A water efficiency classification method for nozzle-type urinal flushing valves

Abstract

The present invention discloses a nozzle-type urinal flush valve water efficiency grading method, including collecting flushing data of a nozzle-type urinal, preprocessing the flushing data, performing correlation analysis on the flushing data according to a water output related score to obtain associated data, obtaining a water volume variable control coefficient according to the associated data, correcting the flushing valve water output of the flushing data according to the water volume variable control coefficient to obtain effective water consumption, constructing a flushing valve water efficiency grading model according to the effective water consumption, optimizing the flushing valve water efficiency grading model, inputting the flushing data to be graded into the optimized flushing valve water efficiency grading model, and outputting a grading result. This method can not only improve the accuracy of the nozzle-type urinal flushing valve water efficiency grading, but also has good interpretability, and can be directly applied to the nozzle-type urinal flushing valve water efficiency grading system.

Description

Technical Field

[0001] The invention relates to the field of classification, and in particular to a water efficiency classification method for nozzle type urinal flushing valves. Background Art

[0002] With the increasing awareness of environmental protection and the increasing scarcity of water resources, the application of water-saving technology in homes and public facilities has become increasingly important. As a widely used water-saving device, the performance of the nozzle-type urinal flush valve directly affects the efficiency of water resources. Therefore, accurate evaluation and classification of its water efficiency is of great significance to promote the development of water-saving technology.

[0003] At present, there are many types of nozzle-type urinal flush valves on the market, but there is no unified standard for water efficiency evaluation methods. Traditional evaluation methods mainly rely on experimental tests. Although this method can directly reflect the actual performance of the flush valve, it is complicated to operate, time-consuming and labor-intensive, and it is difficult to effectively evaluate a large number of products. In addition, traditional evaluation methods also lack quantitative comparisons of water efficiency differences between different flush valves, making it difficult for consumers to make wise decisions when choosing products.

[0004] Therefore, it is necessary to develop a water efficiency grading method for nozzle urinal flush valves, which should be able to effectively evaluate the water efficiency of flush valves and make quantitative comparisons of different products. This method can not only provide consumers with an intuitive purchase reference, but also promote manufacturers to improve product design and improve water-saving performance. Summary of the invention

[0005] The object of the present invention is to provide a method for grading water efficiency of a nozzle type urinal flushing valve.

[0006] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0007] The present invention comprises the following steps:

[0008] collecting flushing data of a nozzle-type urinal and preprocessing the flushing data;

[0009] Performing correlation analysis on the flushing data according to the water output correlation score to obtain correlation data, and obtaining a water volume variable control coefficient according to the correlation data;

[0010] Correct the flushing valve water output of the flushing data according to the water volume variable control coefficient to obtain effective water consumption, and construct a flushing valve water efficiency classification model according to the effective water consumption;

[0011] The flushing valve water efficiency classification model is optimized, the flushing data to be classified is input into the optimized flushing valve water efficiency classification model, and the classification result is output.

[0012] Furthermore, the method of performing correlation analysis on the flushing data according to the water output correlation score to obtain the associated data includes:

[0013] Calculate the importance of flushing data:

[0014]

[0015] Flushing data The importance of The number of flushing data is p, the fuzzy coefficient is ζ, and the fuzzy neighborhood particle of the flushing data attribute τ is Flush data decision attribute collection The fuzzy neighborhood particle is

[0016] Take the flushing data with importance greater than 0 as the selection data and calculate the interaction gain of the selection data:

[0017]

[0018] The cth selected data and the fth selected data The interaction gain is Select Data The fuzzy neighborhood particle of attribute τ is Select Data Policy attribute collection The fuzzy neighborhood particle is

[0019] When the interaction gain is greater than 0, select data and select data Interrelated;

[0020] The water output in the selected data is taken as fixed data, and the selected data with interaction gain greater than 0 with the fixed data is selected as relevant data;

[0021] Calculate the water yield correlation score for the relevant data:

[0022]

[0023] The relevant data are Related data The number of Related data The mutual information gain of the water output c is Related data The adjustment factor is The correlation constant is L;

[0024] The relevant data with water volume correlation scores greater than 0.415 are output as associated data.

[0025] Furthermore, the method for obtaining the water volume variable control coefficient according to the associated data includes:

[0026]

[0027] The number of associated data in the sth measurement is m, and the zth associated data in the sth measurement is g. s,z , associated data g s,z The standard value is g o , the boundary control constant is k, and the variable control factor of the sth measurement is λ s , the correlation coefficient of the sth measurement is The norm is ‖·‖.

[0028] Furthermore, the method for obtaining effective water consumption by correcting the flushing valve water output of the flushing data according to the water volume variable control coefficient includes:

[0029] The flushing valve water output and water volume variable control coefficient of the flushing data are input into the mixed autoregressive moving average model to predict the flushing valve water output;

[0030] Correction constants for calculating flush valve water output and effective water consumption:

[0031]

[0032] The sth correction constant is h s , the number of associated data is U, the actual value of the water output of the sth flushing valve is G(s), the correction constant is b, and the sth correction weight is φ s The predicted value of the water output of the flushing valve for the sth time is The attenuation coefficient is δ;

[0033] Calculate the correction weight according to the water volume variable control coefficient:

[0034]

[0035] The sth correction weight is μ s , calculate the effective water consumption:

[0036] M s =h s +μ s ·G(s)

[0037] The sth water volume variable control coefficient is The effective water consumption for the sth time is M s .

[0038] Furthermore, a method for constructing a flush valve water efficiency classification model according to the effective water consumption includes:

[0039] Classification principle: When the effective water volume is less than or equal to 0.5 liters, the water efficiency level is 1; when the effective water volume is greater than 0.5 liters and less than or equal to 1.5 liters, the water efficiency level is 2; when the effective water volume is greater than 1.5 liters and less than or equal to 2.5 liters, the water efficiency level is 3;

[0040] The flush valve water efficiency classification model includes Bayesian algorithm, time series analysis algorithm, support vector machine algorithm, and gradient descent algorithm;

[0041] The Bayesian algorithm uses prior knowledge and probability models to evaluate the importance of the flushing data to be classified, and screens the flushing data to obtain important data according to the importance of the flushing data to be classified;

[0042] The time series analysis algorithm obtains the loss coefficient of the flushing valve according to the time variation law of important data;

[0043] The support vector machine algorithm classifies the product value of the loss coefficient and the effective water consumption of important data according to the classification principle to obtain the classification result;

[0044] The gradient descent algorithm iteratively adjusts the weights to minimize the error, and adjusts the hyperparameters of the flush valve water efficiency classification model according to the minimized error.

[0045] Furthermore, the method for optimizing the flush valve water efficiency classification model includes:

[0046] The initial population is generated by the chaotic sequence, and the expression of the chaotic map is:

[0047]

[0048] The chaos parameter is ρ, and the sequence value of the w+1th dimension of the ath searcher is The sequence value of the w-th dimension of the a-th searcher is

[0049] Compute the position of the searcher:

[0050]

[0051] The maximum number of iterations is t max , the position of the a-th searcher at the t+1-th iteration is The position of the a-th searcher at the t-th iteration is The optimal position of the a-th searcher at the t-th iteration is A random number between 0 and 1 is d 1 , the sine function is sin(·);

[0052] Calculate the fitness value of the searcher:

[0053]

[0054] The fitness value of the a-th searcher is The actual value of the bth effective water consumption is The average effective water consumption is The estimated effective water consumption is The amount of effective water consumption is

[0055] Update the position of the searcher, the expression is:

[0056]

[0057] The updated position for The spiral coefficient is β, the natural constant is e, the inertia constant is σ, and the control factor is η;

[0058] Continue to iterate until the maximum number of iterations is reached, otherwise recalculate the fitness value of the searcher.

[0059] In a second aspect, an embodiment of the present application further provides an electronic device, including:

[0060] A processor; and a memory arranged to store computer executable instructions, which when executed cause the processor to perform the method steps described in the first aspect.

[0061] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple applications, the electronic device executes the method steps described in the first aspect.

[0062] The beneficial effects of the present invention are:

[0063] The present invention is a method for grading water efficiency of a nozzle-type urinal flushing valve. Compared with the prior art, the present invention has the following technical effects:

[0064] The present invention can improve the accuracy of the water efficiency classification of nozzle-type urinal flushing valves through preprocessing, correlation analysis, acquisition of water volume variable control coefficients, correction data, model construction and model optimization steps, thereby improving the precision of the water efficiency classification of nozzle-type urinal flushing valves. The water efficiency classification of nozzle-type urinal flushing valves can be optimized, which can greatly save resources and improve work efficiency. It can realize the intelligent classification of the water efficiency of nozzle-type urinal flushing valves and perform data correction on the water efficiency classification of nozzle-type urinal flushing valves in real time, which is of great significance to the water efficiency classification of nozzle-type urinal flushing valves and can adapt to the water efficiency classification of nozzle-type urinal flushing valves of different standards and the water efficiency classification requirements of different nozzle-type urinal flushing valves, and has a certain universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A flow chart of the steps of a method for grading water efficiency of a nozzle-type urinal flushing valve according to the present invention;

[0066] Figure 2 It is a schematic diagram of the structure of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION

[0067] The present invention is further described below by means of specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0068] A method for grading water efficiency of a nozzle-type urinal flushing valve of the present invention comprises the following steps:

[0069] like Figure 1 As shown, in this embodiment, the following steps are included:

[0070] Collect flushing data of nozzle-type urinals and pre-process the flushing data; the flushing data includes flushing water volume, flushing water pressure, flushing time, flushing effect, urinal drain water volume, usage time, full flushing water volume, half flushing water volume, pipeline water source conditions, urinal conditions, and flush valve aging degree;

[0071] In the actual evaluation, the flushing data are flushing water volume 2 liters / time, flushing water pressure 0.25MPa, flushing time 4s, flushing effect with slight residue, urinal drain water volume 1.95 liters / time, use time of one and a half years, full flushing water volume 2 liters / time, half flushing water volume 1 liter / time, pipe water source looseness 0.093cm, urinal condition without blockage, flush valve aging degree 0.32;

[0072] Performing correlation analysis on the flushing data according to the water output correlation score to obtain correlation data, and obtaining a water volume variable control coefficient according to the correlation data;

[0073] In the actual evaluation, the relevant data are flushing water volume 2 liters / time, flushing water pressure 0.25MPa, slight residue in flushing effect, urinal drain water volume 1.55 liters / time, full flushing water volume 2 liters / time, half flushing water volume 1 liter / time, pipe water source looseness 0.093cm, urinal condition without blockage, flush valve aging degree 0.32; water volume variable control coefficient is 0.876;

[0074] Correct the flushing valve water output of the flushing data according to the water volume variable control coefficient to obtain effective water consumption, and construct a flushing valve water efficiency classification model according to the effective water consumption;

[0075] In the actual evaluation, the effective water consumption is 1.55 liters, and the average water consumption is calculated as follows:

[0076]

[0077] The effective water volume is V, and the full flushing water volume is V 1 , half flush water consumption is V 2 , the average water consumption is 1.34;

[0078] Optimizing the flushing valve water efficiency classification model, inputting the flushing data to be classified into the optimized flushing valve water efficiency classification model, and outputting the classification result;

[0079] In the actual evaluation, the flushing data to be classified are flushing water volume 1 liter / time, flushing water pressure 0.37MPa, flushing time 3s, flushing effect without residue, urinal drain water volume 0.96 liters / time, use time one year, full flushing water volume 2 liters / time, half flushing water volume 0.5 liters / time, pipeline water source looseness 0.048cm, urinal condition without blockage, flush valve aging degree 0.15;

[0080] The classification result is level 2, and the average water consumption is 1 liter.

[0081] In this embodiment, the method of performing correlation analysis on the flushing data according to the water output correlation score to obtain the associated data includes:

[0082] Calculate the importance of flushing data:

[0083]

[0084] Flushing data The importance of The number of flushing data is p, the fuzzy coefficient is ζ, and the fuzzy neighborhood particle of the flushing data attribute τ is Flush data decision attribute collection The fuzzy neighborhood particle is

[0085] Take the flushing data with importance greater than 0 as the selection data and calculate the interaction gain of the selection data:

[0086]

[0087] The cth selected data and the fth selected data The interaction gain is Select Data The fuzzy neighborhood particle of attribute τ is Select Data Policy attribute collection The fuzzy neighborhood particle is

[0088] When the interaction gain is greater than 0, select data and select data Interrelated;

[0089] The water output in the selected data is taken as fixed data, and the selected data with interaction gain greater than 0 with the fixed data is selected as relevant data;

[0090] Calculate the water yield correlation score for the relevant data:

[0091]

[0092] The relevant data are Related data The number of Related data The mutual information gain of the water output c is Related data The adjustment factor is The correlation constant is L;

[0093] The relevant data with water volume correlation scores greater than 0.415 are output as associated data.

[0094] In this embodiment, the method for obtaining the water volume variable control coefficient according to the associated data includes:

[0095]

[0096] The number of associated data in the sth measurement is m, and the zth associated data in the sth measurement is g. s,z , associated data g s,z The standard value is g o , the boundary control constant is k, and the variable control factor of the sth measurement is λ s , the correlation coefficient of the sth measurement is The norm is ‖·‖.

[0097] In this embodiment, the method for obtaining effective water consumption by correcting the flushing valve water output of the flushing data according to the water volume variable control coefficient includes:

[0098] The flushing valve water output and water volume variable control coefficient of the flushing data are input into the mixed autoregressive moving average model to predict the flushing valve water output;

[0099] Correction constants for calculating flush valve water output and effective water consumption:

[0100]

[0101] The sth correction constant is h s, the number of associated data is U, the actual value of the water output of the sth flushing valve is G(s), the correction constant is b, and the sth correction weight is φ s The predicted value of the water output of the flushing valve for the sth time is The attenuation coefficient is δ;

[0102] Calculate the correction weight according to the water volume variable control coefficient:

[0103]

[0104] The sth correction weight is μ s , calculate the effective water consumption:

[0105] M s =h s +μ s ·G(s)

[0106] The sth water volume variable control coefficient is The effective water consumption for the sth time is M s .

[0107] In this embodiment, the method for constructing a flush valve water efficiency classification model according to the effective water consumption includes:

[0108] Classification principle: When the effective water volume is less than or equal to 0.5 liters, the water efficiency level is 1; when the effective water volume is greater than 0.5 liters and less than or equal to 1.5 liters, the water efficiency level is 2; when the effective water volume is greater than 1.5 liters and less than or equal to 2.5 liters, the water efficiency level is 3;

[0109] The flush valve water efficiency classification model includes Bayesian algorithm, time series analysis algorithm, support vector machine algorithm, and gradient descent algorithm;

[0110] The Bayesian algorithm uses prior knowledge and probability models to evaluate the importance of the flushing data to be classified, and screens the flushing data to obtain important data according to the importance of the flushing data to be classified;

[0111] The time series analysis algorithm obtains the loss coefficient of the flushing valve according to the time variation law of important data;

[0112] The support vector machine algorithm classifies the product value of the loss coefficient and the effective water consumption of important data according to the classification principle to obtain the classification result;

[0113] The gradient descent algorithm iteratively adjusts the weights to minimize the error, and adjusts the hyperparameters of the flush valve water efficiency classification model according to the minimized error.

[0114] In this embodiment, the method for optimizing the flush valve water efficiency classification model includes:

[0115] The initial population is generated by the chaotic sequence, and the expression of the chaotic map is:

[0116]

[0117] The chaos parameter is ρ, and the sequence value of the w+1th dimension of the ath searcher is The sequence value of the w-th dimension of the a-th searcher is

[0118] Compute the position of the searcher:

[0119]

[0120] The maximum number of iterations is t max , the position of the a-th searcher at the t+1-th iteration is The position of the a-th searcher at the t-th iteration is The optimal position of the a-th searcher at the t-th iteration is A random number between 0 and 1 is d 1 , the sine function is sin(·);

[0121] Calculate the fitness value of the searcher:

[0122]

[0123] The fitness value of the a-th searcher is The actual value of the bth effective water consumption is The average effective water consumption is The estimated effective water consumption is The amount of effective water consumption is

[0124] Update the position of the searcher, the expression is:

[0125]

[0126] The updated position for The spiral coefficient is β, the natural constant is e, the inertia constant is σ, and the control factor is η;

[0127] Continue to iterate until the maximum number of iterations is reached, otherwise recalculate the fitness value of the searcher.

[0128] Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 2At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0129] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0130] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0131] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a nozzle-type urinal flush valve water efficiency grading device at the logical level. The processor executes the program stored in the memory and is specifically used to execute any of the above-mentioned nozzle-type urinal flush valve water efficiency grading methods.

[0132] The above application Figure 1The water efficiency classification method of a nozzle-type urinal flushing valve disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in a decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0133] The electronic device may also perform Figure 1 A water efficiency classification method for nozzle type urinal flush valves and implementation Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0134] The embodiment of the present application also proposes a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions. When the instructions are executed by an electronic device including multiple application programs, any of the aforementioned nozzle-type urinal flush valve water efficiency grading methods is executed.

[0135] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0136] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0137] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0138] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0139] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0140] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0141] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0142] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0143] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0144] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for grading water efficiency of a nozzle-type urinal flush valve, characterized in that: The following steps are involved: collecting flushing data of a nozzle-type urinal and preprocessing the flushing data; According to the water output related score, the flushing data is subjected to correlation analysis to obtain correlation data, and the water volume variable control coefficient is obtained according to the correlation data; including: Calculate the importance of flushing data: Flushing data The importance of , the number of flushing data is p, and the fuzziness coefficient is , flush data attributes The fuzzy neighborhood particle is , flush data decision attribute set The fuzzy neighborhood particle is ; Take the flushing data with importance greater than 0 as the selection data and calculate the interaction gain of the selection data: The cth selected data and the fth selected data The interaction gain is , select data property The fuzzy neighborhood particle is , select data Decision attribute set The fuzzy neighborhood particle is ; When the interaction gain is greater than 0, select data and select data Interrelated; The water output in the selected data is taken as fixed data, and the selected data with interaction gain greater than 0 with the fixed data is selected as relevant data; Calculate the water yield correlation score for the relevant data: The relevant data are , related data The number of , related data The mutual information gain of the water output r is , related data The adjustment factor is , the correlation constant is L; Output the relevant data with water volume correlation score greater than 0.415 as associated data; Correct the flushing valve water output of the flushing data according to the water volume variable control coefficient to obtain effective water consumption, and construct a flushing valve water efficiency classification model according to the effective water consumption; The flushing valve water efficiency classification model is optimized, the flushing data to be classified is input into the optimized flushing valve water efficiency classification model, and the classification result is output.

2. A method for grading water efficiency of nozzle-type urinal flush valves according to claim 1, characterized in that: The method for obtaining the water volume variable control coefficient according to the associated data includes: The number of associated data in the sth measurement is m, and the zth associated data in the sth measurement is , Linked Data The standard value is , the boundary control constant is k, and the variable control factor of the sth measurement is , the correlation coefficient of the sth measurement is , the norm is .

3. A method for grading water efficiency of nozzle-type urinal flush valves according to claim 1, characterized in that: The method for obtaining effective water consumption by correcting the flushing valve water output of the flushing data according to the water volume variable control coefficient comprises: The flushing valve water output and water volume variable control coefficient of the flushing data are input into the mixed autoregressive moving average model to predict the flushing valve water output; Correction constants for calculating flush valve water output and effective water consumption: The sth correction constant is , the number of associated data is The actual value of the water output of the flushing valve for the sth time is , the correction constant is b, and the sth correction weight is The predicted value of the water output of the flushing valve for the sth time is , the attenuation coefficient is ; Calculate the correction weight according to the water volume variable control coefficient: The sth revision weight is , calculate the effective water consumption: The sth water volume variable control coefficient is , the effective water consumption for the sth time is .

4. A method for grading water efficiency of nozzle-type urinal flush valves according to claim 1, characterized in that: The method for constructing a flush valve water efficiency classification model according to the effective water consumption comprises: Classification principle: When the effective water volume is less than or equal to 0.5 liters, the water efficiency level is 1; when the effective water volume is greater than 0.5 liters and less than or equal to 1.5 liters, the water efficiency level is 2; when the effective water volume is greater than 1.5 liters and less than or equal to 2.5 liters, the water efficiency level is 3; The flush valve water efficiency classification model includes Bayesian algorithm, time series analysis algorithm, support vector machine algorithm, and gradient descent algorithm; The Bayesian algorithm uses prior knowledge and probability models to evaluate the importance of the flushing data to be classified, and screens the flushing data to obtain important data according to the importance of the flushing data to be classified; The time series analysis algorithm obtains the loss coefficient of the flushing valve according to the time variation law of important data; The support vector machine algorithm classifies the product value of the loss coefficient and the effective water consumption of important data according to the classification principle to obtain the classification result; The gradient descent algorithm iteratively adjusts the weights to minimize the error, and adjusts the hyperparameters of the flush valve water efficiency classification model according to the minimized error.

5. A method for grading water efficiency of nozzle-type urinal flush valves according to claim 1, characterized in that: The method for optimizing the flush valve water efficiency classification model comprises: The initial population is generated by the chaotic sequence, and the expression of the chaotic map is: The chaos parameter is , the sequence value of the w+1th dimension of the ath searcher is , the sequence value of the w-th dimension of the a-th search sub-array is ; Compute the position of the searcher: The maximum number of iterations is , the position of the a-th searcher at the t+1-th iteration is , the position of the a-th searcher at the t-th iteration is , the optimal position of the a-th searcher at the t-th iteration is , a random number from 0 to 1 is , the sine function is ; Calculate the fitness value of the searcher: The fitness value of the a-th searcher is , the actual value of the bth effective water consumption is The average effective water consumption is The estimated effective water consumption is , the amount of effective water consumption is ; Update the position of the searcher, the expression is: The updated position for , the spiral coefficient is , the natural constant is e, and the inertia constant is , the control factor is ; Continue to iterate until the maximum number of iterations is reached, otherwise recalculate the fitness value of the searcher.

6. An electronic device comprising: processor; as well as A memory arranged to store computer executable instructions, which, when executed, cause the processor to perform the method of any one of claims 1 to 5.

7. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of application programs, enables the electronic device to execute the method according to any one of claims 1 to 5.

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