processing based on the word frequency statistics results to obtain a first vector; performing word frequency statistics and word frequency vectorization processing on the main treatment items of each department to obtain multiple second vectors; calculating the similarity between the first vector and each second vector; and taking the department corresponding to the second vector with the highest similarity as the target department obtained by matching.

[0095] In one embodiment, department matching based on word frequency statistics may include: setting a weight value for each word based on the word frequency statistics; determining the element information type of each word and obtaining the weight coefficient of each element information type; multiplying the weight value of each word by the weight coefficient of the element information type to obtain the target weight of each word; and selecting the word with the highest target weight as the target word for department matching.

[0096] In one embodiment, the above-mentioned element information may include but is not limited to at least one of the following: the main complaint data of the target user, the interaction data between the target user and the first device, the disease data associated with the drug inquired by the target user,

[0097] In one embodiment, after the target user is switched from a communication connection with a first device end to a communication connection with a second device end in the target department, element information of the interaction between the target user and the first device end can be transmitted to the second device end.

[0098] In this example, a referral processing device is also provided, which is located at the pharmacist end, and the pharmacist end receives the medication consultation request of the target user; in response to the medication consultation request, the pharmacist communicates with the target user; in the process of doctor-patient communication, the target user is matched with a department through the above-mentioned semantic analysis method, and the target user is referred to the matched department.

[0099] In one embodiment, before matching the target user to a department, the pharmacist may also generate a work order, in which the main complaint data of the target user is carried.

[0100] In one embodiment, after the target user is referred to the matched department, the medical-patient communication data between the pharmacist and the target user may be transmitted to the matched department; and / or the work order may be transmitted to the matched department.

[0101] The embodiments of the present application also provide a specific implementation of an electronic device capable of implementing all the steps in the semantic analysis method in the above embodiments, wherein the electronic device specifically includes the following contents: a processor, a memory, a communication interface, and a bus; wherein the processor, the memory, and the communication interface communicate with each other through the bus; the processor is used to call a computer program in the memory, and when the processor executes the computer program, all the steps in the semantic analysis method in the above embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0102] Step 1: Obtaining element information of the interaction between the target user and the first device;

[0103] Step 2: Perform semantic analysis on the element information to match the target department;

[0104] Step 3: Switch the target user's communication connection from the first device end to the communication connection with the second device end in the target department.

[0105] From the above description, it can be seen that when the target user interacts with the first device end, the embodiment of the present application obtains the element information of the interaction, and then matches the target user to the target department based on the element information, so as to achieve direct and efficient switching from the first device end to the second device end. That is, no matter whether the user is currently facing a nurse, a pharmacist, or a drug seller, etc., he can efficiently switch to the second device end of the target department. The above solution solves the problems of low inquiry efficiency and low data utilization in the existing interactive switching, and achieves the technical effect of effectively improving the interactive switching efficiency and data utilization.

[0106] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps in the semantic analysis method in the above embodiments. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, all the steps in the semantic analysis method in the above embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0107] Step 1: Obtaining element information of the interaction between the target user and the first device;

[0108] Step 2: Perform semantic analysis on the element information to match the target department;

[0109] Step 3: Switch the target user's communication connection from the first device end to the communication connection with the second device end in the target department.

[0110] From the above description, it can be seen that when the target user interacts with the first device end, the embodiment of the present application obtains the element information of the interaction, and then matches the target user to the target department based on the element information, so as to achieve direct and efficient switching from the first device end to the second device end. That is, no matter whether the user is currently facing a nurse, a pharmacist, or a drug seller, etc., he can efficiently switch to the second device end of the target department. The above solution solves the problems of low inquiry efficiency and low data utilization in the existing interactive switching, and achieves the technical effect of effectively improving the interactive switching efficiency and data utilization.

[0111] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the hardware + program embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0112] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0113] Although the present application provides method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-creative labor. The order of steps listed in the embodiments is only one way of executing the order of many steps and does not represent the only execution order. When the actual device or client product is executed, it can be executed in the order of the method shown in the embodiments or the drawings or in parallel (for example, in a parallel processor or multi-threaded processing environment).

[0114] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a vehicle-mounted human-computer interaction device, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0115] Although the present specification embodiment provides the method operation steps as described in the embodiment or flow chart, more or less operation steps may be included based on conventional or non-creative means. The order of steps listed in the embodiment is only one way in the order of execution of many steps, and does not represent a unique execution order. When the device or terminal product in practice is executed, it can be executed in sequence or in parallel (such as a parallel processor or a multi-threaded processing environment, or even a distributed data processing environment) according to the method shown in the embodiment or the accompanying drawings. The term "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, product or equipment including a series of elements not only includes those elements, but also includes other elements not clearly listed, or also includes elements inherent to such process, method, product or equipment. In the absence of more restrictions, it is not excluded that there are other identical or equivalent elements in the process, method, product or equipment including the elements.

[0116] For the convenience of description, the above devices are described in various modules according to their functions. Of course, when implementing the embodiments of this specification, the functions of each module can be implemented in the same or more software and / or hardware, or the module implementing the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0117] Those skilled in the art also know that, in addition to implementing the controller in a purely computer-readable program code, the controller can be made to implement the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the devices for implementing various functions included therein can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules for implementing the method and structures within the hardware component.

[0118] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0119] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0121] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0122] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0123] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0124] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, the embodiments of this specification may take the form of complete hardware embodiments, complete software embodiments or embodiments combining software and hardware. Moreover, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0125] The present specification embodiments may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present specification embodiments may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0126] Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. In the description of this specification, the description of the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of this specification. In this specification, the schematic representation of the above terms does not necessarily target the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, in the absence of contradiction, a person skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0127] The above is only an example of the embodiment of the present specification and is not intended to limit the embodiment of the present specification. For those skilled in the art, the embodiment of the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiment of the present specification shall be included in the scope of the claims of the embodiment of the present specification.

Claims

1. A semantic analysis method, characterized in that: include: Acquire element information of the interaction between the target user and the first device end; Performing semantic analysis on the element information to match and obtain a target cluster; Switching the target user from a communication connection with the first device end to a communication connection with a second device end in the target cluster; After the target user is switched from the communication connection with the first device end to the communication connection with the second device end in the target cluster, the method further includes: Transmitting the element information of the interaction between the target user and the first device end to the second device end; Among them, the element information includes two or more of the following: the target user's chief complaint data, the interaction data between the target user and the first device, and the symptom data associated with the drug inquired by the target user; when the user is seeking medication consultation, the element information includes: the drug and the symptoms associated with the drug.

2. The method according to claim 1, characterized in that The element information is semantically analyzed to match the target cluster including: Performing word segmentation processing on the element information to obtain a full word set; Performing word frequency statistics on each word in the full word set; Cluster matching is performed based on word frequency statistics.

3. The method according to claim 2, characterized in that Cluster matching based on word frequency statistics includes: The word with the highest frequency of occurrence is used as the target word; Cluster matching is performed using the target words.

4. The method according to claim 2, characterized in that: Cluster matching based on word frequency statistics includes: Perform word frequency vectorization processing according to the word frequency statistical result to obtain a first vector; Perform word frequency statistics and word frequency vectorization processing on the main treatment items of each cluster to obtain multiple second vectors; Calculating the similarity between the first vector and each second vector; The cluster corresponding to the second vector with the highest similarity is used as the matched target cluster.

5. The method according to claim 2, characterized in that: Cluster matching based on word frequency statistics includes: Set a weight value for each word based on the word frequency statistics; Determine the element information type of each word, and obtain the weight coefficient of each element information type; Multiply the weight value of each word by the weight coefficient of the element information type to obtain the target weight of each word; Select the word with the highest target weight as the target word for cluster matching.

6. The method according to claim 1, characterized in that The element information includes at least one of the following: the main complaint data of the target user, the interaction data between the target user and the first device, and the disease data associated with the drug inquired by the target user.

7. A referral processing method, characterized in that: include: The pharmacist receives medication consultation requests from target users; In response to the medication consultation request, communicating with the target user; During the doctor-patient communication process, department matching is performed for the target user using the semantic analysis method described in any one of claims 1 to 6, and the target user is referred to the matched department.

8. The method according to claim 7, characterized in that Before department matching for the target user, it also includes: The pharmacist generates a work order, which carries the main complaint data of the target user.

9. The method according to claim 8, characterized in that After referring the target user to the matched department, it also includes: Transmitting the doctor-patient communication data between the pharmacist terminal and the target user to the matched department; and / or, The work order is transmitted to the matched department.

10. A semantic analysis device, characterized in that: include: An acquisition module, used to acquire element information of the interaction between the target user and the first device end; An analysis module, used for performing semantic analysis on the element information to match and obtain a target cluster; A switching module, used to switch the target user from a communication connection with the first device end to a communication connection with a second device end in the target cluster; After the target user is switched from the communication connection with the first device end to the communication connection with the second device end in the target cluster, the method further includes: Transmitting the element information of the interaction between the target user and the first device end to the second device end; Among them, the element information includes two or more of the following: the target user's chief complaint data, the interaction data between the target user and the first device, and the symptom data associated with the drug inquired by the target user; when the user is seeking medication consultation, the element information includes: the drug and the symptoms associated with the drug.

11. An electronic device, comprising a processor and a memory for storing processor-executable instructions, wherein the processor implements the steps of the method according to any one of claims 1 to 6 when executing the instructions.

12. A computer-readable storage medium having computer instructions stored thereon, wherein the instructions, when executed, implement the steps of the method according to any one of claims 1 to 6.

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