Intelligent queuing and calling system

Abstract

The invention provides an intelligent queuing and calling system, which is characterized in that a current called person, information of the current called person, information of a target waiting person and information of intermediate waiting persons are acquired through an information acquisition unit, and the doctor seeing time of the current called person is predicted through a time length prediction unit according to the information of the current called person; the number calling unit is used for calling the number of the person, predicting the doctor-seeing duration of the intermediate waiting person according to the information of the intermediate waiting person, taking the sum of the doctor-seeing duration of the current called person and the doctor-seeing duration of the intermediate waiting person as the waiting duration of the target waiting person through the duration accumulation unit, and displaying the waiting duration when the target waiting person is displayed through the display unit; according to the invention, the waiting personnel can reasonably arrange his / her things according to the waiting time conveniently, the waiting around the outpatient service for a long time is avoided, and the doctor-seeing experience of the waiting personnel is improved.

Description

Technical Field

[0001] This application relates to the field of intelligent control technology, and particularly to an intelligent queuing and calling system. Background Art

[0002] The queuing and calling system is an automated service system widely used in places such as banks, hospitals, government service centers, restaurants, etc. that need to manage the customer waiting process. This system generates and assigns a unique number to each arriving customer, and then notifies the customer to receive service according to the order of the numbers, thus effectively managing and optimizing the customer's waiting experience and improving service efficiency.

[0003] However, for the queuing and calling system in the hospital outpatient department, it usually only displays the queuing order of the current patient being called and the waiting patients, and cannot estimate the waiting time of the waiting patients in real time. The waiting patients also cannot reasonably arrange their own affairs according to the waiting time. In order not to miss the call, the waiting patients can only wait around the outpatient department, or, after leaving for a short time, rush back to the outpatient department in a hurry, which reduces the waiting patients' experience of seeing a doctor. Summary of the Invention

[0004] In view of the above-mentioned disadvantages of the prior art, this application provides an intelligent queuing and calling system to solve the above technical problems.

[0005] An intelligent queuing and calling system provided by this application, the system includes: an information acquisition unit, configured to obtain the current person being called, the information of the current person being called, the target waiting person, and the information of the intermediate waiting person; the information of the current person being called includes: age value, body shape characteristics, number of companions, and type of medical treatment, and the intermediate waiting person is the waiting person between the current person being called and the target waiting person in the calling queue; the information of the intermediate waiting person includes: age value and type of medical treatment; a duration prediction unit, configured to predict the medical treatment duration of the current person being called according to the information of the current person being called; and predict the medical treatment duration of the intermediate waiting person according to the information of the intermediate waiting person; a duration accumulation unit, configured to use the sum of the medical treatment duration of the current person being called and the medical treatment duration of the intermediate waiting person as the waiting duration of the target waiting person; a display unit, configured to display the waiting duration when displaying the target waiting person.

[0006] In an embodiment of the present application, the duration prediction unit includes: a sample collection module for collecting sample data, where the sample data includes first sample object information, the medical treatment duration of the first sample object, second sample object information, and the medical treatment duration of the second sample object; the first sample object information includes: age value, physical characteristics, number of accompanying persons, and type of medical treatment; the second sample object information includes: age value and type of medical treatment; a first model training module for establishing a first duration prediction model and training the first duration training model according to the corresponding relationship between the first sample object information and the medical treatment duration of the first sample object; a first medical treatment duration output module for outputting the medical treatment duration of the currently called person when the currently called person information is input into the trained first duration prediction model; a second model training module for establishing a second duration prediction model and training the second duration training model according to the corresponding relationship between the second sample object information and the medical treatment duration of the second sample object; a second medical treatment duration output module for outputting the medical treatment duration of the intermediate waiting person when the intermediate waiting person information is input into the trained second duration prediction model.

[0007] In an embodiment of the present application, the duration prediction unit includes: a first duration change prediction module for recording the age difference between the age value of the currently called person and a preset age threshold as a first age difference; and predicting the change amount of a preset medical treatment duration, denoted as a first duration change amount, according to the first age difference; the preset medical treatment duration is determined based on the diagnosis duration of all medical treatment personnel within a first preset time period by the medical staff; a second duration change prediction module for predicting the change amount of the preset medical treatment duration, denoted as a second duration change amount, according to the physical characteristics; a third duration change prediction module for predicting the change amount of the preset medical treatment duration, denoted as a third duration change amount, according to the difference between the number of accompanying persons and a preset number threshold; the absolute value of the third duration change amount is positively correlated with the absolute value of the number difference; a fourth duration change prediction module for predicting the change amount of the preset medical treatment duration, denoted as a fourth duration change amount, according to the type of medical treatment of the currently called person; a first duration determination module for obtaining the medical treatment duration of the currently called person based on the preset medical treatment duration, the first duration change amount, the second duration change amount, the third duration change amount, and the fourth duration change amount.

[0008] In an embodiment of the present application, the first duration change prediction module includes: an age difference calculation module for calculating the first age difference; a first positive increment determination module for setting the first duration change amount as a positive increment when the first age difference is positive, and the absolute value of the first duration change amount is positively correlated with the absolute value of the first age difference; a first increment determination module for setting the first duration change amount as a first preset increment when the first age difference is negative or zero.

[0009] In an embodiment of the present application, the second duration change prediction module includes: a second increment determination module for setting the second duration change amount as a second preset increment if the body posture feature belongs to a preset abnormal body posture feature; the preset abnormal body posture features include: spinal curvature, limping; a third increment determination module for setting the second duration change amount as a third preset increment if the body posture feature does not belong to the preset abnormal body posture feature, and the third preset increment is less than the second preset increment.

[0010] In an embodiment of the present application, the third duration change prediction module includes: a number difference calculation module for calculating the number difference; a second positive increment determination module for setting the third duration change amount as a positive increment when the number difference is positive; a first negative increment determination module for setting the third duration change amount as a negative increment when the number difference is negative.

[0011] In an embodiment of the present application, the fourth duration change prediction module includes: a second negative increment determination module for setting the fourth duration change amount as a fourth preset increment when the visit type of the currently called person is a first visit, and the fourth preset increment is a negative increment; a third negative increment determination module for setting the fourth duration change amount as a fifth preset increment when the visit type of the currently called person is a follow-up visit, and the fifth preset increment is a negative increment; the fifth preset increment is greater than the fourth preset increment.

[0012] In an embodiment of the present application, the duration prediction unit further includes: a fifth duration change prediction module for recording the age difference between the age value of the intermediate waiting person and the preset age threshold as the second age difference; and predicting the change amount of the preset visit duration, denoted as the fifth duration change amount, according to the second age difference; a sixth duration change prediction module for predicting the change amount of the preset visit duration, denoted as the sixth duration change amount, according to the visit type of the intermediate waiting person; a second duration determination module for obtaining the visit duration of the intermediate waiting person based on the preset visit duration, the fifth duration change amount, and the sixth duration change amount.

[0013] In an embodiment of the present application, the system further includes: an image acquisition unit, configured to acquire a personnel image within a preset area within a second preset time period after a call number is triggered and obtain a face image of the currently called personnel; the face image is sourced from a medical insurance system or a registration system; an image recognition unit, configured to compare the personnel image with the face image, and under the condition of successful comparison, recognize the body posture feature and the number of accompanying persons; under the condition of unsuccessful comparison, set the body posture feature to a preset body posture feature and set the number of accompanying persons to a preset number of accompanying persons.

[0014] Advantages of the present application: The present application obtains information of the currently called personnel, the currently called personnel information, the target waiting personnel, and the intermediate waiting personnel through an information acquisition unit. The duration prediction unit predicts the consultation duration of the currently called personnel according to the currently called personnel information, and predicts the consultation duration of the intermediate waiting personnel according to the intermediate waiting personnel information. The duration accumulation unit takes the sum of the consultation duration of the currently called personnel and the consultation duration of the intermediate waiting personnel as the waiting duration of the target waiting personnel. When the display unit displays the target waiting personnel, it displays the waiting duration. Through the above process, by predicting the consultation duration of the currently called personnel and the consultation duration of the intermediate waiting personnel, the waiting duration of the target waiting personnel is determined, which facilitates the waiting personnel to reasonably arrange their own affairs according to the waiting duration, avoids waiting around the outpatient department for a long time, and improves the consultation experience of the waiting personnel.

[0015] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. In the drawings:

[0017] Figure 1 is a block diagram of an intelligent queuing and calling system shown in an exemplary embodiment of the present application;

[0018] Figure 2 is a block diagram of a duration prediction unit shown in an exemplary embodiment of the present application;

[0019] Figure 3 is a block diagram of a duration prediction unit shown in another exemplary embodiment of the present application;

[0020] Figure 4It is a block diagram of an intelligent queuing and calling system shown in another exemplary embodiment of the present application. Detailed implementation manners

[0021] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for explaining the present invention, rather than for limiting the protection scope of the present invention.

[0022] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Therefore, only the components related to the present invention are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0023] In the following description, a large number of details are explored to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0024] The implementation details of the technical solutions of the embodiments of the present application are elaborated in detail below:

[0025] Figure 1 It is a block diagram of an intelligent queuing and calling system shown in an exemplary embodiment of the present application. The intelligent queuing and calling system includes:

[0026] The information acquisition unit 101 is used to obtain the current person being called, the information of the current person being called, the target waiting person, and the information of the intermediate waiting person.

[0027] The duration prediction unit 102 is used to predict the consultation duration of the current person being called according to the information of the current person being called; and predict the consultation duration of the intermediate waiting person according to the information of the intermediate waiting person.

[0028] The duration accumulation unit 103 is used to take the sum of the consultation duration of the current person being called and the consultation duration of the intermediate waiting person as the waiting duration of the target waiting person.

[0029] The display unit 104 is used to display the waiting duration when displaying the target waiting person.

[0030] In an embodiment of the present application, the current called person information includes: age value, physical characteristics, number of accompanying persons, and type of medical treatment. The intermediate waiting persons are the waiting persons in the queuing system between the current called person and the target waiting person; the intermediate waiting person information includes: age value and type of medical treatment; the queuing system is derived from the registration system, and the registration system can be deployed on the mobile phone or mobile device side through a small program, etc. The waiting person can view the waiting duration through the registration system on the mobile phone or mobile device side. The age value and type of medical treatment in the current called person information are derived from the registration system or the medical insurance system, and the intermediate waiting person information is derived from the registration system or the medical insurance system.

[0031] In an embodiment of the present application, if the number of intermediate waiting persons is multiple, the waiting duration is the sum of the medical treatment durations of all intermediate waiting persons and the medical treatment duration of the current called person.

[0032] In an embodiment of the present application, after obtaining the waiting duration of the target waiting person, the target waiting person and the waiting duration can be displayed through a display, or while the target waiting person and the waiting duration are displayed through the display, a voice broadcast can be performed to remind the target waiting person to pay attention.

[0033] In an embodiment of the present application, by predicting the medical treatment duration of the current called person and the medical treatment duration of the intermediate waiting persons, the waiting duration of the target waiting person is determined, which facilitates the waiting persons to reasonably arrange their own affairs according to the waiting duration, avoids waiting around the outpatient department for a long time, and improves the medical treatment experience of the waiting persons.

[0034] Figure 2 It is a block diagram of a duration prediction unit shown in an exemplary embodiment of the present application, as Figure 2 shown, the duration prediction unit includes:

[0035] The sample collection module 201 is used to collect sample data.

[0036] The first model training module 202 is used to establish a first duration prediction model and train the first duration training model according to the correspondence between the first sample object information and the medical treatment duration of the first sample object.

[0037] The first medical treatment duration output module 203 is used to output the medical treatment duration of the current called person when the current called person information is input into the trained first duration prediction model.

[0038] The second model training module 204 is used to establish a second duration prediction model and train the second duration training model according to the correspondence between the second sample object information and the medical treatment duration of the second sample object.

[0039] The second visit duration output module 205 is configured to output the visit duration of the intermediate waiting personnel when the intermediate waiting personnel information is input into the trained second duration prediction model.

[0040] In an embodiment of the present application, the sample data includes first sample object information, the visit duration of the first sample object, second sample object information, and the visit duration of the second sample object; the first sample object information includes: age value, body posture characteristics, the number of accompanying persons, and the type of visit; the second sample object information includes: age value and the type of visit. The first sample object is the currently called person, and the second sample object is the intermediate waiting personnel.

[0041] In an embodiment of the present application, the first duration prediction model can be a neural network model or other network models, which are not specifically limited herein. The neural network model is trained with the first sample object information and the visit duration of the first sample object to obtain the trained first duration prediction model, thereby improving the accuracy of predicting the visit duration of the currently called person.

[0042] In an embodiment of the present application, the second duration prediction model can be a neural network model or other network models, which are not specifically limited herein. The neural network model is trained with the second sample object information and the visit duration of the second sample object to obtain the trained second duration prediction model, thereby improving the accuracy of predicting the visit duration of the intermediate waiting personnel.

[0043] Figure 3 is a block diagram of the duration prediction unit shown in another exemplary embodiment of the present application. In Figure 3 the duration prediction unit includes:

[0044] The first duration change prediction module 301 is configured to record the age difference between the age value of the currently called person and the preset age threshold as the first age difference; and predict the change amount of the preset visit duration according to the first age difference, which is denoted as the first duration change amount.

[0045] The second duration change prediction module 302 is configured to predict the change amount of the preset visit duration according to the body posture characteristics, which is denoted as the second duration change amount.

[0046] The third duration change prediction module 303 is configured to predict the change amount of the preset visit duration according to the number difference between the number of accompanying persons and the preset number threshold, which is denoted as the third duration change amount.

[0047] The fourth duration change prediction module 304 is configured to predict the change amount of the preset visit duration according to the type of visit of the currently called person, which is denoted as the fourth duration change amount.

[0048] The first duration determination module 305 is configured to obtain the consultation duration of the currently called person based on a preset consultation duration, a first duration change amount, a second duration change amount, a third duration change amount, and a fourth duration change amount.

[0049] In an embodiment of the present application, the absolute value of the third duration change amount is positively correlated with the absolute value of the difference in the number of people. The preset consultation duration is determined based on the diagnosis durations of all the consulting people within the first preset time period. Here, the consulting staff is the medical service staff of the current queuing call (for example, a certain consulting doctor), and the first preset time period can be one year, half a year, etc. The preset consultation duration is the average value of the durations spent by all the consulting people during the first preset time period.

[0050] In an embodiment of the present application, by fully considering the age value, physical characteristics, number of companions, and type of consultation of the currently called person, predicting the consultation duration of the currently called person is beneficial to improving the accuracy of the consultation duration of the currently called person.

[0051] In an embodiment of the present application, the first duration change prediction module includes:

[0052] An age difference calculation module, configured to calculate a first age difference.

[0053] A first positive increment determination module, configured to, when the first age difference is positive, set the first duration change amount as a positive increment, and the absolute value of the first duration change amount is positively correlated with the absolute value of the first age difference.

[0054] A first negative increment determination module, configured to, when the first age difference is negative or zero, set the first duration change amount as a first preset increment.

[0055] In an embodiment of the present application, the first preset increment is non-negative, and the preset age threshold can be set according to actual situations. For example, at 50 years old, when the first age difference is positive, it indicates that the age value of the currently called person is greater than the preset age threshold. Older consulting people may have longer consultation times due to factors such as inconvenient movement and slow speech speed. Therefore, the first duration change amount is set as a positive increment, and the absolute value of the first duration change amount is positively correlated with the absolute value of the first age difference. When the first age difference is negative or zero, it indicates that the age value of the currently called person is less than or equal to the preset age threshold. Younger consulting people are not restricted by their own factors in terms of movement and have a faster reaction speed. Therefore, it usually does not lead to an increase in the consultation duration. Therefore, the first duration change amount is set as the first preset increment, and the first preset increment can be set to 0 or other values, which are not specifically limited herein.

[0056] In an embodiment of the present application, by considering the influence of age on the consultation duration, the preset consultation duration is adjusted, which is beneficial to predicting the consultation duration of the currently called person according to different age situations of the currently called person, reflecting the real-time, accuracy and flexibility of predicting the consultation duration of the currently called person.

[0057] In an embodiment of the present application, the second duration change prediction module includes:

[0058] A second increment determination module, configured to set the second duration change amount to a second preset increment if the body posture feature belongs to a preset abnormal body posture feature.

[0059] A third increment determination module, configured to set the second duration change amount to a third preset increment if the body posture feature does not belong to a preset abnormal body posture feature.

[0060] In an embodiment of the present application, the preset abnormal body posture features include: spinal curvature, limping, head tilt, etc.; the second preset increment is set according to the actual situation, the second preset increment is a positive value, the third preset increment is a non-negative value, the third preset increment is less than the second preset increment, and the third preset increment can be 0.

[0061] In an embodiment of the present application, when the body posture of the currently called person is abnormal, since the abnormal body posture feature will cause inconvenience in movement and prolong the consultation duration, therefore, the second preset increment is set as a positive increment. And when the body posture of the currently called person is normal, it will not cause inconvenience in movement. Therefore, there will be no increase in the consultation duration in terms of movement. Therefore, the third preset increment is set to 0, or a value close to 0. By considering the influence of the body posture feature on the consultation duration, the preset consultation duration is adjusted, which is beneficial to predicting the consultation duration of the currently called person according to different body posture features of the currently called person, reflecting the real-time, accuracy and flexibility of predicting the consultation duration of the currently called person.

[0062] In an embodiment of the present application, the third duration change prediction module includes:

[0063] A number difference calculation module, configured to calculate the number difference.

[0064] A second positive increment determination module, configured to set the third duration change amount to a positive increment when the number difference is positive.

[0065] A first negative increment determination module, configured to set the third duration change amount to a negative increment when the number difference is negative.

[0066] In an embodiment of the present application, the preset number threshold is set according to the actual situation. For example, the preset number threshold is 1. When the number difference is positive, it indicates that the number of accompanying people is greater than the preset number threshold. Since there are more accompanying people, it will cause an increase in the consultation time, resulting in an increase in the consultation duration. Therefore, the third duration change amount is set as a positive increment. When the number difference is negative, it indicates that the number of accompanying people is less than the preset number threshold, saving the communication time between the accompanying personnel and the attending doctor. Therefore, the third duration change amount is set as a negative increment. When the number difference is zero, it indicates that the number of accompanying people is equal to the preset number threshold. Therefore, the third duration change amount is set as zero. By considering the impact of the number of accompanying people on the consultation duration to adjust the preset consultation duration, it is beneficial to predict the consultation duration of the currently called patient according to the number of accompanying people of the currently called patient, reflecting the real-time, accuracy, and flexibility of predicting the consultation duration of the currently called patient.

[0067] In an embodiment of the present application, the fourth duration change prediction module includes:

[0068] The second negative increment determination module is configured to set the fourth duration change amount as a fourth preset increment when the consultation type of the currently called patient is a first consultation, and the fourth preset increment is a negative increment;

[0069] The third negative increment determination module is configured to set the fourth duration change amount as a fifth preset increment when the consultation type of the currently called patient is a follow-up consultation, and the fifth preset increment is a negative increment.

[0070] In an embodiment of the present application, the preset consultation duration includes the first consultation duration and the follow-up consultation duration. The fifth preset increment is greater than the fourth preset increment. Based on the preset consultation duration, the first consultation duration and the follow-up consultation duration are adjusted according to the consultation type, reflecting the real-time, accuracy, and flexibility of predicting the first consultation duration and the follow-up consultation duration.

[0071] In an embodiment of the present application, the first preset increment, the second preset increment, and the third preset increment are non-negative values. A positive increment represents a positive value, and a negative increment represents a negative value. If the first duration change amount is a positive increment, then during the calculation of the current called person's consultation duration, the first duration change amount is taken as a positive value. If the first duration change amount is a negative increment, then during the calculation of the current called person's consultation duration, the first duration change amount is taken as a negative value. If the second duration change amount is a positive increment, then during the calculation of the current called person's consultation duration, the second duration change amount is taken as a positive value. If the second duration change amount is a negative increment, then during the calculation of the current called person's consultation duration, the second duration change amount is taken as a negative value. If the third duration change amount is a positive increment, then during the calculation of the current called person's consultation duration, the third duration change amount is taken as a positive value. If the third duration change amount is a negative increment, then during the calculation of the current called person's consultation duration, the third duration change amount is taken as a negative value. If the fourth duration change amount is a positive increment, then during the calculation of the current called person's consultation duration, the fourth duration change amount is taken as a positive value. If the fourth duration change amount is a negative increment, then during the calculation of the current called person's consultation duration, the fourth duration change amount is taken as a negative value.

[0072] In an embodiment of the present application, the duration prediction unit further includes:

[0073] A fifth duration change prediction module, configured to record the age difference between the age value of the intermediate waiting person and the preset age threshold as the second age difference; and predict the change amount of the preset consultation duration according to the second age difference, denoted as the fifth duration change amount.

[0074] A sixth duration change prediction module, configured to predict the change amount of the preset consultation duration according to the consultation type of the intermediate waiting person, denoted as the sixth duration change amount.

[0075] A second duration determination module, configured to obtain the consultation duration of the intermediate waiting person based on the preset consultation duration, the fifth duration change amount, and the sixth duration change amount.

[0076] In an embodiment of the present application, the process of predicting the change amount of the preset consultation duration according to the second age difference includes: if the second age difference is a positive value, then the fifth duration change amount is set as a positive increment, and the absolute value of the fifth duration change amount is positively correlated with the absolute value of the second age difference; if the second age difference is a zero value or a negative value, then the fifth duration change amount is set as the sixth preset increment, and the sixth preset increment can be 0 or a value close to 0.

[0077] In an embodiment of the present application, the process of predicting the change amount of the preset consultation duration according to the consultation type of the intermediate waiting person includes: when the consultation type of the intermediate waiting person is a first consultation, setting the sixth duration change amount as the seventh preset increment, and the seventh preset increment is a negative increment; when the consultation type of the intermediate waiting person is a follow-up consultation, setting the sixth duration change amount as the eighth preset increment, and the eighth preset increment is a negative increment; the eighth preset increment is greater than the seventh preset increment.

[0078] In an embodiment of the present application, the process of obtaining the consultation duration of the intermediate waiting person based on the preset consultation duration, the fifth duration change amount, and the sixth duration change amount includes: taking the sum of the preset consultation duration, the fifth duration change amount, and the sixth duration change amount as the consultation duration of the intermediate waiting person. If the fifth duration change amount is a positive increment, then the fifth duration change amount is taken as a positive value in the process of the consultation duration of the intermediate waiting person; if the fifth duration change amount is a negative increment, then the fifth duration change amount is taken as a negative value in the process of the consultation duration of the intermediate waiting person; if the sixth duration change amount is a positive increment, then the sixth duration change amount is taken as a positive value in the process of the consultation duration of the intermediate waiting person; if the sixth duration change amount is a negative increment, then the sixth duration change amount is taken as a negative value in the process of the consultation duration of the intermediate waiting person, and the sixth preset increment is a non-negative value.

[0079] In an embodiment of the present application, on the basis of the preset consultation duration, adjusting the first consultation duration and the follow-up consultation duration according to the consultation type reflects the real-time, accurate, and flexible prediction of the first consultation duration and the follow-up consultation duration.

[0080] In an embodiment of the present application, the intelligent queuing and calling system further includes:

[0081] An image acquisition unit, configured to acquire a personnel image within a preset area and obtain a face image of the currently called person within a second preset time period after triggering the call number.

[0082] An image recognition unit, configured to compare the personnel image and the face image, and under the condition of successful comparison, recognize the body posture feature and the number of accompanying persons; under the condition of unsuccessful comparison, set the body posture feature as a preset body posture feature and set the number of accompanying persons as a preset number of accompanying persons.

[0083] In an embodiment of the present application, the second preset time period can be within 1 minute after triggering the call number, or can be other duration values. The preset area can be within 1 meter outside the outpatient room of the medical staff, or can be within 1.5 meters outside the outpatient room of the medical staff, etc. The image acquisition unit can be a camera or a camera, etc. The installation position of the image acquisition unit meets the requirement of being able to acquire the personnel image within the preset area, and the face image is from the medical insurance system or the registration system.

[0084] In an embodiment of the present application, when the comparison between the personnel image and the face image is successful, it indicates that the currently called personnel is entering the outpatient room, and the body posture characteristics of the currently called personnel are identified. If the personnel image also includes other people, the number of accompanying people of the currently called personnel is determined according to the images of other people. When the comparison between the personnel image and the face image is unsuccessful, it indicates that the currently called personnel has not been collected, then the body posture characteristics of the currently called personnel are set to the preset body posture characteristics, and the number of accompanying people of the currently called personnel is set to the preset number of accompanying people. The preset body posture characteristics are non-abnormal body posture characteristics, and the preset number of accompanying people is 1 person.

[0085] In an embodiment of the present application, the personnel image collected by the image acquisition unit can be a single-frame image or a continuous multi-frame image, which is not specifically limited herein. The method of identifying the body posture characteristics and the number of accompanying people from the personnel image can be implemented with reference to the methods of identifying the body posture characteristics and the number of accompanying people in the related art, which is not specifically limited herein.

[0086] In an embodiment of the present application, the image acquisition unit collects the personnel image in the preset area within the second preset time period after the call is triggered and obtains the face image of the currently called personnel. The image recognition unit compares the personnel image with the face image. Under the condition of successful comparison, the body posture characteristics and the number of accompanying people are recognized, which is beneficial to predicting the medical treatment duration of the currently called personnel according to the age value, body posture characteristics, number of accompanying people and medical treatment type of the currently called personnel, and improves the accuracy of predicting the medical treatment duration of the currently called personnel.

[0087] Figure 4 is a block diagram of an intelligent queuing and calling system shown in another exemplary embodiment of the present application, as Figure 4 shown, the intelligent queuing and calling system includes:

[0088] The image acquisition unit 401 is used to collect the personnel image in the preset area within the second preset time period after the call is triggered and obtain the face image of the currently called personnel.

[0089] The image recognition unit 402 is used to compare the personnel image with the face image. Under the condition of successful comparison, the body posture characteristics and the number of accompanying people are recognized; under the condition of unsuccessful comparison, the body posture characteristics are set to the preset body posture characteristics, and the number of accompanying people is set to the preset number of accompanying people.

[0090] The information acquisition unit 403 is used to obtain the information of the currently called personnel, the currently called personnel information, the target waiting personnel and the intermediate waiting personnel information.

[0091] The duration prediction unit 404 is used to predict the medical treatment duration of the currently called person according to the current called person information; and predict the medical treatment duration of the intermediate waiting persons according to the intermediate waiting person information.

[0092] The duration accumulation unit 405 is used to take the sum of the medical treatment duration of the currently called person and the medical treatment duration of the intermediate waiting persons as the waiting duration of the target waiting person.

[0093] The display unit 406 is used to display the waiting duration when the target waiting person is displayed.

[0094] In an embodiment of the present application, the present application collects the personnel images within the preset area within the second preset time period after the call is triggered by the image acquisition unit and obtains the face image of the currently called person. The personnel images and the face images are compared by the image recognition unit. Under the condition that the comparison is successful, the body features and the number of accompanying persons are recognized. Under the condition that the comparison is unsuccessful, the body features are set to the preset body features, and the number of accompanying persons is set to the preset number of accompanying persons. The information acquisition unit obtains the currently called person, the current called person information, the target waiting person and the intermediate waiting person information. The duration prediction unit predicts the medical treatment duration of the currently called person according to the current called person information, and predicts the medical treatment duration of the intermediate waiting persons according to the intermediate waiting person information. The duration accumulation unit takes the sum of the medical treatment duration of the currently called person and the medical treatment duration of the intermediate waiting persons as the waiting duration of the target waiting person. The display unit displays the waiting duration when the target waiting person is displayed. Through the above process, by predicting the medical treatment duration of the currently called person and the medical treatment duration of the intermediate waiting persons, the waiting duration of the target waiting person is determined, which is convenient for the waiting patients to reasonably arrange their own affairs, avoid waiting around the outpatient department for a long time, and improve the medical treatment experience of the waiting patients.

[0095] The above embodiments only exemplarily illustrate the principles and effects of the present application, rather than limiting the present application. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of the present application. Therefore, all equivalent modifications or changes completed by those with ordinary knowledge in the technical field without departing from the spirit and technical idea disclosed by the present application should still be covered by the claims of the present application.

Claims

1. An intelligent queuing system, characterized in that: The system comprises: The information collection unit is used to obtain the information of the current called person, the current called person information, the target waiting person and the intermediate waiting person; the current called person information includes: age value, physical characteristics, number of accompanying persons and medical treatment type; the intermediate waiting person is the waiting person between the current called person and the target waiting person in the calling queue; the intermediate waiting person information includes: age value and medical treatment type; The duration prediction unit is used to predict the duration of the consultation of the currently called person according to the information of the currently called person; and to predict the duration of the consultation of the intermediate waiting person according to the information of the intermediate waiting person; A duration accumulation unit, used to take the sum of the consultation duration of the currently called person and the consultation duration of the intermediate waiting person as the waiting duration of the target waiting person; A display unit is used to display the waiting time when displaying the target waiting person.

2. The intelligent queuing system according to claim 1 is characterized in that: The duration prediction unit comprises: A sample collection module, used to collect sample data, the sample data including first sample subject information, duration of first sample subject consultation, second sample subject information, duration of second sample subject consultation; the first sample subject information includes: age value, physical characteristics, number of accompanying persons and type of consultation; the second sample subject information includes: age value and type of consultation; A first model training module, configured to establish a first duration prediction model and train the first duration training model according to the correspondence between the first sample subject information and the first sample subject's consultation duration; A first consultation duration output module, for outputting the consultation duration of the current called person when the information of the current called person is input into the trained first duration prediction model; A second model training module, used for establishing a second duration prediction model and training the second duration training model according to the correspondence between the second sample object information and the second sample object's consultation duration; The second consultation duration output module is used to output the consultation duration of the intermediate waiting personnel when the information of the intermediate waiting personnel is input into the trained second duration prediction model.

3. The intelligent queuing system according to claim 1 is characterized in that: The duration prediction unit comprises: The first duration change prediction module is used to record the age difference between the age value of the currently called person and the preset age threshold as the first age difference; and predict the change of the preset consultation duration based on the first age difference, which is recorded as the first duration change; the preset consultation duration is determined based on the diagnosis duration of all the patients in the first preset time period by the visiting person; A second duration change prediction module is used to predict the change of the preset consultation duration according to the body shape characteristics, which is recorded as a second duration change; A third duration change prediction module is used to predict the change of the preset consultation duration according to the difference between the number of accompanying persons and the preset number threshold, which is recorded as a third duration change; the absolute value of the third duration change is positively correlated with the absolute value of the difference in the number of persons; The fourth duration change prediction module is used to predict the change of the preset consultation duration according to the consultation type of the currently called person, which is recorded as the fourth duration change; The first duration determination module is used to obtain the consultation duration of the current called person based on the preset consultation duration, the first duration change, the second duration change, the third duration change and the fourth duration change.

4. The intelligent queuing system according to claim 3 is characterized in that: The first duration change prediction module includes: An age difference calculation module, used to calculate the first age difference; a first positive increment determination module, configured to set the first duration change as a positive increment when the first age difference is a positive value, and the absolute value of the first duration change is positively correlated with the absolute value of the first age difference; The first increment determination module is configured to set the first duration change to a first preset increment when the first age difference is a negative value or a zero value.

5. The intelligent queuing system according to claim 3 is characterized in that: The second duration change prediction module includes: A second increment determination module is used to set the second time length change amount as a second preset increment if the body shape feature belongs to a preset abnormal body shape feature; the preset abnormal body shape feature includes: spinal curvature and lameness; The third increment determination module is used to set the second duration change to a third preset increment if the body feature does not belong to the preset abnormal body feature, and the third preset increment is smaller than the second preset increment.

6. The intelligent queuing system according to claim 3 is characterized in that: The third duration change prediction module includes: A number difference calculation module, used to calculate the number difference; A second positive increment determination module, configured to set the third duration change as a positive increment when the number difference is a positive value; The first negative increment determination module is used to set the third duration change as a negative increment when the number difference is a negative value.

7. The intelligent queuing system according to claim 3 is characterized in that: The fourth duration change prediction module includes: A second negative increment determination module, configured to set the fourth duration change to a fourth preset increment when the consultation type of the currently called person is a first visit, and the fourth preset increment is a negative increment; The third negative increment determination module is used to set the fourth duration change to the fifth preset increment when the consultation type of the currently called person is a follow-up visit, and the fifth preset increment is a negative increment; the fifth preset increment is greater than the fourth preset increment.

8. The intelligent queuing system according to any one of claims 3 to 7, characterized in that: The duration prediction unit further includes: A fifth duration change prediction module is used to record the age difference between the age value of the middle waiting person and the preset age threshold as a second age difference; and predict the change amount of the preset consultation duration according to the second age difference, which is recorded as a fifth duration change amount; A sixth duration change prediction module is used to predict the change of the preset consultation duration according to the consultation type of the intermediate waiting person, which is recorded as the sixth duration change; The second duration determination module is used to obtain the consultation duration of the intermediate waiting person based on the preset consultation duration, the fifth duration change and the sixth duration change.

9. The intelligent queuing system according to any one of claims 1 to 7, characterized in that: The system further comprises: An image acquisition unit, used to acquire images of people in a preset area and obtain a facial image of the person currently being called within a second preset time period after the call is triggered; the facial image is derived from a medical insurance system or a registration system; The image recognition unit is used to compare the person image with the face image, and if the comparison is successful, identify the body features and the number of accompanying persons; if the comparison is unsuccessful, set the body features as preset body features, and set the number of accompanying persons as preset accompanying persons.

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