Oral medicine hospital guide management method and system

Abstract

The invention provides a stomatology hospital guide management method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: estimating the remaining waiting time of a patient when a new patient in a stomatology department is waiting; when the remaining waiting time length exceeds a preset first time length threshold value, inquiring the hospital guide demand of the patient; when the remaining waiting duration exceeds a preset second duration threshold corresponding to the hospital guide demand, generating an artificial intelligence hospital guide model based on the remaining waiting duration and a preset hospital guide material library corresponding to the hospital guide demand; and performing corresponding hospital guide on the patient based on the artificial intelligence hospital guide model. The system can automatically determine whether the patient can be guided or not and correspondingly guide the patient after determining that the patient can be guided, waiting reception personnel do not need to be set to answer the problem of the patient, the labor cost is reduced, in addition, the patient can receive system guide through a smartphone carried by the patient, parallel one-to-one guide is achieved, and the patient experience is improved. And the hospital guide efficiency and the hospital guide effect are improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an oral medicine consultation management method and system. Background Art

[0002] At present, when patients are waiting for treatment in the dental department, they often have different guidance needs. For example, some patients want to know the consultation process, while others are concerned about oral health knowledge. Especially if the patient is about to undergo oral surgery, they also want to know the detailed surgical process so that they can be mentally prepared.

[0003] However, current medical guidance services often rely on a certain number of receptionists to answer patients' questions. This approach not only has high labor costs, but also has limitations in the service capabilities of receptionists, resulting in the inability of medical guidance efficiency and effectiveness to meet patients' needs.

[0004] With the development of artificial intelligence technology, how to use AI to improve the efficiency and quality of medical guidance services has become a problem that needs to be solved urgently.

[0005] Therefore, a solution is urgently needed. Summary of the invention

[0006] One of the purposes of the present invention is to provide a method for managing oral medicine guidance. When a new patient is waiting for consultation in an oral medicine department, the remaining waiting time of the patient is estimated. When the remaining waiting time exceeds a preset first time threshold, it is preliminarily determined that the patient can be guided, and the patient's guidance needs are inquired. When the remaining waiting time exceeds a preset second time threshold corresponding to the guidance needs, it is completely determined that the patient can be guided, and an artificial intelligence guidance model is generated. Based on the artificial intelligence guidance model, the patient is guided, and the system automatically determines whether the patient can be guided and guides the patient accordingly after determining that the patient can be guided. There is no need to set up waiting reception staff to answer the patient's questions, which reduces labor costs. In addition, patients can receive system guidance through their own smart phones, etc., to achieve parallel one-to-one guidance, which improves the guidance efficiency and effect.

[0007] An embodiment of the present invention provides a method for managing oral medical consultation, comprising:

[0008] When new patients are waiting for consultation in the Department of Stomatology, estimate the remaining waiting time for the patients;

[0009] When the remaining waiting time exceeds the preset first time threshold, the patient is asked for consultation needs;

[0010] When the remaining waiting time exceeds the second time threshold preset corresponding to the guidance demand, an artificial intelligence guidance model is generated based on the remaining waiting time and the guidance material library preset corresponding to the guidance demand;

[0011] Based on the artificial intelligence guidance model, patients are guided accordingly.

[0012] Optionally, the artificial intelligence-based diagnosis guidance model is used to provide corresponding diagnosis guidance to the patient, including:

[0013] Based on the display control timeline of the artificial intelligence guidance model, start controlling the artificial intelligence guidance model to display to the patient;

[0014] Planning the nearest future detection period and the corresponding optimization period on the real-time remaining time axis of the display control time axis;

[0015] When the time for controlling the display of the artificial intelligence guidance model to the patient enters the detection period, the dynamic individual intention of the patient to view the displayed artificial intelligence guidance model is detected;

[0016] Based on dynamic individual intentions, the local time axis within the optimization period on the real-time remaining time axis is optimized;

[0017] Based on the real-time remaining timeline after local timeline optimization, the artificial intelligence guidance model begins to be relayed to display to the patient.

[0018] Optionally, planning the nearest future detection period and the corresponding optimization period on the real-time remaining time axis of the display control time axis includes:

[0019] The control stage that meets the constraints of the first stage in the future on the real-time remaining time axis is taken as the first target stage;

[0020] The control period of the first target stage on the real-time remaining time axis is used as the nearest detection period in the future;

[0021] The control stage that meets the second stage constraint among the preset number of control stages after the first target stage on the real-time remaining time axis is used as the second target stage;

[0022] The control period of the second target stage on the real-time remaining time axis is used as the optimization period corresponding to the detection period;

[0023] The first stage constraints include:

[0024] The phase types for the control phase exist in the standard phase type library;

[0025] Or, there are at least one of all first standard association relationships in the same first standard association relationship set between the control stage and the control stage corresponding to the patient's historical dynamic personality intention or historical detection period;

[0026] The second stage constraints include:

[0027] The control phase does not meet the first phase constraints;

[0028] Furthermore, all the second standard association relationships in at least one identical second standard association relationship set exist between the control stage and the first target stage.

[0029] Optionally, the detecting of the patient's dynamic individual intention to view the displayed artificial intelligence guidance model includes:

[0030] When the patient first maintains the first viewing perspective to view the displayed artificial intelligence diagnosis guidance model for more than the first time threshold, if the patient then sequentially maintains multiple second viewing perspectives to view the displayed artificial intelligence diagnosis guidance model for each time period not exceeding the second time threshold and the overlap between the two second viewing perspectives sequentially maintained thereafter does not exceed the overlap threshold, the first historical visible entity in the displayed artificial intelligence diagnosis guidance model at the first viewing perspective and the same second historical visible entity in the displayed artificial intelligence diagnosis guidance model at each second viewing perspective are respectively obtained;

[0031] When the first historical visible entity is exactly the same as the second historical visible entity, a first knowledge search rule is generated based on the first historical visible entity or the second historical visible entity and the positional relationship between the first viewing perspective and the second viewing perspective last maintained by the patient; based on the first knowledge search rule, a first target knowledge is searched from a preset intention determination knowledge base; based on the first target knowledge, a dynamic individual intention of the patient to view the displayed artificial intelligence guidance model is determined;

[0032] When there is a third historical visible entity different from the first historical visible entity in the second historical visible entity, a second knowledge search rule is generated based on the third historical visible entity; based on the second knowledge search rule, a second target knowledge is searched from the intention determination knowledge base; based on the second target knowledge, a dynamic individual intention of the patient to view the displayed artificial intelligence guidance model is determined;

[0033] The first duration threshold is the product of a preset first proportional coefficient and the total duration of the detection period; and the second duration threshold is the product of a preset second proportional coefficient and the first duration threshold.

[0034] Optionally, the optimizing of the local time axis within the optimization period on the real-time remaining time axis based on the dynamic personalized intention includes:

[0035] Generate strategy search rules based on the optimization decision-making basis on the local time axis;

[0036] Based on the strategy search rules, search for optimization strategies in the preset optimization strategy library corresponding to the dynamic personalized intent;

[0037] Based on the optimization strategy, the local timeline is optimized accordingly.

[0038] An embodiment of the present invention provides an oral medical consultation management system, comprising:

[0039] An estimation module is used to estimate the remaining waiting time of patients when new patients are waiting for consultation in the oral medicine department;

[0040] An inquiry module, used for inquiring the patient about the need for consultation when the remaining waiting time exceeds a preset first time threshold;

[0041] A generation module, for generating an artificial intelligence guidance model based on the remaining waiting time and a preset guidance material library corresponding to the guidance demand when the remaining waiting time exceeds a second time threshold preset corresponding to the guidance demand;

[0042] The guidance module is used to provide corresponding guidance to patients based on the artificial intelligence guidance model.

[0043] Optionally, the guidance module provides corresponding guidance to the patient based on the artificial intelligence guidance model, including:

[0044] Based on the display control timeline of the artificial intelligence guidance model, start controlling the artificial intelligence guidance model to display to the patient;

[0045] Planning the nearest future detection period and the corresponding optimization period on the real-time remaining time axis of the display control time axis;

[0046] When the time for controlling the display of the artificial intelligence guidance model to the patient enters the detection period, the dynamic individual intention of the patient to view the displayed artificial intelligence guidance model is detected;

[0047] Based on dynamic individual intentions, the local time axis within the optimization period on the real-time remaining time axis is optimized;

[0048] Based on the real-time remaining timeline after local timeline optimization, the artificial intelligence guidance model begins to be relayed to display to the patient.

[0049] Optionally, planning the nearest future detection period and the corresponding optimization period on the real-time remaining time axis of the display control time axis includes:

[0050] The control stage that meets the constraints of the first stage in the future on the real-time remaining time axis is taken as the first target stage;

[0051] The control period of the first target stage on the real-time remaining time axis is used as the nearest detection period in the future;

[0052] The control stage that meets the second stage constraint among the preset number of control stages after the first target stage on the real-time remaining time axis is used as the second target stage;

[0053] The control period of the second target stage on the real-time remaining time axis is used as the optimization period corresponding to the detection period;

[0054] The first stage constraints include:

[0055] The phase types for the control phase exist in the standard phase type library;

[0056] Or, there are at least one of all first standard association relationships in the same first standard association relationship set between the control stage and the control stage corresponding to the patient's historical dynamic personality intention or historical detection period;

[0057] The second stage constraints include:

[0058] The control phase does not meet the first phase constraints;

[0059] Furthermore, all the second standard association relationships in at least one identical second standard association relationship set exist between the control stage and the first target stage.

[0060] Optionally, the detecting of the patient's dynamic individual intention to view the displayed artificial intelligence guidance model includes:

[0061] When the patient first maintains the first viewing perspective to view the displayed artificial intelligence diagnosis guidance model for more than the first time threshold, if the patient then sequentially maintains multiple second viewing perspectives to view the displayed artificial intelligence diagnosis guidance model for each time period not exceeding the second time threshold and the overlap between the two second viewing perspectives sequentially maintained thereafter does not exceed the overlap threshold, the first historical visible entity in the displayed artificial intelligence diagnosis guidance model at the first viewing perspective and the same second historical visible entity in the displayed artificial intelligence diagnosis guidance model at each second viewing perspective are respectively obtained;

[0062] When the first historical visible entity is exactly the same as the second historical visible entity, a first knowledge search rule is generated based on the first historical visible entity or the second historical visible entity and the positional relationship between the first viewing perspective and the second viewing perspective last maintained by the patient; based on the first knowledge search rule, a first target knowledge is searched from a preset intention determination knowledge base; based on the first target knowledge, a dynamic individual intention of the patient to view the displayed artificial intelligence guidance model is determined;

[0063] When there is a third historical visible entity different from the first historical visible entity in the second historical visible entity, a second knowledge search rule is generated based on the third historical visible entity; based on the second knowledge search rule, a second target knowledge is searched from the intention determination knowledge base; based on the second target knowledge, a dynamic individual intention of the patient to view the displayed artificial intelligence guidance model is determined;

[0064] The first duration threshold is the product of a preset first proportional coefficient and the total duration of the detection period; and the second duration threshold is the product of a preset second proportional coefficient and the first duration threshold.

[0065] Optionally, the optimizing of the local time axis within the optimization period on the real-time remaining time axis based on the dynamic personalized intention includes:

[0066] Generate strategy search rules based on the optimization decision-making basis on the local time axis;

[0067] Based on the strategy search rules, search for optimization strategies in the preset optimization strategy library corresponding to the dynamic personalized intent;

[0068] Based on the optimization strategy, the local timeline is optimized accordingly.

[0069] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0070] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0072] Figure 1 A schematic diagram of a method for oral medical consultation management in an embodiment of the present invention;

[0073] Figure 2 It is a schematic diagram of an oral medical consultation guidance management system in an embodiment of the present invention. DETAILED DESCRIPTION

[0074] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0075] The embodiment of the present invention provides a method for managing oral medical consultation. Figure 1 As shown, including:

[0076] S1. When new patients are waiting for consultation in the Department of Stomatology, estimate the remaining waiting time for the patients;

[0077] In S1, when estimating the remaining waiting time of the patient, first determine the queue list of patients currently waiting to be seen by the patient's appointment doctor, then determine the types of visits of other patients before the patient from the queue list, then determine the average time taken by the patient's appointment doctor to diagnose each patient of the same type in history, and finally add up the determined average times to obtain the remaining waiting time;

[0078] S2. When the remaining waiting time exceeds the preset first time threshold, inquire about the patient's need for consultation;

[0079] In S2, the first time threshold is a time threshold representing that the patient is not about to see a doctor, for example, 5 minutes; when the remaining waiting time exceeds the first time threshold, it means that the patient is not about to see a doctor, and it is preliminarily determined that the patient can be guided, and the patient is asked about the guidance needs, which may be that the patient wants to know about his or her own medical process, relevant oral health knowledge, and the surgical process of the oral surgery he or she is about to undergo, etc. When asking, relevant information can be pushed to the smartphone carried by the patient for inquiry;

[0080] S3. When the remaining waiting time exceeds the second time threshold preset corresponding to the guidance demand, an artificial intelligence guidance model is generated based on the remaining waiting time and the guidance material library preset corresponding to the guidance demand;

[0081] In S3, the second duration threshold is a duration threshold representing that the patient's guidance time that meets his or her guidance needs is sufficient, for example: 6 minutes; when the remaining waiting time exceeds the second duration threshold, it is completely certain that the patient can be guided; there are a large number of materials in the guidance material library that can be used to guide patients in accordance with their guidance needs through visual display, for example: the guidance demand is that the patient wants to know certain oral health knowledge, then the corresponding material in the guidance material library is an animated video introducing the oral health knowledge, etc.; when generating an artificial intelligence guidance model, determine the material whose playback time is closest to the remaining waiting time from the guidance material library, perform visual modeling based on the material, and obtain the artificial intelligence guidance model. For example, if the material is an animated video introducing oral health knowledge, a visual interface is generated for patients to view and operate the animated video and equipped with an artificial intelligence virtual assistant, and used as an artificial intelligence guidance model. The artificial intelligence virtual assistant is a virtual assistant obtained by artificial intelligence training based on a large amount of guidance experience, guidance dialogues, and information of different materials. It can answer patients' questions, etc.; the second duration threshold can be set to the minimum playback duration of the material in the guidance material library multiplied by a preset proportional coefficient, and the proportional coefficient is greater than 0.5 and less than 1, to ensure that when the remaining waiting time exceeds the second duration threshold, at least the minimum playback time material can be played for at least half of the time and the patient can be guided as much as possible;

[0082] S4. Provide corresponding guidance to patients based on the artificial intelligence guidance model.

[0083] In S4, visualization leads to model generation, based on which patients are guided.

[0084] In this application, when a new patient is waiting for consultation in the oral medicine department, the remaining waiting time of the patient is estimated. When the remaining waiting time exceeds a preset first time threshold, it is preliminarily determined that the patient can be guided, and the patient's guidance needs are asked. When the remaining waiting time exceeds a preset second time threshold corresponding to the guidance needs, it is completely determined that the patient can be guided, and an artificial intelligence guidance model is generated. Based on the artificial intelligence guidance model, the patient is guided, and the system automatically determines whether the patient can be guided and guides the patient accordingly after determining that the patient can be guided. There is no need to set up waiting reception staff to answer patients' questions, which reduces labor costs. In addition, patients can receive system guidance through their own smartphones, etc., to achieve parallel one-to-one guidance, which improves the guidance efficiency and effectiveness.

[0085] In one embodiment, the step S4, providing corresponding guidance to the patient based on the artificial intelligence guidance model, includes:

[0086] S41, based on the display control timeline of the artificial intelligence diagnosis guidance model, start controlling the artificial intelligence diagnosis guidance model to display to the patient;

[0087] In S41, the display control timeline is a timeline indicating how to control the artificial intelligence guidance model to display content in the future, which is determined by the material based on which the artificial intelligence guidance model is generated; when guiding the patient based on the artificial intelligence guidance model, first control the artificial intelligence guidance model to display to the patient based on the display control timeline; when displaying, it can be pushed to the smart phone carried by the patient and displayed;

[0088] S42, planning the nearest future detection period and the corresponding optimization period on the real-time remaining time axis of the display control time axis;

[0089] In S42, when the control visualization causes the model to be displayed to the patient, the progress pointer on the display control timeline will begin to move as the real time passes, and the real-time remaining timeline refers to the part of the timeline on the display control timeline after the progress pointer currently moved to; when the patient views the artificial intelligence guidance model displayed to him / her, he / she may generate dynamic individual intentions, and the dynamic individual intentions refer to the patient's intention to view the artificial intelligence guidance model in a personalized manner; when the patient generates dynamic individual intentions, it is necessary to optimize the display control of the real-time remaining timeline control artificial intelligence guidance model displayed to the patient to adapt to the patient's dynamic individual intentions; therefore, the detection period is the nearest future period on the real-time remaining timeline that is suitable for detecting the dynamic individual intentions generated by the patient, and the corresponding optimization period is the period on the real-time remaining timeline that is suitable for display control optimization based on the dynamic individual intentions detected during the detection period;

[0090] S43, when the time for controlling the artificial intelligence diagnosis guidance model to be displayed to the patient enters the detection period, detecting the patient's dynamic individual intention to view the displayed artificial intelligence diagnosis guidance model;

[0091] In S43, the detection period is used as a guide for the detection timing, and when the time displayed by the artificial intelligence guidance model to the patient enters the detection period, the dynamic individual intention of the patient to view the displayed artificial intelligence guidance model is detected;

[0092] S44, optimizing the local time axis within the optimization period on the real-time remaining time axis based on the dynamic individual intention;

[0093] In S44, the optimization period is used as a guide for the optimization opportunity, and based on the dynamic individual intention, the local time axis within the optimization period on the real-time remaining time axis is optimized; during the optimization, the time length of the local time axis is not changed;

[0094] S45. Based on the real-time remaining timeline after the local timeline optimization, the relay control of the artificial intelligence guidance model begins to display to the patient.

[0095] In S45, after the local timeline is optimized, the artificial intelligence guidance model can be relayed to display to the patient based on the current real-time remaining timeline. Relay means using the real-time remaining timeline after the local timeline is optimized to continue controlling the artificial intelligence guidance model to display to the patient after the time node to which the progress pointer has most recently moved.

[0096] Generally, when patients view the artificial intelligence guidance model displayed to them, they may have dynamic personalized intentions. In order to improve humanization, it is necessary to optimize the display control of the real-time remaining timeline control artificial intelligence guidance model displayed to patients based on the dynamic personalized intentions. However, it is necessary to determine the appropriate detection timing and optimization timing. If it cannot be determined, it is necessary to continuously detect whether the user has dynamic personalized intentions in the display of the artificial intelligence guidance model, and continuously optimize the subsequent display control based on the detected dynamic personalized intentions. Doing so not only increases the occupation of detection resources and optimization resources, but also causes waste of resources. Secondly, it seriously reduces the accuracy of detection and optimization, resulting in limited improvement in humanization, and even affects the patient viewing experience.

[0097] The embodiments of the present invention can solve these problems as described above: the nearest future detection period and the corresponding optimization period are planned on the real-time remaining time axis of the display control time axis. When the time displayed by the artificial intelligence guidance model to the patient enters the detection period, the patient's dynamic individual intention to view the displayed artificial intelligence guidance model is detected. Based on the dynamic individual intention, the local time axis within the optimization period on the real-time remaining time axis is optimized. The detection period and the optimization period are used as guides for the detection timing and the optimization timing respectively. Depending on whether the time displayed by the artificial intelligence guidance model to the patient enters the detection period and the optimization period, the dynamic individual intention detection and display control optimization are triggered, which greatly reduces the occupation of detection resources and optimization resources, avoids waste of resources, and greatly improves the accuracy of detection and optimization, ensuring that the system improves the degree of humanization and avoids affecting the patient's viewing experience.

[0098] In one embodiment, the S42, planning the nearest future detection period and the corresponding optimization period on the real-time remaining time axis of the display control time axis, includes:

[0099] S421, taking the control stage on the real-time remaining time axis that is closest to the first stage constraint as the first target stage;

[0100] In S421, when the real-time remaining time axis performs display control on the artificial intelligence guidance model, it is controlled in multiple control stages. For example, if the artificial intelligence guidance model is used to display and introduce the oral surgery process, the multiple control stages are used to control the artificial intelligence guidance model to display detailed process contents of different surgical stages. When the control stage meets the constraints of the first stage, it means that when the display control of the control stage is performed, the patient may have a dynamic personality intention and take the corresponding control stage as the first target stage.

[0101] S422, taking the control period of the first target stage on the real-time remaining time axis as the nearest detection period in the future;

[0102] In S422, when the display control of the first target stage is performed, the patient may have a dynamic individual intention, and the control period of the first target stage on the real-time remaining time axis is used as the nearest detection period in the future, and the control period is the period for performing the display control work of the first target stage in the future, and the determination of the detection period is completed;

[0103] S423, taking a control stage that meets the second stage constraint among a preset number of control stages after the first target stage on the real-time remaining time axis as the second target stage;

[0104] In S423, the preset number may be 3; when the control stage meets the second stage constraint, it means that the display control work of the control stage needs to be optimized; because the dynamic individual intention generated by the patient is staged and temporary, therefore, based on the second stage constraint, the control stages of the preset number after the first target stage are screened to avoid invalid display control optimization for the control stages that will be displayed later in the future;

[0105] S424, taking the control period of the second target stage on the real-time remaining time axis as the optimization period corresponding to the detection period;

[0106] In S424, if the display control work of the second target stage needs to be optimized, the control period of the second target stage on the real-time remaining time axis is used as the optimization period corresponding to the detection period;

[0107] The first stage constraints include:

[0108] Constraint 1: The stage type of the control stage exists in the standard stage type library;

[0109] or, constraint 2, there exists at least one of all first standard association relationships in the same first standard association relationship set between the control stage and the control stage corresponding to the patient's historical dynamic personality intention or historical detection period;

[0110] The second stage constraints include:

[0111] Constraint 3: The control phase does not meet the constraints of the first phase;

[0112] Furthermore, constraint 4: all second standard association relationships in at least one identical second standard association relationship set exist between the control stage and the first target stage.

[0113] In constraint one, the standard stage type library has multiple standard stage types. The standard stage type is a control stage type that is pre-set and that the patient may have dynamic individual intentions when performing display control. For example, multiple perspectives are used to display the changes in teeth before and after oral dental surgery (the patient may have a dynamic individual intention to change the display perspective), preoperative psychological counseling display (the patient may have a dynamic individual intention to select resources suitable for his or her own psychological state for display), and postoperative recovery guidance display (the patient may have a dynamic individual intention to adjust the level of detail of the guidance content). When the stage type of a control stage exists in the standard stage type library, it means that when performing display control of the control stage, the patient may A dynamic personality intention will be generated; in constraint 2, the historical detection period refers to the detection period determined on the part of the time axis before the real-time remaining time axis on the display control time axis in history. Correspondingly, the historical dynamic personality intention is the dynamic personality intention of the patient detected in the historical detection period; when there is at least one first standard association relationship in the same first standard association relationship set between the control stage and the patient's historical dynamic personality intention, it means that when performing the display control of the control stage, the patient may generate a dynamic personality intention. For example: the control stage is to display a variety of oral treatment risks, and the historical dynamic personality intention is to pay attention to a certain oral treatment risk. Then the same first standard relationship between the two If all the first standard association relationships in the association relationship set are consistent with the control stage and the historical dynamic personality intention, and there is an inclusion relationship between the control stage and the historical dynamic personality intention (a certain oral treatment risk that the patient is concerned about is among the multiple oral treatment risks displayed), the patient may have a new dynamic personality intention to pay attention to other oral treatment risks; when there are at least one of the same first standard association relationships in the first standard association relationship set between the control stage and the control stage corresponding to the historical detection period, it also means that when performing display control of the control stage, the patient may have a dynamic personality intention, for example: the control stage is to display oral treatment risks, and the control stage corresponding to the historical detection period is to display appointment doctors Introduction, all the first standard association relationships in the same first standard association relationship set between the two are that there is a content echo between the control stage and the control stage corresponding to the historical detection period (the doctor's relevant experience in dealing with various oral treatment risks in the doctor's introduction) and there is information influence reinforcement between the control stage and the control stage corresponding to the historical detection period (first check the doctor's introduction, then check the oral treatment risks, strengthen the trust in the doctor, and the patient may have a new dynamic personality intention to pay attention to the doctor's ability to deal with certain oral treatment risks); in summary, when the control stage meets constraint one or constraint two, it means that when the display control of the control stage is performed, the patient may have a dynamic personality intention.

[0114] In constraint three, since the constraint of the first stage is met, it means that it is suitable to detect whether the patient has a dynamic individual intention in the control period of the corresponding control stage, that is, the display control of the content in the control period needs to be executed as usual. Therefore, the display control optimization cannot be performed, which ensures that the control stage does not meet the constraint of the first stage; in constraint four, when there is at least one second standard association relationship in the same second standard association relationship set between the control stage and the first target stage, it means that the display control work of the control stage needs to be optimized. For example: the control stage is to display the operational measures for postoperative dental care from multiple perspectives, and the first target stage is to display the changes of teeth before and after oral dental surgery from multiple perspectives. Then, there is at least one second standard association relationship in the same second standard association relationship set between the control stage and the first target stage, and all the second standard association relationships are the same display method (multi-perspective display) and the same display subject (the same teeth). Then the patient may need to continue viewing in a viewing method that is consistent with the dynamic individual intention generated in the first target stage, and the display control of the control stage needs to be optimized; in summary, when the control stage meets the constraint of the second stage, it means that the display control work of the control stage needs to be optimized.

[0115] The embodiment of the present invention introduces first-stage constraints and second-stage constraints to accurately, comprehensively and efficiently plan the nearest future detection period and the corresponding optimization period on the real-time remaining time axis of the display control time axis, thereby greatly improving the applicability of the system; secondly, based on the second-stage constraints, screening is performed in a preset number of control stages after the first target stage to avoid invalid display control optimization for control stages that will perform display control later in the future.

[0116] In one embodiment, in S43, detecting the patient's dynamic personality intention to view the displayed artificial intelligence guidance model includes:

[0117] S431. When the patient first maintains the first viewing perspective to view the displayed artificial intelligence diagnosis guidance model for more than the first time threshold, if the patient subsequently maintains multiple second viewing perspectives to view the displayed artificial intelligence diagnosis guidance model, each of which does not exceed the second time threshold and the overlap between the two second viewing perspectives subsequently maintained does not exceed the overlap threshold, respectively obtain the first historical visible entity in the displayed artificial intelligence diagnosis guidance model at the first viewing perspective and the same second historical visible entity in the displayed artificial intelligence diagnosis guidance model at each second viewing perspective;

[0118] In S341, the first duration threshold is a threshold representing a longer viewing angle holding time, for example: 30 seconds; the second duration threshold is a threshold representing a shorter viewing angle holding time, for example: 5 seconds; the overlap threshold is a threshold representing a greater degree of overlap in viewing angles; when the patient first maintains the first viewing angle to view the displayed artificial intelligence guidance model for more than the first duration threshold, if the patient then successively maintains multiple second viewing angles to view the displayed artificial intelligence guidance model, each of which does not exceed the second duration threshold and the overlap between the two second viewing angles maintained successively does not exceed the overlap threshold, it means that the patient does not want to continue viewing the artificial intelligence guidance model with the first viewing angle The artificial intelligence guidance model displayed by the viewing angle is constantly fine-tuning the second viewing angle to find the best viewing angle that the user wants to maintain. At this time, the user will generate a dynamic personality intention about the best viewing angle; therefore, the first historical visible entity and the second historical visible entity are obtained, and the dynamic personality intention is to be determined; the first historical visible entity is a model entity that was visible when the user maintained the first viewing angle to view the displayed artificial intelligence guidance model, such as: multiple virtual teeth; correspondingly, the second historical visible entity is a model entity that was visible when the user maintained the second viewing angle to view the displayed artificial intelligence guidance model;

[0119] S432. When the first historical visible entity is exactly the same as the second historical visible entity, a first knowledge search rule is generated based on the first historical visible entity or the second historical visible entity and the positional relationship between the first viewing perspective and the second viewing perspective last maintained by the patient; based on the first knowledge search rule, a first target knowledge is searched from a preset intention determination knowledge base; based on the first target knowledge, a dynamic individual intention of the patient to view the displayed artificial intelligence guidance model is determined;

[0120] In S432, when the first historical visible entity is exactly the same as the second historical visible entity, it means that the patient keeps viewing the object unchanged and only wants to find a new suitable viewing angle; the positional relationship between the first viewing angle and the second viewing angle that the patient last maintained refers to the spatial relative position between the two, etc.; the first historical visible entity is exactly the same as the second historical visible entity, therefore, the first historical visible entity or the second historical visible entity is selected to generate the first knowledge search rule; the first knowledge search rule is a rule for searching for intention determination knowledge related to the first historical visible entity or the second historical visible entity and the positional relationship; there is a large amount of intention determination knowledge in the intention determination knowledge base, and the intention determination knowledge is used to indicate how to determine the dynamic individual intention of the artificial intelligence guidance model that the patient views and displays; based on the first knowledge search rule, the first target knowledge is searched from the intention determination knowledge base, for example: the first historical visible entity or the second historical visible entity is a plurality of virtual teeth, and the positional relationship between the first viewing angle and the second viewing angle that the patient last maintained is a conversion from a front-viewing tooth to a reverse-viewing tooth, then the first target knowledge is to take the patient's desire to view the details of the back of the virtual teeth as the dynamic individual intention of the artificial intelligence guidance model that the patient views and displays;

[0121] S433. When there is a third historical visible entity different from the first historical visible entity in the second historical visible entity, a second knowledge search rule is generated based on the third historical visible entity; based on the second knowledge search rule, a second target knowledge is searched from the intention determination knowledge base; based on the second target knowledge, a dynamic individual intention of the patient to view the displayed artificial intelligence guidance model is determined;

[0122] In S433, when there is a third historical visible entity different from the first historical visible entity in the second historical visible entity, it means that the patient has newly viewed the third historical visible entity, which means that the patient's needs have changed; the second knowledge search rule is a rule for searching for intention determination knowledge related to the third historical visible entity; based on the second knowledge search rule, the second target knowledge is searched from the intention determination knowledge base, for example: the third historical visible entity is the structure of a virtual tooth root, then the second target knowledge is the dynamic personality intention of the artificial intelligence guidance model that focuses on the tooth root as the patient's viewing display;

[0123] The first duration threshold is the product of a preset first proportional coefficient and the total duration of the detection period; and the second duration threshold is the product of a preset second proportional coefficient and the first duration threshold.

[0124] The first proportional coefficient and the second proportional coefficient are both greater than 0 and less than 1, and the first proportional coefficient is greater than the second proportional coefficient. The first duration threshold and the second duration threshold are reasonably set based on the first proportional coefficient and the second proportional coefficient, so that the first duration threshold and the second duration threshold adapt to the detection period.

[0125] The embodiment of the present invention determines whether to start determining the patient's dynamic personality intention according to the specific situation of the patient switching the viewing perspective of the artificial intelligence guidance model displayed, thereby improving the appropriateness of the timing of determining the dynamic personality intention; the patient's dynamic personality intention is determined separately for two situations: that the first historical visual entity is exactly the same as the second historical visual entity and that the second historical visual entity contains a third historical visual entity that is different from the first historical visual entity, thereby greatly improving the comprehensiveness and accuracy of the dynamic personality intention determination and greatly improving the applicability of the system.

[0126] In one embodiment, the step S44, optimizing the local time axis within the optimization period on the real-time remaining time axis based on the dynamic personalized intention, includes:

[0127] S441, generating a strategy search rule based on the optimization decision basis on the local time axis;

[0128] In S441, the optimization decision basis is the display control scheme executed at different time nodes on the local time axis, and the strategy search rule is a rule for searching for optimization strategies related to the display control scheme executed at different time nodes;

[0129] S442, based on the strategy search rule, searching for an optimization strategy in a preset optimization strategy library corresponding to the dynamic personalized intent;

[0130] In S442, the optimization strategy library contains a large number of strategies for indicating how to optimize the display control in accordance with the dynamic personality intention based on the optimization decision basis. For example, if the dynamic personality intention is that the patient wants to see the details of the back of the virtual teeth, and the optimization decision basis is to show the change state of the virtual teeth when undergoing surgery, then the strategy in the optimization strategy library is to control the virtual teeth to only show the change state of the back of the virtual teeth when undergoing surgery.

[0131] S443. Based on the optimization strategy, the local time axis is optimized accordingly.

[0132] In S443, after the optimization strategy is searched out, the local time axis is optimized based on the optimization strategy.

[0133] The embodiment of the present invention is based on dynamic personalized intention. When optimizing the local timeline within the optimization period on the real-time remaining timeline, an optimization decision basis is introduced, a strategy search rule is generated based on it, an optimization strategy library is introduced, and optimization strategies are searched therein based on the strategy search rules. Finally, based on the optimization strategy, the local timeline is optimized accordingly, which greatly improves the accuracy and efficiency of optimizing the local timeline.

[0134] The embodiment of the present invention provides a dental medical consultation management system. Figure 2As shown, including:

[0135] Estimation module 1, used for estimating the remaining waiting time of patients when new patients are waiting for consultation in the oral medicine department;

[0136] Inquiry module 2, used for inquiring the patient's need for consultation when the remaining waiting time exceeds a preset first time threshold;

[0137] A generating module 3 is used to generate an artificial intelligence guidance model based on the remaining waiting time and a preset guidance material library corresponding to the guidance demand when the remaining waiting time exceeds a second time threshold preset corresponding to the guidance demand;

[0138] The guidance module 4 is used to provide corresponding guidance to patients based on the artificial intelligence guidance model.

[0139] The guidance module provides corresponding guidance to patients based on the artificial intelligence guidance model, including:

[0140] Based on the display control timeline of the artificial intelligence guidance model, start controlling the artificial intelligence guidance model to display to the patient;

[0141] Planning the nearest future detection period and the corresponding optimization period on the real-time remaining time axis of the display control time axis;

[0142] When the time for controlling the display of the artificial intelligence guidance model to the patient enters the detection period, the dynamic individual intention of the patient to view the displayed artificial intelligence guidance model is detected;

[0143] Based on dynamic individual intentions, the local time axis within the optimization period on the real-time remaining time axis is optimized;

[0144] Based on the real-time remaining timeline after local timeline optimization, the artificial intelligence guidance model begins to be relayed to display to the patient.

[0145] The planning of the nearest future detection period and the corresponding optimization period on the real-time remaining time axis of the display control time axis includes:

[0146] The control stage that meets the constraints of the first stage in the future on the real-time remaining time axis is taken as the first target stage;

[0147] The control period of the first target stage on the real-time remaining time axis is used as the nearest detection period in the future;

[0148] The control stage that meets the second stage constraint among the preset number of control stages after the first target stage on the real-time remaining time axis is used as the second target stage;

[0149] The control period of the second target stage on the real-time remaining time axis is used as the optimization period corresponding to the detection period;

[0150] The first stage constraints include:

[0151] The phase types for the control phase exist in the standard phase type library;

[0152] Or, there are at least one of all first standard association relationships in the same first standard association relationship set between the control stage and the control stage corresponding to the patient's historical dynamic personality intention or historical detection period;

[0153] The second stage constraints include:

[0154] The control phase does not meet the first phase constraints;

[0155] Furthermore, all the second standard association relationships in at least one identical second standard association relationship set exist between the control stage and the first target stage.

[0156] The detecting of the dynamic individual intention of the patient viewing the displayed artificial intelligence guidance model includes:

[0157] When the patient first maintains the first viewing perspective to view the displayed artificial intelligence diagnosis guidance model for more than the first time threshold, if the patient then sequentially maintains multiple second viewing perspectives to view the displayed artificial intelligence diagnosis guidance model for each time period not exceeding the second time threshold and the overlap between the two second viewing perspectives sequentially maintained thereafter does not exceed the overlap threshold, the first historical visible entity in the displayed artificial intelligence diagnosis guidance model at the first viewing perspective and the same second historical visible entity in the displayed artificial intelligence diagnosis guidance model at each second viewing perspective are respectively obtained;

[0158] When the first historical visible entity is exactly the same as the second historical visible entity, a first knowledge search rule is generated based on the first historical visible entity or the second historical visible entity and the positional relationship between the first viewing perspective and the second viewing perspective last maintained by the patient; based on the first knowledge search rule, a first target knowledge is searched from a preset intention determination knowledge base; based on the first target knowledge, a dynamic individual intention of the patient to view the displayed artificial intelligence guidance model is determined;

[0159] When there is a third historical visible entity different from the first historical visible entity in the second historical visible entity, a second knowledge search rule is generated based on the third historical visible entity; based on the second knowledge search rule, a second target knowledge is searched from the intention determination knowledge base; based on the second target knowledge, a dynamic individual intention of the patient to view the displayed artificial intelligence guidance model is determined;

[0160] The first duration threshold is the product of a preset first proportional coefficient and the total duration of the detection period; and the second duration threshold is the product of a preset second proportional coefficient and the first duration threshold.

[0161] The optimization of the local time axis within the optimization period on the real-time remaining time axis based on the dynamic individual intention includes:

[0162] Generate strategy search rules based on the optimization decision-making basis on the local time axis;

[0163] Based on the strategy search rules, search for optimization strategies in the preset optimization strategy library corresponding to the dynamic personalized intent;

[0164] Based on the optimization strategy, the local timeline is optimized accordingly.

[0165] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for oral medical consultation management, characterized in that: include: When new patients are waiting for consultation in the Department of Stomatology, estimate the remaining waiting time for the patients; When the remaining waiting time exceeds the preset first time threshold, the patient is asked for consultation needs; When the remaining waiting time exceeds the second time threshold preset corresponding to the guidance demand, an artificial intelligence guidance model is generated based on the remaining waiting time and the guidance material library preset corresponding to the guidance demand; Based on the artificial intelligence guidance model, patients are guided accordingly.

2. The oral medical consultation management method according to claim 1, characterized in that: The artificial intelligence-based diagnosis guidance model provides corresponding diagnosis guidance to patients, including: Based on the display control timeline of the artificial intelligence guidance model, start controlling the artificial intelligence guidance model to display to the patient; Planning the nearest future detection period and the corresponding optimization period on the real-time remaining time axis of the display control time axis; When the time for controlling the display of the artificial intelligence guidance model to the patient enters the detection period, the dynamic individual intention of the patient to view the displayed artificial intelligence guidance model is detected; Based on dynamic individual intentions, the local time axis within the optimization period on the real-time remaining time axis is optimized; Based on the real-time remaining timeline after local timeline optimization, the artificial intelligence guidance model begins to be relayed to display to the patient.

3. The oral medical consultation management method according to claim 2, characterized in that: The planning of the nearest future detection period and the corresponding optimization period on the real-time remaining time axis of the display control time axis includes: The control stage that meets the constraints of the first stage in the future on the real-time remaining time axis is taken as the first target stage; The control period of the first target stage on the real-time remaining time axis is used as the nearest detection period in the future; The control stage that meets the second stage constraint among the preset number of control stages after the first target stage on the real-time remaining time axis is used as the second target stage; The control period of the second target stage on the real-time remaining time axis is used as the optimization period corresponding to the detection period; The first stage constraints include: The phase types for the control phase exist in the standard phase type library; Or, there are at least one of all first standard association relationships in the same first standard association relationship set between the control stage and the control stage corresponding to the patient's historical dynamic personality intention or historical detection period; The second stage constraints include: The control phase does not meet the first phase constraints; Furthermore, all the second standard association relationships in at least one identical second standard association relationship set exist between the control stage and the first target stage.

4. The oral medical consultation management method according to claim 2, characterized in that: The detecting of the dynamic individual intention of the patient viewing the displayed artificial intelligence guidance model includes: When the patient first maintains the first viewing perspective to view the displayed artificial intelligence diagnosis guidance model for more than the first time threshold, if the patient then sequentially maintains multiple second viewing perspectives to view the displayed artificial intelligence diagnosis guidance model for each time period not exceeding the second time threshold and the overlap between the two second viewing perspectives sequentially maintained thereafter does not exceed the overlap threshold, the first historical visible entity in the displayed artificial intelligence diagnosis guidance model at the first viewing perspective and the same second historical visible entity in the displayed artificial intelligence diagnosis guidance model at each second viewing perspective are respectively obtained; When the first historical visible entity is exactly the same as the second historical visible entity, a first knowledge search rule is generated based on the first historical visible entity or the second historical visible entity and the positional relationship between the first viewing perspective and the second viewing perspective last maintained by the patient; based on the first knowledge search rule, a first target knowledge is searched from a preset intention determination knowledge base; based on the first target knowledge, a dynamic individual intention of the patient to view the displayed artificial intelligence guidance model is determined; When there is a third historical visible entity different from the first historical visible entity in the second historical visible entity, a second knowledge search rule is generated based on the third historical visible entity; based on the second knowledge search rule, a second target knowledge is searched from the intention determination knowledge base; based on the second target knowledge, a dynamic individual intention of the patient to view the displayed artificial intelligence guidance model is determined; The first duration threshold is the product of a preset first proportional coefficient and the total duration of the detection period; and the second duration threshold is the product of a preset second proportional coefficient and the first duration threshold.

5. The oral medical consultation management method according to claim 2, characterized in that: The optimization of the local time axis within the optimization period on the real-time remaining time axis based on the dynamic individual intention includes: Generate strategy search rules based on the optimization decision-making basis on the local time axis; Based on the strategy search rules, search for optimization strategies in the preset optimization strategy library corresponding to the dynamic personalized intent; Based on the optimization strategy, the local timeline is optimized accordingly.

6. An oral medical consultation management system, characterized in that: include: An estimation module is used to estimate the remaining waiting time of patients when new patients are waiting for consultation in the oral medicine department; An inquiry module, used for inquiring the patient about the need for consultation when the remaining waiting time exceeds a preset first time threshold; A generation module, for generating an artificial intelligence guidance model based on the remaining waiting time and a preset guidance material library corresponding to the guidance demand when the remaining waiting time exceeds a second time threshold preset corresponding to the guidance demand; The guidance module is used to provide corresponding guidance to patients based on the artificial intelligence guidance model.

7. The oral medical consultation management system according to claim 6, characterized in that: The guidance module provides corresponding guidance to patients based on the artificial intelligence guidance model, including: Based on the display control timeline of the artificial intelligence guidance model, start controlling the artificial intelligence guidance model to display to the patient; Planning the nearest future detection period and the corresponding optimization period on the real-time remaining time axis of the display control time axis; When the time for controlling the display of the artificial intelligence guidance model to the patient enters the detection period, the dynamic individual intention of the patient to view the displayed artificial intelligence guidance model is detected; Based on dynamic individual intentions, the local time axis within the optimization period on the real-time remaining time axis is optimized; Based on the real-time remaining timeline after local timeline optimization, the artificial intelligence guidance model begins to be relayed to display to the patient.

8. The oral medical consultation management system according to claim 7, characterized in that: The planning of the nearest future detection period and the corresponding optimization period on the real-time remaining time axis of the display control time axis includes: The control stage that meets the constraints of the first stage in the future on the real-time remaining time axis is taken as the first target stage; The control period of the first target stage on the real-time remaining time axis is used as the nearest detection period in the future; The control stage that meets the second stage constraint among the preset number of control stages after the first target stage on the real-time remaining time axis is used as the second target stage; The control period of the second target stage on the real-time remaining time axis is used as the optimization period corresponding to the detection period; The first stage constraints include: The phase types for the control phase exist in the standard phase type library; Or, there are at least one of all first standard association relationships in the same first standard association relationship set between the control stage and the control stage corresponding to the patient's historical dynamic personality intention or historical detection period; The second stage constraints include: The control phase does not meet the first phase constraints; Furthermore, all the second standard association relationships in at least one identical second standard association relationship set exist between the control stage and the first target stage.

9. The oral medical consultation guidance management system according to claim 7, characterized in that: The detecting of the dynamic individual intention of the patient viewing the displayed artificial intelligence guidance model includes: When the patient first maintains the first viewing perspective to view the displayed artificial intelligence diagnosis guidance model for more than the first time threshold, if the patient then sequentially maintains multiple second viewing perspectives to view the displayed artificial intelligence diagnosis guidance model for each time period not exceeding the second time threshold and the overlap between the two second viewing perspectives sequentially maintained thereafter does not exceed the overlap threshold, the first historical visible entity in the displayed artificial intelligence diagnosis guidance model at the first viewing perspective and the same second historical visible entity in the displayed artificial intelligence diagnosis guidance model at each second viewing perspective are respectively obtained; When the first historical visible entity is exactly the same as the second historical visible entity, a first knowledge search rule is generated based on the first historical visible entity or the second historical visible entity and the positional relationship between the first viewing perspective and the second viewing perspective last maintained by the patient; based on the first knowledge search rule, a first target knowledge is searched from a preset intention determination knowledge base; based on the first target knowledge, a dynamic individual intention of the patient to view the displayed artificial intelligence guidance model is determined; When there is a third historical visible entity different from the first historical visible entity in the second historical visible entity, a second knowledge search rule is generated based on the third historical visible entity; based on the second knowledge search rule, a second target knowledge is searched from the intention determination knowledge base; based on the second target knowledge, a dynamic individual intention of the patient to view the displayed artificial intelligence guidance model is determined; The first duration threshold is the product of a preset first proportional coefficient and the total duration of the detection period; and the second duration threshold is the product of a preset second proportional coefficient and the first duration threshold.

10. The oral medical consultation management system according to claim 7, characterized in that: The optimization of the local time axis within the optimization period on the real-time remaining time axis based on the dynamic individual intention includes: Generate strategy search rules based on the optimization decision-making basis on the local time axis; Based on the strategy search rules, search for optimization strategies in the preset optimization strategy library corresponding to the dynamic personalized intent; Based on the optimization strategy, the local timeline is optimized accordingly.

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