A cloud health service platform for online real-time detection of drinking water based on multi-dimensional parameters

Abstract

The present invention belongs to the technical field of drinking water quality detection, and specifically discloses a cloud health service platform for online real-time detection of drinking water based on multi-dimensional parameters, including a detection record season classification module, a sensitive water quality parameter identification module, a drinking water record season classification module, a preferred drinking water time period analysis module, a water quality detection frequency adjustment module and a water quality detection abnormality display module. The historical water quality detection records of shared drinking water points are divided by season, and the sensitive water quality parameters corresponding to each season are analyzed, and then these sensitive water quality parameters are displayed and associated with the drinking water sensing terminal, thereby realizing key detection and intuitive display based on multi-dimensional water quality detection. This method not only avoids excessive consumption of detection resources, but also avoids the inconvenience and increased operation and management costs caused by using smart water cups for display association by using drinking water sensing terminals, thereby virtually enhancing the reliability and sustainability of the system.

Description

Technical Field

[0001] The present invention belongs to the technical field of drinking water quality detection, and in particular relates to shared drinking water quality detection, and specifically discloses a cloud health service platform for online real-time detection of drinking water based on multi-dimensional parameters. Background Art

[0002] With the growth of the global population and the acceleration of urbanization, water shortage and water quality safety issues are becoming increasingly serious. As an emerging service model, shared drinking water is widely used in public places and residential areas through centralized water purification equipment and intelligent management systems to provide users with safe and healthy drinking water. However, once shared drinking water has water quality problems, it will cause serious harm, especially in residential areas, where the scope and degree of harm of water quality problems will be more serious due to the wide range of water users and high frequency of water use. Therefore, it is particularly important to conduct real-time and effective water quality testing of shared drinking water.

[0003] There are also technical solutions related to shared drinking water quality detection in the prior art. For example, the Chinese invention patent with publication number CN107192802B discloses a method and system for online monitoring of shared direct drinking water quality. The neural network input sample set is established according to the control parameters affecting the water quality in the constant water tank, and then the neural network output sample set is established according to the water quality indicators in the constant water tank measured in real time. Then, the input sample set and the output sample set are normalized to construct a GRNN network model to achieve real-time prediction of the water quality in the constant water tank and realize the adaptive dynamic self-learning online monitoring of the shared direct drinking water quality. This solution achieves high-precision prediction of the shared drinking water quality through a large amount of historical data training, but does not fully consider the correlation between water quality and season. The meteorological conditions in different seasons are significantly different, resulting in different fluctuation characteristics of water quality parameters in different seasons. Comprehensive detection of all water quality parameters at all times not only fails to highlight the detection focus, but also leads to excessive consumption of detection resources and increases unnecessary detection costs. In addition, comprehensive detection will generate a large amount of data, many of which may not have practical significance in a specific season, resulting in data redundancy.

[0004] Another example is the Chinese invention patent with publication number CN107727820A, which discloses a control system for a smart drinking water cup. By associating the TDS test results of shared drinking water with the smart water cup, the quality of the water is directly reflected through the red / green / yellow LED lights on the water cup, allowing users to instantly and intuitively understand the water quality. However, only TDS testing can only partially reflect the water quality and cannot fully capture changes in other important water quality parameters, which may lead to some water quality problems not being discovered in time. In addition, the smart water cup requires users to carry it with them at all times or place it at a shared drinking point, which increases the inconvenience of use for users and increases the operating and management costs of shared drinking points. Summary of the invention

[0005] To this end, one purpose of an embodiment of the present application is to provide a cloud health service platform for online real-time detection of drinking water based on multi-dimensional parameters for shared drinking water in residential areas. By integrating and optimizing the solutions of water quality training using historical water quality detection data and water quality detection using smart drinking water cups, intuitive, focused and targeted multi-dimensional water quality detection of shared drinking water is achieved.

[0006] The purpose of the present invention can be achieved through the following technical solutions: a cloud health service platform for online real-time detection of drinking water based on multi-dimensional parameters, including: a detection record seasonal classification module, which is used to retrieve water quality detection records from the water quality detection terminal of a shared drinking water point, and divide the water quality detection records according to seasonal distribution periods to form a water quality detection record set for each season.

[0007] The sensitive water quality parameter identification module is used to perform sensitive water quality parameter identification on the water quality detection record sets of each season to obtain the sensitive water quality parameters corresponding to each season.

[0008] The drinking water record seasonal classification module is used to use the drinking water sensing terminal set up at the shared drinking water point to sense the drinking water, thereby retrieving the drinking water records from the smart sensing terminal, and dividing the drinking water records according to the seasonal distribution period to form a drinking water record set for each season.

[0009] The preferred drinking water time period analysis module is used to analyze the preferred drinking water time period in each season based on the drinking water record set in each season.

[0010] The water quality detection frequency adjustment module is used to adjust the water quality detection frequency based on the sensitive water quality parameters corresponding to each season and the preferred drinking water time period.

[0011] The water quality detection abnormality display module is used to display and associate the sensitive water quality parameters corresponding to each season with the drinking water sensing terminal, and then use the display association to display the abnormality of the sensitive water quality parameters when there is drinking water sensing at the drinking water sensing terminal.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The present invention divides the historical water quality detection records of shared drinking water points by season, analyzes the sensitive water quality parameters corresponding to each season, and then displays and associates these sensitive water quality parameters with the drinking water sensing terminal, thereby realizing key detection and intuitive display based on multi-dimensional water quality detection. This method not only avoids excessive consumption of detection resources, but also avoids the inconvenience and increased operating management costs caused by using smart water cups for display association by utilizing the drinking water sensing terminal for display association, thereby invisibly enhancing the reliability and sustainability of the system.

[0013] 2. After analyzing the sensitive water quality parameters corresponding to each season, the present invention also uses the drinking water records of each season to analyze the preferred drinking water time period corresponding to each season, and then increases the detection frequency of sensitive water quality parameters during the preferred drinking water time period, thereby realizing dynamic adjustment of water quality detection frequency, which not only ensures water quality safety during critical periods, but also avoids all-weather high-frequency detection, optimizes the allocation of detection resources, improves detection efficiency, and reduces the number of unnecessary detections. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0015] Figure 1 It is a schematic diagram of the connection of each module of the platform in the present invention.

[0016] Figure 2 This is an operation diagram of the shared drinking water quality detection and display process in the present invention.

[0017] Figure 3 This is a schematic diagram of the operation of sensitive identification of water quality parameters in the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] A cloud health service platform for online real-time detection of drinking water based on multi-dimensional parameters, including a detection record season classification module, a sensitive water quality parameter identification module, a drinking water record season classification module, a preferred drinking water time period analysis module, a water quality detection frequency adjustment module and a water quality detection abnormality display module.

[0020] See also Figure 1 As shown, the detection record season classification module is connected to the sensitive water quality parameter identification module, the drinking water record season classification module is connected to the preferred drinking water period analysis module, the sensitive water quality parameter identification module and the preferred drinking water period analysis module are both connected to the water quality detection frequency adjustment module, and the water quality detection frequency adjustment module is connected to the water quality detection abnormality display module.

[0021] The detection record seasonal classification module is used to retrieve water quality detection records from the water quality detection terminal of the shared drinking water point, and divide the water quality detection records into seasonal water quality detection record sets according to seasonal distribution periods.

[0022] It should be noted that the drinking water in the shared drinking water point mentioned in the present invention is directly connected to the water supply pipeline of the residential area, and the tap water in the pipeline is purified by purification equipment and stored in the water tank. Therefore, the shared drinking water is continuously supplied and there is no need to replace the water bucket regularly.

[0023] It is necessary to explain again that a water quality detection terminal is set in the shared drinking water storage tank to realize real-time detection of the water quality parameters of the drinking water in the storage tank and generate a water quality detection record. The water quality detection record will record the detection time and the detection data of each water quality parameter, where the detection time includes the detection date and the specific detection time, and the detection date includes the year, month and day of the detection. The water quality detection terminal is equipped with a storage unit for local storage of the generated water quality detection records. In the absence of a storage unit, the water quality detection terminal will upload the generated water quality detection records to the cloud for long-term storage to ensure the integrity and reliability of the data.

[0024] It should be further explained that the water quality test records retrieved should be limited to the current year and span multiple years. This is to provide a large number of test data samples and improve the accuracy of identifying seasonal changes in water quality parameters through years of data. At the same time, the current year is limited to avoid selecting more distant years, which will make the retrieved water quality test records unreferenceable.

[0025] Applied to the above scheme, the water quality detection records are divided according to the seasonal distribution period to form a water quality detection record set for each season. See the following process: locate the area where the shared drinking water point is located, and then obtain the climate type corresponding to the area from the climate distribution map.

[0026] The above-mentioned mesoclimate distribution map is a kind of geographical information display, which shows the distribution areas corresponding to various climate types. For example, the climate types include tropical rainforest climate, tropical savanna climate, tropical monsoon climate, subtropical monsoon climate, etc.

[0027] The climate type corresponding to the area where the shared drinking water point is located is matched with the seasonal distribution periods corresponding to various meteorological types to obtain the seasonal distribution periods corresponding to the area where the shared drinking water point is located.

[0028] In the above operation example, since different climate types correspond to different climate characteristics, the resulting climate distribution is different, which makes the distribution time periods corresponding to different climate types different. The distribution period here refers to the distribution month, specifically the distribution month corresponding to spring, the distribution month corresponding to summer, the distribution month corresponding to autumn, and the distribution month corresponding to winter.

[0029] The test date is extracted from the retrieved historical water quality test records, and then the test date is compared with the seasonal distribution period corresponding to the area where the shared drinking water point is located, thereby classifying the historical water quality test records into each seasonal distribution period.

[0030] The sensitive water quality parameter identification module is used to perform water quality parameter sensitive identification on the water quality detection record sets of each season to obtain the sensitive water quality parameters corresponding to each season.

[0031] As a way to implement the above solution, see Figure 3 As shown, water quality parameter sensitive identification is implemented as follows: each water quality test record in each season water quality test record set is divided into water quality test record groups corresponding to each test year according to the test year in the test date.

[0032] It should be understood that by dividing the water quality test record sets of each season into water quality test record groups corresponding to each test year, the water quality test records within a test year form a continuous time series, which is convenient for time series analysis. Continuous time series data can more accurately identify seasonal changes in water quality parameters.

[0033] The detection data of each water quality parameter in each water quality detection record in the water quality detection record group corresponding to each detection year constitutes a water quality parameter time series, wherein the water quality parameter time series includes each water quality parameter time series.

[0034] It is important to know that water quality parameters include but are not limited to turbidity, dissolved oxygen, bacterial content, total dissolved solids, etc.

[0035] A coordinate system is constructed with the detection time as the horizontal axis and the water quality detection data as the vertical axis. The water quality parameter change curves for each season corresponding to each detection year are generated in the constructed coordinate system based on the water quality parameter time series composed of the water quality detection records corresponding to each detection year in the water quality detection record set of each season.

[0036] The overlap degree is calculated by comparing and overlapping the change curves of the same water quality parameter in each test year in the same season. The overlap degree is obtained by obtaining the length of the overlapped segment when overlapping and comparing the change curves of the same water quality parameter in different test years, and dividing the length of the overlapped segment by the average curve length of the same water quality parameter change curve in the test year.

[0037] It should be pointed out that the overlapping segments mentioned above refer to the overlapping parts of the change curves of the same water quality parameter in different testing years. Since there are multiple testing years, there may be multiple overlapping segments, and the number and length of overlapping curves in each overlapping segment are also different. For example, suppose we have the change curves of a water quality parameter in summer in three testing years (A, B, C). Through overlap comparison, the curves of year A and year B overlap from July 1 to July 10, the curves of year B and year C overlap from July 5 to July 15, and the curves of year A and year C overlap from July 1 to July 5. At this time, there are three overlapping segments. The first overlapping segment comes from the curves of year A and year B. There are 2 overlapping curves, and the overlapping length is the length of the curve segment between July 1 and July 10 in the curves of year A and year B. The second overlapping segment comes from the curves of year B and year C. There are 2 overlapping curves, and the overlapping length is the length of the curve segment between July 5 and July 15 in the curves of year B and year C. The third overlapping segment comes from the curves of year A and year C. There are 2 overlapping curves, and the overlapping length is the length of the curve segment between July 1 and July 5 in the curves of year A and year C.

[0038] It should be emphasized that since the same testing frequency is used in each year of historical water quality testing, this means that the water quality parameter change curve for each testing year starts from the same time point and covers the same time span for each testing year. Therefore, the starting end and length of the same water quality parameter change curve in each testing year are the same.

[0039] Under the above description, the following formula can be used to calculate the overlap degree: Calculate, where Indicates the degree of overlap, Indicates The number of curves with overlapping segments, Indicates the overlap segment number, , represents the number of overlapping segments, Indicates the total number of water quality parameter change curves, Indicates The overlap length of the overlap segment, Indicates The average curve length of the overlapping curves where the overlapping segments are located. From this formula, it can be seen that the closer the number of overlapping curves is to the total number of water quality parameter change curves, the more the water quality parameter change curves of each test year overlap in the same time period. The longer the overlapping length of the overlapping segments, the greater the degree of overlap.

[0040] It should be understood that the overlap of the change curves of the same water quality parameter in each test year corresponding to the same season is calculated because the overlap reflects whether the water quality parameter has periodic changes. If multiple curves overlap highly in certain time periods, it indicates that the water quality test data of different years have high similarity in the same time period, which largely reflects that the changes in water quality parameters in these time periods have obvious periodicity.

[0041] The degree of overlap of the change curves of each water quality parameter in each season is compared with the preset threshold. For example, the preset threshold is 0.8. If the degree of overlap of the change curve of a water quality parameter reaches the preset threshold, the water quality parameter is recorded as a periodic water quality parameter, otherwise the water quality parameter is recorded as a non-periodic water quality parameter.

[0042] It should be understood that when a water quality parameter is identified as a periodic water quality parameter, if the water quality parameter has a large volatility in a certain test year, this indicates that the water quality parameter may also show similar volatility in other test years. Therefore, the sensitivity identification of periodic water quality parameters can reduce the number of test years that require detailed analysis, improve the efficiency of water quality parameter sensitivity identification, and reduce unnecessary data processing and analysis work.

[0043] The water quality detection record group corresponding to the most recent detection year is extracted from the water quality detection record groups corresponding to each detection year divided into the seasonal water quality detection record sets, and then the periodic water quality parameter change curve corresponding to the water quality detection record group is screened out.

[0044] It should be noted that for the sensitivity identification of periodic water quality parameters, the water quality parameter change curve of the most recent detection year can be used for sensitivity identification, which can ensure the timeliness and accuracy of the identification results.

[0045] The change rate of the screened periodic water quality parameter change curve is calculated, and the calculation result is compared with the set critical change rate. If the change rate of a certain periodic water quality parameter change curve reaches the critical change rate, for example, the critical change rate is 0.3, and the critical change rate is set in this way, the sensitive water quality parameters that fluctuate can be captured in time, then the periodic water quality parameter will be used as the sensitive water quality parameter.

[0046] It should be emphasized that the change rate corresponding to the above-mentioned periodic water quality parameter change curve refers to a significant negative change in the water quality parameters, that is, a trend of water quality deterioration. In water quality testing, the deterioration trend of different water quality parameters is not necessarily synchronized with the direction of change of their values. For some water quality parameters, the larger the value, the greater the deterioration trend, such as turbidity, bacterial content, total dissolved solids, etc. For some water quality parameters, the smaller the value, the greater the deterioration trend, such as dissolved oxygen, total phosphorus, total nitrogen, etc. Therefore, when calculating the change rate, it is necessary to obtain the deterioration change rate based on the deterioration trend of the water quality parameters and the direction of change of the value.

[0047] In the preferred implementation of the above scheme, the rate of change of the periodic water quality parameter change curve refers to the overall change rate of the curve, specifically refers to the average change rate of the water quality parameter from the initial value to the final value in the entire time series. By way of example, the slope of each point in the periodic water quality parameter change curve can be obtained, and the slope of each point is averaged to obtain the overall change rate. The closer the overall change rate is to 0, the more it means that the periodic water quality parameter has almost no fluctuation in the time series, which means that the periodic water quality parameter is relatively stable in the time series and therefore has no sensitivity. Conversely, the greater the overall change rate is than 0, the more it means that the periodic water quality parameter fluctuates in the time series and has sensitivity.

[0048] It is important to know that when a water quality parameter is not identified as a periodic water quality parameter, it may still become a sensitive water quality parameter. The definition of sensitive water quality parameters is not limited to periodic changes in water quality parameters.

[0049] For non-periodic water quality parameters, the change rate of non-periodic water quality parameter change curves in each season corresponding to each detection year is calculated, and the calculation result is compared with the set critical change rate, thereby summarizing the proportion of detection years reaching the critical change rate, and then comparing with the configured effective proportion. Exemplarily, the effective proportion is 0.7. If the proportion of detection years in which a non-periodic water quality parameter reaches the critical change rate is greater than or equal to the effective proportion, it means that the non-periodic water quality parameter shows volatility in most detection years, and the periodic water quality parameter is used as a sensitive water quality parameter.

[0050] It should be added that in water quality testing, since multiple water quality parameters are involved, more than one sensitive water quality parameter may be identified. When multiple sensitive water quality parameters are identified, there may be a mutual influence relationship between these parameters, that is, the fluctuation of a sensitive water quality parameter may be caused by the fluctuation of another sensitive water quality parameter. Based on this situation, it is necessary to conduct correlation analysis on multiple sensitive water quality parameters, which can reveal the causal relationship between related sensitive water quality parameters, optimize monitoring strategies, focus on these parameters, and improve monitoring efficiency.

[0051] As an implementation of the above supplementary explanation, sensitive identification of water quality parameters continues to include: counting the number of sensitive water quality parameters identified in each season; if more than one sensitive water quality parameter is identified in a season, retrieving the sensitive water quality change curves in each test year corresponding to that season, and grouping the sensitive water quality parameters in pairs to form sensitive water quality parameter groups.

[0052] A correlation analysis is performed on the two sensitive water quality change curves in the corresponding sensitive water quality parameter group in the same detection year. If a correlation is found in a certain sensitive water quality parameter group, the sensitive water quality parameter group is recorded as the relevant sensitive water quality parameter group.

[0053] In the manner that can be implemented in the above scheme, the correlation analysis can be analyzed using the Pearson correlation coefficient or the Spearman rank correlation coefficient, which is a widely used analysis method for the correlation between variables in existing operations. The specific analysis process will not be repeated here. The obtained correlation coefficient takes a value between -1 and 1, where a value of 1 indicates a complete positive correlation, a value of -1 indicates a complete negative correlation, and a value of 0 indicates no correlation. Since the identification of sensitive water quality parameters in the present invention is based on the deterioration change rate, the impact correlation between multiple sensitive water quality parameters also focuses on the deterioration correlation, that is, a positive correlation. The positive correlation indicates that there is a synergistic effect between the sensitive water quality parameters, and the deterioration of one water quality parameter may lead to the deterioration of another water quality parameter.

[0054] Furthermore, when judging whether there is a correlation between sensitive water quality parameter groups, the correlation coefficient calculated for the sensitive water quality parameter group can be compared with a correlation coefficient threshold, for example, 0.6. When the correlation coefficient calculated for a sensitive water quality parameter group reaches the correlation coefficient threshold, it can be considered that the sensitive water quality parameter group is correlated.

[0055] The inflection points of two sensitive water quality change curves in the relevant sensitive water quality parameter group in each testing year are marked, and the marked inflection points are mapped to obtain a number of inflection point groups.

[0056] In the description of the above operation, the purpose of mapping the marked inflection points is to correspond the time of each fluctuation change of a sensitive water quality parameter in the water quality change curve in the relevant sensitive water quality parameter group with the fluctuation change time of another sensitive water quality parameter in the water quality change curve.

[0057] In a specific example, assuming that the sensitive water quality parameters in the relevant sensitive water quality parameter group are a and b, the inflection point identified in the change curve of a certain detection year is , , , , , At this time, the identified inflection points are mapped to form inflection point groups, which are , , .

[0058] The detection time of each inflection point in each inflection point group in each detection year on the horizontal axis is obtained respectively, and the sensitive water quality parameter corresponding to the inflection point of the previous detection time is extracted as the independent variable sensitive water quality parameter.

[0059] The self-variable sensitive water quality parameters of each inflection point group in all detection years are compared, and the self-variable sensitive water quality parameters with the highest occurrence frequency are extracted as the self-variable sensitive water quality parameters corresponding to the relevant sensitive water quality parameter group.

[0060] The drinking water record seasonal classification module is used to perform drinking water sensing using the drinking water sensing terminal set up at the shared drinking water point, thereby retrieving drinking water records from the smart sensing terminal, and dividing the drinking water records according to seasonal distribution time periods to form drinking water record sets for each season. The drinking water record sets for each season divided according to seasonal distribution time periods are similarly referred to the seasonal classification of water quality detection records.

[0061] Applied to the above solution, the drinking water sensing terminal can be a smart faucet, which can sense drinking water by detecting the opening state of the faucet. A storage unit is provided inside the smart faucet. When the faucet is sensed to be turned on, the opening time will be recorded and a drinking water record will be generated and stored in the storage unit.

[0062] The preferred drinking time period analysis module is used to analyze the preferred drinking time period of each season based on the drinking record set of each season. The specific analysis is as follows: cluster analysis is performed on the drinking time of each drinking record in the drinking record set of each season to obtain the classification cluster of the drinking records corresponding to each season.

[0063] It should be understood that the above cluster analysis of drinking time adopts the KMeans clustering algorithm, and the clustering result can classify similar drinking times into one category.

[0064] The drinking time of each drinking record in the classification cluster is compared, and the earliest drinking time and the latest drinking time are extracted to form the drinking time period corresponding to the classification cluster, which can accurately reflect the time range of each classification cluster.

[0065] By counting the number of drinking water records in each seasonal classification cluster, we can understand the representativeness of each classification cluster, and then extract the drinking water time period corresponding to the classification cluster with the largest number of drinking water records as the preferred drinking water time period of the corresponding season.

[0066] The present invention intuitively provides the time period distribution of drinking water time by adopting the drinking water time cluster analysis of the drinking water records in each season, and the preferred drinking water time period obtained thereby has a high credibility.

[0067] It should be noted that for shared drinking water in residential areas, since users are not active in their homes all day long, their activity time periods in their homes show a certain regularity. Specifically, users are usually at work during the day and are not at home, but return home at night, so shared drinking water activities in the home are mostly concentrated in a certain time period in the evening.

[0068] The water quality detection frequency adjustment module is used to adjust the water quality detection frequency based on the sensitive water quality parameters and preferred drinking water time periods corresponding to each season. The implementation is as follows: the original detection frequency of the sensitive water quality parameters is obtained, and then the detection frequency of the sensitive water quality parameters in the preferred drinking water time period in each season is determined by combining it with the change rate of the sensitive water quality parameter change curve in each season. Specifically, the original detection frequency of the sensitive water quality parameters and the change rate of the sensitive water quality parameter change curve in each season can be introduced into the calculation formula Obtain the detection frequency of sensitive water quality parameters during the preferred drinking time in each season , where Indicates the season number, Can be taken , , , ,in , , , Representing spring, summer, autumn and winter respectively. represents the raw detection frequency of sensitive water quality parameters, represents a set constant, and , for example , Indicates the rate of change of the sensitive water quality parameter change curve, Represents the critical rate of change.

[0069] It should be understood that the detection frequency mentioned above refers to the detection frequency per unit time, for example, 20 times of detection within 10 days. From the above formula, it can be seen that the greater the rate of change of sensitive water quality parameters, the greater the fluctuation, and the more it is necessary to increase the detection frequency.

[0070] Maintain original testing frequencies for sensitive water quality parameters during non-preferred drinking periods of each season.

[0071] It should be pointed out that increasing the detection frequency of sensitive water quality parameters during the preferred drinking water periods in each season does not mean that other water quality parameters will not be tested. Other water quality parameters should be tested according to the original detection frequency. In non-preferred drinking water periods, in addition to maintaining the original detection frequency of sensitive water quality parameters, the original detection frequency of non-sensitive water quality parameters should also be maintained.

[0072] After analyzing and obtaining the sensitive water quality parameters corresponding to each season, the present invention also uses the drinking water records of each season to analyze the preferred drinking water time period corresponding to each season, and then increases the detection frequency of sensitive water quality parameters during the preferred drinking water time period, thereby realizing dynamic adjustment of the water quality detection frequency, which not only ensures the water quality safety during the critical period, but also avoids all-weather high-frequency detection, optimizes the allocation of detection resources, improves detection efficiency, and reduces the number of unnecessary detections.

[0073] The water quality detection abnormality display module is used to display and associate the sensitive water quality parameters corresponding to each season with the drinking water sensing terminal, and then use the display association to display the abnormality of the sensitive water quality parameters when there is drinking water sensing at the drinking water sensing terminal.

[0074] The above display is associated with the following operations: a display is set on the drinking water sensing terminal, wherein the display can be an LED light display or a liquid crystal display. If there is only one sensitive water quality parameter in a certain season, the display is directly wirelessly connected to the detection data of the sensitive water quality parameter. If there is more than one sensitive water quality parameter in a certain season, it is identified whether there is an associated sensitive water quality parameter group. If there is an associated sensitive water quality parameter group, the display is wirelessly connected to the detection data of the self-variable sensitive water quality parameter corresponding to the associated sensitive water quality parameter group. If there is no associated sensitive water quality parameter group, the display is wirelessly connected to the detection data of all sensitive water quality parameters.

[0075] Based on the above operations, it needs to be explained that when there is only one sensitive water quality parameter in a season, directly wirelessly connecting the display to the detection data of the parameter can ensure that the user pays attention to the most important water quality indicators and avoid information overload; when there are multiple sensitive water quality parameters in a season, first identify whether there is an associated sensitive water quality parameter group. If there is an associated sensitive water quality parameter group, wirelessly connecting the display to the detection data of the self-variable sensitive water quality parameter can highlight the main factors that cause changes in other parameters, helping users to quickly understand the root causes of water quality changes. When there is no associated sensitive water quality parameter group, the display is wirelessly connected to the detection data of all sensitive water quality parameters to ensure that users can fully understand the water quality status and avoid missing important information. Through such operations, unnecessary information display can be reduced, the effectiveness of data display can be improved, and the readability and comprehensibility of information can be enhanced, making it easier for users to grasp the water quality status.

[0076] It should be added that the wireless connection between the sensitive water quality parameters and the display is not fixed. In the process of water quality testing, not only sensitive water quality parameters need to be tested, but also non-sensitive water quality parameters. In actual testing, if no abnormality is found in the sensitive water quality parameters, but a non-sensitive water quality parameter is abnormal, the non-sensitive water quality parameter can be wirelessly connected to the display for real-time display and monitoring. This method can flexibly respond to different situations and ensure water quality safety and health.

[0077] Furthermore, the abnormal display is performed as follows: the detection data of the sensitive water quality parameter is compared with the normal value of the corresponding water quality parameter. If the detection data of the sensitive water quality parameter does not conform to the normal value, the display is used to display the abnormality.

[0078] The shared drinking water quality detection and display process in the present invention refers to Figure 2 shown.

[0079] The normal data of the above-mentioned sensitive water quality parameters can refer to the shared drinking water quality standards issued by the state, and the normal data can be stored in a reference database for easy retrieval.

[0080] In the above operation example, when the display is an LED light display, the color of the LED light can be used to prompt the user of the sensitive water quality parameter status. The specific display method is as follows: Green LED light: indicates that the water quality parameters are normal and the water quality is safe; Yellow LED light: indicates that the water quality parameters are slightly abnormal and need attention; Red LED light: indicates that the water quality parameters are seriously abnormal and immediate measures need to be taken.

[0081] In another example of the above operation, when the display is a liquid crystal display screen, specific values ​​of sensitive water quality parameters can be displayed on the liquid crystal display screen, and a warning icon is used to prompt when the value is abnormal.

[0082] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A cloud health service platform for online real-time detection of drinking water based on multi-dimensional parameters, characterized in that: include: The test record seasonal classification module is used to retrieve water quality test records from the water quality test terminal of the shared drinking water point and divide them into seasonal distribution to form a water quality test record set for each season; The sensitive water quality parameter identification module is used to perform sensitive water quality parameter identification on the water quality detection record sets of each season and determine the sensitive water quality parameters of each season; The drinking water record seasonal classification module is used to obtain drinking water records using the drinking water sensing terminal in the shared drinking water point and divide them into drinking water record sets for each season according to seasonal distribution; The preferred drinking water time period analysis module is used to analyze the preferred drinking water time period of each season based on the drinking water record set of each season; Water quality testing frequency adjustment module, used to dynamically adjust the water quality testing frequency according to sensitive water quality parameters and preferred drinking water time periods in each season; The water quality detection abnormality display module is used to associate sensitive water quality parameters with the drinking water sensing terminal display, and then display the abnormality of sensitive water quality parameters when drinking water is sensed; The sensitive identification of water quality parameters is implemented as follows: The water quality test record sets of each season are divided according to the test year to form an annual water quality test record group, and the test data of each water quality parameter in each record is extracted to generate a water quality parameter time series; With the detection time as the horizontal axis and the water quality detection data as the vertical axis, the change curves of each water quality parameter in each detection year corresponding to each season are drawn; The change curves of the same water quality parameters in the same season corresponding to each test year are overlapped and compared to calculate the overlap degree; Compare the overlap of water quality parameter change curves in different years in the same season. If the overlap reaches the preset threshold, it is marked as a periodic water quality parameter; otherwise, it is marked as a non-periodic water quality parameter. For periodic water quality parameters, the water quality detection record group corresponding to the most recent detection year is extracted from the water quality detection record group corresponding to each detection year divided into the water quality detection record set of each season, and then the periodic water quality parameter change curve corresponding to the water quality detection record group is screened out; The change rate of the selected periodic water quality parameter change curve is calculated and compared with the set critical change rate. If the critical change rate is reached, it is determined to be a sensitive water quality parameter; For non-periodic water quality parameters, calculate the change rate of the non-periodic water quality parameter change curve in each season corresponding to each test year, and compare it with the critical change rate. The proportion of the test year that reaches the critical change rate is summarized, and then compared with the configured effective proportion. If the proportion is greater than or equal to the effective proportion, it is judged to be a sensitive water quality parameter; Count the number of sensitive water quality parameters identified in each season. If multiple sensitive water quality parameters are identified in a season, retrieve the sensitive water quality change curve of the corresponding test year and group the parameters into sensitive water quality parameter groups. Conduct correlation analysis on the two change curves of sensitive water quality parameter groups in the same testing year. If there is a correlation, mark them as related sensitive water quality parameter groups; Mark the inflection points of the two change curves in the relevant sensitive water quality parameter group in each test year, and map them to generate several inflection point groups; Obtain the detection time of each inflection point on the horizontal axis in the inflection point group of each detection year, and extract the sensitive water quality parameter corresponding to the previous detection time as the independent variable sensitive water quality parameter; Compare the self-variable sensitive water quality parameters of the inflection point groups of each detection year, and extract the self-variable sensitive water quality parameters with the highest frequency as the relevant sensitive water quality parameter group; To associate sensitive water quality parameters with the drinking water sensor terminal display, do the following: A display is set on the drinking water sensing terminal. If there is only one sensitive water quality parameter in the season, the display is directly connected to the detection data of the sensitive water quality parameter; if there are multiple sensitive water quality parameters, first determine whether there is an associated sensitive water quality parameter group. If so, the display is connected to the detection data of the self-variable sensitive water quality parameter, otherwise it is connected to the detection data of all sensitive water quality parameters.

2. The cloud health service platform for online real-time detection of drinking water based on multi-dimensional parameters as claimed in claim 1, characterized in that: The process of forming the water quality detection record set for each season by dividing it according to seasonal distribution is as follows: Locate the area where the shared drinking water point is located, and then obtain the climate type corresponding to the area where the shared drinking water point is located from the climate distribution map; The climate type corresponding to the area where the shared drinking water point is located is matched with the seasonal distribution periods corresponding to various meteorological types to obtain the seasonal distribution period corresponding to the area where the shared drinking water point is located; The test date is extracted from the retrieved water quality test records, and then the test date is compared with the seasonal distribution period corresponding to the area where the shared drinking water point is located, thereby classifying the water quality test records into each seasonal distribution period.

3. The cloud health service platform for online real-time detection of drinking water based on multi-dimensional parameters as claimed in claim 1, characterized in that: The analysis of the preferred drinking time period in each season based on the drinking water record set in each season refers to the following process: Cluster analysis was performed on the drinking time of each drinking record in each season’s drinking record set to obtain the classification clusters of drinking records corresponding to each season. Compare the drinking time of each drinking record in the classification cluster, extract the earliest drinking time and the latest drinking time to form the drinking time period corresponding to the classification cluster; The number of drinking water records in each seasonal classification cluster is counted, and then the drinking water time period corresponding to the classification cluster with the largest number of drinking water records is extracted as the preferred drinking water time period of the corresponding season.

4. The cloud health service platform for online real-time detection of drinking water based on multi-dimensional parameters as claimed in claim 1, characterized in that: The water quality detection frequency adjustment refers to the following process: The original detection frequency of sensitive water quality parameters is obtained, and then combined with the change rate of the sensitive water quality parameter change curve in each season to determine the detection frequency of sensitive water quality parameters in the preferred drinking water period in each season; Maintain original testing frequencies for sensitive water quality parameters during non-preferred drinking periods of each season.

5. The cloud health service platform for online real-time detection of drinking water based on multi-dimensional parameters as claimed in claim 4, characterized in that: The determination of the detection frequency of sensitive water quality parameters in the preferred drinking water period in each season is performed as follows: The original detection frequency of sensitive water quality parameters and the change rate of sensitive water quality parameter change curves in each season are introduced into the calculation formula Obtain the detection frequency of sensitive water quality parameters during the preferred drinking time in each season , where Indicates the season number, Can be taken , , , ,in , , , Representing spring, summer, autumn and winter respectively. represents the raw detection frequency of sensitive water quality parameters, represents a set constant, and , Indicates the rate of change of the sensitive water quality parameter change curve, Represents the critical rate of change.

6. The cloud health service platform for online real-time detection of drinking water based on multi-dimensional parameters as claimed in claim 1, characterized in that: The exception shows the following actions: The detection data of the sensitive water quality parameters are compared with the normal values ​​of the corresponding water quality parameters. If the detection data of the sensitive water quality parameters do not conform to the normal values, the display will be used to display the abnormality.

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