A mobile supervision and management method and system for vaccination clinics

Abstract

The present invention relates to the technical field of outpatient supervision and management, and specifically discloses a mobile supervision and management method and system for vaccination outpatient clinics, which ensures timely feedback on the prevention and supervision rectification rates of vaccination outpatient departments at all levels and generates supervision information to complete an intelligent and reasonable supervision process. It includes: collecting the registered supervision expert file information and appointment cancellation supervision expert file information of historical vaccination outpatient management institutions at all levels within a preset time period; performing multi-dimensional data analysis based on the registered supervision expert file information and appointment cancellation supervision expert file information to obtain supervision rectification rate parameters and sending supervision information to vaccination outpatient management institutions at all levels; statistically collecting the supervision rectification rate parameter sets of historical vaccination outpatient management institutions at all levels and inputting them into a GRU recurrent neural network model for training to obtain a vaccination supervision model; real-time obtaining the supervision rectification rate parameter sets of vaccination outpatient clinics at all levels and inputting them into the vaccination supervision model for analysis, and generating supervision reminders according to the magnitude of the reminder status coefficient.

Description

Technical Field

[0001] The present invention relates to the technical field of outpatient supervision and management, and particularly to a mobile supervision and management method and system for vaccination outpatient clinics. Background Art

[0002] The mobile supervision and management of vaccination outpatient clinics involves many department management processes, and the management process also requires the ability to integrate online and offline data information; therefore, localities have actively promoted the informatization construction of vaccination outpatient clinics. By building an informatization management platform for the whole process of vaccination, functions such as online appointment, vaccination information query, and self-reminder have been realized to improve the work efficiency and service quality of outpatient departments, and also provide a data basis for mobile supervision and management.

[0003] In the existing mobile supervision and management system for vaccination outpatient clinics, it is necessary to use mobile terminal devices to confirm user vaccination information and install relevant supervision and management application programs to guide the vaccination units in the supervision and management process; however, since the supervision work needs to be implemented in grass-roots departments, the paper-based statistical method is prone to non-standard assessment items or missing relevant assessment items during the supervision and assessment process, and there are problems such as a large amount of archive materials storage and extremely inconvenient access to historical archive materials, which in turn affect the supervision and rectification efficiency and reduce the outpatient service quality.

[0004] Therefore, it is necessary to analyze and mine the data related to outpatient supervision and management by means of big data and cloud computing technologies, further strengthen the supervision and rectification feedback mechanism, improve the monitoring efficiency, and then formulate more scientific and intelligent supervision plans and strategies. Summary of the Invention

[0005] The purpose of the present invention is to provide a mobile supervision and management method and system for vaccination outpatient clinics, and solve the following technical problems:

[0006] How to ensure the timely feedback of the prevention supervision and rectification rate of vaccination outpatient departments at all levels and generate supervision information to complete an intelligent and reasonable supervision process.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] A mobile supervision and management method for vaccination outpatient clinics, the method includes:

[0009] S1. Collect the registered supervision expert file information of vaccination outpatient management institutions at all levels in a preset time period, and count the number of supervision experts and the expert supervision dates according to the registered supervision expert file information;

[0010] S2. Collect the appointment cancellation supervision expert file information of the historical vaccination outpatient management institutions at all levels during the preset time period, and count the number of unappointed supervision experts and the date of the last supervision of the experts according to the appointment cancellation supervision expert file information;

[0011] S3. Perform multi-dimensional data analysis based on the registered supervision expert file information and the appointment cancellation supervision expert file information to obtain the supervision rectification rate parameter, judge the high or low of the supervision rectification rate, and send supervision information to the vaccination outpatient management institutions at all levels;

[0012] S4. Count the set of supervision rectification rate parameters of the historical vaccination outpatient management institutions at all levels and input them into the GRU recurrent neural network model for training to obtain the vaccination supervision model;

[0013] S5. Real-time obtain the set of supervision rectification rate parameters of each vaccination outpatient, input them into the vaccination supervision model for analysis, obtain the reminder status coefficient of each vaccination outpatient, and generate a supervision reminder according to the size of the reminder status coefficient.

[0014] Preferably, the process of multi-dimensional data analysis in S3 is as follows:

[0015] Screen the number of supervision experts and the date of expert supervision in the registered supervision expert file information and record them as the first input information;

[0016] Screen the number of unappointed supervision experts and the date of the last supervision of the experts in the appointment cancellation supervision expert file information and record them as the second input information;

[0017] Perform information merging processing on the first input information and the second input information to respectively confirm the number of people who were supervised last time and are supervised this time, the number of people who were supervised last time but not this time, and the number of problems in the last supervision, and mark them;

[0018] Obtain the total number of people in the registered supervision expert file information and the total number of supervision times, and use the marked number of people who were supervised last time and are supervised this time as the first positive example sample and the number of people who were supervised last time but not this time as the first negative example sample;

[0019] Input the first positive example sample and the first negative example sample into the BP neural network model for training, construct an analysis model, and establish a feedback learning mechanism for the analysis model.

[0020] Preferably, the judgment process of the supervision rectification rate is as follows:

[0021] Calculate the target cycle change within the preset time period based on the feedback learning mechanism of the analysis model to obtain the supervision rectification rate parameter:

[0022]

[0023] Among them, is the supervision rectification rate parameter; is the target cycle within a preset time period; is the number of rectification personnel who were supervised last time and have been supervised this time within the duration; is the number of rectification personnel who were supervised last time but not supervised this time within the duration; is the number of rectification personnel who were not supervised either last time or this time within the duration, regardless of whether they were supervised before; is the total number of registered expert file information; is the weight coefficient of the regional registration number within the duration, and 0 < < 1; is the number of supervision times.

[0024] Preferably, compare the supervision rectification rate parameter with the standard threshold interval of the preset supervision rectification rate parameter and is the minimum value of the standard supervision rectification rate parameter, is the maximum value of the standard supervision rectification rate parameter:

[0025] If > , it is determined that the supervision rectification rate is high;

[0026] If ≤ ≤ , it is determined that the supervision rectification rate is medium;

[0027] If < , it is determined that the supervision rectification rate is low.

[0028] Preferably, the method of statistically collecting the supervision rectification rate parameter set of historical vaccination outpatient management institutions at all levels in S4 and inputting it into the GRU recurrent neural network model for training to obtain the vaccination supervision model is as follows:

[0029] Obtain positive and negative samples: Use the supervision rectification rate parameter set with a high supervision rectification rate as the second positive example sample and the supervision rectification rate parameter sets with medium and low supervision rectification rates as the second negative example samples;

[0030] Input the second positive example sample and the second negative example sample into the GRU recurrent neural network model for training and learning to obtain the vaccination supervision model.

[0031] Preferably, the reminder status coefficient:

[0032] Obtained by the formula calculate the reminder status coefficient of the ;

[0033] Among them, is the target cycle within a preset time period; is the previous cycle period of two adjacent cycles of the vaccination clinic within the preset time period; is the next cycle period of two adjacent cycles of the vaccination clinic within the preset time period; is the first preset weight coefficient, is the second preset weight coefficient; and , are both greater than 0; is the real-time supervision and rectification rate parameter of the -level vaccination clinic.

[0034] Preferably, the method for generating supervision reminders according to the reminder status coefficient is as follows:

[0035] Compare the reminder status coefficient with the preset reminder status coefficient threshold to compare their magnitudes:

[0036] If ≤ , it is determined that the completion degree of the supervision of the vaccination work in the current-level vaccination clinic meets the requirements;

[0037] If > , it is determined that the completion degree of the supervision of the vaccination work in the current-level vaccination clinic does not meet the requirements, and a supervision reminder is generated.

[0038] It also includes:

[0039] Register the evaluation information after supervision and rectification and the reasons for canceling supervision, extract the information related to the effect of supervision and rectification, and match the corresponding-level vaccination clinics to send supervision and rectification notices.

[0040] A mobile supervision management system for vaccination clinics is used to implement a mobile supervision management method for vaccination clinics. The system includes:

[0041] A collection unit, which is used to collect the registered supervision expert file information of historical vaccination clinic management institutions at all levels within a preset time period, and count the number of supervision experts and the expert supervision dates according to the registered supervision expert file information; and is used to collect the reserved cancellation supervision expert file information of historical vaccination clinic management institutions at all levels within a preset time period, and count the number of unreserved supervision experts and the expert's last supervision date;

[0042] An analysis unit, which is used to perform multi-dimensional data analysis on the registered supervision expert file information and the reserved cancellation supervision expert file information to obtain the supervision and rectification rate parameter, judge the level of the supervision and rectification rate, and send supervision information to vaccination clinic management institutions at all levels;

[0043] A model construction unit is configured to statistically collect a parameter set of the supervision and rectification rates of historical vaccination outpatient management institutions at all levels and input the set into a GRU recurrent neural network model for training to obtain a vaccination supervision model.

[0044] A supervision unit is configured to obtain in real time a parameter set of the supervision and rectification rates of vaccination outpatient clinics at all levels, input the set into the vaccination supervision model for analysis, obtain a reminder status coefficient for each level of vaccination outpatient clinic, and generate a supervision reminder according to the magnitude of the reminder status coefficient.

[0045] Advantages of the present invention:

[0046] (1) The present invention obtains the parameter of the supervision and rectification rate through multi-dimensional data analysis, judges the high or low of the supervision and rectification rate, sends supervision information to vaccination outpatient management institutions at all levels, and confirms the information of the outpatient management institutions that send the supervision information; through predictive analysis of the high or low of the supervision and rectification rate, timely sends supervision information to vaccination outpatient management institutions at all levels to ensure that each outpatient department generates the parameter of the supervision and rectification rate based on historical vaccination data as a reference.

[0047] (2) The present invention statistically collects a parameter set of the supervision and rectification rates of historical vaccination outpatient management institutions at all levels according to the sent supervision information and inputs the set into a GRU recurrent neural network model for training to obtain a vaccination supervision model; analyzes the dynamic change situation of the supervision and rectification rate parameters in adjacent periods to obtain a reminder status coefficient, and determines the real-time stage situation of the vaccination work during this period according to the reminder status coefficient; furthermore, makes corresponding supervision feedback on the vaccination work in a timely manner according to the output of the magnitude of the reminder status coefficient.

[0048] Of course, it is not necessary for any product implementing the present invention to achieve all the advantages described above simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0050] Figure 1 It is a flowchart of a mobile supervision management method for vaccination outpatient clinics according to the present invention;

[0051] Figure 2 It is a unit diagram of a mobile supervision management system for vaccination outpatient clinics according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with 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. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0053] Please refer to Figure 1 As shown, the present invention is a mobile supervision and management method for vaccination clinics, and the method includes:

[0054] S1. Collect the registration supervision expert file information of each level of vaccination clinic management institution in the preset time period, and count the number of supervision experts and the expert supervision dates according to the registration supervision expert file information;

[0055] S2. Collect the appointment cancellation supervision expert file information of each level of vaccination clinic management institution in the preset time period, and count the number of unappointed supervision experts and the expert's last supervision date according to the appointment cancellation supervision expert file information;

[0056] S3. Perform multi-dimensional data analysis based on the registration supervision expert file information and the appointment cancellation supervision expert file information to obtain the supervision rectification rate parameter, judge the high or low of the supervision rectification rate, and send supervision information to each level of vaccination clinic management institution;

[0057] S4. Statistically collect the supervision rectification rate parameter sets of each level of vaccination clinic management institution in history and input them into the GRU recurrent neural network model for training to obtain the vaccination supervision model;

[0058] S5. Real-time obtain the supervision rectification rate parameter sets of each level of vaccination clinic and input them into the vaccination supervision model for analysis, obtain the reminder status coefficients of each level of vaccination clinic, and generate supervision reminders according to the sizes of the reminder status coefficients.

[0059] In the above technical solution, by collecting the login information of supervision experts on mobile terminal devices such as mobile phones and tablets, and obtaining the supervision data of the information supervision management department of vaccination clinics through the mobile information receiving end, it mainly realizes the accurate acquisition of the vaccination situation of each level of vaccination departments in history during the supervision process by collecting the supervision registration information of each level of vaccination clinic management institution in the preset time period and screening the supervision file information of supervised registration vaccinations.

[0060] Specifically, first, collect the registration supervision expert file information of the historical vaccination outpatient management institutions at all levels during the preset time period, and count the number of supervision experts and the expert supervision dates according to the registration supervision expert file information; and collect the appointment cancellation supervision expert file information of the historical vaccination outpatient management institutions at all levels during the preset time period, and count the number of non-appointed supervision experts and the expert's last supervision date according to the appointment cancellation supervision expert file information. Moreover, since there are various situations where experts are registered but not supervised, further data screening and processing are required to ensure the realization of the multi-dimensional data analysis process based on the registration supervision expert file information and the appointment cancellation supervision expert file information. The multi-dimensional data analysis technology can screen the data related to the supervision vaccination status. In this embodiment, the supervision rectification rate parameter is calculated and obtained through multi-dimensional data analysis, the level of the supervision rectification rate is judged, and the supervision information is sent to the vaccination outpatient management institutions at all levels, and the information of the outpatient management institutions that send the supervision information is confirmed; through the predictive analysis of the level of the supervision rectification rate, the supervision information is sent to the vaccination outpatient management institutions at all levels in a timely manner to ensure that each outpatient department generates the supervision rectification rate parameter based on the historical vaccination data as a reference; furthermore, based on machine learning and data model selection, a further mining and analysis process is carried out on the supervision rectification rate parameter.

[0061] Furthermore, the mining and analysis process is specifically to count the set of supervision rectification rate parameters of the historical vaccination outpatient management institutions at all levels according to the sent supervision information and input it into the GRU recurrent neural network model for training to obtain the vaccination supervision model; optimize the supervision rectification information to realize the accurate input process of monitoring the supervision vaccination data; finally, obtain the set of supervision rectification rate parameters of each vaccination outpatient in real time and input it into the vaccination supervision model for analysis to realize the real-time data detection process, so as to obtain the reminder status coefficient of each vaccination outpatient, and generate supervision reminders in real time according to the size of the reminder status coefficient to ensure that each outpatient department receives accurate supervision message prompts and optimize the supervision process.

[0062] As an implementation manner of the present invention, the process of multi-dimensional data analysis in S3 is as follows:

[0063] The number of supervision experts and the expert supervision dates in the screened registration supervision expert file information are recorded as the first input information;

[0064] The number of non-appointed supervision experts and the expert's last supervision date in the screened appointment cancellation supervision expert file information are recorded as the second input information;

[0065] The first input information and the second input information are merged and processed to respectively confirm the number of people who were supervised last time and are supervised this time, the number of people who were supervised last time but not this time, and the number of problems in the last supervision and mark them;

[0066] Obtain the total number of registered supervision expert file information and the total number of supervisions, and use the number of people who were supervised last time and are supervised this time that have been marked as the first positive example sample, and the number of people who were supervised last time but not supervised this time as the first negative example sample;

[0067] Input the first positive example sample and the first negative example sample into a training based on a BP neural network model, construct an analysis model, and establish a feedback learning mechanism for the analysis model.

[0068] In the above technical solution, the specific process of setting multi-dimensional data analysis is to screen the number of supervision experts and the expert supervision dates in the registered supervision expert file information as the first input information; and screen the number of unscheduled supervision experts and the expert's last supervision date in the reserved cancellation supervision expert file information as the second input information; determine the number of currently registered and supervised experts through the first input data, and confirm the number of currently registered but not participating in supervision this time through the second input information; according to the first input information and the second input information, perform data insertion and table merging and other statistical knowledge, and four situations can be confirmed: supervised last time and supervised this time, supervised last time but not supervised this time, neither supervised last time nor this time, not supervised last time but supervised this time. Extract conditional information through the input data, screen and confirm the number of people who were supervised last time and are supervised this time, the number of people who were supervised last time but not supervised this time, and the number of last supervision problems and mark them.

[0069] Then, obtain the total number of registered supervision expert file information and the total number of supervisions, extract the total number of supervision expert data and the total number of supervision times data from the supervision expert file information; then determine the sample information, use the number of people who were supervised last time and are supervised this time that have been marked as the first positive example sample and the number of people who were supervised last time but not supervised this time as the first negative example sample. After the obtained sample information is confirmed, the training process of the machine model such as the BP neural network model is completed. By inputting the first positive example sample and the first negative example sample into a training based on the BP neural network model, an analysis model is constructed, and a feedback learning mechanism is established for the analysis model, completing the construction of the analysis model and performing the preliminary training process according to the above historical data set.

[0070] As an implementation manner of the present invention, the judgment process of the supervision rectification rate is as follows:

[0071] Calculate the supervision rectification rate parameter based on the feedback learning mechanism of the analysis model for the target cycle change within a preset time period:

[0072]

[0073] Wherein, is the supervision rectification rate parameter; is the target cycle within a preset time period; is The number of rectified people who were last supervised within the time period and have been supervised this time; is The number of rectified people who were last supervised within the time period but have not been supervised this time; is The number of rectified people who have not been supervised either last time or this time within the time period, and whether they were supervised before is not taken into account; is the total number of people registered in the expert file; is The weight coefficient of the number of people registered in the region within the time period, and 0 < < 1; is the number of supervision times.

[0074] In the above technical solution, by defining a preset time period, several time intervals within this time period are evenly collected as target cycles, and the supervision and rectification rate parameter is obtained through calculation according to the change of the target cycle; the specific process of judging the size of the supervision and rectification rate parameter is obtained through calculation by a feedback learning mechanism, and the specific calculation process is through the formula Calculate to obtain the supervision and rectification rate parameter ; Since the data range related to the supervision and rectification rate includes: the number of people supervised and rectified and this vaccination content (obtained through hospital diagnosis and CDC statistics), and because the supervision and rectification process has periodicity, seasonality and regionality due to the vaccination disease situation, only by statistically confirming the fixed number of people supervised and rectified cannot accurately reflect the current epidemic prevention effect, and it is also necessary to add the judgment process of the number of people rectified before and after the vaccination supervision; specifically, the analysis process is to judge the weight of this number Under the state, the number of people rectified in single supervision , multiple supervision and non-supervision and other situations are combined and statistically analyzed, and in this formula represents the degree of rectification of the last supervision but not this time; The larger it is, the lower the supervision and rectification degree and the higher the supervision and rectification efficiency; Indicates the rectification ratio of the last supervision and this supervision, and The smaller it is, the larger the relative rectification ratio The value of, indicating the higher the supervision and rectification rate. In addition, the weight coefficient is determined according to the ratio of the number of people supervised and rectified (including those supervised once or more) to the total number of registered people, and is a value that can be statistically determined. Therefore, judging the supervision and rectification rate is through statistically analyzing the change of the number of people supervised and rectified, that is, The size of; actually, by judging that when The overall value is larger, and 0 < < 1, then the supervision and rectification rate parameter It will increase as the number of people supervised for rectification increases; is the number of supervision times, and > 0.

[0075] As an implementation manner of the present invention, the supervision and rectification rate parameter is compared with the standard threshold interval of the preset supervision and rectification rate parameter and is the minimum value of the standard supervision and rectification rate parameter, is the maximum value of the standard supervision and rectification rate parameter:

[0076] If > , it is determined that the supervision and rectification rate is high;

[0077] If ≤ ≤ , it is determined that the supervision and rectification rate is medium;

[0078] If < , it is determined that the supervision and rectification rate is low.

[0079] In the above technical solution, when the supervision and rectification rate parameter is greater than the maximum value of the preset interval, it indicates that the supervision and rectification rate is high. On the contrary, it indicates that the supervision and rectification rate is general or low. During the period of strengthening supervision and rectification, this result needs to be promoted for vaccination. The vaccination institutions should be supervised and the further expansion of the scope of this epidemic should be reduced, thereby reducing the impact on people's normal life.

[0080] As an implementation manner of the present invention, the method for statistically collecting the supervision and rectification rate parameter sets of historical vaccination outpatient management institutions at all levels in step S4 and inputting them into the GRU recurrent neural network model for training to obtain the vaccination supervision model is as follows:

[0081] Obtain positive and negative samples: The set of supervision and rectification rate parameters with a high supervision and rectification rate is used as the second positive example sample, and the set of supervision and rectification rate parameters with a medium and low supervision and rectification rate is used as the second negative example sample;

[0082] Input the second positive example sample and the second negative example sample into the GRU recurrent neural network model for training and learning to obtain the vaccination supervision model.

[0083] In the above technical solution, the supervision and rectification rate parameter set of historical vaccination outpatient management institutions at all levels is statistically obtained through the obtained supervision information for machine training. The training method first requires the use of a machine model, and the structure of a new GRU model is defined using the deep learning framework of the GRU recurrent neural network model (such as PyTorch, TensorFlow, etc.), including the input layer, GRU layer, output layer and other architectures. The trained model (vaccination supervision model) can predict the change trend of the supervision and rectification rate parameters, and the amount of data that GRU needs to calculate is relatively small, which can quickly give feedback on the real-time obtained data and optimize the supervision process. Specifically, first obtain positive and negative sample information, and use the supervision and rectification rate parameter set with a high supervision and rectification rate as the second positive example sample; and use the supervision and rectification rate parameter sets with medium and low supervision and rectification rates as the second negative example samples; then input the second positive example samples and the second negative example samples into the GRU recurrent neural network model for training and learning to construct a vaccination supervision model.

[0084] As an implementation manner of the present invention, the reminder status coefficient:

[0085] is calculated through the formula to obtain the reminder status coefficient of the -th level vaccination outpatient;

[0086] wherein, is the target period within the preset time period; is the previous cycle period of two adjacent cycles within the preset time period for the vaccination outpatient; is the subsequent cycle period of two adjacent cycles within the preset time period for the vaccination outpatient; is the first preset weight coefficient, is the second preset weight coefficient; and , are both greater than 0; is the real-time supervision and rectification rate parameter of the -th level vaccination outpatient.

[0087] In the above technical solution, after constructing the vaccination supervision model, the real-time obtained supervision and rectification rate parameters are input into the model for test analysis. Specifically, the supervision and rectification rate parameter sets of each level of vaccination outpatient obtained in real time are input into the vaccination supervision model for testing; after the test is completed, according to the test output result, the change state of the supervision and rectification rate parameters of adjacent cycles needs to be calculated for dynamic data, and it is determined whether the currently trained vaccination meets the requirements through coefficient calculation, and a supervision reminder is generated for those that do not meet the requirements. Specifically, the reminder status coefficient is calculated through the formula to obtain the reminder status coefficient of the It reflects the periodic completion effect and completion degree of the vaccination work in the vaccination clinics at the current level; therefore, by judging the dynamic changes in adjacent cycles, the real-time stage of the vaccination work during this period can be explained; furthermore, according to the output of the reminder status coefficient, corresponding supervision and feedback can be given to the vaccination work in a timely manner.

[0088] Among them, it should be explained that the first preset weight coefficient and the second preset weight coefficient are calculated by the CRITIC weight method for the parameter sets of the real-time supervision and rectification rates of the two cycles respectively. The product of the contrast intensity (represented by the standard deviation of all supervision and rectification rate parameters in each cycle) and the conflict index (represented by the correlation coefficient of all supervision and rectification rate parameters in each cycle, usually considering the non-rectification rate after supervision as the correlation coefficient) is calculated and normalized to obtain the weight value.

[0089] As an implementation manner of the present invention, the method for generating a supervision reminder according to the reminder status coefficient is as follows:

[0090] Compare the reminder status coefficient with the preset reminder status coefficient threshold to determine the size:

[0091] If ≤ , it is determined that the completion degree of the supervision and vaccination work in the vaccination clinics at the current level meets the requirements;

[0092] If > , it is determined that the completion degree of the supervision and vaccination work in the vaccination clinics at the current level does not meet the requirements, and a supervision reminder is generated.

[0093] In the above technical solution, as shown in Table 1, the method for generating a supervision reminder includes generating a supervision selection form for the corresponding department, which is updated for a period of time, generally updated in a periodic manner. According to the dynamic supervision form, the corresponding time period is selected to arrange supervisors to go to vaccination clinics at all levels in a timely manner to conduct supervision and vaccination and vaccination publicity. In this application, first, data is input, and the rectification rate parameters of outpatient clinics at all levels are collected in real time. The rectification rate parameters are the rectification-related data obtained daily (cumulative number of problems found, cumulative number of rectifications implemented, problem rectification rate (%), number of outpatient clinics not fully rectified, outpatient clinic full rectification rate (%)); then, the rectification rate data of each outpatient clinic is integrated according to the time window, and the formula can be simplified to: ; where is the number of data points within the cycle period, and the cycle period includes , , and ; is the time interval between data points; is the supervision and rectification rate parameter of the -level vaccination clinic at the th data point; if > , it is determined as "not meeting the requirements", and a supervision reminder is triggered; the standard threshold needs to be adjusted according to historical data (such as determining the normal fluctuation range through statistical methods).

[0094] Table 1 Supervision Reminder for Vaccination Cycle

[0095]

[0096] As an implementation manner of the present invention, it further includes:

[0097] Register the evaluation information after supervision and rectification and the reasons for canceling supervision, extract the information related to the effect of supervision and rectification, and match the corresponding - level vaccination clinics for supervision and rectification notice.

[0098] In the above - mentioned technical solution, the information on the reasons for canceling supervision and the evaluation information after supervision and rectification are deconstructed by using a language model, the evaluation related to the effect of supervision and rectification is extracted, the number of evaluation information and the vaccinated clinics being evaluated are counted, and supervision notices are sent in a timely manner.

[0099] Please refer to Figure 2 as shown. The present invention also sets up a mobile supervision and management system for vaccination clinics to implement a mobile supervision and management method for vaccination clinics. The system includes:

[0100] A collection unit, which is used to collect the registration and supervision expert file information of historical vaccination clinic management institutions at all levels within a preset time period, and count the number of supervision experts and the expert supervision dates according to the registration and supervision expert file information; and is used to collect the reservation - cancellation supervision expert file information of historical vaccination clinic management institutions at all levels within a preset time period, and count the number of non - reserved supervision experts and the expert's last supervision date according to the reservation - cancellation supervision expert file information;

[0101] An analysis unit, which is used to perform multi - dimensional data analysis on the registration and supervision expert file information and the reservation - cancellation supervision expert file information to obtain the supervision and rectification rate parameter, judge the level of the supervision and rectification rate, and send supervision information to vaccination clinic management institutions at all levels;

[0102] A model construction unit, which is used to count the set of supervision and rectification rate parameters of historical vaccination clinic management institutions at all levels and input them into a GRU recurrent neural network model for training to obtain a vaccination supervision model;

[0103] A supervision unit, which is used to input the set of supervision and rectification rate parameters of vaccination clinics at all levels in real - time into the vaccination supervision model for analysis, obtain the reminder status coefficient of vaccination clinics at all levels, and generate a supervision reminder according to the size of the reminder status coefficient.

[0104] Table 2 and Table 3 reflect the analysis through the mobile supervision and management system for vaccination clinics, and statistically analyze the implementation and rectification of outpatient clinic supervision in different regions over the years; by analyzing the cumulative number of problems found and the cumulative data of implemented rectification, the rectification rate information is obtained, and the rectification process is optimized and implemented.

[0105] Implementation and rectification situation in partial regions in Table 2

[0106]

[0107] Table 3 Implementation and rectification situation in different rounds in multiple regions cumulatively

[0108]

[0109] Table 4 Rectification situation of supervision problems

[0110]

[0111] In Table 4 above, the number of problems in the previous supervision is counted according to the third-level headings; under the condition of selecting the time period / task name, it is the sum of the problems found in the previous regular supervision of all supervised units in this area ("sum of the number of 'latest anomalies' problems").

[0112] Number of problems with rectification completed: Only counted when selecting regular supervision; under the condition of selecting the time period / task name, it is the number of problems selected as [rectified] in this regular supervision of all supervised units in this area;

[0113] Rectification rate (%): Number of problems with rectification completed / Number of problems in the previous supervision * 100; Only counted when selecting regular supervision;

[0114] Number of supervision problems: The definition remains unchanged. Under the condition of selecting the time period / task name, it is the number of problems found in this regular supervision of all supervised units in this area (note: including the problems that were found last time and selected as [not rectified] this time);

[0115] Number of problems in the previous supervision, number of supervision problems: The problem lists can be opened separately for viewing.

[0116] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of devices, equipment, and non-volatile computer storage media, since they are basically similar to the method embodiments, the description is relatively simple, and for the relevant parts, reference can be made to the partial description of the method embodiments.

[0117] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended documents. In some cases, the actions or steps recited in this application may be performed in a different order than in the embodiments and still achieve the desired results. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0118] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the specific embodiments described, or use similar methods to substitute, as long as they do not deviate from the concept of the invention or exceed the scope defined by this application, they should all fall within the protection scope of the present invention.

Claims

1. A mobile supervision and management method for vaccination clinics, characterized in that, The method includes: S1. Collect the registration and supervision expert file information of each historical vaccination outpatient management institution during a preset time period, and count the number of supervision experts and the expert supervision dates according to the registration and supervision expert file information; S2. Collect the appointment cancellation supervision expert file information of each historical vaccination outpatient management institution during a preset time period, and count the number of non-appointed supervision experts and the expert's last supervision date according to the appointment cancellation supervision expert file information; S3. Perform multi-dimensional data analysis based on the registration and supervision expert file information and the appointment cancellation supervision expert file information to obtain the supervision rectification rate parameter, judge the level of the supervision rectification rate, and send supervision information to each level of vaccination outpatient management institution; The process of multi-dimensional data analysis in S3 is as follows: Screen the number of supervision experts and the expert supervision dates in the registration and supervision expert file information and record them as the first input information; Screen the number of non-appointed supervision experts and the expert's last supervision date in the appointment cancellation supervision expert file information and record them as the second input information; Perform information merging processing on the first input information and the second input information to respectively confirm the number of people who were supervised last time and are supervised this time, the number of people who were supervised last time but not supervised this time, and the number of problems in the last supervision, and mark them; Obtain the total number of people in the registration and supervision expert file information and the total number of supervision times, and use the marked number of people who were supervised last time and are supervised this time as the first positive example sample and the number of people who were supervised last time but not supervised this time as the first negative example sample; Input the first positive example sample and the first negative example sample into a BP neural network model for training, construct an analysis model, and establish a feedback learning mechanism for the analysis model; S4. Statistically obtain the set of supervision rectification rate parameters of each historical vaccination outpatient management institution according to the supervision information, and input it into a GRU recurrent neural network model for training to obtain a vaccination supervision model; S5. Real-time obtain the set of supervision rectification rate parameters of each level of vaccination outpatient, input it into the vaccination supervision model for analysis, obtain the reminder status coefficient of each level of vaccination outpatient, and generate a supervision reminder according to the size of the reminder status coefficient; The reminder status coefficient: Obtained through the formula Calculate to obtain the Reminder status coefficient of the vaccination clinic at level ; Among them, is the target cycle within the preset time period; is the previous cycle period of two adjacent cycles of the vaccination clinic within the preset time period; is the subsequent cycle period of two adjacent cycles of the vaccination clinic within the preset time period; is the first preset weight coefficient, is the second preset weight coefficient; and , are both greater than 0; is the real-time supervision and rectification rate parameter of the vaccination clinic at level 2. The mobile supervision and management method for vaccination clinics according to claim 1, wherein The judgment process of the supervision rectification rate is as follows: Calculate the supervision rectification rate parameter based on the feedback learning mechanism of the analysis model for the target cycle change within a preset time period: ； Among them, is the supervision and rectification rate parameter; is the target cycle within the preset time period; is the number of rectified people who were supervised last time and have been supervised this time within the is the number of rectified people who were supervised last time but not supervised this time within the is the number of rectified people who have not been supervised either last time or this time within the , and whether they were supervised before is not taken into account; is the total number of registered expert file information; is the weight coefficient of the regional registration number within the , and 0 < < 1; is the number of supervision times.

3. The mobile supervision and management method for vaccination clinics according to claim 2, characterized in that, Compare the supervision and rectification rate parameter with the standard threshold range of the preset supervision and rectification rate parameter and is the minimum value of the standard supervision and rectification rate parameter, is the maximum value of the standard supervision and rectification rate parameter: If > , it is determined that the supervision and rectification rate is high; If ≤ ≤ , then it is judged in the supervision and rectification rate; If < , it is determined that the supervision and rectification rate is low.

4. A mobile supervision and management method for vaccination clinics according to claim 1, characterized in that The method of statistically obtaining the set of supervision rectification rate parameters of each historical vaccination outpatient management institution in S4 and inputting it into a GRU recurrent neural network model for training to obtain a vaccination supervision model is as follows: Obtain positive and negative samples: Use the set of supervision rectification rate parameters with a high supervision rectification rate as the second positive example sample and the set of supervision rectification rate parameters with a medium and low supervision rectification rate as the second negative example sample; Input the second positive example sample and the second negative example sample into a GRU recurrent neural network model for training and learning to obtain a vaccination supervision model.

5. The mobile supervision and management method for vaccination clinics according to claim 1, characterized in that, The method of generating a supervision reminder according to the size of the reminder status coefficient is as follows: Compare the reminder status coefficient with a preset reminder status coefficient threshold to determine their relative magnitudes: If ≤ , it is determined that the completion degree of the supervision of the vaccination work in the current-level vaccination clinic meets the requirements; If > , it is determined that the completion degree of the supervision of the vaccination work in the current-level vaccination clinic does not meet the requirements, and a supervision reminder is generated.

6. The mobile supervision and management method for vaccination clinics according to claim 1, characterized in that, It also includes: Register the evaluation information after supervision rectification and the reasons for canceling supervision, extract the information related to the supervision rectification effect, and match the corresponding level of vaccination outpatient for supervision rectification notice.

7. A mobile supervision and management system for vaccination clinics, characterized in that, To implement a mobile supervision management method for vaccination outpatient clinics described in any one of claims 1-6, the system includes: The collection unit is used to collect the registration and supervision expert file information of the historical vaccination outpatient management institutions at all levels in a preset time period, and count the number of supervision experts and the expert supervision dates according to the registration and supervision expert file information; and is used to collect the appointment cancellation supervision expert file information of the historical vaccination outpatient management institutions at all levels in a preset time period, and count the number of non-appointed supervision experts and the expert's last supervision date according to the appointment cancellation supervision expert file information. The analysis unit is used to perform multi-dimensional data analysis based on the registration and supervision expert file information and the appointment cancellation supervision expert file information to obtain the supervision rectification rate parameter, judge the level of the supervision rectification rate and send supervision information to the vaccination outpatient management institutions at all levels. The model construction unit is used to count the set of supervision rectification rate parameters of the historical vaccination outpatient management institutions at all levels and input them into the GRU recurrent neural network model for training to obtain the vaccination supervision model. The supervision unit is used to obtain the set of supervision rectification rate parameters of each vaccination outpatient in real time, input them into the vaccination supervision model for analysis, obtain the reminder status coefficient of each vaccination outpatient, and generate a supervision reminder according to the size of the reminder status coefficient.

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