Self-adaptive water purification method and device

Abstract

The invention discloses a self-adaptive water purification device and method. The device comprises an environment detection module, a water quality monitoring module, a filtering module group, a water purification module group and a resource control module group, the water quality monitoring module is connected with the environment detection module and is used for measuring the water quality to obtain a water quality parameter information set and evaluating the environment monitoring data set and the water quality parameter information set to obtain water purification demand information; the resource control module group is used for processing the water purification demand information to obtain filtering module switch instruction information and water purification module switch instruction information; filtering the raw water by using a filtering module corresponding to the electric switch; and turning on an electric switch corresponding to the water purification module switch instruction information, and carrying out water purification treatment on effluent of the filtering module by utilizing a water purification module corresponding to the electric switch.

Description

Technical Field

[0001] The present invention relates to the fields of water purification technology, intelligent control and strategy optimization technology, and in particular to an adaptive water purification method and device. Background Art

[0002] As people's living standards improve, the demand for drinking water quality is also getting higher and higher. Traditional water purification devices usually use fixed filtration and purification processes and cannot adapt to different water quality and environmental conditions. This fixed-mode water purification device has the following technical problems:

[0003] First, traditional water purification systems are unable to monitor the impact of environmental factors on water quality in real time. For example, changes in environmental parameters such as air pressure, temperature, and humidity can indirectly affect the water quality characteristics of the source water. Traditional water purification systems ignore these factors, resulting in suboptimal water purification results.

[0004] Secondly, traditional water purification systems cannot flexibly adjust the combination and operating mode of filtration and purification modules when treating water with varying degrees of contamination. The same treatment process is used for both high-turbidity and low-residual-chlorine water, which can lead to low purification efficiency and wasteful resources.

[0005] Furthermore, traditional water purification systems lack effective resource control mechanisms when multiple modules work together. Dynamic connection and switching between the filtration and purification modules is impossible, resulting in poor flexibility and adaptability of the entire water purification system.

[0006] Finally, traditional water purification devices are relatively simple in data processing, unable to classify, analyze and accurately evaluate complex water quality data, and it is difficult to dynamically adjust water purification strategies according to actual needs. Summary of the Invention

[0007] The present invention mainly solves the problem that traditional water purification devices are relatively simple in data processing, cannot classify and analyze complex water quality data and accurately evaluate them, and find it difficult to dynamically adjust water purification strategies according to actual needs. The present invention discloses an adaptive water purification method and device.

[0008] In a first aspect of an embodiment of the present invention, an adaptive water purification device is disclosed, comprising: an environment detection module, a water quality monitoring module, a filtration module group, a water purification module group, and a resource control module group;

[0009] The environmental monitoring module is used to collect and obtain an environmental monitoring data set; the environmental monitoring data set includes an air pressure sequence, a PM2.5 content sequence, a temperature sequence, a humidity sequence, and an oxygen content sequence;

[0010] The water quality monitoring module is connected to the environmental detection module and is used to measure water quality to obtain a water quality parameter information set, evaluate and process the environmental monitoring data set and the water quality parameter information set to obtain water purification demand information; the water quality parameter information set includes a pH value sequence, a dissolved oxygen value sequence, a turbidity value sequence, a conductivity value sequence, and a residual chlorine content sequence;

[0011] The filter module group includes several types of filter modules and an electric switch connected to each filter module;

[0012] The water purification module group includes several types of water purification modules and an electric switch connected to each water purification module;

[0013] The resource control module group is connected to the water quality monitoring module, the filtration module group, and the water purification module group, respectively, and is used to process the water purification demand information to obtain the filtration module switch instruction information and the water purification module switch instruction information; according to the filtration module switch instruction information, the electric switch corresponding to the filtration module switch instruction information is turned on, and the raw water is filtered using the filtration module corresponding to the electric switch; according to the water purification module switch instruction information, the electric switch corresponding to the water purification module switch instruction information is turned on, and the water purification module corresponding to the electric switch is used to purify the water output from the filtration module.

[0014] The water quality monitoring module includes a pH sensor, a dissolved oxygen sensor, a turbidity sensor, a conductivity sensor, a residual chlorine sensor and a data processing submodule;

[0015] The pH sensor, dissolved oxygen sensor, turbidity sensor, conductivity sensor and residual chlorine sensor are used to collect and obtain pH value sequence, dissolved oxygen value sequence, turbidity value sequence, conductivity value sequence and residual chlorine content sequence respectively;

[0016] The data processing submodule is used to evaluate and process the environmental monitoring data set and the water quality parameter information set to obtain water purification demand information.

[0017] A second aspect of the embodiments of the present invention discloses an adaptive water purification method, which is implemented using the adaptive water purification device, comprising:

[0018] S1, using the environmental detection module to collect an environmental monitoring data set;

[0019] S2, using the water quality monitoring module to measure water quality to obtain a water quality parameter information set, and evaluating and processing the environmental monitoring data set and the water quality parameter information set to obtain water purification demand information;

[0020] S3, using the resource control module group to process the water purification demand information to obtain filter module switch instruction information and water purification module switch instruction information;

[0021] S4. According to the filter module switch instruction information, turn on the electric switch corresponding to the filter module switch instruction information, and use the filter module corresponding to the electric switch to filter the raw water; according to the water purification module switch instruction information, turn on the electric switch corresponding to the water purification module switch instruction information, and use the water purification module corresponding to the electric switch to purify the water output from the filter module.

[0022] The evaluation and processing of the environmental monitoring data set and the water quality parameter information set to obtain the water purification demand information includes:

[0023] S21, performing classification analysis on the environmental monitoring data set and the water quality parameter information set to obtain a water quality data set of each type;

[0024] S22, performing evaluation processing on each type of water quality data set to obtain an evaluation sub-value of each type;

[0025] S23, taking each type of evaluation sub-value as a demand component, constructing and obtaining water purification demand information; the categories of the demand component include filtration demand component and water purification demand component.

[0026] The environmental monitoring data set and the water quality parameter information set are classified and analyzed to obtain each type of water quality data set, including:

[0027] S211, representing all sequences in the environmental monitoring data set as a first matrix; the row vectors of the first matrix include an air pressure sequence, a PM2.5 content sequence, a temperature sequence, a humidity sequence, and an oxygen content sequence;

[0028] S212, performing normalization processing on each row vector of the first matrix;

[0029] S213, using all column vectors of the first matrix as variables to be classified, performing classification processing on the variables to be classified, and obtaining type information and column vectors included in each type;

[0030] S214, performing classification matrix estimation and boundary estimation processing on all column vectors of the same type, to obtain an intermediate matrix and edge value corresponding to the type;

[0031] S215, using all types of intermediate matrices and edge quantities, a water quality classification model is constructed;

[0032] S216, using the water quality classification model, processing the water quality parameter information set to obtain a water quality data set for each type;

[0033] The expression of the classification matrix estimation is:

[0034]

[0035] Among them, a i,kj is the jth element of the kth column vector contained in the i-th type, β ij is the element in the i-th row and j-th column of the intermediate matrix, ω1 and ω2 are the preset first and second weighting factors respectively, P is the number of types, K is the total number of vectors contained in a type, is the mean of the kth column vector contained in the i-th type, is the mean of the jth element of all column vectors of the i-th type;

[0036] The boundary estimation expression is:

[0037]

[0038] Among them, J is the number of elements contained in a column vector, δ i The maximum eigenvalue of the type matrix constructed for all column vectors contained in the i-th type, μ i,kj The jth element of the kth eigenvector of the type matrix constructed for all column vectors contained in the i-th type, γ i is the edge quantity of the i-th type; the column vector of the type matrix is ​​all column vectors contained in a type.

[0039] The discriminant expression of the i-th type of the water quality classification model is:

[0040]

[0041] Among them, φ j is the jth element of the water quality parameter vector to be classified, and J is the number of elements in the water quality parameter vector; the water quality parameter vector is constructed using the collected values ​​of all sequences in the water quality parameter information set at the same time.

[0042] The water quality data set of each type is evaluated and processed to obtain the evaluation sub-value of each type, including:

[0043] S221, obtaining the pH standard value, dissolved oxygen standard value, turbidity standard value, conductivity standard value and residual chlorine content standard value;

[0044] S222, subtracting the elements of the water quality parameter vector of each type of water quality data set from the corresponding standard value, and constructing a difference vector of the type using all the values ​​obtained after the subtraction for each type;

[0045] S223, performing fusion evaluation calculation on all difference vectors of each type to obtain an evaluation sub-value of each type;

[0046] The expression for the fusion evaluation calculation is:

[0047]

[0048] Among them, pgr is a type of evaluation sub-value, and are the i-th elements of the first and second eigenvectors, respectively, z ij is the jth element of the ith difference vector of a type, N is the number of elements in the difference vector, and M is the number of difference vectors contained in a type.

[0049] According to a third aspect of an embodiment of the present invention, an adaptive water purification device is disclosed, comprising:

[0050] a memory storing executable program code;

[0051] a processor coupled to the memory;

[0052] The processor calls the executable program code stored in the memory to execute the adaptive water purification method.

[0053] According to a fourth aspect of an embodiment of the present invention, a computer-storable medium is disclosed, wherein the computer-storable medium stores computer instructions. When the computer instructions are called by a computer, the computer instructions are used to execute the adaptive water purification method.

[0054] A fifth aspect of the embodiments of the present invention discloses an information data processing terminal, which is used to implement the adaptive water purification method.

[0055] The beneficial effects of the present invention are:

[0056] The adaptive water purification device provided by this invention, through the collaborative operation of an environmental monitoring module and a water quality monitoring module, can collect real-time environmental monitoring data such as air pressure, PM2.5 content, temperature, humidity, and oxygen content, as well as water quality parameters such as pH value, dissolved oxygen, turbidity, conductivity, and residual chlorine content. This comprehensive monitoring method enables the water purification device to fully understand the impact of the external environment on water quality, providing data support for accurate water purification needs assessment.

[0057] The present invention uses a data processing submodule to classify, analyze, and evaluate environmental monitoring data and water quality parameters, constructing a water quality classification model. This model then dynamically adjusts the operating states of the filtration and purification modules based on the water purification demand information output by the model. This adaptive processing approach allows for the flexible selection of appropriate filtration and purification module combinations based on varying water quality conditions, improving water purification efficiency and resource utilization.

[0058] The filtration and water purification modules of the present invention are connected to the resource control module group via branch switches, enabling dynamic connection and switching between the modules. The flexible and controllable switching design of the filtration and water purification modules ensures the stability and reliability of the entire water purification system while improving its flexibility and adaptability. This modular design not only facilitates maintenance and upgrades but also allows for flexible expansion of system functionality based on actual needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 Flow chart for the implementation of the method of the present invention;

[0060] Figure 2 It is a composition diagram of the device of the present invention. DETAILED DESCRIPTION

[0061] In order to better understand the content of the present invention, an embodiment is given here.

[0062] Figure 1 4 is an implementation flow chart of the method of the present invention. Figure 2 It is a composition diagram of the device of the present invention.

[0063] In a first aspect of an embodiment of the present invention, an adaptive water purification device is disclosed, comprising: an environment detection module, a water quality monitoring module, a filtration module group, a water purification module group, and a resource control module group;

[0064] The environmental monitoring module is used to collect and obtain an environmental monitoring data set; the environmental monitoring data set includes an air pressure sequence, a PM2.5 content sequence, a temperature sequence, a humidity sequence, and an oxygen content sequence;

[0065] The water quality monitoring module is connected to the environmental detection module and is used to measure water quality to obtain a water quality parameter information set, evaluate and process the environmental monitoring data set and the water quality parameter information set to obtain water purification demand information; the water quality parameter information set includes a pH value sequence, a dissolved oxygen value sequence, a turbidity value sequence, a conductivity value sequence, and a residual chlorine content sequence;

[0066] The filter module group includes several types of filter modules and an electric switch connected to each filter module;

[0067] The water purification module group includes several types of water purification modules and an electric switch connected to each water purification module;

[0068] The resource control module group is connected to the water quality monitoring module, the filtration module group, and the water purification module group, respectively, and is used to process the water purification demand information to obtain filtration module switch instruction information and water purification module switch instruction information; according to the filtration module switch instruction information, turn on the electric switch corresponding to the filtration module switch instruction information, and use the filtration module corresponding to the electric switch to filter the raw water; according to the water purification module switch instruction information, turn on the electric switch corresponding to the water purification module switch instruction information, and use the water purification module corresponding to the electric switch to purify the water output from the filtration module;

[0069] The filter module group is connected to the water purification module group. Specifically, the filter module group and the water purification module are connected through a branch switch; the branch switch remains connected; the electric switch connected to each filter module is connected to the branch switch, and the electric switch connected to each water purification module is connected to the branch switch.

[0070] The water quality monitoring module includes a pH sensor, a dissolved oxygen sensor, a turbidity sensor, a conductivity sensor, a residual chlorine sensor and a data processing submodule;

[0071] The pH sensor, dissolved oxygen sensor, turbidity sensor, conductivity sensor and residual chlorine sensor are used to collect and obtain pH value sequence, dissolved oxygen value sequence, turbidity value sequence, conductivity value sequence and residual chlorine content sequence respectively;

[0072] The data processing submodule is used to evaluate and process the environmental monitoring data set and the water quality parameter information set to obtain water purification demand information.

[0073] The data processing submodule evaluates and processes the environmental monitoring data set and the water quality parameter information set to obtain water purification demand information, including:

[0074] The data processing submodule performs classification analysis on the environmental monitoring data set and the water quality parameter information set to obtain each type of water quality data set;

[0075] Each type of water quality data set is evaluated and processed to obtain the evaluation sub-value of each type;

[0076] Each type of evaluation sub-value is used as a demand component to construct water purification demand information; the categories of the demand components include filtration demand components and water purification demand components.

[0077] The environmental monitoring data set and the water quality parameter information set are classified and analyzed to obtain each type of water quality data set, including:

[0078] Representing all sequences in the environmental monitoring data set as a first matrix; the row vectors of the first matrix include an air pressure sequence, a PM2.5 content sequence, a temperature sequence, a humidity sequence, and an oxygen content sequence;

[0079] Performing normalization processing on each row vector of the first matrix;

[0080] Taking all column vectors of the first matrix as variables to be classified, classifying the variables to be classified to obtain type information and column vectors contained in each type;

[0081] For all column vectors of the same type, classification matrix estimation and boundary estimation processing are performed respectively to obtain the intermediate matrix and edge quantity corresponding to the type;

[0082] Using all types of intermediate matrices and edge quantities, a water quality classification model is constructed;

[0083] The water quality classification model is used to process the water quality parameter information set to obtain each type of water quality data set.

[0084] The normalization process is to divide each element in the vector by the maximum value of the elements in the vector.

[0085] The expression of the classification matrix estimation is:

[0086]

[0087] Among them, a i,kj is the jth element of the kth column vector contained in the i-th type, β ij is the element in the i-th row and j-th column of the intermediate matrix, ω1 and ω2 are the preset first and second weighting factors respectively, P is the number of types, K is the total number of vectors contained in a type, is the mean of the kth column vector contained in the i-th type, is the mean of the jth element of all column vectors of the i-th type.

[0088] The expression for classification matrix estimation comprehensively considers the logarithmic proportional relationship and sinusoidal variation characteristics of the data. Logarithmic operations can effectively highlight relative differences between data and capture details of data changes; sinusoidal functions can further explore the data's periodic or volatile characteristics. This enables a more comprehensive and detailed description of the characteristics of different types of water quality data when classifying and analyzing environmental monitoring datasets and water quality parameter information sets, avoiding the omission of important information. This expression introduces a preset first weighting factor ω1 and a second weighting factor ω2. By properly adjusting these two parameters, the weights of different calculation components can be flexibly assigned based on the actual application scenario and data characteristics. For example, when environmental factors have a significant impact on water quality, the weight of ω1 can be appropriately increased, allowing the calculation of the logarithmic proportional relationship to play a greater role in the classification, thereby more accurately classifying water quality data and improving the accuracy and adaptability of the classification.

[0089] This expression fully considers factors such as the number of categories P in the dataset and the total number of vectors K within a category, making it adaptable to water quality data of varying sizes and structures. Whether processing a small number of water quality data samples or large, complex datasets, it can reliably perform classification analysis, laying a solid foundation for subsequently determining water purification needs based on different water quality types.

[0090] The boundary estimation expression is:

[0091]

[0092] Among them, J is the number of elements contained in a column vector, δ i The maximum eigenvalue of the type matrix constructed for all column vectors contained in the i-th type, μ i,kj The jth element of the kth eigenvector of the type matrix constructed for all column vectors contained in the i-th type, γ i is the edge quantity of the i-th type; the column vector of the type matrix is ​​all the column vectors contained in a type;

[0093] The expression of the bound estimate is obtained by introducing the maximum eigenvalue δ of the type matrix i , eigenvector element μ ikjParameters such as , analyze each type of water quality data set from the perspective of matrix characteristics, and accurately estimate the boundary conditions of the data. Accurate boundary estimation helps to clarify the range of different water quality types, so as to better judge whether the water quality data is in the normal or abnormal range, and provide a reliable basis for timely adjustment of water purification strategies; by comprehensively considering factors such as the minimum and maximum values ​​of the data, it can effectively deal with fluctuations in water quality data that may occur during the actual collection process. When water quality data fluctuates due to environmental factors or other interference, this boundary estimation method can stably determine the data boundaries, avoid misjudgment of water quality types due to data fluctuations, and enhance the adaptability and robustness of water purification equipment to complex water quality changes.

[0094] The discriminant expression of the i-th type of the water quality classification model is:

[0095]

[0096] Among them, φ j is the jth element of the water quality parameter vector to be classified, and J is the number of elements in the water quality parameter vector; the water quality parameter vector is constructed using the collected values ​​of all sequences in the water quality parameter information set at the same time;

[0097] The water quality data set of each type is evaluated and processed to obtain the evaluation sub-value of each type, including:

[0098] Obtain standard values ​​for pH, dissolved oxygen, turbidity, conductivity and residual chlorine content;

[0099] For each type of water quality data set, the elements of the water quality parameter vector are subtracted from the corresponding standard values, and the difference vector is constructed using all the values ​​obtained after subtraction;

[0100] Perform fusion evaluation calculation on all difference vectors of each type to obtain the evaluation sub-value of each type;

[0101] The expression for the fusion evaluation calculation is:

[0102]

[0103] Among them, pgr is a type of evaluation sub-value, and are the i-th elements of the first and second eigenvectors, respectively, z ij is the jth element of the ith difference vector of a type, N is the number of elements in the difference vector, and M is the number of difference vectors contained in a type.

[0104] The fusion evaluation expression is based on the difference vectors obtained by subtracting each type of water quality dataset from the standard value. This comprehensively considers the differences between water quality parameters and the standard values ​​across multiple dimensions (such as pH, dissolved oxygen, and turbidity). By integrating these difference vectors, the fusion evaluation avoids the one-sidedness of single-parameter assessments and more accurately reflects the overall degree of difference between water quality and the standard, providing a more accurate quantitative indicator for determining water purification needs.

[0105] The fusion evaluation formula leverages the properties of sine and logarithmic functions to highlight key features in the data. The sine function is sensitive to data trends and can capture fluctuations; the logarithmic function amplifies data variance, allowing even small differences to be reflected in the assessment. Combining these two methods can more effectively uncover important information hidden in water quality data, improving the sensitivity and accuracy of the assessment.

[0106] The resource control module group processes the water purification demand information to obtain filter module switch instruction information and water purification module switch instruction information, including:

[0107] Acquire a filtering capability value sequence of each filtering module of the filtering module group; each filtering capability value of the filtering capability value sequence has a corresponding filtering requirement component;

[0108] Obtaining a water purification capacity value sequence of each water purification module of the water purification module group; each water purification capacity value of the water purification capacity value sequence has a corresponding water purification demand component;

[0109] Extracting all filtration demand components from the water purification demand information and constructing a filtration demand vector;

[0110] Extracting all clean water demand components from the clean water demand information and constructing a clean water demand vector;

[0111] performing Euclidean distance calculation on the filter capability value sequence of each filter module in the filter module group and the filter requirement vector respectively to obtain the filter module with the smallest Euclidean distance, and determining information of the electric switch connected to the filter module with the smallest Euclidean distance as the filter module switch instruction information;

[0112] Performing Euclidean distance calculation on the water purification capacity value sequence of each water purification module in the water purification module group and the water purification demand vector, obtaining the water purification module with the smallest Euclidean distance, and determining information of the electric switch connected to the water purification module with the smallest Euclidean distance as the water purification module switch instruction information;

[0113] The water quality classification model is used to process the water quality parameter information set to obtain each type of water quality data set, including:

[0114] When it is determined that the water quality parameter vector to be classified satisfies a discriminant expression of a type, determining that the water quality parameter vector to be classified belongs to the corresponding type;

[0115] Using water quality parameter vectors belonging to the same type, a water quality dataset of the corresponding type is constructed;

[0116] The eigenvalues ​​of a matrix are obtained using the matrix eigenvalue solving algorithm.

[0117] The elements of the water quality parameter vector are the collected values ​​of the pH value sequence, dissolved oxygen value sequence, turbidity value sequence, and conductivity value sequence at a certain moment;

[0118] The water quality parameter vector has the same number of elements as the column vector of the first matrix, which is 5.

[0119] The filter module group includes several filter units; the filter units mainly use ceramic membrane water purification technology and are composed of a circulation pump, a ceramic membrane assembly, a backwash water tank, an air compressor, etc. The circulation pump, ceramic membrane assembly, backwash water tank, and air compressor are connected in sequence;

[0120] The filtration performance indicators of each filter unit are different. The pore size of the ceramic membrane is 50nm, which can greatly reduce the turbidity, suspended matter, bacteria and viruses in the water, and reduce the working pressure of the subsequent RO membrane.

[0121] The working principle of ceramic membranes: Ceramic membranes use static pressure differentials as the driving force, utilizing the "sieving" effect of the mesh-like filter medium to perform membrane separation. Ceramic membranes employ a cross-flow filtration method. Driven by a pump, raw water flows parallel to the ceramic membrane surface within the membrane pores. The water that passes through the ceramic membrane is purified water, and the concentrated water is discharged from the other end of the membrane channel. The shear force generated by the water flowing through the ceramic membrane surface removes some impurities trapped on the membrane surface, thereby reducing membrane surface contamination and keeping the contamination layer at a relatively thin level.

[0122] The backwash tank structure adopts a stainless steel shell, with an air valve on the top, which is connected to the air compressor. When backwashing is required, the air valve opens and the clean water in the tank enters the ceramic membrane assembly under the pressure of air pressure, thereby completing the backwash process.

[0123] The water purification module utilizes an RO membrane assembly. RO membranes are made of aromatic polyamide composite membrane material, which offers advantages such as low operating pressure, strong acid and alkali resistance, high water yield, high salt rejection rate, and enhanced chemical stability. The selected LP series membrane elements are suitable for surface water with a salinity below approximately 10,000 ppm. The high-pressure pump is a key component of the RO system, providing a smooth, uninterrupted flow and appropriate pressure to the membrane assembly.

[0124] The water purification performance indicators of each water purification module are different;

[0125] The beneficial effects of the present invention are mainly reflected in the following aspects: First, the adaptability of the water purification device is improved, and the water purification strategy can be dynamically adjusted according to the environment and water quality conditions; second, through precise water quality assessment and modular design, the water purification efficiency and resource utilization rate are improved; third, the flexibility and reliability of the system are enhanced, which is convenient for maintenance and expansion; fourth, through advanced data processing technology, the classification analysis and precise assessment of complex water quality data are realized, providing technical support for intelligent water purification.

[0126] A second aspect of the embodiments of the present invention discloses an adaptive water purification method, which is implemented using the adaptive water purification device, comprising:

[0127] S1, using the environmental detection module to collect an environmental monitoring data set;

[0128] S2, using the water quality monitoring module to measure water quality to obtain a water quality parameter information set, and evaluating and processing the environmental monitoring data set and the water quality parameter information set to obtain water purification demand information;

[0129] S3, using the resource control module group to process the water purification demand information to obtain filter module switch instruction information and water purification module switch instruction information;

[0130] S4. According to the filter module switch instruction information, turn on the electric switch corresponding to the filter module switch instruction information, and use the filter module corresponding to the electric switch to filter the raw water; according to the water purification module switch instruction information, turn on the electric switch corresponding to the water purification module switch instruction information, and use the water purification module corresponding to the electric switch to purify the water output from the filter module.

[0131] The evaluation and processing of the environmental monitoring data set and the water quality parameter information set to obtain the water purification demand information includes:

[0132] S21, performing classification analysis on the environmental monitoring data set and the water quality parameter information set to obtain a water quality data set of each type;

[0133] S22, performing evaluation processing on each type of water quality data set to obtain an evaluation sub-value of each type;

[0134] S23, taking each type of evaluation sub-value as a demand component, constructing and obtaining water purification demand information; the categories of the demand component include filtration demand component and water purification demand component.

[0135] The classified analysis processing is performed on the environment monitoring data set and the water quality parameter information set, and water quality data sets of each type are obtained, including:

[0136] S211, all sequences in the environment monitoring data set are expressed as a first matrix, and a row vector of the first matrix includes a pressure sequence, a PM2.5 content sequence, a temperature sequence, a humidity sequence and an oxygen content sequence.

[0137] S212, each row vector of the first matrix is normalized respectively.

[0138] S213, all column vectors of the first matrix are used as classified variables, and the classified variables are classified to obtain type information and column vectors contained in each type.

[0139] S214, all column vectors of the same type are classified and estimated to obtain an intermediate matrix and an edge quantity corresponding to the type.

[0140] S215, a water quality classification model is constructed by using the intermediate matrix and the edge quantity of all types.

[0141] S216, the water quality classification model is used to process the water quality parameter information set to obtain water quality data sets of each type.

[0142] The normalization processing is to divide each element in the vector by the maximum value of the elements in the vector.

[0143] The expression of the classified matrix estimation is:

[0144]

[0145] Wherein, a i,kj is the jth element of the kth column vector contained in the ith type, β ij is an element in the ith row and the jth column of the intermediate matrix, ω1 and ω2 are a first preset weighting factor and a second preset weighting factor respectively, P is a type number, K is a total number of vectors contained in a type, is a mean value of the kth column vector contained in the ith type, is a mean value of the jth element of all column vectors of the ith type.

[0146] The expression of the boundary estimation is:

[0147]

[0148] Wherein, J is a number of elements contained in a column vector, δ ithe maximum eigenvalue of the type matrix constructed by all column vectors of the i-th type, μ i,kj the j-th element of the k-th eigenvector of the type matrix constructed by all column vectors of the i-th type, γ i the edge quantity of the i-th type; the column vector of the type matrix, all column vectors of a type;

[0149] The classification processing of the variable to be classified can adopt a principal component analysis algorithm or a K-means algorithm.

[0150] The discriminant expression of the i-th type of the water quality classification model is:

[0151]

[0152] wherein, φ j the j-th element of the water quality parameter vector to be classified, J being the number of elements of the water quality parameter vector; the water quality parameter vector being constructed by using the collection values of all sequences of the same moment in the water quality parameter information set;

[0153] The evaluation processing of each type of water quality data set to obtain each type of evaluation sub-value includes:

[0154] S221, obtaining PH standard value, dissolved oxygen standard value, turbidity standard value, conductivity standard value and residual chlorine content standard value;

[0155] S222, subtracting the elements of the water quality parameter vector of each type of water quality data set from the corresponding standard value respectively, and constructing a difference vector by using all the values obtained after subtraction;

[0156] S223, performing fusion evaluation calculation on all difference vectors of each type to obtain evaluation sub-value of each type;

[0157] The expression of the fusion evaluation calculation is:

[0158]

[0159] wherein, pgr is the evaluation sub-value of a type, and respectively, the i-th element of the first eigenvector and the second eigenvector, z ij the j-th element of the i-th difference vector of a type, N being the number of elements of the difference vector, and M being the number of difference vectors of a type.

[0160] The processing of the water purification demand information to obtain filter module switch instruction information and water purification module switch instruction information includes:

[0161] S31, obtaining a filtering capability value sequence of each filtering module of the filtering module group; each filtering capability value of the filtering capability value sequence has a corresponding filtering requirement component;

[0162] S32, obtaining a water purification capacity value sequence of each water purification module of the water purification module group; each water purification capacity value in the water purification capacity value sequence has a corresponding water purification demand component;

[0163] S33, extracting all filtration demand components from the water purification demand information and constructing a filtration demand vector;

[0164] S34, extracting all water purification demand components from the water purification demand information and constructing a water purification demand vector;

[0165] S35, performing Euclidean distance calculation on the filter capability value sequence of each filter module in the filter module group and the filter requirement vector, obtaining the filter module with the smallest Euclidean distance, and determining information of the electric switch connected to the filter module with the smallest Euclidean distance as the filter module switch instruction information;

[0166] S36, performing Euclidean distance calculation on the water purification capacity value sequence of each water purification module in the water purification module group and the water purification demand vector, obtaining the water purification module with the smallest Euclidean distance, and determining information of the electric switch connected to the water purification module with the smallest Euclidean distance, which serves as the water purification module switch instruction information;

[0167] The water quality classification model is used to process the water quality parameter information set to obtain each type of water quality data set, including:

[0168] When it is determined that the water quality parameter vector to be classified satisfies a discriminant expression of a type, determining that the water quality parameter vector to be classified belongs to the corresponding type;

[0169] Using water quality parameter vectors belonging to the same type, a water quality dataset of the corresponding type is constructed;

[0170] The eigenvalues ​​of a matrix are obtained using the matrix eigenvalue solving algorithm.

[0171] The elements of the water quality parameter vector are the collected values ​​of the pH value sequence, dissolved oxygen value sequence, turbidity value sequence, and conductivity value sequence at a certain moment;

[0172] The water quality parameter vector has the same number of elements as the column vector of the first matrix, which is 5.

[0173] According to a third aspect of an embodiment of the present invention, an adaptive water purification device is disclosed, comprising:

[0174] a memory storing executable program code;

[0175] a processor coupled to the memory;

[0176] The processor calls the executable program code stored in the memory to execute the adaptive water purification method.

[0177] According to a fourth aspect of an embodiment of the present invention, a computer-storable medium is disclosed, wherein the computer-storable medium stores computer instructions. When the computer instructions are called by a computer, the computer instructions are used to execute the adaptive water purification method.

[0178] A fifth aspect of the embodiments of the present invention discloses an information data processing terminal, which is used to implement the adaptive water purification method.

[0179] The foregoing is merely an embodiment of the present invention and is not intended to limit the present invention. It will be apparent to those skilled in the art that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be included within the scope of the claims of the present invention.

Claims

1. An adaptive water purification device, characterized in that: include: Environmental detection module, water quality monitoring module, filtration module group, water purification module group and resource control module group; The environmental monitoring module is used to collect and obtain an environmental monitoring data set; the environmental monitoring data set includes an air pressure sequence, a PM2.5 content sequence, a temperature sequence, a humidity sequence, and an oxygen content sequence; The water quality monitoring module is connected to the environmental detection module and is used to measure water quality to obtain a water quality parameter information set, evaluate and process the environmental monitoring data set and the water quality parameter information set to obtain water purification demand information; the water quality parameter information set includes a pH value sequence, a dissolved oxygen value sequence, a turbidity value sequence, a conductivity value sequence, and a residual chlorine content sequence; The filter module group includes several types of filter modules and an electric switch connected to each filter module; The water purification module group includes several types of water purification modules and an electric switch connected to each water purification module; The resource control module group is connected to the water quality monitoring module, the filtration module group, and the water purification module group respectively, and is used to process the water purification demand information to obtain the filtration module switch instruction information and the water purification module switch instruction information; According to the filter module switch instruction information, turn on the electric switch corresponding to the filter module switch instruction information, and use the filter module corresponding to the electric switch to filter the raw water; according to the water purification module switch instruction information, turn on the electric switch corresponding to the water purification module switch instruction information, and use the water purification module corresponding to the electric switch to purify the water output from the filter module.

2. The adaptive water purification device according to claim 1, characterized in that The water quality monitoring module includes a pH sensor, a dissolved oxygen sensor, a turbidity sensor, a conductivity sensor, a residual chlorine sensor and a data processing submodule; The pH sensor, dissolved oxygen sensor, turbidity sensor, conductivity sensor and residual chlorine sensor are used to collect and obtain pH value sequence, dissolved oxygen value sequence, turbidity value sequence, conductivity value sequence and residual chlorine content sequence respectively; The data processing submodule is used to evaluate and process the environmental monitoring data set and the water quality parameter information set to obtain water purification demand information.

3. An adaptive water purification method, characterized in that: This is achieved by using the adaptive water purification device according to any one of claims 1 to 2, comprising: S1, using the environmental detection module to collect an environmental monitoring data set; S2, using the water quality monitoring module to measure water quality to obtain a water quality parameter information set, and evaluating and processing the environmental monitoring data set and the water quality parameter information set to obtain water purification demand information; S3, using the resource control module group to process the water purification demand information to obtain filter module switch instruction information and water purification module switch instruction information; S4. According to the filter module switch instruction information, turn on the electric switch corresponding to the filter module switch instruction information, and use the filter module corresponding to the electric switch to filter the raw water; according to the water purification module switch instruction information, turn on the electric switch corresponding to the water purification module switch instruction information, and use the water purification module corresponding to the electric switch to purify the water output from the filter module.

4. The adaptive water purification method according to claim 3, wherein: The evaluation and processing of the environmental monitoring data set and the water quality parameter information set to obtain the water purification demand information includes: S21, performing classification analysis on the environmental monitoring data set and the water quality parameter information set to obtain a water quality data set of each type; S22, performing evaluation processing on each type of water quality data set to obtain an evaluation sub-value of each type; S23, taking each type of evaluation sub-value as a demand component, constructing and obtaining water purification demand information; the categories of the demand component include filtration demand component and water purification demand component.

5. The adaptive water purification method according to claim 4, wherein: The environmental monitoring data set and the water quality parameter information set are classified and analyzed to obtain each type of water quality data set, including: S211, representing all sequences in the environmental monitoring data set as a first matrix; the row vectors of the first matrix include an air pressure sequence, a PM2.5 content sequence, a temperature sequence, a humidity sequence, and an oxygen content sequence; S212, performing normalization processing on each row vector of the first matrix; S213, using all column vectors of the first matrix as variables to be classified, performing classification processing on the variables to be classified, and obtaining type information and column vectors included in each type; S214, performing classification matrix estimation and boundary estimation processing on all column vectors of the same type, to obtain an intermediate matrix and edge value corresponding to the type; S215, using all types of intermediate matrices and edge quantities, a water quality classification model is constructed; S216, using the water quality classification model, processing the water quality parameter information set to obtain a water quality data set for each type; The expression of the classification matrix estimation is: Among them, a i,kj is the jth element of the kth column vector contained in the i-th type, β ij is the element in the i-th row and j-th column of the intermediate matrix, ω1 and ω2 are the preset first and second weighting factors respectively, P is the number of types, K is the total number of vectors contained in a type, is the mean of the kth column vector contained in the i-th type, is the mean of the jth element of all column vectors of the i-th type; The boundary estimation expression is: Among them, J is the number of elements contained in a column vector, δ i The maximum eigenvalue of the type matrix constructed for all column vectors contained in the i-th type, μ i,kj The jth element of the kth eigenvector of the type matrix constructed for all column vectors contained in the i-th type, γ i is the edge quantity of the i-th type; the column vector of the type matrix is ​​all column vectors contained in a type.

6. The adaptive water purification method according to claim 5, characterized in that: The discriminant expression of the i-th type of the water quality classification model is: Among them, φ j is the jth element of the water quality parameter vector to be classified, and J is the number of elements in the water quality parameter vector; the water quality parameter vector is constructed using the collected values ​​of all sequences in the water quality parameter information set at the same time.

7. The adaptive water purification method according to claim 4, wherein: The water quality data set of each type is evaluated and processed to obtain the evaluation sub-value of each type, including: S221, obtaining the pH standard value, dissolved oxygen standard value, turbidity standard value, conductivity standard value and residual chlorine content standard value; S222, subtracting the elements of the water quality parameter vector of each type of water quality data set from the corresponding standard value, and constructing a difference vector of the type using all the values ​​obtained after the subtraction for each type; S223, performing fusion evaluation calculation on all difference vectors of each type to obtain an evaluation sub-value of each type; The expression for the fusion evaluation calculation is: Among them, pgr is a type of evaluation sub-value, and are the i-th elements of the first and second eigenvectors, respectively, z ij is the jth element of the ith difference vector of a type, N is the number of elements in the difference vector, and M is the number of difference vectors contained in a type.

8. An adaptive water purification device, characterized in that: The device comprises: a memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the adaptive water purification method according to any one of claims 3 to 7.

9. A computer storable medium, characterized in that The computer storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the adaptive water purification method according to any one of claims 3 to 7.

10. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the adaptive water purification method according to any one of claims 3 to 7.

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