CN112542236A

CN112542236A - Online task distribution method and device, electronic equipment and storage medium

Info

Publication number: CN112542236A
Application number: CN202011502990.0A
Authority: CN
Inventors: 平晓丽; 刘磊
Original assignee: Weiyiyun Hangzhou Holding Co ltd
Current assignee: Weiyiyun Hangzhou Holding Co ltd
Priority date: 2020-12-18
Filing date: 2020-12-18
Publication date: 2021-03-23

Abstract

The invention discloses an online task distribution method, an online task distribution device, electronic equipment and a storage medium, wherein the online task distribution method comprises the following steps: acquiring disease information in a task to be distributed, and determining a target department corresponding to the disease information; determining at least one to-be-selected consultation user associated with the target department, and determining at least one to-be-determined consultation user according to the attribute evaluation value corresponding to the attribute category information to which the at least one to-be-selected consultation user belongs; for each user to be determined, determining an attribute evaluation value and a dynamic characteristic attribute of the current user to be determined, comment attribute information of the target user to the current user to be determined, and determining a characteristic evaluation value of the current user to be determined; according to the characteristic evaluation value of each user to be determined, at least one target user to be diagnosed is screened out from at least one user to be determined, and the task to be distributed is distributed to at least one target user to be diagnosed, so that the effect of improving the matching degree of the target user to be diagnosed and the target user to be diagnosed is achieved.

Description

Online task distribution method and device, electronic equipment and storage medium

Technical Field

The embodiment of the invention relates to the technical field of medical information, in particular to an online task distribution method and device, electronic equipment and a storage medium.

Background

With the popularization of networks, an inquiry method in which a patient communicates with a doctor on line gradually appears. Compared with an offline hospital, the patient can conveniently and quickly meet the inquiry requirement only by operating the application program.

At present, most of on-line inquiry is to randomly allocate corresponding treatment users to treatment users according to symptoms described by users, but at the moment, the treatment users allocated to the treatment users are not matched with the treatment users, so that the treatment efficiency and the matching degree with the treatment users are low.

Disclosure of Invention

The invention provides an online task distribution method, an online task distribution device, electronic equipment and a storage medium, and aims to achieve the technical effects of high efficiency and adaptability of allocating treatment receiving users to target treatment users.

In a first aspect, an embodiment of the present invention provides an online task dispatching method, where the method includes:

acquiring disease information in a task to be distributed, and determining a target department corresponding to the disease information; the disease information is a disease description text corresponding to the target visiting user;

determining at least one to-be-selected patient receiving user associated with the target department, and determining at least one to-be-determined patient receiving user according to the attribute evaluation value corresponding to the attribute category information to which the at least one to-be-selected patient receiving user belongs;

for each user to be determined, determining an attribute evaluation value and a dynamic characteristic attribute of the current user to be determined, comment attribute information of the target user to the current user to be determined, and determining a characteristic evaluation value of the current user to be determined;

and screening at least one target consultation user from the at least one user to be determined according to the characteristic evaluation value of each user to be determined, and distributing the task to be distributed to the at least one target consultation user.

In a second aspect, an embodiment of the present invention further provides an online task dispatching device, where the device includes:

the target department determining module is used for acquiring disease information in the task to be distributed and determining a target department corresponding to the disease information; the disease information is a disease description text corresponding to the target visiting user;

the to-be-determined consultation user determining module is used for determining at least one to-be-selected consultation user associated with the target department and determining at least one to-be-determined consultation user according to the attribute evaluation value corresponding to the attribute category information to which the at least one to-be-selected consultation user belongs;

the characteristic evaluation value determining module is used for determining the attribute evaluation value and the dynamic characteristic attribute of the current to-be-determined consultation user, the comment attribute information of the target consultation user to the current to-be-determined consultation user and the characteristic evaluation value of the current to-be-determined consultation user for each to-be-determined consultation user;

and the target diagnosis receiving user determining module is used for screening out at least one target diagnosis receiving user from the at least one to-be-determined diagnosis receiving user according to the characteristic evaluation value of each to-be-determined diagnosis receiving user and distributing the to-be-distributed task to the at least one target diagnosis receiving user.

In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes:

one or more processors;

a storage device for storing one or more programs,

when the one or more programs are executed by the one or more processors, the one or more processors implement the online task distribution method according to any one of the embodiments of the present invention.

In a fourth aspect, the present invention further provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are used for executing the online task dispatching method according to any one of the embodiments of the present invention.

According to the technical scheme of the embodiment of the invention, the disease text description in the task to be distributed is obtained, the text description is processed to determine the corresponding target department, the server can search each user to be selected and received treatment associated with the target department, the user to be determined and received treatment meeting certain conditions is determined according to the user attribute type of each user to be selected and received treatment, at least one target received treatment user matched with the target user to be treated is determined by combining the dynamic attribute and the comment attribute of the user to be determined, the determined target received treatment user has higher adaptation degree with the target user to be treated, and the technical effect of user experience is further improved.

Drawings

In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, a brief description is given below of the drawings used in describing the embodiments. It should be clear that the described figures are only views of some of the embodiments of the invention to be described, not all, and that for a person skilled in the art, other figures can be derived from these figures without inventive effort.

Fig. 1 is a flowchart illustrating an online task dispatching method according to a first embodiment of the present invention;

fig. 2 is a flowchart illustrating an online task dispatching method according to a second embodiment of the present invention;

fig. 3 is a schematic diagram illustrating a characteristic evaluation value determination method according to a second embodiment of the present invention;

fig. 4 is a schematic structural diagram of an online task dispatching device according to a third embodiment of the present invention;

fig. 5 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.

Detailed Description

The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.

Example one

Fig. 1 is a flowchart of an online task dispatching method according to an embodiment of the present invention, where the present embodiment is applicable to a situation where a task to be distributed corresponding to a target visiting user is distributed to at least one target visiting user, and the at least one target visiting user visits, the method may be executed by an online task dispatching device, and the device may be implemented in the form of software and/or hardware, where the hardware may be an electronic device, and optionally, the electronic device may be a PC terminal, and the like.

Before the embodiments of the present invention are described, an application scenario is described. The online task dispatching method can be integrated in the application program, so that when the user triggers the application program, the method provided by the embodiment can be adopted to distribute the corresponding online task to the corresponding service user; or, the online task dispatching method is integrated in the page of the web page, so that when the user triggers the page, the corresponding consultation user can be allocated to the user based on the method provided by the embodiment. Of course, the on-line task dispatching method may also be integrated into a triage consultation robot in a certain hospital system, so as to determine the corresponding consultation user according to the problem description of the consultation user, and further guide the consultation user to the position of the corresponding consultation user.

For example, when the application program is integrated in the application program, the corresponding application program may be installed on the mobile terminal, and the user may trigger the control corresponding to the online task dispatch in the application program. Specifically, a condition information input control is triggered, in which text related to the current disease symptom is entered. When the target visit user finishes inputting the symptom text, the confirmation button can be triggered to send the symptom text to the server corresponding to the application program. After the server receives the disease information of the target visiting user, a task to be distributed can be established for the target visiting user based on the disease information of the target visiting user. And the target receiving users matched with the tasks to be distributed can be determined, and the tasks to be distributed are sent to the terminal equipment of the corresponding target receiving users, so that at least one target receiving user receives the tasks to be distributed, and the order grabbing effect is achieved.

As shown in fig. 1, the method of this embodiment specifically includes the following steps:

and S110, acquiring disease information in the task to be distributed, and determining a target department corresponding to the disease information.

The task to be distributed is a task generated after the visiting user describes the problem to be consulted. If the visiting user needs to find a relevant doctor to consult the medical problem information, the problem consultation can be description of symptoms, for example, the user is headache, a content editing control on a display interface or a content editing control on an application program can be triggered, and the edited information can be: "I had three days to do headache". After detecting that the user triggers the confirmation control, the task to be distributed can be generated based on the edited content. Accordingly, the condition information may be a user-edited content such as "i am suffering from headache for three days. That is, the disease information is a disease description text of the target visiting user. The target visiting user is a user matched with the edited content, for example, if the user a edits the content aiming at the disease of the user a, the target visiting user is the user a, and if the user a edits the content aiming at the disease of the user B, the target visiting user is the user B. The information of the disease condition that each department can process can be determined according to the prior knowledge and/or the book knowledge. The target department corresponds to the disease information. Each doctor can edit the basic information in the application program or the page in advance, and the basic information comprises the department to which the doctor belongs, so that after the disease information is determined, the target department can be determined according to the disease information which can be processed by each department.

Specifically, the target doctor may trigger an application installed on the mobile terminal, or trigger a web page on the PC, edit a disease text description in the application or the web page, and send the disease text description to the server after the disease text description is completed, so that the server generates a task to be distributed according to the disease text information, and determines a target department associated with the disease information.

For example, when the target visit user asks for a diagnosis online, the disease information can be edited in the disease information editing control, such as the input disease information is: high fever at 39 degrees, sore throat and cough. After the information editing is completed, a 'confirm' key can be triggered, the server can acquire disease information of 'high fever 39 degrees, angina and cough', keywords of 'high fever 39 degrees, angina and cough', such as 'high fever, angina and cough', can be extracted, and a target department, such as a medical department, can be determined according to the keywords and the predetermined relation of disease information which can be processed by each department.

In the present embodiment, the advantages of determining the target department are: the method and the system can determine the diagnosis receiving users matched with the target diagnosis users from all the to-be-selected diagnosis receiving users associated with the target department, and improve the matching degree between the determined diagnosis receiving users and the diagnosis receiving users. The problem that the treatment user is selected from all the treatment users to be matched with the treatment user in the prior art is solved, and the problem that the treatment user is randomly allocated with the corresponding treatment user even if the treatment user selects the corresponding department in the prior art, so that the treatment user is not adaptive is solved.

S120, at least one to-be-selected patient receiving user related to the target department is determined, and at least one to-be-determined patient receiving user is determined according to the attribute evaluation value corresponding to the attribute category information to which the at least one to-be-selected patient receiving user belongs.

The user to be selected for taking a doctor is a user to be taken a doctor associated with the target department, that is, the user may be a user for performing diagnosis and treatment on the target user, for example: a doctor associated with the target department. The attribute category information may include at least three categories, such as information obtained by dividing the information according to the diagnosis and treatment level of the user to be selected, the service level of the user to be selected during the diagnosis treatment, and the recent activity level of the user to be selected. The attribute evaluation value is an evaluation value corresponding to the attribute category information after being processed. And the to-be-determined reception user is the reception user screened out according to the attribute evaluation value after processing the attribute category information of each to-be-selected reception user.

Specifically, for each user to be selected, the current user to be selected associated with the target department is obtained, various attribute category information of the current user to be selected is determined, and the attribute evaluation value of the current user to be selected is determined according to the attribute category information corresponding to the current user to be selected. According to the attribute evaluation value of the to-be-selected visiting user, at least one to-be-determined visiting user capable of providing service for the target visiting user can be determined, for example: and determining the front 100 to-be-selected clinic users with the attribute evaluation values as the to-be-determined clinic users.

It should be noted that, other users to be selected for receiving a doctor may respectively determine whether the users are the users to be determined for receiving a doctor based on the above manner.

In this embodiment, the attribute category information to which the to-be-selected consultation user belongs includes basic attribute category information, service evaluation attribute category information, and activity evaluation attribute category information of the to-be-selected consultation user.

In order to clearly introduce each category information in the attribute category information, the basic attribute category information, the service evaluation attribute category information, and the activity evaluation attribute category information may be introduced one by one.

First, basic attribute category information.

The basic attribute category information can be used for measuring the diagnosis and treatment level of the user to be selected for treatment. The diagnosis level may be determined by at least one of a hospital ranking of a hospital to which the user to be selected belongs, a department ranking of a department to which the user belongs, a hospital grade of the department, a city grade of the hospital to which the hospital belongs, and a title of the user to be selected.

The hospital ranking can be a comprehensive ranking of hospitals nationwide, or the ranking can be divided into a plurality of grades according to a certain division; the ranking of the same department can be the ranking nationwide, and the ranking can also be divided into levels; the hospital grade of the hospital can be three grades which are divided according to the 'hospital grading management standard', wherein each grade comprises three grades, namely A, B, C and the like, the three grades also comprise special grade, and the grade is divided into three grades, namely ten grades; the grade of the city where the hospital belongs to can be one to four grades divided according to the medical level of each city, or can be other division standards, and the specific division standard can be set according to the requirements of the inquiry system, which is not specifically limited in this embodiment; the job title of the user to be selected for consultation may be a resident, an attending physician, a secondary or primary physician, etc.

Note that the evaluation values corresponding to different levels are different, for example, the evaluation value of the main president in the job title of the user to be selected for consultation is 0.5, the evaluation value of the subordinate president is 0.3, the evaluation value of the main president is 0.15, and the evaluation value of the inpatient is 0.05. Correspondingly, when the levels of the users to be selected for consultation are different, the determined attribute evaluation values are different.

It should be noted that the determination of each piece of information in the basic attribute category may be determined based on a preset function.

And secondly, service evaluation attribute category information.

The service evaluation attribute category information can be used for measuring the service level of the user to be selected for receiving a call at the time of receiving a call. The service level at the time of the call-up may be determined by at least one of an accumulated call-up amount, an average response time, a total number of words of a single response, a good rate, a bad rate, a voice response rate, and a prescription rate.

The accumulated number of the received visits can be the total number of the visiting users receiving the visits by the selected receiving users; the average reply duration can be the average of the first reply duration of each visiting user waiting for the to-be-selected visiting user; the total word number in single reply can be the average value of the word numbers used when the selected doctor-taking user replies the doctor-seeing user; the good evaluation rate and the poor evaluation rate can be evaluation values of the past treatment user to the to-be-selected treatment user; the voice reply rate can be the ratio of the number of times of replying by the user to be selected for receiving a doctor by using voice to the total number of times of replying; the prescription rate can be the ratio of the number of times of prescription of the user to be selected for treatment to the number of times of the user to be treated for treatment.

It should be noted that, the evaluation values corresponding to different levels are different, and correspondingly, the determined attribute evaluation values are different when the levels to which the users to be selected for consultation belong are different.

It should be further noted that the determination of each piece of information in the service evaluation attribute category may be determined based on a preset function.

And thirdly, evaluating the attribute category information by liveness.

The activity evaluation attribute category information can be used for measuring the recent activity degree of the user to be selected for treatment, and can be determined through the activity duration within the third preset duration and the task receiving activity duration.

Specifically, the third preset time period may be a preset time period based on the current date, and may be a recent time period, for example, a recent 30 days. The active time within the third preset time period may be the active days of the user to be selected for consultation in approximately 30 days; the task reception active time within the third preset time period may be an active number of days to be selected for the order taking of the hospitalizing user within approximately 30 days.

It should be further noted that the determination of each piece of information in the activity evaluation attribute category may be determined based on a preset function. For example: assuming that the third preset time period is 30 days, the evaluation value is recorded as s, the number of active days of the user to be selected for consultation is recorded as h, and s is 0.1+0.03 h.

Based on the first, second, and third, determining the attribute evaluation value may be: and processing and determining the basic attribute type information, the service evaluation attribute type information and the activity evaluation attribute type information. For each user to be selected in the target department, attribute category information of each user to be selected can be acquired, and an attribute evaluation value can be determined according to the attribute category information. After determining the attribute evaluation value of each user to be selected for treatment, the attribute evaluation values may be sorted to determine a certain number of users to be determined for treatment, for example: and determining the user to be selected as the user to be determined, wherein the user to be selected is the user to be determined, and the user to be selected is the user to be determined before the attribute evaluation value ranking.

Illustratively, the user Y to be selected for consultation includes 3 attribute category information, that is, basic attribute category information, service evaluation attribute category information, and activity evaluation attribute category information, where an attribute evaluation value corresponding to the basic attribute category information of the user Y to be selected for consultation is 0.80, an attribute evaluation value corresponding to the service evaluation attribute category information is 0.95, and an attribute evaluation value corresponding to the activity evaluation attribute category information is 0.60. Further, it may be determined that the attribute evaluation value of the user to be selected for consultation Y is 2.35 to 0.80+0.95+ 0.60.

S130, aiming at each user to be determined, determining the attribute evaluation value and the dynamic characteristic attribute of the user to be determined currently, the comment attribute information of the target user to the user to be determined currently, and determining the characteristic evaluation value of the user to be determined currently.

The dynamic characteristic attribute can measure the activity degree of the user to be determined for receiving a call in the current time period. The comment attribute information is used for measuring the satisfaction degree of the target visiting user to the current to-be-determined visiting user. The characteristic evaluation value can represent the matching degree of the user to be determined for receiving a doctor and the target user for seeing a doctor.

Specifically, after obtaining the attribute evaluation value and the dynamic characteristic attribute of the current user to be determined, and the comment attribute information of the target user to the current user to be determined, the three information are processed to determine the characteristic evaluation value of the current user to be determined.

In this embodiment, the dynamic characteristic attribute includes a task receiving activity level within a first preset time duration and a task processing amount level within a second preset time duration.

The first preset time is set according to actual requirements, for example, the first preset time is a time with a preset time interval based on the current time. For example, the preset value is 5 minutes or 15 minutes. The task receiving activity level can be measured by the task amount received in the preset time length, and can also be measured by the up-down line frequency in the preset time length. The second preset duration may be greater than the first preset duration. The task processing capacity level can be measured by the task capacity processed in the second preset time, and the higher the task capacity is, the higher the level is.

In this embodiment, the comment attribute information includes a comment score of each user to be selected for consultation by the target user.

Specifically, if the user to be selected for taking a doctor provides diagnosis and treatment services for the target user and the target user reviews the user, the review score of the target user for taking a doctor on the user to be selected can be obtained. The comment score can be obtained according to the star-level score, or the comment content is input into a pre-trained model, and the score of the target visit user on the user to be selected for treatment is determined according to the result output by the model and the star-level score. If the user to be selected for taking a doctor never provides the diagnosis service for the target user or the target user for taking a doctor never performs comment scoring on the user to be selected for taking a doctor, the comment scoring may be set to a default value, for example: 0.5.

specifically, for each user to be determined, the attribute evaluation value and the dynamic characteristic attribute of the current user to be determined, and the comment attribute information of the target user to the current user to be determined may be obtained. And further determining the characteristic evaluation value of the current user to be determined for receiving a consultation according to the information. For example: the attribute evaluation value of the current user to be determined is 3.75, the evaluation value corresponding to the dynamic characteristic attribute is 1.25, and the evaluation value corresponding to the comment attribute information of the current user to be determined by the target user is 0.75, at this time, the characteristic evaluation value of the current user to be determined may be 3.75+1.25+0.75 — 5.75.

S140, screening at least one target consultation user from the at least one user to be determined according to the characteristic evaluation value of each user to be determined, and distributing the task to be distributed to the at least one target consultation user.

When the user to be determined meets the preset conditions, the user to be determined can be used as a target user, for example: the determination of the target receiving user according to the preset condition may be: the target receiving user may be a receiving user to be determined whose feature evaluation value is higher than a preset threshold, or may be a receiving user to be determined who ranks within a preset number as the target receiving user by sorting the receiving users to be determined from high to low according to the feature evaluation value.

Specifically, at least one target user for receiving a diagnosis can be determined according to the feature evaluation value of each user to be determined. The task to be distributed may be distributed to each target treatment user, i.e. to each target treatment user, so that each target treatment user may receive the task. For example: when the current target receiving user selects a certain task to be distributed in the diagnosis and treatment system and triggers the confirmation key, the current target receiving user can be considered to receive the task to be distributed. Optionally, when any one target diagnosis receiving user receives the task to be distributed, the task to be distributed received by the other target diagnosis receiving users is invalid.

In this embodiment, after the task to be distributed is sent to each target user, if the target user logs in the application program or is in an online state, the task to be distributed may be pushed to the display interface corresponding to each target user in a pop-up window, push, or "drip" reminding manner, so as to achieve an effect of reminding the target user to receive orders, thereby achieving a technical effect of ordering each target user.

Example two

Fig. 2 is a flowchart illustrating an online task serving method according to a second embodiment of the present invention, where in this embodiment, on the basis of the foregoing embodiment, at least one to-be-determined user is determined according to an attribute evaluation value, and a specific determination manner for determining a feature evaluation value of a current to-be-determined user according to the attribute evaluation value, a dynamic feature attribute, and comment attribute information of a target user on the current to-be-determined user may refer to a technical solution of this embodiment. Explanations of the same or corresponding terms as those in the above embodiments are omitted here.

As shown in fig. 2, the online task dispatching method specifically includes the following steps:

s210, acquiring a disease description text for describing the disease of the target visiting user in the task to be distributed.

Specifically, the target doctor user can establish a task to be distributed corresponding to the target doctor user before performing inquiry, prompt the target doctor user to input a disease description text to describe the current disease, and when the input is completed, the target doctor user can click a corresponding confirmation control to send the disease description text. And when the confirmation control is triggered, acquiring a disease description text of the target visiting user for the current disease in the task to be distributed.

And S220, processing the disease description text to determine keywords of the disease information.

Wherein, the keywords can be related words used for diagnosis and treatment in the disease description text. The determination of the keywords may be implemented by a key extraction model, for example, the disease description text is input into the keyword extraction model, and the keyword extraction model may perform word segmentation processing on the disease description text and eliminate stop words. And determining word vectors of each participle based on the word vector dictionary, and processing the word vectors to determine keywords. The keywords may also be determined according to preset corpus information.

Specifically, keywords in the disease description text can be identified and extracted by setting keyword matching; or, identifying and extracting keywords in the disease description text by setting a regular expression.

And S230, determining a target department corresponding to the disease information based on the keywords.

Specifically, according to the determined keywords of the disease information, the target department may be determined according to the correspondence between the key words and departments.

For example, if the determined keywords are pregnancy, abdominal pain and anorexia, the target department may be determined to be gynaecology and obstetrics according to the correspondence between the key words. Or, a plurality of target departments are determined, and the plurality of target departments are provided to the terminal device of the target visiting user for the target visiting user to select. For example, if the determined keywords are pregnancy, abdominal pain and anorexia, the target department can be determined to be obstetrics and gynecology according to the corresponding relationship between the keyword departments, and the target visiting user can determine one of the two target departments to be the target department.

The determination of the target department based on S220 and S230 has the advantages that the disease text description can be input to the trained keyword extraction model to obtain the keywords, or the keywords are determined according to the pre-generated corpus, and then the target department is determined based on the keywords, at this time, the determined department is highly matched with the target visiting user, and when the target visiting user is determined by executing the methods provided in S240 to S270 on the basis, the matching degree between the target visiting user and the target visiting user can be further improved, so that the treatment efficiency of the user and the technical effect of the user experience are improved. Wherein the corpus is corpus information associated with each department.

S240, at least one to-be-selected patient receiving user related to the target department is determined, and at least one to-be-determined patient receiving user is determined according to the attribute evaluation value corresponding to the attribute category information to which the at least one to-be-selected patient receiving user belongs.

In order to more accurately determine the attribute evaluation value of each user to be selected for treatment and further determine the user to be determined for treatment, the following steps can be used:

the method comprises the steps of firstly, determining a hospital ranking, a department ranking, a hospital grade, a city grade and a job title of a user to be selected for receiving a consultation aiming at each user to be selected, calling weight values corresponding to the hospital ranking, the department ranking, the hospital grade, the city grade and the job title respectively, and determining a first attribute value corresponding to the user to be selected for receiving a consultation based on the weight values.

It should be noted that, the same manner is adopted for determining the first attribute value of each user to be selected for medical examination, and for clarity of describing the technical solution of the present embodiment, the description is given by taking the determination of the first attribute value of one of the users to be selected for medical examination as an example.

The hospital ranking may be a comprehensive ranking of hospitals across the country, or the ranking may be divided into a plurality of levels according to a certain division. Determining the weighted value corresponding to the hospital ranking according to the hospital ranking may be according to the function total _ rank1 × 95% + rank2 × 5% +2, where total _ rank represents the weighted value corresponding to the hospital ranking, rank1 represents the score of the national comprehensive hospital board for the hospital, rank2 represents the score of the regional comprehensive hospital board for the hospital, and rank1 and rank2 are not greater than 100. Moreover, the calculation modes of rank1 and rank2 can be determined by integrating hospital ranking information of last 3 years, such as: rank [ (score1 × 70% + score2 × 20% + score3 × 10%)/100 ] × 23, where score1 is the annual score information of the present year of the hospital, score2 is the annual score information of the last year of the hospital, score3 is the annual score information of the previous year of the hospital, and rank1 and rank2 can be calculated by using the above formula.

The ranking of the same department may be a national ranking or a ranking divided into levels. Determining the weight value corresponding to the department rank according to the department rank may be according to a function total _ rank is rank1 × 95% + rank2 × 5% +2, where total _ rank represents the weight value corresponding to the hospital department rank, rank1 represents the hospital department national rank score, rank2 represents the hospital department regional score, and rank1 and rank2 are not greater than 100. Moreover, the calculation modes of rank1 and rank2 can be determined by integrating the ranking information of hospital departments in the last 3 years, such as: rank [ (score1 × 70% + score2 × 20% + score3 × 10%)/100 ] × 23, where score1 is the annual score information of the present year of the department, score2 is the annual score information of the last year of the department, score3 is the annual score information of the previous year of the department, and rank1 and rank2 can both be calculated by using the above formula.

The city grade can be one to four grades divided according to the medical level of each city, or can be other division standards, and the specific division standard can be set according to the requirements of the inquiry system. Determining the corresponding weight value according to the city class may be determining a matched weight value according to the city class, for example: the cities are divided into 5 levels, the medical level of the cities is decreased from the first level to the fifth level in sequence, at the moment, the weight value of the first level city is determined to be 4, the weight value of the second level city is determined to be 3, the weight value of the third level city is determined to be 2, the weight value of the fourth level city is determined to be 1, and the weight value of the fifth level city is determined to be 0.

The hospital grade can be determined according to the hospital grade management standard, the hospital is determined to be in three grades after being reviewed, each grade is divided into three grades, namely, A, B, C and the like, the third grade hospital is additionally provided with a special grade, so that the hospital is divided into three grades, namely, ten grades, the highest grade is three grades, namely, special grade, and the lowest grade is one grade, namely, C and the like. The primary hospital is a primary health care organization, which generally refers to a primary hospital providing comprehensive medical, prevention, rehabilitation and health care services for communities. Secondary hospitals generally refer to regional hospitals that provide health services across several communities, and are the technical centers for regional medical prevention. The third-level hospital is a hospital which provides medical and health services across regions, provinces and cities and nationwide, and is a medical prevention technical center with comprehensive medical, teaching and scientific research capabilities. Determining the corresponding weight value according to the hospital level may be determining a matched weight value according to the hospital level, such as: may be reduced in order from tertiary par to primary par, for example: the weight values are sequentially reduced from 10 corresponding to three levels of special grade to 1 corresponding to one level of third grade.

The title may be set according to a related regulation document of the department of health, or may be set in stages according to specific situations of each department. The corresponding weight value determined according to the job title of the user to be selected for taking a doctor may be a weight value determined according to the job title to be matched, for example: the corresponding weight values of the national experts, the provincial experts, the chief and chief physicians and the chief and chief physicians are 10, the corresponding weight values of the assistant and chief physicians and assistant physicians are 9, the corresponding weight values of the chief and chief physicians and the assistant physicians are 7, the corresponding weight values of the physicians and the hospitalizers are 2, and the corresponding weight values of the rest job titles are 0. The rest of the job title may include a principal pharmacist, a principal examiner, a principal technician, a director nurse, etc.

After the weight values of the to-be-selected user for taking a diagnosis are obtained, the first attribute value corresponding to the to-be-selected user for taking a diagnosis can be determined through the forms of summation, weighted summation, multiplication and the like.

For example, the weight value corresponding to the hospital ranking of the user to be selected for consultation is 20, the weight value corresponding to the department ranking is 18, the weight value corresponding to the hospital level is 7, the weight value corresponding to the city level is 4, and the weight value corresponding to the job title is 7, so that it can be determined that the first attribute value corresponding to the user to be selected for consultation is 20+18+7+4+7 ═ 56; the hospital ranking, department ranking, hospital ranking, city ranking and job title of the user to be selected are 15%, 15%, 30%, 20% and 20%, respectively, and based on the above example, it can be determined that the first attribute value corresponding to the user to be selected is 20 × 15% +18 × 15% +7 × 30% +4 × 20% +7 × 20% + 10.

And secondly, determining the accumulated quantity of the received calls, the average reply time length, the total word number of single reply, the high rating rate, the poor rating rate, the voice reply rate and the prescription rate of the current user to be selected for receiving the calls for the next call, and determining the weighted values corresponding to the accumulated quantity of the received calls, the average reply time length, the total word number of single reply, the high rating rate, the poor rating rate, the voice reply rate and the prescription rate respectively so as to determine a second attribute value corresponding to the current user to be selected for receiving the calls for the next call based on the weighted values.

It should be noted that, the same manner is adopted for determining the second attribute value of each user to be selected for medical examination, and for clarity of describing the technical solution of the present embodiment, the description is given by taking the determination of the second attribute value of one of the users to be selected for medical examination as an example.

Wherein, the accumulated quantity of the received diagnoses is the number of times of the internal diagnosis in a certain time. Corresponding weight values can be determined according to the accumulated quantity of the received calls, for example, the higher the accumulated quantity of the received calls is within 3 months, the higher the weight values are, namely, the higher the weight values are distributed to the users to be selected for receiving the calls with the accumulated quantity of the received calls; the higher the accumulated treatment receiving quantity in the last 3 months is, the lower the weight value is, so as to provide more opportunities for new treatment receiving users to be selected. For example: when the accumulated diagnosis receiving amount is less than or equal to 10 units, the corresponding weight value can be determined according to the formula y being 1/(x +1), wherein y represents the weight value corresponding to the accumulated diagnosis receiving amount, and x represents the accumulated diagnosis receiving amount; when the accumulated number of received diagnoses is more than 10, the corresponding weight value is 0.

The average reply duration is the duration of receiving the order after receiving the task to be distributed within the preset duration, or the average reply duration for replying the question of the target visiting user, for example, the average reply duration may be the average duration from the order receiving time to the first reply time within about 3 months of the user to be selected. The shorter the average reply duration, the higher its weight value. The determination method of the average reply duration weight value may be: when the average reply duration is less than 2 minutes, the weight value may be determined according to a formula y ═ 1/(x/60+1), where y represents the weight value corresponding to the average reply duration, and x represents the average reply duration; when the average recovery time is greater than or equal to 2 minutes, the corresponding weight value is 0.

The total word number of the single reply can be an average value of the recorded word numbers of each reply, and when the word numbers of the replies are different, the user to be selected for receiving a doctor also has corresponding influence. The weight value corresponding to the number of the single reply words can be determined, such as: based on the average value of the total word number of single reply within 3 months of the user to be selected for consultation. The more total words are replied in a single time, the higher the weight value, for example: when the total word number in single reply is less than or equal to 30 words, the corresponding weight value is 0; when the total word number in one reply is greater than 30 words and less than or equal to 220 words, the corresponding weight value can be (x/220) according to the formula y²Determining, wherein y represents a weight value corresponding to the total word number in single reply, and x represents the total word number in single reply; when the total word number in a single reply is greater than 220 words, the corresponding weight value is 1.

The good evaluation rate and the bad evaluation rate may be evaluation values of the past visiting user to the user to be selected for receiving a visit. According to the good rating and the bad rating of the user to be selected for receiving a diagnosis, the corresponding weight values can be respectively determined, for example: can be determined according to a good rating and a bad rating of nearly 3 months. It should be noted that the evaluation of the visit user as the user to be selected for the visit can be divided into one to five starsThe evaluation is good evaluation of five stars, poor evaluation of one star, two stars and three stars, the good evaluation rate is the ratio of good evaluation in the evaluation of all the users to be selected for reception, and the poor evaluation rate is the ratio of poor evaluation in the evaluation of all the users to be selected for reception. The higher the goodness score is, the higher the weight value is, the higher the bad score is, the lower the weight value is, for example: when the rating is greater than 75%, the corresponding weight value may be x according to the formula y²Determining, wherein y represents a weight value corresponding to the high rating, and x represents the high rating; when the favorable rating is more than 0 and less than or equal to 75 percent, the corresponding weight value is 0; when the rating is 0, the corresponding weight value is-0.25. When the poor evaluation rate is 0, the corresponding weight value is 0; when the poor rating is greater than 0, the corresponding weight value may be determined according to the formula of (1/x +0.5) -2, where y represents the weight value corresponding to the poor rating and x represents the poor rating.

The voice reply rate may be a ratio of the number of times that the user to be selected for consultation replies with voice to the total number of times of replying. The weight value corresponding to the voice response rate determined according to the voice response rate of the user to be selected for consultation can be determined according to the voice response rate of the user to be selected for consultation in the last 3 months. The higher the voice reply rate, the higher the weight value, for example: the voice response rate may be used as a weight value.

The prescription rate can be the ratio of the number of times of prescription of the user to be selected for reception and treatment to the number of times of the user to be received and treated. The weight value corresponding to the prescription rate determined according to the prescription rate of the user to be selected for taking a doctor may be determined according to the prescription rate of the user to be selected for taking a doctor in the last 3 months. The higher the prescription rate, the higher the weight value, for example: the prescription rate may be used as a weighted value.

After the weight values of the to-be-selected patient receiving users are obtained, second attribute values corresponding to the to-be-selected patient receiving users can be determined.

Illustratively, the weight value corresponding to the accumulated diagnosis receiving amount of the user to be selected for diagnosis receiving is 0.2, the weight value corresponding to the average reply duration is 0.7, the weight value corresponding to the total word number of single reply is 0.6, the weight value corresponding to the high rating rate is 0.25, the weight value corresponding to the low rating rate is-0.3, the weight value corresponding to the voice reply rate is 0.3, and the weight value corresponding to the prescription rate is 0.45, so that it can be determined that the second attribute value corresponding to the user to be selected for diagnosis receiving is 0.2+0.7+0.6+0.25-0.3+0.45 ═ 1.9.

And step three, aiming at each user to be selected for taking a call, determining the active time length and the task receiving active time length of the current user to be selected for taking a call within a third preset time length, respectively determining the weight values corresponding to the active time length and the task receiving active time length, and determining a third attribute value corresponding to the current user to be selected for taking a call based on the weight values.

It should be noted that, the same manner is adopted for determining the third attribute value of each user to be selected for medical examination, and for clarity of describing the technical solution of the present embodiment, the third attribute value of one of the users to be selected for medical examination is taken as an example for description.

Specifically, the active duration of the to-be-selected medical examination user within a third preset duration and the task reception active duration may be acquired from pre-stored activity level information related to the to-be-selected medical examination user, where the third preset duration may be a recent period of time, for example: for approximately 30 days. Furthermore, the corresponding weight values can be respectively determined according to the active time length of the to-be-selected visit user within the third preset time length and the weight value determination mode corresponding to the task receiving active time length. Based on the weighted values, a third attribute value corresponding to the user to be selected for consultation can be determined through summation.

For example, the weight value determined according to the active time length of the to-be-selected consultation user within the third preset time length may be higher as the number of active days of the last 30 days is greater, wherein the number of active days may be the number of days for the to-be-selected consultation user to log in the system. For example: determining a weight value corresponding to the active time length of the user to be selected for consultation in a third preset time length according to a formula y which is x/30, wherein y represents the weight value corresponding to the active time length, and x represents the active time length; determining that the corresponding weight value can be more order grabbing days of nearly 30 days and higher according to the task receiving active time length of the to-be-selected reception user in the third preset time length, wherein the order grabbing days can be the days for the to-be-selected reception user to log in the system and grab an order. For example: when the task receiving active time is less than 5 days, the corresponding weight value is 0; when the task receiving active time length is greater than or equal to 5 days, the corresponding weight value may be determined according to the formula y ═ x/25-1/5, where y represents the weight value corresponding to the task receiving active time length, and x represents the task receiving active time length.

After the weight values of the to-be-selected patient receiving users are obtained, a third attribute value corresponding to the to-be-selected patient receiving user can be determined.

Illustratively, the weight value corresponding to the active duration of the to-be-selected visiting user in the third preset duration is 0.83, and the weight value corresponding to the task receiving active duration in the third preset duration is 0.8, so that it can be determined that the third attribute value corresponding to the to-be-selected visiting user is 0.83+0.8, which is 1.63; the ratio of the active time length of the to-be-selected visiting user in the third preset time length to the task receiving active time length of the to-be-selected visiting user in the third preset time length is 40% and 60%, respectively, and based on the above example, it may be determined that the third attribute value corresponding to the to-be-selected visiting user is 0.83 × 40% +0.8 × 60% — 0.812.

And fourthly, determining the attribute evaluation value of the current user to be selected for receiving a diagnosis according to the first attribute value, the second attribute value and the third attribute value of the current user to be selected for receiving a diagnosis.

Specifically, after the first attribute value, the second attribute value and the third attribute value of the current user to be selected for treatment are obtained, the attribute evaluation value of the current user to be selected for treatment can be determined through summation or weighted summation.

For example, if the first attribute value of the current user to be selected for consultation is 9.8, the second attribute value is 1.92, and the third attribute value is 0.83, it may be further determined that the evaluation value of the attribute of the user to be selected for consultation is 9.8+1.92+0.83 — 12.55.

Illustratively, the ratio of the first attribute value, the second attribute value and the third attribute value of the user to be selected is 20%, 60% and 20%, respectively, and on the basis of the above example, it can be determined that the attribute evaluation value of the user to be selected is 9.8 × 20% +1.92 × 60% +0.83 × 20% ═ 3.278.

And step five, determining a first preset number of to-be-determined patients according to the attribute evaluation value of each to-be-selected patient.

Specifically, the first preset number may be 100, and after determining the attribute evaluation value of each user to be selected for treatment, the attribute evaluation values may be sorted from high to low, so as to determine a first preset number of users to be determined for treatment, for example: and determining the user to be selected as the user to be determined, wherein the user to be selected is the user to be determined, and the user to be selected is the user to be determined before the attribute evaluation value ranking.

S250, aiming at each user to be determined, determining the attribute evaluation value and the dynamic characteristic attribute of the user to be determined currently, the comment attribute information of the target user to the user to be determined currently, and determining the characteristic evaluation value of the user to be determined currently.

In order to determine a more accurate characteristic evaluation value for each user to be determined, the following steps are performed:

step one, determining a task receiving activity level and a task processing capacity level of a current user to be determined for receiving a consultation, and respectively determining a weight value of the task receiving activity level and the task processing capacity level so as to determine a dynamic attribute value of the current user to be determined for receiving the consultation based on the weight value.

Specifically, the task receiving activity level and the task processing capacity level of the user to be determined may be obtained from pre-stored information related to the user to be determined. Furthermore, the corresponding weight values can be respectively determined according to the task receiving activity level of the user to be determined for receiving a doctor and the weight value determination mode corresponding to the task processing capacity level. Based on each weight value, the dynamic attribute value corresponding to the user to be determined for receiving a doctor can be determined through summation.

For example, the task receiving activity level of the user to be determined for taking a visit may be divided into two levels, namely, active level and inactive level, where the weight value corresponding to the active level is 1 and the weight value corresponding to the inactive level is 0.01. The task receiving activity level of the user to be determined for receiving a doctor can be divided into an activity level in a first period and an activity level in a second period, for example: the first period of time is approximately 15 minutes and the second period of time is approximately 2 minutes. At this time, the weight value corresponding to the active level in the first time period is 1, and the weight value corresponding to the inactive level is 0.01; the weight value corresponding to the active level in the second period is 1, and the weight value corresponding to the inactive level is 0.6. The task throughput level of the user to be determined for the treatment can be divided into two levels, for example: if the task processing amount of the user to be determined is the first level, a weighted value corresponding to the task processing amount of the user to be determined can be determined according to a formula y of-0.2 x/100+1.2, and if the task processing amount of the user to be determined is the second level, the weighted value corresponding to the task processing amount of the user to be determined can be determined to be 1.

For example, if the weight value of the task receiving activity level of the currently-to-be-determined user is 0.6 and the weight value of the task processing capacity level is 1, it may be determined that the dynamic attribute value of the currently-to-be-determined user is 0.6+1 — 1.6.

And step two, determining the comment grade of the current to-be-determined receiving user and the corresponding weight value of the target receiving user, and determining the comment attribute value of the current to-be-determined receiving user based on the weight value.

Specifically, for each user to be determined, the comment score of the current user to be determined can be obtained. For the comment scores, the comment attribute value of the current user to be determined for receiving a call can be further determined.

Illustratively, the corresponding weight value is determined according to the average score of each visiting user on the active comment of the currently determined visiting user. The active comment is the score of the visiting user, which is the current user to be determined to receive a doctor, but not the default score. If the review score has a value ranging from 0 to 1, then the value may be 0.2 according to the formula y^xAnd determining the weight value. Further, the weight value can be used as a comment attribute value of the current user to be determined for receiving a doctor.

And step three, determining the feature evaluation value of the current user to be determined for receiving a diagnosis according to the dynamic attribute value, the comment attribute value and the attribute evaluation value.

Specifically, the manner of determining the feature evaluation value of the current user to be determined for treatment may be summation, weighted summation, product calculation or determination according to a preset function according to the dynamic attribute value, the comment attribute value and the attribute evaluation value, and the specific manner of determining the feature evaluation value may be determined according to an actual situation.

If the determination manner of the feature evaluation value is weighted summation, the weights of the dynamic attribute value, the comment attribute value and the attribute evaluation value can be adjusted according to the preference of the target visiting user, for example: the target visit user is more inclined to the user to be determined for the quick order receiving, so that the weight of the dynamic attribute value can be improved, and the weight of the comment attribute value and the attribute evaluation value can be reduced; the target visit user is more inclined to the basic medical capability of the user to be determined for receiving a doctor, so that the weight of the attribute evaluation value can be increased, and the weight of the comment attribute value and the dynamic attribute value can be reduced.

Alternatively, the server may determine the feature evaluation value of each user to be determined for the treatment by using a feature evaluation value determination method as shown in fig. 3.

As shown in fig. 3, the feature evaluation value may include: doctor static scoring, doctor dynamic scoring and user personalized scoring. The doctor static score corresponds to the attribute evaluation value in the embodiment, the doctor dynamic score corresponds to the dynamic characteristic attribute in the embodiment, and the user personalized score corresponds to the comment attribute information of the target visiting user to the current user to be determined.

The doctor static score can be determined according to a doctor base score, a doctor business score and a doctor active score. The doctor basic score corresponds to the basic attribute category information, the doctor business score corresponds to the business evaluation attribute category information, and the doctor active score corresponds to the activity evaluation attribute category information. The doctor basic score can be determined according to the national hospital ranking score, the national department ranking score, the city ranking score, the hospital ranking score and the doctor professional title score; the doctor service score can be determined according to the accumulated receiving quantity score, the average first return time length score, the single average return total word number score, the quality inspection grade score, the 5-star active good score rate score, the 123-star active bad score rate score, the voice return rate score and the prescription order rate score; the physician activity score may be determined based on the number of days the physician is active for approximately 30 days and the number of days the physician is active for approximately 30 days to preempt a single activity. The doctor dynamic score can be determined according to the close 15-minute order taking activity score, the close 2-minute order taking activity score and the doctor rapid order taking quantity score in the same day. The user personalization score may be determined from the user's active review score for the doctor.

And S260, determining a second preset number of target diagnosis receiving users from high to low according to the characteristic evaluation value of each user to be determined.

Specifically, the second preset number may be 30, and after determining the feature evaluation value of each user to be determined, the feature evaluation values may be sorted from high to low, so as to determine a second preset number of target users, for example: and determining the user to be determined who receives a consultation before the ranking 30 of the characteristic evaluation value as a target user.

And S270, distributing the tasks to be distributed to each target reception user so that each target reception user receives the tasks to be distributed.

Specifically, when the task to be distributed is distributed to each target examination user, the task to be distributed may be distributed to the diagnosis and treatment system of each target examination user, so that each target examination user may selectively perform an examination. For example: when the current target receiving user selects a certain task to be distributed in the diagnosis and treatment system and triggers the confirmation key, the current target receiving user can be considered to receive the task to be distributed. Optionally, when any one target diagnosis receiving user receives the task to be distributed, the task to be distributed received by the other target diagnosis receiving users is invalid.

EXAMPLE III

Fig. 4 is a schematic structural diagram of an online task dispatching device according to a third embodiment of the present invention, where the online task dispatching device includes: a target department determining module 310, a to-be-determined consultation user determining module 320, a feature evaluation value determining module 330, and a target consultation user determining module 340.

The target department determining module 310 is configured to obtain disease information in the task to be distributed, and determine a target department corresponding to the disease information; the disease information is a disease description text corresponding to the target visiting user; the to-be-determined consultation user determining module 320 is configured to determine at least one to-be-selected consultation user associated with the target department, and determine at least one to-be-determined consultation user according to the attribute evaluation value corresponding to the attribute category information to which the at least one to-be-selected consultation user belongs; the feature evaluation value determining module 330 is configured to determine, for each user to be determined, an attribute evaluation value and a dynamic feature attribute of the current user to be determined, and comment attribute information of the target user to the current user to be determined, and determine a feature evaluation value of the current user to be determined; and the target diagnosis receiving user determining module 340 is configured to screen at least one target diagnosis receiving user from the at least one to-be-determined diagnosis receiving user according to the feature evaluation value of each to-be-determined diagnosis receiving user, and distribute the to-be-distributed task to the at least one target diagnosis receiving user.

Optionally, the target department determining module 310 is specifically configured to obtain a disease description text describing a disease of the target visiting user in the task to be distributed; determining keywords of the disease information by processing the disease description text; based on the keywords, a target department corresponding to the disease information is determined.

Optionally, the attribute category information to which the to-be-selected consultation user belongs includes basic attribute category information, service evaluation attribute category information, and activity evaluation attribute category information of the to-be-selected consultation user; the dynamic characteristic attributes comprise a task receiving activity level in a first preset time length and a task processing capacity level in a second preset time length; the comment attribute information comprises comment scores of the target visit user on all the to-be-selected visit users.

Optionally, the basic attribute category information includes at least one of a hospital rank of a hospital to which the to-be-selected reception user belongs, a department rank of a department to which the to-be-selected reception user belongs, a hospital grade of the hospital to which the to-be-selected reception user belongs, a city grade of the hospital to which the to-be-selected reception user belongs, and a title of the to-be-selected reception user; the business evaluation attribute category information comprises at least one of accumulated quantity of treatment receiving, average reply duration, total word number of single reply, high evaluation rate, low evaluation rate, voice reply rate and prescription rate; the activity evaluation attribute category information comprises an activity duration within a third preset duration and a task receiving activity duration.

Optionally, the to-be-determined consultation user determining module 420 is specifically configured to determine, for each to-be-selected consultation user, a hospital rank, a department rank, a hospital level, a city level, and a job title of the current to-be-selected consultation user, and call weight values corresponding to the hospital rank, the department rank, the hospital level, the city level, and the job title, respectively, so as to determine, based on the weight values, a first attribute value corresponding to the current to-be-selected consultation user; aiming at each user to be selected for receiving a diagnosis, determining the accumulated quantity of receiving a diagnosis, the average reply duration, the total word number of single reply, the high rating rate, the poor rating rate, the voice reply rate and the prescription rate of the current user to be selected for receiving a diagnosis, and respectively determining the weighted values corresponding to the accumulated quantity of receiving a diagnosis, the average reply duration, the total word number of single reply, the high rating rate, the poor rating rate, the voice reply rate and the prescription rate so as to determine a second attribute value corresponding to the current user to be selected for receiving a diagnosis based on the weighted values; aiming at each user to be selected for taking a call, determining the active time length and the task receiving active time length of the current user to be selected for taking a call within a third preset time length, respectively determining the weight values corresponding to the active time length and the task receiving active time length, and determining a third attribute value corresponding to the current user to be selected for taking a call based on the weight values; for each user to be selected for receiving a diagnosis, determining an attribute evaluation value of the current user to be selected for receiving a diagnosis according to the first attribute value, the second attribute value and the third attribute value of the current user to be selected for receiving a diagnosis; and determining a first preset number of to-be-determined treatment users according to the attribute evaluation value of each to-be-selected treatment user.

Optionally, the feature evaluation value determining module 330 is specifically configured to determine a task receiving activity level and a task throughput level of the current user to be determined, and respectively determine weighted values of the task receiving activity level and the task throughput level, so as to determine a dynamic attribute value of the current user to be determined based on the weighted values; determining the comment grade of the current to-be-determined reception user and a corresponding weight value of the target reception user, and determining the comment attribute value of the current to-be-determined reception user based on the weight value; and determining the characteristic evaluation value of the current user to be determined for receiving a diagnosis according to the dynamic attribute value, the comment attribute value and the attribute evaluation value.

Optionally, the target receiving user determining module 340 is specifically configured to determine a second preset number of target receiving users from high to low according to the feature evaluation value of each user to be determined; and distributing the tasks to be distributed to each target reception user so that each target reception user receives the tasks to be distributed.

According to the technical scheme of the embodiment of the invention, by acquiring the disease information in the task to be distributed, determining the target department corresponding to the disease information, preliminarily distributing the disease information to the target visiting user, determining at least one user to be selected and associated with the target department, determining at least one user to be determined according to the attribute evaluation value corresponding to the attribute category information of the at least one user to be selected and associated with the target department, preliminarily matching the medical basic level and the order taking capability of the user to be determined and the target visiting user, further determining the attribute evaluation value and the dynamic characteristic attribute of the user to be determined and the comment attribute information of the user to be determined and reviewed by the target visiting user, determining the characteristic evaluation value of the user to be determined, and screening out at least one target visiting user from the at least one user to be determined, the task to be distributed is distributed to at least one target visiting user, the problem that the matching degree of the target visiting user and the target visiting user is low is solved, the problem that the target visiting user is difficult to provide diagnosis and treatment services for the target visiting user in time is solved, the multi-dimensional influence factors are considered, and the technical effect of improving the matching degree of the target visiting user and the target visiting user is achieved.

The online task dispatching device provided by the embodiment of the invention can execute the online task dispatching method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.

It should be noted that, the units and modules included in the apparatus are merely divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the embodiment of the invention.

Example four

Fig. 5 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention. FIG. 5 illustrates a block diagram of an exemplary electronic device 40 suitable for use in implementing embodiments of the present invention. The electronic device 40 shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiment of the present invention.

As shown in fig. 5, electronic device 40 is embodied in the form of a general purpose computing device. The components of electronic device 40 may include, but are not limited to: one or more processors or processing units 401, a system memory 402, and a bus 403 that couples the various system components (including the system memory 402 and the processing unit 401).

Bus 403 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

Electronic device 40 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by electronic device 40 and includes both volatile and nonvolatile media, removable and non-removable media.

The system memory 402 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)404 and/or cache memory 405. The electronic device 40 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 406 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, commonly referred to as a "hard drive"). Although not shown in FIG. 5, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 403 by one or more data media interfaces. Memory 402 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.

A program/utility 408 having a set (at least one) of program modules 407 may be stored, for example, in memory 402, such program modules 407 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 407 generally perform the functions and/or methods of the described embodiments of the invention.

The electronic device 40 may also communicate with one or more external devices 409 (e.g., keyboard, pointing device, display 410, etc.), with one or more devices that enable a user to interact with the electronic device 40, and/or with any devices (e.g., network card, modem, etc.) that enable the electronic device 40 to communicate with one or more other computing devices. Such communication may be through input/output (I/O) interface 411. Also, the electronic device 40 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 412. As shown, the network adapter 412 communicates with the other modules of the electronic device 40 over the bus 403. It should be appreciated that although not shown in FIG. 5, other hardware and/or software modules may be used in conjunction with electronic device 40, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

The processing unit 401 executes various functional applications and data processing by executing programs stored in the system memory 402, for example, to implement the online task distribution method provided by the embodiment of the present invention.

EXAMPLE five

An embodiment of the present invention further provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform an online task dispatching method, where the method includes:

determining at least one to-be-selected consultation user associated with the target department, and determining at least one to-be-determined consultation user according to the attribute evaluation value corresponding to the attribute category information to which the at least one to-be-selected consultation user belongs;

Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for embodiments of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. An online task dispatching method is characterized by comprising the following steps:

2. The method of claim 1, wherein the obtaining of the disease information in the task to be distributed and the determining of the target department corresponding to the disease information comprises:

acquiring a disease description text for describing the disease of a target visiting user in a task to be distributed;

determining keywords of the disease description text by processing the disease description text;

and determining a target department corresponding to the disease information based on the keywords.

3. The method according to claim 1, wherein the attribute category information to which the to-be-selected consultation user belongs includes basic attribute category information, service evaluation attribute category information, and activity evaluation attribute category information of the to-be-selected consultation user; the dynamic characteristic attributes comprise a task receiving activity level in a first preset time length and a task processing capacity level in a second preset time length; the comment attribute information comprises comment scores of the target visit user on all the to-be-selected visit users.

4. The method of claim 3, wherein the basic attribute category information comprises at least one of a hospital ranking of a hospital to which the to-be-selected user is affiliated, a department ranking of a department to which the user is affiliated, a hospital grade of the hospital to which the user is affiliated, a city grade of the hospital to which the user is affiliated, and a title of the to-be-selected user; the business evaluation attribute category information comprises at least one of accumulated quantity of received diagnoses, average reply duration, total word number of single reply, high evaluation rate, low evaluation rate, voice reply rate and prescription rate; the activity evaluation attribute category information comprises an activity duration within a third preset duration and a task receiving activity duration.

5. The method according to claim 4, wherein the determining at least one to-be-determined consultation user according to the attribute evaluation value corresponding to the attribute category information to which the at least one to-be-selected consultation user belongs comprises:

determining a hospital ranking, a department ranking, a hospital grade, a city grade and a job title of a current user to be selected for receiving a consultation, calling weight values corresponding to the hospital ranking, the department ranking, the hospital grade, the city grade and the job title respectively, and determining a first attribute value corresponding to the current user to be selected for receiving a consultation based on the weight values;

determining the accumulated number of the current to-be-selected patients, the average reply duration, the total word number of single reply, the high rating rate, the poor rating rate, the voice reply rate and the prescription rate, and determining the weight values corresponding to the accumulated number of the to-be-selected patients, the average reply duration, the total word number of single reply, the high rating rate, the poor rating rate, the voice reply rate and the prescription rate respectively, so as to determine a second attribute value corresponding to the current to-be-selected patients based on the weight values;

aiming at each user to be selected for taking a call, determining the active time length and the task receiving active time length of the current user to be selected for taking a call within a third preset time length, respectively determining the weight values corresponding to the active time length and the task receiving active time length, and determining a third attribute value corresponding to the current user to be selected for taking a call based on the weight values;

for each user to be selected for receiving a diagnosis, determining an attribute evaluation value of the current user to be selected for receiving a diagnosis according to a first attribute value, a second attribute value and a third attribute value of the current user to be selected for receiving a diagnosis;

and determining a first preset number of to-be-determined treatment users according to the attribute evaluation value of each to-be-selected treatment user.

6. The method of claim 3, wherein the determining of the attribute evaluation value and the dynamic characteristic attribute of the current to-be-determined visiting user, the comment attribute information of the target visiting user to the current to-be-determined visiting user, and the determining of the characteristic evaluation value of the current to-be-determined visiting user comprise:

determining a task receiving activity level and a task processing capacity level of a current user to be determined for receiving a consultation, and respectively determining a weight value of the task receiving activity level and the task processing capacity level so as to determine a dynamic attribute value of the current user to be determined for receiving the consultation based on the weight value; and the number of the first and second groups,

determining a comment score of a target visiting user for the current to-be-determined visiting user and a corresponding weight value, and determining a comment attribute value of the current to-be-determined visiting user based on the weight value;

and determining the feature evaluation value of the current user to be determined for receiving a diagnosis according to the dynamic attribute value, the comment attribute value and the attribute evaluation value.

7. The method according to claim 1, wherein the step of screening out at least one target user from the at least one user to be determined and distributing the task to be distributed to the at least one target user according to the characteristic evaluation value of each user to be determined comprises:

determining a second preset number of target diagnosis receiving users from high to low according to the characteristic evaluation value of each user to be determined;

and distributing the tasks to be distributed to each target reception user so that each target reception user receives the tasks to be distributed.

8. An online task distribution apparatus, comprising:

9. An electronic device, characterized in that the electronic device comprises:

one or more processors;

a storage device for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement the online task serving method of any of claims 1-7.

10. A storage medium containing computer-executable instructions for performing the online task serving method of any one of claims 1-7 when executed by a computer processor.