Auxiliary reproduction patient management system based on mobile terminal

Abstract

The invention belongs to the field of reproductive health care management, and discloses an assisted reproductive patient management system based on a mobile terminal. Comprising an offline cache and synchronization module used for performing multivariate processing on data of a mobile terminal in an offline state, representing the data as a multi-tuple structure, performing priority calculation on a multi-tuple corresponding to each piece of data by using an incremental synchronization mechanism, dividing the data into K batches, and uploading the data to a cloud; the data processing and screening module is used for collecting comprehensive information of the cloud, performing time alignment on the comprehensive information by setting time synchronization parameters, performing conflict classification by setting a conflict detection mechanism, and obtaining the current reproductive state of a patient, and the scheme renovation and visualization module is used for generating a new treatment scheme according to an overall reproductive state report. Arranging the information on a visual interface, and interacting with the patient; and efficient management of assisted reproduction patients based on the mobile terminal is realized.

Description

Technical Field

[0001] The present invention relates to the field of reproductive health management. More specifically, the present invention relates to an assisted reproductive patient management system based on a mobile terminal. Background Art

[0002] A mobile terminal for assisted reproductive patient management refers to an application tool that provides management and services for patients undergoing assisted reproductive treatments (such as in vitro fertilization, artificial insemination, etc.) through mobile devices such as mobile phones and tablets. Such a terminal usually exists in the form of an APP, a mini-program, or a platform, aiming to optimize the assisted reproductive process and improve the patient's treatment experience and management efficiency.

[0003] Generally speaking, the terminal performs poorly under high concurrency or unstable network conditions, directly affecting the patient's usage experience. The massive aggregation or one-time push of outdated messages makes it difficult for patients to quickly screen out the key information related to the current treatment. During the disconnection period or when the treatment plan adjusted through other channels fails to be synchronized with the terminal in real time, information mismatch occurs, interfering with the patient's normal implementation of the treatment plan.

[0004] In view of this, the present invention proposes an assisted reproductive patient management system based on a mobile terminal to solve the above problems. Summary of the Invention

[0005] To overcome the above-mentioned defects of the prior art and to achieve the above object, the present invention provides the following technical solution: An assisted reproductive patient management system based on a mobile terminal, comprising: 1. An assisted reproductive patient management system based on a mobile terminal, characterized in that it comprises:

[0006] An offline cache and synchronization module: used for performing multi-element processing on data in the offline state of the mobile terminal, presenting the data in a multi-element group structure, and using an incremental synchronization mechanism to calculate the priority of each multi-element group corresponding to the data, and uploading the data to the cloud after dividing it into K batches;

[0007] A data processing and screening module: collecting comprehensive information from the cloud, aligning the time of the comprehensive information by setting time synchronization parameters, classifying conflicts through setting a conflict detection mechanism, and obtaining the current reproductive state of the patient;

[0008] The conflict detection mechanism includes a conflict detection logic, and the set conflict categories are time conflict C time 、dosage conflict C dose and operation content conflict C con ;

[0009] The conflict detection logic is

[0010] Wherein, Q ca_fRepresents the dose in the medication data or injection data, Q ca_d Represents the dose of medication or injection in the treatment step, Q cf Represents the dose deviation threshold, L ca_d Represents the injection site, M ca_f Represents the drug name in the medication data, M ca_d Represents the drug name in the injection data, t ca_f Is the timestamp of the patient's execution data, t ca_d Is the timestamp t of the doctor's treatment plan ca_d , Δt cf Is the time window threshold Δt cf , S cf Is the doctor's treatment step;

[0011] Treatment plan renovation and visualization module: Generates a new treatment plan based on the overall reproductive status report and arranges it on the visualization interface to interact with the patient.

[0012] Preferably, the method for performing multivariate processing on the data in the offline state of the mobile terminal includes:

[0013] During the offline state of the mobile terminal, each piece of data is formed into a multivariate group structure and cached in the local storage;

[0014] For each piece of data, the manifestation form of the multivariate group structure is {x i |(t, v, s, w)}, where x i Represents the i-th piece of data, i represents the index of the data, t represents the timestamp attached to the data, v represents the version number of the data, the version number is the number of times the data is modified, used to judge the latest degree of the data, s represents the operation source of the data, w represents the operation status of the data, including complete and incomplete, the operation status is represented by a numerical value, the numerical value 1 represents complete, and the numerical value 2 represents incomplete;

[0015] The method for uploading the processed data to the cloud using the incremental synchronization mechanism is:

[0016] Identify the operation status of each piece of data and perform integrity verification on the data with w = 1. The integrity verification includes mandatory field verification, timestamp verification, and operation logic verification. If the data meets the integrity verification conditions, when the mobile terminal is in the non-offline state, the multivariate groups corresponding to all the data during the offline state are uploaded to the cloud through the incremental synchronization mechanism, otherwise, they are retained in the mobile terminal cache.

[0017] Preferably, the method for uploading the multivariate groups corresponding to all the data during the offline state to the cloud through the incremental synchronization mechanism includes:

[0018] Calculate the priority of each data transmission according to the multi-tuple, and design the priority calculation formula Pr = ω1×ag + ω2×So + ω3×St, where ag represents the age of the data, So represents the performance coefficient of the data operation source, and St represents the performance coefficient of the data operation state. If w = 1, then St = 1; if w = 2, then St = 0. ω1, ω2, and ω3 are weight coefficients, and 0 < ω1, ω2, ω3 < 1, which are used to adjust the influence of different factors on the priority.

[0019] Use the K-Means clustering algorithm to group the data. Set the input of the clustering algorithm to the priority and timestamp, and the output to K data groups. The clustering objective function is where, G j represents the j-th clustering group, and μ j represents the center of the j-th clustering group, and K represents the total number of clustering groups.

[0020] Divide the data into K batches according to the K data groups, and upload each batch to the cloud in descending order of priority.

[0021] Preferably, the comprehensive information includes the patient's execution data, the doctor's treatment plan, and auxiliary data;

[0022] The patient's execution data includes medication data and injection data;

[0023] The medication data includes the timestamp, drug name, dose, and medication status; the injection data includes the timestamp, drug name, dose, and injection site;

[0024] The doctor's treatment plan includes the timestamp, treatment steps, and target status;

[0025] The auxiliary data includes the patient's physiological data and environmental data. The physiological data includes the timestamp, body temperature, heart rate, and hormone level;

[0026] The environmental data includes the timestamp, temperature, and humidity.

[0027] Preferably, the method for performing data comparison and conflict detection based on the comprehensive information and obtaining the patient's current reproductive status includes:

[0028] Set the time synchronization parameters, which include the time window threshold Δt cf and the time calibration deviation Δt of ;

[0029] Use the time calibration formula t ca = t lo + Δt of to synchronize the timestamp t lo on the patient's mobile terminal with the timestamp t caAlignment;

[0030] Set the time window mechanism as |t ca_f -t ca_d |≤Δt cf , if the timestamp t of the patient's execution data ca_f and the timestamp t of the doctor's treatment plan ca_d meet the time window mechanism, then it is judged that the time is synchronized, otherwise, the time is not synchronized;

[0031] Set up a conflict detection mechanism, classify conflicts according to the calculated comprehensive conflict impact value, and output an evaluation data set;

[0032] Set up a reproductive status evaluation mechanism, calculate the reproductive status evaluation coefficient, and output an overall reproductive status report in combination with the evaluation data set.

[0033] Preferably, the setting method of the time synchronization parameter includes:

[0034] For the time window threshold Δt cf , set Δt cf ∈(Δt cf -δ1, Δt cf +δ1), where δ1 represents the limit deviation time, automatically adjust the time window threshold based on a dynamic model, initialize the time window threshold, set the input of the dynamic model as historical execution data, and the output as the adjusted time window threshold, and the constraint condition is to maximize the treatment effect, that is, the deviation between the patient's physiological data and the target state is minimized;

[0035] For the time calibration deviation Δt of , set the calibration frequency, collect the historical time calibration deviations within the current r time periods, and calculate the average value as the time calibration deviation Δt of .

[0036] Preferably, the method of setting up a conflict detection mechanism, classifying conflicts according to the calculated comprehensive conflict impact value, and outputting an evaluation data set includes:

[0037] The conflict detection mechanism also includes a conflict flag;

[0038] The conflict flag is

[0039] where C f represents the conflict score of the medication data, C z represents the conflict score of the injection data, ω1 and ω2 represent weight coefficients, and 0 < ω1, ω2 < 1, the conflict score C f or C z is calculated by calculating the time conflict C time and the dose conflict C doseConflict with operation content C con Obtained by summation;

[0040] Wherein, TH1 and TH2 are conflict degree judgment thresholds, H1 represents high-risk conflict, H2 represents medium-risk conflict, and H3 represents low-risk conflict;

[0041] The output evaluation data set is D c ={(t1, M, Q, SL, C imp , H)|C imp >0};

[0042] Wherein, in the conflict state, t1 represents the time stamp when taking medicine data or injection data, M represents the drug name in the taking medicine data or injection data, Q represents the dose when taking medicine data or injection data, SL represents the taking medicine plan or injection site, and H represents the conflict degree, taking values of H1, H2, and H3 respectively.

[0043] Preferably, the method of setting the reproductive state evaluation mechanism, calculating the reproductive state evaluation coefficient, and outputting the overall reproductive state report in combination with the evaluation data set includes:

[0044] Extract the physiological data, environmental data, and target state in the treatment plan when the time stamp is the largest in the auxiliary data, and calculate the hormone level deviation ΔJ, body temperature deviation ΔM, heart rate abnormality deviation, and environmental impact deviation;

[0045] Wherein, the hormone level deviation and body temperature deviation are obtained by calculating the absolute difference between the data in the current auxiliary data and the data in the target state, and the heart rate abnormality deviation The environmental impact deviation ΔE = α1×(T cu -T opt ) + α2×(U cu -U opt ) + α3×B cu ;

[0046] Set the normal heart rate range as [X min , X max , X cu represents the heart rate in the auxiliary data at the current time, α1, α2, and α3 are weight coefficients, T cu represents the temperature at the current time, T opt represents the optimal environmental temperature, U cu represents the humidity at the current time, U opt represents the optimal environmental humidity, and B cu represents the air quality at the current time;

[0047] Calculate the reproductive state evaluation coefficient P cu =P ba-(ΔJ + ΔM + ΔX + ΔE + ΔJ tr + ΔM tr ), output the overall reproductive status report Mn cu = (D c , P cu );

[0048] Among them, ΔJ tr represents the change rate of hormone level, and ΔM tr represents the change rate of body temperature.

[0049] Preferably, the method for generating a new treatment plan according to the overall reproductive status report and arranging it on a visualization interface for interaction with the patient includes:

[0050] When the network is restored and the mobile terminal is not in the offline state, terminate the push of all data in the offline state, and only push the overall reproductive status report and the new treatment plan to the patient and display them on the mobile terminal;

[0051] The patient operates and interacts with the displayed information through the mobile terminal.

[0052] Preferably, the method for generating a new treatment plan according to the overall reproductive status report includes:

[0053] Send the overall reproductive status report to the doctor. The doctor evaluates the patient's reproductive status according to the overall reproductive status report and issues a new treatment plan;

[0054] Upload the new treatment plan to the cloud for storage.

[0055] The technical effects and advantages of an assisted reproductive patient management system based on a mobile terminal according to the present invention:

[0056] Through the multi - tuple structure and integrity check of offline data, the secure storage and integrity of data in the offline state are ensured. The incremental synchronization mechanism avoids data loss or repeated uploads and guarantees data consistency.

[0057] Accurate conflict detection can timely detect the situation where the patient fails to follow the doctor's advice and remind the patient or doctor to take corrective measures. Comprehensive reproductive status assessment can help the doctor better understand the patient's condition and formulate a more personalized treatment plan. Timely update of the treatment plan can adjust the treatment plan according to the actual situation of the patient and improve the treatment effect.

[0058] Priority calculation and batch upload mechanism can reduce network congestion and server load and improve the efficiency of data synchronization. Automated conflict detection and reproductive status assessment can reduce the doctor's workload and allow the doctor to have more time to focus on the diagnosis and treatment of patients.

[0059] The offline data processing and synchronization mechanism ensures that patients can use mobile terminals normally even when the network is unstable or unavailable, and guarantees that data will not be lost.

[0060] By optimizing the utilization of medical resources, the efficiency of medical services can be improved and operating costs can be reduced. Through the collected data, analysis can be carried out to provide data support for doctors.

[0061] In summary, through a series of innovative designs, this technical solution solves key problems such as offline data processing, synchronization, conflict detection, and treatment plan update in the field of medical health, has significant technical advantages and beneficial effects, can improve the quality, efficiency, and safety of medical services, and promote the health and well-being of patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a schematic structural diagram of an assisted reproductive patient management system based on a mobile terminal according to the present invention;

[0063] Figure 2 It is a schematic step diagram of an assisted reproductive patient management system based on a mobile terminal according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0065] Embodiment 1

[0066] Please refer to Figure 1 and Figure 2 As shown, an assisted reproductive patient management system based on a mobile terminal described in this embodiment includes:

[0067] The mobile terminal for assisted reproductive patient management refers to an application tool that provides management and services for patients undergoing assisted reproductive treatments (such as in vitro fertilization, artificial insemination, etc.) through mobile devices such as mobile phones and tablets. Such terminals usually exist in the form of APPs, mini-programs, or platforms, aiming to optimize the assisted reproductive process and improve the treatment experience and management efficiency of patients.

[0068] When APPs, mini-programs, or platforms exist on the mobile terminal, they need to be connected to the network. When the network conditions are relatively poor or incompatible, problems such as crashes, freezes, and login failures may occur during use. As a frequently occurring event, unstable network not only affects the patient's usage experience, especially at critical time nodes (such as medication reminders or appointment registrations);

[0069] Secondly, when the network is restored, the system pushes all messages that were not sent during the disconnection period together without screening the timeliness of the messages. The message timestamps are not effectively marked, resulting in patients being unable to distinguish which messages are the latest and which are expired, causing patients to receive a large number of messages with complicated information, making it difficult to quickly identify important information related to the current treatment plan;

[0070] In addition, during the network disconnection, patients or doctors may modify the treatment plan through other means (such as offline communication, telephone adjustment, etc.), but the terminal system is not updated in time. The mobile terminal still performs reminders, plan plans, and other operations according to the data process before the disconnection. Patients may perform incorrect operations (such as wrong medication time, wrong medical appointment arrangement, etc.) based on outdated information on the terminal, which reduces the efficiency of doctor-patient communication, may lead to obvious deviations in the treatment plan, and even affect the treatment effect.

[0071] In general, the terminal performs poorly under high concurrency or unstable network conditions, which directly affects the patient experience. The large number of outdated messages or one-time pushes make it difficult for patients to quickly filter out key information related to the current treatment. Treatment plans adjusted during disconnection or through other channels fail to synchronize with the terminal in real time, resulting in information mismatch and interfering with the patient's normal execution of the treatment plan.

[0072] In order to solve the problems of crash, freeze, message overload, information asynchrony, etc. that may occur in the assisted reproductive management system under network instability and high concurrency, this paper designs a patient management system based on mobile terminals, aiming to optimize the patient experience and improve the accuracy and timeliness of information processing. The following is a description of the system design in modules:

[0073] Offline cache and synchronization module: used to perform multi-processing on the data in the offline state of the mobile terminal, and use the incremental synchronization mechanism to upload the processed data to the cloud;

[0074] When the network is unstable, the patient's operations (such as appointments, reading messages, and medication records) cannot be uploaded to the server in real time, resulting in data loss or asynchrony. Conversely, after the network is restored, the latest treatment plans or messages in the cloud may not be updated to the patient's terminal in time. The offline cache and synchronization module ensures that the terminal data is securely stored during network outages and is efficiently synchronized to the cloud or server after the network is restored.

[0075] Perform multi-processing on the offline data of the mobile terminal and upload the processed data to the cloud using an incremental synchronization mechanism. The method includes:

[0076] When the mobile terminal is in an offline state, each piece of data (e.g., patient operation and message record) is formed into a tuple structure and cached in local storage;

[0077] For each piece of data, the representation form of the multi - tuple structure is {x i |(t, v, s, w)}, where x i represents the i - th piece of data, i represents the index of the data, t represents the timestamp attached to the data, v represents the version number of the data. The version number is the number of times the data has been modified and is used to judge the currency of the data. The version number automatically increments each time the data is modified. s represents the operation source of the data (such as "patient, terminal system, or user operation") and is used for priority judgment. w represents the operation status of the data, including complete and incomplete. The operation status is represented by a numerical value. The numerical value 1 represents complete, and the numerical value 2 represents incomplete. This is mainly used to handle the situation during network disconnection, where the patient's operations may be incomplete (such as an incomplete appointment, or incomplete message upload). Failure to perform integrity verification on network - disconnection operations may result in incorrect data synchronization. Therefore, an integrity verification mechanism is added to ensure that the data is complete before uploading.

[0078] Identify the operation status of each piece of data and perform integrity verification on the data with w = 1. The goal is to ensure that data generated during network disconnection is only allowed to be uploaded when it is complete, preventing incorrect data from entering the cloud. Integrity verification includes mandatory field verification (used to check whether the mandatory fields of the data are complete), timestamp verification (used to check whether the timestamp is valid, for example, non - empty and in the correct format), and operation logic verification (used to check whether the data meets the expectations according to the business logic, for example, whether the appointment record has complete appointment information). If the data meets the integrity verification conditions (marked as complete data), when the mobile terminal is in a non - offline state, the multi - tuples corresponding to all data during the offline state are uploaded to the cloud through the incremental synchronization mechanism. Otherwise, they are stored in the mobile - terminal cache and wait for the user to complete the operation.

[0079] The method of uploading the multi - tuples corresponding to all data during the offline state to the cloud through the incremental synchronization mechanism includes:

[0080] Calculate the priority of each data transmission according to the multi-tuple. The goal is to sort the data by priority to ensure that critical data is uploaded first, reducing the impact of synchronization latency on the patient experience. Design the priority calculation formula Pr = ω1×ag + ω2×So + ω3×St, where ag represents the age of the data (i.e., the interval between the data and the current time), which is positively correlated with the priority. So represents the performance coefficient of the data operation source. For example, if the operation source is the patient, set So = 1; if the operation source is the system of the mobile terminal, set So = 0.5, etc. St represents the performance coefficient of the data operation state. If w = 1, then St = 1; if w = 2, then St = 0. ω1, ω2, and ω3 are weight coefficients, and 0 < ω1, ω2, ω3 < 1, which are used to adjust the influence of different factors on the priority. According to the actual scenario, ω1 can be set to 0.5 (higher time weight), ω2 = 0.3 (medium source weight), and ω3 = 0.2 (lower state weight);

[0081] Use the K-Means clustering algorithm to group the data, reduce the upload pressure in the case of high concurrency, and optimize the upload efficiency. Set the input of the clustering algorithm to the priority and timestamp, and the output to K data groups. The clustering objective function is where, G j represents the j-th clustering group, μ j represents the center of the j-th clustering group, and K represents the total number of clustering groups; the purpose of the objective function is to minimize the sum of the squares of the Euclidean distances from all data points to the center of their respective clustering groups. By iteratively updating the clustering group centers and reassigning the data points to the nearest clustering group centers, the K-Means algorithm gradually optimizes this objective function until convergence.

[0082] Divide the data into K batches according to the K data groups, and upload each batch to the cloud in descending order of priority.

[0083] Among them, each time a batch of data is uploaded, the batch size is determined by the current load of the server. Therefore, scalable nodes can be used to dynamically expand the load according to the batch size.

[0084] Through offline caching, multi-tuple processing, data integrity verification, priority calculation, clustering analysis, and incremental synchronization mechanisms, the system can achieve the following functions in the case of unstable network or high concurrency scenarios, ensuring data integrity (offline data is verified to ensure that no incorrect data is uploaded), optimizing the upload order (priority calculation ensures that critical data is processed first), improving the upload efficiency (clustering analysis uploads in batches to avoid system performance degradation caused by high concurrency), and data consistency (the incremental synchronization mechanism ensures that locally added or modified data is synchronized to the cloud, facilitating comprehensive analysis together with the cloud data, enabling patients to obtain accurate treatment information).

[0085] Data Processing and Screening Module: Collect comprehensive information from the cloud, conduct data comparison and conflict detection based on the comprehensive information, and obtain the patient's current reproductive status;

[0086] The comprehensive information includes the patient's execution data, the doctor's treatment plan, and auxiliary data;

[0087] The patient's execution data includes medication data and injection data;

[0088] The execution data is automatically recorded by a mobile terminal (such as a mobile application, intelligent medicine box, injection recorder, etc.) or manually input by the patient to record medication and injection data.

[0089] Mobile terminals such as smartwatches and health monitoring devices are used to record the patient's physiological data.

[0090] The medication data includes a timestamp (medication time), drug name (name of the drug taken), dose (drug dosage taken), and medication status (whether the medication is taken on time, such as "taken" or "not taken"); the injection data includes a timestamp (injection time), drug name (name of the injected drug), dose (drug injection dosage), and injection site (such as the abdomen, arm, etc.);

[0091] The doctor's treatment plan includes a timestamp (time of plan modification), treatment steps (such as drug name, dose, medication time, injection time, etc.), and target status (the patient's physiological status expected by the doctor, such as hormone level, ovulation time, etc.);

[0092] The doctor inputs the treatment plan through an electronic medical record system or a mobile terminal and stores it in the cloud.

[0093] The auxiliary data includes the patient's physiological data and environmental data. The physiological data includes a timestamp (data recording time), body temperature (the patient's body temperature), heart rate (the patient's heart rate), and hormone level (the patient's hormone level, such as progesterone, estrogen, etc.).

[0094] The environmental data includes a timestamp (environmental data recording time), temperature (environmental temperature), humidity (environmental humidity), and air quality (environmental air quality).

[0095] Smartwatches, health monitoring devices, etc. record the patient's physiological data and environmental data.

[0096] For example, a patient is undergoing assisted reproductive treatment, and the doctor formulates a medication and injection plan for the patient. The patient uses an intelligent medicine box and a health monitoring device to record medication, injection, and physiological data.

[0097] Data Example

[0098] Medication data: [2023-10-01 08:00, progesterone, 200 mg, taken].

[0099] Injection data: [2023-10-01 20:00, ovulation induction injection, 75 IU, abdomen].

[0100] Treatment steps: [2023-10-01 07:00, progesterone, 200 mg, daily at 08:00].

[0101] Target status: [2023-10-01 07:00, progesterone level, 20 ng / mL].

[0102] Physiological data: [2023-10-01 08:00, 36.5 °C, 75 bpm, 18 ng / mL].

[0103] Environmental data: [2023-10-01 08:00, 22 °C, 60%, good].

[0104] By comparing the patient's operation data with the doctor's treatment plan data, detect whether the patient's execution meets the doctor's requirements, mark potential conflicts, and evaluate the severity of the conflicts on the treatment effect.

[0105] Based on the comprehensive information, conduct data comparison and conflict detection, and obtain the patient's current reproductive status. The methods include:

[0106] Set time synchronization parameters, where the time synchronization parameters include the time window threshold Δt cf and the time calibration deviation Δt of , and the time calibration and time window mechanisms. Their role is to ensure that in a distributed medical system, the patient's operation time and the doctor's plan time can be accurately aligned, thus avoiding misjudgment of conflicts caused by time deviation. The following is a detailed explanation of these two parts:

[0107] In the system, there may be a deviation between the local clock of the patient terminal and the cloud clock. To ensure that all timestamps have a unified benchmark, it is necessary to calibrate the local timestamps. Even after time calibration, due to network latency or minor delays in device operations, there may still be a minor deviation between the patient's operation time and the doctor's plan time. To reduce unnecessary conflict detection, a time window mechanism is introduced.

[0108] Use the time calibration formula t ca = t lo + Δt of , to calibrate the timestamp t lo on the patient's mobile terminal with the timestamp t caAlignment; if the device time deviates significantly from the cloud time, forced calibration is required. If the device has no network connection, the patient can be prompted to manually adjust the time.

[0109] Set the time window mechanism as |t ca_f -t ca_d |≤Δt cf , if the timestamp t of the patient's executed data ca_f and the timestamp t of the doctor's treatment plan ca_d meet the time window mechanism, then time synchronization is judged, otherwise, time is not synchronized;

[0110] Function: Through calibration, all local timestamps are converted into timestamps consistent with the cloud time, eliminating problems caused by inconsistent device clocks; ensuring that all operation timestamps are compared and processed under the same time basis, providing accurate time data for subsequent conflict detection. By allowing a certain range of time deviation, unnecessary conflict detection caused by minor time differences is avoided; even if there are small delays in actual operations, the system can still operate normally without misjudging conflicts due to time deviation. The time calibration and the time window mechanism work together to ensure that the patient's operation time and the doctor's plan time can be accurately aligned, thus providing a reliable time basis for conflict detection.

[0111] Set up a conflict detection mechanism, classify conflicts according to the calculated comprehensive conflict impact value, and output an evaluation data set;

[0112] Set up a reproductive status evaluation mechanism, calculate the reproductive status evaluation coefficient, and output an overall reproductive status report in combination with the evaluation data set.

[0113] The setting method of time synchronization parameters includes:

[0114] The time window threshold Δt cf is the allowed time deviation range for judging whether the patient's operation time is synchronized with the doctor's plan time. For example, the patient is allowed to operate within a certain range before and after the doctor's planned time without being judged as a conflict.

[0115] The time calibration deviation Δt of is the deviation between the local time of the patient's device and the cloud standard time, which is used to calibrate all timestamps.

[0116] For the time window threshold Δt cf , set Δt cf ∈(Δt cf -δ1, Δt cf+δ1), where δ1 represents the limit deviation time, that is, if the timestamp of the patient's execution data deviates from the planned time of the treatment plan by more than δ1, it will lead to a decline in the treatment effect. Automatically adjust the time window threshold based on the dynamic model, initialize the time window threshold, set the input of the dynamic model as the historical execution data, the output as the adjusted time window threshold, and the constraint condition as maximizing the treatment effect, that is, minimizing the deviation between the patient's physiological data and the target state;

[0117] The dynamic model adjustment mechanism is based on the patient's behavior data, specifically including:

[0118] Analyze the historical execution data and statistically analyze the time distribution of the actual operations completed by the patient before and after the planned time. For example, if 90% of the patients complete the operation within ±30 minutes, the adjustment range can be set to ±30 minutes.

[0119] In the initial stage of the system, the time window threshold can be initialized according to the treatment type and clinical experience. For example: 2 hours for ordinary drugs (such as vitamin supplements), 30 minutes for hormonal drugs or antibiotics.

[0120] Dynamically adjust according to the patient's compliance (behavior habits): If the patient often operates in advance or delays, but does not affect the treatment effect, it can be appropriately relaxed; if the patient's operation deviates too far from the planned time and leads to a decline in the treatment effect, the value should be reduced.

[0121] For the time calibration deviation Δt of , set the calibration frequency. In high-frequency operation scenarios (such as taking medicine or injecting multiple times a day), it is recommended to calibrate the time every 1 hour, and the calibration frequency in low-frequency operation scenarios (such as operating once a week) can be reduced to once a day. Collect the historical time calibration deviations within the current r time periods, and calculate the average value as the time calibration deviation Δt at the current time of .

[0122] When designing the data comparison and conflict detection scheme, the selection and setting of the above parameters are very crucial. They directly affect the fault tolerance of the system and the detection accuracy. The above is a detailed description of the parameters, including suggestions on how to obtain, set, and adjust.

[0123] Set up a conflict detection mechanism, classify conflicts according to the calculated comprehensive conflict impact value, and the methods for outputting the evaluation data set include:

[0124] Set the conflict category as time conflict C time 、dose conflict C dose and operation content conflict C con , and the conflict detection mechanism includes conflict detection logic and conflict flags;

[0125] The conflict detection logic is

[0126] Among them, Q ca_f represents the dose in the medication data or injection data, and Q ca_d represents the dose of taking medicine or injection in the treatment step, and Q cf represents the dose deviation threshold, which is set by the doctor for different drugs, and L ca_d represents the injection site, and M ca_f represents the drug name in the medication data, and M ca_d represents the drug name in the injection data;

[0127] The conflict flag is

[0128] Among them, C f represents the conflict score of the medication data, and C z represents the conflict score of the injection data, ω1 and ω2 represent weight coefficients, and 0 < ω1, ω2 < 1. The conflict score C f or C z is obtained by calculating the sum of the time conflict C time , the dose conflict C dose and the operation content conflict C con ;

[0129] Among them, TH1 and TH2 are conflict degree judgment thresholds, which can be set through experimental data analysis or according to experience. H1 represents a high-risk conflict, such as a serious dose deviation or a key drug omission. H2 represents a medium-risk conflict, such as a minor dose deviation or a small time delay. H3 represents a low-risk conflict; the patient's operation process is normal.

[0130] The output evaluation data set is D c ={(t1, M, Q, SL, C imp , H)|C imp >0};

[0131] Among them, in the conflict state, t1 represents the timestamp when taking the medication data or injection data, M represents the drug name in the medication data or injection data, Q represents the dose when taking the medication data or injection data, SL represents the medication plan (for example, three times a day or taking medicine at 8:00, 12:00, 6:00, 9:00 every day) or the injection site, and H represents the conflict degree, taking values of H1, H2, and H3 respectively. The conflicts are classified into different types (such as dose conflict, time conflict, etc.), and specific cause analysis is provided for patients or doctors.

[0132] The complete usage process is as follows:

[0133] 1. Input data: patient operation data, doctor treatment plan, time synchronization parameters, dose deviation threshold, and weight coefficients.

[0134] 2. Data processing: Calibrate all timestamps and map them to the cloud time; Apply the time window mechanism to avoid unnecessary time conflicts.

[0135] 3. Conflict detection: Detect whether the patient's operation time exceeds the allowed window; Detect whether the patient's operation dose exceeds the tolerance range; Operation content conflict: Detect whether the drug name or injection site meets the protocol requirements.

[0136] 4. Comprehensive conflict assessment

[0137] Calculate the comprehensive conflict impact value for each operation, classify the conflict data according to time conflict, dose conflict, and operation content conflict, and extract the corresponding sets.

[0138] 5. Output results

[0139] The output evaluation data set provides specific reason explanations for each conflict, such as time out of range, dose deviation, drug name mismatch, etc. According to the comprehensive conflict impact value, the conflicts are divided into three risk levels: high, medium, and low, and priority suggestions are provided.

[0140] Example is as follows:

[0141] Input data:

[0142] Patient operation data

[0143] Taking medicine: (10:00, Drug A, 50mg)

[0144] Injection: (10:30, Drug B, 100, abdomen)

[0145] Doctor's treatment plan

[0146] Taking medicine: (09:00, Drug A, 40mg)

[0147] Injection: (10:00, Drug B, 100mg, abdomen)

[0148] Time window: (1 hour)

[0149] Dose deviation threshold: (5mg).

[0150] Weights: (0.6, 0.4).

[0151] Conflict degree judgment threshold: (0.8, 0.4).

[0152] Detection process

[0153] Time conflict detection for taking medicine: (|10:00 - 09:00| = 1 hour) → Time synchronization, no conflict. Injection: (|10:30 - 10:00| = 0.5 hour) → Time synchronization, no conflict.

[0154] Dosage conflict detection for taking medicine: (|50 - 40| = 10mg > 5mg) → Dosage conflict.

[0155] Injection: (|100 - 100| = 0mg < 5mg) → No dosage conflict.

[0156] Detection of operation content conflict

[0157] Taking medicine: (Drug A) → No content conflict.

[0158] Injection: (Drug B, abdomen) → No content conflict.

[0159] Comprehensive evaluation

[0160] Impact value of taking medicine conflict: 0.6×(1 + 0) = 0.6

[0161] Impact value of injection conflict: 0.4×(0 + 0) = 0

[0162] Introduce time window and tolerance threshold to enhance fault tolerance. Through the weight mechanism and conflict classification, highlight key issues, improve the practicality of detection results, flexibly handle time and dosage deviations, avoid false alarms, and provide detailed conflict explanations and risk assessments. This solution realizes efficient, comprehensive and accurate data comparison and conflict detection, and is applicable to complex scenarios.

[0163] The evaluation of reproductive status is complex and is affected by the dynamic change laws of factors such as body temperature, hormone levels, and environment.

[0164] The method of setting up a reproductive status evaluation mechanism, calculating the reproductive status evaluation coefficient, and outputting an overall reproductive status report in combination with the evaluation data set includes:

[0165] Extract the physiological data, environmental data and the target status in the treatment plan when the timestamp is the largest (i.e., the latest time) from the auxiliary data, and calculate the hormone level deviation ΔJ, body temperature deviation ΔM, heart rate abnormality deviation and environmental impact deviation;

[0166] Among them, the hormone level deviation and body temperature deviation are obtained by calculating the absolute difference between the data in the current auxiliary data and the data in the target status, and the heart rate abnormality deviation The environmental impact deviation ΔE = α1×(T cu -T opt ) + α2×(U cu -U opt ) + α3×B cu ;

[0167] Set the normal heart rate range as [X min , X max , X cuIndicates the heart rate in the auxiliary data at the current time, where α1, α2, and α3 are weight coefficients, and T cu Indicates the temperature at the current time, T opt Indicates the optimal environmental temperature, U cu Indicates the humidity at the current time, U opt Indicates the optimal environmental humidity, B cu Indicates the air quality at the current time;

[0168] Calculate the reproductive status evaluation coefficient P cu = P ba -(ΔJ + ΔM + ΔX + ΔE + ΔJ tr + ΔM tr ), and output the overall reproductive status report Mn cu = (D c , P cu );

[0169] Among them, ΔJ tr Indicates the hormone level change rate, which is the change trend of the hormone level in the offline state. The calculation method is the absolute difference between the starting and ending hormone levels divided by the offline time. ΔM tr Indicates the body temperature change rate, and its calculation method is the same as that of the hormone level change rate.

[0170] Scheme renovation and visualization module: Generate a new treatment plan according to the overall reproductive status report, arrange it on the visualization interface, and interact with the patient;

[0171] The method of generating a new treatment plan according to the overall reproductive status report and arranging it on the visualization interface to interact with the patient includes:

[0172] When the network is restored and the mobile terminal is not in the offline state, terminate the push of all data in the offline state, and only push the overall reproductive status report and the new treatment plan to the patient and display them on the mobile terminal;

[0173] The patient operates and interacts with the displayed information through the mobile terminal, including querying, marking, and deleting.

[0174] The method of generating a new treatment plan according to the overall reproductive status report includes:

[0175] Send the overall reproductive status report to the doctor. The doctor evaluates the patient's reproductive status according to the overall reproductive status report and issues a new treatment plan;

[0176] The new treatment plan is uploaded to the cloud for storage.

[0177] Example 2

[0178] Please refer toFigure 1 As shown in Figure 1 , for the parts not described in detail in this embodiment, refer to the description in Embodiment 1. A method for managing assisted reproductive patients based on a mobile terminal is provided, including:

[0179] Step S1: Represent the data as a multi - tuple structure, and use an incremental synchronization mechanism to calculate the priority of each multi - tuple corresponding to the data. After dividing the data into K batches, upload it to the cloud;

[0180] Step S2: Collect the comprehensive information from the cloud and align the time of the comprehensive information by setting time synchronization parameters;

[0181] Step S3: Set time synchronization parameters to align the time of the comprehensive information;

[0182] Step S4: Set a conflict detection mechanism, classify conflicts according to the calculated comprehensive conflict impact value, and output an evaluation data set; Set a reproductive status evaluation mechanism, calculate the reproductive status evaluation coefficient, and output an overall reproductive status report in combination with the evaluation data set;

[0183] Step S5: Generate a new treatment plan according to the overall reproductive status report, arrange it on the visualization interface, and interact with the patient.

[0184] 1. Using a multi - tuple structure to encapsulate each piece of data provides comprehensive metadata for the data, making the management and synchronization of offline data more reliable and traceable. The version number mechanism can effectively track the modification history of the data, avoid data overwriting or loss, and ensure data consistency. Through the "complete" and "incomplete" status flags, it is possible to distinguish whether the data is complete, preventing incomplete or incorrect data from being uploaded to the cloud. Mandatory field verification, timestamp verification, and operation logic verification are introduced to ensure that only complete and valid data will be synchronized to the cloud, avoiding dirty data from contaminating the cloud database.

[0185] 2. Introduce a priority calculation formula, comprehensively consider data age, operation source, and operation status, and sort the data by priority. This ensures that critical data (such as emergency medical operations) can be uploaded first, reducing the impact of synchronization delay on the patient experience. Use the K - Means clustering algorithm to group the data, grouping similar data (based on priority and timestamp) into one group. This helps to reduce the upload pressure in high - concurrency situations, optimize the upload efficiency, and avoid network congestion. According to the clustering results, divide the data into multiple batches and upload them in the order of priority. This way can smooth the upload traffic and avoid sudden large - volume data uploads causing server overload.

[0186] 3. Introduce a time window threshold and a time calibration deviation to synchronize the time between the patient terminal and the cloud. This eliminates the misjudgment of conflicts caused by device clock differences and ensures the accuracy of conflict detection. The time window threshold can be dynamically adjusted according to the patient's behavior habits and treatment types, improving the flexibility and adaptability of the system. Classify conflicts into time conflicts, dose conflicts, and operation content conflicts, and detect them separately. This fine-grained conflict detection helps identify the root cause of problems and provides more specific solutions. Use flags and scores to quantify the severity of conflicts and classify them into high, medium, and low risks. Introduce a dose deviation threshold to allow a certain range of dose deviations, avoiding false alarms caused by overly strict conflict detection.

[0187] 4. Comprehensively consider the patient's physiological data, environmental data, and the doctor's treatment plan to comprehensively evaluate the patient's reproductive status. Consider various deviations and rates of change, calculate the reproductive status evaluation coefficient, and provide a quantitative indicator for the doctor to evaluate the patient's reproductive status.

[0188] 5. Send the overall reproductive status report to the doctor. The doctor evaluates the patient's reproductive status based on the report and issues a new treatment plan. This ensures the professionalism and safety of the treatment plan. The new treatment plan will be pushed to the patient, and the patient can view and operate on it (query, mark, delete) through the mobile terminal. This improves the patient's participation and compliance.

[0189] Embodiment 3

[0190] This embodiment discloses and provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the operation mode of the above-provided auxiliary reproductive patient management system based on a mobile terminal.

[0191] Since the electronic device introduced in this embodiment is the electronic device used in an auxiliary reproductive patient management system based on a mobile terminal in an embodiment of the present application, based on the auxiliary reproductive patient management system based on a mobile terminal introduced in an embodiment of the present application, those skilled in the art can understand the specific implementation manner and various forms of change of the electronic device in this embodiment. Therefore, the specific implementation of how this electronic device implements the method in the embodiment of the present application will not be described in detail here. As long as those skilled in the art implement the electronic device used in an auxiliary reproductive patient management system based on a mobile terminal in an embodiment of the present application, it falls within the scope of protection of the present application.

[0192] The above formulas are all dimensionless and take their numerical calculations. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0193] The above are only the preferred embodiments of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions within the idea of the present invention belong to the protection scope of the present invention. It should be noted that for ordinary users in the technical field, several improvements and refinements made without departing from the principle of the present invention should also be regarded as within the protection scope of the present invention.

Claims

1. An assisted reproductive patient management system based on a mobile terminal, characterized in that, Including: Offline Cache and Synchronization Module: It is used to perform multi - dimensional processing on the data of the mobile terminal in the offline state, represent the data in a multi - tuple structure, and use the incremental synchronization mechanism to calculate the priority of each multi - tuple corresponding to the data, and upload the data to the cloud after dividing it into K batches; Data Processing and Screening Module: Collect the comprehensive information from the cloud, align the time of the comprehensive information by setting time synchronization parameters, classify conflicts by setting a conflict detection mechanism, and obtain the current reproductive status of the patient; The conflict detection mechanism includes conflict detection logic, setting the conflict type as time conflict C time 、Dose conflict C dose Conflict with operation content C con ; The conflict detection logic is Among them, Q ca_f represents the dose in the medication data or injection data, Q ca_d represents the dose of taking medicine or injection in the treatment step, Q cf represents the dose deviation threshold, L ca_d represents the injection site, M ca_f represents the drug name in the medication data, M ca_d represents the drug name in the injection data, t ca_f is the timestamp of the patient's execution data, t ca_d is the timestamp of the doctor's treatment plan t ca_d , Δt cf is the time window threshold Δt cf , S cf is the doctor's treatment step; Solution Refurbishment and Visualization Module: Generate a new treatment plan according to the overall reproductive status report, and arrange it on the visualization interface to interact with the patient.

2. The assisted reproductive patient management system based on a mobile terminal according to claim 1, wherein The method of performing multi - dimensional processing on the data of the mobile terminal in the offline state includes: During the offline state of the mobile terminal, form a multi - tuple structure for each piece of data and cache it in the local storage; For each piece of data, the manifestation form of the multi - tuple structure is {x i |(t, v, s, w)}, where x i represents the i - th piece of data, i represents the index of the data, t represents the timestamp attached to the data, v represents the version number of the data, the version number is the number of modifications of the data, which is used to judge the latest degree of the data, s represents the operation source of the data, and w represents the operation status of the data, including complete and incomplete. The operation status is represented by a numerical value, where the numerical value 1 represents complete and the numerical value 2 represents incomplete; Use the incremental synchronization mechanism to upload the processed data to the cloud. The method is: Identify the operation status of each piece of data, and perform integrity verification on the data with w = 1. The integrity verification includes mandatory field verification, timestamp verification, and operation logic verification. If the data meets the integrity verification conditions, when the mobile terminal is in the non - offline state, upload all the multi - tuples corresponding to the data during the offline state to the cloud through the incremental synchronization mechanism, otherwise, keep them in the mobile terminal cache.

3. The assisted reproductive patient management system based on a mobile terminal according to claim 2, characterized in that, The method of uploading all the multi - tuples corresponding to the data during the offline state to the cloud through the incremental synchronization mechanism includes: Calculate the transmission priority of each piece of data according to the multi - tuple, and design the priority calculation formula Pr = ω1×ag + ω2×So + ω3×St, where ag represents the age of the data, So represents the performance coefficient of the data operation source, St represents the performance coefficient of the data operation status. If w = 1, then St = 1; if w = 2, then St = 0. ω1, ω2, and ω3 are weight coefficients, and 0 < ω1, ω2, ω3 < 1, which are used to adjust the influence of different factors on the priority; Group the data using the K-Means clustering algorithm. Set the input of the clustering algorithm to the priority and timestamp, and the output to K data groups. The clustering objective function is where G j represents the j-th clustering group, and μ j represents the center of the j-th clustering group, and K represents the total number of clustering groups; Divide the data into K batches according to K data groups, and upload each batch to the cloud in the order of priority from high to low.

4. The assisted reproductive patient management system based on a mobile terminal according to claim 3, characterized in that, The comprehensive information includes the patient's execution data, the doctor's treatment plan, and auxiliary data; The patient's execution data includes medication data and injection data; The medication data includes timestamp, drug name, dosage, and medication status; the injection data includes timestamp, drug name, dosage, and injection site; The doctor's treatment plan includes timestamp, treatment steps, and target status; The auxiliary data includes the patient's physiological data and environmental data. The physiological data includes timestamp, body temperature, heart rate, and hormone level; The environmental data includes timestamp, temperature, and humidity.

5. The assisted reproductive patient management system based on a mobile terminal according to claim 4, characterized in that The method of aligning the time of the comprehensive information by setting time synchronization parameters, classifying conflicts by setting a conflict detection mechanism, and obtaining the current reproductive status of the patient includes: Set time synchronization parameters, where the time synchronization parameters include a time window threshold Δt cf and a time calibration deviation Δt of ; Use the time calibration formula t ca = t lo + Δt of , and align the timestamp t lo on the patient mobile terminal with the timestamp t ca on the cloud; Set the time window mechanism as |t ca_f -t ca_d |≤Δt cf , if the timestamp t of the patient's execution data ca_f and the timestamp t of the doctor's treatment plan ca_d meet the time window mechanism, then it is judged that the time is synchronized, otherwise, the time is not synchronized; Set a conflict detection mechanism, classify conflicts according to the calculated comprehensive conflict influence value, and output an evaluation data set; Set a reproductive status evaluation mechanism, calculate the reproductive status evaluation coefficient, and output an overall reproductive status report in combination with the evaluation data set.

6. The assisted reproductive patient management system based on a mobile terminal according to claim 5, characterized in that, The method of setting the time synchronization parameters includes: For the time window threshold Δt cf , set Δt cf ∈(Δt cf -δ1, Δt cf +δ1), where δ1 represents the limit deviation time, automatically adjust the time window threshold based on the dynamic model, initialize the time window threshold, set the input of the dynamic model as the historical execution data, the output as the adjusted time window threshold, and the constraint condition is to maximize the treatment effect, that is, the deviation between the patient's physiological data and the target state is minimized; For the time calibration deviation Δt of , set the calibration frequency, collect the historical time calibration deviations within the current r time periods, and calculate the average value as the time calibration deviation Δt at the current time of .

7. The assisted reproductive patient management system based on a mobile terminal according to claim 6, characterized in that, The method of setting a conflict detection mechanism to classify conflicts according to the calculated comprehensive conflict impact value and output an evaluation data set includes: The conflict detection mechanism further includes a conflict flag; The conflict flag is Among them, C f represents the conflict score of medication data, and C z represents the conflict score of injection data. ω1 and ω2 represent weight coefficients, and 0 < ω1, ω2 < 1. The conflict score C f or C z is obtained by calculating the sum of the time conflict C time , the dose conflict C dose and the operation content conflict C con . Among them, TH1 and TH2 are conflict degree judgment thresholds, H1 represents a high-risk conflict, H2 represents a medium-risk conflict, and H3 represents a low-risk conflict; The output evaluation data set is D c ={(t1, M, Q, SL, C imp , H)|C imp > 0}; Among them, in the conflict state, t1 represents the time stamp when taking medicine data or injection data, M represents the drug name in the taking medicine data or injection data, Q represents the dose when taking medicine data or injection data, SL represents the taking medicine plan or injection site, and H represents the conflict degree, which are respectively valued as H1, H2, and H3.

8. The assisted reproductive patient management system based on a mobile terminal according to claim 7, characterized in that, The method of setting a reproductive state evaluation mechanism, calculating a reproductive state evaluation coefficient, and outputting an overall reproductive state report in combination with the evaluation data set includes: Extract the physiological data, environmental data, and the target state in the treatment plan when the time stamp is the largest from the auxiliary data, and calculate the hormone level deviation ΔJ, body temperature deviation ΔM, heart rate abnormality deviation, and environmental impact deviation; Among them, the hormone level deviation and body temperature deviation are obtained by calculating the absolute difference between the data in the current auxiliary data and the data in the target state, and the heart rate abnormality deviation The environmental impact deviation ΔE = α1×(T cu - T opt ) + α2×(U cu - U opt ) + α3×B cu ; Set the normal range of heart rate as [X min , X max , where X cu represents the heart rate in the auxiliary data at the current time, α1, α2, and α3 are weight coefficients, T cu represents the temperature at the current time, T opt represents the optimal ambient temperature, U cu represents the humidity at the current time, U opt represents the optimal ambient humidity, and B cu represents the air quality at the current time; Calculate the reproductive status evaluation coefficient P cu = P ba -(ΔJ + ΔM + ΔX + ΔE + ΔJ tr + ΔM tr ), and output the overall reproductive status report Mn cu = (D c , P cu ); Among them, ΔJ tr represents the change rate of hormone level, and ΔM tr represents the change rate of body temperature.

9. The assisted reproductive patient management system based on a mobile terminal according to claim 8, characterized in that The method of generating a new treatment plan according to the overall reproductive state report and arranging it on a visual interface to interact with the patient includes: When the network is restored and the mobile terminal is not in the offline state, terminate the push of all data in the offline state, and only push the overall reproductive state report and the new treatment plan to the patient and display them on the mobile terminal; The patient operates and interacts with the displayed information through the mobile terminal.

10. The assisted reproductive patient management system based on a mobile terminal according to claim 9, wherein The method of generating a new treatment plan according to the overall reproductive state report includes: Send the overall reproductive state report to the doctor, and the doctor evaluates the patient's reproductive state according to the overall reproductive state report and issues a new treatment plan; The new treatment plan is uploaded to the cloud for storage.

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