WO2021151332A1 - Medical insurance data processing method and apparatus based on fixed time window, and device and medium - Google Patents

Abstract

The present application relates to the technical field of artificial intelligence, and is applied to the field of smart healthcare. Disclosed are a medical insurance data processing method and apparatus based on a fixed time window, and a device and a medium. The method comprises: acquiring trigger time points of medical insurance use events within a preset time period and the total number of times the medical insurance use events are triggered; displaying the medical insurance use events and the trigger time points thereof on a preset time axis, and acquiring a time interval between two adjacent medical insurance use events on the preset time axis; segmenting the preset time axis into several sub time axes according to a preset fixed time window and the time interval; recording a sub time axis including the maximum total number of medical insurance use events as a first sub time axis; determining, according to the preset fixed time window and a preset frequency determination method, a first densest frequency corresponding to the first sub time axis; and when the first densest frequency meets a preset frequency standard, warning that the medical insurance use events within the preset time period are abnormal. By means of the present application, the efficiency of processing abnormal data is improved.

Description

Medical insurance data processing method, device, equipment and medium based on fixed time window

This application claims the priority of the Chinese patent application filed with the Chinese Patent Office on September 7, 2020, with the application number 202010929662.2, and the invention title "Medical insurance data processing method, device, equipment and medium based on a fixed time window", all of which The content is incorporated in this application by reference.

Technical field

This application relates to the field of data processing, and in particular to a medical insurance data processing method, device, equipment and medium based on a fixed time window.

Background technique

With the development of science and technology, data processing technologies such as special data query and abnormal data processing have also developed. Data processing technology is also used in different fields, such as medical field, application field and so on.

The inventor realizes that, in the medical field, it is often necessary to query abnormal data for medical insurance data, such as querying the number of times a patient visits the same department in a week under the premise of using medical insurance; or under the premise of using medical insurance, the patient’s The number of internal medicine withdrawals is used to make abnormal judgments on the patient’s medical insurance data based on the above data. In the prior art, a sliding time window method is generally used to achieve this. The sliding time window method refers to the preset time window of interest, on the time axis constructed based on the time point of the event, sliding the unit of the time window from left to right in order to obtain the current time window Contains the number of events. This method has the following shortcomings: this method requires an exhaustive list of the entire time axis to get the results corresponding to each time window, and the detection time is longer, which leads to lower detection efficiency.

Application content

The embodiments of the present application provide a medical insurance data processing method, device, equipment, and medium based on a fixed time window to solve the problems of long detection time and low detection efficiency.

A medical insurance data processing method based on a fixed time window includes:

Acquiring the trigger time point of the medical insurance use event and the total number of triggers of the medical insurance use event within the preset time period;

According to a preset display rule, display the medical insurance use event and its trigger time on a preset time axis, and obtain the time interval between all two adjacent medical insurance use events on the preset time axis;

Cutting the preset time axis into a plurality of sub time axes according to a preset fixed time window and the time interval;

Acquiring the total number of the medical insurance usage events included in each of the sub-time axes, and recording the sub-time axis containing the largest total number of medical insurance usage events as the first sub-time axis;

According to the preset fixed time window and the preset frequency determination method, the first most intensive frequency corresponding to the first sub-time axis is determined; the first most intensive frequency characterization corresponds to the preset fixed time window, so The maximum frequency of triggering the medical insurance use event existing in the first sub-time axis;

When the first most intensive frequency meets the preset frequency standard, it is prompted that the medical insurance use event within the preset time period is abnormal.

A medical insurance data processing device based on a fixed time window includes:

The data acquisition module is used to acquire the trigger time point of the medical insurance use event and the total number of triggers of the medical insurance use event within a preset time period;

The data display module is used to display the medical insurance use event and its trigger time point on a preset time axis according to a preset display rule, and obtain all of the two adjacent medical insurance use events on the preset time axis Time interval between

The time axis cutting module is configured to cut the preset time axis into a plurality of sub time axes according to a preset fixed time window and the time interval;

The first sub-time axis determination module is used to obtain the total number of the medical insurance use events contained in each of the sub-time axes, and record the sub-time axis containing the most total number of medical insurance use events as the first sub-time axis;

The first frequency determining module is configured to determine the first densest frequency corresponding to the first sub-time axis according to the preset fixed time window and the preset frequency determining method; the first densest frequency characterization corresponds to all The preset fixed time window, the maximum frequency of triggering the medical insurance use event existing in the first sub-time axis;

The first abnormality prompting module is configured to prompt an abnormality in the medical insurance use event within the preset time period when the first most intensive frequency meets the preset frequency standard.

A computer device includes a memory, a processor, and computer-readable instructions that are stored in the memory and can run on the processor, and the processor implements the following steps when the processor executes the computer-readable instructions:

Acquiring the trigger time point of the medical insurance use event and the total number of triggers of the medical insurance use event within the preset time period;

According to a preset display rule, display the medical insurance use event and its trigger time on a preset time axis, and obtain the time interval between all two adjacent medical insurance use events on the preset time axis;

Cutting the preset time axis into a plurality of sub time axes according to a preset fixed time window and the time interval;

Acquiring the total number of the medical insurance usage events included in each of the sub-time axes, and recording the sub-time axis containing the largest total number of medical insurance usage events as the first sub-time axis;

According to the preset fixed time window and the preset frequency determination method, the first most intensive frequency corresponding to the first sub-time axis is determined; the first most intensive frequency characterization corresponds to the preset fixed time window, so The maximum frequency of triggering the medical insurance use event existing in the first sub-time axis;

When the first most intensive frequency meets the preset frequency standard, it is prompted that the medical insurance use event within the preset time period is abnormal.

One or more readable storage media storing computer readable instructions, when the computer readable instructions are executed by one or more processors, the one or more processors execute the following steps:

Acquiring the trigger time point of the medical insurance use event and the total number of triggers of the medical insurance use event within the preset time period;

According to a preset display rule, display the medical insurance use event and its trigger time on a preset time axis, and obtain the time interval between all two adjacent medical insurance use events on the preset time axis;

Cutting the preset time axis into a plurality of sub time axes according to a preset fixed time window and the time interval;

Acquiring the total number of the medical insurance usage events included in each of the sub-time axes, and recording the sub-time axis containing the largest total number of medical insurance usage events as the first sub-time axis;

According to the preset fixed time window and the preset frequency determination method, the first most intensive frequency corresponding to the first sub-time axis is determined; the first most intensive frequency characterization corresponds to the preset fixed time window, so The maximum frequency of triggering the medical insurance use event existing in the first sub-time axis;

When the first most intensive frequency meets the preset frequency standard, it is prompted that the medical insurance use event within the preset time period is abnormal.

The above-mentioned medical insurance data processing method, device, device and medium based on a fixed time window obtain the trigger time point of medical insurance use event and the total number of triggers of the medical insurance use event within a preset time period; The medical insurance use event and its trigger time point are displayed on a preset time axis, and the time interval between all two adjacent medical insurance use events on the preset time axis is acquired; according to the preset fixed time window and the Time interval, cutting the preset time axis into several sub-time axes; obtaining the total number of the medical insurance usage events contained in each of the sub-time axes, and will include the sub-time with the largest total number of medical insurance usage events The axis record is the first sub-time axis; according to the preset fixed time window and the preset frequency determination method, the first most intensive frequency corresponding to the first sub-time axis is determined; the first most intensive frequency characterization corresponds to The preset fixed time window, the maximum frequency that triggers the medical insurance use event existing in the first sub-time axis; when the first most intensive frequency meets the preset frequency standard, it is prompted to be within the preset time period The medical insurance use event is abnormal.

This application first cuts the preset time axis by cutting the time interval on the time axis with the preset fixed time window; effectively pruning the preset time axis without exhausting the time axis, thereby saving abnormal data processing Secondly, perform the first test on the first sub-time axis that contains the most medical insurance usage events. When the first most intensive frequency corresponding to the first sub-time axis meets the preset frequency standard, it can be determined that the medical insurance is within the preset time period. If there is an abnormal phenomenon in the use event, the remaining sub-time axis can be deleted, which reduces the amount of system calculation while shortening the detection time, so that whether there is an abnormal phenomenon in the preset time period can be verified more quickly; through the above method, For medical insurance use incidents with a large amount of data, it is possible to more quickly detect whether abnormal phenomena occur, so as to promote the construction of smart cities.

The details of one or more embodiments of the present application are presented in the following drawings and description, and other features and advantages of the present application will become apparent from the description, drawings and claims.

Description of the drawings

In order to explain the technical solutions of the embodiments of the present application more clearly, the following will briefly introduce the drawings that need to be used in the description of the embodiments of the present application. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative labor.

FIG. 1 is a schematic diagram of an application environment of a medical insurance data processing method based on a fixed time window in an embodiment of the present application;

2 is a flowchart of a medical insurance data processing method based on a fixed time window in an embodiment of the present application;

3 is a flowchart of S30 in the method for processing medical insurance data based on a fixed time window in an embodiment of the present application;

4 is a flowchart of S40 in the medical insurance data processing method based on a fixed time window in an embodiment of the present application;

5 is a flowchart of S50 in the medical insurance data processing method based on a fixed time window in an embodiment of the present application;

Fig. 6 is a functional block diagram of a medical insurance data processing device based on a fixed time window in an embodiment of the present application;

FIG. 7 is a functional block diagram of the time axis cutting module in the medical insurance data processing device based on a fixed time window in an embodiment of the present application;

8 is a functional block diagram of the first sub-time axis determination module in the medical insurance data processing device based on a fixed time window in an embodiment of the present application;

9 is a functional block diagram of the first frequency determining module in the medical insurance data processing device based on a fixed time window in an embodiment of the present application;

Fig. 10 is a schematic diagram of a computer device in an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of them. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of this application.

The medical insurance data processing method based on a fixed time window provided by the embodiment of the present application can be applied to the application environment as shown in FIG. 1. Specifically, the medical insurance data processing method based on a fixed time window is applied to a medical insurance data processing system based on a fixed time window. The medical insurance data processing system based on a fixed time window includes a client and a server as shown in FIG. Communicate with the server through the network to solve the problems of long detection time and low detection efficiency. Among them, the client is also called the client, which refers to the program that corresponds to the server and provides local services to the client. The client can be installed on, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices. The server can be implemented as an independent server or a server cluster composed of multiple servers.

In one embodiment, as shown in FIG. 2, a method for processing medical insurance data based on a fixed time window is provided in the server shown in FIG. 1, and the method includes the following steps:

S10: Acquire the trigger time point of the medical insurance use event and the total number of triggers of the medical insurance use event within a preset time period.

Among them, the preset time period can be set according to actual scene requirements. Illustratively, the preset time period can be one week or one month, etc. Exemplarily, the medical insurance use event may be a record of the patient's visit to the same department; or the number of times the patient has taken medicine. The trigger time point refers to the time point corresponding to the occurrence of the medical insurance use event monitored within the preset time period. The total number of triggers refers to the total number of medical insurance usage events triggered in a preset time period.

S20: Display the medical insurance use event and its trigger time point on a preset time axis according to a preset display rule, and obtain the time interval between all two adjacent medical insurance use events on the preset time axis .

Among them, the preset display rule can be to sort each medical insurance use event according to the sequence of the trigger time points corresponding to each medical insurance use event, and the interval between the medical insurance use events is the corresponding trigger time point. Time interval. That is, each coordinate point on the preset time axis represents the time point corresponding to each medical insurance use event.

Specifically, after acquiring the trigger time point of the medical insurance use event within the preset time period and the total number of triggers of the medical insurance use event, each medical insurance use event is displayed at the preset time in the order of its corresponding trigger time point. On the axis, you can then clearly understand the time point corresponding to each medical insurance use event and the total number of triggers of the medical insurance use event (that is, the number of coordinate points on the preset time axis) on the preset time axis, and get the preset The time interval between all two adjacent medical insurance use events on the time axis.

S30: According to a preset fixed time window and the time interval, the preset time axis is cut into a plurality of sub time axes.

Among them, the preset fixed time window refers to a fixed monitoring window, that is, the trigger frequency of a medical insurance use event within a preset fixed time window can be used to determine whether the medical insurance use event is abnormal, and the preset fixed time window can be based on Set the actual application scenario. The sub-time axis refers to the time axis obtained by cutting the preset time axis and dividing it, that is, the sub-time axis is a part of the preset time axis.

In a specific embodiment, as shown in FIG. 3, step S30 includes the following steps:

S301: Compare each of the time intervals with the preset fixed time window.

S302: When the time interval is greater than the preset fixed time window, check whether the time interval is the first time interval; the first time interval refers to the time interval that is in the first position on the preset time axis .

S303: When the time interval is the first time interval, delete the first time interval and the first medical insurance use event located before the first time interval from the preset time axis.

S304: When the time interval is not the first time interval, delete the time interval from the preset time axis, and cut the preset time axis at a position corresponding to the time interval, which will be located in the time interval The subsequent medical insurance use event is recorded as the start event of the next sub-time axis, and the medical insurance use event located before the time interval is recorded as the end event of the previous sub-time axis.

Specifically, according to a preset display rule, the medical insurance use event and its trigger time point are displayed on a preset time axis, and all the medical insurance use events between two adjacent medical insurance use events on the preset time axis are obtained. After the time interval, the preset fixed time window is compared with each time interval to determine whether the time interval is greater than the preset fixed time window.

Since the preset fixed time window is set according to specific application scenarios, when the time interval is greater than the preset fixed time window, it indicates that the time interval between the triggering time points of two adjacent medical insurance use events corresponding to the time interval is very long, and The monitoring time exceeds the preset fixed time window, which means that there is no abnormality in the two adjacent medical insurance usage events corresponding to the time interval, that is, normal data, and then delete the time interval to save time for finding abnormal data and improve abnormal data processing efficiency.

Further, when the time interval is greater than the preset fixed time window, the time interval needs to be cut on the preset time axis, and the first time interval corresponds to two adjacent medical insurance usage events, one of which is in the preset time interval. The first medical insurance usage event on the time axis before the first time interval. After cutting the first time interval, if the first medical insurance usage event is used as a sub-time axis alone, the sub-time axis must not be An abnormality occurs, so when the time interval greater than the preset fixed time window is the first time interval, the first time interval and the first medical insurance use event before the first time interval are deleted from the preset time axis, thereby Save time for abnormal data processing.

Further, when the time interval is not the first time interval, the time interval is deleted from the preset time axis, and the preset time axis is cut at a position corresponding to the time interval, which will be located at the time interval. The medical insurance use event after the interval is recorded as the start event of the next sub-time axis, and the medical insurance use event before the time interval is recorded as the end event of the previous sub-time axis.

Further, when the time interval is the end time interval (that is, the last time interval on the preset time axis), and the end time interval is greater than the preset fixed time window, after cutting the end time interval, it will be The previous medical insurance use event corresponding to the end time interval is regarded as the end event of the previous sub-time axis, and the rest is the last medical insurance use event. When the last medical insurance use event is used as a sub-timeline alone, the sub-time The axis must not be abnormal, so the last time interval and the last medical insurance use event after the last time interval can be deleted from the preset time axis.

In another specific embodiment, when the time interval is less than or equal to the preset fixed time window, it is characterized that the time interval meets the monitoring range of the preset fixed time window, so the time interval is retained, that is, the time interval is not deleted.

S40: Obtain the total number of the medical insurance use events included in each of the sub-time axes, and record the sub-time axis that contains the most total number of medical insurance use events as the first sub-time axis.

Specifically, after cutting the preset time axis into a number of sub time axes according to a preset fixed time window and the time interval, acquiring the total number of the medical insurance use events included in each of the sub time axes, And record the sub-time axis that contains the most total number of medical insurance usage events as the first sub-time axis.

Further, as shown in FIG. 4, in step S40, the following steps are further included:

S401: Delete a qualified time axis, where the qualified time axis refers to the sub-time axis in which the total number of medical insurance usage events included is less than a preset number.

Among them, the preset number refers to the number of times the medical insurance use event is allowed to be triggered; for example, in a medical scenario, under the premise that a patient uses medical insurance, the number of visits to the same department within a week is allowed to occur up to three times; or The number of withdrawals within a week is the most appropriate four times and so on.

S402: When the number of the sub-time axes after the qualified time axis is deleted is greater than or equal to one, the sub-time axis containing the largest total number of medical insurance use events is recorded as the first sub-time axis.

S403: When the number of the sub-time axes after the qualified time axis is deleted is equal to zero, it is prompted that there is no abnormality in the medical insurance use event within the preset time period.

Specifically, after cutting the preset time axis into a number of sub time axes according to a preset fixed time window and the time interval, the total number of the medical insurance usage events included in each of the sub time axes is obtained ; When the total number of medical insurance usage events on any sub-time axis is less than the preset number, delete the sub-time axis, that is, the qualified time axis; after deleting all qualified time axes, check the number of remaining sub-time axes , If the number of remaining sub-time axes is greater than or equal to one, which means that there is still an unqualified time axis at this time, the sub-time axis that contains the most total number of medical insurance use events is recorded as the first sub-time axis for subsequent In the step, the first position detection is performed on the first sub-time axis.

Further, if the number of remaining sub-time axes is equal to zero, it indicates that there is no unqualified time axis at this time, that is, there is no abnormality in the medical insurance use events on each sub-time axis, so it can be prompted to use medical insurance in the preset time period. There was no exception in the event.

S50: Determine the first densest frequency corresponding to the first sub-time axis according to the preset fixed time window and the preset frequency determination method; the first densest frequency characterization corresponds to the preset fixed time window , The maximum frequency of triggering the medical insurance use event existing in the first sub-time axis.

Among them, the preset frequency determination method is used to determine the most intensive frequency on any sub-time axis.

Specifically, after acquiring the total number of the medical insurance use events contained in each of the sub-time axes, and recording the sub-time axis containing the largest total number of medical insurance use events as the first sub-time axis, according to the The preset fixed time window and the preset frequency determination method determine that there is a maximum frequency of triggering medical insurance usage events in the first sub-time axis within the preset fixed time window, and record the maximum frequency as the first sub-time axis corresponding to the first sub-time axis. One of the most intensive frequency.

Further, as shown in FIG. 5, step S50 specifically includes the following steps:

S501: Select a preset number of medical insurance use events on the first sub-time axis in chronological order as the initialization element group, and record the total duration of the time interval between adjacent medical insurance use events in the initialization element group as the first A cumulative time interval.

Among them, the time sequence refers to the sequence of trigger time points corresponding to the medical insurance use event. The preset number refers to the number of times the medical insurance use event is allowed to be triggered; for example, in a medical scenario, under the premise that a patient uses medical insurance, the number of visits to the same department within a week is allowed to occur up to three times; or one week The maximum number of internal medicine withdrawals is allowed four times and so on. The initial element group is a combination containing a preset number of medical insurance usage events on the first sub-time axis.

Specifically, after acquiring the total number of the medical insurance use events contained in each of the sub-time axes, and recording the sub-time axis containing the most total number of medical insurance use events as the first sub-time axis, according to the first sub-time axis, The sequence of the trigger time points corresponding to each medical insurance use event on the sub-time axis, and a preset number of medical insurance use events are selected as the initial element group. Understandably, the first medical insurance use event and the second medical insurance use event on the first sub-time axis can be selected as the initial element group. The first medical insurance use event and the third medical insurance use event on the first sub-time axis should not be selected as the initial element group, because the maximum frequency that needs to be determined in this embodiment is related to each medical insurance use event. If the medical insurance use event is not selected continuously Event, it may cause the final maximum frequency to be wrong, reducing the accuracy of abnormal data processing. In this embodiment, the first medical insurance use event of the first sub-time axis is used as the starting point, that is, a preset number of medical insurance use events are selected as the initial element group from the first medical insurance use event.

S502: When the first cumulative time interval is less than or equal to the preset fixed time window, store the first cumulative time interval in association with the initialization element group as first frequency data.

Specifically, a preset number of medical insurance use events on the first sub-time axis are selected in chronological order as the initialization element group, and the total duration of the time interval between adjacent medical insurance use events in the initialization element group is recorded After the first accumulation time interval, the first accumulation time interval is compared with the preset fixed time window, and when the first accumulation time interval is less than or equal to the preset fixed time window, it indicates that the first accumulation time interval conforms to the preset time window. Set the monitoring requirement of a fixed time window, associate the first cumulative time interval with the initialization element group and store it as the first frequency data.

S503: Detect whether there is a medical insurance use event after the last medical insurance use event in the initialization element group.

Specifically, when the first cumulative time interval is less than or equal to the preset fixed time window, after the first cumulative time interval and the initialization element group are associated and stored as first frequency data, the initialization element group is detected Whether there is a medical insurance use event after the last medical insurance use event in. Illustratively, assuming that the first sub-time axis contains five medical insurance use events arranged in chronological order, and assuming that the initialization element group includes the first medical insurance use event, the second medical insurance use event, and the third medical insurance use event, then After the last medical insurance use event in the initialization element group (that is, the third medical insurance use event), there is still a medical insurance use event.

S504: When there is no medical insurance use event after the last medical insurance use event in the initialization element group, record the first frequency data as the first most intensive frequency corresponding to the first sub-time axis.

Specifically, after detecting whether there is a medical insurance use event after the last medical insurance use event in the initialization element group, when there is no medical insurance use event after the last medical insurance use event in the initialization element group, the first child is characterized After the time axis is checked, the first frequency data is recorded as the first most intensive frequency corresponding to the first sub-time axis.

Further, after step S503, it further includes:

S505: When there is a medical insurance use event after the last medical insurance use event in the initialization element group, add the medical insurance use event after the last medical insurance use event in the initialization element group to the initialization element group to form a second medical insurance use event. An element group, recording the total duration of the time interval between adjacent medical insurance use events in the second element group as the second cumulative time interval.

Specifically, after detecting whether there is a medical insurance use event after the last medical insurance use event in the initialization element group, if there is a medical insurance use event after the last medical insurance use event in the initialization element group, the first sub-time is represented If the axis has not been calibrated, the medical insurance use event after the last medical insurance use event in the initial element group is added to the initial element group to form the second element group, and the adjacent medical insurance use events in the second element group The total duration of the time interval is recorded as the second cumulative time interval.

S506: When the second cumulative time interval is less than or equal to the preset fixed time window, if there is no medical insurance use event after the last medical insurance use event in the second element group, the second cumulative time interval The time interval is stored in association with the second element group as the first most intensive frequency corresponding to the first sub-time axis.

Specifically, the medical insurance use event after the last medical insurance use event in the initialization element group is added to the initialization element group to form a second element group, and adjacent medical insurance use events in the second element group After recording the total duration of the time interval as the second cumulative time interval, compare the second cumulative time interval with the preset fixed time window. When the second cumulative time interval is less than or equal to the preset fixed time window, detect Whether there is a medical insurance use event after the last medical insurance use event in the second element group, if there is no medical insurance use event after the last medical insurance use event in the second element group, store the second cumulative time interval in association with the second element group Is the first dense frequency corresponding to the first sub-time axis. Understandably, if after adding a new medical insurance use event, the second cumulative time interval corresponding to the second element group is still less than or equal to the preset fixed time window, then the time between the medical insurance use event and other medical insurance use events The interval is short, so the second cumulative time interval is associated with the second element group and stored as the first dense frequency corresponding to the first sub-time axis.

Further, if there is still a medical insurance use event after the last medical insurance use event in the second element group, steps S505 to S506 are repeated to obtain the fourth element group, the fifth element group, etc., until all the medical insurance use events in the first sub-time axis The medical insurance usage events are all traversed.

In a specific embodiment, after step S501, a preset number of medical insurance use events on the first sub-time axis are selected in chronological order as the initialization element group, and adjacent medical insurance use events in the initialization element group are selected. After the total duration of the time interval between is recorded as the first cumulative time interval, the following steps are also included:

S507: When the first cumulative time interval is greater than the preset fixed time window, if there is a medical insurance use event after the last medical insurance use event in the initialization element group, use the last medical insurance use event in the initialization element group. The medical insurance use event after the event is added to the initial element group, and the first medical insurance use event in the initial element group is deleted in chronological order to form a third element group, and adjacent elements in the third element group The total duration of the time interval between medical insurance use events is recorded as the third cumulative time interval.

Specifically, after recording the total duration of the time interval between adjacent medical insurance use events in the initialization element group as the first cumulative time interval, the first cumulative time interval is compared with a preset fixed time window, and When the first cumulative time interval is greater than the preset fixed time window, it is detected whether there is a medical insurance use event after the last medical insurance use event in the initialization element group; when the first cumulative time interval is greater than the preset fixed time window, if There is a medical insurance use event after the last medical insurance use event in the initialization element group, and the medical insurance use event after the last medical insurance use event in the initialization element group is added to the initialization element group, and the medical insurance use events are deleted in chronological order. The first medical insurance use event in the element group is initialized to form a third element group, and the total duration of the time interval between adjacent medical insurance use events in the third element group is recorded as the third cumulative time interval.

S508: When the third cumulative time interval is less than or equal to the preset fixed time window, store the third cumulative time interval in association with the third element group as third frequency data.

S509: When there is no medical insurance use event after the last medical insurance use event in the third element group, record the one with the highest frequency density among the first frequency data and the third frequency data as the same as that of the first child. The first most intensive frequency corresponding to the time axis.

Among them, the frequency density refers to the ratio of the number of medical insurance usage events in the element group corresponding to the first frequency data (or the third frequency data) to the cumulative time interval.

Specifically, when the third cumulative time interval is less than or equal to the preset fixed time window, the third cumulative time interval and the third element are associated and stored as third frequency data; and the last medical insurance use event in the third element group is detected Whether there is a medical insurance use event afterwards, if there is no medical insurance use event after the last medical insurance use event in the third element group, the frequency density of the first frequency data is obtained, that is, the initial element group Chinese medical insurance use event in the first frequency data The ratio of the number to the first cumulative time interval; the frequency density of the third frequency data is obtained, that is, the ratio of the number of medical insurance use events in the third element group in the third frequency data to the third cumulative time interval; and then the first The frequency data and the third frequency data with the highest frequency density are recorded as the first densest frequency corresponding to the first sub-time axis.

In another specific embodiment, after step S507, that is, after the first cumulative time interval is greater than the preset fixed time window, the method further includes:

When there is no medical insurance use event after the last medical insurance use event in the initialization element group, it is prompted that there is no abnormality in the medical insurance use event within the preset time period.

Understandably, the initialization element group contains a preset number of medical insurance use events, that is, the number of medical insurance use events in the initialization element group meets the requirements, so after the first cumulative time interval is greater than the preset fixed time window, When there is no medical insurance use event after the last medical insurance use time in the initialization element group, it means that the medical insurance use time on the first sub-time axis is abnormal, so the medical insurance use event within the preset time period can be counted. unusual phenomenon.

S60: When the first most intensive frequency meets the preset frequency standard, prompting that the medical insurance use event within the preset time period is abnormal.

Among them, the preset frequency standard can be set according to different scenarios; suppose that in the medical scenario, a patient should obtain the same prescription no more than five times in a month, and more than five times indicate that there may be abnormalities such as suspicion of fraudulent insurance. Therefore, the default frequency standard is set to be that the patient obtains the same prescription more than five times in a month.

Specifically, after determining the first densest frequency corresponding to the first sub-time axis according to the preset fixed time window and the preset frequency determining method, the first densest frequency is compared with the preset frequency standard, Determine whether the first most intensive frequency meets the preset frequency standard. If the first most intensive frequency meets the preset frequency standard, it will prompt that there is an abnormal phenomenon in the medical insurance usage event within the preset time period for the checker to calibrate it. Test.

Further, when the first most intensive frequency meets the preset frequency standard, it indicates that it has been determined on the first sub-time axis that there is an abnormal phenomenon in the medical insurance use event within the preset time period, and the remaining sub-time axis can be deleted, and the remaining sub-time axis can be deleted. The amount of system calculations also shortens the processing time of abnormal data.

In a specific embodiment, after step S50, that is, after determining the first most intensive frequency corresponding to the first sub-time axis according to the preset fixed time window and the preset frequency determining method, the method further includes the following steps :

S70: When the first most intensive frequency does not meet the preset frequency standard, record the sub-time axis including the total number of medical insurance usage events as the second sub-time axis.

Specifically, after determining the first densest frequency corresponding to the first sub-time axis according to the preset fixed time window and the preset frequency determining method, the first densest frequency is compared with the preset frequency standard, Determine whether the first most intensive frequency meets the preset frequency standard. If the first most intensive frequency does not meet the preset frequency standard, it means that there is no abnormal phenomenon in the medical insurance usage event on the first sub-time axis. Therefore, it is necessary to continue the calibration. Check whether there is an abnormal phenomenon in the medical insurance use event in the remaining sub-time axis, and then record the sub-time axis that contains the total number of medical insurance use events as the second sub-time axis.

S80: Determine the second densest frequency corresponding to the second sub-time axis according to the preset fixed time window and the preset frequency determination method; the second densest frequency characterization corresponds to the preset fixed time window , The maximum frequency of triggering the medical insurance use event existing in the second sub-time axis.

Specifically, when the first most intensive frequency does not meet the preset frequency standard, after recording the sub-time axis containing the total number of medical insurance usage events as the second sub-time axis, according to the preset fixed time window And the method for determining the preset frequency is to determine that there is a maximum frequency of triggering medical insurance use events in the second sub-time axis within the preset fixed time window, and record the maximum frequency as the second most intensive frequency corresponding to the second sub-time axis.

S90: When the second most intensive frequency meets the preset frequency standard, prompting that the medical insurance use event within the preset time period is abnormal.

Specifically, after determining the second densest frequency corresponding to the second sub-time axis according to the preset fixed time window and the preset frequency determination method, compare the second densest frequency with the preset frequency standard, Determine whether the second most intensive frequency meets the preset frequency standard. If the second most intensive frequency meets the preset frequency standard, it will prompt that there is an abnormal phenomenon in the medical insurance use event during the preset time period, so that the checker can calibrate it. Test.

Further, when the second most intensive frequency meets the preset frequency standard, it indicates that it has been determined on the second sub-time axis that there is an abnormal phenomenon in the medical insurance usage event within the preset time period, and the remaining sub-time axis can be deleted, and the remaining sub-time axis can be deleted. The amount of system calculations also shortens the processing time of abnormal data.

Further, when the second most intensive frequency does not meet the preset frequency standard, check the sub-time axis that contains the third most total target time, and if it still does not meet the preset frequency standard, traverse all remaining sub-time axes in turn , When the most intensive frequency corresponding to any sub-time axis meets the preset frequency standard, stop the verification process, and prompt that there is an abnormal phenomenon in the medical insurance use event within the preset time period, or the remaining sub-time axis is zero, and all When the most intensive frequency corresponding to the sub-time axis does not meet the preset frequency standard, the verification will be stopped, and there will be no abnormal phenomenon in the medical insurance use event within the preset time period.

In this embodiment, firstly, the preset time axis is cut by cutting the time interval on the time axis with the preset fixed time window; the preset time axis is effectively pruned to save the time of abnormal data processing; The first sub-time axis containing the most medical insurance usage events is performed for the first detection. When the first most intensive frequency corresponding to the first sub-time axis meets the preset frequency standard, it can be determined that there is an abnormal phenomenon in the medical insurance usage event within the preset time period. , You can delete the remaining sub-time axis, which reduces the amount of system calculation while shortening the detection time, so that it can be more quickly verified whether there is an abnormal phenomenon within the preset time period; through the above method, the preset time period can be checked. When there is a large amount of data in the memory, it is faster to detect whether an abnormal phenomenon occurs.

In another specific embodiment, in order to ensure the privacy and security of the medical insurance use event in the foregoing embodiment, the medical insurance use event may be stored in the blockchain. Among them, the Blockchain is an encrypted and chained transaction storage structure formed by blocks.

For example, the header of each block can not only include the hash value of all transactions in the block, but also the hash value of all transactions in the previous block, so as to achieve tamper-proof transactions in the block based on the hash value And anti-counterfeiting; newly generated transactions are filled in the block and after the consensus of the nodes in the block chain network, they will be appended to the end of the block chain to form chain growth.

It should be understood that the size of the sequence number of each step in the foregoing embodiment does not mean the order of execution. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

In one embodiment, a medical insurance data processing device based on a fixed time window is provided. The medical insurance data processing device based on a fixed time window corresponds to the medical insurance data processing method based on a fixed time window in the above-mentioned embodiment in a one-to-one correspondence. As shown in Figure 6, the medical insurance data processing based on a fixed time window includes the following modules:

The data acquisition module 10 is configured to acquire the trigger time point of the medical insurance use event and the total number of triggers of the medical insurance use event within a preset time period;

The data display module 20 is configured to display the medical insurance use event and its trigger time point on a preset time axis according to a preset display rule, and obtain all two adjacent medical insurance use events on the preset time axis The time interval between

The time axis cutting module 30 is configured to cut the preset time axis into a plurality of sub time axes according to a preset fixed time window and the time interval;

The first sub-time axis determination module 40 is configured to obtain the total number of the medical insurance use events contained in each of the sub-time axes, and record the sub-time axis that contains the most total number of medical insurance use events as the first sub-time axis Timeline

The first frequency determining module 50 is configured to determine the first densest frequency corresponding to the first sub-time axis according to the preset fixed time window and the preset frequency determining method; the first densest frequency characterization corresponds to The preset fixed time window, the maximum frequency of triggering the medical insurance use event existing in the first sub-time axis;

The first abnormality prompting module 60 is configured to prompt an abnormality of the medical insurance use event within the preset time period when the first most intensive frequency meets the preset frequency standard.

Preferably, as shown in FIG. 7, the time axis cutting module 30 includes the following units:

The data comparison unit 301 is configured to compare each of the time intervals with the preset fixed time window;

The position detection unit 302 is configured to check whether the time interval is the first time interval when the time interval is greater than the preset fixed time window; the first time interval refers to the first time interval on the preset time axis. A time interval;

The first data deletion unit 303 is configured to delete the first time interval and the first medical insurance use event located before the first time interval from the preset time axis when the time interval is the first time interval;

The second data deletion unit 304 is configured to delete the time interval from the preset time axis when the time interval is not the first time interval, and perform data processing on the preset time axis at a position corresponding to the time interval. Cutting, recording the medical insurance use event after the time interval as the start event of the next sub-time axis, and at the same time recording the medical insurance use event before the time interval as the end event of the previous sub-time axis.

Preferably, the medical insurance data processing device based on a fixed time window further includes the following modules:

The second sub-time axis determining module 70 is configured to record the sub-time axis including the total number of medical insurance usage events as the second sub-time axis when the first most intensive frequency does not meet the preset frequency standard;

The second frequency determining module 80 is configured to determine the second most intensive frequency corresponding to the second sub-time axis according to the predetermined fixed time window and the predetermined frequency determining method; the second most intensive frequency characterization corresponds to The preset fixed time window, the maximum frequency of triggering the medical insurance use event existing in the second sub-time axis;

The second abnormality prompting module 90 is configured to prompt an abnormality in the medical insurance use event within the preset time period when the second most intensive frequency meets the preset frequency standard.

Preferably, as shown in FIG. 8, the first sub-time axis determination module 40 includes the following units:

The time axis deleting unit 401 is configured to delete a qualified time axis, where the qualified time axis refers to the sub-time axis in which the total number of medical insurance use events contained is less than a preset number.

The sub-time axis determining unit 402 is configured to record the sub-time axis with the largest total number of medical insurance usage events as the first sub-time axis when the number of the sub-time axes after the qualified time axis is deleted is greater than or equal to one.

The first abnormality prompting unit 403 is configured to prompt that there is no abnormality in the medical insurance use event within the preset time period when the number of the sub-time axes after the qualified time axis is deleted is equal to zero.

Preferably, as shown in FIG. 9, the first frequency determining module 50 includes the following units:

The element group determining unit 501 is configured to select a preset number of medical insurance use events on the first sub-time axis as an initialization element group in chronological order, and set the time interval between adjacent medical insurance use events in the initialization element group The total duration of is recorded as the first cumulative time interval.

The first frequency data determining unit 502 is configured to store the first cumulative time interval in association with the initialization element group as the first frequency when the first cumulative time interval is less than or equal to the preset fixed time window data;

The event detection unit 503 is configured to detect whether there is a medical insurance use event after the last medical insurance use event in the initialization element group;

The first frequency determining unit 504 is configured to record the first frequency data as the first frequency data corresponding to the first sub-time axis when there is no medical insurance use event after the last medical insurance use event in the initialization element group One of the most intensive frequency.

Preferably, the first frequency determining module 50 further includes the following units:

The first element group adjustment unit 505 is configured to add the medical insurance use event after the last medical insurance use event in the initialization element group to the initialization when there is a medical insurance use event after the last medical insurance use event in the initialization element group In the element group, to form a second element group, the total duration of the time interval between adjacent medical insurance use events in the second element group is recorded as the second cumulative time interval;

The second frequency determining unit 506 is configured to, when the second cumulative time interval is less than or equal to the preset fixed time window, if there is no medical insurance use event after the last medical insurance use event in the second element group , Storing the second cumulative time interval in association with the second element group as the first most intensive frequency corresponding to the first sub-time axis.

Preferably, the first frequency determining module 50 further includes the following units:

The second element group adjustment unit 507 is configured to: when the first cumulative time interval is greater than the preset fixed time window, if there is a medical insurance use event after the last medical insurance use event in the initialization element group, change the The medical insurance use event after the last medical insurance use event in the initialization element group is added to the initialization element group, and the first medical insurance use event in the initialization element group is deleted in chronological order to form the third element group. The total duration of the time interval between adjacent medical insurance use events in the third element group is recorded as the third cumulative time interval;

The third frequency determining unit 508 is configured to store the third cumulative time interval in association with the third element group as a third frequency when the third cumulative time interval is less than or equal to the preset fixed time window data;

The fourth frequency determining unit 509 is configured to record the first frequency data and the third frequency data with the highest frequency density when there is no medical insurance use event after the last medical insurance use event in the third element group Is the first densest frequency corresponding to the first sub-time axis.

Preferably, the first frequency determining module 50 further includes the following units:

The second abnormality prompting unit is configured to prompt that there is no abnormality in the medical insurance use event within the preset time period when there is no medical insurance use event after the last medical insurance use event in the initialization element group.

For the specific limitation of the medical insurance data processing device based on a fixed time window, please refer to the above limitation on the medical insurance data processing method based on a fixed time window, which will not be repeated here. The various modules in the medical insurance data processing device based on a fixed time window can be implemented in whole or in part by software, hardware, and a combination thereof. The above-mentioned modules may be embedded in the form of hardware or independent of the processor in the computer equipment, or may be stored in the memory of the computer equipment in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. 10. The computer equipment includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide calculation and control capabilities. The memory of the computer device includes a readable storage medium and an internal memory. The readable storage medium stores an operating system, computer readable instructions, and a database. The internal memory provides an environment for the operation of the operating system and computer readable instructions in the readable storage medium. The database of the computer equipment is used for the data used in the medical insurance data processing method based on a fixed time window in the foregoing embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer-readable instructions are executed by the processor to realize a medical insurance data processing method based on a fixed time window. The readable storage medium provided in this embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.

In one embodiment, a computer device is provided, including a memory, a processor, and computer readable instructions stored in the memory and capable of running on the processor, and the processor implements the following steps when the processor executes the computer readable instructions:

Acquiring the trigger time point of the medical insurance use event and the total number of triggers of the medical insurance use event within the preset time period;

According to a preset display rule, display the medical insurance use event and its trigger time on a preset time axis, and obtain the time interval between all two adjacent medical insurance use events on the preset time axis;

Cutting the preset time axis into a plurality of sub time axes according to a preset fixed time window and the time interval;

Acquiring the total number of the medical insurance usage events included in each of the sub-time axes, and recording the sub-time axis containing the largest total number of medical insurance usage events as the first sub-time axis;

According to the preset fixed time window and the preset frequency determination method, the first most intensive frequency corresponding to the first sub-time axis is determined; the first most intensive frequency characterization corresponds to the preset fixed time window, so The maximum frequency of triggering the medical insurance use event existing in the first sub-time axis;

When the first most intensive frequency meets the preset frequency standard, it is prompted that the medical insurance use event within the preset time period is abnormal.

In one embodiment, one or more readable storage media storing computer readable instructions are provided. The readable storage media provided in this embodiment include non-volatile readable storage media and volatile readable storage. Medium; the readable storage medium stores computer readable instructions, and when the computer readable instructions are executed by one or more processors, the one or more processors implement the following steps:

Acquiring the trigger time point of the medical insurance use event and the total number of triggers of the medical insurance use event within the preset time period;

According to a preset display rule, display the medical insurance use event and its trigger time on a preset time axis, and obtain the time interval between all two adjacent medical insurance use events on the preset time axis;

Cutting the preset time axis into a plurality of sub time axes according to a preset fixed time window and the time interval;

Acquiring the total number of the medical insurance usage events included in each of the sub-time axes, and recording the sub-time axis containing the largest total number of medical insurance usage events as the first sub-time axis;

According to the preset fixed time window and the preset frequency determination method, the first most intensive frequency corresponding to the first sub-time axis is determined; the first most intensive frequency characterization corresponds to the preset fixed time window, so The maximum frequency of triggering the medical insurance use event existing in the first sub-time axis;

When the first most intensive frequency meets the preset frequency standard, it is prompted that the medical insurance use event within the preset time period is abnormal.

A person of ordinary skill in the art can understand that all or part of the processes in the methods of the foregoing embodiments can be implemented by instructing relevant hardware through computer-readable instructions. The computer-readable instructions can be stored in a non-volatile computer. In a readable storage medium or a volatile computer readable storage medium, when the computer readable instruction is executed, it may include the processes of the above-mentioned method embodiments. Wherein, any reference to memory, storage, database, or other media used in the embodiments provided in this application may include non-volatile and/or volatile memory. Non-volatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not a limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Channel (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the division of the above functional units and modules is used as an example. In practical applications, the above functions can be allocated to different functional units and modules as needed. Module completion, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.

The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, a person of ordinary skill in the art should understand that it can still implement the foregoing The technical solutions recorded in the examples are modified, or some of the technical features are equivalently replaced; these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the application, and should be included in Within the scope of protection of this application.

Claims (20)

1. A medical insurance data processing method based on a fixed time window, which includes:

   Acquiring the trigger time point of the medical insurance use event and the total number of triggers of the medical insurance use event within the preset time period;

   According to a preset display rule, display the medical insurance use event and its trigger time on a preset time axis, and obtain the time interval between all two adjacent medical insurance use events on the preset time axis;

   Cutting the preset time axis into a plurality of sub time axes according to a preset fixed time window and the time interval;

   Acquiring the total number of the medical insurance usage events included in each of the sub-time axes, and recording the sub-time axis containing the largest total number of medical insurance usage events as the first sub-time axis;

   According to the preset fixed time window and the preset frequency determination method, the first most intensive frequency corresponding to the first sub-time axis is determined; the first most intensive frequency characterization corresponds to the preset fixed time window, so The maximum frequency of triggering the medical insurance use event existing in the first sub-time axis;

   When the first most intensive frequency meets the preset frequency standard, it is prompted that the medical insurance use event within the preset time period is abnormal.
2. The medical insurance data processing method based on a fixed time window according to claim 1, wherein the cutting the preset time axis into a plurality of sub time axes according to the preset fixed time window and the time interval comprises:

   Comparing each of the time intervals with the preset fixed time window;

   When the time interval is greater than the preset fixed time window, it is checked whether the time interval is the first time interval; the first time interval refers to the time interval located first on the preset time axis;

   When the time interval is the first time interval, delete the first time interval and the first medical insurance use event located before the first time interval from the preset time axis;

   When the time interval is not the first time interval, the time interval is deleted from the preset time axis, and the preset time axis is cut at the position corresponding to the time interval. The medical insurance use event is recorded as the start event of the next sub-time axis, and the medical insurance use event located before the time interval is recorded as the end event of the previous sub-time axis.
3. The method for processing medical insurance data based on a fixed time window according to claim 1, wherein after the determining the first most intensive frequency corresponding to the first sub-time axis, the method further comprises:

   When the first most intensive frequency does not meet the preset frequency standard, recording the sub-time axis including the total number of medical insurance usage events as the second sub-time axis;

   According to the preset fixed time window and the preset frequency determination method, the second most intensive frequency corresponding to the second sub-time axis is determined; the second most intensive frequency characterization corresponds to the preset fixed time window, so The maximum frequency of triggering the medical insurance use event existing in the second sub-time axis;

   When the second most intensive frequency meets the preset frequency standard, it is prompted that the medical insurance use event within the preset time period is abnormal.
4. The medical insurance data processing method based on a fixed time window according to claim 1, wherein said acquiring the total number of medical insurance use events included in each of said sub-time axes will include the total number of medical insurance use events The most sub-time axis is recorded as the first sub-time axis, which also includes:

   Deleting a qualified time axis, where the qualified time axis refers to the sub-time axis in which the total number of medical insurance usage events contained is less than a preset number;

   When the number of the sub-time axes after the qualified time axis is deleted is greater than or equal to one, the sub-time axis containing the largest number of medical insurance usage events is recorded as the first sub-time axis;

   When the number of the sub-time axes after the qualified time axis is deleted is equal to zero, it is prompted that there is no abnormality in the medical insurance use event within the preset time period.
5. The medical insurance data processing method based on a fixed time window according to claim 1, wherein said determining the first densest time corresponding to said first sub-time axis according to said preset fixed time window and a preset frequency determination method. Frequency, including:

   A preset number of medical insurance use events on the first sub-time axis are selected in chronological order as the initial element group, and the total duration of the time interval between adjacent medical insurance use events in the initial element group is recorded as the first cumulative time interval;

   When the first cumulative time interval is less than or equal to the preset fixed time window, storing the first cumulative time interval in association with the initialization element group as first frequency data;

   Detecting whether there is a medical insurance use event after the last medical insurance use event in the initialization element group;

   When there is no medical insurance use event after the last medical insurance use event in the initialization element group, the first frequency data is recorded as the first most intensive frequency corresponding to the first sub-time axis.
6. The medical insurance data processing method based on a fixed time window according to claim 5, wherein after detecting whether there is a medical insurance use event after the last medical insurance use event in the initialization element group, the method further comprises:

   When there is a medical insurance use event after the last medical insurance use event in the initialization element group, the medical insurance use event after the last medical insurance use event in the initialization element group is added to the initialization element group to form a second element group , Recording the total duration of the time interval between adjacent medical insurance use events in the second element group as the second cumulative time interval;

   When the second cumulative time interval is less than or equal to the preset fixed time window, if there is no medical insurance use event after the last medical insurance use event in the second element group, the second cumulative time interval It is stored in association with the second element group as the first most intensive frequency corresponding to the first sub-time axis.
7. The medical insurance data processing method based on a fixed time window according to claim 5, wherein the total duration of the time interval between adjacent medical insurance use events in the initialization element group is recorded as after the first cumulative time interval, further include:

   When the first cumulative time interval is greater than the preset fixed time window, if there is a medical insurance use event after the last medical insurance use event in the initialization element group, set the medical insurance use event after the last medical insurance use event in the initialization element group Add the medical insurance use event of the initial element group to the initial element group, and delete the first medical insurance use event in the initial element group in chronological order to form a third element group, and combine the adjacent medical insurance in the third element group The total duration of the time interval between usage events is recorded as the third cumulative time interval;

   When the third cumulative time interval is less than or equal to the preset fixed time window, storing the third cumulative time interval in association with the third element group as third frequency data;

   When there is no medical insurance use event after the last medical insurance use event in the third element group, the first frequency data and the third frequency data with the highest frequency density are recorded as being related to the first sub-time axis The corresponding first most intensive frequency.
8. A medical insurance data processing device based on a fixed time window, which includes:

   The data acquisition module is used to acquire the trigger time point of the medical insurance use event and the total number of triggers of the medical insurance use event within a preset time period;

   The data display module is used to display the medical insurance use event and its trigger time point on a preset time axis according to a preset display rule, and obtain all of the two adjacent medical insurance use events on the preset time axis Time interval between

   The time axis cutting module is configured to cut the preset time axis into a plurality of sub time axes according to a preset fixed time window and the time interval;

   The first sub-time axis determination module is used to obtain the total number of the medical insurance use events contained in each of the sub-time axes, and record the sub-time axis that contains the most total number of medical insurance use events as the first sub-time axis;

   The first frequency determining module is configured to determine the first densest frequency corresponding to the first sub-time axis according to the preset fixed time window and the preset frequency determining method; the first densest frequency characterization corresponds to all The preset fixed time window, the maximum frequency of triggering the medical insurance use event existing in the first sub-time axis;

   The first abnormality prompting module is configured to prompt an abnormality in the medical insurance use event within the preset time period when the first most intensive frequency meets the preset frequency standard.
9. A computer device includes a memory, a processor, and computer-readable instructions that are stored in the memory and can run on the processor, wherein the processor implements the following steps when the processor executes the computer-readable instructions:

   Acquiring the trigger time point of the medical insurance use event and the total number of triggers of the medical insurance use event within the preset time period;

   According to a preset display rule, display the medical insurance use event and its trigger time on a preset time axis, and obtain the time interval between all two adjacent medical insurance use events on the preset time axis;

   Cutting the preset time axis into a plurality of sub time axes according to a preset fixed time window and the time interval;

   Acquiring the total number of the medical insurance usage events included in each of the sub-time axes, and recording the sub-time axis containing the largest total number of medical insurance usage events as the first sub-time axis;

   According to the preset fixed time window and the preset frequency determination method, the first most intensive frequency corresponding to the first sub-time axis is determined; the first most intensive frequency characterization corresponds to the preset fixed time window, so The maximum frequency of triggering the medical insurance use event existing in the first sub-time axis;

   When the first most intensive frequency meets the preset frequency standard, it is prompted that the medical insurance use event within the preset time period is abnormal.
10. 9. The computer device according to claim 9, wherein the cutting the preset time axis into a plurality of sub time axes according to a preset fixed time window and the time interval comprises:

    Comparing each of the time intervals with the preset fixed time window;

    When the time interval is greater than the preset fixed time window, it is checked whether the time interval is the first time interval; the first time interval refers to the time interval located first on the preset time axis;

    When the time interval is the first time interval, delete the first time interval and the first medical insurance use event located before the first time interval from the preset time axis;

    When the time interval is not the first time interval, the time interval is deleted from the preset time axis, and the preset time axis is cut at the position corresponding to the time interval. The medical insurance use event is recorded as the start event of the next sub-time axis, and the medical insurance use event located before the time interval is recorded as the end event of the previous sub-time axis.
11. 9. The computer device according to claim 9, wherein after the determining the first most intensive frequency corresponding to the first sub-time axis, the processor further implements the following steps when executing the computer-readable instruction:

    When the first most intensive frequency does not meet the preset frequency standard, recording the sub-time axis including the total number of medical insurance usage events as the second sub-time axis;

    According to the preset fixed time window and the preset frequency determination method, the second most intensive frequency corresponding to the second sub-time axis is determined; the second most intensive frequency characterization corresponds to the preset fixed time window, so The maximum frequency of triggering the medical insurance use event existing in the second sub-time axis;

    When the second most intensive frequency meets the preset frequency standard, it is prompted that the medical insurance use event within the preset time period is abnormal.
12. The computer device according to claim 9, wherein said acquiring the total number of the medical insurance use events contained in each of the sub-time axes, and recording the sub-time axis containing the largest total number of medical insurance use events as The first sub-timeline includes:

    Deleting a qualified time axis, where the qualified time axis refers to the sub-time axis in which the total number of medical insurance usage events contained is less than a preset number;

    When the number of the sub-time axes after the qualified time axis is deleted is greater than or equal to one, the sub-time axis containing the largest number of medical insurance usage events is recorded as the first sub-time axis;

    When the number of the sub-time axes after the qualified time axis is deleted is equal to zero, it is prompted that there is no abnormality in the medical insurance use event within the preset time period.
13. 9. The computer device according to claim 9, wherein the determining the first most intensive frequency corresponding to the first sub-time axis according to the preset fixed time window and the preset frequency determining method comprises:

    A preset number of medical insurance use events on the first sub-time axis are selected in chronological order as the initialization element group, and the total duration of the time interval between adjacent medical insurance use events in the initialization element group is recorded as the first cumulative time interval;

    When the first cumulative time interval is less than or equal to the preset fixed time window, storing the first cumulative time interval in association with the initialization element group as first frequency data;

    Detecting whether there is a medical insurance use event after the last medical insurance use event in the initialization element group;

    When there is no medical insurance use event after the last medical insurance use event in the initialization element group, the first frequency data is recorded as the first most intensive frequency corresponding to the first sub-time axis.
14. The computer device according to claim 13, wherein after the detection of whether there is a medical insurance use event after the last medical insurance use event in the initialization element group, the processor executes the computer-readable instruction. The following steps:

    When there is a medical insurance use event after the last medical insurance use event in the initial element group, the medical insurance use event after the last medical insurance use event in the initial element group is added to the initial element group to form a second element group , Recording the total duration of the time interval between adjacent medical insurance use events in the second element group as the second cumulative time interval;

    When the second cumulative time interval is less than or equal to the preset fixed time window, if there is no medical insurance use event after the last medical insurance use event in the second element group, the second cumulative time interval It is stored in association with the second element group as the first most intensive frequency corresponding to the first sub-time axis.
15. One or more readable storage media storing computer readable instructions, where when the computer readable instructions are executed by one or more processors, the one or more processors execute the following steps:

    Acquiring the trigger time point of the medical insurance use event and the total number of triggers of the medical insurance use event within the preset time period;

    According to a preset display rule, display the medical insurance use event and its trigger time on a preset time axis, and obtain the time interval between all two adjacent medical insurance use events on the preset time axis;

    Cutting the preset time axis into a plurality of sub time axes according to a preset fixed time window and the time interval;

    Acquiring the total number of the medical insurance usage events included in each of the sub-time axes, and recording the sub-time axis containing the largest total number of medical insurance usage events as the first sub-time axis;

    According to the preset fixed time window and the preset frequency determination method, the first most intensive frequency corresponding to the first sub-time axis is determined; the first most intensive frequency characterization corresponds to the preset fixed time window, so The maximum frequency of triggering the medical insurance use event existing in the first sub-time axis;

    When the first most intensive frequency meets the preset frequency standard, it is prompted that the medical insurance use event within the preset time period is abnormal.
16. 15. The readable storage medium of claim 15, wherein the cutting the preset time axis into a plurality of sub time axes according to a preset fixed time window and the time interval comprises:

    Comparing each of the time intervals with the preset fixed time window;

    When the time interval is greater than the preset fixed time window, it is checked whether the time interval is the first time interval; the first time interval refers to the time interval located first on the preset time axis;

    When the time interval is the first time interval, delete the first time interval and the first medical insurance use event located before the first time interval from the preset time axis;

    When the time interval is not the first time interval, the time interval is deleted from the preset time axis, and the preset time axis is cut at the position corresponding to the time interval. The medical insurance use event is recorded as the start event of the next sub-time axis, and the medical insurance use event located before the time interval is recorded as the end event of the previous sub-time axis.
17. 15. The readable storage medium according to claim 15, wherein after the first most intensive frequency corresponding to the first sub-time axis is determined, when the computer-readable instructions are executed by one or more processors, such that The one or more processors further execute the following steps:

    When the first most intensive frequency does not meet the preset frequency standard, recording the sub-time axis including the total number of medical insurance usage events as the second sub-time axis;

    According to the preset fixed time window and the preset frequency determination method, the second most intensive frequency corresponding to the second sub-time axis is determined; the second most intensive frequency characterization corresponds to the preset fixed time window, so The maximum frequency of triggering the medical insurance use event existing in the second sub-time axis;

    When the second most intensive frequency meets the preset frequency standard, it is prompted that the medical insurance use event within the preset time period is abnormal.
18. The readable storage medium according to claim 15, wherein said acquiring the total number of the medical insurance usage events contained in each of the sub-time axes will include the sub-time axis with the largest total number of medical insurance usage events Recorded as the first sub-timeline, including:

    Deleting a qualified time axis, where the qualified time axis refers to the sub-time axis in which the total number of medical insurance usage events contained is less than a preset number;

    When the number of the sub-time axes after the qualified time axis is deleted is greater than or equal to one, the sub-time axis containing the largest number of medical insurance usage events is recorded as the first sub-time axis;

    When the number of the sub-time axes after the qualified time axis is deleted is equal to zero, it is prompted that there is no abnormality in the medical insurance use event within the preset time period.
19. 15. The readable storage medium of claim 15, wherein the determining the first most intensive frequency corresponding to the first sub-time axis according to the preset fixed time window and the preset frequency determining method comprises:

    A preset number of medical insurance use events on the first sub-time axis are selected in chronological order as the initial element group, and the total duration of the time interval between adjacent medical insurance use events in the initial element group is recorded as the first cumulative time interval;

    When the first cumulative time interval is less than or equal to the preset fixed time window, storing the first cumulative time interval in association with the initialization element group as first frequency data;

    Detecting whether there is a medical insurance use event after the last medical insurance use event in the initialization element group;

    When there is no medical insurance use event after the last medical insurance use event in the initialization element group, the first frequency data is recorded as the first most intensive frequency corresponding to the first sub-time axis.
20. The readable storage medium according to claim 19, wherein after the detection of whether there is a medical insurance use event after the last medical insurance use event in the initialization element group, the computer-readable instructions are processed by one or more When the processor executes, the one or more processors further execute the following steps:

    When there is a medical insurance use event after the last medical insurance use event in the initialization element group, the medical insurance use event after the last medical insurance use event in the initialization element group is added to the initialization element group to form a second element group , Recording the total duration of the time interval between adjacent medical insurance use events in the second element group as the second cumulative time interval;

    When the second cumulative time interval is less than or equal to the preset fixed time window, if there is no medical insurance use event after the last medical insurance use event in the second element group, the second cumulative time interval It is stored in association with the second element group as the first most intensive frequency corresponding to the first sub-time axis.

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