Medical parameter determination method and device, computer equipment and storage medium

Abstract

The invention relates to a medical parameter determination method and device, computer equipment and a storage medium. Obtaining a first prediction value according to the target parameter of the user and a preset first prediction model, and obtaining a second prediction value according to time information and a target period of a preset event generated by the user in a historical time period, thereby obtaining a target prediction value according to the first prediction value and the second prediction value; according to the method, the first predicted value determined through the target parameter represents the real-time probability of the preset event occurring in the target time period of the user, and the second predicted value determined according to the time information represents the regular probability of the preset event occurring in the target time period of the user, so that the user experience is improved. According to the target prediction value obtained according to the first prediction value and the second prediction value, the probability that the user has the preset event in the target time period can be predicted in real time, so that the user is reminded according to the target prediction value.

Description

Technical Field

[0001] The present application relates to the field of medical technology, and in particular to a method, apparatus, computer device and storage medium for determining medical parameters. Background Art

[0002] In daily life, the occurrence of various diseases with seizure cycles will affect the patient's daily activities. For example, epilepsy is a brain disease caused by various reasons. When an epileptic seizure occurs, it will cause epileptic myoclonus, eyelid myoclonus, gelastic seizures, etc. Therefore, predicting the probability of the user's disease onset can avoid affecting the user's daily activities as much as possible.

[0003] In traditional technology, the probability of a user's illness is mainly predicted based on historical data. However, traditional technology is unable to predict the probability of a user's illness in the future time period in real time. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment and storage medium for determining medical parameters that can predict in real time the probability of a preset event occurring to a user within a future time period in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for determining a medical parameter, the method comprising:

[0006] Obtaining a first prediction value according to a user's target parameter and a preset first prediction model; the target parameter includes a physiological parameter of the user and a motion parameter of the user; the first prediction value is used to characterize a first initial probability of a preset event occurring to the user in a target time period;

[0007] According to the time information of the preset event occurring to the user in the historical time period and the target period, a second prediction value is obtained; the second prediction value is used to represent a second initial probability of the preset event occurring to the user in the target time period;

[0008] A target prediction value is obtained according to the first prediction value and the second prediction value; the target prediction value is used to characterize the target probability of the preset event occurring to the user in the target time period.

[0009] In one embodiment, obtaining the second prediction value according to the time information of the preset event occurring to the user in the historical time period and the target period includes:

[0010] Taking the target period as a unit, dividing the time information into a plurality of period data;

[0011] Determine, according to the plurality of periodic data, a third probability value of the occurrence of the preset event for the user within a unit time period in the target period;

[0012] The second prediction value is obtained according to each of the third probability values ​​and the target time period.

[0013] In one embodiment, obtaining the second prediction value according to each of the third probability values ​​and the target time period includes:

[0014] Determine a target unit time period corresponding to the target time period;

[0015] The third probability value corresponding to the target unit time period is determined as the second prediction value.

[0016] In one embodiment, the method further comprises:

[0017] For each preset period in a plurality of preset periods, a target operation is performed; the target operation comprises: determining an average coordinate point of the plurality of target moments in each preset period on the unit circle according to the coordinates of the plurality of target moments on the unit circle at which the preset event occurs to the user in the time information;

[0018] The preset period corresponding to the maximum distance between the average coordinate points corresponding to the preset periods and the center of the unit circle is determined as the target period.

[0019] In one embodiment, the method further comprises:

[0020] For each of the preset periods, each of the target moments is determined according to the initial moment in the time information and the preset period; the initial moment is the initial moment when the preset event occurs to the user in the time information;

[0021] Performing a modulo operation on each of the target moments according to the preset period to obtain a modulo value corresponding to each of the target moments;

[0022] Determine, according to the residual value corresponding to each of the target moments, the preset period and the pi, the time parameter corresponding to each of the target moments in the preset period;

[0023] The coordinates of each target moment on the unit circle are obtained according to the trigonometric function value corresponding to the time parameter corresponding to each target moment.

[0024] In one embodiment, the determining of the time parameter corresponding to each target moment in the preset period according to the residual value corresponding to each target moment, the preset period and pi includes:

[0025] Obtaining the ratio of the residual value corresponding to each of the target moments to the preset period;

[0026] According to the ratio corresponding to each of the target moments and the pi, the time parameter corresponding to each of the target moments in the preset period is determined.

[0027] In one embodiment, obtaining a target prediction value according to the first prediction value and the second prediction value includes:

[0028] Determine a first weight value corresponding to the first prediction value and a second weight value corresponding to the second prediction value;

[0029] The first predicted value and the second predicted value are weighted and summed according to the first weight value and the second weight value to obtain the target predicted value.

[0030] In one embodiment, determining a first weight value corresponding to the first prediction value and a second weight value corresponding to the second prediction value includes:

[0031] Determine the time difference between the current time and the target time period;

[0032] Obtaining a tangent function value of a target ratio; the target ratio is a ratio between a first parameter and a first preset parameter; the first parameter is a sum of the time difference and the second preset parameter;

[0033] Determine the difference between 1 and the tangent function value as the first weight value;

[0034] The tangent function value is determined as the second weight value.

[0035] In a second aspect, the present application further provides a device for determining a medical parameter, the device comprising:

[0036] A first acquisition module, configured to acquire a first prediction value according to a user's target parameter and a preset first prediction model; the target parameter includes a physiological parameter of the user and a motion parameter of the user; the first prediction value is used to characterize a first initial probability that a preset event occurs to the user in a target time period;

[0037] A second acquisition module is used to acquire a second prediction value according to the time information of the preset event occurring to the user in the historical time period and the target period; the second prediction value is used to represent a second initial probability of the preset event occurring to the user in the target time period;

[0038] The third acquisition module is used to acquire a target prediction value according to the first prediction value and the second prediction value; the target prediction value is used to represent the target probability of the preset event occurring to the user in the target time period.

[0039] In a third aspect, the present application further provides a computer device, the computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the following steps when executing the computer program:

[0040] Obtaining a first prediction value according to a user's target parameter and a preset first prediction model; the target parameter includes a physiological parameter of the user and a motion parameter of the user; the first prediction value is used to characterize a first initial probability of a preset event occurring to the user in a target time period;

[0041] According to the time information of the preset event occurring to the user in the historical time period and the target period, a second prediction value is obtained; the second prediction value is used to represent a second initial probability of the preset event occurring to the user in the target time period;

[0042] A target prediction value is obtained according to the first prediction value and the second prediction value; the target prediction value is used to characterize the target probability of a preset event occurring to the user in the target time period.

[0043] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0044] Obtaining a first prediction value according to a user's target parameter and a preset first prediction model; the target parameter includes a physiological parameter of the user and a motion parameter of the user; the first prediction value is used to characterize a first initial probability of a preset event occurring to the user in a target time period;

[0045] According to the time information of the preset event occurring to the user in the historical time period and the target period, a second prediction value is obtained; the second prediction value is used to represent a second initial probability of the preset event occurring to the user in the target time period;

[0046] A target prediction value is obtained according to the first prediction value and the second prediction value; the target prediction value is used to characterize the target probability of a preset event occurring to the user in the target time period.

[0047] In a fifth aspect, the present application further provides a computer program product, the computer program product comprising a computer program, which implements the following steps when executed by a processor:

[0048] Obtaining a first prediction value according to a user's target parameter and a preset first prediction model; the target parameter includes a physiological parameter of the user and a motion parameter of the user; the first prediction value is used to characterize a first initial probability of a preset event occurring to the user in a target time period;

[0049] According to the time information of the preset event occurring to the user in the historical time period and the target period, a second prediction value is obtained; the second prediction value is used to represent a second initial probability of the preset event occurring to the user in the target time period;

[0050] A target prediction value is obtained according to the first prediction value and the second prediction value; the target prediction value is used to characterize the target probability of a preset event occurring to the user in the target time period.

[0051] The above-mentioned method, device, computer equipment and storage medium for determining medical parameters obtain a first prediction value according to the user's target parameter and a preset first prediction model, and obtain a second prediction value according to the time information and target period of the preset event occurring in the historical time period of the user, so as to obtain a target prediction value according to the first prediction value and the second prediction value; wherein the target parameter includes the user's physiological parameter and the user's motion parameter, the first prediction value is used to characterize the first initial probability of the user's preset event occurring in the target time period, the second prediction value is used to characterize the second initial probability of the user's preset event occurring in the target time period, and the target prediction value is used to characterize the target probability of the user's preset event occurring in the target time period. In this method, the first prediction value determined by the target parameter characterizes the real-time probability of the user's preset event occurring in the target time period, and the second prediction value of the target time period determined according to the time information characterizes the regular probability of the user's preset event occurring in the target time period. Therefore, the target prediction value obtained according to the first prediction value and the second prediction value can predict the probability of the user's preset event occurring in the target time period in real time, and the first prediction value and the second prediction value are combined to make the probability of the user's preset event occurring in the target time period more accurate, so as to remind the user according to the target prediction value. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is an application environment diagram of a method for determining a medical parameter in one embodiment;

[0053] Figure 2 is a schematic flow chart of a method for determining a medical parameter in an embodiment;

[0054] Figure 3 A schematic diagram of a network structure of a first prediction model according to an embodiment;

[0055] Figure 4 A schematic diagram of a process for obtaining a target prediction value in one embodiment;

[0056] Figure 5 is a schematic flow chart of a method for obtaining a second prediction value in one embodiment;

[0057] Figure 6 A schematic diagram of a flow chart of a method for determining a target period in one embodiment;

[0058] Figure 7 is a schematic diagram of coordinates of each target moment on a unit circle in one embodiment;

[0059] Figure 8 is a schematic diagram of a target cycle in one embodiment;

[0060] Fig. 9 It is a schematic diagram of a process of obtaining the coordinates of each target on the unit circle at a certain moment in an embodiment;

[0061] Fig.10 is a schematic diagram of time information of an epileptic seizure in one embodiment;

[0062] Fig.11 A schematic flow chart of a method for obtaining a target prediction value in another embodiment;

[0063] Fig.12 A schematic flow chart of a method for obtaining a target prediction value in another embodiment;

[0064] Fig.13 is a structural block diagram of a device for determining medical parameters in one embodiment;

[0065] Fig.14 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0067] In the related art, the probability of occurrence of preset events in each time period of a day is calculated through the time information of historical time periods. The advantage of this technology is that it can predict the probability of occurrence of preset events after a long time, so that users can make time plans in advance and avoid the impact of preset events on the user's schedule as much as possible. However, the calculation results obtained by this method are fixed and cannot respond to changes in the user's physiological state in real time. Therefore, it cannot cope with the occurrence of preset events in low-probability time periods, and cannot adjust the probability of occurrence of each time period according to the user's state. Therefore, the present application proposes a method, device, computer equipment and storage medium for determining medical parameters.

[0068] The method for determining medical parameters provided in the embodiments of the present application can be applied to Figure 1 The application environment includes a computer device, which may be a server, and its internal structure diagram may be as shown in FIG. Figure 1 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store relevant data for determining medical parameters. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for determining medical parameters is implemented. The server can be implemented as an independent server or a server cluster consisting of multiple servers.

[0069] In one embodiment, Figure 2 As shown, a method for determining a medical parameter is provided, and the method is applied to Figure 1 The computer device in the example is used to illustrate, including the following steps:

[0070] S201, obtaining a first prediction value according to a user's target parameters and a preset first prediction model; the target parameters include a user's physiological parameters and a user's motion parameters; the first prediction value is used to characterize a first initial probability of a preset event occurring to the user in a target time period.

[0071] Optionally, the preset event may be a disease with periodic attacks such as epilepsy, migraine, etc. Exemplarily, migraine involves periodic attacks of headaches, nausea, or other periodic symptoms.

[0072] Among them, the target parameters include the user's physiological parameters and the user's motion parameters. The target parameters can capture the precursor symptoms before the preset event occurs. The physiological parameters can be the user's skin electrical parameters, body temperature parameters, etc., and the motion parameters can be the acceleration parameters, speed parameters, and force parameters generated during the user's exercise. Optionally, body temperature parameters, skin electrical parameters, and motion parameters can be collected through wearable devices, and flexible electrochemical sensors can be used to detect neurons to obtain skin electrical parameters.

[0073] In this embodiment, the computer device can obtain the first prediction value of the target time period according to the user's target parameter and the preset first prediction model, wherein the target parameter can be any parameter or multiple parameters. Figure 3 As shown, Figure 3This is a schematic diagram of the network structure of the first prediction model in an embodiment. When the target parameter is any parameter, the target parameter is input into the first prediction model, and the target parameter passes through the Conv1D layer, the Dropout layer, the Conv1D layer, the Maxpool1D layer and the Dense layer in sequence to obtain the first prediction value.

[0074] If the target parameter is multiple parameters, each parameter obtains the eigenvalues ​​corresponding to the multiple parameters after passing through the Conv1D layer and the Dropout layer. It is necessary to use Concatenate to fuse the eigenvalues ​​corresponding to the multiple parameters, and then input the fused eigenvalues ​​into the Conv1D layer, the Maxpool1D layer and the Dense layer to obtain the first prediction value.

[0075] Figure 4 FIG. 1 is a flow chart of a method for obtaining a target prediction value in an embodiment. Figure 4 As shown, the target parameter samples are divided according to the time information of the preset events that occur in the historical time period of the user, and the target sub-parameter samples are obtained. The first initial prediction model is trained using the target sub-parameter samples to obtain the first prediction model corresponding to each time period, so as to obtain the first prediction value according to the first prediction model and the target parameter. For example, the target sub-parameter samples between t1 hours before the epileptic seizure and t2 hours before the epileptic seizure are obtained according to the time information of the user's epileptic seizure, and the first initial prediction model for predicting the epileptic seizure after t1 hours is trained according to the target sub-parameter samples, and the first prediction model after t1 hours is obtained. By adjusting t1 and t2, the first prediction model for epileptic seizures in different time periods can be obtained. For example, if the time information is 12:00, t1 is 1 hour, and t2 is 2 hours, then the target sub-parameter samples are the target parameter samples corresponding to 10:00-11:00 in the target parameter samples. The first initial prediction model is trained according to the target parameter samples corresponding to 10:00-11:00, and the first prediction model after 1 hour can be obtained. If t1 is 2 hours and t2 is 4 hours, the first initial prediction model can be trained according to the target parameter samples corresponding to 8:00-10:00, and the first prediction model after 2 hours can be obtained.

[0076] The target parameter represents the real-time physiological information of the user. The first prediction value determined based on the target parameter reflects the real-time probability of a preset event occurring to the user, and is used to remind the user.

[0077] S202, obtaining a second prediction value according to the time information of the preset event occurring to the user in the historical time period and the target period; the second prediction value is used to represent a second initial probability of the preset event occurring to the user in the target time period.

[0078] In this embodiment, as described above Figure 4As shown, the target period can be determined according to the time information, so as to obtain the second prediction value based on the target period. Specifically, after obtaining the target period, the time information of the preset event occurring in the historical time period of the user is divided according to the target period to obtain a plurality of period data, and the third prediction value of each unit time period of the user in the target period is obtained by using the plurality of period data, so as to determine the second prediction value of the target time period according to the third prediction value of each unit time period and the target time period.

[0079] S203, obtaining a target prediction value according to the first prediction value and the second prediction value; the target prediction value is used to characterize the target probability of a preset event occurring to the user in a target time period.

[0080] In this embodiment, the average of the first prediction value and the second prediction value can be used as the target prediction value; a first weight value corresponding to the first prediction value and a second weight value corresponding to the second prediction value can also be determined, and the first prediction value and the second prediction value can be weighted using the first weight value and the second weight value to obtain the target prediction value.

[0081] It should be noted that the first predicted value, second predicted value and target predicted value obtained in the present application are only intermediate values, which are only used to prompt and refer to users, rather than the final diagnosis results. Medical staff need to judge whether a preset event occurs to the user based on the target predicted value and the user's actual situation.

[0082] In the above-mentioned method for determining medical parameters, a first prediction value is obtained according to the user's target parameter and a preset first prediction model, and a second prediction value is obtained according to the time information and the target period of the preset event occurring in the historical time period of the user, thereby obtaining a target prediction value according to the first prediction value and the second prediction value; wherein the target parameter includes the user's physiological parameter and the user's motion parameter, the first prediction value is used to characterize the first initial probability of the user's preset event occurring in the target time period, the second prediction value is used to characterize the second initial probability of the user's preset event occurring in the target time period, and the target prediction value is used to characterize the target probability of the user's preset event occurring in the target time period. In this method, the first prediction value determined by the target parameter characterizes the real-time probability of the user's preset event occurring in the target time period, and the second prediction value of the target time period determined according to the time information characterizes the regular probability of the user's preset event occurring in the target time period. Therefore, the target prediction value obtained according to the first prediction value and the second prediction value can predict the probability of the user's preset event occurring in the target time period in real time, and the first prediction value and the second prediction value are combined to make the probability of the user's preset event occurring in the target time period more accurate, so as to remind the user according to the target prediction value.

[0083] Figure 5FIG. 1 is a flow chart of a method for obtaining a second prediction value in an embodiment. Figure 5 As shown, the embodiment of the present application relates to a possible implementation method of how to obtain a second prediction value according to time information of a preset event occurring in a historical time period of a user and a target period, including the following steps:

[0084] S501, dividing the time information into a plurality of periodic data based on the target period.

[0085] S502: Determine, based on the plurality of period data, a third probability value of a preset event occurring to the user within a unit time period in the target period.

[0086] Optionally, the unit time period may be 1 hour, 2 hours, or a day, etc.

[0087] In this embodiment, the time information of the preset event occurring in the historical time period of the user is divided into multiple periods according to the target period, and multiple period data are obtained. The data of the unit time period in each period data is used to calculate the third probability value of the preset event occurring in the unit time period of the user. For example, the target period is 24h, the unit time period is 1h, and the time information is one week. According to the target period, the data is divided to obtain 7 period data. Determine the number of times the preset event occurs in 0-1h in the 7 period data, and input the number of times the preset event occurs in 0-1h into the kernel density estimation method to calculate the third probability value of the preset event occurring in 0-1h. The third probability value of the preset event occurring in each unit time period is obtained in turn using the above method. Optionally, the kernel function in the kernel density estimation method uses a Gaussian kernel function.

[0088] In a possible implementation, the third probability value of the preset event occurring within 0-1h may also be directly determined based on the ratio of the number of times the preset event occurs within 0-1h in the 7-period data to the 7-period data.

[0089] S503: Obtain a second prediction value according to each third probability value and the target time period.

[0090] In this embodiment, the target unit time period corresponding to the target time period, the previous unit time period and the next unit time period corresponding to the target unit time period can be determined, and the average of the third probability value of the target unit time period, the third probability value of the previous unit time period and the third probability value of the next unit time period can be used as the second prediction value.

[0091] Specifically, “obtaining a second prediction value according to each third probability value and a target time period” includes: determining a target unit time period corresponding to the target time period; and determining the third probability value corresponding to the target unit time period as the second prediction value.

[0092] In this embodiment, the target unit time period corresponding to the target time period is determined according to the target time period, and the third probability value corresponding to the target unit time period is used as the second prediction value. For example, if the target time period is 3:00-4:00 the day after tomorrow, the third probability value corresponding to 3:00-4:00 in the target period is used as the second prediction value.

[0093] In the embodiment of the present application, the time information is divided into a plurality of periodic data with the target period as the unit, and the third probability value of the occurrence of the preset event by the user in the unit time period in the target period is determined according to the plurality of periodic data, and the second prediction value is obtained according to each third probability value and the target time period. In the present embodiment, the second prediction value is obtained by using the third probability value of the occurrence of the preset event by the user in the unit time period in the target period, and the probability of the occurrence of the preset event after a long time can be predicted, so as to remind the user according to the second prediction value, so that the user can make time planning in advance according to the second prediction value, and avoid the occurrence of the preset event affecting the schedule as much as possible.

[0094] Figure 6 FIG. 1 is a flow chart of a method for determining a target period in an embodiment. Figure 6 As shown, the following steps are included:

[0095] S601, performing a target operation for each preset period in a plurality of preset periods; the target operation includes: determining an average coordinate point of a plurality of target moments in each preset period on the unit circle according to the coordinates of a plurality of target moments on the unit circle when a preset event occurs to the user in the time information.

[0096] The target time includes the time when the preset event occurs for the first time in the time information, and the time when the preset event occurs for the first time after a preset period based on each occurrence of the preset event as a starting point.

[0097] In this embodiment, multiple target moments are obtained for each preset period, and the x-coordinates and y-coordinates of the multiple target moments on the unit circle are averaged to obtain the average coordinate points of the multiple target moments on the unit circle. Figure 7 As shown, the triangle on the circumference of the unit circle is the coordinates of multiple target moments on the unit circle in a preset period. Get the average coordinate point, such as Figure 7 The position of * in , where Ns is the number of target moments in each preset cycle, vi is the coordinate of the target moment on the unit circle, and P is the average coordinate point.

[0098] S602: Determine the preset period corresponding to the maximum distance between the average coordinate points corresponding to the preset periods and the center of the unit circle as the target period.

[0099] In this embodiment, if the center coordinates of the unit circle are (x0, y0), the distance R is determined according to the average coordinate point P corresponding to each preset period and the center coordinates of the unit circle (x0, y0), and the preset period corresponding to the maximum distance R among the distances R corresponding to each preset period is taken as the target period. Figure 8 As shown, Figure 8 is a schematic diagram of a target cycle in an embodiment, in which the time information ( Figure 8 The target period is marked on the corresponding period length in Figure 8 * in .

[0100] In the embodiment of the present application, for each of the multiple preset periods, the average coordinate point of the multiple target moments on the unit circle in each preset period is determined according to the coordinates of the multiple target moments on the unit circle when the preset event occurs in the time information, and the preset period corresponding to the maximum distance between the average coordinate point corresponding to each preset period and the center of the unit circle is determined as the target period. In this embodiment, the target period is determined according to the coordinates of the multiple target moments on the unit circle in the time information, and the determination of the target period is more efficient and concise, which lays a foundation for the subsequent determination of the second prediction value based on the target period.

[0101] Fig. 9 FIG. 1 is a flow chart of obtaining the coordinates of each target on the unit circle at each time in one embodiment. Fig. 9 As shown, the following steps are included:

[0102] S901, for each preset period, determine each target time according to the initial time in the time information and the preset period; the initial time is the initial time when a preset event occurs in the time information of the user.

[0103] In this embodiment, for each preset period, the initial time when the preset event occurs in the time information of the user is first determined. Based on the initial time, the time when the preset event occurs for the first time after the preset period is determined as the target time. The target time is continued as the basis to determine the time when the preset event occurs for the first time after the preset period. This step is repeated until all the target times in the time information are searched. Fig.10 As shown, Fig.10 is a schematic diagram of time information of an epileptic seizure in an embodiment, based on Fig.10 From the time information at the bottom, we can see that the time information of epileptic seizures includes the time of epileptic seizures within 40 days. The triangle marks are the time when the preset events occurred, which occurred 13 times in total. They are mainly concentrated on days 0-4 and 36-38. Assuming that the preset cycle is 24h, the time information of days 0-4 and 36-38 is refined to hours, and we get Fig.10Based on the time information graph in the middle, we can see that the first epileptic seizure is mainly concentrated in the 5th hour. By refining the time information around the 5th hour to seconds, we get Fig.10 The top time information graph is used to determine the initial time of the epileptic seizure as 4:00. Based on the initial time of 4:00, after the preset period of 24 hours, the time of the first epileptic seizure after 24 hours is determined to be around the 35th hour. The time information around the 35th hour is refined to seconds, and the time of the first epileptic seizure after 24 hours is obtained as the target time. By analogy, the time of the first epileptic seizure after 24 hours is used as the basis to determine the time of the first epileptic seizure after 24 hours in the next preset period of 24 hours, until all the time information of 40 days is searched and all the target times are obtained.

[0104] S902, performing a modulo operation on each target moment according to a preset period to obtain a modulo value corresponding to each target moment.

[0105] In this embodiment, a modulo operation is performed on each target time according to a preset period to obtain a modulo value corresponding to each target time, that is, a logical time corresponding to each target time in the preset period. For example, the above 4:00 modulo operation is 4:00, and 35h modulo operation on the preset period is 11:00.

[0106] S903: Determine the time parameter corresponding to each target moment in the preset period according to the residual value corresponding to each target moment, the preset period and pi.

[0107] In this embodiment, the product of the remainder corresponding to each target moment and pi may be determined first, and the ratio of the product to the preset period is used as the time parameter corresponding to each target moment in the preset period.

[0108] In a possible implementation, the product of the residual value, the preset value and pi corresponding to each target moment may be determined first, and the ratio of the product result to the preset period may be used as the time parameter corresponding to each target moment in the preset period.

[0109] S904, obtaining the coordinates of each target moment on the unit circle according to the trigonometric function value corresponding to the time parameter corresponding to each target moment.

[0110] In this embodiment, the time parameters corresponding to each target time are substituted into the trigonometric function to obtain the corresponding trigonometric function value, which is the coordinate of each target time on the unit circle. Get the coordinates v of each target on the unit circle at each moment i ,in, is the horizontal axis value, is the vertical coordinate value, is the time parameter.

[0111] In the embodiment of the present application, for each preset period, each target moment is determined according to the initial moment and the preset period in the time information, and a modulus operation is performed on each target moment according to the preset period to obtain a residual value corresponding to each target moment, and the time parameter corresponding to each target moment in the preset period is determined according to the residual value, the preset period and the pi corresponding to each target moment, and the coordinates of each target moment on the unit circle are obtained according to the trigonometric function value corresponding to the time parameter corresponding to each target moment. In this embodiment, a modulus operation is performed on each target moment to obtain a residual value corresponding to the target moment, that is, a logical moment corresponding to the target moment in the preset period, so that each target moment is mapped to the unit circle, and the coordinates of each target moment on the unit circle are obtained. The implementation method is simple and intuitive, and the efficiency of subsequent target period determination is improved.

[0112] In one embodiment, according to the residual value corresponding to each target moment, the preset period and the pi, the time parameter corresponding to each target moment in the preset period is determined, including:

[0113] Obtain the ratio of the residual value corresponding to each target moment to the preset period; and determine the time parameter corresponding to each target moment in the preset period according to the ratio corresponding to each target moment and pi.

[0114] In this embodiment, the formula Among them, mod(s i ,Tc) is the residual value corresponding to each target time, Tc is the preset period, s i For each target moment.

[0115] In an embodiment of the present application, by obtaining the ratio of the residual value corresponding to each target moment and the preset period, the time parameters corresponding to each target moment in the preset period are determined according to the ratio corresponding to each target moment and pi. The implementation method is simple and the efficiency of determining the time parameters is improved.

[0116] Fig.11 FIG. 1 is a flow chart of a method for obtaining a target prediction value in another embodiment. Fig.11 As shown, the embodiment of the present application relates to a possible implementation method of how to obtain a target prediction value according to a first prediction value and a second prediction value, including the following steps:

[0117] S1101, determine a first weight value corresponding to a first prediction value and a second weight value corresponding to a second prediction value.

[0118] In this embodiment, the first weight value corresponding to the first prediction value and the second weight value corresponding to the second prediction value can be determined according to the time difference between the current moment and the target time period. For example, if the time difference is greater than the preset time difference, it is determined that the second weight value is greater than the first weight value, and the sum of the first weight value and the second weight value is 1.

[0119] In a possible implementation, the first weight value and the second weight value may be directly preset, as long as the sum of the first weight value and the second weight value is 1.

[0120] S1102, performing weighted summation on the first prediction value and the second prediction value according to the first weight value and the second weight value to obtain a target prediction value.

[0121] In this embodiment, assuming that the first prediction value is 0.4, the first weight probability value is 0.8, the second prediction value is 0.7, and the first weight probability value is 0.2, the target prediction value is 0.46.

[0122] In the embodiment of the present application, a first weight value corresponding to the first prediction value and a second weight value corresponding to the second prediction value are determined, and the first prediction value and the second prediction value are weighted and summed according to the first weight value and the second weight value to obtain a target prediction value. In the present embodiment, the target prediction value is determined by combining the first prediction value and the second prediction value at the same time, so that the user can obtain the probability of a preset event occurring in real time within multiple days, which is more suitable for actual needs.

[0123] In one embodiment, determining a first weight value corresponding to a first prediction value and a second weight value corresponding to a second prediction value includes: determining a time difference between a current moment and a target time period; obtaining a tangent function value of a target ratio; the target ratio is a ratio between a first parameter and a first preset parameter; the first parameter is a sum of the time difference and the second preset parameter; determining a difference between 1 and the tangent function value as a first weight value; and determining the tangent function value as a second weight value.

[0124] In this embodiment, the above determination of the first weight value corresponding to the first prediction value and the second weight value corresponding to the second prediction value can be implemented by using the following formula:

[0125] The first weight value is: The second weight value is: Among them, Δt is the time difference, a is the second preset parameter, b / a is the first preset parameter, Δt / a is the first parameter, and a and b are used to adjust the shape of the target prediction value.

[0126] When the time difference is larger, the tangent function value is closer to 1, and when the time difference is smaller, the tangent function value is closer to 0. Therefore, the tangent function value is between 0 and 1, and the difference between 1 and the tangent function value is: 1-tangent function value = difference, and the difference is also between 0 and 1. The closer the tangent function value is to 1, the larger the second weight value of the second prediction value determined based on the time information is, and the closer the tangent function value is to 0, the larger the first weight value of the first prediction value determined based on the target parameter is.

[0127] In the embodiment of the present application, the time difference between the current moment and the target time period is determined, the tangent function value of the target ratio is obtained, the difference between 1 and the tangent function value is determined as the first weight value, and the tangent function value is determined as the second weight value. The first weight value and the second weight value are determined according to the time difference, and the influence of the target parameters and time information on the target prediction value under different time differences is considered, so that the determined first weight value and the second weight value are more accurate, thereby obtaining a more accurate target prediction value.

[0128] In one embodiment, Fig.12 As shown, when predicting the probability of occurrence of a preset event, it is first determined whether the user reports new data, that is, the time information of the new preset event and the target parameter sample. If new data is reported, the first prediction model is updated according to the new data to obtain a new first prediction model, thereby determining the first prediction value according to the target parameters and the new first prediction model, determining the second prediction value according to the new time information, and obtaining the target prediction value according to the first prediction value and the second prediction value.

[0129] If the user does not report new data, the first prediction value is determined according to the first prediction model and the target parameter, and the second prediction value is determined according to the time information, so as to obtain the target prediction value according to the first prediction value and the second prediction value.

[0130] When the amount of data of the acquired target parameter samples is small, if the time difference is less than the preset time, the first prediction model based on the target parameter samples can only predict the probability of the preset event occurring in a shorter time; if the time difference is not less than the preset time, the target information based on the historical time period can only predict the fixed probability of the preset event occurring in each future time period.

[0131] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0132] Based on the same inventive concept, the embodiment of the present application also provides a medical parameter determination device for implementing the medical parameter determination method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more medical parameter determination devices provided below can refer to the limitations of the medical parameter determination method above, and will not be repeated here.

[0133] In one embodiment, Fig.13 As shown, a device for determining a medical parameter is provided, comprising: a first acquisition module 11, a second acquisition module 12 and a third acquisition module 13, wherein:

[0134] A first acquisition module 11 is used to acquire a first prediction value according to a user's target parameter and a preset first prediction model; the target parameter includes a user's physiological parameter and a user's motion parameter; the first prediction value is used to characterize a first initial probability that a preset event will occur to the user in a target time period;

[0135] The second acquisition module 12 is used to acquire a second prediction value according to the time information of the preset event occurring to the user in the historical time period and the target period; the second prediction value is used to represent the second initial probability of the preset event occurring to the user in the target time period;

[0136] The third acquisition module 13 is used to acquire a target prediction value according to the first prediction value and the second prediction value; the target prediction value is used to represent the target probability of a preset event occurring to the user in a target time period.

[0137] In one embodiment, the second acquisition module includes:

[0138] A division unit, used to divide the time information into a plurality of periodic data based on a target period;

[0139] A first determining unit, configured to determine, based on the plurality of periodic data, a third probability value of a preset event occurring to a user within a unit time period in a target period;

[0140] The first acquisition unit is used to acquire the second prediction value according to each third probability value and the target time period.

[0141] In one embodiment, the first acquisition unit is further used to determine a target unit time period corresponding to the target time period; and determine the third probability value corresponding to the target unit time period as the second prediction value.

[0142] In one embodiment, the medical parameter determination device further comprises:

[0143] An execution module is used to execute a target operation for each preset period in a plurality of preset periods; the target operation includes: determining an average coordinate point of a plurality of target moments in each preset period on the unit circle according to the coordinates of a plurality of target moments on the unit circle when a preset event occurs to the user in the time information;

[0144] The first determination module is used to determine the preset period corresponding to the maximum distance between the average coordinate points corresponding to each preset period and the center of the unit circle as the target period.

[0145] In one embodiment, the medical parameter determination device further comprises:

[0146] The second determination module is used to determine each target time for each preset period according to the initial time in the time information and the preset period; the initial time is the initial time when the preset event of the user occurs in the time information;

[0147] A third determination module is used to perform a remainder operation on each target moment according to a preset period to obtain a remainder value corresponding to each target moment;

[0148] A fourth determination module, used to determine the time parameter corresponding to each target moment in the preset period according to the residual value corresponding to each target moment, the preset period and the pi;

[0149] The fourth acquisition module is used to acquire the coordinates of each target moment on the unit circle according to the trigonometric function value corresponding to the time parameter corresponding to each target moment.

[0150] In one embodiment, the fourth determining module includes:

[0151] A second acquisition unit is used to acquire the ratio of the residual value corresponding to each target moment to a preset period;

[0152] The second determining unit is used to determine the time parameter corresponding to each target moment in a preset period according to the ratio and pi corresponding to each target moment.

[0153] In one embodiment, the third acquisition module includes:

[0154] A third determining unit, used to determine a first weight value corresponding to the first prediction value and a second weight value corresponding to the second prediction value;

[0155] The fourth determination unit is used to perform weighted summation on the first prediction value and the second prediction value according to the first weight value and the second weight value to obtain a target prediction value.

[0156] In one embodiment, the third determination unit is also used to determine the time difference between the current moment and the target time period; obtain the tangent function value of the target ratio; the target ratio is the ratio between the first parameter and the first preset parameter; the first parameter is the sum of the time difference and the second preset parameter; the difference between 1 and the tangent function value is determined as the first weight value; and the tangent function value is determined as the second weight value.

[0157] Each module in the above-mentioned medical parameter determination device can be implemented in whole or in part by software, hardware or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0158] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Fig.14 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for determining a medical parameter is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0159] Those skilled in the art will understand that Fig.14 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0160] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0161] Obtaining a first prediction value according to a user's target parameter and a preset first prediction model; the target parameter includes a user's physiological parameter and a user's motion parameter; the first prediction value is used to characterize a first initial probability that a preset event will occur to the user in a target time period;

[0162] According to the time information of the preset event occurring in the historical time period of the user and the target period, a second prediction value is obtained; the second prediction value is used to represent the second initial probability of the preset event occurring in the target time period of the user;

[0163] A target prediction value is obtained according to the first prediction value and the second prediction value; the target prediction value is used to characterize the target probability of a preset event occurring to the user in a target time period.

[0164] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0165] Taking the target period as the unit, the time information is divided into multiple period data;

[0166] Determine, based on the plurality of periodic data, a third probability value of a preset event occurring to the user within a unit time period in the target period;

[0167] A second prediction value is obtained according to each third probability value and the target time period.

[0168] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0169] Determine the target unit time period corresponding to the target time period;

[0170] The third probability value corresponding to the target unit time period is determined as the second prediction value.

[0171] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0172] For each preset period in the plurality of preset periods, a target operation is performed; the target operation includes: determining an average coordinate point of the plurality of target moments in each preset period on the unit circle according to the coordinates of the plurality of target moments on the unit circle when the user has a preset event in the time information;

[0173] The preset period corresponding to the maximum distance between the average coordinate points corresponding to each preset period and the center of the unit circle is determined as the target period.

[0174] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0175] For each preset period, each target time is determined according to the initial time in the time information and the preset period; the initial time is the initial time when the preset event occurs in the time information of the user;

[0176] Perform a remainder operation on each target moment according to a preset period to obtain a remainder value corresponding to each target moment;

[0177] According to the residual value corresponding to each target moment, the preset period and the pi, the time parameter corresponding to each target moment in the preset period is determined;

[0178] According to the trigonometric function value corresponding to the time parameter corresponding to each target moment, the coordinates of each target moment on the unit circle are obtained.

[0179] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0180] Obtain the ratio of the residual value corresponding to each target moment to the preset period;

[0181] According to the ratio and pi corresponding to each target moment, the time parameter corresponding to each target moment in the preset period is determined.

[0182] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0183] Determine a first weight value corresponding to the first prediction value and a second weight value corresponding to the second prediction value;

[0184] The first prediction value and the second prediction value are weighted and summed according to the first weight value and the second weight value to obtain a target prediction value.

[0185] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0186] Determine the time difference between the current time and the target time period;

[0187] Obtaining a tangent function value of a target ratio; the target ratio is a ratio between a first parameter and a first preset parameter; the first parameter is a sum of a time difference and a second preset parameter;

[0188] The difference between 1 and the tangent function value is determined as the first weight value;

[0189] The tangent function value is determined as the second weight value.

[0190] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0191] Obtaining a first prediction value according to a user's target parameter and a preset first prediction model; the target parameter includes a user's physiological parameter and a user's motion parameter; the first prediction value is used to characterize a first initial probability that a preset event will occur to the user in a target time period;

[0192] According to the time information of the preset event occurring in the historical time period of the user and the target period, a second prediction value is obtained; the second prediction value is used to represent the second initial probability of the preset event occurring in the target time period of the user;

[0193] A target prediction value is obtained according to the first prediction value and the second prediction value; the target prediction value is used to characterize the target probability of a preset event occurring to the user in a target time period.

[0194] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0195] Taking the target period as the unit, the time information is divided into multiple period data;

[0196] Determine, based on the plurality of periodic data, a third probability value of a preset event occurring to the user within a unit time period in the target period;

[0197] A second prediction value is obtained according to each third probability value and the target time period.

[0198] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0199] Determine the target unit time period corresponding to the target time period;

[0200] The third probability value corresponding to the target unit time period is determined as the second prediction value.

[0201] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0202] For each preset period in the plurality of preset periods, a target operation is performed; the target operation includes: determining an average coordinate point of the plurality of target moments in each preset period on the unit circle according to the coordinates of the plurality of target moments on the unit circle when the user has a preset event in the time information;

[0203] The preset period corresponding to the maximum distance between the average coordinate points corresponding to each preset period and the center of the unit circle is determined as the target period.

[0204] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0205] For each preset period, each target time is determined according to the initial time in the time information and the preset period; the initial time is the initial time when the preset event occurs in the time information of the user;

[0206] Perform a remainder operation on each target moment according to a preset period to obtain a remainder value corresponding to each target moment;

[0207] According to the residual value corresponding to each target moment, the preset period and the pi, the time parameter corresponding to each target moment in the preset period is determined;

[0208] According to the trigonometric function value corresponding to the time parameter corresponding to each target moment, the coordinates of each target moment on the unit circle are obtained.

[0209] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0210] Obtain the ratio of the residual value corresponding to each target moment to the preset period;

[0211] According to the ratio and pi corresponding to each target moment, the time parameter corresponding to each target moment in the preset period is determined.

[0212] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0213] Determine a first weight value corresponding to the first prediction value and a second weight value corresponding to the second prediction value;

[0214] The first prediction value and the second prediction value are weighted and summed according to the first weight value and the second weight value to obtain a target prediction value.

[0215] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0216] Determine the time difference between the current time and the target time period;

[0217] Obtaining a tangent function value of a target ratio; the target ratio is a ratio between a first parameter and a first preset parameter; the first parameter is a sum of a time difference and a second preset parameter;

[0218] The difference between 1 and the tangent function value is determined as the first weight value;

[0219] The tangent function value is determined as the second weight value.

[0220] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0221] Obtaining a first prediction value according to a user's target parameter and a preset first prediction model; the target parameter includes a user's physiological parameter and a user's motion parameter; the first prediction value is used to characterize a first initial probability that a preset event will occur to the user in a target time period;

[0222] According to the time information of the preset event occurring in the historical time period of the user and the target period, a second prediction value is obtained; the second prediction value is used to represent the second initial probability of the preset event occurring in the target time period of the user;

[0223] A target prediction value is obtained according to the first prediction value and the second prediction value; the target prediction value is used to characterize the target probability of a preset event occurring to the user in a target time period.

[0224] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0225] Taking the target period as the unit, the time information is divided into multiple period data;

[0226] Determine, based on the plurality of periodic data, a third probability value of a preset event occurring to the user within a unit time period in the target period;

[0227] A second prediction value is obtained according to each third probability value and the target time period.

[0228] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0229] Determine the target unit time period corresponding to the target time period;

[0230] The third probability value corresponding to the target unit time period is determined as the second prediction value.

[0231] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0232] For each preset period in the plurality of preset periods, a target operation is performed; the target operation includes: determining an average coordinate point of the plurality of target moments in each preset period on the unit circle according to the coordinates of the plurality of target moments on the unit circle when the user has a preset event in the time information;

[0233] The preset period corresponding to the maximum distance between the average coordinate points corresponding to each preset period and the center of the unit circle is determined as the target period.

[0234] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0235] For each preset period, each target time is determined according to the initial time in the time information and the preset period; the initial time is the initial time when the preset event occurs in the time information of the user;

[0236] Perform a remainder operation on each target moment according to a preset period to obtain a remainder value corresponding to each target moment;

[0237] According to the residual value corresponding to each target moment, the preset period and the pi, the time parameter corresponding to each target moment in the preset period is determined;

[0238] According to the trigonometric function value corresponding to the time parameter corresponding to each target moment, the coordinates of each target moment on the unit circle are obtained.

[0239] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0240] Obtain the ratio of the residual value corresponding to each target moment to the preset period;

[0241] According to the ratio and pi corresponding to each target moment, the time parameter corresponding to each target moment in the preset period is determined.

[0242] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0243] Determine a first weight value corresponding to the first prediction value and a second weight value corresponding to the second prediction value;

[0244] The first prediction value and the second prediction value are weighted and summed according to the first weight value and the second weight value to obtain a target prediction value.

[0245] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0246] Determine the time difference between the current time and the target time period;

[0247] Obtaining a tangent function value of a target ratio; the target ratio is a ratio between a first parameter and a first preset parameter; the first parameter is a sum of a time difference and a second preset parameter;

[0248] The difference between 1 and the tangent function value is determined as the first weight value;

[0249] The tangent function value is determined as the second weight value.

[0250] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0251] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0252] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0253] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for determining a medical parameter, characterized in that: The method comprises: Obtaining a first prediction value according to a user's target parameter and a preset first prediction model; the target parameter includes a physiological parameter of the user and a motion parameter of the user; the first prediction value is used to characterize a first initial probability of a preset event occurring to the user in a target time period; According to the time information of the preset event occurring to the user in the historical time period and the target period, a second prediction value is obtained; the second prediction value is used to represent a second initial probability of the preset event occurring to the user in the target time period; A target prediction value is obtained according to the first prediction value and the second prediction value; the target prediction value is used to characterize the target probability of the preset event occurring to the user in the target time period.

2. The method according to claim 1, characterized in that The acquiring a second prediction value according to the time information of the preset event occurring in the historical time period by the user and the target period includes: Taking the target period as a unit, dividing the time information into a plurality of period data; Determine, according to the plurality of periodic data, a third probability value of the occurrence of the preset event for the user within a unit time period in the target period; The second prediction value is obtained according to each of the third probability values ​​and the target time period.

3. The method according to claim 2, characterized in that The acquiring the second prediction value according to each of the third probability values ​​and the target time period includes: Determine a target unit time period corresponding to the target time period; The third probability value corresponding to the target unit time period is determined as the second prediction value.

4. The method according to any one of claims 1 to 3, characterized in that: The method further comprises: For each preset period in a plurality of preset periods, a target operation is performed; the target operation comprises: determining an average coordinate point of the plurality of target moments in each preset period on the unit circle according to the coordinates of the plurality of target moments on the unit circle at which the preset event occurs to the user in the time information; The preset period corresponding to the maximum distance between the average coordinate points corresponding to the preset periods and the center of the unit circle is determined as the target period.

5. The method according to claim 4, characterized in that The method further comprises: For each of the preset periods, each of the target moments is determined according to the initial moment in the time information and the preset period; the initial moment is the initial moment when the preset event occurs to the user in the time information; Performing a modulo operation on each of the target moments according to the preset period to obtain a modulo value corresponding to each of the target moments; Determine, according to the residual value corresponding to each of the target moments, the preset period and the pi, the time parameter corresponding to each of the target moments in the preset period; The coordinates of each target moment on the unit circle are obtained according to the trigonometric function value corresponding to the time parameter corresponding to each target moment.

6. The method according to claim 5, characterized in that The determining, according to the residual value corresponding to each target moment, the preset period and the pi, the time parameter corresponding to each target moment in the preset period includes: Obtaining the ratio of the residual value corresponding to each of the target moments to the preset period; According to the ratio corresponding to each of the target moments and the pi, the time parameter corresponding to each of the target moments in the preset period is determined.

7. The method according to any one of claims 1 to 3, characterized in that: The obtaining a target prediction value according to the first prediction value and the second prediction value includes: Determine a first weight value corresponding to the first prediction value and a second weight value corresponding to the second prediction value; The first predicted value and the second predicted value are weighted and summed according to the first weight value and the second weight value to obtain the target predicted value.

8. The method according to claim 7, characterized in that The determining a first weight value corresponding to the first prediction value and a second weight value corresponding to the second prediction value includes: Determine the time difference between the current time and the target time period; Obtaining a tangent function value of a target ratio; the target ratio is a ratio between a first parameter and a first preset parameter; the first parameter is a sum of the time difference and the second preset parameter; Determine the difference between 1 and the tangent function value as the first weight value; The tangent function value is determined as the second weight value.

9. A device for determining a medical parameter, characterized in that: The device comprises: A first acquisition module, configured to acquire a first prediction value according to a user's target parameter and a preset first prediction model; the target parameter includes a physiological parameter of the user and a motion parameter of the user; the first prediction value is used to characterize a first initial probability that a preset event occurs to the user in a target time period; A second acquisition module is used to acquire a second prediction value according to the time information of the preset event occurring to the user in the historical time period and the target period; the second prediction value is used to represent a second initial probability of the preset event occurring to the user in the target time period; The third acquisition module is used to acquire a target prediction value according to the first prediction value and the second prediction value; the target prediction value is used to represent the target probability of the preset event occurring to the user in the target time period.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

12. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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