Intelligent temperature monitoring system for hydroelectric power shower apparatus

Abstract

The application relates to the field of intelligent monitoring, in particular to an intelligent temperature monitoring system for a hydropower bathroom device, which comprises a power generation evaluation module, which is used to determine whether to perform energy storage effective distribution analysis according to a stage energy storage evaluation coefficient; an analysis execution module, which is used to determine whether to perform water use behavior analysis or monitoring management analysis on the bathroom monitoring process according to a monitoring demand state of a target analysis period; a behavior analysis module, which is used to determine whether to perform dynamic adaptation analysis according to an operation stability index and a water use duration index; a dynamic matching module, which is used to determine optimization analysis on an initial temperature setting based on a prediction evaluation coefficient; and a supervision analysis module, which is used to determine whether to adjust a blade water-impingement area according to a state duration index. The application guarantees the effectiveness of the temperature monitoring process and the stability of the power supply of the device.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent monitoring, in particular to an intelligent temperature monitoring system for a hydropower bathroom device. BACKGROUND

[0002] The hydropower bathroom device drives a micro power generation device by water flow to provide power storage for the bathroom device, thereby providing driving power for the temperature monitoring module, data processing module, data display module and other related modules existing in the bathroom device. However, the actual use process of the bathroom device is non-continuous, and the self-generated power stored by the device cannot always provide stable power supply for each module existing in the bathroom device. Therefore, how to coordinate the accuracy requirement of water temperature monitoring and the power supply distribution tendency according to the actual use state of the bathroom device to ensure the temperature monitoring effect and the stability of the self-generated power supply is a problem to be solved by those skilled in the art.

[0003] Chinese Patent Publication No. CN114812863A discloses a low-power consumption control display data system and method, belonging to the technical field of bathroom devices, comprising: a display module, an induction switch and a temperature sensor. When the induction switch is inducted, the temperature sensor collects the current temperature and compares it with the starting display temperature range to determine whether to turn on the display. After the display is turned on, the temperature sensor monitors whether the temperature changes and determines whether the difference q before and after the temperature change is within the range of the temperature change difference Q set by the controller to determine the display time. Chinese Patent Application Publication No. CN119602444A discloses a hydropower generator output circuit, a shower and an electric energy conversion method, belonging to the technical field of bathroom devices. The hydropower generator output circuit includes a rectifier module, an overvoltage protection module, a voltage stabilizing module, a channel identification switching module, a high-voltage channel module, a low-voltage channel module, a charge management module and a load interaction module. The rectifier module is used to rectify the alternating current power output by the hydropower generator into direct current power. The overvoltage protection module is used to detect the direct current power to output direct current power within a preset voltage range. The voltage stabilizing module is used to output a reference voltage to the channel identification switching module. The channel identification switching module is used to control the on-off state of the high-voltage channel module and the low-voltage channel module according to the reference voltage and the direct current power output by the overvoltage protection module. The low-voltage channel module is used to supply power to the charge management module and the load interaction module. However, the above technical solutions have the following defects: they fail to coordinate the temperature monitoring accuracy and the actual distribution of self-generated power according to the actual use process of the bathroom, making it impossible to balance the accuracy requirement of temperature monitoring of the bathroom device and the stability of the power supply of the self-generated device in the actual process. SUMMARY

[0004] To this end, the application provides an intelligent temperature monitoring system for a hydroelectric power generation bathroom device, so as to overcome the problem that the prior art fails to coordinate the temperature monitoring accuracy and the actual distribution of power generation electric energy according to the actual bathroom use process, so that the temperature monitoring accuracy of the bathroom device and the stability of the power supply of the device self-power generation cannot be considered in the actual process.

[0005] To achieve the above-mentioned purpose, the application provides an intelligent temperature monitoring system for a hydroelectric power generation bathroom device, comprising:

[0006] A power generation evaluation module is configured to periodically determine a stage energy storage evaluation coefficient according to the water pressure transmission parameter and the stage water use parameter, and determine whether to perform energy storage effective distribution analysis for the bathroom monitoring process according to the stage energy storage evaluation coefficient;

[0007] An analysis execution module is connected with the power generation evaluation module and configured to perform energy storage effective distribution analysis, determine a monitoring demand state of each execution evaluation period according to a real-time water use index and a water use temperature fluctuation index, and determine whether to perform water use behavior analysis or monitoring management analysis for the bathroom monitoring process according to the monitoring demand state in which the target analysis period is located;

[0008] A behavior analysis module is connected with the analysis execution module and configured to determine an initial temperature monitoring coefficient according to the water use temperature fluctuation index and a fluctuation proportion index, determine an initial temperature monitoring parameter, and determine whether to perform dynamic adaptation analysis according to an operation stability index and a water use duration index;

[0009] A dynamic matching module is connected with the behavior analysis module and configured to determine a real-time matching curve and a reference adaptation curve thereof based on bathroom working parameters, determine a prediction evaluation coefficient, and determine whether to perform optimization analysis for the initial temperature setting based on the prediction evaluation coefficient;

[0010] A supervision analysis module is connected with the analysis execution module and configured to set a temperature monitoring parameter for the target analysis period according to the stage energy storage evaluation coefficient, and determine whether to adjust a blade water-impingement area according to a state duration index.

[0011] Further, if the stage energy storage evaluation coefficient of the real-time evaluation stage is less than or equal to a preset stage energy storage evaluation coefficient, it is determined that the analysis execution module performs energy storage effective distribution analysis for the bathroom monitoring process;

[0012] The stage energy storage evaluation coefficient has a positive correlation with the water pressure transmission parameter and the stage water use parameter, respectively.

[0013] Further, the monitoring demand state includes a first-type monitoring demand state and a second-type monitoring demand state, wherein,

[0014] The execution evaluation period in the first monitoring demand state is an execution evaluation period in which the real-time water consumption index is greater than a preset real-time water consumption index or the water temperature fluctuation index is greater than a preset water temperature fluctuation index.

[0015] The execution evaluation period in the second monitoring demand state is an execution evaluation period in which the real-time water consumption index is less than or equal to a preset real-time water consumption index and the water temperature fluctuation index is less than or equal to a preset water temperature fluctuation index.

[0016] The target analysis period is an execution evaluation period in which the current time is taken as a starting time.

[0017] Further, if the target analysis period is in the first monitoring demand state, it is determined that the behavior analysis module performs water consumption behavior analysis for the bathroom monitoring process.

[0018] Further, the initial temperature monitoring parameter includes an enabled monitoring proportion and a temperature collection frequency, and the enabled monitoring proportion and the temperature collection frequency are in a positive correlation relationship with an initial temperature monitoring coefficient.

[0019] Further, if the operation stability index of the target analysis period is greater than a preset operation stability index or the water consumption duration index is greater than a preset water consumption duration index, it is determined that the dynamic matching module performs dynamic adaptation analysis, wherein,

[0020] The real-time matching curve is determined according to the bathroom work parameters of each execution evaluation period in the dynamic analysis stage;

[0021] The prediction evaluation coefficient is determined according to the corresponding bathroom work parameters in the prediction evaluation stage of the reference adaptation curve;

[0022] The coincidence stage proportion index of any reference adaptation curve to the real-time matching curve is greater than a preset coincidence stage proportion index, and the trend correlation proportion index is greater than a preset trend correlation proportion index.

[0023] Further, if the prediction evaluation coefficient is greater than a preset prediction evaluation coefficient, optimization analysis is performed on the initial temperature setting, and whether the initial temperature monitoring parameter is adjusted is determined according to the prediction fluctuation parameter and the change coordination parameter;

[0024] If the prediction fluctuation parameter is less than a preset prediction fluctuation parameter, the temperature collection frequency of the initial setting is adjusted to be reduced according to the prediction fluctuation parameter;

[0025] If the change coordination parameter is greater than a preset change coordination parameter, the enabled monitoring proportion of the initial setting is adjusted to be reduced according to the change coordination parameter;

[0026] The decrease value of the temperature collection frequency is in a positive correlation with the predicted floating parameter, and the decrease value of the enabled monitoring proportion is in a positive correlation with the change coordination parameter.

[0027] Further, if the prediction evaluation coefficient is less than or equal to a preset prediction evaluation coefficient, no optimization analysis is performed on the initial temperature setting, and the enabled distribution tendency parameter is determined according to the reference water temperature index;

[0028] The enabled distribution tendency parameter is in a positive correlation with the reference water temperature index.

[0029] Further, if the target analysis period is in a second type of monitoring demand state, it is determined that the supervision analysis module performs monitoring management analysis on the bathroom monitoring process.

[0030] The temperature monitoring parameter includes an enabled monitoring proportion and a temperature collection frequency, and the enabled monitoring proportion and the temperature collection frequency are in a positive correlation with the stage energy storage evaluation coefficient.

[0031] Further, if the state duration index is greater than a preset state duration index, the blade water-approaching area is increased according to the state duration index.

[0032] The increase value of the blade water-approaching area is in a positive correlation with the state duration index.

[0033] Compared with the prior art, the present application has the beneficial effects that the technical scheme of the present application periodically determines the stage energy storage evaluation coefficient according to the water pressure transmission parameter and the stage water parameter to determine whether to perform further analysis, the stage energy storage evaluation coefficient represents the amount of electricity stored by the bathroom equipment itself within a certain time range based on power generation, and whether there is a risk of energy storage is determined, further energy storage effective distribution analysis is required for the time range with poor energy storage effect, and the supply of electrical energy for the temperature monitoring process is optimized, the present application reduces the burden of electrical energy supply of the bathroom equipment under the premise of ensuring the effectiveness of the temperature monitoring process, and ensures the stability of the electrical energy supply of the equipment self-power generation.

[0034] Further, in the present application, the monitoring demand state of each execution evaluation period is determined according to the real-time water index and the water temperature floating index, the real-time water index and the water temperature floating index represent the actual water consumption and the water temperature change in the stage, and the demand degree for temperature monitoring accuracy is represented, so as to determine the specific means for the energy storage effective distribution analysis process, ensure that the energy storage effective distribution analysis process is more in line with the actual working scene, and effectively ensure the effectiveness of the optimization result of the electrical energy supply decision for the temperature monitoring process.

[0035] Further, in the present application, the water use behavior analysis is carried out for the target analysis period in a type of monitoring demand state. At this time, due to the large water consumption or large water temperature change, the temperature monitoring accuracy requirement in the actual water use process is relatively high. The initial setting of the temperature monitoring parameter needs to be carried out based on the actual situation, so as to ensure the basic requirement of the temperature monitoring process, and the real-time matching curve and the reference adaptive curve are determined to predict the subsequent stage of water use, so as to make optimization adjustment. The present application can reduce unnecessary power consumption as much as possible while ensuring the quality of temperature monitoring.

[0036] Further, in the present application, the water use monitoring curve is determined based on the bathroom working parameter, and the reference adaptive curve for predicting the water use characteristics of the subsequent stage is determined according to the coincidence stage proportion index and the trend related proportion index. The overall coincidence of the matching process and the trend similarity degree are represented by the coincidence stage proportion index and the trend related proportion index, so as to avoid the calculation resource consumption caused by one-to-one matching of the data corresponding to each time. The data analysis efficiency is improved while ensuring the prediction and evaluation quality.

[0037] Further, in the present application, the initial temperature setting is optimized and analyzed based on the prediction and evaluation coefficient, or the distribution tendency parameter is enabled according to the reference water temperature index. According to the prediction and evaluation coefficient, the similarity degree of the subsequent water use characteristics is determined when the current stage water use characteristics are consistent in the historical water use record. The reference quality of the reference adaptive curve corresponding to the prediction and evaluation stage of the reference adaptive curve is judged, and then the optimization method is determined, so as to further ensure the effectiveness of the optimization setting of the temperature monitoring process. The present application ensures the stability of the power supply of the equipment self-generation under the premise of ensuring the effectiveness of the temperature monitoring process. BRIEF DESCRIPTION OF DRAWINGS

[0038] Fig. 1 It is a module connection diagram of the intelligent temperature monitoring system for the water power generation bathroom equipment of the present application.

[0039] Fig. 2 It is a flowchart for determining whether to analyze the energy storage effective distribution of the bathroom monitoring process according to the stage energy storage evaluation coefficient.

[0040] Fig. 3 It is a flowchart for determining the monitoring demand state of each execution evaluation period according to the real-time water use index and the water temperature floating index.

[0041] Fig. 4 It is a flowchart for determining the water use behavior analysis or monitoring management analysis of the bathroom monitoring process according to the monitoring demand state of the target analysis period. DETAILED DESCRIPTION

[0042] In order to make the objects and advantages of the present application clearer, the following further describes the present application with reference to the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0043] The preferred embodiments of the present application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and are not used to limit the protection scope of the present application.

[0044] It should be noted that, in the description of the present application, the terms indicating the direction or positional relationship such as "upper", "lower", "left", "right", "inner", "outer" and the like are based on the direction or positional relationship shown in the drawings, which is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application.

[0045] In addition, it should also be noted that, in the description of the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connection" should be understood in a broad sense, for example, it can be fixed connection, or detachable connection, or integrally connected; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or the internal communication of two elements. Those skilled in the art can understand the specific meaning of the above terms in the present application according to the specific circumstances.

[0046] Please refer to Figs. 1 to 4 As shown in the drawings, the present application provides an intelligent temperature monitoring system for a hydropower bathroom device, comprising:

[0047] A power generation evaluation module is configured to periodically determine a stage energy storage evaluation coefficient according to the water pressure transmission parameter and the stage water use parameter, and determine whether to perform energy storage effective distribution analysis for the bathroom monitoring process according to the stage energy storage evaluation coefficient;

[0048] An analysis execution module is connected with the power generation evaluation module and configured to perform energy storage effective distribution analysis, determine the monitoring demand state of each execution evaluation period according to the real-time water use index and the water use temperature fluctuation index, and determine whether to perform water use behavior analysis or monitoring management analysis for the bathroom monitoring process according to the monitoring demand state of the target analysis period;

[0049] A behavior analysis module is connected with the analysis execution module and configured to determine an initial temperature monitoring coefficient according to the water use temperature fluctuation index and the fluctuation proportion index, determine an initial temperature monitoring parameter, and determine whether to perform dynamic adaptation analysis according to the running stability index and the water use duration index;

[0050] a dynamic matching module connected with the behavior analysis module, configured to determine a real-time matching curve and a reference adaptive curve based on the bathroom working parameters, determine a prediction evaluation coefficient, and determine whether to perform optimization analysis on the initial temperature setting based on the prediction evaluation coefficient;

[0051] a supervision analysis module connected with the analysis execution module, configured to set temperature monitoring parameters for the target analysis period according to the stage energy storage evaluation coefficient, and determine whether to adjust the blade water-facing area according to the state duration index.

[0052] In the present application, the supply and distribution decision of the self-generated stored power of the hydroelectric power generation bathroom equipment is optimized, and the temperature monitoring module and other modules that need to be driven by the self-generated stored power in the hydroelectric power generation bathroom equipment can ensure the actual task processing requirements while ensuring the stability of the supply of the self-generated stored power of the bathroom equipment to each module of the bathroom equipment. The hydroelectric power generation bathroom equipment that will optimize the power supply distribution decision is referred to as a target execution equipment. There are several sub-modules that need to be driven by the self-generated stored power in the target execution equipment, including but not limited to temperature monitoring modules, data processing modules, and data display modules. The present application does not specifically limit the types of sub-modules in the target execution equipment, but there must be temperature monitoring modules and data processing modules. The temperature monitoring module is used for water temperature monitoring during the use of the target execution equipment. The temperature monitoring module includes several temperature sensors that respectively acquire the water temperature at different positions. The positions of the temperature sensors include but are not limited to the water inlet, the inside of the mixing valve, and the water outlet. The data processing module is used to perform data analysis and processing for the optimization process of the supply and distribution decision of the self-generated stored power of the hydroelectric power generation bathroom equipment.

[0053] In the present application, several distribution optimization records are applied. Each distribution optimization record records the stage energy storage evaluation coefficient, the real-time water consumption index, the water temperature fluctuation index, the water temperature fluctuation index, the operation stability index, the water consumption duration index, the parameter difference index, the parameter fluctuation difference index, the coincidence stage proportion index, the trend correlation proportion index, the prediction evaluation coefficient, the water flow fluctuation index, the state duration index, and the bathroom working parameters acquired each time in the process of optimizing the supply and distribution decision of the self-generated stored power of the hydroelectric power generation bathroom equipment. Each distribution optimization record corresponds to a qualified mark. The qualified mark records whether the effectiveness of the optimization result of the supply and distribution of the self-generated power of the target execution equipment meets the user's demand.

[0054] Specifically, if the stage energy storage evaluation coefficient of the real-time evaluation stage is less than or equal to the preset stage energy storage evaluation coefficient, it is determined that the analysis execution module performs effective energy storage distribution analysis for the bathroom monitoring process.

[0055] The stage energy storage evaluation coefficient is in positive correlation with the water pressure transmission parameter and the stage water use parameter respectively.

[0056] In the present application, a cycle of energy storage evaluation period is applied, and the length of the energy storage evaluation period can be determined by the user. The higher the user's requirement for the effectiveness of the optimization result of the power supply distribution of the target execution device, the shorter the length of the energy storage evaluation period. A length of the energy storage evaluation period is provided, which is 48h. At the end of each energy storage evaluation period, the stage energy storage evaluation coefficient of the target execution device is detected.

[0057] If the current time is the end time of an energy storage evaluation period, the time range corresponding to the energy storage evaluation period is recorded as a real-time evaluation stage. For a single energy storage evaluation period, the stage energy storage evaluation coefficient is the sum of the product of the water pressure transmission parameter and the stage water use parameter of the energy storage evaluation period and the corresponding evaluation weight coefficient. The water pressure transmission parameter is the average value of the minimum value of the water inlet pressure in each use process of the target execution device in the energy storage evaluation period / the maximum value of the water inlet pressure in the use process of the target execution device. The stage water use parameter is (the average value of the bathroom water outlet flow in each use process of the target execution device in the energy storage evaluation period / the maximum value of the bathroom water outlet flow in each use process in the distribution optimization record)+(the sum of the time length of each use process of the target execution device in the energy storage evaluation period / the length of the energy storage evaluation period). The user can determine the value of the evaluation weight coefficient corresponding to the water pressure transmission parameter and the stage water use parameter according to the actual working scene. A value of the evaluation weight coefficient corresponding to the water pressure transmission parameter and the stage water use parameter is provided. The value of the evaluation weight coefficient corresponding to the water pressure transmission parameter is 0.6, and the value of the evaluation weight coefficient corresponding to the stage water use parameter is 0.4. The water inlet pressure is the static pressure value at the water inlet interface of the municipal pipe network or the outlet of the booster pump. The bathroom water outlet flow is the volume flow of the mixed final water outlet per unit time. How to determine the water inlet pressure and the bathroom water outlet flow in the use process of the target execution device is a matter that has been mastered by those skilled in the art, and will not be described here.

[0058] If the stage energy storage evaluation coefficient corresponding to the real-time evaluation stage is less than or equal to the preset stage energy storage evaluation coefficient, it indicates that the actual use of the target execution device in the time range corresponding to the real-time evaluation stage is poor for the stability of the self-generated power energy storage, and the energy storage effective distribution analysis is performed for the bathroom monitoring process to optimize the power supply distribution decision of the temperature monitoring module. The value of the preset stage energy storage evaluation coefficient can be determined by the user according to the actual working scene. For example, the user can set it according to the distribution optimization record. The higher the user's requirement for the effectiveness of the optimization result of the self-generated power supply distribution of the target execution device, the greater the value of the preset stage energy storage evaluation coefficient. A method for determining the value of the preset stage energy storage evaluation coefficient is provided. The average value of the stage energy storage evaluation coefficient corresponding to the real-time evaluation stage in the distribution optimization record that meets the user's requirement for the effectiveness of the optimization result of the self-generated power supply distribution of the target execution device is taken as the preset stage energy storage evaluation coefficient.

[0059] Specifically, the monitoring demand state includes a first monitoring demand state and a second monitoring demand state, wherein,

[0060] The execution evaluation period in the first monitoring demand state is an execution evaluation period in which the real-time water consumption index is greater than a preset real-time water consumption index or the water temperature fluctuation index is greater than a preset water temperature fluctuation index.

[0061] The execution evaluation period in the second monitoring demand state is an execution evaluation period in which the real-time water consumption index is less than or equal to the preset real-time water consumption index and the water temperature fluctuation index is less than or equal to the preset water temperature fluctuation index.

[0062] The target analysis period is an execution evaluation period with the current time as the starting time.

[0063] In the present application, a cycle of execution evaluation period is applied. The length of the execution evaluation period can be determined by the user. The higher the user's requirement for the effectiveness of the optimization result of the self-generated power supply distribution of the target execution device, the shorter the length of the execution evaluation period. A length of the execution evaluation period is provided, which is 4 min. At the starting time of each execution evaluation period, the monitoring demand state in which the execution evaluation period is located is detected. In the present application, a cycle of temperature monitoring period is also applied. The length of the execution evaluation period can be determined by the user. The higher the user's requirement for the effectiveness of the optimization result of the self-generated power supply distribution of the target execution device, the shorter the length of the execution evaluation period. A length of the temperature monitoring period is provided, which is 5 s. At the end time of each temperature monitoring period, each temperature sensor included in the temperature monitoring module acquires the water temperature at the corresponding position.

[0064] For a single execution evaluation period, the real-time water consumption index = the actual water consumption of the target execution device in the last execution evaluation period of the execution evaluation period / the maximum water consumption that can be achieved in the execution evaluation period, the water temperature fluctuation index is the average value of the water temperature fluctuation indexes of each temperature sensor enabled in the last two execution evaluation periods before the execution evaluation period, for any temperature sensor enabled in the last execution evaluation period of the last execution evaluation period, the water temperature fluctuation index , is the average value of the water temperature values obtained each time in the last execution evaluation period of the last execution evaluation period, is the average value of the water temperature values obtained each time in the last execution evaluation period of the last execution evaluation period of the last execution evaluation period;

[0065] The values of the preset real-time water consumption index and the preset water temperature fluctuation index can be determined by the user according to the actual working scene. For example, the user can set according to the distribution optimization record. The higher the user's requirement for the effectiveness of the optimization result of the supply distribution of the self-generated power of the target execution device, the smaller the value of the preset real-time water consumption index, and the smaller the value of the preset water temperature fluctuation index. A method for determining the value of the preset real-time water consumption index is provided. The maximum value of the real-time water consumption index of the execution evaluation period in the distribution optimization record that meets the user's requirement for the effectiveness of the optimization result of the supply distribution of the self-generated power of the target execution device is recorded as the preset real-time water consumption index. A method for determining the value of the preset water temperature fluctuation index is provided. The maximum value of the water temperature fluctuation index of the execution evaluation period in the distribution optimization record that meets the user's requirement for the effectiveness of the optimization result of the supply distribution of the self-generated power of the target execution device is recorded as the preset water temperature fluctuation index.

[0066] Specifically, if the target analysis period is in the first monitoring demand state, it is determined that the behavior analysis module performs water consumption behavior analysis for the bathroom monitoring process.

[0067] If the target analysis period is in the first monitoring demand state, that is, in the case of large water consumption or large water temperature change, the control process of the outlet water temperature is heavily burdened, and the demand for temperature monitoring accuracy in the actual water consumption process is relatively high. Further analysis is needed for the actual water consumption behavior of the target execution device to reduce the power consumption caused by the temperature monitoring module as much as possible under the premise of ensuring the accuracy requirement of temperature monitoring.

[0068] Specifically, the initial temperature monitoring parameters include an enabled monitoring proportion and a temperature collection frequency, and the enabled monitoring proportion and the temperature collection frequency are positively correlated with the initial temperature monitoring coefficient.

[0069] The initial temperature monitoring coefficient is the sum of the water temperature fluctuation index and the fluctuation proportion index, the fluctuation proportion index = the number of existing fluctuation sensors corresponding to the target analysis period / the number of temperature sensors enabled in the two adjacent execution evaluation periods before the target analysis period, the fluctuation sensor is a temperature sensor that is enabled in the two adjacent execution evaluation periods before the target analysis period and has a water temperature fluctuation index greater than a preset water temperature fluctuation index, the preset water temperature fluctuation index can be determined by the user according to the actual working scene, for example, the user can set it according to the allocation optimization record, the higher the user's requirement for the effectiveness of the optimization result of the supply allocation of the self-generated power of the target execution device, the smaller the value of the preset water temperature fluctuation index, and a method for determining the value of the preset water temperature fluctuation index is provided, which is the minimum value of the water temperature fluctuation index of the fluctuation sensor in the allocation optimization record that meets the user's requirement for the effectiveness of the optimization result of the supply allocation of the self-generated power of the target execution device, and is recorded as the preset water temperature fluctuation index;

[0070] The water temperature fluctuation index represents the overall fluctuation degree of the water temperature monitored by different temperature sensors within a certain time range before the target analysis period, and the fluctuation proportion index represents the proportion of the number of temperature sensors that have relatively obvious water temperature fluctuation within a certain time range before the target analysis period. By using the water temperature fluctuation index and the fluctuation proportion index, it is shown that the data obtained by the enabled temperature sensors in the actual use process has a certain fluctuation, and then the demand degree of the response speed to temperature change in the subsequent time range is indicated. In this way, the initial temperature monitoring coefficient is set, and the enabled temperature sensors in the temperature monitoring process are adjusted in time to ensure that the precision requirement of the temperature monitoring result meets the actual situation. According to the initial temperature monitoring coefficient, the initial temperature monitoring parameters in the next execution evaluation period of the target analysis period are set, including the enabled monitoring proportion and the temperature acquisition frequency. The enabled monitoring proportion and the temperature acquisition frequency in the next execution evaluation period of the target analysis period are in a positive correlation with the initial temperature monitoring coefficient. The temperature acquisition frequency is the number of times of obtaining temperature by each temperature sensor per unit time. The enabled monitoring proportion = the number of temperature sensors enabled in the next execution evaluation period of the target analysis period / the number of temperature sensors existing in the temperature monitoring module.

[0071] Specifically, if the running stability index of the target analysis period is greater than a preset running stability index or the water consumption duration index is greater than a preset water consumption duration index, it is determined that the dynamic matching module performs dynamic adaptation analysis, wherein,

[0072] The real-time matching curve is determined according to the bathroom working parameters in each execution evaluation period in the dynamic analysis stage;

[0073] The prediction evaluation coefficient is determined according to the corresponding bathroom working parameters in the prediction evaluation stage of the reference adaptive curve;

[0074] The coincidence stage proportion index of any reference adaptive curve to the real-time matching curve is greater than the preset coincidence stage proportion index, and the trend correlation proportion index is greater than the preset trend correlation proportion index.

[0075] Wherein, for a single execution evaluation period, the running stability index = the number of execution smooth periods existing in the dynamic analysis stage / the number of execution evaluation periods contained in the dynamic analysis stage, the execution smooth period is an execution evaluation period with a smooth execution coefficient greater than a preset smooth execution coefficient, the water use duration index = the number of execution evaluation periods with a real-time water use index greater than a preset real-time water use index in the dynamic analysis stage / the number of execution evaluation periods contained in the dynamic analysis stage, for a single execution evaluation period, the smooth execution coefficient = 1 / sum of water temperature fluctuation index and water difference index of the execution evaluation period, the water difference index , is the real-time water use index of the previous execution evaluation period of the execution evaluation period, is the real-time water use index of the execution evaluation period, the end time of the dynamic analysis stage is the end time of the execution evaluation period, the duration of the dynamic analysis stage, the user can determine according to the actual working scene, the higher the user's requirement for the effectiveness of the optimization result of the user's spontaneous power supply allocation to the target execution device, the greater the value of the duration of the dynamic analysis stage, a value of the duration of the dynamic analysis stage is provided, the duration of the dynamic analysis stage is 15 times the duration of the execution evaluation period;

[0076] If the running stability index is greater than the preset running stability index or the water consumption persistence index is greater than the preset water consumption persistence index, it indicates that the water consumption is in a high demand state or the water consumption is relatively stable in a long time range, there is a certain matching feature, and the actual water consumption has a long-term impact on the monitoring environment. Through dynamic adaptation analysis, the water consumption matching the historical water consumption process corresponding to the dynamic analysis stage of the target analysis period is analyzed to estimate the subsequent water consumption process of the target analysis period, and then the temperature monitoring process is optimized. The values of the preset running stability index and the preset water stability index can be determined by the user according to the actual working scene. For example, the user can set it according to the distribution optimization record. The higher the user's requirement for the effectiveness of the optimization result of the supply distribution of the self-generated power of the target execution device, the smaller the value of the preset running stability index, and the smaller the value of the preset water consumption persistence index. A method for determining the value of the preset running stability index is provided. The average value of the running stability index corresponding to each execution evaluation period in the distribution optimization record that meets the user's requirement for the effectiveness of the optimization result of the supply distribution of the self-generated power of the target execution device is recorded as the preset running stability index. A method for determining the value of the preset water consumption persistence index is provided. The average value of the water consumption persistence index corresponding to each execution evaluation period in the distribution optimization record that meets the user's requirement for the effectiveness of the optimization result of the supply distribution of the self-generated power of the target execution device is recorded as the preset water consumption persistence index.

[0077] Based on the water flow value, water temperature value and corresponding time obtained each time, the bathroom working parameter is recorded, and the water consumption monitoring curve is determined according to the bathroom working parameter. The water consumption monitoring curve in the time range corresponding to the behavior analysis stage is recorded as the real-time matching curve. The water consumption monitoring curve includes: time-water flow value curve and time-water temperature value curve. How to determine the water consumption monitoring curve based on the bathroom working parameter is mastered by those skilled in the art, and will not be repeated here. The end time of the behavior analysis stage is the end time of the target analysis period. The length of the behavior analysis stage can be determined by the user according to the actual working scene. A method for determining the length of the behavior analysis stage is provided. The length of the behavior analysis stage is 10 times the execution evaluation period.

[0078] The reference adaptation curve is determined from the determined real-time matching curve based on the allocation optimization record, the time length covered by the time range corresponding to any reference adaptation curve is consistent with the time length covered by the time range corresponding to the real-time matching curve, for a single reference adaptation curve, the coincidence stage proportion index = the number of coincident adaptation stage combinations / the number of evaluation adaptation stage combinations, the trend correlation proportion index = the number of trend correlation stage combinations / the number of evaluation adaptation stage combinations, when determining the reference adaptation curve, a to-be-matched curve with a time length consistent with the time length of the time range corresponding to the implementation matching curve is randomly intercepted, the to-be-matched curve and the implementation matching curve are respectively divided into a plurality of evaluation adaptation stages with the same time length, two evaluation adaptation stages with the same order in the to-be-matched curve or the implementation matching curve are recorded as an evaluation adaptation stage combination, for a single evaluation adaptation stage combination, if the parameter difference index of the evaluation adaptation stage combination is less than the preset parameter difference index, the evaluation adaptation stage combination is recorded as a coincident adaptation stage combination, if the parameter floating difference index of the evaluation adaptation stage combination is less than the preset parameter floating difference index, the evaluation adaptation stage combination is recorded as a trend correlation stage combination, the parameter difference index = (the difference between the average values of the water flow values obtained in the two evaluation adaptation stages in the evaluation adaptation stage combination / the average value of the average values of the water flow values obtained in the two evaluation adaptation stages in the evaluation adaptation stage combination) + (the difference between the average values of the water temperature values obtained in the two evaluation adaptation stages in the evaluation adaptation stage combination / the average value of the average values of the water temperature values obtained in the two evaluation adaptation stages in the evaluation adaptation stage combination), the parameter floating difference index = (the difference between the floating indexes of the water temperature values of the two evaluation adaptation stages in the evaluation adaptation stage combination / the average value of the difference between the floating indexes of the water temperature values of the two evaluation adaptation stages in the evaluation adaptation stage combination) + (the difference between the floating indexes of the water flow values of the two evaluation adaptation stages in the evaluation adaptation stage combination / the average value of the difference between the floating indexes of the water flow values of the two evaluation adaptation stages in the evaluation adaptation stage combination), for a single evaluation adaptation stage, the floating index of the water flow value = |the maximum value of the water flow value obtained in the previous evaluation adaptation stage of the evaluation adaptation stage-the maximum value of the water flow value obtained in the evaluation adaptation stage| / the maximum value of the water flow value obtained in the previous evaluation adaptation stage of the evaluation adaptation stage, the floating index of the water temperature value = |the maximum value of the water temperature value obtained in the previous evaluation adaptation stage of the evaluation adaptation stage-the maximum value of the water temperature value obtained in the evaluation adaptation stage| / the maximum value of the water temperature value obtained in the previous evaluation adaptation stage of the evaluation adaptation stage;

[0079] The preset parameter difference index and the preset parameter floating difference index can be determined by the user according to the actual working scene. For example, the user can set according to the allocation optimization record. The higher the user's requirement for the effectiveness of the optimization result of the supply allocation of the self-generated power of the target execution device, the smaller the value of the preset parameter difference index, and the smaller the value of the preset parameter floating difference index. A method for determining the value of the preset parameter difference index is provided. The maximum value of the parameter difference index of the combined parameter difference index in the allocation optimization record that meets the user's requirement for the effectiveness of the optimization result of the supply allocation of the self-generated power of the target execution device is recorded as the preset parameter difference index. A method for determining the value of the preset parameter floating difference index is provided. The maximum value of the parameter floating difference index of the combined parameter floating difference index in the allocation optimization record that meets the user's requirement for the effectiveness of the optimization result of the supply allocation of the self-generated power of the target execution device is recorded as the preset parameter floating difference index.

[0080] The preset coincidence stage proportion index and the preset trend correlation proportion index can be determined by the user according to the actual working scene. For example, the user can set according to the allocation optimization record. The higher the user's requirement for the effectiveness of the optimization result of the supply allocation of the self-generated power of the target execution device, the greater the value of the preset coincidence stage proportion index, and the greater the value of the preset trend correlation proportion index. A method for determining the value of the preset coincidence stage proportion index is provided. The minimum value of the coincidence stage proportion index of the reference adaptation curve in the allocation optimization record that meets the user's requirement for the effectiveness of the optimization result of the supply allocation of the self-generated power of the target execution device is recorded as the preset coincidence stage proportion index. A method for determining the value of the preset trend correlation proportion index is provided. The minimum value of the trend correlation proportion index of the reference adaptation curve in the allocation optimization record that meets the user's requirement for the effectiveness of the optimization result of the supply allocation of the self-generated power of the target execution device is recorded as the preset trend correlation proportion index.

[0081] After the determination of the reference adaptation curve is completed, the water consumption monitoring curve in the time range corresponding to the prediction evaluation stage of each reference adaptation curve is recorded as a reference prediction curve, the prediction evaluation coefficient is determined based on the bathroom working parameters corresponding to the reference prediction curve, the difference of the water consumption in a certain time range after the determined reference adaptation curve is characterized according to the prediction evaluation coefficient, each reference adaptation curve is divided into several prediction evaluation stages with the same length, the prediction evaluation stages with the same order in each reference adaptation curve are recorded as a prediction evaluation combination, the prediction evaluation coefficient = 1 / average value of the reference evaluation difference index of each prediction evaluation combination, for a single prediction evaluation combination, the reference evaluation difference index = (maximum difference between the average values of the water flow values obtained in each prediction evaluation stage in the prediction evaluation combination / the average value of the average values of the water flow values obtained in each prediction evaluation stage in the prediction evaluation combination) + (maximum difference between the average values of the water temperature values obtained in each prediction evaluation stage in the prediction evaluation combination / the average value of the average values of the water temperature values obtained in each prediction evaluation stage in the prediction evaluation combination).

[0082] Specifically, if the prediction evaluation coefficient is greater than the preset prediction evaluation coefficient, the initial temperature setting is optimized and analyzed, and whether the initial temperature monitoring parameter is adjusted is determined according to the prediction floating parameter and the change coordination parameter;

[0083] If the prediction floating parameter is less than the preset prediction floating parameter, the initial set temperature acquisition frequency is reduced according to the prediction floating parameter;

[0084] If the change coordination parameter is greater than the preset change coordination parameter, the initial set enable monitoring proportion is reduced according to the change coordination parameter;

[0085] The reduction value of the temperature acquisition frequency and the prediction floating parameter have a positive correlation, and the reduction value of the enable monitoring proportion and the change coordination parameter have a positive correlation.

[0086] The prediction evaluation coefficient greater than the preset prediction evaluation coefficient indicates that the subsequent water use characteristics of the historical water use stage corresponding to the dynamic analysis stage of the target evaluation period are consistent, and the subsequent water use characteristics are analyzed to optimize the temperature monitoring process. The prediction floating parameter = (the maximum value of the water temperature value obtained each time in each reference adaptive curve - the minimum value of the water temperature value obtained each time in each reference adaptive curve) / the average value of the water temperature value obtained each time in each reference adaptive curve. The change coordination parameter = the number of coordination change combinations / the number of prediction evaluation combinations. For a single prediction evaluation combination, if the combination temperature floating index of the prediction evaluation combination is greater than the preset water temperature floating index and the combination water flow floating index is greater than the preset water flow floating index, the prediction evaluation combination is recorded as a coordination change combination. The combination temperature floating index = | the average value of the water temperature value obtained each time in each reference adaptive curve corresponding to the prediction evaluation combination - the average value of the water temperature value obtained each time in each reference adaptive curve corresponding to the last sequence of prediction evaluation combination | / the average value of the water temperature value obtained each time in each reference adaptive curve corresponding to the last sequence of prediction evaluation combination. The combination water flow floating index = | the average value of the water flow value obtained each time in each reference adaptive curve corresponding to the prediction evaluation combination - the average value of the water flow value obtained each time in each reference adaptive curve corresponding to the last sequence of prediction evaluation combination | / the average value of the water flow value obtained each time in each reference adaptive curve corresponding to the last sequence of prediction evaluation combination.

[0087] The preset prediction evaluation coefficient and the preset water flow floating index can be determined by the user according to the actual working scene. For example, the user can set according to the distribution optimization record. The higher the user's requirement for the effectiveness of the optimization result of the supply distribution of the self-generated power of the target execution device, the greater the value of the preset prediction evaluation coefficient, and the smaller the value of the preset water flow floating index. A method for determining the value of the preset prediction evaluation coefficient is provided. The distribution optimization record for which the initial temperature setting is optimized is recorded as an adjustment reference record. The minimum value of the prediction evaluation coefficient in the adjustment reference record that meets the user's requirement for the effectiveness of the optimization result of the supply distribution of the self-generated power of the target execution device is recorded as the preset prediction evaluation coefficient. A method for determining the value of the preset water flow floating index is provided. The distribution optimization record in which the enabled monitoring proportion of the initial setting is reduced is recorded as a floating reference record. The minimum value of the water flow floating index in the floating reference record that meets the user's requirement for the effectiveness of the optimization result of the supply distribution of the self-generated power of the target execution device is recorded as the preset water flow floating index.

[0088] Specifically, if the prediction evaluation coefficient is less than or equal to the preset prediction evaluation coefficient, no optimization analysis is performed on the initial temperature setting, and the distribution tendency parameter is enabled according to the reference water temperature index;

[0089] The distribution tendency parameter is in a positive correlation with the reference water temperature index.

[0090] Wherein, when the prediction evaluation coefficient is less than or equal to the preset prediction evaluation coefficient, it indicates that there is a large difference between the subsequent water consumption characteristics of the historical water consumption stage corresponding to the dynamic analysis stage of the target evaluation period, at this time, it is not possible to adjust the current temperature monitoring process according to the historical water consumption stage corresponding to the matched reference adaptation curve, but it is possible to determine the influence degree of water temperature on the temperature monitoring process by analyzing the water temperature of the subsequent stage, and adjust it accordingly, the reference water temperature index is the average of the water temperature values obtained each time in each reference adaptation curve, the larger the reference water temperature index, the more the temperature value obtained by the temperature sensor close to the water outlet (i.e. the more the temperature sensor contacts the water environment) is susceptible to long-term water environment interference, and the reliability of the data cannot be guaranteed, without affecting the initial temperature monitoring parameter, the distribution proportion of the temperature sensor enabled in the outlet area is reduced to ensure the overall temperature monitoring accuracy, the distribution tendency parameter enabled = the number of temperature sensors not located in the outlet among the enabled temperature sensors / the number of enabled temperature sensors.

[0091] Specifically, if the target analysis period is in a two-class monitoring demand state, it is determined that the supervision analysis module performs monitoring management analysis on the bathroom monitoring process.

[0092] The temperature monitoring parameters include an enabled monitoring proportion and a temperature collection frequency, and the enabled monitoring proportion and the temperature collection frequency are in a positive correlation with the stage energy storage evaluation coefficient.

[0093] Specifically, if the state duration index is greater than the preset state duration index, the state duration index is used to increase the blade water-facing area.

[0094] The increase value of the blade water-facing area is in a positive correlation with the state duration index.

[0095] Wherein, if the target analysis period is in a two-class monitoring data state, that is, in a condition of small water consumption and small water temperature change, at this time, the demand for temperature monitoring accuracy of the actual water consumption process is relatively low, and the frequency of temperature monitoring and the proportion of enabled temperature sensors can be adjusted based on the actual situation to avoid excessive power consumption caused by useless temperature monitoring behavior.

[0096] For the target analysis period in the second type of monitoring demand state, the state persistence index = the number of execution evaluation periods in the second type of monitoring demand state in the dynamic analysis stage of the target analysis period / the number of execution evaluation periods in the dynamic analysis stage of the target analysis period, if the state persistence index is larger, it indicates that the water consumption is poor for a long time range, there is long-term energy storage consumption, by adjusting the blade surface area to ensure the blade speed in low water consumption, the blade surface area is the projection area of the impeller blade in the working state, which directly contacts the water flow direction and can effectively undertake the driving force of the water flow, how to increase the blade surface area according to the automatic adjustment of the blade angle is easy for those skilled in the art to understand, and will not be repeated here.

[0097] The preset state persistence index value can be determined by the user according to the actual working scene, for example, the user can set it according to the distribution optimization record, the higher the user's requirement for the effectiveness of the optimization result of the supply distribution of the self-generated power of the target execution device, the smaller the preset state persistence index value, a method for providing a preset state persistence index value is provided, the distribution optimization record for increasing the blade surface area is recorded as the energy storage optimization record, and the minimum value of the state persistence index in the energy storage optimization record that meets the user's requirement for the effectiveness of the optimization result of the supply distribution of the self-generated power of the target execution device is recorded as the preset state persistence index.

[0098] So far, the technical solutions of the present application have been described in conjunction with the preferred embodiments shown in the drawings, but those skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to related technical features without departing from the principles of the present application, and the technical solutions after the changes or replacements will fall within the protection scope of the present application.

[0099] The above description is only the preferred embodiments of the present application and is not intended to limit the present application; for those skilled in the art, the present application can have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. An intelligent temperature monitoring system for a hydroelectric bathing apparatus, characterized in that, The method comprises the following steps: a power generation evaluation module is configured to periodically determine a stage energy storage evaluation coefficient according to water pressure transmission parameters and stage water use parameters, and determine whether to perform energy storage effective distribution analysis for the bathroom monitoring process according to the stage energy storage evaluation coefficient; an analysis execution module connected to the power generation evaluation module is configured to perform energy storage effective distribution analysis, determine a monitoring demand state of each execution evaluation period according to a real-time water use index and a water use temperature fluctuation index, and determine whether to perform water use behavior analysis or monitoring management analysis for the bathroom monitoring process according to a monitoring demand state of a target analysis period; a behavior analysis module connected to the analysis execution module is configured to determine an initial temperature monitoring coefficient according to the water use temperature fluctuation index and a fluctuation proportion index, determine an initial temperature monitoring parameter, and determine whether to perform dynamic adaptation analysis according to an operation stability index and a water use duration index; a dynamic matching module connected to the behavior analysis module is configured to determine a real-time matching curve and a reference adaptation curve based on bathroom working parameters, determine a prediction evaluation coefficient, and determine whether to perform optimization analysis for the initial temperature setting based on the prediction evaluation coefficient; a monitoring management analysis module connected to the analysis execution module is configured to set a temperature monitoring parameter for the target analysis period according to the stage energy storage evaluation coefficient, and determine whether to adjust a blade water-impingement area according to a state duration index; the monitoring demand state comprises a first monitoring demand state and a second monitoring demand state, wherein an execution evaluation period in the first monitoring demand state is an execution evaluation period in which the real-time water use index is greater than a preset real-time water use index or the water use temperature fluctuation index is greater than a preset water use temperature fluctuation index; an execution evaluation period in the second monitoring demand state is an execution evaluation period in which the real-time water use index is less than or equal to the preset real-time water use index and the water use temperature fluctuation index is less than or equal to the preset water use temperature fluctuation index; the target analysis period is an execution evaluation period in which the current time is the starting time; if the target analysis period is in the first monitoring demand state, it is determined that the behavior analysis module performs water use behavior analysis for the bathroom monitoring process; the initial temperature monitoring parameter comprises an enabled monitoring proportion and a temperature collection frequency, and the enabled monitoring proportion and the temperature collection frequency are in a positive correlation with the initial temperature monitoring coefficient; if the operation stability index of the target analysis period is greater than a preset operation stability index or the water use duration index is greater than a preset water use duration index, it is determined that the dynamic matching module performs dynamic adaptation analysis, wherein the real-time matching curve is determined according to bathroom working parameters of each execution evaluation period in a dynamic analysis stage; the prediction evaluation coefficient is determined according to corresponding bathroom working parameters in a prediction evaluation stage of the reference adaptation curve; the coincidence stage proportion index of any reference adaptation curve with respect to the real-time matching curve is greater than a preset coincidence stage proportion index, and the trend correlation proportion index is greater than a preset trend correlation proportion index. If the predicted evaluation coefficient is greater than the preset predicted evaluation coefficient, an optimization analysis is performed on the initial temperature setting, and it is determined whether to adjust the initial temperature monitoring parameter according to the predicted floating parameter and the change coordination parameter; If the predicted floating parameter is less than the preset predicted floating parameter, the initial set temperature collection frequency is reduced according to the predicted floating parameter; If the change coordination parameter is greater than the preset change coordination parameter, the initial set monitoring proportion is reduced according to the change coordination parameter; The reduction value of the temperature collection frequency and the predicted floating parameter are in a positive correlation, and the reduction value of the monitoring proportion and the change coordination parameter are in a positive correlation.

2. The intelligent temperature monitoring system for hydroelectric bathing apparatus as claimed in claim 1, wherein, If the stage energy storage evaluation coefficient of the real-time evaluation stage is less than or equal to the preset stage energy storage evaluation coefficient, it is determined that the analysis execution module performs energy storage effective distribution analysis on the bathroom monitoring process; The stage energy storage evaluation coefficient is positively correlated with the water pressure transmission parameter and the stage water consumption parameter.

3. The intelligent temperature monitoring system for hydroelectric bathing apparatus as claimed in claim 1, wherein, If the predicted evaluation coefficient is less than or equal to the preset predicted evaluation coefficient, no optimization analysis is performed on the initial temperature setting, and the use distribution tendency parameter is determined according to the reference water temperature index; The use distribution tendency parameter and the reference water temperature index are in a positive correlation.

4. The intelligent temperature monitoring system for hydroelectric bathing apparatus as claimed in claim 1, wherein, If the target analysis period is in a two-class monitoring demand state, it is determined that the supervision analysis module performs monitoring management analysis on the bathroom monitoring process; The temperature monitoring parameter includes the monitoring proportion and the temperature collection frequency, and the monitoring proportion and the temperature collection frequency are positively correlated with the stage energy storage evaluation coefficient.

5. The intelligent temperature monitoring system for hydroelectric bathing apparatus as claimed in claim 1, wherein, If the state duration index is greater than the preset state duration index, the blade water-approaching area is increased according to the state duration index; The increase value of the blade water-approaching area and the state duration index are in a positive correlation.

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