Intelligent follow-up visit management system for otolaryngological patients based on applet and cloud platform

Abstract

The invention discloses an otolaryngology department patient follow-up visit intelligent management system based on an applet and a cloud platform, and belongs to the technical field of follow-up visit management. The method is used for solving the technical problems that in an existing scheme, the implementation effect of personalized services for follow-up visit of different otolaryngological patients is poor, and the independent supervision and optimization effect of the personalized services is poor. Historical doctor-seeing information and doctor-seeing inquiry information of different otorhinolaryngological patients are counted, corresponding data influence processing and analysis marking are carried out on the counted different information, and meanwhile targeted follow-up visit processing can be carried out on different patients. And performing periodic multi-dimensional supervision and evaluation on follow-up implementation data of different marked otolaryngological patients, performing integrated analysis on mark type group follow-up effect data corresponding to the different marked otolaryngological patients, determining implementation effects corresponding to existing follow-up differentiation management schemes, and performing targeted optimization management.

Description

Technical Field

[0001] The present invention relates to the technical field of follow-up management, and particularly to an intelligent management system for follow-up of otolaryngology patients based on a mini-program and a cloud platform. Background Art

[0002] The follow-up of otolaryngology patients refers to a series of follow-up examinations and services carried out by doctors after the patients complete the initial diagnosis and treatment in order to further observe the changes in the condition, evaluate the treatment effect, and timely detect and handle possible complications or recurrence. Follow-up is crucial for ensuring the effectiveness of treatment, adjusting the treatment plan, and improving the quality of life of patients.

[0003] Since otolaryngology involves the head and neck region with complex anatomical structures, the symptoms of patients may be very diverse and complex, increasing the difficulty of diagnosis and treatment; many otolaryngological diseases such as allergic rhinitis, chronic pharyngitis, etc. require long-term management and regular follow-up, which requires more meticulous and continuous follow-up services; however, when implementing the existing follow-up management programs for otolaryngology patients, many patients fail to follow up on time after discharge, resulting in untimely condition monitoring and possibly missing the best intervention opportunity. At the same time, the existing follow-up management is mostly in a unified mode and fails to make personalized adjustments and services according to the specific conditions of patients. Some patients do not fully recognize the importance of follow-up, or are unwilling to participate in follow-up due to inconvenient transportation, time arrangements, etc., resulting in poor implementation effects of personalized services for different otolaryngology patients and poor optimization effects of autonomous supervision of personalized services. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent management system for follow-up of otolaryngology patients based on a mini-program and a cloud platform, which is used to solve the technical problems of poor implementation effects of personalized services for different otolaryngology patients and poor optimization effects of autonomous supervision of personalized services in the existing solutions.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] An intelligent management system for follow-up of otolaryngology patients based on a mini-program and a cloud platform includes a multi-dimensional supervision and processing marking module for patient information, which is used to count the historical medical information and medical inquiry information of different otolaryngology patients, and process, analyze and mark the corresponding data impacts of the counted different information, so as to obtain the supervision marking data corresponding to different otolaryngology patients and upload them to the follow-up cloud platform in real time;

[0007] The patient follow-up implementation evaluation and optimization module is used to conduct periodic multi-dimensional supervision and evaluation on the follow-up implementation data of otolaryngology patients with different tags, and to integrate and analyze the follow-up effect data of the tag type groups corresponding to otolaryngology patients with different tags, determine the implementation effect corresponding to the existing follow-up differential management plan, and conduct targeted optimization management.

[0008] Preferably, obtain the corresponding historical medical treatment information according to the medical treatment identification of otolaryngology patients in the cloud platform, obtain the medical treatment results in the historical medical treatment information, identify and combine the different medical treatment sub-results in the medical treatment results to obtain a medical treatment sub-result identification array;

[0009] Successively input the medical treatment keywords in different medical treatment sub-result identification arrays into the medical treatment result influence table for traversal matching and summation to obtain the local medical treatment influence coefficients corresponding to different medical treatment sub-result identification arrays;

[0010] Sum up the local medical treatment influence coefficients corresponding to all medical treatment sub-result identification arrays obtained by processing the medical treatment results to obtain the integrated medical treatment influence coefficient corresponding to the otolaryngology patients.

[0011] Preferably, obtain the medical treatment inquiry information corresponding to otolaryngology patients, obtain the one-way journey duration and journey cost in the medical treatment inquiry information, and perform data analysis on the one-way journey duration and journey cost through an external influence identification function to output the corresponding external influence coefficient;

[0012] Sort and combine the integrated medical treatment influence coefficient and the external influence coefficient obtained by corresponding processing of the patient to obtain the corresponding supervision combination data.

[0013] Preferably, when processing, analyzing and marking the supervision combination data of the patient, calculate the sum of the integrated medical treatment influence coefficient, the external influence coefficient and the value in the supervision combination data to obtain the follow-up influence value corresponding to the patient;

[0014] Arrange different patients in descending order according to the value of the follow-up influence value, mark all patients greater than or equal to the follow-up influence limit value as the first patients, and mark the remaining patients as the second patients;

[0015] Sort and combine the supervision combination data, the follow-up influence value and the mark corresponding to different patients to obtain the supervision mark data corresponding to different patients.

[0016] Preferably, based on the cloud platform, monitor and count whether otolaryngology patients with different tags have been followed up within the corresponding required time period. If they have been followed up, add one to the total number of follow-ups of the corresponding tag type of the patient through the small program;

[0017] If follow-up is not conducted, the total number of patients who have not had the corresponding follow-up for the marked type will be incremented by one through the mini-program.

[0018] Preferably, obtain the total number of first follow-ups conducted and the total number of first follow-ups not conducted for all first patients corresponding to the marked type, and obtain the total number of second follow-ups conducted and the total number of second follow-ups not conducted for all patients corresponding to the marked type, and respectively calculate the first follow-up effect coefficient and the second follow-up effect coefficient for different marked types.

[0019] Preferably, obtain the first follow-up effect coefficient for the first patient corresponding to the marked type and the second follow-up effect coefficient for the second patient corresponding to the marked type, and calculate the first scheme effect coefficient corresponding to the follow-up differential management scheme;

[0020] Moreover, calculate the second scheme effect coefficient corresponding to the follow-up differential management scheme by calculating the first follow-up effect coefficient for the first patient corresponding to the marked type and the second scheme effect requirement value.

[0021] Preferably, conduct a combined analysis of the calculated first scheme effect coefficient and the second scheme effect coefficient to determine the implementation effect of the existing follow-up differential management scheme and perform targeted optimization management.

[0022] Preferably, if the first scheme effect coefficient ≥ K and the second scheme effect coefficient ≥ K, it is determined that the implementation effect of the existing follow-up differential management scheme is overall effective, and its subsequent implementation is maintained, and corresponding push notifications are sent through the mini-program and the cloud platform; K is a real number;

[0023] If the first scheme effect coefficient < K and the second scheme effect coefficient < K, it is determined that the implementation effect of the existing follow-up differential management scheme is overall ineffective, and its overall scheme is optimized, and corresponding push notifications are sent through the mini-program and the cloud platform;

[0024] In other cases, it is determined that the implementation effect of the existing follow-up differential management scheme is partially ineffective, and its local scheme is optimized, and corresponding push notifications are sent through the mini-program and the cloud platform.

[0025] Compared with the existing scheme, the beneficial effects achieved by the present invention:

[0026] The present invention statistically analyzes the historical medical record information and medical inquiry information of different otolaryngology patients, processes and analyzes the marked corresponding data impacts of the statistical information, and can also perform targeted follow-up processing on different patients, realizing differential follow-up evaluation and processing for different otolaryngology patients, and improving the implementation effect of personalized services for follow-up of different otolaryngology patients.

[0027] The present invention implements periodic multi-dimensional supervision and evaluation on the follow-up implementation data of otolaryngology patients with different tags, and integrates and analyzes the follow-up effect data of the tag type groups corresponding to otolaryngology patients with different tags, determines the implementation effects corresponding to the existing follow-up differential management schemes and conducts targeted optimization management, realizes the extended analysis of the effects of different follow-up methods for otolaryngology patients in the early stage and the autonomous optimization management, and improves the autonomous supervision and optimization effects of personalized services for the follow-up of different otolaryngology patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The present invention will be further described below with reference to the accompanying drawings.

[0029] Figure 1 It is a flow block diagram of the intelligent management system for the follow-up of otolaryngology patients based on the mini-program and the cloud platform of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0031] As Figure 1 shown, the present invention is an intelligent management system for the follow-up of otolaryngology patients based on the mini-program and the cloud platform, including a multi-dimensional supervision and processing tag module for patient information and a follow-up implementation evaluation and optimization module for patients;

[0032] The multi-dimensional supervision and processing tag module for patient information is used to count the historical medical treatment information and medical treatment inquiry information of different otolaryngology patients, and perform processing, analysis and tagging of the corresponding data impacts on the counted different information, obtain the supervision tag data corresponding to different otolaryngology patients and upload them to the follow-up cloud platform in real time; including:

[0033] Obtain the corresponding historical medical treatment information according to the medical treatment identification of otolaryngology patients in the cloud platform. The medical treatment information of otolaryngology patients is encrypted and stored in the cloud platform. The medical treatment identification can specifically be the medical treatment card number, and obtain the medical treatment result in the historical medical treatment information. Identify and combine keywords for different medical treatment sub-results in the medical treatment result to obtain a medical treatment sub-result identification array;

[0034] Among them, the medical treatment result contains at least one medical treatment sub-result, and the format of the medical treatment sub-result can be restricted. For example, it includes specific abnormal reasons and abnormal degrees; identifying keywords for different medical treatment sub-results is a conventional technical solution in the prior art, and the specific implementation steps are not described here in detail;

[0035] The visit keywords in the different visit result recognition arrays are successively input into the visit result impact table for traversal matching and summation to obtain the local visit impact coefficient JYJ corresponding to the different visit result recognition arrays;

[0036] Among them, the visit result impact table contains several sample abnormal reasons, and several different sample abnormal degrees and abnormal impact coefficients are associated with different sample abnormal reasons;

[0037] The abnormal impact coefficient is used to numerically represent the belonging sample abnormal degree, and the specific value can be determined by professional technicians in this field according to work experience;

[0038] In addition, when the visit keywords in the visit result recognition array do not match completely or partially in the visit result impact table, the otolaryngology patients corresponding to the visit result recognition array and their associated historical visit information are pushed to the administrator for manual review;

[0039] Sum up the local visit impact coefficients corresponding to all the visit result recognition arrays obtained by processing the visit results to obtain the integrated visit impact coefficient JYZ corresponding to the otolaryngology patients;

[0040] And, obtain the visit inquiry information corresponding to the otolaryngology patients. Through the attending doctor's active inquiry, statistical record and upload to the cloud platform, obtain the one-way journey duration and journey cost in the visit inquiry information, with the units being minutes and yuan respectively, and perform data analysis on the one-way journey duration and journey cost through the external impact recognition function to output the corresponding external impact coefficient WYX;

[0041] Among them, the expression of the external impact recognition function is In the formula, x and y are respectively the one-way journey duration and journey cost in the patient's visit inquiry information; x0 and y0 are respectively the one-way journey classification duration and journey classification cost corresponding to the patient, and both can be determined according to the median of all the one-way journey durations and the median of all the journey costs corresponding to the patients with normal historical follow-up;

[0042] The external impact coefficient includes the values of 0, 1, and 2;

[0043] Judge that there is no external impact on the patient according to the external impact coefficient with a value of 0;

[0044] Judge that the external impact on the patient is medium according to the external impact coefficient with a value of 1;

[0045] Judge that the external impact on the patient is high according to the external impact coefficient with a value of 2;

[0046] Sort and combine the integrated medical visit impact coefficient and external impact coefficient obtained for the patient to obtain the corresponding regulatory combination data;

[0047] It should be noted that by processing and digitally representing the impacts on the subsequent follow-up of different patients from different dimensions respectively, it is possible to obtain the follow-up impacts of different patients corresponding to different dimensions, and it can also provide reliable multi-dimensional local regulatory processing data support for the overall impact processing and analysis of the subsequent follow-up of different patients;

[0048] When processing, analyzing, and marking the regulatory combination data of the patient, the value in the regulatory combination data is calculated through the formula to obtain the follow-up impact value SY corresponding to the patient; in the formula, A is the integrated medical visit impact classification value, which can be determined according to the application requirements data of the actual application scenario, or can be determined according to the median of the follow-up impact values obtained by processing all historical patients;

[0049] Among them, the follow-up impact value is used to process and calculate the regulatory processing data of different dimensions to digitally represent the follow-up impact status corresponding to the patient;

[0050] The larger the follow-up impact value, the more it indicates that the corresponding patient needs to implement targeted follow-up processing, such as targeted follow-up visits, online follow-up processing, and other follow-up methods, to avoid the subsequent patients not being able to be followed up in time, thereby generating greater health hazards;

[0051] Arrange different patients in descending order according to the value of the follow-up impact value, and mark all patients greater than or equal to the follow-up impact limit value as the first patients, and mark the remaining patients as the second patients; the follow-up impact limit value can be determined according to the application requirements data of the actual application scenario, or can be determined according to the test data of the otolaryngology patients in the early stage;

[0052] Combine the regulatory combination data, follow-up impact value, and marking obtained for different patients to obtain the regulatory marking data corresponding to different patients;

[0053] Implement targeted follow-up processing for different patients according to the regulatory marking data;

[0054] Specifically, conduct targeted follow-up processing according to the marking of the first patients; conduct regular follow-up processing according to the marking of the second patients;

[0055] In the embodiments of the present invention, by statistically analyzing the historical medical treatment information and medical treatment inquiry information of different otolaryngology patients, processing and analyzing and marking the corresponding data impacts of the statistically different information, and at the same time, targeted follow-up processing can be implemented for different patients, differential follow-up evaluation and processing of different otolaryngology patients are realized, and the implementation effect of personalized services for follow-up of different otolaryngology patients is improved.

[0056] The patient follow-up implementation evaluation and optimization module is used to perform periodic multi-dimensional supervision and evaluation on the follow-up implementation data of otolaryngology patients with different marks, and integratively analyze the follow-up effect data of the mark type groups corresponding to different marked otolaryngology patients, determine the implementation effect corresponding to the existing follow-up differential management plan and perform targeted optimization management; including:

[0057] Based on the cloud platform, monitor and count whether different marked otolaryngology patients have been followed up within the corresponding required time periods. The required time periods corresponding to different patients are determined by their attending physicians. If a follow-up has been carried out, the total number of follow-ups of the corresponding mark type of the patient is incremented by one through the small program;

[0058] If the follow-up has not been carried out, the total number of non-followed-up patients of the corresponding mark type of the patient is incremented by one through the small program;

[0059] Obtain the total number of first follow-ups and the total number of non-first follow-ups of all first patients corresponding to the mark type, and obtain the total number of second follow-ups and the total number of non-second follow-ups of all patients corresponding to the mark type, and respectively use the formula Calculate and obtain the follow-up effect coefficient SFXk of different mark types; in the formula, k is 1, 2, which are the mark types corresponding to the first patient and the second patient respectively; N1k is N11, N12, which are the total number of first follow-ups and the total number of second follow-ups corresponding to different mark types respectively; N0k is N01, N02, which are the sum of the total number of first follow-ups and the total number of non-first follow-ups within the required time period corresponding to different mark types and the sum of the total number of second follow-ups and the total number of non-second follow-ups respectively;

[0060] Obtain the first follow-up effect coefficient SFX1 of the first patient corresponding to the mark type and the second follow-up effect coefficient SFX2 of the second patient corresponding to the mark type, and use the formula Calculate and obtain the first scheme effect coefficient FX1 corresponding to the follow-up differential management plan; in the formula, B is the first scheme effect requirement value, which is determined according to the actual design requirement data of the follow-up differential management plan;

[0061] And, use the formula for the first follow-up effect coefficient of the first patient corresponding to the mark type Calculate and obtain the second program effectiveness coefficient FX2 corresponding to the follow-up differential management program; where C is the required value of the second program effectiveness, which is determined according to the actual design requirement data of the follow-up differential management program;

[0062] It should be noted that the first program effectiveness coefficient is used for data calculation and analysis of the implementation effects corresponding to different follow-up methods; the second program effectiveness coefficient is used for data calculation and analysis of the implementation data corresponding to the targeted follow-up method and its standard data;

[0063] The differential calculation of the first program effectiveness coefficient and the second program effectiveness coefficient realizes the diversified supervision and processing analysis of the implementation effects of the follow-up differential management program, and can effectively improve the diversity and reliability of data processing and analysis;

[0064] When the calculated first program effectiveness coefficient and the second program effectiveness coefficient are jointly analyzed to determine the implementation effect corresponding to the existing follow-up differential management program and conduct targeted optimization management;

[0065] If FX1≥K and FX2≥K, it is determined that the overall implementation effect corresponding to the existing follow-up differential management program is effective, and its subsequent implementation is maintained, and corresponding push notifications are sent through the applet and the cloud platform; K is a real number, specifically, it can take the value of 1, or it can be adjusted and customized according to the application requirements of the actual application scenario;

[0066] If FX1<K and FX2<K, it is determined that the overall implementation effect corresponding to the existing follow-up differential management program is ineffective, and its overall program is optimized, and corresponding push notifications are sent through the applet and the cloud platform;

[0067] In other cases, it is determined that the implementation effect corresponding to the existing follow-up differential management program is partially ineffective, and its local program is optimized, and corresponding push notifications are sent through the applet and the cloud platform;

[0068] Among them, for the overall program optimization management of the existing follow-up differential management program, specifically, it can be to add, delete, or modify the overall process of data analysis and the standard parameters involved;

[0069] For the local program optimization management of the existing follow-up differential management program, specifically, it can be to add, delete, or modify the standard parameters of its data analysis process.

[0070] In the embodiments of the present invention, through the periodic multi-dimensional supervision and evaluation of the follow-up implementation data of otolaryngology patients with different markers, and the integrated analysis of the follow-up effect data of the marker type groups corresponding to otolaryngology patients with different markers, the implementation effect corresponding to the existing follow-up differential management plan is determined and targeted optimization management is carried out, realizing the extended analysis of the effects of different follow-up methods for otolaryngology patients in the early stage and the autonomous optimization management, and improving the autonomous supervision and optimization effect of personalized services for different otolaryngology patients' follow-up.

[0071] In several embodiments provided by the present invention, it should be understood that the disclosed system can be implemented in other ways. For example, the above-described invention embodiments are merely illustrative. For example, the division of modules is only a logical function division, and there can be other division methods in actual implementation.

[0072] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0073] In addition, in each embodiment of the present invention, the functional modules can be integrated in a processing module, or each module can exist physically alone, or two or more modules can be integrated in one module. The above integrated modules can be implemented in the form of hardware or in the form of a combination of hardware and software functional modules.

[0074] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent management system for follow-up of ENT patients based on mini-programs and cloud platforms, characterized by: It includes a patient information multi-dimensional supervision processing and marking module, which is used to collect statistics on the historical medical information and medical inquiry information of different ENT patients, and to process and analyze the corresponding data impact of the different statistical information, so as to obtain the corresponding supervision marking data of different ENT patients and upload it to the follow-up cloud platform in real time; The patient follow-up implementation evaluation and optimization module is used to conduct periodic multi-dimensional supervision and evaluation of the follow-up implementation data of ENT patients with different markers, and to integrate and analyze the follow-up effect data of marker type groups corresponding to ENT patients with different markers, determine the implementation effect of the existing differentiated follow-up management plan and conduct targeted optimization management.

2. The intelligent management system for follow-up of otolaryngology patients based on mini-programs and cloud platforms according to claim 1 is characterized in that: Obtain the corresponding historical medical information according to the medical identification of the otolaryngology patient in the cloud platform, and obtain the medical results in the historical medical information, perform keyword recognition and combination on different medical sub-results in the medical results, and obtain the medical sub-result recognition array; Input the visit keywords in different visit result identification arrays into the visit result influence table in turn, perform traversal matching and sum, and obtain the local visit influence coefficients corresponding to the different visit result identification arrays; The local visit influence coefficients corresponding to all the visit result identification arrays obtained by processing the visit results are summed up to obtain the integrated visit influence coefficient corresponding to the otolaryngology patient.

3. The intelligent management system for follow-up of otolaryngology patients based on mini-programs and cloud platforms according to claim 2 is characterized in that: Obtain the consultation inquiry information corresponding to the ENT patient, obtain the one-way journey time and journey cost in the consultation inquiry information, and perform data analysis on the one-way journey time and journey cost through the external influence identification function, and output the corresponding external influence coefficient; The integrated medical treatment impact coefficient and external impact coefficient obtained for the corresponding treatment of the patient are sorted and combined to obtain the corresponding regulatory combination data.

4. The intelligent management system for follow-up of otolaryngology patients based on mini-programs and cloud platforms according to claim 3 is characterized in that: When processing, analyzing and marking the patient's supervision combination data, the integrated visit impact coefficient and the external impact coefficient and the value in the supervision combination data are calculated to obtain the follow-up impact value corresponding to the patient; Arrange different patients in descending order according to the values ​​of the follow-up impact values, mark all patients whose values ​​are greater than or equal to the follow-up impact limit value as first patients, and mark the remaining patients as second patients; The regulatory combination data, follow-up impact values ​​and marker rankings obtained from the corresponding treatments of different patients are combined to obtain the regulatory marker data corresponding to different patients.

5. The intelligent management system for follow-up of otolaryngology patients based on mini-programs and cloud platforms according to claim 4 is characterized in that: Based on the cloud platform, monitor and count whether ENT patients with different marks have been followed up within the corresponding required time period. If they have been followed up, the total number of follow-up visits for the corresponding marked type of patients will be increased by one through the mini program; If no follow-up is conducted, the total number of patients with the corresponding marked type who have not undergone follow-up will be increased by one through the mini program.

6. The intelligent management system for follow-up of otolaryngology patients based on mini-programs and cloud platforms according to claim 5 is characterized in that: Obtain the total number of patients who underwent the first follow-up and the total number of patients who did not undergo the first follow-up for the corresponding marking type for all first patients, as well as the total number of patients who underwent the second follow-up and the total number of patients who did not undergo the second follow-up for the corresponding marking type for all patients, and obtain the first follow-up effect coefficient and the second follow-up effect coefficient of different marking types by calculation respectively.

7. The intelligent management system for follow-up of otolaryngology patients based on mini-programs and cloud platforms according to claim 6, characterized in that: Obtaining a first follow-up effect coefficient of the marker type corresponding to the first patient and a second follow-up effect coefficient of the marker type corresponding to the second patient, and obtaining a first scheme effect coefficient corresponding to the follow-up differentiated management scheme by calculation; Furthermore, the first follow-up effect coefficient of the marking type corresponding to the first patient and the second scheme effect requirement value are calculated to obtain the second scheme effect coefficient corresponding to the follow-up differentiated management scheme.

8. The intelligent management system for follow-up of otolaryngology patients based on mini-programs and cloud platforms according to claim 7 is characterized in that: The calculated effect coefficients of the first and second plans are jointly analyzed to determine the corresponding implementation effects of the existing follow-up differentiated management plans and conduct targeted optimization management.

9. The intelligent management system for follow-up of otolaryngology patients based on mini-programs and cloud platforms according to claim 8, characterized in that: If the effect coefficient of the first plan ≥ K and the effect coefficient of the second plan ≥ K, the implementation effect of the existing follow-up differentiated management plan is determined to be effective as a whole, and its subsequent implementation is maintained, and corresponding push prompts are made through the mini program and cloud platform; K is a real number; If the effect coefficient of the first plan is less than K and the effect coefficient of the second plan is less than K, the implementation effect of the existing follow-up differentiated management plan is determined to be invalid as a whole, and the overall plan is optimized and managed, and corresponding push notifications are made through the mini program and cloud platform; In other cases, the implementation effect of the existing differentiated follow-up management plan will be judged to be partially invalid, and the local plan will be optimized and managed, and corresponding push prompts will be made through the mini program and cloud platform.

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