Drinking water taste information detection method and system based on electronic sense organ

Abstract

The invention discloses a drinking water taste information detection method and system based on an electronic sense organ, and relates to the technical field of drinking water taste detection.The method comprises the steps that a water source water sample is obtained, the water source water sample is filtered and disinfected to obtain a pretreated water sample, and the pretreated water sample is subjected to first, second and third pretreatment sub-water samples; first, second and third feature information sets are obtained through corresponding detection, first, second and third difference influence factors are obtained through evaluation according to the difference and difference relation of feature information of all sub water samples in the first, second and third feature information sets, and the water smell information of the pretreatment water sample is detected to obtain comprehensive pretreatment smell parameters; and carrying out data differential compensation integration on the comprehensive pretreatment taste parameter and the first, second and third difference influence factors to obtain a factory water final taste measurement parameter. According to the drinking water taste information detection method and system based on the electronic sense organ, the accuracy of the taste information detection result of drinking water in life can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of drinking water taste detection, and in particular to a drinking water taste information detection method and system based on electronic sensory organs. Background Art

[0002] Artificial intelligence electronic sensors collect water quality data through sensors and other equipment, and combine advanced artificial intelligence algorithms for real-time analysis and processing, which can quickly and accurately identify abnormal conditions in water quality, such as excessive pollutants and microbial contamination. This efficient detection method greatly improves the accuracy and efficiency of water quality detection, helps to promptly discover and solve water quality problems, and ensure the safety and quality of drinking water.

[0003] In traditional technologies, subjective factors have a great influence, and it is often difficult to objectively and accurately evaluate the taste of water because it relies on human sensory experience, which is affected by many factors, such as personal preferences, drinking habits, etc. There is no further in-depth understanding of the correlation mechanism between water components and taste, and the establishment of an objective and scientific taste evaluation system to reduce the influence of subjective factors. Summary of the invention

[0004] In order to overcome the above-mentioned deficiencies of the prior art, the present application provides a method and system for detecting the taste information of drinking water based on electronic sensory organs.

[0005] In a first aspect, the present application provides a method for detecting drinking water taste information based on electronic sensory organs, the method comprising:

[0006] Obtain a source water sample, filter and disinfect the source water sample to obtain a pretreated water sample, and divide the pretreated water sample into a first pretreated sub-water sample, a second pretreated sub-water sample, and a third pretreated sub-water sample;

[0007] Performing characteristic information detection on each sub-water sample in the first pre-processed sub-water sample to obtain a first characteristic information set, and evaluating and obtaining a first difference influencing factor according to the similarities and differences between the characteristic information of each sub-water sample in the first characteristic information set;

[0008] Performing characteristic information detection on each sub-water sample in the second pre-treated sub-water sample to obtain a second characteristic information set, and evaluating and obtaining a second difference influencing factor according to the similarities and differences between the characteristic information of each sub-water sample in the second characteristic information set;

[0009] Performing characteristic information detection on each sub-water sample in the third pre-processed sub-water sample to obtain a third characteristic information set, and evaluating and obtaining a third difference influencing factor according to the similarities and differences between the characteristic information of each sub-water sample in the third characteristic information set;

[0010] The pretreated water samples are subjected to water taste information detection of the factory to which they belong to obtain comprehensive pretreatment taste parameters, and the comprehensive pretreatment taste parameters, the first difference influencing factor, the second difference influencing factor and the third difference influencing factor are integrated after data micro-difference compensation to obtain the final taste parameters of the factory water.

[0011] Preferably, a water source sample is obtained, the water source sample is subjected to coarse filtration to obtain a coarse filtered water sample, and the coarse filtered water sample is subjected to ultrafiltration membrane filtration to obtain an ultrafiltered water sample;

[0012] Disinfecting the ultrafiltration water sample to obtain a pretreated water sample;

[0013] Dividing the pretreated water sample into a number of sub-water samples of equal size to obtain a sub-water sample set;

[0014] Performing factory water taste information detection on a number of sub-water samples of the same molecular weight in the sub-water sample set to obtain a factory water taste parameter set;

[0015] Extracting the same factory water taste parameter from the factory water taste parameter set to obtain a first pre-processed taste parameter;

[0016] extracting corresponding first pretreated sub-water samples from the sub-water sample set according to the first pretreated taste parameter;

[0017] Preset a difference segmentation interval value, extract the factory water taste parameters whose factory water taste parameter difference degrees are within the difference segmentation interval value from the factory water taste parameter set to obtain the second pre-processed taste parameter;

[0018] extracting corresponding second pretreated sub-water samples from the sub-water sample set according to the second pretreated taste parameter;

[0019] Extract the factory water taste parameters whose factory water taste parameter difference degrees are outside the difference cut interval value from the factory water taste parameter set to obtain the third pre-processing taste parameter;

[0020] According to the third pre-processed taste parameter, a corresponding third pre-processed sub-water sample is extracted from the sub-water sample set.

[0021] Preferably, the characteristic information of each sub-water sample in the first pre-processed sub-water sample is detected to obtain a first characteristic information set;

[0022] If the characteristic information of each sub-water sample in the first characteristic information set is the same, or the characteristic information of each sub-water sample in the first characteristic information set is different, then output the first condition case 1;

[0023] Extract the different parts in the first feature information set, and then output the second condition case 1;

[0024] According to the first case of the second condition, the correlation between the different parts in the first feature information set and the corresponding first preprocessing parameters is evaluated to obtain the first difference impact factor.

[0025] Preferably, the characteristic information of each sub-water sample in the second pre-processed sub-water sample is detected to obtain a second characteristic information set;

[0026] If the characteristic information of each sub-water sample in the second characteristic information set is the same, output the first condition case 2;

[0027] According to the first condition, case 2, averaging the preprocessing taste parameters in the second preprocessing taste parameter to obtain a second preprocessing average taste parameter;

[0028] Extract the different parts of the second feature information set and output the second condition case 2;

[0029] According to the second condition case 2, the second preprocessing flavor parameters corresponding to different parts of the second feature information set are subtracted from each other to obtain a parameter difference value 1;

[0030] The second difference impact factor is obtained by calculating the ratio between the different parts in the second feature information set and the parameter difference one.

[0031] Preferably, the characteristic information of each sub-water sample in the third pre-processed sub-water sample is detected to obtain a third characteristic information set;

[0032] If the characteristic information of each sub-water sample in the third characteristic information set is the same, the third pre-processing taste parameter is misjudged, and the third pre-processing taste parameter is invalidated, and the first condition case 3 is output;

[0033] Extract the different parts of the third feature information set and output the second condition case 3;

[0034] According to the second condition case 3, the preprocessing flavor parameters in the third preprocessing flavor parameters corresponding to different parts of the third feature information set are subtracted to obtain parameter difference value 2;

[0035] The third difference impact factor is obtained by calculating the ratio between the different parts in the third feature information set and the parameter difference value 2.

[0036] Preferably, the pre-treated water sample is subjected to water taste information detection of the factory to which it belongs to obtain a comprehensive pre-treated taste parameter;

[0037] According to the first condition case 1, the first condition case 2 and the first condition case 3, the comprehensive pretreatment taste parameter, the first pretreatment taste parameter and the second pretreatment average taste parameter are averaged to obtain the final taste parameter of the factory water;

[0038] Alternatively, according to the second condition case one, the second condition case two and the second condition case three, the comprehensive pretreatment taste parameters, the first difference influencing factor, the second difference influencing factor and the third difference influencing factor are integrated with data slight difference compensation to obtain the final taste parameters of the factory water.

[0039] In the second aspect, a drinking water taste information detection system based on electronic sensory organs comprises:

[0040] A water sample pretreatment module is used to obtain a water source sample, filter and disinfect the water source sample to obtain a pretreated water sample, and divide the pretreated water sample into a first pretreated sub-water sample, a second pretreated sub-water sample, and a third pretreated sub-water sample;

[0041] The sub-water sample parameter analysis module 1 is used to detect the characteristic information of each sub-water sample in the first pre-processed sub-water sample to obtain a first characteristic information set, and evaluate and obtain a first difference influencing factor according to the similarities and differences between the characteristic information of each sub-water sample in the first characteristic information set;

[0042] The second sub-water sample parameter analysis module is used to detect the characteristic information of each sub-water sample in the second pre-processed sub-water sample to obtain a second characteristic information set, and evaluate and obtain a second difference influencing factor according to the similarities and differences between the characteristic information of each sub-water sample in the second characteristic information set;

[0043] The sub-water sample parameter analysis module three is used to detect the characteristic information of each sub-water sample in the third pre-processed sub-water sample to obtain a third characteristic information set, and evaluate and obtain a third difference influencing factor according to the similarities and differences between the characteristic information of each sub-water sample in the third characteristic information set;

[0044] The final measurement module for factory water parameters is used to detect the water taste information of the factory to which the pretreated water samples belong to obtain comprehensive pretreatment taste parameters, and integrate the comprehensive pretreatment taste parameters, the first difference influencing factor, the second difference influencing factor and the third difference influencing factor with data micro-difference compensation to obtain the final measurement taste parameters of factory water.

[0045] Compared with the prior art, the present invention has the following characteristics and beneficial effects:

[0046] The pretreated water sample is divided into several sub-water samples so as to preliminarily detect the individual parameters and the characteristic information of the several sub-water samples, so as to extract from the detected data some sub-water samples with the same taste parameters, some sub-water samples with parameter differences within the difference cutting interval value, and some sub-water samples with parameter differences outside the difference cutting interval value, and respectively judge the similarities and differences of the characteristic information contained in these three kinds of partial sub-water samples, so as to obtain two conditions respectively, and extract the taste parameters of some sub-water samples with different characteristic information to analyze the difference influencing factors, so as to obtain the first difference influencing factor, the second difference influencing factor and the taste parameter of the partial sub-water samples. The effect factor and the third difference influencing factor, that is, both are parameter difference compensation amounts, so as to reduce the error of the detection data. Finally, the comprehensive pretreatment taste parameters are obtained by detecting the factory water taste information of the pretreated water samples. The parameter difference compensation amounts are integrated for the comprehensive pretreatment taste parameters to obtain the final factory water final taste parameters. This result is obtained by integrating the results of parameter detection in two cases, and the single processing method used in traditional technology is avoided by integrating the parameter detection results in the two cases. That is, the changes in the development correlation between information are fully considered to conduct researchable analysis and enhance the reliability and accuracy of the entire information detection work. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flowchart of a method for detecting drinking water taste information based on electronic sensory organs, which is mainly embodied in this embodiment.

[0048] Figure 2 This is a structural block diagram of a drinking water taste information detection system based on electronic sensory organs, which is mainly embodied in this embodiment. DETAILED DESCRIPTION

[0049] The present invention is further described in detail below in conjunction with the following examples.

[0050] Reference Figure 1 , a method for detecting drinking water taste information based on electronic sensory organs, the method comprising the following steps:

[0051] S1. Obtain a source water sample, filter and disinfect the source water sample to obtain a pretreated water sample, and divide the pretreated water sample into a first pretreated sub-water sample, a second pretreated sub-water sample, and a third pretreated sub-water sample.

[0052] S2. Detect the characteristic information of each sub-water sample in the first pre-processed sub-water sample to obtain a first characteristic information set, and evaluate and obtain a first difference influencing factor based on the similarities and differences between the characteristic information of each sub-water sample in the first characteristic information set.

[0053] S3. Detect the characteristic information of each sub-water sample in the second pre-processed sub-water sample to obtain a second characteristic information set, and evaluate and obtain a second difference influencing factor based on the similarities and differences between the characteristic information of each sub-water sample in the second characteristic information set.

[0054] S4. Detect the characteristic information of each sub-water sample in the third pre-processed sub-water sample to obtain a third characteristic information set, and evaluate and obtain a third difference influencing factor based on the similarities and differences between the characteristic information of each sub-water sample in the third characteristic information set.

[0055] S5. Detect the water taste information of the factory to which the pre-treated water sample belongs to obtain the comprehensive pre-treatment taste parameters, and integrate the comprehensive pre-treatment taste parameters, the first difference influencing factor, the second difference influencing factor and the third difference influencing factor with data micro-difference compensation to obtain the final taste parameters of the factory water.

[0056] Specifically, the pretreated water sample is divided into several sub-water samples so as to preliminarily detect the individual parameters and the characteristic information of the several sub-water samples, so as to extract from the detected data some sub-water samples with the same taste parameters, some sub-water samples with parameter differences within the difference cutting interval value, and some sub-water samples with parameter differences outside the difference cutting interval value, and respectively judge the similarities and differences of the characteristic information contained in these three kinds of partial sub-water samples, so as to obtain two conditions respectively, and extract the taste parameters of some sub-water samples with different characteristic information contained in the sub-water samples to analyze the difference influencing factors, so as to obtain the first difference influencing factor, the second difference influencing factor and the taste parameter of the partial sub-water samples. The difference influencing factor and the third difference influencing factor, that is, both are parameter difference compensation amounts, so as to reduce the error of the detection data. Finally, the comprehensive pretreatment taste parameters are obtained by detecting the factory water taste information of the pretreated water samples. The parameter difference compensation amounts are integrated for the comprehensive pretreatment taste parameters to obtain the final factory water final taste parameters. This result is obtained by integrating the results of parameter detection in two cases, and the single processing method used in traditional technology is avoided by integrating the parameter detection results in the two cases. That is, the changes in the development correlation between information are fully considered to conduct researchable analysis and enhance the reliability and accuracy of the entire information detection work.

[0057] The specific step S1 includes the following sub-steps:

[0058] Obtain a source water sample, perform coarse filtration on the source water sample to obtain a coarse filtered water sample, and perform ultrafiltration membrane filtration on the coarse filtered water sample to obtain an ultrafiltered water sample.

[0059] The ultrafiltration water sample is disinfected to obtain a pretreated water sample.

[0060] The pretreated water sample is divided into several equal sub-water samples to obtain a sub-water sample set.

[0061] The factory water taste information of several identical sub-water samples in the sub-water sample set is detected to obtain the factory water taste parameter set.

[0062] The same factory water taste parameters are extracted from the factory water taste parameter set to obtain the first pre-processed taste parameters.

[0063] According to the first preprocessed taste parameter, a corresponding first preprocessed sub-water sample is extracted from the sub-water sample set.

[0064] A difference segmentation interval value is preset, and factory water taste parameters whose factory water taste parameter difference degrees are within the difference segmentation interval value are extracted from the factory water taste parameter set to obtain second pre-processed taste parameters.

[0065] According to the second pre-processed taste parameter, a corresponding second pre-processed sub-water sample is extracted from the sub-water sample set.

[0066] The factory water taste parameters whose difference degree of factory water taste parameters is outside the difference cutting interval value are extracted from the factory water taste parameter set to obtain the third pre-processed taste parameter.

[0067] According to the third preprocessed taste parameter, a corresponding third preprocessed sub-water sample is extracted from the sub-water sample set.

[0068] Specifically, such as coarse filtration water samples (i.e., removing impurities in water source samples through coarse filtration), pre-treated water samples (such as adding disinfection sodium hypochlorite, ozone, ultraviolet disinfection, etc., to simulate the taste of tap water after disinfection. The taste here refers to the five basic flavors plus taste, sweet, salty, sour, bitter, fresh, and astringent. The disinfected water samples are collected through a collection tank), sub-water sample sets (such as pre-treated water sample A, sub-water sample set: if it contains a1, a2, a3, a4, a5, a6, a7, a8, a9), factory water taste parameter set (need It should be noted that the factory water analyzed in this article refers to factory water. For example, the electronic tongue detects the water taste information of a1, a2, a3, a4, a5, a6, a7, a8, and a9 in turn. The electronic tongue here includes the establishment of a database: by collecting historical data record information detected in historical periods, the historical data record information is plotted into a data comparison table, that is, a database. The database also contains a standard radar chart style, which is a graphical information converted from historical data record information, which can be used in advance for the final detection obtained later. The data is compared with the radar spectrum of the standard sample to determine the specification level of the final test data obtained later. The water taste information may include hardness, total dissolved solids, carbonate, chloride, sulfate, iron, etc. If the taste parameters of the sub-water samples a1, a2, a3, a4, a5, a6, a7, a8, and a9 correspond to b1, b2, b3, b4, b5, b6, b7, b8, and b9 respectively, the first pretreatment taste parameter (if b1, b2, and b3 are the same), the first pretreatment sub-water sample ( a1, a2, a3), difference segmentation interval value (if expressed in percentage, 0%-10%), second pretreatment taste parameter (first calculate the difference between the taste parameters corresponding to each sub-water sample, so as to extract the sub-water sample taste parameters with a difference between 0%-10%, if it is b4, b5, b6), second pretreatment sub-water sample (then a4, a5, a6), third pretreatment taste parameter (if it is b7, b8, b9), third pretreatment sub-water sample (then a7, a8, a9).

[0069] The specific step S2 includes the following sub-steps:

[0070] The characteristic information of each sub-water sample in the first preprocessed sub-water sample is detected to obtain a first characteristic information set.

[0071] If the characteristic information of each sub-water sample in the first characteristic information set is the same, or the characteristic information of each sub-water sample in the first characteristic information set is different, then the first conditional case 1 is output.

[0072] The different parts in the first feature information set are extracted, and the second condition case one is output.

[0073] According to the second condition, case 1, the correlation between the different parts in the first feature information set and the corresponding first preprocessing parameters is evaluated to obtain the first difference impact factor.

[0074] Specifically, for the first characteristic information set (e.g., characteristic information such as hardness, total dissolved solids, carbonate, chloride, sulfate, iron, etc., if c1, c2, c3 correspond to a1, a2, a3 respectively), the first conditional situation 1 (e.g., the characteristic information contained in c1, c2, c3 are all the same, or are not the same: c1: carbonate, c2: chloride, c3: sulfate, iron, then c1, c2, c3 (three with the same value) in the first pretreatment taste parameter are used as the final taste parameter detection data of the first pretreatment sub-water sample) , the second condition case one (if c1 is the same as c2: carbonate, and different from c3: sulfate, iron), the first difference influencing factor (if c1 is the same as c2: carbonate, and different from c3: sulfate, iron, then c1 and c3 are extracted, here c1 and c3 are used as examples for explanation, c2 and c3 can also be extracted, if the corresponding parameter difference is S1, and the carbonate content of c1 is h1, and the sulfate and iron of c3 are h2 and h3 respectively, then the correlation evaluation is: (S1 / h1+S1 / h2+S1 / h3) / 3=W1).

[0075] The specific step S3 includes the following sub-steps:

[0076] The characteristic information of each sub-water sample in the second pre-processed sub-water sample is detected to obtain a second characteristic information set.

[0077] If the characteristic information of each sub-water sample in the second characteristic information set is the same, the first condition case 2 is output.

[0078] According to the second case of the first condition, the average value of each preprocessing flavor parameter in the second preprocessing flavor parameter is obtained to obtain the second preprocessing average flavor parameter.

[0079] The different parts in the second feature information set are extracted, and the second condition case 2 is output.

[0080] According to the second condition, case two, the difference between the preprocessing flavor parameters in the second preprocessing flavor parameters corresponding to the different parts in the second feature information set is calculated to obtain a parameter difference value one.

[0081] The second difference impact factor is obtained by calculating the ratio between the different parts in the second feature information set and the parameter difference one.

[0082] Specifically, for example, the second characteristic information set (such as hardness, total dissolved solids, carbonate, chloride, sulfate, iron, etc., if the characteristic information corresponding to a4, a5, and a6 is c4, c5, and c6 respectively), the first condition case 2 (such as the characteristic information contained in c4, c5, and c6 are all the same: c4: chloride, c5: chloride, c6: chloride), the second pre-processing average taste parameter (therefore, the error rate of the detected taste parameter in this case is low, that is, the detected data is more accurate, and the average value of the data is processed to obtain the comprehensive data as the final detection data of this sub-sample, if it is P ), the second condition case two (if c4 and c5 are the same: chloride, and different from c6: total dissolved solids, iron), the second difference influencing factor (if c4 and c5 are the same: chloride, and different from c6: total dissolved solids, iron, then c5 and c6 are extracted, here c5 and c6 are used as examples for explanation, c4 and c6 can also be extracted, if the corresponding parameter difference is S2, and the chloride content of c5 is h4, the total dissolved solids and iron of c3 are h5 and h6 respectively, then the correlation evaluation is: (S2 / h4+S2 / h5+S2 / h6) / 3=W2).

[0083] The specific step S4 includes the following sub-steps:

[0084] The characteristic information of each sub-water sample in the third pre-processed sub-water sample is detected to obtain a third characteristic information set.

[0085] If the characteristic information of each sub-water sample in the third characteristic information set is the same, the third pre-processing taste parameter is misjudged, and the third pre-processing taste parameter is invalidated, and the first condition case three is output.

[0086] Extract the different parts in the third feature information set and output the second condition case three.

[0087] According to the third condition of the second condition, the difference between each preprocessing flavor parameter in the third preprocessing flavor parameters corresponding to the different parts in the third feature information set is calculated to obtain parameter difference value 2.

[0088] The third difference impact factor is obtained by calculating the ratio between the different parts in the third feature information set and the parameter difference value 2.

[0089] Specifically, the third characteristic information set (such as hardness, total dissolved solids, carbonate, chloride, sulfate, iron, etc., if the characteristic information contained in these sets corresponds to c7, c8, c9 respectively with a7, a8, a9), the first condition case three (if the characteristic information of each sub-water sample in the third characteristic information set is the same, and on the contrary, the differences between the respective taste parameters of the third pre-treatment taste parameters are large, it means that there is a great possibility that a data mismeasurement problem has occurred, and the taste parameters detected this time are invalid), the second condition case three (if c7 is different from c8 and c9, c7: iron, c8: carbonate, c9: chloride), the third difference influencing factor (If c7 is different from c8 and c9, c7: iron, c8: carbonate, c9: chloride, if the corresponding parameter difference is two (the parameter difference between b7 and b8 is s31, the parameter difference between b7 and b9 is s32, the parameter difference between b8 and b9 is s33), and among them c7: iron content h7, c8 carbonate content h8, c9 chloride content h9, then the correlation evaluation is: [(s31 / h7+s31 / h8+s31 / h9) / 3+(s32 / h7+s32 / h8+s32 / h9) / 3+(s33 / h7+s33 / h8+s33 / h9) / 3] / 3=W3).

[0090] The specific step S5 includes the following sub-steps:

[0091] The pre-treated water samples are tested for water taste information of the factory to obtain comprehensive pre-treatment taste parameters.

[0092] According to the first condition case one, the first condition case two and the first condition case three, the comprehensive pretreatment taste parameter, the first pretreatment taste parameter and the second pretreatment average taste parameter are averaged to obtain the final taste parameter of the factory water.

[0093] Alternatively, according to the second condition case one, the second condition case two and the second condition case three, the comprehensive pretreatment taste parameters, the first difference influencing factor, the second difference influencing factor and the third difference influencing factor are integrated with data slight difference compensation to obtain the final taste parameters of the factory water.

[0094] Specifically, if the comprehensive pretreatment taste parameter (i.e., the taste parameter detected corresponding to the pretreated water sample A is K), if the detection conditions meet the first condition case one, the first condition case two, and the first condition case three, then the comprehensive pretreatment taste parameter, the first pretreatment taste parameter, the second pretreatment average taste parameter are averaged to obtain the final taste parameter of the factory water (i.e., for example, (c1+P+K) / 3=Z1, i.e., the final taste parameter of the factory water), or, if the detection result meets the second condition case one, the second condition case two, and the second condition case three, then the comprehensive pretreatment taste parameter, the first difference influencing factor, the second difference influencing factor are averaged to obtain the final taste parameter of the factory water. The difference influencing factors and the third difference influencing factors are integrated for data micro-difference compensation to obtain the final taste parameters of the factory water (i.e. K+(W1+W2+W3) / 3=Z2, i.e. the final taste parameters of the factory water). The taste information of Z1 or Z2 collected by the electronic tongue is paired with the common taste database of drinking water established in advance for detection. At the same time, the acquired electrical signals are subjected to intelligent learning to optimize the database to improve the recognition sensitivity. The electrical signals that cannot be recognized are classified, stored and collected for subsequent supplementation of the taste database. These operations are all processed in the background data software, and finally a prompt is given as to whether there is any unrecognized taste information.

[0095] A drinking water taste information detection system based on electronic sensory organs, by applying the above-mentioned drinking water taste information detection method based on electronic sensory organs, comprises a water sample pretreatment module, a sub-water sample parameter analysis module 1, a sub-water sample parameter analysis module 2, a sub-water sample parameter analysis module 3 and a factory water parameter final measurement module, referring to Figure 2 , obtain a water source water sample through a water sample pretreatment module, filter and disinfect the water source water sample to obtain a pretreated water sample, and divide the pretreated water sample into a first pretreated sub-water sample, a second pretreated sub-water sample and a third pretreated sub-water sample; perform characteristic information detection on each sub-water sample in the first pretreated sub-water sample through a sub-water sample parameter analysis module one to obtain a first characteristic information set, and evaluate and obtain a first difference influencing factor based on the similarities and differences between the characteristic information of each sub-water sample in the first characteristic information set; perform characteristic information detection on each sub-water sample in the second pretreated sub-water sample through a sub-water sample parameter analysis module two to obtain a second characteristic information set, and evaluate and obtain a first difference influencing factor based on the similarities and differences between the characteristic information of each sub-water sample in the first characteristic information set. The similarities and differences between the characteristic information of each sub-water sample in the information set are evaluated to obtain the second difference influencing factor; the characteristic information of each sub-water sample in the third pretreated sub-water sample is detected through the sub-water sample parameter analysis module to obtain the third characteristic information set, and the third difference influencing factor is evaluated according to the similarities and differences between the characteristic information of each sub-water sample in the third characteristic information set; the pretreated water sample is subjected to the factory water taste information detection through the factory water parameter final measurement module to obtain the comprehensive pretreatment taste parameter, and the comprehensive pretreatment taste parameter, the first difference influencing factor, the second difference influencing factor and the third difference influencing factor are integrated after data micro-difference compensation to obtain the factory water final measurement taste parameter.

[0096] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for detecting drinking water taste information based on electronic sensory organs, characterized in that: The following steps are involved: Obtain a source water sample, filter and disinfect the source water sample to obtain a pretreated water sample, and divide the pretreated water sample into a first pretreated sub-water sample, a second pretreated sub-water sample, and a third pretreated sub-water sample; Performing characteristic information detection on each sub-water sample in the first pre-processed sub-water sample to obtain a first characteristic information set, and evaluating and obtaining a first difference influencing factor according to the similarities and differences between the characteristic information of each sub-water sample in the first characteristic information set; Performing characteristic information detection on each sub-water sample in the second pre-treated sub-water sample to obtain a second characteristic information set, and evaluating and obtaining a second difference influencing factor according to the similarities and differences between the characteristic information of each sub-water sample in the second characteristic information set; Performing characteristic information detection on each sub-water sample in the third pre-processed sub-water sample to obtain a third characteristic information set, and evaluating and obtaining a third difference influencing factor according to the similarities and differences between the characteristic information of each sub-water sample in the third characteristic information set; The pretreated water samples are subjected to water taste information detection of the factory to which they belong to obtain comprehensive pretreatment taste parameters, and the comprehensive pretreatment taste parameters, the first difference influencing factor, the second difference influencing factor and the third difference influencing factor are integrated after data micro-difference compensation to obtain the final taste parameters of the factory water.

2. The method for detecting drinking water taste information based on electronic sensory organs according to claim 1, characterized in that: The steps of obtaining a source water sample, filtering and disinfecting the source water sample to obtain a pretreated water sample, and dividing the pretreated water sample into a first pretreated sub-water sample, a second pretreated sub-water sample, and a third pretreated sub-water sample are specifically as follows: Obtain a source water sample, perform coarse filtration on the source water sample to obtain a coarse filtered water sample, and perform ultrafiltration membrane filtration on the coarse filtered water sample to obtain an ultrafiltered water sample; Disinfecting the ultrafiltration water sample to obtain a pretreated water sample; Dividing the pretreated water sample into a number of sub-water samples of equal size to obtain a sub-water sample set; Performing factory water taste information detection on a number of sub-water samples of the same molecular weight in the sub-water sample set to obtain a factory water taste parameter set; Extracting the same factory water taste parameter from the factory water taste parameter set to obtain a first pre-processed taste parameter; extracting corresponding first pretreated sub-water samples from the sub-water sample set according to the first pretreated taste parameter; Preset a difference segmentation interval value, extract the factory water taste parameters whose factory water taste parameter difference degrees are within the difference segmentation interval value from the factory water taste parameter set to obtain the second pre-processed taste parameter; extracting corresponding second pretreated sub-water samples from the sub-water sample set according to the second pretreated taste parameter; Extract the factory water taste parameters whose factory water taste parameter difference degrees are outside the difference cut interval value from the factory water taste parameter set to obtain the third pre-processing taste parameter; According to the third pre-processed taste parameter, a corresponding third pre-processed sub-water sample is extracted from the sub-water sample set.

3. The method for detecting drinking water taste information based on electronic sensory organs according to claim 2, characterized in that: The steps of detecting the characteristic information of each sub-water sample in the first pre-treated sub-water sample to obtain a first characteristic information set, and evaluating and obtaining a first difference influencing factor according to the similarities and differences between the characteristic information of each sub-water sample in the first characteristic information set, are specifically as follows: Performing characteristic information detection on each sub-water sample in the first pre-processed sub-water sample to obtain a first characteristic information set; If the characteristic information of each sub-water sample in the first characteristic information set is the same, or the characteristic information of each sub-water sample in the first characteristic information set is different, then output the first condition case 1; Extract the different parts in the first feature information set, and then output the second condition case 1; According to the first case of the second condition, the correlation between the different parts in the first feature information set and the corresponding first preprocessing parameters is evaluated to obtain the first difference impact factor.

4. The method for detecting drinking water taste information based on electronic sensory organs according to claim 2, characterized in that: The step of detecting the characteristic information of each sub-water sample in the second pretreated sub-water sample to obtain a second characteristic information set, and evaluating and obtaining a second difference influencing factor according to the similarities and differences between the characteristic information of each sub-water sample in the second characteristic information set, is specifically: Performing characteristic information detection on each sub-water sample in the second pre-processed sub-water sample to obtain a second characteristic information set; If the characteristic information of each sub-water sample in the second characteristic information set is the same, output the first condition case 2; According to the first condition, case 2, averaging the preprocessing taste parameters in the second preprocessing taste parameter to obtain a second preprocessing average taste parameter; Extract the different parts of the second feature information set and output the second condition case 2; According to the second condition case 2, the second preprocessing flavor parameters corresponding to different parts of the second feature information set are subtracted from each other to obtain a parameter difference value 1; The second difference impact factor is obtained by calculating the ratio between the different parts in the second feature information set and the parameter difference one.

5. The method for detecting drinking water taste information based on electronic sensory organs according to claim 2, characterized in that: The steps of detecting the characteristic information of each sub-water sample in the third pre-treated sub-water sample to obtain a third characteristic information set, and evaluating and obtaining a third difference influencing factor according to the similarities and differences of the characteristic information of each sub-water sample in the third characteristic information set, are specifically as follows: Performing characteristic information detection on each sub-water sample in the third pre-processed sub-water sample to obtain a third characteristic information set; If the characteristic information of each sub-water sample in the third characteristic information set is the same, the third pre-processing taste parameter is misjudged, and the third pre-processing taste parameter is invalidated, and the first condition case 3 is output; Extract the different parts of the third feature information set and output the second condition case 3; According to the second condition case 3, the preprocessing flavor parameters in the third preprocessing flavor parameters corresponding to different parts of the third feature information set are subtracted to obtain parameter difference value 2; The third difference impact factor is obtained by calculating the ratio between the different parts in the third feature information set and the parameter difference value 2.

6. The method for detecting drinking water taste information based on electronic sensory organs according to claim 5, characterized in that: The steps of performing a water taste information test on the pre-treated water sample to obtain a comprehensive pre-treated taste parameter, and integrating the comprehensive pre-treated taste parameter, the first difference influencing factor, the second difference influencing factor and the third difference influencing factor to obtain the final taste parameter of the out-of-factory water are specifically as follows: The pre-treated water samples are tested for water taste information of the factory to obtain comprehensive pre-treated taste parameters; According to the first condition case 1, the first condition case 2 and the first condition case 3, the comprehensive pretreatment taste parameter, the first pretreatment taste parameter and the second pretreatment average taste parameter are averaged to obtain the final taste parameter of the factory water; Alternatively, according to the second condition case one, the second condition case two and the second condition case three, the comprehensive pretreatment taste parameters, the first difference influencing factor, the second difference influencing factor and the third difference influencing factor are integrated with data slight difference compensation to obtain the final taste parameters of the factory water.

7. A drinking water taste information detection system based on electronic sensory organs, characterized in that: The system is used to implement the method for detecting drinking water taste information based on electronic sense organs according to any one of claims 1 to 6, comprising: A water sample pretreatment module is used to obtain a water source sample, filter and disinfect the water source sample to obtain a pretreated water sample, and divide the pretreated water sample into a first pretreated sub-water sample, a second pretreated sub-water sample, and a third pretreated sub-water sample; The sub-water sample parameter analysis module 1 is used to detect the characteristic information of each sub-water sample in the first pre-processed sub-water sample to obtain a first characteristic information set, and evaluate and obtain a first difference influencing factor according to the similarities and differences between the characteristic information of each sub-water sample in the first characteristic information set; The second sub-water sample parameter analysis module is used to detect the characteristic information of each sub-water sample in the second pre-processed sub-water sample to obtain a second characteristic information set, and evaluate and obtain a second difference influencing factor according to the similarities and differences between the characteristic information of each sub-water sample in the second characteristic information set; The sub-water sample parameter analysis module three is used to detect the characteristic information of each sub-water sample in the third pre-processed sub-water sample to obtain a third characteristic information set, and evaluate and obtain a third difference influencing factor according to the similarities and differences between the characteristic information of each sub-water sample in the third characteristic information set; The final measurement module for factory water parameters is used to detect the water taste information of the factory to which the pretreated water samples belong to obtain comprehensive pretreatment taste parameters, and integrate the comprehensive pretreatment taste parameters, the first difference influencing factor, the second difference influencing factor and the third difference influencing factor with data micro-difference compensation to obtain the final measurement taste parameters of factory water.

Images