An apparatus and method for the irrigation of the anal region

Abstract

The application provides an anus part flushing device and a flushing method, and belongs to the technical field of medical equipment, and specifically comprises: a data monitoring module responsible for monitoring and analyzing flushing data, a flushing module responsible for flushing treatment of the anus of a patient, and a flushing control module responsible for adjusting the flushing flow of the flushing module, so that personalized adjustment of the flushing mode of the patient is realized.

Description

Technical Field

[0001] This invention belongs to the field of medical device technology, and in particular relates to a flushing device and flushing method for the anal area. Background Technology

[0002] When a patient has anal diseases, it is often necessary to clean or disinfect the patient's anus. Existing technical solutions often use mechanical devices or manual methods for cleaning or disinfection, but inevitably, there are problems such as poor cleaning efficiency or poor effect.

[0003] To improve the efficiency and effectiveness of cleaning, patent application CN202322175554.2, "An Anal Irrigator," proposes selecting different irrigation methods for different diseases, thereby improving the cleaning efficiency and effectiveness when patients irrigate their anus. However, the above technical solution has the following problems: Even for the same type of disease, the amount of rinsing varies among patients. If a uniform rinsing method is used without rinsing control based on the analysis of rinsing data, the cleaning results cannot be guaranteed.

[0004] To address the aforementioned technical problems, the present invention provides a device and method for rinsing the anal area. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted: According to one aspect of the present invention, a rinsing method is provided, specifically comprising: S1 acquires the patient's flushing data within a preset time period, and uses the analysis results of the flushing data to determine the similarity between the patient's flushing data and that of different historical patients, and determines the comprehensive similarity coefficient of the patient's similar reference patients and different similar reference patients based on the similarity. S2 uses the analysis results of the flushing data of the similar reference patients to determine the flushing time of different similar reference patients, and combines the comprehensive similarity coefficient of different similar reference patients to determine that real-time detection and processing of the flushing data of the patients is not required, and then proceeds to the next step; S3 determines the variation of the flushing data of different similar reference patients based on the analysis results of the flushing data of different similar reference patients, and determines the detection time of the flushing data of the patient based on the variation. S4 performs flushing treatment on the patient at a fixed flow rate until the detection time is reached, and dynamically adjusts the flushing flow rate of the patient based on the detection results of the flushing data after the detection time.

[0006] The beneficial effects of this invention are as follows: By using the flushing duration of different similar reference patients, it is possible to determine whether real-time monitoring and processing of patient flushing data is required. This avoids the technical problem of the high difficulty of monitoring and processing all patients' flushing data in real time. It also avoids the technical problem of not being able to adjust the flushing flow in time if the flushing duration is too short and real-time monitoring and processing cannot be performed. This reduces the difficulty of monitoring and processing and improves the reliability of flushing.

[0007] By dynamically adjusting the patient's flushing flow rate based on the flushing data after the detection time, the technical problem of low flushing efficiency caused by using a fixed flushing flow rate is avoided. It also avoids the technical problem of wasting sub-resources in flushing treatment caused by using a fixed flushing flow rate, thus realizing dynamic adjustment of the patient's flushing flow rate from multiple perspectives.

[0008] A further technical solution is that the preset duration is determined according to the patient's disease type, wherein the longer the rinsing time required for the patient's disease type, the longer the preset duration.

[0009] A further technical solution is that the similar situations include the deviation in the patient's drug absorption rate and the content of contaminants at different times.

[0010] A further technical solution is that the method for determining the similar reference patients is as follows: Based on the aforementioned similarities, the deviation between the patient's drug absorption rate and the content of contaminants at different times was determined. The deviation coefficient at different times is determined by the deviation of drug absorption rate and the deviation of dirt content at different times. The similar time points are determined based on the deviation coefficient, and the comprehensive similarity coefficient is determined using the proportion of similar time points. Based on the comprehensive similarity coefficient, it is determined whether the historical patients are similar reference patients.

[0011] A further technical solution is that the deviation coefficient is determined based on the average of the deviation rates of drug absorption rate and the deviation rates of dirt content at different times.

[0012] A further technical solution involves determining the detection time of the patient's irrigation data as follows: Based on the changes in the irrigation data of the similar reference patients, the irrigation data of the similar reference patients at different times are determined; The timing of changes in different similar reference patients is determined by using flushing data at different times, and the detection time of the flushing data of the patient is determined by using the timing of changes in different similar reference patients.

[0013] A further technical solution is that the change time is the earliest time when the change in the flushing data at adjacent times does not meet the requirements.

[0014] A further technical solution is that the detection time of the patient's rinsing data is determined based on the interval between the change time and the initial rinsing time of different similar reference patients, and the detection time is determined by using the average value of the interval between the change time and the initial rinsing time of different similar reference patients.

[0015] Secondly, the present invention provides a flushing device for the anal area, employing the above-mentioned flushing method, specifically including: Data monitoring module, flushing module, flushing control module; The data monitoring module is responsible for monitoring and analyzing the flushing data, the flushing module is responsible for flushing the patient's anus, and the flushing control module is responsible for adjusting the flushing flow rate of the flushing module.

[0016] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0019] Figure 1 This is a flowchart of a rinsing method.

[0020] Figure 2 This is a flowchart of the method for identifying similar reference patients.

[0021] Figure 3 This is a flowchart illustrating the method for determining the timing of patient flushing data detection.

[0022] Figure 4 This is a frame diagram of a device for rinsing the anal area. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0024] Example 1 like Figure 1 As shown, this application provides a rinsing method, specifically including: S1 acquires the patient's flushing data within a preset time period, and uses the analysis results of the flushing data to determine the similarity between the patient's flushing data and that of different historical patients, and determines the comprehensive similarity coefficient of the patient's similar reference patients and different similar reference patients based on the similarity. Comprehensive similarity coefficient: determined based on the proportion of similar moments within a preset time period. Specifically, similar moments are determined by the proportion of moments where the deviation between the flushing data of other patients and the patient is within 5%. Similar reference patients: other patients with a comprehensive similarity coefficient greater than 0.9.

[0025] S2 uses the analysis results of the flushing data of the similar reference patients to determine the flushing time of different similar reference patients, and combines the comprehensive similarity coefficient of different similar reference patients to determine that real-time detection and processing of the flushing data of the patients is not required, and then proceeds to the next step; It was determined that real-time detection and processing of the patient's flushing data was not required: Similar reference patients were divided into multiple intervals based on the comprehensive similarity coefficient of different similar reference patients. The comprehensive similarity coefficients corresponding to the intervals were 0.7-0.8, 0.8-0.9, and 0.9-1.0, respectively. When the proportion of similar reference patients with flushing time greater than 10 minutes in the 0.9-1.0 interval was greater than 0.4, it was determined that real-time detection and processing of the patient's flushing data was not required.

[0026] S3 determines the variation of the flushing data of different similar reference patients based on the analysis results of the flushing data of different similar reference patients, and determines the detection time of the flushing data of the patient based on the variation. The detection time of the patient's flushing data: The detection time of the patient's flushing data is determined by averaging the fluctuating times of the flushing data of different similar reference patients. Specifically, the fluctuating time is the time when the flushing data is within a preset range.

[0027] S4 performs flushing treatment on the patient at a fixed flow rate until the detection time is reached, and dynamically adjusts the flushing flow rate of the patient based on the detection results of the flushing data after the detection time.

[0028] Dynamic adjustment of patient flushing flow rate: Determine the number of abnormal fluctuation moments within a preset time period based on the changes in drug absorption rate and dirt content between different adjacent moments, and determine the abnormal fluctuation coefficient within the preset time period based on the changes in drug absorption rate and dirt content at different abnormal fluctuation moments. Determine whether the patient needs dynamic adjustment of flushing flow rate based on the abnormal fluctuation coefficient.

[0029] Furthermore, the preset duration is determined based on the patient's disease type, wherein the longer the rinsing time required for the patient's disease type, the longer the preset duration.

[0030] It should be noted that the flushing data includes the patient's drug absorption rate and the content of dirt and grime.

[0031] Understandably, the similarities include variations in the patient's drug absorption rate and the amount of contaminants at different times.

[0032] Specifically, such as Figure 2 As shown, the method for determining the similar reference patients is as follows: The flushing data refers to the drug absorption rate and the amount of waste material recorded for the current patient within a preset time period. The drug absorption rate reflects the degree to which the patient absorbs the flushing medication during the flushing process, and the amount of waste material reflects the amount of residual waste in the patient's anal area. The similarity data refers to the deviation between the drug absorption rate and the amount of waste material at different times between the current patient and historical patients.

[0033] Assuming the preset duration is the past 7 calendar days, the current patient is denoted as Patient A, and the historical patient set includes Patient B, Patient C, Patient D, Patient E, and Patient F, then the drug absorption rate and dirt content data for Patient A for each calendar day within the past 7 days are obtained. At the same time, the drug absorption rate and dirt content data for Patients B to F at corresponding moments within the past 7 days are obtained, and the deviation of the drug absorption rate and the deviation of the dirt content between Patient A and each historical patient at each corresponding moment are calculated.

[0034] This step provides a data foundation for subsequent screening of similar reference patients. Its significance lies in ensuring that the calculation of the comprehensive similarity coefficient is based on complete time-series bias data, avoiding inaccurate similarity judgments due to missing data, thereby improving the reliability of screening of similar reference patients.

[0035] S11 determines the deviation coefficient at different times based on the deviation of drug absorption rate and the deviation of dirt content at different times.

[0036] The deviation coefficient refers to a similarity index obtained by comprehensively quantifying the average deviation rate of drug absorption rate and the deviation rate of dirt content at a specific moment. It reflects the degree of deviation between the flushing data of the current patient and a historical patient at that moment.

[0037] Assume that at time T on the third natural day, patient A's drug absorption rate is 0.75 and the amount of waste material is 15 mg / cm³. 2 Patient B's drug absorption rate was 0.78, and the content of contaminated material was 14.5 mg / cm³. 2 The deviation rate of drug absorption is 3%, the deviation rate of dirt content is 3.33%, and the deviation coefficient at this moment is (3% + 3.33%) ÷ 2 = 3.165%.

[0038] This step establishes a unified standard for quantifying time dimension deviation. Its significance lies in normalizing the deviation between the two dimensions into a single coefficient, providing a comparable quantitative basis for subsequent determination of similar times.

[0039] S12 determines similar times based on the deviation coefficient, and uses the proportion of similar times to determine a comprehensive similarity coefficient, and determines whether historical patients are similar reference patients based on the comprehensive similarity coefficient.

[0040] The "similar time" refers to the moment when the deviation coefficient is within a preset range, reflecting a high degree of similarity between the current patient's flushing data and that of a historical patient at that moment. The "comprehensive similarity coefficient" refers to the proportion of similar moments within a preset time period to the total number of moments, quantifying the overall temporal similarity between a historical patient and the current patient. The "similar reference patient" refers to a historical patient whose comprehensive similarity coefficient is greater than a preset similarity coefficient threshold; their flushing data has high reference value.

[0041] Assuming the preset coefficient range is the time range where the deviation coefficient does not exceed 5%, there are 168 statistical time points in the past 7 calendar days (24 time points per calendar day). Patient B has a deviation coefficient of less than 5% for 152 time points. Therefore, the comprehensive similarity coefficient of patient B is 152 ÷ 168 ≈ 0.905. Assuming the preset similarity coefficient threshold is 0.90, then patient B's comprehensive similarity coefficient of 0.905 is greater than 0.90, and patient B is determined to be a similar reference patient to patient A.

[0042] This step achieves quantitative screening of similar reference patients by integrating similarity coefficients. Its significance lies in filtering out historical patient groups that are highly consistent with the current patient's flushing behavior using a unified threshold, providing a reliable reference basis for subsequent flushing duration analysis and detection timing determination.

[0043] It should be noted that when the overall similarity coefficient is greater than the preset similarity coefficient threshold, the historical patient is determined to be a similar reference patient.

[0044] When the overall similarity coefficient of a historical patient is greater than the preset similarity coefficient threshold, it indicates that the overall similarity between the historical patient and the current patient's flushing data within the preset time period is high, and it is sufficient to serve as a reference basis. Therefore, it is included in the set of similar reference patients.

[0045] Continuing with the example above, suppose the overall similarity coefficient for patient C is 0.893, for patient D it is 0.921, for patient E it is 0.876, and for patient F it is 0.908. With a preset similarity coefficient threshold of 0.90, patients B (0.905), D (0.921), and F (0.908) all have overall similarity coefficients greater than 0.90 and are therefore identified as similar reference patients; patients C (0.893) and E (0.876) do not exceed the threshold and are not considered similar reference patients.

[0046] This condition establishes the inclusion criteria for similar reference patients. Its significance lies in defining the similarity boundary with a threshold, ensuring that all historical patients entering subsequent analysis have sufficient wash data similarity to the current patient, and avoiding interference from low-similarity patients in the detection and judgment.

[0047] This embodiment establishes a similar reference patient screening system based on time-series deviation analysis through S11 and S12. Its core value is reflected in three aspects: First, it normalizes the multi-dimensional deviation into a single deviation coefficient, simplifying the complexity of similarity quantification; second, it uses the proportion of similar times as the comprehensive similarity coefficient, realizing the quantitative assessment of overall similarity; and third, it automatically screens high-similarity patients by setting a preset similarity coefficient threshold, reducing the subjectivity of manual intervention.

[0048] Specifically, it was determined that real-time monitoring and processing of the patient's irrigation data was not required, including: S21 divides the similar reference patients into different flushing time intervals based on the flushing time of different similar reference patients.

[0049] Similar reference patients were initially classified according to the comparison results between the irrigation time and the preset irrigation time, where the preset irrigation time was used as a reference benchmark to determine whether the irrigation time was too short, and it was determined according to the patient's disease type.

[0050] This step establishes an initial classification framework for the duration of similar reference patients. Its significance lies in quickly identifying similar reference patients with abnormal flushing durations by comparing them with preset flushing durations, thus providing a classification basis for subsequent refined judgments.

[0051] If, based on the rinsing time of different similar reference patients, it is determined that there are no similar reference patients whose rinsing time is less than the preset rinsing time, then it is determined that no real-time detection processing is required. If there are similar reference patients whose rinsing time is less than the preset rinsing time, proceed to step S212.

[0052] The preset flushing duration refers to the lower limit of the standard flushing time required for a single flush, determined based on the patient's disease type. It reflects the minimum duration required for normal flushing. The screened patients refer to similar reference patients whose flushing duration is less than the preset flushing duration, as abnormal flushing durations may affect the reliability of the judgment without real-time detection.

[0053] Assuming the preset flushing time is 12 minutes, patient B's flushing time is 18 minutes, patient D's flushing time is 22 minutes, and patient F's flushing time is 15 minutes, and the flushing time of the three similar reference patients is not less than 12 minutes, then it is determined that there are no similar reference patients whose flushing time is less than the preset flushing time, and it is directly determined that real-time detection and processing of patient A is not required.

[0054] Assuming that patient B's rinsing time is 18 minutes, patient D's rinsing time is 9 minutes, and patient F's rinsing time is 15 minutes, then patient D's rinsing time is less than 12 minutes. This indicates that there are similar reference patients whose rinsing time is less than the preset rinsing time, and we need to proceed to step S212 for further judgment.

[0055] This step establishes the first filtering condition for exemption from real-time detection by setting the rinsing time. Its significance lies in the fact that when the rinsing time of all similar reference patients is within the normal range, the conclusion of exemption from real-time detection is given directly, thereby improving the efficiency of judgment.

[0056] S212 Select similar reference patients whose rinsing time is less than the preset rinsing time as screening patients. When the proportion of the number of the screening patients is greater than the preset proportion, it is determined that real-time detection processing is required. When the proportion of the number of the screening patients is not greater than the preset proportion, proceed to step S213.

[0057] The screened patients refer to similar reference patients whose rinsing time is less than the preset rinsing time, reflecting the existence of individuals with insufficient rinsing time within the similar reference patient group. The proportion refers to the ratio of the screened patients to the total number of similar reference patients, reflecting the distribution ratio of similar reference patients with abnormal rinsing time in the overall population.

[0058] Assume the preset flushing time is 12 minutes and the preset number ratio is 0.30. Patient B's flushing time is 18 minutes, Patient D's flushing time is 9 minutes, and Patient F's flushing time is 15 minutes. Patient D is selected as the first patient, and the total number of similar reference patients is 3. The selected patient's number ratio = 1 ÷ 3 ≈ 0.333, which is greater than the preset number ratio of 0.30. Therefore, it is determined that real-time monitoring and processing of Patient A is required.

[0059] This step uses the proportion of screened patients as a necessary condition for real-time detection. Its significance lies in the fact that when the proportion of similar reference patients with insufficient rinsing time is too high, it indicates that there may be uncertainty in the current patient's rinsing process, and real-time detection is required to ensure safety.

[0060] S213 Based on the rinsing time of different similar reference patients, the similar reference patients are divided into different rinsing time intervals. When there is a rinsing time interval in which the proportion of similar reference patients is greater than a preset proportion, it is determined whether real-time detection processing is required based on the average rinsing time of similar reference patients in the rinsing time interval in which the proportion of similar reference patients is greater than the preset proportion. When there is no rinsing time interval in which the proportion of similar reference patients is greater than the preset proportion, proceed to step S22.

[0061] The irrigation duration intervals refer to time periods categorized based on the numerical range of irrigation duration, with each interval corresponding to a specific duration range. The average irrigation duration refers to the arithmetic mean of the irrigation durations of all similar reference patients within the same irrigation duration interval, reflecting the typical irrigation duration level of that interval.

[0062] This step uses the distribution of rinsing time intervals as the second-level judgment dimension. Its significance lies in that when the proportion of screened patients is not high, the interval distribution analysis can be used to further assess the concentration trend of rinsing time in similar reference patient groups. If there is a high proportion interval, the average time of that interval can be used directly as the judgment basis.

[0063] S22 uses the comprehensive similarity coefficient of different similar reference patients in different irrigation duration intervals as a basis to determine the irrigation duration confidence coefficient for different irrigation duration intervals.

[0064] The reliability coefficient of the irrigation duration refers to the average of the comprehensive similarity coefficients of all similar reference patients within a certain irrigation duration interval, reflecting the reliability of that irrigation duration interval in the similarity dimension. The higher the reliability coefficient of the irrigation duration, the more consistent the irrigation behavior of similar reference patients in that interval is with the current patient, and the more valuable the irrigation duration of that interval is as a reference.

[0065] For example, suppose the similar reference patients and their composite similarity coefficients for the three rinse duration intervals are as follows: Patient D is included in the 8 to 12 minute interval (composite similarity coefficient 0.923), and the confidence coefficient for the rinse duration in this interval is 0.923; Patient F is included in the 12 to 16 minute interval (composite similarity coefficient 0.911), and the confidence coefficient for the rinse duration in this interval is 0.911; Patient B is included in the 16 to 20 minute interval (composite similarity coefficient 0.905), and the confidence coefficient for the rinse duration in this interval is 0.905.

[0066] This step introduces a weighted evaluation of the flushing duration intervals using a comprehensive similarity coefficient. The significance of this is to ensure that subsequent judgments not only depend on the numerical distribution of flushing durations, but also take into account the similarity weights of similar reference patients in each interval, thus avoiding the undue influence of patients with low similarity.

[0067] S221 uses the comprehensive similarity coefficient of different similar reference patients in different irrigation duration intervals as a basis to determine the irrigation duration confidence coefficient for different irrigation duration intervals, and takes the irrigation duration interval with the largest irrigation duration confidence coefficient as the confidence interval.

[0068] The confidence interval refers to the flushing time interval with the highest confidence coefficient, which represents the time range of the subgroup with the highest similarity in the similar reference patient group.

[0069] Continuing with the above example, the confidence coefficient for the rinsing duration in the 8-12 minute interval is 0.923, the confidence coefficient for the rinsing duration in the 12-16 minute interval is 0.911, and the confidence coefficient for the rinsing duration in the 16-20 minute interval is 0.905. Among them, the confidence coefficient for the 8-12 minute interval is the largest at 0.923, and it is determined to be the confidence interval.

[0070] This step maximizes the confidence coefficient to select the most valuable rinsing time range. Its significance lies in focusing on the subgroup with the highest similarity, providing a core basis for subsequent detailed judgment.

[0071] S222 When the number of similar reference patients in the confidence interval is greater than the preset patient number threshold, proceed to step S223; when the number of similar reference patients in the confidence interval is not greater than the preset patient number threshold, proceed to step S23.

[0072] The preset patient number threshold refers to the minimum number of similar reference patients within the confidence interval to determine whether the number of such patients is sufficient. It reflects the minimum sample size required for statistical judgment.

[0073] This step uses the sample size of the confidence interval as a threshold for judgment. Its significance is that when the number of similar reference patients within the confidence interval is insufficient, the direct judgment of that interval is skipped and the analysis of all confidence intervals is turned to avoid judgment bias caused by small samples.

[0074] S223 When the proportion of similar reference patients in the confidence interval is greater than the preset patient proportion threshold, the average flushing time of the similar reference patients corresponding to the confidence interval is used to determine whether real-time detection processing is required. When the proportion of similar reference patients in the confidence interval is not greater than the preset patient proportion threshold, proceed to step S23.

[0075] The preset patient number percentage threshold refers to the proportion benchmark for determining whether the proportion of similar reference patients within the confidence interval reaches statistical significance, and it reflects the minimum percentage requirement required for duration determination.

[0076] This step, based on a sufficient sample size, further examines the significance of the proportion of the confidence interval. Its purpose is to ensure that the intervals used for judgment are sufficiently representative and to avoid a single interval dominating the overall conclusion.

[0077] S23 determines the reliable flushing time range based on the reliability coefficient of the flushing time range for different flushing time ranges, and determines the reference flushing time for the patient based on the reliable flushing time range, and uses the reference flushing time to determine whether real-time detection and processing is required.

[0078] The credible flushing duration interval refers to the flushing duration interval where the flushing duration credibility coefficient is greater than the preset credibility coefficient. It represents the duration range of a subgroup with high similarity to the current patient. The reference flushing duration refers to the average flushing duration of all similar reference patients within the credible flushing duration interval, which reflects the typical flushing duration level of highly similar patients.

[0079] Assuming a preset confidence coefficient of 0.90, the confidence coefficients for flushing durations in the 8-12 minute interval are 0.923 (greater than 0.90), 12-16 minute intervals are 0.911 (greater than 0.90), and 16-20 minute intervals are 0.905 (greater than 0.90). Therefore, the 8-12 minute, 12-16 minute, and 16-20 minute intervals are all considered reliable flushing duration intervals. Similar reference patients within these reliable flushing duration intervals include Patient D (10-minute flushing), Patient F (15-minute flushing), and Patient B (18-minute flushing). The reference flushing duration is approximately (10 + 15 + 18) ÷ 3 ≈ 14.33 minutes. Since the preset duration threshold is 13 minutes, and the reference flushing duration of 14.33 minutes is not less than the duration threshold of 13 minutes, it is determined that real-time monitoring and processing for Patient A is not required.

[0080] This step uses the credible rinsing time range as the final analysis scope. Its significance lies in integrating the information of all highly similar ranges and giving the final conclusion by comparing the reference rinsing time with the time threshold, ensuring that the judgment is based on the most valuable sample.

[0081] This embodiment establishes a multi-level progressive real-time detection exemption judgment system through S21 to S23. Its core value is reflected in three aspects: First, it uses initial duration screening to quickly exclude similar reference patients with obvious abnormalities, thereby improving judgment efficiency; second, it captures the duration concentration trend of patients through the analysis of the flushing duration interval distribution, thereby achieving refined assessment; and third, it uses the comprehensive similarity coefficient to weight the credibility of each interval, ensuring that the judgment result reflects both the duration distribution and the similarity weight, avoiding undue interference from patients with low similarity.

[0082] Specifically, such as Figure 3 As shown, the method for determining the detection time of the patient's flushing data is as follows: The core objective of this embodiment is to determine the detection time for the current patient's flushing data based on the fluctuations in flushing data from similar reference patients, providing a timing benchmark for subsequent dynamic flow adjustments. Its core logic is as follows: based on the analysis of fluctuation times from similar reference patients, through comparison of reference value coefficients within a unit time period, and through multiple levels of screening including no fluctuation times, insufficient number of patients per unit time period, and reference value coefficients outside the range, the final set of detection times requiring focused attention is determined using the reference unit time period as a benchmark, combined with deviation judgments and risk value calculations from other reference unit time periods. The overall logic follows the process of "determining fluctuation times → analyzing unit time periods → calculating reference value coefficients → branch judgments for five types of cases → determining detection times".

[0083] S31 determines the flushing data of the similar reference patient at different times based on the changes in the flushing data of the similar reference patient.

[0084] The drug absorption rate of similar reference patients at each statistical moment within a preset time period is used as the flushing data at that moment to form a complete time series dataset.

[0085] Continuing with the examples of S1 and S2, the flushing data for patient B at 24 time points on the first calendar day are as follows: drug absorption rate at time t1 is 0.60, drug absorption rate at time t2 is 0.63, and so on up to drug absorption rate at time t24 is 0.92.

[0086] This step transforms the raw flushing data from similar reference patients into an analyzable time series, which is significant in that it provides a complete temporal data foundation for the identification of subsequent changes.

[0087] S32 determines the time of change of different similar reference patients by using the flushing data at different times, and determines the detection time of the flushing data of the patient by using the time of change of different similar reference patients.

[0088] The change time refers to the earliest time when the change in flushing data compared to adjacent times does not meet the requirements; it reflects the moment when a significant change first occurs during the flushing process. The detection time refers to the time point to be detected, determined based on the average interval between the change time and the initial flushing time of different similar reference patients; it represents the typical detection opportunity that needs to be monitored for the current patient.

[0089] Assume the variation requirements are that the change in drug absorption rate between adjacent time points does not exceed 0.05 and the change in the content of contaminants does not exceed 2 mg / cm³. 2 Between t7 and t8, patient B's drug absorption rate changed from 0.70 to 0.78. The change of 0.08 exceeded 0.05, which did not meet the change requirement. Furthermore, t8 was the first time in this unit of time that the change did not meet the requirement. Therefore, t8 was determined to be the time of change for patient B in this unit of time.

[0090] This step extracts key change nodes from similar reference patients by identifying the time of change. Its significance lies in transforming continuous time series data into sparse, key change events, which facilitates subsequent statistical analysis and determination of detection time.

[0091] In scenario 1, the flushing data of the similar reference patients at different times is determined based on the changes in the flushing data of the similar reference patients. The change times of different similar reference patients are determined by the flushing data at different times. When there are no change times within the unit time period, it is determined that the time within the unit time period does not belong to the detection time.

[0092] When the changes in adjacent flushing data at all times within a certain time period meet the change requirements, it indicates that the flushing process of similar reference patients within that time period is in a stable state and there are no abnormal changes that require special attention. Therefore, all times within that time period are not used as detection times.

[0093] Assuming the time period consists of 4 statistical time points, within the second time period (t5 to t8), the variation in drug absorption rate for patients B, D, and F at each adjacent time point does not exceed 0.05, and the variation in the content of soiled substances does not exceed 2 mg / cm³. 2 If it is determined that there are no changing moments within the second time unit, then moments t5, t6, t7, and t8 within that time unit are not considered detection moments.

[0094] This establishes the first filtering condition for candidate units of detection time. Its significance lies in excluding periods when the rinsing process is completely stable and focusing on periods where there are indeed changes and fluctuations, so as to avoid including too many times in the detection range and reducing detection efficiency.

[0095] In scenario 2, when there are changing moments within the unit time period, the number of similar reference patients in different unit time periods is determined by the similar reference patients corresponding to the changing moments in different unit time periods. When the number of similar reference patients in the unit time period is less than the patient number threshold, it is determined that the moment in the unit time period does not belong to the detection moment.

[0096] The number of similar reference patients within a given time period refers to the number of similar reference patients whose time of change occurred within the same time period, reflecting the patient coverage of the change event within that time period. When the number of changing patients is too small, it indicates that the change within that time period belongs only to individual patients rather than a group characteristic, and is not representative of the current patient detection time.

[0097] Assuming each time period consists of 4 statistical moments, and the preset patient number threshold is 2, the entire statistical duration of 168 moments is divided into 42 time periods. In the 5th time period (times t17 to t20), patient B's time of change is at time t18, patient D's time of change is at time t19, and patient F's time of no change is within this time period. Therefore, the similar reference patients corresponding to the time of change within this time period include patients B and D, a total of 2 similar reference patients, which is exactly equal to the preset patient number threshold of 2. Thus, the process proceeds to the next level of judgment.

[0098] In this case, the variable patient coverage is used as the second filtering condition. Its significance is to exclude time periods with sparse changes or those involving only individual patients, ensuring that the time periods included in the subsequent analysis have sufficient population representativeness.

[0099] Case 3: When the number of similar reference patients within the unit time period is not less than the patient number threshold, the reference value coefficient within different unit time periods is determined by the number of similar reference patients within different unit time periods and the comprehensive similarity coefficient of different similar reference patients. When the reference value coefficient within the unit time period is not within the preset reference coefficient range, it is determined that the time within the unit time period does not belong to the detection time.

[0100] The reference value coefficient refers to a composite index that comprehensively considers the number of patients changing within a certain unit of time and their overall similarity coefficient. It reflects the value and reliability of that unit of time as a testing time. When the reference value coefficient is not within the preset reference coefficient range, it indicates that the testing value of that time period is too high or too low, and does not conform to the expected distribution of testing opportunities.

[0101] Assuming the preset reference coefficient range is 0.60 to 0.80, and the preset patient number threshold is 2, within the 5th time unit (t17 to t20), the similar reference patients corresponding to the changing time points include patient B (overall similarity coefficient 0.905) and patient D (overall similarity coefficient 0.923), a total of 2. The reference value coefficient = number of changing patients × average overall similarity coefficient = 2 × (0.905 + 0.923) ÷ 2 = 2 × 0.914 = 1.828. Since the reference value coefficient of 1.828 is above the preset reference coefficient range of 1, the process proceeds to the next step.

[0102] In this case, the reference value coefficient is used as the third filtering condition. Its significance lies in further screening out the time periods that meet the expected distribution of detection value, ensuring that the finally determined detection time has appropriate detection necessity and reference value.

[0103] Case 4: When the reference value coefficient within the unit time period is within the preset reference value coefficient range, the unit time period with the largest reference value coefficient is taken as the reference unit time period. When the deviations of the reference value coefficients of different unit time periods from the reference value coefficient of the reference unit time period are all outside the preset deviation range (i.e., all have large deviations from the reference value coefficient of the reference unit time period), the time within the preset duration before and after the change time of similar reference patients within the reference unit time period is taken as the detection time of the patient's flushing data.

[0104] The reference unit time period refers to the time period with the highest reference value coefficient among all unit time periods that pass the first three filtering conditions, representing the moment with the highest detection value. The preset deviation interval refers to the threshold range used to determine whether the deviation of other candidate time periods from the reference unit time period is small. When the deviations are all outside the preset deviation interval (i.e., the deviations are large), it indicates that there are no other time periods with a value close to that of the reference unit time period, and therefore the detection time is determined solely based on the reference unit time period.

[0105] This establishes a mechanism for determining the reference time period. Its significance lies in selecting the most valuable time period from the candidate time periods that have passed the first three filters, and judging whether there are similar time periods that can be referenced by the value deviation between other time periods and the reference time period, thus providing a basis for determining the subsequent detection time range.

[0106] Case 5: When there is a unit time period whose deviation from the reference value coefficient of the reference unit time period is not within the preset deviation range (i.e., the deviation is small), the unit time period whose deviation from the reference value coefficient of the reference unit time period is not within the preset deviation range (i.e., the deviation is small) is taken as another reference unit time period. The detection and processing risk value in the reference unit time period is determined by the number of other reference unit time periods and the reference value coefficient of the other reference unit time periods. It is determined whether the detection and processing risk value in the reference unit time period is greater than the preset risk threshold. If so, the time within a preset duration before and after the change time of the reference unit time period and other reference unit time periods is taken as the detection time of the patient's flushing data. If not, the time within a preset duration before and after the change time of similar reference patients in the reference unit time period is taken as the detection time of the patient's flushing data, and the time within a second preset duration before and after the change time of other reference unit time periods is taken as the detection time of the patient's flushing data.

[0107] The other reference unit time periods refer to candidate unit time periods with small deviations from the reference value coefficient of the reference unit time period (within a preset deviation range), representing referable time periods with similar values ​​to the reference unit time period. The detection and processing risk value refers to an indicator used to assess the degree of risk faced when relying solely on the reference unit time period for detection and processing, taking into account the number of other reference unit time periods and their reference value coefficients. The second preset duration refers to the subsequent time window used to determine the detection time range under the condition of other reference unit time periods, and it is shorter than the preset duration.

[0108] The detection and processing risk value = number of other reference unit time periods × average reference value coefficient of other reference unit time periods = 2.97. The preset risk threshold is 2.5. Since the detection and processing risk value of 2.97 is greater than 0.70, the time within 8 statistical time periods before and after the change time of the reference unit time period and other reference unit time periods are all used as detection time periods.

[0109] This situation establishes a joint judgment mechanism with multiple reference unit time periods. Its significance lies in determining the coverage of the detection time through risk assessment when other time periods with similar value to the reference unit time period exist. When the risk is high, the coverage is expanded to improve the reliability of detection. When the risk is low, the reference unit time period is used as the main reference and other time periods are covered with a narrower second duration, so as to ensure the detection effect while avoiding over-detection.

[0110] It should be noted that the preset duration is longer than the second preset duration.

[0111] The relationship between the preset duration and the second preset duration ensures that the detection time coverage of the main reference unit time period is greater than that of other reference unit time periods. Its significance lies in highlighting the core position of the reference unit time period, while taking into account other reference unit time periods with a narrower second duration, thus achieving a clear distribution of detection time with primary and secondary characteristics.

[0112] This embodiment establishes a multi-level detection time determination system based on variation analysis through S31 to S32 in conjunction with Situations 1 to 5. Its core value is reflected in four aspects: First, it filters out time periods with detection value layer by layer using progressively filtering conditions such as no variation time periods, insufficient number of patients per unit time period, and reference value coefficients not being in the range; second, it determines the reference unit time period by maximizing the reference value coefficient, establishing the core benchmark for detection time; third, it judges whether there are similar time periods that can be referenced by the value deviation between other time periods and the reference time period, realizing intelligent time period correlation analysis; and fourth, it determines the coverage strategy of detection time based on the detection processing risk value, optimizing detection efficiency while ensuring detection reliability.

[0113] Example 2 Secondly, such as Figure 4 As shown, this application provides a device for rinsing the anal area, employing one of the above-mentioned rinsing methods, specifically including: Data monitoring module, flushing module, flushing control module; The data monitoring module is responsible for monitoring and analyzing the flushing data, the flushing module is responsible for flushing the patient's anus, and the flushing control module is responsible for adjusting the flushing flow rate of the flushing module based on the analysis results of the flushing data.

[0114] Specifically, the data monitoring module is responsible for monitoring and analyzing the patient's flushing data. It includes a drug absorption rate sensor and a dirt content sensor, which collect data on the drug absorption rate and dirt content of the patient's anus during the flushing process in real time, and transmit the data to the flushing control module.

[0115] The flushing module is responsible for flushing the patient's anus. It includes a flushing pipe, a flow control valve, and a nozzle. The flushing pipe is connected to an external medicine supply device. The flow control valve receives the flow command from the flushing control module and adjusts the flow rate of the medicine. The nozzle is located at the end of the flushing pipe and is aimed at the patient's anus.

[0116] The flushing control module is responsible for adjusting the flushing flow rate of the flushing module based on the analysis results of the flushing data. Specifically, it includes a data receiving unit, an analysis and judgment unit, and a flow command unit. The data receiving unit obtains flushing data from the data monitoring module, the analysis and judgment unit determines whether dynamic flow rate adjustment is needed based on the flushing data, and the flow command unit sends a flow rate adjustment command to the flow control valve of the flushing module.

[0117] Furthermore, the flushing data includes the patient's drug absorption rate and the amount of dirt.

[0118] Specifically, adjusting the flushing flow rate of the flushing module includes: Using the analysis results of the flushing data, the changes in the patient's drug absorption rate and the content of dirt were determined; The flushing flow rate of the flushing module is adjusted based on the changes in the patient's drug absorption rate.

[0119] Furthermore, when the variation in the patient's drug absorption rate exceeds a preset absorption rate threshold or the variation in the content of contaminants exceeds a preset content threshold, the flushing flow rate of the flushing module is adjusted according to a preset flow rate.

[0120] Furthermore, the flushing data includes the patient's drug absorption rate and the amount of dirt.

[0121] Specifically, adjusting the flushing flow rate of the flushing module includes: using the analysis results of the flushing data to determine the changes in the patient's drug absorption rate and the content of dirt; and using the changes in the patient's drug absorption rate to adjust the flushing flow rate of the flushing module.

[0122] Furthermore, when the variation in the patient's drug absorption rate exceeds a preset absorption rate threshold or the variation in the content of contaminants exceeds a preset content threshold, the flushing flow rate of the flushing module is adjusted according to a preset flow rate.

[0123] Specifically, the dynamic adjustment scheme for the flushing flow rate is as follows: The flow command unit of the flushing control module sends a corresponding flow adjustment command to the flow control valve based on the abnormal variation coefficient output by the analysis and judgment unit. The abnormal variation coefficient is calculated as follows: Assume there are N adjacent time pairs within a preset time period. The difference in drug absorption rate between the i-th adjacent time and time i-1 is ΔA_i, and the preset absorption rate threshold is A_th. When ΔA_i is greater than A_th, the i-th adjacent time pair is determined to be an abnormal variation time. Abnormal variation coefficient = (Average sum of ΔA_i for different abnormal variation times) ÷ A_th.

[0124] The specific adjustment schemes include the following three modes: The first mode is a fixed baseline mode. When the abnormal fluctuation coefficient does not exceed the preset abnormal fluctuation coefficient threshold, the flow command unit sends a maintenance command to the flow control valve to maintain the current flow rate. The flow control valve maintains its current opening, and the flushing module continues to perform flushing treatment at the current flow rate. For example, when the abnormal fluctuation coefficient is 1.1 and the preset abnormal fluctuation coefficient threshold is 1.3, the flow command unit sends a maintenance command, and the flow control valve maintains its opening to keep the flushing flow rate at the current 80 mL / min.

[0125] The second mode is incremental adjustment. When the abnormal variation coefficient exceeds the preset abnormal variation coefficient threshold but does not exceed the incremental adjustment upper limit coefficient, the flow command unit sends an incremental adjustment command to the flow control valve according to the preset flow increment value. The flow control valve increases its opening according to the increment value to increase the flushing flow rate. For example, when the abnormal variation coefficient is 1.4, the preset abnormal variation coefficient threshold is 1.3, the preset incremental adjustment upper limit coefficient is 1.5, and the preset flow increment is 20 mL / min, the flow command unit sends an incremental adjustment command, and the flow control valve increases its opening to increase the flushing flow rate from the current 80 mL / min to 100 mL / min.

[0126] The third mode is the large-scale adjustment mode. When the abnormal fluctuation coefficient exceeds the upper limit of the incremental adjustment coefficient, the flow command unit sends a large-scale adjustment command to the flow control valve according to the preset large-scale flow adjustment value. The flow control valve significantly increases its opening according to the large-scale adjustment value to significantly increase the flushing flow rate. At the same time, an alert signal is sent to the monitoring terminal to prompt medical staff to pay attention. For example, when the abnormal fluctuation coefficient is 1.51, which is greater than 1.5, and the preset large-scale flow adjustment value is 40 mL / min, the flow command unit sends a large-scale adjustment command. The flow control valve significantly increases its opening, increasing the flushing flow rate from the current 80 mL / min to 120 mL / min. At the same time, an alert signal is sent to the monitoring terminal to prompt medical staff that there is a significant abnormal fluctuation in the current patient flushing process.

[0127] It should be noted that when the abnormal fluctuation coefficient gradually falls back to the corresponding range, the rinsing treatment is carried out according to the flow rate in the corresponding range, such as 100ml / min or 80ml / min.

[0128] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0129] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0130] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A rinsing method, characterized in that, Specifically, it includes: Acquire patient flushing data within a preset time period, and use the analysis results of the flushing data to determine the similarity between the patient and the flushing data of different historical patients. Based on the similarity, determine the comprehensive similarity coefficient of the patient's similar reference patients and different similar reference patients. Based on the analysis results of the flushing data of the similar reference patients, the flushing time of different similar reference patients is determined, and when the comprehensive similarity coefficient of different similar reference patients determines that real-time detection and processing of the flushing data of the patients is not required, the process proceeds to the next step. Based on the analysis results of the flushing data of different similar reference patients, the variation of the flushing data of different similar reference patients is determined, and the detection time of the flushing data of the patient is determined based on the variation. The patient is flushed at a fixed flow rate until the detection time is reached. The flushing flow rate is then dynamically adjusted based on the flushing data after the detection time.

2. The rinsing method as described in claim 1, characterized in that, The preset duration is determined based on the patient's disease type, wherein the longer the required rinsing time is for the patient's disease type, the longer the preset duration is.

3. The rinsing method as described in claim 1, characterized in that, The similarities include the deviations in the patient's drug absorption rate and the amount of contaminants at different times.

4. The rinsing method as described in claim 1, characterized in that, The method for determining the similar reference patients is as follows: Based on the aforementioned similarities, the deviation between the patient's drug absorption rate and the content of contaminants at different times was determined. The deviation coefficient at different times is determined by the deviation of drug absorption rate and the deviation of dirt content at different times. The similar time points are determined based on the deviation coefficient, and the comprehensive similarity coefficient is determined using the proportion of similar time points. Based on the comprehensive similarity coefficient, it is determined whether the historical patients are similar reference patients.

5. The rinsing method as described in claim 4, characterized in that, The deviation coefficient is determined based on the average of the deviation rates of drug absorption rate and the deviation rates of contaminant content at different times.

6. The rinsing method as described in claim 1, characterized in that, The method for determining the detection time of the patient's irrigation data is as follows: Based on the changes in the irrigation data of the similar reference patients, the irrigation data of the similar reference patients at different times are determined; The timing of changes in different similar reference patients is determined by using flushing data at different times, and the detection time of the flushing data of the patient is determined by using the timing of changes in different similar reference patients.

7. A device for rinsing the anal area, employing the rinsing method according to any one of claims 1-6, characterized in that, Specifically, it includes: Data monitoring module, flushing module, flushing control module; The data monitoring module is responsible for monitoring and analyzing the flushing data, the flushing module is responsible for flushing the patient's anus, and the flushing control module is responsible for adjusting the flushing flow rate of the flushing module based on the analysis results of the flushing data.

8. The anal irrigation device as described in claim 7, characterized in that, The flushing data includes the patient's drug absorption rate and the amount of dirt.

9. The anal irrigation device as described in claim 7, characterized in that, Adjusting the flushing flow rate of the flushing module specifically includes: Using the analysis results of the flushing data, the changes in the patient's drug absorption rate and the content of dirt were determined; The flushing flow rate of the flushing module is adjusted based on the changes in the patient's drug absorption rate.

10. The anal irrigation device as described in claim 7, characterized in that, When the patient's drug absorption rate changes by more than a preset absorption rate threshold or the content of dirt and grime changes by more than a preset content threshold, the flushing flow rate of the flushing module is adjusted according to the preset flow rate.

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