Intelligent water drinking reminding method and device, computer equipment and readable storage medium

Abstract

The invention relates to an intelligent water drinking reminding method and device, computer equipment and a readable storage medium. The method comprises the following steps: traversing a target object in unit time after drinking water, and obtaining a body temperature change value of the target object in the traversed unit time; according to the body temperature change value and the duration of the traversed unit time, determining the body temperature change rate of the target object in the traversed unit time; according to the body temperature change value and the body temperature change rate, determining body temperature adjustment parameters of the target object in the traversed unit time; when the body temperature adjusting parameters of the target object in the traversed unit time meet the preset water drinking condition, traversing is stopped, and the traversed unit time serves as the water drinking waiting time. By adopting the method, invalid and even harmful early or delayed water drinking behaviors can be avoided, formation of good living habits can be promoted, and long-term health maintenance and enhancement of personal immunity are further supported.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to an intelligent drinking water reminder method, device, computer equipment, computer-readable storage medium and computer program product. Background Art

[0002] In modern society, personal health management and immune system enhancement have gradually become the focus of people's attention. Basal body temperature is one of the important indicators reflecting human health status. The regulation of basal body temperature is of great significance to maintaining physical health and enhancing immunity. However, there is a lack of methods in the market that can monitor and intelligently regulate human basal body temperature in real time to achieve optimal management of the immune system.

[0003] The traditional method mainly relies on users to manually record body temperature and water intake and make adjustments based on past experience. However, this method is not only time-consuming and laborious, but also difficult to ensure the scientificity and accuracy of the adjustment. With the continuous development of science and technology, it has become increasingly feasible and urgent to develop a method that can optimize basal body temperature through real-time monitoring, accurate prediction and scientific regulation. Summary of the invention

[0004] Based on this, it is necessary to provide an intelligent drinking water reminder method, device, computer equipment, computer-readable storage medium and computer program product that can monitor in real time, accurately predict and scientifically regulate to optimize basal body temperature in response to the above technical problems.

[0005] In a first aspect, the present application provides an intelligent drinking water reminder method, comprising:

[0006] Traversing the unit time of the target object after drinking the water, and obtaining the temperature change value of the target object within the traversed unit time;

[0007] Determine the temperature change rate of the target object within the unit time traversed according to the temperature change value and the duration of the unit time traversed to;

[0008] Determine the temperature adjustment parameter of the target object within the unit time of traversal according to the temperature change value and the temperature change rate;

[0009] When the body temperature regulation parameter of the target object in the traversed unit time meets the preset drinking water condition, the traversal is stopped, and the traversed unit time is used as the waiting drinking water time.

[0010] In one embodiment, the preset drinking water condition is a body temperature parameter threshold; when the body temperature adjustment parameter of the target object in the traversed unit time meets the preset drinking water condition, the traversal is stopped, and the traversed unit time is used as the waiting drinking water time, including:

[0011] The body temperature regulation parameter of the target object in the traversed unit time is matched with the body temperature parameter threshold to obtain a parameter matching result; when the parameter matching result indicates that the body temperature regulation parameter of the target object in the traversed unit time is greater than the body temperature parameter threshold, the traversal is stopped, and the traversed unit time is used as the waiting time for drinking water.

[0012] In one embodiment, determining the temperature adjustment parameter of the target object within the unit time of traversing to according to the temperature change value and the temperature change rate includes:

[0013] For each of the multiple unit times traversed to, determine the adjacent unit time behind the unit time traversed to; determine the rate of change of the temperature change rate of the target object in the unit time traversed to according to the length of the unit time traversed to, the temperature change rate of the target object in the unit time traversed to, and the temperature change rate of the adjacent unit time; determine the temperature regulation parameters of the target object in the unit time traversed to based on the length of the unit time traversed to, the temperature change rate of the target object in the unit time traversed to, and the temperature change rate of the target object in the unit time traversed to.

[0014] In one embodiment, the body temperature adjustment parameter of the target object in the unit time traversed to is determined based on the duration of the unit time traversed to, the body temperature change rate of the target object in the unit time traversed to, and the change rate of the body temperature change rate of the target object in the unit time traversed to, and is implemented by the following calculation formula:

[0015] d 2 T / dt 2 -μ(1-T 2 )dT / dt+T=0

[0016] Among them, d 2 T / dt 2 is the rate of change of the target object's body temperature change rate during the unit time traversed to, dT / dt is the rate of change of the target object's body temperature change rate during the unit time traversed to, μ is the body temperature regulation parameter of the target object during the unit time traversed to, T is the body temperature change value of the target object during the unit time traversed to, and t is the duration of the unit time traversed to.

[0017] In one of the embodiments, the body temperature value, body temperature change value, body temperature change rate and change rate of the body temperature change rate of the target object during the waiting time for drinking water are obtained; based on the body temperature value, body temperature change value, body temperature change rate and change rate of the body temperature change rate of the target object during the waiting time for drinking water, an image to be detected is generated; a drinking water prediction model is obtained, and the image to be detected is processed by the drinking water prediction model to obtain the drinking water amount and drinking water temperature required by the target object.

[0018] In one embodiment, the drinking water detection model includes at least a backbone network, a drinking water volume branch module, and a drinking water temperature branch module; the drinking water prediction model is used to process the image to be detected to obtain the drinking water volume and drinking water temperature required by the target object, including:

[0019] Acquire a water intake weight matrix and a water intake temperature weight matrix; perform feature extraction on the image to be detected through the backbone network to obtain an initial feature map; perform linear transformation and nonlinear transformation on the initial feature map corresponding to the water intake weight matrix through the water intake branch module to obtain a water intake convolution feature map, and determine a water intake pooling feature map according to the maximum eigenvalue of the water intake convolution feature map on each feature channel; perform linear transformation and nonlinear transformation on the initial feature map corresponding to the water intake temperature weight matrix through the water intake temperature branch module to obtain a water intake temperature convolution feature map, According to the average eigenvalue of the drinking water temperature convolution feature map on each feature channel, a drinking water temperature pooling feature map is determined; the drinking water amount pooling feature map and the drinking water temperature pooling feature map are flattened and straightened respectively to obtain a drinking water amount feature vector and a drinking water temperature feature vector; the drinking water amount feature vector is converted into a drinking water amount probability distribution feature vector, and the drinking water temperature feature vector is converted into a drinking water temperature probability distribution feature vector; according to the drinking water amount probability distribution feature vector, the drinking water amount required by the target object is determined, and according to the drinking water temperature probability distribution feature vector, the drinking water temperature required by the target object is determined.

[0020] In one of the embodiments, the body temperature data fed back by the target object regarding the drinking water amount and the drinking water temperature is received; the body temperature parameter threshold is updated according to the body temperature data fed back by the target object regarding the drinking water amount and the drinking water temperature to obtain an updated body temperature parameter threshold; and the updated body temperature parameter threshold is used as the preset drinking water condition for the target object after the waiting time for drinking water.

[0021] In a second aspect, the present application also provides an intelligent water drinking reminder device, comprising:

[0022] An acquisition module is used to traverse the unit time of the target object after drinking the water, and obtain the temperature change value of the target object within the traversed unit time;

[0023] A first determination module is used to determine the body temperature change rate of the target object within the unit time traversed to according to the body temperature change value and the duration of the unit time traversed to;

[0024] A second determination module is used to determine the temperature adjustment parameter of the target object within the unit time of traversal to according to the temperature change value and the temperature change rate;

[0025] The stopping module is used to stop traversing when the body temperature adjustment parameter of the target object in the traversed unit time meets the preset drinking water condition, and use the traversed unit time as the waiting drinking water time.

[0026] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0027] Traversing the unit time of the target object after drinking the water, and obtaining the temperature change value of the target object within the traversed unit time;

[0028] Determine the temperature change rate of the target object within the unit time traversed according to the temperature change value and the duration of the unit time traversed to;

[0029] Determine the temperature adjustment parameter of the target object within the unit time of traversal according to the temperature change value and the temperature change rate;

[0030] When the body temperature regulation parameter of the target object in the traversed unit time meets the preset drinking water condition, the traversal is stopped, and the traversed unit time is used as the waiting drinking water time.

[0031] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0032] Traversing the unit time of the target object after drinking the water, and obtaining the temperature change value of the target object within the traversed unit time;

[0033] Determine the temperature change rate of the target object within the unit time traversed according to the temperature change value and the duration of the unit time traversed to;

[0034] Determine the temperature adjustment parameter of the target object within the unit time of traversal according to the temperature change value and the temperature change rate;

[0035] When the body temperature regulation parameter of the target object in the traversed unit time meets the preset drinking water condition, the traversal is stopped, and the traversed unit time is used as the waiting drinking water time.

[0036] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0037] Traversing the unit time of the target object after drinking the water, and obtaining the temperature change value of the target object within the traversed unit time;

[0038] Determine the temperature change rate of the target object within the unit time traversed according to the temperature change value and the duration of the unit time traversed to;

[0039] Determine the temperature adjustment parameter of the target object within the unit time of traversal according to the temperature change value and the temperature change rate;

[0040] When the body temperature regulation parameter of the target object in the traversed unit time meets the preset drinking water condition, the traversal is stopped, and the traversed unit time is used as the waiting drinking water time.

[0041] The above-mentioned intelligent drinking water reminder method, device, computer equipment, computer-readable storage medium and computer program product, by traversing the unit time of the target object after drinking water, obtains the temperature change value of the target object within the traversed unit time; and achieves detailed tracking of the user's body temperature changes after drinking water, and can more accurately capture the trend of body temperature changes over time, thereby ensuring the timeliness and accuracy of the data.

[0042] According to the temperature change value and the duration of the unit time traversed to, the temperature change rate of the target object in the unit time traversed to is determined; understanding the temperature change rate can help the system better understand the user's physiological response pattern and the degree of influence of drinking water on his body temperature. The calculation of the temperature change rate provides a basis for scientifically evaluating the timing and amount of drinking water, making the adjustment measures more targeted and effective.

[0043] According to the temperature change value and the temperature change rate, the temperature adjustment parameters of the target object in the unit time traversed to are determined; the specific temperature change information and the change rate are combined to set personalized temperature adjustment parameters. These parameters reflect the user's current physical state and its response to specific water volume, water temperature and intake time. Based on this, more reasonable suggestions or automatic adjustment strategies can be made to achieve the best health management and immunity improvement effects.

[0044] When the target object's body temperature regulation parameters meet the preset drinking conditions within the traversed unit time, the traversal is stopped, and the traversed unit time is used as the waiting time for drinking water. Once the appropriate time to drink water is determined (that is, when the body temperature regulation parameters meet the preset ideal range), action will be taken immediately or corresponding guidance will be provided to the user. This not only avoids ineffective or even harmful premature or delayed drinking behavior, but also promotes the formation of good living habits, further supporting long-term health maintenance and personal immunity enhancement. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 A diagram showing an application environment of an intelligent water drinking reminder method in an embodiment;

[0047] Figure 2 A schematic diagram of a flow chart of an intelligent water drinking reminder method in one embodiment;

[0048] Figure 3 A schematic diagram of a flow chart of an intelligent water drinking reminder method in another embodiment;

[0049] Figure 4 It is a structural block diagram of an intelligent water drinking reminder device in one embodiment;

[0050] Figure 5 It is a structural block diagram of an intelligent water drinking reminder device in another embodiment;

[0051] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0052] 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.

[0053] The intelligent drinking water reminder method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The terminal 102 is used to generate an intelligent drinking water reminder request, and send the intelligent drinking water reminder request to the server 104, so that the server 104 stops traversing when the temperature adjustment parameter of the target object in the unit time traversed to meets the preset drinking water condition, and uses the unit time traversed to as the waiting drinking water time. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, projection devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. Head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. The server 104 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0054] In an exemplary embodiment, Figure 2 As shown, a smart drinking water reminder method is provided, which is applied to Figure 1 The server 104 in the example is used as an example to illustrate, including the following steps 202 to 208. Among them:

[0055] Step 202, traverse the unit time of the target object after drinking the water, and obtain the temperature change value of the target object in the traversed unit time.

[0056] The target object refers to the user who uses the intelligent drinking water regulation method. By monitoring the user's various physiological parameters (such as body temperature, heart rate, etc.) and environmental factors (such as ambient temperature), personalized health management suggestions are provided to the user.

[0057] Unit time refers to the time interval used to monitor and analyze the user's body temperature changes in segments. For example, every minute or every five minutes can be used as a unit time for data collection and processing. By continuously observing the user's physiological state within each unit time period, more detailed body temperature fluctuation patterns can be captured.

[0058] The temperature change value refers to the change in the user's body temperature in a specific unit time relative to the previous unit time or baseline level. Specifically, it is the difference between the body temperature measurement result at the current unit time point and the body temperature measurement result at the previous unit time point. This indicator is very important for understanding how drinking behavior affects human body temperature, and is also the basis for subsequent calculations of the body temperature change rate and determination of body temperature regulation parameters.

[0059] Specifically, the first step is to define the unit time and the monitoring start point, set a reasonable unit time length (for example, every 5 minutes or every 10 minutes), which will be used as the time interval for analyzing the user's body temperature changes. And record the specific time point when the user actually drinks the recommended amount of water. This time point will become the starting point for subsequent traversal analysis.

[0060] The second step is to start traversing the unit time, starting from the moment the user drinks water, and set the first unit time window (for example, the 0th to 5th minute after drinking water). In each unit time, the user's body temperature is continuously monitored through the sensor and the reading is saved.

[0061] The third step is to calculate the temperature change value within a unit time. When the end of the unit time is reached (for example, the 5th minute), record the user's body temperature at this time. Subtract this temperature value from the end of the previous unit time (if it is the first unit time, it may be the baseline body temperature before drinking water) or the body temperature at the time of drinking water to obtain the temperature change value within this unit time. The calculated temperature change value is saved together with the corresponding timestamp for further analysis.

[0062] The fourth step is to iterate through more unit times. If the stop condition has not been reached (i.e., the body temperature regulation parameters meet the preset drinking conditions), the current unit time is advanced by one unit time length (e.g., from the 5th minute to the 10th minute), and the second and third steps are repeated. This process continues until a unit time is found during which the body temperature regulation parameters meet the preset standards.

[0063] Step 204, determining the temperature change rate of the target object within the unit time traversed according to the temperature change value and the duration of the unit time traversed to.

[0064] The unit time refers to a fixed time period set to monitor the user's physiological response after drinking water. For example, 1 minute, 5 minutes, or 10 minutes can be set as a unit time. The user's body temperature data is collected during this fixed time period and analyzed accordingly. It can be understood that the choice of unit time depends on the required data resolution and the requirements for real-time performance; a shorter unit time can provide more detailed change trends, but may require more computing resources.

[0065] The temperature change rate refers to the speed at which body temperature changes in a given unit of time. It can be calculated by dividing the temperature change value by the corresponding unit time length (temperature change rate = temperature change value / unit time length). This indicator reflects the speed of the trend of body temperature change over time, which is very important for understanding how drinking water affects human body temperature. The temperature change rate helps to identify situations where the body temperature rises or falls rapidly, thereby helping the system decide when and how to intervene to achieve the ideal temperature regulation effect.

[0066] Specifically, first obtain the temperature change value, and obtain the user's temperature reading at the end of a specific unit time from the sensor. Compare the body temperature at the end of the current unit time with the body temperature at the end of the previous unit time (or the basal body temperature before drinking water) to obtain the change in body temperature during this period. The temperature change value in this step is expressed as , where Tend is the body temperature at the end of the current unit time, and Tstart is the body temperature at the end of the previous unit time.

[0067] Then confirm the unit time length and make sure that the specific duration of each unit time has been defined, such as 5 minutes, 10 minutes, etc. Optionally, if the actual duration is slightly deviated due to some reasons during the actual monitoring process, the actual time interval needs to be recorded for subsequent accurate calculation of the temperature change rate.

[0068] Next, calculate the temperature change rate, using the known temperature change value ΔT and the corresponding unit time length Δt to calculate the temperature change rate. The temperature change rate R can be obtained by the following formula: R=ΔT\Δt, where ΔT is the temperature change value and Δt is the actual length of the unit time. In order to facilitate comparison between different time periods, it may be necessary to standardize the calculated temperature change rate, such as converting it into changes per minute or per hour.

[0069] Finally, the temperature change rate is recorded and analyzed, and the calculated temperature change rate is stored together with the corresponding time point for subsequent data analysis. Based on the temperature change rate in multiple consecutive unit times, the user's temperature change trend can be further analyzed to identify any significant change patterns or abnormalities.

[0070] Step 206, determining the temperature adjustment parameter of the target object in the unit time traversed to according to the temperature change value and the temperature change rate.

[0071] Among them, the body temperature regulation parameters refer to a set of data used to evaluate or guide body temperature regulation determined based on the target object's body temperature change value and body temperature change rate. It can be understood that these parameters are obtained by analyzing the user's physiological data (such as body temperature, heart rate, ambient temperature, water intake, and exercise volume, etc.) based on the Vanderpol oscillator equation and machine learning algorithms.

[0072] Specifically, the first step is to analyze the trend of body temperature changes, and use the Vanderpol oscillator equation combined with historical body temperature data to predict the possible trend of body temperature changes in the future. Combined with machine learning algorithms, the current body temperature change value and change rate are analyzed and compared with the patterns in previous similar situations to find out the rules.

[0073] Then determine the temperature regulation parameters, based on the temperature change value and temperature change rate, combined with the prediction results of the Vanderpol oscillator equation and the evaluation of the drinking water intervention effect by the machine learning model, set a series of evaluation indicators as the temperature regulation parameters. It can be understood that the temperature regulation parameters can be set according to actual needs. For example:

[0074] Optionally, the temperature regulation parameter is the expected body temperature, which predicts the future body temperature level based on the current body temperature change trend.

[0075] Optionally, the temperature regulation parameter is the temperature adjustment speed, which is the time required for the body temperature to reach the expected healthy range from the current value.

[0076] Optionally, the temperature regulation parameter is an intervention effectiveness score that quantifies the effect of drinking water on temperature regulation, such as whether the temperature drop meets the expected value.

[0077] Step 208, when the temperature regulation parameter of the target object in the traversed unit time meets the preset drinking water condition, the traversal is stopped, and the traversed unit time is used as the waiting drinking water time.

[0078] The preset drinking water conditions refer to a series of standards or thresholds set according to the user's health management and body temperature regulation goals. When the conditions are met, it means that drinking water can most effectively help the user achieve their health management goals, especially the optimization and regulation of basal body temperature. It can be understood that the preset drinking water conditions can be set according to actual needs. For example:

[0079] Optionally, the body temperature changes within a certain range, such as a decrease in body temperature to a level believed to be beneficial for the health of the immune system.

[0080] Optionally, the body temperature change rate (i.e., the rate at which the body temperature changes over time) meets certain requirements, such as the rate at which the body temperature drops is as expected.

[0081] Optionally, other physiological parameters (such as heart rate, ambient temperature, etc.) are in an ideal state or have no negative impact on body temperature regulation.

[0082] Optionally, the body temperature prediction result calculated based on the Vanderpol oscillator equation and the machine learning algorithm indicates that it is the best time to drink water.

[0083] The time to drink water refers to the best time to drink water determined after traversal analysis. Specifically, after drinking water, the system will monitor the user's body temperature changes per unit time (for example, per minute) and calculate the corresponding body temperature change rate. When the monitored data meets the preset drinking conditions, further traversal is stopped, and the corresponding unit time at this time is marked as the best drinking time recommended to the user. This time point is to ensure that the user can consume water at the most appropriate time to achieve effective regulation of basal body temperature and promote improvement of overall health.

[0084] Specifically, first check whether the preset drinking water conditions are met, and compare the body temperature regulation parameters in the current unit time with the ideal health management standards or thresholds preset by the system. Then judge and record the time to drink water. If the body temperature regulation parameters in the current unit time meet the preset drinking water conditions, it indicates that this is the ideal time to drink water. Stop further traversal process. Mark the current traversed unit time as the time to drink water and record it. Finally, the user interface module displays the time to drink water and the recommended specific drinking water temperature and amount to the user. Optionally, allow users to manually adjust drinking water parameters according to personal circumstances, and incorporate user feedback into subsequent model optimization.

[0085] In one of the embodiments, the temperature regulation parameter of the target object in the traversed unit time is matched with the temperature parameter threshold to obtain a parameter matching result; when the parameter matching result indicates that the temperature regulation parameter of the target object in the traversed unit time is greater than the temperature parameter threshold, the traversal is stopped, and the traversed unit time is used as the waiting time for drinking water.

[0086] The temperature parameter threshold refers to a set of values ​​preset by the system to determine whether the user's body temperature has reached the ideal health standard. It can be understood that the temperature parameter threshold is used to ensure that drinking water intervention can effectively and safely regulate body temperature to achieve the purpose of optimizing immune function.

[0087] The parameter matching result is the result obtained by comparing the actual measured temperature regulation parameters with the preset temperature parameter thresholds. This process involves evaluating whether the temperature regulation parameters meet the preset threshold conditions. If the temperature regulation parameters exceed the preset thresholds (for example, the temperature drops too much or the temperature changes too quickly), it is considered that the health requirements are not met; conversely, if the temperature regulation parameters remain within the ideal range, it is considered to meet the health management standards.

[0088] Specifically, data collection is first performed, and the user's physiological parameters are monitored in real time through sensors (such as body temperature sensors, heart rate sensors, etc.). After the user drinks a certain amount of water, the user's body temperature changes in each unit time (for example, every minute) are recorded.

[0089] Then the temperature regulation parameters are calculated. For each unit time point, the temperature change value (i.e. the temperature difference between the current time point and the previous time point) is calculated. The temperature change rate is calculated based on the temperature change value and the unit time length. The Vanderpol oscillator equation is used to predict future temperature trends, and the data are analyzed in combination with machine learning algorithms to determine the temperature regulation parameters within the unit time.

[0090] Next, the temperature parameter thresholds are set, and a series of temperature parameter thresholds are pre-set. Optionally, these thresholds are ideal health ranges defined based on medical standards or research results. And parameter matching is performed, and the temperature regulation parameters calculated in each unit time are compared with the preset temperature parameter thresholds. If a temperature regulation parameter exceeds the corresponding threshold (for example, the temperature change rate is greater than the maximum allowable change rate), it is considered that the time point has been reached to stop traversal and recommend drinking water. It can be understood that the "matching" in this step means checking whether the actually measured temperature regulation parameters meet or exceed the pre-set health management standards.

[0091] Then judge the parameter matching results and analyze the matching results. If it is found that the body temperature regulation parameters have exceeded the preset threshold, it means that the current unit time is a critical node and further traversal should be stopped. For example, if the body temperature change rate exceeds the preset maximum value, it indicates that the body temperature regulation effect is significant and has reached the ideal state.

[0092] Finally, the time to drink water is determined. When the parameter matching result shows that the body temperature regulation parameter is greater than the preset threshold, the system marks the current traversed unit time as the time to drink water. It can be understood that the time to drink water is the best time for the system to recommend the user to drink water next time. Drinking water at this time is considered to be the most effective way to help the user maintain a healthy body temperature level. Optionally, the recommended time to drink water and related drinking water suggestions, such as the recommended drinking water temperature and amount, are displayed through the user interface. Allow users to view specific monitoring data, prediction results, and personalized drinking water suggestions, and provide manual adjustment options.

[0093] Since the temperature parameter threshold is pre-set, if the temperature regulation parameter exceeds the preset threshold, it may indicate that the body is undergoing some adverse changes. Timely detection of these situations can help users take measures to avoid physical discomfort or other health problems caused by improper drinking.

[0094] In one of the embodiments, for each of a plurality of unit times traversed to, an adjacent unit time located behind the unit time traversed to is determined; based on the length of the unit time traversed to, the temperature change rate of the target object in the unit time traversed to, and the temperature change rate in the adjacent unit time, the rate of change of the temperature change rate of the target object in the unit time traversed to is determined; based on the length of the unit time traversed to, the temperature change rate of the target object in the unit time traversed to, and the rate of change of the temperature change rate of the target object in the unit time traversed to, the temperature regulation parameters of the target object in the unit time traversed to are determined.

[0095] Among them, the adjacent unit time refers to the next unit time immediately after the unit time currently traversed. For example, if the current traversed time period is from the nth minute to the n+1th minute, then the adjacent unit time is from the n+1th minute to the n+2th minute. It can be understood that by comparing the data of the current unit time and its adjacent unit time, the trend of body temperature change can be analyzed more carefully, thereby providing more accurate information for the subsequent calculation of body temperature regulation parameters.

[0096] The rate of change of the body temperature rate of change refers to how the body temperature rate of change itself changes over time. It describes how the rate of change of body temperature accelerates or decelerates over time. In short, it is the first-order derivative of the body temperature rate of change. For example, if the body temperature rate of change is 0.1°C / minute in a certain unit time, and the body temperature rate of change becomes 0.2°C / minute in the next unit time, then the rate of change of the body temperature rate of change is (0.2 - 0.1) / unit time length = 0.1°C / minute.

[0097] Specifically, the adjacent unit time is first determined, and multiple unit times (for example, every minute) after the user drinks the water are processed one by one. For each unit time currently traversed, the adjacent unit time behind it is found. For example, if the current unit time is from the tth minute to the t+1th minute, the adjacent unit time is from the t+1th minute to the t+2th minute.

[0098] Then calculate the rate of change of body temperature change rate. Based on the user's body temperature change value and unit time length in the current unit time, calculate the body temperature change rate R in the current unit time. t Similarly, based on the user's body temperature change value and unit time length in adjacent unit time, the body temperature change rate R in adjacent unit time is calculated. t+1 The temperature change rate ΔR is calculated by using the temperature change rate of the current unit time and the adjacent unit time. The specific formula is: ΔR = (R t+1 -R t )\Δt.

[0099] Among them, Δt is the duration of unit time, ΔR is the rate of change of body temperature change rate, R t is the rate of change of body temperature per unit time. t+1 is the rate of change of body temperature in adjacent unit time.

[0100] Finally, the temperature regulation parameters are determined based on the duration of the unit time traversed to and the temperature change rate R in the current unit time. t , and the rate of change of body temperature change rate ΔR, to comprehensively analyze the user's body temperature regulation state. And based on these data analysis results, determine the body temperature regulation parameters of the target object in the current unit time.

[0101] By analyzing the temperature changes of each unit time and its adjacent unit time in detail, the system can capture the subtle dynamics of temperature changes. This provides more information than temperature measurement at a single time point, helping to understand the user's physiological state more accurately. And by comprehensively considering the individual's temperature change rate and its rate of change, the system can provide personalized drinking water recommendations. This personalized health management program can better meet the specific needs of users and improve user experience and satisfaction.

[0102] In one embodiment, based on the duration of the unit time traversed to, the temperature change rate of the target object in the unit time traversed to, and the change rate of the temperature change rate of the target object in the unit time traversed to, the temperature adjustment parameter of the target object in the unit time traversed to is determined, which is implemented by the following calculation formula: 2 T / dt 2 -μ(1-T 2 )dT / dt+T=0.

[0103] Among them, d 2 T / dt 2 is the rate of change of the target object's body temperature change rate during the unit time traversed to, dT / dt is the rate of change of the target object's body temperature during the unit time traversed to, μ is the body temperature regulation parameter of the target object during the unit time traversed to, T is the body temperature change value of the target object during the unit time traversed to, and t is the duration of the unit time traversed to.

[0104] Specifically, data collection is first performed, and the user's physiological parameters are monitored in real time through the sensor module, including body temperature, heart rate, ambient temperature, water intake, exercise volume, etc. The temperature change value T of the user in each unit time after drinking water is recorded.

[0105] Then calculate the body temperature change rate. For each unit time, calculate the body temperature change rate dT / dt. This can be obtained by dividing the body temperature difference between two adjacent time points by the unit time length: dT / dt=(T t+1 -Tt )\Δt.

[0106] Among them, T t and T t+1 are the body temperature values ​​at the current time and the next time point respectively, and Δt is the duration of the unit time.

[0107] Next, calculate the rate of change of the body temperature change rate, and calculate the rate of change of the body temperature change rate d 2 T / dt 2 , which can be obtained by dividing the difference between the body temperature change rates in two adjacent unit times by the unit time length:

[0108] d 2 T / dt 2 =[(dT / dt) t+1 -(dT / dt) t ] / Δt.

[0109] Where, (dT / dt) t and (dT / dt) t+1 are the body temperature change rates of the current unit time and the next unit time respectively.

[0110] Then determine the thermoregulatory parameter μ, and use the Vanderpol oscillator equation to determine the thermoregulatory parameter μ. 2 T / dt 2 , dT / dt and T value are substituted into the equation: 2 T / dt 2 -μ(1-T 2 )dT / dt+T=0. By solving this nonlinear equation, the value of μ can be obtained.

[0111] Finally, the μ value calculated through the above steps reflects the intensity of body temperature regulation required in the current unit time in order to make the body temperature change conform to the pattern described by the Vanderpol oscillator equation. For example, if μ is large, it may indicate that the body temperature changes more drastically and requires stronger regulation; if μ is small, it may indicate that the body temperature changes more slowly and the regulation requirement is lower. Optionally, based on the calculated μ value and other relevant data, personalized drinking water recommendations can be generated, such as the optimal drinking time, temperature, and amount, to help users scientifically regulate basal body temperature, thereby enhancing immune system function and overall health.

[0112] The use of the Vanderpol oscillator equation can more accurately simulate and predict the periodic changes in body temperature. This nonlinear differential equation can capture the complex dynamics of body temperature changes and provide more accurate predictions than simple linear models. And the determination of the temperature regulation parameter μ enables the system to scientifically regulate the timing, temperature and amount of drinking water. By optimizing these parameters, the basal body temperature can be more effectively regulated, thereby improving the function of the immune system.

[0113] In one of the embodiments, the body temperature value, body temperature change value, body temperature change rate and change rate of the body temperature change rate of the target object during the drinking time are obtained; based on the body temperature value, body temperature change value, body temperature change rate and change rate of the body temperature change rate of the target object during the drinking time, an image to be detected is generated; a drinking water prediction model is obtained, and the image to be detected is processed by the drinking water prediction model to obtain the amount of drinking water and the drinking water temperature required by the target object.

[0114] The body temperature value refers to the actual body temperature value measured by the target object at a specific time point (i.e., the time to drink water). This is usually data collected in real time by the body temperature sensor in the sensor module, reflecting the user's current body temperature status.

[0115] The image to be detected refers to a visual representation generated based on the target object's body temperature value, body temperature change value, body temperature change rate, and the rate of change of the body temperature change rate during the drinking time. It can be understood that the image to be detected is generated to facilitate the analysis and processing of the machine learning model, and it can intuitively reflect the user's physiological state. The image to be detected can be in various forms, for example:

[0116] Optional, a time series graph showing the trend of body temperature over time.

[0117] Optionally, a scatter plot or heat map showing the relationship between different physiological parameters.

[0118] Optionally, a multi-dimensional feature vector graph combines multiple physiological parameters into a feature vector and represents it in the form of an image.

[0119] The drinking water prediction model refers to a machine learning model trained based on historical data, and its purpose is to predict the optimal amount and temperature of drinking water based on input physiological parameters (such as body temperature values, body temperature change values, etc.). This application does not specifically limit the drinking water prediction model, and it can be set according to actual needs. For example, the model can use a variety of algorithms, such as neural networks, support vector machines, decision trees, etc. By learning from a large amount of historical data, the model can identify the complex relationship between body temperature changes and drinking water needs, and make accurate predictions based on this.

[0120] The amount of water you drink refers to the total amount of water you are advised to consume during the drinking time. This amount is determined based on the user's current physiological state (including body temperature and its changes) and personal health goals, and is intended to help users maintain good physical condition and optimize immune function.

[0121] Drinking water temperature refers to the specific temperature of water recommended for users to drink. This temperature is also recommended based on the user's current physiological state and health needs. Different body temperature regulation needs may correspond to different drinking water temperatures to achieve the best body temperature regulation effect.

[0122] Specifically, first obtain physiological parameters, and use sensor modules (such as body temperature sensors, heart rate sensors, etc.) to monitor the user's body temperature in real time during the waiting time for drinking water. Record the user's body temperature change from the previous unit time to the current waiting time for drinking water. For example, if the body temperature in the previous unit time is T t-1 , the current body temperature at the time of drinking water is T t , then the temperature change is Δt=T t -T t-1 . Based on the temperature change value and the duration of the unit time, calculate the temperature change rate. The formula is: dT / dt=ΔT\Δt. Where Δt is the duration of the unit time. Determine the temperature change rate in adjacent unit times and calculate the rate of change of the temperature change rate. The formula is:

[0123] d 2 T / dt 2 =[(dT / dt) t+1 -(dT / dt) t ] / Δt.

[0124] Where, (dT / dt) t and (dT / dt) t+1 are the body temperature change rates of the current unit time and the next unit time respectively.

[0125] Then the temperature value, temperature change value, temperature change rate, and temperature change rate calculated above are integrated. Based on these physiological parameters, a visualization image is generated. The generated image is preprocessed as necessary, such as normalization and noise reduction, to facilitate subsequent machine learning model processing.

[0126] This application does not make any specific limitation on the visual image, and it can be set according to actual needs.

[0127] Optional, a time series graph to show the temperature trend over time.

[0128] Optionally, a scatter plot or heat map can be used to show the relationship between different physiological parameters.

[0129] Optionally, a multi-dimensional feature vector map is used to combine multiple physiological parameters into a feature vector and represent it in the form of an image.

[0130] Finally, the pre-trained water drinking prediction model is loaded. The model may be trained based on historical data, using algorithms such as neural networks, support vector machines, and decision trees. The generated image to be tested is provided as input to the water drinking prediction model. The input image is analyzed and processed by the water drinking prediction model, and the model will identify the patterns in the image and make predictions based on them. After the model is processed, the required water drinking amount and water temperature of the target object are output. These prediction results are recommended based on the user's current physiological state and health needs.

[0131] Since users can intuitively see their physiological status through visualization (such as images to be detected) and understand how the system generates drinking water recommendations based on these data, this transparency increases user trust and participation. Drinking water recommendations are generated based on the user's actual physiological parameters to ensure that the recommended amount and temperature of water are tailored to individual needs. This personalized recommendation can better meet the user's specific health needs than general recommendations, thereby improving the effectiveness of health management.

[0132] In one embodiment, a water intake weight matrix and a water temperature weight matrix are obtained; a feature extraction is performed on the image to be detected through a backbone network to obtain an initial feature map; a linear transformation and a nonlinear transformation corresponding to the water intake weight matrix are performed on the initial feature map through a water intake branch module to obtain a water intake convolution feature map; a water intake pooling feature map is determined according to the maximum eigenvalue of the water intake convolution feature map on each feature channel; a linear transformation and a nonlinear transformation corresponding to the water intake temperature weight matrix are performed on the initial feature map through a water intake temperature branch module to obtain a water intake temperature convolution feature map; The drinking water temperature pooling feature map is determined according to the average eigenvalue of the drinking water temperature convolution feature map on each feature channel; the drinking water amount pooling feature map and the drinking water temperature pooling feature map are flattened and straightened respectively to obtain the drinking water amount feature vector and the drinking water temperature feature vector; the drinking water amount feature vector is converted into the drinking water amount probability distribution feature vector, and the drinking water temperature feature vector is converted into the drinking water temperature probability distribution feature vector; the drinking water amount required by the target object is determined according to the drinking water amount probability distribution feature vector, and the drinking water temperature required by the target object is determined according to the drinking water temperature probability distribution feature vector.

[0133] The water intake weight matrix refers to a pre-trained weight matrix used for linear transformation in the water intake branch module. It contains all the weight parameters required in the conversion process from the initial feature map to the water intake convolution feature map.

[0134] The water temperature weight matrix refers to another pre-trained weight matrix used for linear transformation in the water temperature branch module. It contains all the weight parameters required in the conversion process from the initial feature map to the water temperature convolution feature map.

[0135] The backbone network refers to a deep neural network structure, which is usually composed of multiple convolutional layers, pooling layers and activation functions. Its main function is to extract high-level feature representations (i.e., initial feature maps) from the input image to be detected.

[0136] The water intake branch module is a submodule of the water intake prediction model, which is specifically used to process features related to water intake. It further processes the initial feature map extracted by the backbone network to generate the final water intake prediction result.

[0137] The water intake convolution feature map refers to the intermediate result obtained by linearly transforming the initial feature map (using the water intake weight matrix) and nonlinearly transforming (such as the ReLU activation function) through the water intake branch module. This feature map contains detailed feature information related to water intake.

[0138] The water intake pooling feature map is obtained by taking the maximum value (maximum pooling operation) on each feature channel of the water intake convolution feature map. This step helps to reduce the spatial dimension of the feature map and retain the most important feature information.

[0139] The initial feature map refers to the high-level feature representation extracted by the backbone network from the input image to be detected. It is the basis for subsequent branch module processing.

[0140] The drinking water temperature branch module refers to another submodule of the drinking water prediction model, which is specifically used to process features related to drinking water temperature. It further processes the initial feature map extracted by the backbone network to generate the final drinking water temperature prediction result.

[0141] The drinking water temperature convolution feature map refers to the intermediate result obtained by linearly transforming the initial feature map (using the drinking water temperature weight matrix) and nonlinearly transforming (such as the ReLU activation function) through the drinking water temperature branch module. This feature map contains detailed feature information related to the drinking water temperature.

[0142] The drinking water temperature pooled feature map is obtained by taking the average value of the drinking water temperature convolution feature map on each feature channel (average pooling operation). This step also helps to reduce the spatial dimension of the feature map and retain important feature information.

[0143] The water intake feature vector refers to the vector obtained by flattening and straightening the water intake pooling feature map. This vector contains all the feature information related to water intake and can be used for subsequent probability distribution calculations.

[0144] The drinking water temperature feature vector refers to the vector obtained by flattening and straightening the drinking water temperature pooling feature map. This vector contains all the feature information related to the drinking water temperature and can be used for subsequent probability distribution calculations.

[0145] The probability distribution feature vector of water intake refers to a vector obtained by converting the water intake feature vector into a probability distribution form. Each element in this vector represents the probability of different water intake amounts, reflecting the system's confidence in different water intake amounts.

[0146] The drinking water temperature probability distribution feature vector refers to a vector obtained by converting the drinking water temperature feature vector into a probability distribution form. Each element in this vector represents the probability of different drinking water temperatures, reflecting the system's confidence in different drinking water temperatures.

[0147] Specifically, first obtain the water intake weight matrix and the water temperature weight matrix. Input an image to be processed into the backbone network part of the model. The backbone network usually contains multiple convolutional layers, activation functions such as ReLU (Rectified Linear Unit) and pooling layers to extract high-level abstract features from the original input image. After a series of operations, one or more high-dimensional feature maps are output as the "initial feature map", which captures the key visual information of the input image.

[0148] The resulting "initial feature map" is then fed into the water intake branch module. The feature map is linearly transformed using the water intake weight matrix, and then a nonlinear activation function (such as ReLU) is applied to generate a water intake convolution feature map. For the water intake convolution feature map on each channel, the maximum value is selected for pooling to form a new feature map, namely the water intake pooling feature map. This process helps to emphasize the most important features and reduce computational complexity.

[0149] Next, the "initial feature map" is also used and input into the water temperature branch module. The water temperature weight matrix is ​​applied to implement linear transformation and combined with nonlinear activation to produce the water temperature convolution feature map. After that, an average pooling strategy is adopted for each channel to create a water temperature pooled feature map. Average pooling helps to maintain more details while reducing dimensions.

[0150] Then, the drinking water amount pooled feature map and the drinking water temperature pooled feature map are straightened into a one-dimensional vector form, namely, the so-called drinking water amount feature vector and the drinking water temperature feature vector. It can be understood that this step converts the data originally having a spatial structure into a format that is easy to process later.

[0151] Finally, the water intake feature vector is converted into a water intake probability distribution feature vector through a fully connected layer or other appropriate methods, where each element represents the probability of a different water intake level. The same method is applied to the water temperature feature vector to obtain a water temperature probability distribution feature vector, indicating the possibility of different temperature levels. Then, based on the water intake probability distribution feature vector, the water intake with the highest probability is selected as the optimal water intake recommended to the user. Similarly, based on the water temperature probability distribution feature vector, the water temperature that is most likely to be suitable for the user is determined.

[0152] The above scheme can achieve the following beneficial effects:

[0153] First, by using the pre-trained water intake weight matrix and water temperature weight matrix, personalized water intake suggestions can be provided based on individual differences of users, which can better meet the needs of different users.

[0154] Second, the backbone network extracts features from the input image to be tested and can capture key information in the image, which provides high-quality basic features for subsequent predictions of water intake and water temperature.

[0155] Third aspect: The model contains both the water intake branch module and the water temperature branch module, which can predict both the water intake and the water temperature in one forward propagation process. This multi-task learning method improves the efficiency and accuracy of the model.

[0156] Fourth aspect: Through linear transformation and nonlinear transformation, convolution feature map of drinking water volume and convolution feature map of drinking water temperature are generated. These feature maps contain rich detailed information, which helps to predict drinking water volume and drinking water temperature more accurately.

[0157] Fifth aspect: The water intake pooling feature map uses the maximum pooling operation to retain the most important features in each feature channel. The water temperature pooling feature map uses the average pooling operation to smooth the features and reduce noise. These two pooling methods combine their respective advantages to improve the effectiveness and robustness of the features.

[0158] Sixth aspect: Flattening and straightening the pooled feature map into a feature vector simplifies the data structure and facilitates subsequent fully connected layer processing. This can efficiently map high-dimensional features to low-dimensional space and reduce computational complexity.

[0159] Seventh aspect: By converting the feature vector into a probability distribution feature vector, the system can output the probability of each possible drinking amount and drinking temperature. This method not only provides specific prediction results, but also gives the confidence of the prediction, which increases the reliability and interpretability of the system.

[0160] Aspect 8: The final amount and temperature of drinking water are determined based on the probability distribution feature vector, making the recommendations more scientific and reasonable. This data-driven approach can help users better regulate body temperature, improve the function of the immune system, and thus improve overall health.

[0161] In one of the embodiments, the body temperature data of the target object regarding the amount of water drunk and the temperature of the water drunk are received; the body temperature parameter threshold is updated according to the body temperature data of the target object regarding the amount of water drunk and the temperature of the water drunk, to obtain an updated body temperature parameter threshold; and the updated body temperature parameter threshold is used as the preset drinking condition for the target object after the waiting time for drinking.

[0162] Among them, body temperature data refers to the actual body temperature measurement value fed back by the target object after drinking water according to the amount and temperature recommended by the system. This data is usually collected in real time through sensors (such as body temperature sensors) and records the changes in the user's body temperature after drinking water.

[0163] The updated temperature parameter threshold refers to the result of adjusting and optimizing the original temperature parameter threshold based on the actual temperature data fed back by the user. This update is based on the individual differences and actual effects of the user, making the threshold more in line with the user's actual situation, thereby improving the personalization and accuracy of health management. For example, if the user's body temperature changes after drinking water as recommended by the system are not in line with expectations, the system may adjust the threshold of the temperature change rate to better suit the user's physiological characteristics.

[0164] The preset drinking conditions after the waiting time are the ideal conditions set by the system for the next drinking after the user has finished drinking. These conditions are based on the updated temperature parameter thresholds to ensure that the body temperature can be regulated more effectively the next time the user drinks.

[0165] Specifically, after the target subject drinks water according to the amount and temperature recommended by the system, the user's body temperature data is collected in real time through a sensor (such as a body temperature sensor). The instantaneous body temperature value after drinking water and the body temperature changes over a period of time are recorded.

[0166] Then, the collected feedback temperature data is analyzed to evaluate the difference between the actual temperature change and the expected effect. Based on the analysis results, the original temperature parameter threshold is adjusted. Optionally, a machine learning algorithm or other optimization method is used to continuously iterate and optimize the temperature parameter threshold to make it more suitable for the individual differences and actual conditions of the user. For example, the gradient descent method, genetic algorithm, etc. can be used to find the optimal threshold.

[0167] Finally, the updated temperature parameter threshold is stored as a new preset drinking water condition. These conditions will be used as a reference standard for the next drinking water. It can be understood that based on the updated temperature parameter threshold, a new drinking water recommendation is generated, including: determining the best time for the next drinking water according to the temperature change trend and the prediction model, recommending the appropriate amount of water to drink according to the temperature change rate and the user's historical data, and recommending the best drinking water temperature according to the temperature change and user feedback.

[0168] By receiving actual feedback data from users, the system can continuously adjust and optimize the temperature parameter thresholds to make them more suitable for individual differences of users. This personalized management method can provide more accurate health advice to meet the specific needs of different users. Through continuous data feedback and model updates, the system can continuously learn and improve, gradually improving its performance. This continuous optimization capability enables the system to become more and more intelligent and effective over time.

[0169] In an exemplary embodiment, Figure 3 As shown, it includes steps 302 to 312. Among them:

[0170] Step 302, traverse the unit time of the target object after drinking the water, and obtain the temperature change value of the target object in the traversed unit time;

[0171] Step 304, determining the temperature change rate of the target object within the unit time traversed to according to the temperature change value and the duration of the unit time traversed to;

[0172] Step 306, for each of the multiple traversed unit times, determine an adjacent unit time behind the traversed unit time; determine a rate of change of the target object's temperature change rate in the traversed unit time according to the duration of the traversed unit time, the temperature change rate of the target object in the traversed unit time, and the temperature change rate in the adjacent unit time; determine a temperature regulation parameter of the target object in the traversed unit time based on the duration of the traversed unit time, the temperature change rate of the target object in the traversed unit time, and the temperature change rate of the target object in the traversed unit time;

[0173] Step 308, matching the temperature regulation parameter of the target object in the traversed unit time with the temperature parameter threshold to obtain a parameter matching result; when the parameter matching result indicates that the temperature regulation parameter of the target object in the traversed unit time is greater than the temperature parameter threshold, the traversal is stopped, and the traversed unit time is used as the waiting time for drinking water;

[0174] Step 310, obtaining the body temperature value, body temperature change value, body temperature change rate and change rate of the body temperature change rate of the target object during the drinking time; generating an image to be detected based on the body temperature value, body temperature change value, body temperature change rate and change rate of the body temperature change rate of the target object during the drinking time; obtaining a drinking water prediction model, obtaining a drinking water weight matrix and a drinking water temperature weight matrix; performing feature extraction on the image to be detected through the backbone network to obtain an initial feature map; performing linear transformation and nonlinear transformation on the initial feature map corresponding to the drinking water weight matrix through the drinking water branch module to obtain a drinking water convolution feature map, and determining a drinking water pooling feature map according to the maximum eigenvalue of the drinking water convolution feature map on each feature channel; The water temperature branch module performs linear and nonlinear transformations on the initial feature map corresponding to the drinking water temperature weight matrix to obtain a drinking water temperature convolution feature map, and determines a drinking water temperature pooling feature map based on the average eigenvalue of the drinking water temperature convolution feature map on each feature channel; flattens and straightens the drinking water volume pooling feature map and the drinking water temperature pooling feature map respectively to obtain a drinking water volume feature vector and a drinking water temperature feature vector; converts the drinking water volume feature vector into a drinking water volume probability distribution feature vector, and converts the drinking water temperature feature vector into a drinking water temperature probability distribution feature vector; determines the drinking water volume required by the target object based on the drinking water volume probability distribution feature vector, and determines the drinking water temperature required by the target object based on the drinking water temperature probability distribution feature vector;

[0175] Step 312, receiving the body temperature data of the target object regarding the amount of water drunk and the temperature of the water drunk; updating the body temperature parameter threshold according to the body temperature data of the target object regarding the amount of water drunk and the temperature of the water drunk, to obtain an updated body temperature parameter threshold; and using the updated body temperature parameter threshold as the preset drinking condition for the target object after the waiting time for drinking.

[0176] 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.

[0177] Based on the same inventive concept, the embodiment of the present application also provides an intelligent drinking water reminder device for implementing the intelligent drinking water reminder 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 one or more intelligent drinking water reminder device embodiments provided below can refer to the limitations of the intelligent drinking water reminder method above, and will not be repeated here.

[0178] In an exemplary embodiment, Figure 4 As shown, a smart drinking water reminder device 400 is provided, including: an acquisition module 402, a first determination module 404, a second determination module 406 and a stop module 408, wherein:

[0179] The acquisition module 402 is used to traverse the unit time of the target object after drinking the water, and obtain the temperature change value of the target object in the traversed unit time;

[0180] A first determination module 404 is used to determine the temperature change rate of the target object within the unit time traversed to according to the temperature change value and the duration of the unit time traversed to;

[0181] The second determination module 406 is used to determine the temperature adjustment parameter of the target object within the unit time of traversal to according to the temperature change value and the temperature change rate;

[0182] The stop module 408 is used to stop traversing when the temperature adjustment parameter of the target object in the traversed unit time meets the preset drinking water condition, and use the traversed unit time as the waiting drinking water time.

[0183] In one of the embodiments, the stop module 408 is used to match the temperature regulation parameter of the target object in the unit time traversed to and the temperature parameter threshold to obtain a parameter matching result; when the parameter matching result indicates that the temperature regulation parameter of the target object in the unit time traversed to is greater than the temperature parameter threshold, the traversal is stopped, and the traversed unit time is used as the time to wait for drinking water.

[0184] In one embodiment, the second determination module 406 is used to determine, for each of a plurality of unit times traversed to, an adjacent unit time located behind the unit time traversed to; determine the rate of change of the target object's temperature change rate in the unit time traversed to according to the length of the unit time traversed to, the temperature change rate of the target object in the unit time traversed to, and the temperature change rate in the adjacent unit time; determine the temperature regulation parameters of the target object in the unit time traversed to based on the length of the unit time traversed to, the temperature change rate of the target object in the unit time traversed to, and the temperature change rate of the target object in the unit time traversed to.

[0185] In one embodiment, the second determination module 406 is implemented by the following calculation formula:

[0186] d 2 T / dt 2 -μ(1-T 2 )dT / dt+T=0

[0187] Among them, d 2 T / dt 2 is the rate of change of the target object's body temperature change rate during the unit time traversed to, dT / dt is the rate of change of the target object's body temperature during the unit time traversed to, μ is the body temperature regulation parameter of the target object during the unit time traversed to, T is the body temperature change value of the target object during the unit time traversed to, and t is the duration of the unit time traversed to.

[0188] In one embodiment, the intelligent drinking water reminder device also includes a prediction module 410, which is used to obtain the body temperature value, body temperature change value, body temperature change rate and change rate of the body temperature change rate of the target object during the drinking water waiting time; based on the body temperature value, body temperature change value, body temperature change rate and change rate of the body temperature change rate of the target object during the drinking water waiting time, generate an image to be detected; obtain a drinking water prediction model, and process the image to be detected through the drinking water prediction model to obtain the drinking water amount and drinking water temperature required by the target object.

[0189] In one embodiment, the intelligent drinking water reminder device also includes a prediction module 410, which is used to obtain a drinking water weight matrix and a drinking water temperature weight matrix; the backbone network is used to extract features of the image to be detected to obtain an initial feature map; the drinking water branch module is used to perform linear transformation and nonlinear transformation corresponding to the drinking water weight matrix on the initial feature map to obtain a drinking water convolution feature map, and the drinking water pooling feature map is determined according to the maximum eigenvalue of the drinking water convolution feature map on each feature channel; the drinking water temperature branch module is used to perform linear transformation and nonlinear transformation corresponding to the drinking water temperature weight matrix on the initial feature map The drinking water temperature convolution feature map is obtained by transformation, and the drinking water temperature pooling feature map is determined according to the average eigenvalue of the drinking water temperature convolution feature map on each feature channel; the drinking water amount pooling feature map and the drinking water temperature pooling feature map are flattened and straightened respectively to obtain the drinking water amount feature vector and the drinking water temperature feature vector; the drinking water amount feature vector is converted into the drinking water amount probability distribution feature vector, and the drinking water temperature feature vector is converted into the drinking water temperature probability distribution feature vector; according to the drinking water amount probability distribution feature vector, the drinking water amount required by the target object is determined, and according to the drinking water temperature probability distribution feature vector, the drinking water temperature required by the target object is determined.

[0190] In one of the embodiments, the intelligent drinking water reminder device also includes an updating module 412, which is used to receive the body temperature data of the target object regarding the drinking water amount and the drinking water temperature; based on the body temperature data of the target object regarding the drinking water amount and the drinking water temperature, the body temperature parameter threshold is updated to obtain an updated body temperature parameter threshold; and the updated body temperature parameter threshold is used as the preset drinking water condition for the target object after the drinking water waiting time.

[0191] In another embodiment, if Figure 5 As shown, Figure 5It is a structural block diagram of an intelligent drinking water reminder device in another embodiment, including: an acquisition module 402, a first determination module 404, a second determination module 406 and a stop module 408. Among them, the intelligent drinking water reminder device 400 also includes a prediction module 410 and an update module 412. The prediction module 410 is used to obtain the body temperature value, body temperature change value, body temperature change rate and the rate of change of the body temperature change rate of the target object during the drinking time; based on the body temperature value, body temperature change value, body temperature change rate and the rate of change of the body temperature change rate of the target object during the drinking time, generate an image to be detected; obtain a drinking water prediction model, obtain a drinking water weight matrix and a drinking water temperature weight matrix; extract features of the image to be detected through the backbone network to obtain an initial feature map; through the drinking water branch module, perform linear transformation and nonlinear transformation on the initial feature map corresponding to the drinking water weight matrix to obtain a drinking water convolution feature map, and determine the drinking water pooling feature map according to the maximum eigenvalue of the drinking water convolution feature map on each feature channel; through The drinking water temperature branch module performs linear transformation and nonlinear transformation corresponding to the drinking water temperature weight matrix on the initial feature map to obtain the drinking water temperature convolution feature map, and determines the drinking water temperature pooling feature map according to the average eigenvalue of the drinking water temperature convolution feature map on each feature channel; flattens and straightens the drinking water volume pooling feature map and the drinking water temperature pooling feature map respectively to obtain the drinking water volume feature vector and the drinking water temperature feature vector; converts the drinking water volume feature vector into the drinking water volume probability distribution feature vector, and converts the drinking water temperature feature vector into the drinking water temperature probability distribution feature vector; determines the drinking water volume required by the target object according to the drinking water volume probability distribution feature vector, and determines the drinking water temperature required by the target object according to the drinking water temperature probability distribution feature vector. The updating module 412 is used to receive the body temperature data fed back by the target object for the drinking water volume and the drinking water temperature; updates the body temperature parameter threshold according to the body temperature data fed back by the target object for the drinking water volume and the drinking water temperature, and obtains the updated body temperature parameter threshold; and uses the updated body temperature parameter threshold as the preset drinking water condition for the target object after the waiting drinking time.

[0192] Each module in the above-mentioned intelligent drinking water reminder device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0193] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 6As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. 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 data related to intelligent drinking water reminders. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication 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 smart drinking water reminder method is implemented.

[0194] Those skilled in the art will understand that Figure 6 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.

[0195] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0196] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0197] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0198] 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, and the collection, use and processing of relevant data must comply with relevant regulations.

[0199] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments 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 this application can include at least one of non-volatile memory 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), magnetic 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, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0200] 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 application.

[0201] 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. An intelligent drinking water reminder method, characterized in that: The method comprises: Traversing the unit time of the target object after drinking the water, and obtaining the temperature change value of the target object within the traversed unit time; Determine the temperature change rate of the target object within the unit time traversed according to the temperature change value and the duration of the unit time traversed to; Determine the temperature adjustment parameter of the target object within the unit time of traversal according to the temperature change value and the temperature change rate; When the body temperature regulation parameter of the target object in the traversed unit time meets the preset drinking water condition, the traversal is stopped, and the traversed unit time is used as the waiting drinking water time.

2. The method according to claim 1, characterized in that The preset drinking water condition is a body temperature parameter threshold; The traversal is stopped when the temperature adjustment parameter of the target object in the traversed unit time meets the preset drinking water condition, and the traversed unit time is used as the waiting drinking water time, including: Matching the temperature adjustment parameter of the target object within the unit time traversed to with the temperature parameter threshold to obtain a parameter matching result; When the parameter matching result indicates that the temperature regulation parameter of the target object in the traversed unit time is greater than the temperature parameter threshold, the traversal is stopped, and the traversed unit time is used as the waiting time for drinking water.

3. The method according to claim 1, characterized in that The step of determining the temperature adjustment parameter of the target object within the unit time of traversing to according to the temperature change value and the temperature change rate includes: For each of the plurality of traversed unit times, determining an adjacent unit time located behind the traversed unit time; Determine the rate of change of the target object's body temperature change rate within the targeted unit time traversed to according to the duration of the unit time traversed to, the body temperature change rate of the target object within the targeted unit time traversed to, and the body temperature change rate within the adjacent unit time; Based on the duration of the unit time traversed to, the temperature change rate of the target object in the unit time traversed to, and the rate of change of the temperature change rate of the target object in the unit time traversed to, the temperature regulation parameters of the target object in the unit time traversed to are determined.

4. The method according to claim 3, characterized in that The determination of the temperature adjustment parameter of the target object in the unit time traversed to based on the duration of the unit time traversed to, the temperature change rate of the target object in the unit time traversed to, and the change rate of the temperature change rate of the target object in the unit time traversed to is achieved by the following calculation formula: d 2 T / dt 2 -μ(1-T 2 )dT / dt+T=0; Among them, d 2 T / dt 2 is the rate of change of the target object's body temperature change rate during the unit time traversed to, dT / dt is the rate of change of the target object's body temperature change rate during the unit time traversed to, μ is the body temperature regulation parameter of the target object during the unit time traversed to, T is the body temperature change value of the target object during the unit time traversed to, and t is the duration of the unit time traversed to.

5. The method according to claim 3, characterized in that: The method further comprises: Obtaining the body temperature value, body temperature change value, body temperature change rate, and change rate of the body temperature change rate of the target object during the waiting time for drinking water; Generate an image to be detected based on the body temperature value, body temperature change value, body temperature change rate and change rate of the body temperature change rate of the target object during the waiting time for drinking water; A drinking water prediction model is obtained, and the image to be detected is processed by the drinking water prediction model to obtain the drinking water amount and drinking water temperature required by the target object.

6. The method according to claim 5, characterized in that The drinking water detection model at least includes a backbone network, a drinking water volume branch module and a drinking water temperature branch module; The step of processing the image to be detected by using the drinking water prediction model to obtain the drinking water amount and drinking water temperature required by the target object includes: Obtain the water intake weight matrix and the water temperature weight matrix; Performing feature extraction on the image to be detected through the backbone network to obtain an initial feature map; Through the water intake branch module, the initial feature map is subjected to linear transformation and nonlinear transformation corresponding to the water intake weight matrix to obtain a water intake convolution feature map, and a water intake pooling feature map is determined according to the maximum eigenvalue of the water intake convolution feature map on each feature channel; The drinking water temperature branch module performs a linear transformation and a nonlinear transformation corresponding to the drinking water temperature weight matrix on the initial feature map to obtain a drinking water temperature convolution feature map, and determines a drinking water temperature pooling feature map according to the average eigenvalue of the drinking water temperature convolution feature map on each feature channel; Flatten and straighten the drinking water amount pooled feature map and the drinking water temperature pooled feature map respectively to obtain a drinking water amount feature vector and a drinking water temperature feature vector; Converting the drinking water amount feature vector into a drinking water amount probability distribution feature vector, and converting the drinking water temperature feature vector into a drinking water temperature probability distribution feature vector; The amount of drinking water required by the target object is determined according to the drinking water amount probability distribution feature vector, and the drinking water temperature required by the target object is determined according to the drinking water temperature probability distribution feature vector.

7. The method according to claim 6, characterized in that The method further comprises: receiving body temperature data fed back by the target subject regarding the drinking water amount and the drinking water temperature; According to the body temperature data fed back by the target subject regarding the drinking water amount and the drinking water temperature, the body temperature parameter threshold is updated to obtain an updated body temperature parameter threshold; The updated body temperature parameter threshold is used as the preset drinking water condition for the target object after the waiting time for drinking water.

8. An intelligent water drinking reminder device, characterized in that: The device comprises: An acquisition module is used to traverse the unit time of the target object after drinking the water, and obtain the temperature change value of the target object within the traversed unit time; A first determination module is used to determine the body temperature change rate of the target object within the unit time traversed to according to the body temperature change value and the duration of the unit time traversed to; A second determination module is used to determine the temperature adjustment parameter of the target object within the unit time of traversal to according to the temperature change value and the temperature change rate; The stopping module is used to stop traversing when the body temperature adjustment parameter of the target object in the traversed unit time meets the preset drinking water condition, and use the traversed unit time as the waiting drinking water time.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. 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 7 are implemented.

11. 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 7 are implemented.

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