Intelligent temperature monitoring system for hydroelectric generation bathroom equipment

Abstract

The invention relates to the field of intelligent monitoring, in particular to an intelligent temperature monitoring system for hydroelectric generation bathroom equipment, and the system comprises a power generation evaluation module which is used for determining whether to carry out energy storage effective distribution analysis or not according to a stage energy storage evaluation coefficient; the analysis execution module is used for determining water consumption behavior analysis or monitoring management analysis aiming at the bathroom monitoring process according to the monitoring demand state of the target analysis period; the behavior analysis module is used for determining whether to carry out dynamic adaptation analysis or not according to the operation stability index and the water consumption duration index; the dynamic matching module is used for performing optimization analysis on initial temperature setting based on prediction evaluation coefficient determination; and the supervision and analysis module is used for determining whether to adjust the upstream area of the blade according to the state duration index, and the electric energy supply stability of equipment self-power generation is ensured on the premise that the effectiveness of the temperature monitoring process is ensured.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring, and in particular to an intelligent temperature monitoring system for hydropower generation bathroom equipment. Background Art

[0002] Hydropower bathroom fixtures use water flow to drive micro-generators, which store electricity for the fixtures. This power is used to power the temperature monitoring, data processing, data display, and other related modules. However, the actual use of bathroom fixtures is intermittent, and the stored energy from the fixtures' self-generated electricity cannot consistently provide a stable power supply for each module. Therefore, how to coordinate the accuracy requirements of water temperature monitoring with the energy distribution preferences based on the actual use of bathroom fixtures to ensure effective temperature monitoring and stable self-generated electricity supply is an urgent problem for those skilled in the art.

[0003] Chinese patent publication number CN114812863A discloses a system and method for controlling display data with low power consumption, belonging to the technical field of bathroom equipment. The system includes: a display module, an induction switch and a temperature sensor. When the induction switch is sensed, the temperature sensor collects the current temperature and compares the current temperature with the startup display temperature range to determine whether to light up the display; after the display is lit, 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 number CN119602444A discloses a hydroelectric generator output circuit, a shower, and an electric energy conversion method. In the field of bathroom technology, the hydroelectric generator output circuit includes a rectifier module, an overvoltage protection module, a voltage stabilizing module, a channel identification and switching module, a high-voltage channel module, a low-voltage channel module, a charging management module, and a load interaction module. The rectifier module is used to rectify the AC power output of the hydroelectric generator into a DC power supply. The overvoltage protection module is used to detect the DC power supply and output a DC power supply within a preset voltage range. The voltage stabilizing module is used to output a reference voltage to the channel identification and switching module. The channel identification and switching module is used to control the on / off states of the high-voltage channel module and the low-voltage channel module respectively according to the reference voltage and the DC power output of the overvoltage protection module. The low-voltage channel module is used to supply power to the charging management module and the load interaction module. However, the above technical solution has the following defects: it fails to coordinate the temperature monitoring accuracy and the actual distribution of generated electricity according to the actual use process of the bathroom, making it impossible to take into account the temperature monitoring accuracy requirements of the bathroom equipment and the stability of the power supply of the equipment's self-generated electricity in actual use. Summary of the Invention

[0004] To this end, the present invention provides an intelligent temperature monitoring system for hydropower bathroom equipment to overcome the problem that the existing technology fails to coordinate the temperature monitoring accuracy and the actual distribution of generated electricity according to the actual bathroom usage process, making it impossible to take into account both the temperature monitoring accuracy of the bathroom equipment and the stability of the power supply of the equipment's self-generated electricity in the actual process.

[0005] To achieve the above objectives, the present invention provides an intelligent temperature monitoring system for hydropower generation bathroom equipment, comprising: The power generation evaluation module is used to periodically determine the stage energy storage evaluation coefficient based on the water pressure transmission parameters and the stage water consumption parameters, and determine whether to perform energy storage effective allocation analysis for the bathroom monitoring process based on the stage energy storage evaluation coefficient; An analysis execution module, connected to the power generation assessment module, is used to perform energy storage effective allocation analysis, determine the monitoring demand status of each execution assessment cycle based on the real-time water usage index and the water temperature fluctuation index, and determine whether to perform water use behavior analysis or monitoring management analysis for the bathroom monitoring process based on the monitoring demand status of the target analysis cycle; A behavior analysis module, connected to the analysis execution module, is used to determine an initial temperature monitoring coefficient based on the water temperature fluctuation index and the fluctuation ratio index to determine the initial temperature monitoring parameters, and to determine whether to perform dynamic adaptation analysis based on the operation stability index and the water use continuity index; A dynamic matching module, connected to the behavior analysis module, is used to determine a real-time matching curve and a reference adaptation curve based on bathroom operating parameters to determine a prediction evaluation coefficient, and determine whether to perform an optimization analysis on the initial temperature setting based on the prediction evaluation coefficient; The supervision and analysis module is connected to the analysis execution module and is used to set the temperature monitoring parameters of 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 continuity index.

[0006] Further, 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 allocation analysis for the bathroom monitoring process; The stage energy storage evaluation coefficient is positively correlated with the water pressure transmission parameter and the stage water use parameter.

[0007] Furthermore, the monitoring demand state includes a first-class monitoring demand state and a second-class monitoring demand state, wherein: The execution evaluation period in the first type of monitoring demand state is the execution evaluation period in which the real-time water consumption index is greater than the preset real-time water consumption index or the water temperature fluctuation index is greater than the preset water temperature fluctuation index; The execution evaluation period in the second type of monitoring demand state is the 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; The target analysis cycle is an execution evaluation cycle starting at the current moment.

[0008] Furthermore, if the target analysis period is in a type I monitoring demand state, it is determined that the behavior analysis module performs water usage behavior analysis on the bathroom monitoring process.

[0009] Furthermore, the initial temperature monitoring parameters include an enabled monitoring ratio and a temperature acquisition frequency, and both the enabled monitoring ratio and the temperature acquisition frequency are positively correlated with the initial temperature monitoring coefficient.

[0010] Furthermore, if the operation stability index of the target analysis period is greater than the preset operation stability index or the water use continuity index is greater than the preset water use continuity index, it is determined that the dynamic matching module performs dynamic adaptation analysis, wherein, Determine the real-time matching curve based on the bathroom operating parameters of each evaluation cycle during the dynamic analysis phase; The prediction and evaluation coefficient is determined based on the corresponding bathroom operating parameters in the prediction and evaluation phase of the reference adaptation curve; The overlap phase ratio index of any reference adaptation curve for the real-time matching curve is greater than the preset overlap phase ratio index and the trend-related ratio index is greater than the preset trend-related ratio index.

[0011] Furthermore, if the prediction evaluation coefficient is greater than the preset prediction evaluation coefficient, an optimization analysis is performed on the initial temperature setting, and it is determined whether to adjust the initial temperature monitoring parameters based on the predicted floating parameters and the changed coordination parameters; If the predicted floating parameter is less than the preset predicted floating parameter, the initially set temperature acquisition frequency is reduced according to the predicted floating parameter; If the changed coordination parameter is greater than the preset changed coordination parameter, the initially set enabled monitoring ratio will be reduced according to the changed coordination parameter; The reduction value of the temperature acquisition frequency is positively correlated with the predicted floating parameter, and the reduction value of the enabled monitoring ratio is positively correlated with the changed coordination parameter.

[0012] Furthermore, if the prediction evaluation coefficient is less than or equal to the preset prediction evaluation coefficient, the optimization analysis is not performed for the initial temperature setting, and the activation distribution tendency parameter is determined based on the reference water temperature index; The activation distribution tendency parameter is positively correlated with the reference water temperature index.

[0013] Furthermore, if the target analysis period is in the second-class monitoring demand state, it is determined that the supervision analysis module performs monitoring management analysis on the bathroom monitoring process; The temperature monitoring parameters include an enabled monitoring ratio and a temperature acquisition frequency, and both the enabled monitoring ratio and the temperature acquisition frequency are positively correlated with the stage energy storage evaluation coefficient.

[0014] Furthermore, if the state continuity index is greater than a preset state continuity index, the blade water-facing area is increased and adjusted according to the state continuity index; The increase in the water-facing area of ​​the blade is positively correlated with the state continuity index.

[0015] Compared with the prior art, the beneficial effect of the present invention lies in that the technical solution of the present invention periodically determines the stage energy storage evaluation coefficient based on the water pressure transmission parameters and the stage water consumption parameters to determine whether further analysis is required. The stage energy storage evaluation coefficient is used to characterize the amount of electricity stored based on the bathroom equipment's own power generation within a certain time range to determine whether there is an energy storage risk. For the time range with poor energy storage effect, further energy storage effective distribution analysis is required to optimize the power supply distribution for the temperature monitoring process. On the premise of ensuring the effectiveness of the temperature monitoring process, the present invention reduces the power supply burden of the bathroom equipment and ensures the stability of the power supply of the equipment's self-generated power.

[0016] Furthermore, the present invention determines the monitoring demand status of each execution evaluation cycle based on the real-time water consumption index and the water temperature fluctuation index. The real-time water consumption index and the water temperature fluctuation index are used to characterize the large actual water consumption and water temperature changes in the stage, and then characterize the degree of demand for temperature monitoring accuracy. In this way, specific targeted means are determined for the effective energy storage allocation analysis process, ensuring that the effective energy storage allocation analysis process is more in line with the actual working scenario, and thus effectively ensuring the effectiveness of the optimization results of the power supply decision-making in the temperature monitoring process.

[0017] Furthermore, the present invention conducts water use behavior analysis for a target analysis period in a type of monitoring demand state. At this time, due to the large water consumption or large water temperature changes, the demand for temperature monitoring accuracy in the actual water use process is relatively high. It is necessary to perform initial settings for temperature monitoring parameters based on actual conditions to ensure the basic requirements of the temperature monitoring process, and make predictions for water use in subsequent stages by determining real-time matching curves and reference adaptation curves to make optimization adjustments. The present invention reduces unnecessary energy consumption as much as possible while ensuring the quality of temperature monitoring.

[0018] Furthermore, the present invention determines a water consumption monitoring curve based on bathroom working parameters, and determines a reference adaptation curve for predicting water consumption characteristics in subsequent stages according to the overlapping stage proportion index and the trend-related proportion index. The overlapping stage proportion index and the trend-related proportion index are used to characterize the overall overlap and trend similarity of the matching process, thereby avoiding the consumption of computing resources caused by one-to-one matching of data corresponding to each moment, and improving the efficiency of data analysis while ensuring the quality of prediction and evaluation.

[0019] Furthermore, the present invention determines an optimization analysis for the initial temperature setting based on the prediction evaluation coefficient or determines the activation distribution tendency parameter based on the reference water use temperature index. The prediction evaluation coefficient characterizes the similarity of subsequent water use characteristics when the historical water use records are consistent with the water use characteristics of the current stage. The reference quality of the reference adaptation curve corresponding to the prediction evaluation stage corresponding to the determined reference adaptation curve is judged, and then the optimization method is determined to further ensure the effectiveness of the optimization setting of the temperature monitoring process. On the premise of ensuring the effectiveness of the temperature monitoring process, the present invention ensures the stability of the power supply of the equipment's self-generated electricity. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a module connection diagram of the intelligent temperature monitoring system for hydropower sanitary equipment of the present invention; Figure 2 This is a flow chart of the present invention for determining whether to perform energy storage effective allocation analysis for the bathroom monitoring process based on the stage energy storage evaluation coefficient; Figure 3 This is a flow chart of the present invention for determining the monitoring demand state of each execution evaluation cycle based on the real-time water use index and the water temperature fluctuation index; Figure 4 This is a flow chart of the present invention that determines water usage behavior analysis or monitoring management analysis for a bathroom monitoring process according to the monitoring demand state of a target analysis cycle. DETAILED DESCRIPTION

[0021] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0022] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0023] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This 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. Therefore, it cannot be understood as a limitation on the present invention.

[0024] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0025] See also Figures 1 to 4 As shown, the present invention provides an intelligent temperature monitoring system for hydropower sanitary equipment, comprising: The power generation evaluation module is used to periodically determine the stage energy storage evaluation coefficient based on the water pressure transmission parameters and the stage water consumption parameters, and determine whether to perform energy storage effective allocation analysis for the bathroom monitoring process based on the stage energy storage evaluation coefficient; An analysis execution module, connected to the power generation assessment module, is used to perform energy storage effective allocation analysis, determine the monitoring demand status of each execution assessment cycle based on the real-time water usage index and the water temperature fluctuation index, and determine whether to perform water use behavior analysis or monitoring management analysis for the bathroom monitoring process based on the monitoring demand status of the target analysis cycle; A behavior analysis module, connected to the analysis execution module, is used to determine an initial temperature monitoring coefficient based on the water temperature fluctuation index and the fluctuation ratio index to determine the initial temperature monitoring parameters, and to determine whether to perform dynamic adaptation analysis based on the operation stability index and the water use continuity index; A dynamic matching module, connected to the behavior analysis module, is used to determine a real-time matching curve and a reference adaptation curve based on bathroom operating parameters to determine a prediction evaluation coefficient, and determine whether to perform an optimization analysis on the initial temperature setting based on the prediction evaluation coefficient; The supervision and analysis module is connected to the analysis execution module and is used to set the temperature monitoring parameters of 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 continuity index.

[0026] The present invention is used to optimize the supply and allocation decision for the electricity stored in the self-generated power of a hydropower sanitary ware, ensuring that the temperature monitoring module and other modules in the hydropower sanitary ware are driven by the electricity stored in the self-generated power. This ensures the stability of the electricity supply from the self-generated power of the sanitary ware to the various modules in the sanitary ware while ensuring the actual task processing requirements. The present invention refers to the hydropower sanitary ware for which the electricity supply and allocation decision optimization is performed as a target execution device. The target execution device contains several submodules that require the electricity stored in the self-generated power to be driven. The submodules include, but are not limited to, a temperature monitoring module, a data processing module, and a data display module. The present invention does not specifically limit the types of submodules in the target execution device, but a temperature monitoring module and a data processing module are required. The temperature monitoring module is used to monitor the water temperature during the use of the target execution device. The temperature monitoring module includes several temperature sensors that respectively obtain the water flow temperature at different locations. The locations where the temperature sensors are located include, but are not limited to, the water inlet, the interior of the mixing valve, and the water outlet. The data processing module is used to perform data analysis and processing for the process of optimizing the supply and allocation decision for the electricity stored in the self-generated power of the hydropower sanitary ware. The present invention applies several allocation optimization records, and any allocation optimization record records at least one stage energy storage evaluation coefficient, real-time water use index, water use temperature fluctuation index, water temperature fluctuation index, operation stability index, water use continuity index, parameter difference index, parameter fluctuation difference index, overlap stage proportion index, trend-related proportion index, prediction evaluation coefficient, water flow fluctuation index, state continuity index and bathroom working parameters obtained at each time during the optimization process of the supply allocation decision of the self-generated electricity stored in the hydropower bathroom equipment, and each allocation optimization record corresponds to a qualified mark, which records whether the effectiveness of the optimization result of the supply allocation of the self-generated electricity of the target execution equipment meets the user's needs.

[0027] 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 energy storage effective allocation analysis for the bathroom monitoring process; The stage energy storage evaluation coefficient is positively correlated with the water pressure transmission parameter and the stage water use parameter.

[0028] The present invention uses a cyclic energy storage assessment cycle. The duration of the energy storage assessment cycle can be determined by the user. The higher the user's requirements for the effectiveness of the optimization results of the supply and distribution of self-generated electricity of the target execution device, the shorter the energy storage assessment cycle. A duration of the energy storage assessment cycle is provided, and the duration of the energy storage assessment cycle is 48 hours. At the end of each energy storage assessment cycle, the stage energy storage assessment coefficient of the target execution device is tested. If the current moment is taken as the end moment of an energy storage evaluation cycle, the time range corresponding to the energy storage evaluation cycle is recorded as the real-time evaluation stage. For a single energy storage evaluation cycle, the stage energy storage evaluation coefficient is the sum of the products of the water pressure transmission parameter and the stage water use parameter of the energy storage evaluation cycle and the corresponding evaluation weight coefficient. The water pressure transmission parameter = the average value of the minimum water inlet pressure during each use of the target execution device during the energy storage evaluation cycle / the maximum value that the water inlet pressure can reach during the use of the target execution device. The stage water use parameter = (the average value of the bathroom water outlet flow rate during each use process of the target execution device during the energy storage evaluation cycle / the maximum value of the bathroom water outlet flow rate during each use process in the allocation optimization record) + (the average value of the bathroom water outlet flow rate during each use process of the target execution device during the energy storage evaluation cycle The sum of the duration of each use process of the equipment / the duration of the energy storage evaluation cycle), the values ​​of the evaluation weight coefficients corresponding to the water pressure transmission parameters and the stage water use parameters can be determined by the user according to the actual working scenario, and the values ​​of the evaluation weight coefficients corresponding to the water pressure transmission parameters and the stage water use parameters are 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 bathroom equipment entering the municipal pipe network or the booster pump outlet. The bathroom water outlet flow rate is the volume flow rate of the final water outlet after mixing per unit time. How to determine the water inlet pressure and bathroom water outlet flow rate during the use process of the target execution equipment is content that has been mastered by those skilled in the art and will not be elaborated here; 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 usage of the target execution device within the time range corresponding to the real-time evaluation stage has poor stability in the storage of self-generated electricity. An effective energy storage allocation analysis is performed on the bathroom monitoring process to optimize the power supply allocation 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 scenario. 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 electricity of the target execution device, the larger 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 of the real-time evaluation stage corresponding to the effective energy storage allocation analysis for the bathroom monitoring process 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 electricity of the target execution device is recorded as the preset stage energy storage evaluation coefficient.

[0029] Specifically, the monitoring demand state includes a first-class monitoring demand state and a second-class monitoring demand state, wherein: The execution evaluation period in the first type of monitoring demand state is the execution evaluation period in which the real-time water consumption index is greater than the preset real-time water consumption index or the water temperature fluctuation index is greater than the preset water temperature fluctuation index; The execution evaluation period in the second type of monitoring demand state is the 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; The target analysis cycle is an execution evaluation cycle starting at the current moment.

[0030] Among them, the present invention applies a cyclic execution evaluation cycle, and the duration of the execution evaluation cycle can be determined by the user. The higher the user's requirements for the effectiveness of the optimization results of the supply and distribution of self-generated electricity of the target execution device, the shorter the duration of the execution evaluation cycle. A duration of an execution evaluation cycle is provided, and the duration of the execution evaluation cycle is 4 minutes. At the starting moment of each execution evaluation cycle, the monitoring demand state of the execution evaluation cycle is detected. The present invention also applies a cyclic temperature monitoring cycle, and the duration of the execution evaluation cycle can be determined by the user. The higher the user's requirements for the effectiveness of the optimization results of the supply and distribution of self-generated electricity of the target execution device, the shorter the duration of the execution evaluation cycle. A duration of a temperature monitoring cycle is provided, and the duration of the temperature monitoring cycle is 5 seconds. At the end moment of each temperature monitoring cycle, each temperature sensor included in the temperature monitoring module obtains the water temperature at the corresponding location; For a single execution evaluation cycle, the real-time water consumption index = the actual water consumption of the target execution device in the execution evaluation cycle before the execution evaluation cycle / the maximum water consumption that can be achieved in the execution evaluation cycle. The water temperature fluctuation index is the average of the water temperature fluctuation indexes of each temperature sensor enabled in the two adjacent execution evaluation cycles before the execution evaluation cycle. For any temperature sensor enabled in the execution evaluation cycle before the execution evaluation cycle, the water temperature fluctuation index is , is the average value of the water temperature values ​​obtained in the previous execution evaluation cycle of this execution evaluation cycle, The average value of the water temperature values ​​obtained in each execution evaluation cycle before the execution evaluation cycle of the execution evaluation cycle; The values ​​of the preset real-time water use index and the preset water use temperature floating index can be determined by the user according to the actual working scenario. For example, the user can set them according to the allocation optimization record. The higher the user's requirements for the effectiveness of the optimization results of the supply and allocation of self-generated electricity of the target execution device, the smaller the value of the preset real-time water use index and the smaller the value of the preset water use temperature floating index. A method for determining the value of the preset real-time water use index is provided, and the maximum value of the real-time water use index of the execution evaluation cycle in the allocation optimization record that meets the user's requirements for the effectiveness of the optimization results of the supply and allocation of self-generated electricity of the target execution device is recorded as the preset real-time water use index. A method for determining the value of the preset water use temperature floating index is provided, and the maximum value of the water use temperature floating index of the execution evaluation cycle in the allocation optimization record that meets the user's requirements for the effectiveness of the optimization results of the supply and allocation of self-generated electricity of the target execution device is recorded as the preset water use temperature floating index.

[0031] Specifically, if the target analysis cycle is in a type one monitoring demand state, it is determined that the behavior analysis module performs water usage behavior analysis on the bathroom monitoring process.

[0032] Among them, if the target analysis cycle is in a monitoring demand state, that is, the water consumption is large or the water temperature changes greatly, it will cause a heavy burden on the control process of the outlet water temperature, which will lead to a relatively high demand for temperature monitoring accuracy during actual water use. Further analysis of the actual water use behavior of the target execution equipment is required to minimize the power consumption caused by the temperature monitoring module while ensuring the accuracy requirements of temperature monitoring.

[0033] Specifically, the initial temperature monitoring parameters include an enabled monitoring ratio and a temperature acquisition frequency, and both the enabled monitoring ratio and the temperature acquisition frequency are positively correlated with the initial temperature monitoring coefficient.

[0034] Among them, for the target analysis period in a type of monitoring demand state, the initial temperature monitoring coefficient is the sum of the water temperature floating index and the floating proportion index, the floating proportion index=the number of existing floating 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 floating sensor is a temperature sensor that is enabled in the two adjacent execution evaluation periods before the target analysis period and whose water temperature floating index is greater than the preset water temperature floating index, the value of the preset water temperature floating index can be determined by the user according to the actual working scenario, 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 electricity of the target execution device, the smaller the value of the preset water temperature floating index, and a method for determining the value of the preset water temperature floating index is provided, and the minimum value of the water temperature floating index of the floating 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 electricity of the target execution device is recorded as the preset water temperature floating index; The water temperature fluctuation index represents the overall fluctuation of water temperatures monitored by different temperature sensors within a period of time prior to the target analysis cycle. The fluctuation ratio index represents the proportion of temperature sensors with significant water temperature fluctuation within a period of time prior to the target analysis cycle. The water temperature fluctuation index and the fluctuation ratio index represent the fluctuation of data acquired by enabled temperature sensors during actual use, thereby indicating the degree of response speed required for temperature changes in the subsequent time range. Based on this, the initial temperature monitoring coefficient is set, and the activation of temperature sensors during temperature monitoring is adjusted in a timely manner to ensure that the accuracy requirements of temperature monitoring results meet actual conditions. The initial temperature monitoring parameters for the next execution evaluation cycle of the target analysis cycle are set based on the initial temperature monitoring coefficient. The initial temperature monitoring parameters include the activation monitoring ratio and the temperature acquisition frequency. Both the activation monitoring ratio and the temperature acquisition frequency in the next execution evaluation cycle of the target analysis cycle are positively correlated with the initial temperature monitoring coefficient. The temperature acquisition frequency is the number of temperature acquisitions per unit time by each temperature sensor. The activation monitoring ratio = the number of temperature sensors enabled in the next execution evaluation cycle of the target analysis cycle / the number of temperature sensors in the temperature monitoring module.

[0035] Specifically, if the operation stability index of the target analysis period is greater than the preset operation stability index or the water use continuity index is greater than the preset water use continuity index, it is determined that the dynamic matching module performs dynamic adaptation analysis, wherein, Determine the real-time matching curve based on the bathroom operating parameters of each evaluation cycle during the dynamic analysis phase; The prediction and evaluation coefficient is determined based on the corresponding bathroom operating parameters in the prediction and evaluation phase of the reference adaptation curve; The overlap phase ratio index of any reference adaptation curve for the real-time matching curve is greater than the preset overlap phase ratio index and the trend-related ratio index is greater than the preset trend-related ratio index.

[0036] Among them, for a single execution evaluation cycle, the operation stability index = the number of execution smooth cycles existing in the dynamic analysis stage / the number of execution evaluation cycles included in the dynamic analysis stage, the execution smooth cycle is the execution evaluation cycle with a smooth execution coefficient greater than the preset smooth execution coefficient, the water use continuity index = the number of execution evaluation cycles with a real-time water use index greater than the preset real-time water use index in the dynamic analysis stage / the number of execution evaluation cycles included in the dynamic analysis stage, for a single execution evaluation cycle, the smooth execution coefficient = 1 / the sum of the water temperature fluctuation index and the water use difference index of the execution evaluation cycle, the water use difference index , is the real-time water use index of the previous execution evaluation cycle before this execution evaluation cycle. is the real-time water usage index of the execution evaluation cycle. The end time of the dynamic analysis phase is the end time of the execution evaluation cycle. The duration of the dynamic analysis phase can be determined by the user based on the actual work scenario. The higher the user's requirements for the effectiveness of the optimization results of the supply and distribution of self-generated electricity of the target execution device, the longer the value of the dynamic analysis phase duration is. A value for the duration of the dynamic analysis phase is provided, which is 15 times the duration of the execution evaluation cycle. If the operation stability index is greater than the preset operation stability index or the water use continuity index is greater than the preset water use continuity index, it indicates that the water demand is high or the water use situation is relatively stable over a long period of time, there are certain matching characteristics and the actual water use situation has a long-term impact on the monitoring environment. By performing dynamic adaptation analysis, the water use situation corresponding to the dynamic analysis stage of the target analysis cycle is matched with the historical water use process, and the subsequent water use process of the target analysis cycle is estimated by analyzing the historical water use process, and then the temperature monitoring process is optimized. The values ​​of the preset operation stability index and the preset water use stability index can be determined by the user according to the actual working scenario. For example, the user can set it according to the allocation optimization record, and the user can set the target execution The higher the requirement for the effectiveness of the optimization result of the supply distribution of the self-generated electricity of the target execution device, the smaller the value of the preset operation stability index, and the smaller the value of the preset water use continuity index. A method for determining the value of the preset operation stability index is provided, and the average value of the operation stability index corresponding to each execution evaluation cycle of the dynamic adaptation analysis 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 electricity of the target execution device is recorded as the preset operation stability index. A method for determining the value of the preset water use continuity index is provided, and the average value of the water use continuity index corresponding to each execution evaluation cycle of the dynamic adaptation analysis 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 electricity of the target execution device is recorded as the preset water use continuity index. Based on the water flow value, water temperature value and corresponding time obtained each time, they are recorded as bathroom working parameters, and the water use monitoring curve is determined according to the bathroom working parameters. The water use monitoring curve within the time range corresponding to the behavior analysis stage is recorded as a real-time matching curve. The water use monitoring curve includes: a time-water flow value curve and a time-water temperature value curve. How to determine the water use monitoring curve based on bathroom working parameters is a content that those skilled in the art have mastered and will not be elaborated on here. The end time of the behavior analysis stage is the end time of the target analysis cycle. The duration of the behavior analysis stage can be determined by the user according to the actual work scenario. A value for the duration of the behavior analysis stage is provided, and the value for the duration of the behavior analysis stage is 10 times the execution evaluation cycle. Based on the determined real-time matching curve, a reference adaptation curve is determined from 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 overlap stage ratio index = the number of overlapped adaptation stage combinations / the number of evaluated adaptation stage combinations, the trend-related ratio index = the number of trend-related stage combinations / the number of evaluated adaptation stage combinations. When determining the reference adaptation curve, a corresponding time range is randomly intercepted, the time length of the to-be-matched curve is consistent with the time length of the time range corresponding to the implementation matching curve, and the to-be-matched curve and the implementation matching curve are respectively divided into several equal time lengths. The evaluation and adaptation stage of the curve to be matched or the implementation matching curve with the same order is recorded as an evaluation and adaptation stage combination. For a single evaluation and adaptation stage combination, if the parameter difference index of the evaluation and adaptation stage combination is less than the preset parameter difference index, the evaluation and adaptation stage combination is recorded as a re-matching stage combination. If the parameter floating difference index of the evaluation and adaptation stage combination is less than the preset parameter floating difference index, the evaluation and adaptation stage combination is recorded as a trend-related stage combination. The parameter difference index = (the difference between the average values ​​of the water flow values ​​obtained in each of the two evaluation and adaptation stages in the evaluation and adaptation stage combination / the average value of the two evaluation and adaptation stages in the evaluation and adaptation stage combination) The parameter floating difference index = (the average value of the average value of the water flow value obtained each time in the two evaluation adaptation stages in the evaluation adaptation stage combination / the average value of the average value of the water temperature value obtained each time in the two evaluation adaptation stages in the evaluation adaptation stage combination), the parameter floating difference index = (the average value of the difference between the floating indices of the water temperature value of the two evaluation adaptation stages in the evaluation adaptation stage combination / the average value of the difference between the floating indices of the water temperature value of the two evaluation adaptation stages in the evaluation adaptation stage combination) + (the difference between the floating indices of the water flow value of the two evaluation adaptation stages in the evaluation adaptation stage combination / the average value of the difference between the floating indices of the water flow value of the two evaluation adaptation stages in the evaluation adaptation stage combination The floating index of water flow value is the average value of the difference between the floating indices of the two evaluation and adaptation stages within the evaluation and adaptation stage), for a single evaluation and adaptation stage, the floating index of water flow value = |the maximum value of water flow value obtained in the previous evaluation and adaptation stage of this evaluation and adaptation stage - the maximum value of water flow value obtained in this evaluation and adaptation stage | / the maximum value of water flow value obtained in the previous evaluation and adaptation stage of this evaluation and adaptation stage; the floating index of water temperature value = |the maximum value of water temperature value obtained in the previous evaluation and adaptation stage of this evaluation and adaptation stage - the maximum value of water temperature value obtained in this evaluation and adaptation stage | / the maximum value of water temperature value obtained in the previous evaluation and adaptation stage of this evaluation and adaptation stage; The values ​​of the preset parameter difference index and the preset parameter floating difference index can be determined by the user according to the actual working scenario. For example, the user can set them according to the allocation optimization record. The higher the user's requirements for the effectiveness of the optimization results of the supply allocation of the self-generated electricity 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, and the maximum value of the parameter difference index of the re-matching stage combination in the allocation optimization record that meets the user's requirements for the effectiveness of the optimization results of the supply allocation of the self-generated electricity 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, and the maximum value of the parameter floating difference index of the trend-related stage combination in the allocation optimization record that meets the user's requirements for the effectiveness of the optimization results of the supply allocation of the self-generated electricity of the target execution device is recorded as the preset parameter floating difference index. The values ​​of the preset overlap stage proportion index and the preset trend-related proportion index can be determined by the user according to the actual working scenario. For example, the user can set them according to the allocation optimization record. The higher the user's requirements for the effectiveness of the optimization results of the supply distribution of the self-generated electricity of the target execution device, the larger the value of the preset overlap stage proportion index and the larger the value of the preset trend-related proportion index. A method for determining the value of the preset overlap stage proportion index is provided, and the minimum value of the overlap stage proportion index of the reference adaptation curve in the allocation optimization record that meets the user's requirements for the effectiveness of the optimization results of the supply distribution of the self-generated electricity of the target execution device is recorded as the preset overlap stage proportion index. A method for determining the value of the preset trend-related proportion index is provided, and the minimum value of the trend-related proportion index of the reference adaptation curve in the allocation optimization record that meets the user's requirements for the effectiveness of the optimization results of the supply distribution of the self-generated electricity of the target execution device is recorded as the preset trend-related proportion index.

[0037] After the reference adaptation curve is determined, the water use monitoring curve within the time range corresponding to the prediction and evaluation stage of each reference adaptation curve is recorded as the reference prediction curve, and the prediction and evaluation coefficient is determined based on the bathroom working parameters corresponding to the reference prediction curve. The difference in water use within a certain time range after the determined reference adaptation curve is characterized by the prediction and evaluation coefficient. Each reference adaptation curve obtained is divided into several prediction and evaluation stages of the same duration. The prediction and evaluation stages with the same order in each reference adaptation curve are recorded as a prediction and evaluation combination. The prediction and evaluation coefficient = 1 / the average value of the reference evaluation difference index of each prediction and evaluation combination. For a single prediction and evaluation combination, the reference evaluation difference index = (the maximum difference between the average values ​​of the water flow values ​​obtained each time in each prediction and evaluation stage in the prediction and evaluation combination / the average value of the average values ​​of the water flow values ​​obtained each time in each prediction and evaluation stage in the prediction and evaluation combination) + (the maximum difference between the average values ​​of the water temperature values ​​obtained each time in each prediction and evaluation stage in the prediction and evaluation combination / the average value of the average values ​​of the water temperature values ​​obtained each time in each prediction and evaluation stage in the prediction and evaluation combination).

[0038] Specifically, if the prediction evaluation coefficient is greater than the preset prediction evaluation coefficient, an optimization analysis is performed on the initial temperature setting, and whether to adjust the initial temperature monitoring parameters is determined based on the prediction floating parameters and the change coordination parameters; If the predicted floating parameter is less than the preset predicted floating parameter, the initially set temperature acquisition frequency is reduced according to the predicted floating parameter; If the changed coordination parameter is greater than the preset changed coordination parameter, the initially set enabled monitoring ratio will be reduced according to the changed coordination parameter; The reduction value of the temperature acquisition frequency is positively correlated with the predicted floating parameter, and the reduction value of the enabled monitoring ratio is positively correlated with the changed coordination parameter.

[0039] Among them, when the prediction evaluation coefficient is greater than the preset prediction evaluation coefficient, it indicates that the subsequent water use characteristics of the historical water use stage consistent with the water use situation corresponding to the dynamic analysis stage of the target evaluation period are consistent. By analyzing the subsequent water use characteristics, the temperature monitoring process is optimized. The prediction floating parameter = (the maximum value of the water use temperature value obtained each time in each reference adaptation curve - the minimum value of the water use temperature value obtained each time in each reference adaptation curve) / the average value of the water use temperature value obtained each time in each reference adaptation curve. The change coordination parameter = the number of coordinated change combinations / the number of prediction evaluation combinations. For a single prediction evaluation combination, if the combined temperature floating index of the prediction evaluation combination is greater than the preset water temperature floating index and the combined water flow floating index is greater than the preset water flow floating index, then the prediction evaluation combination is recorded as a coordinated change combination. The combined temperature fluctuation index = | the average value of the water temperature values ​​obtained in each reference adaptation curve corresponding to the prediction and evaluation combination - the average value of the water temperature values ​​obtained in each reference adaptation curve corresponding to the prediction and evaluation combination of the previous order | / the average value of the water temperature values ​​obtained in each reference adaptation curve corresponding to the prediction and evaluation combination of the previous order; the combined water flow fluctuation index = | the average value of the water flow values ​​obtained in each reference adaptation curve corresponding to the prediction and evaluation combination - the average value of the water flow values ​​obtained in each reference adaptation curve corresponding to the prediction and evaluation combination of the previous order | / the average value of the water flow values ​​obtained in each reference adaptation curve corresponding to the prediction and evaluation combination of the previous order; The values ​​of the preset prediction evaluation coefficient and the preset water flow floating index can be determined by the user according to the actual working scenario. For example, the user can set them according to the allocation optimization record. The higher the user's requirements for the effectiveness of the optimization results of the supply distribution of the self-generated electricity of the target execution device, the larger 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, and the allocation optimization record for the optimization analysis of the initial temperature setting is recorded as the adjustment reference record, and the minimum value of the prediction evaluation coefficient in the adjustment reference record that meets the user's requirements for the effectiveness of the optimization results of the supply distribution of the self-generated electricity 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, and the allocation optimization record for reducing the enabled monitoring ratio set for the initial setting is recorded as the floating reference record, and the minimum value of the water flow floating index in the floating reference record that meets the user's requirements for the effectiveness of the optimization results of the supply distribution of the self-generated electricity of the target execution device is recorded as the preset water flow floating index.

[0040] Specifically, if the prediction evaluation coefficient is less than or equal to the preset prediction evaluation coefficient, no optimization analysis is performed for the initial temperature setting, and the activation distribution tendency parameter is determined based on the reference water temperature index; The activation distribution tendency parameter is positively correlated with the reference water temperature index.

[0041] Among them, when the prediction evaluation coefficient is less than or equal to the preset prediction evaluation coefficient, it indicates that there is a significant difference between the subsequent water use characteristics of the historical water use stage consistent with the water use situation corresponding to the dynamic analysis stage of the target evaluation period. At this time, it is impossible to make adjustments to the current temperature monitoring process based on the historical water use stage corresponding to the matched reference adaptation curve. However, by analyzing the water use temperature situation in the subsequent stage, the degree of influence of the water use temperature on the temperature monitoring process can be determined and adjusted accordingly. The reference water use temperature index is the average of the water use temperature values ​​obtained each time in each reference adaptation curve. The larger the reference water use temperature index, the more susceptible the temperature value obtained by the temperature sensor closer to the water outlet (i.e., the closer it is to the water use environment), the more likely it is to be affected by the long-term water use environment, and the data reliability cannot be guaranteed. Without affecting the initial temperature monitoring parameters, the number of enabled temperature sensors is reduced to reduce the distribution proportion of the water outlet area to ensure the overall temperature monitoring accuracy. The enabled distribution tendency parameter = the number of temperature sensors not located at the water outlet among the enabled temperature sensors / the number of enabled temperature sensors.

[0042] Specifically, if the target analysis cycle is in the second-class monitoring demand state, it is determined that the supervision analysis module performs monitoring management analysis on the bathroom monitoring process; The temperature monitoring parameters include an enabled monitoring ratio and a temperature acquisition frequency, and both the enabled monitoring ratio and the temperature acquisition frequency are positively correlated with the stage energy storage evaluation coefficient.

[0043] Specifically, if the state continuity index is greater than the preset state continuity index, the blade water-facing area is increased according to the state continuity index; The increase in the water-facing area of ​​the blade is positively correlated with the state continuity index.

[0044] Among them, if the target analysis cycle is in the second-class monitoring data state, that is, when the water consumption is small and the water temperature change is small, the demand for the temperature monitoring accuracy of the actual water use process is relatively low. The frequency of temperature monitoring and the proportion of enabled temperature sensors can be adjusted based on actual conditions to avoid excessive energy consumption due to useless temperature monitoring behavior.

[0045] For the target analysis cycle in the second-category monitoring demand state, the state persistence index = the number of execution evaluation cycles in the second-category monitoring demand state during the dynamic analysis phase of the target analysis cycle / the number of execution evaluation cycles during the dynamic analysis phase of the target analysis cycle. If the state persistence index is large, it indicates that the water consumption is poor for a long time, and there is long-term energy storage consumption. The blade speed at low water consumption is ensured by adjusting the blade frontal area. The blade frontal area is the projected area of ​​the impeller blade that is in direct contact with the water flow direction and can effectively bear the driving force of the water flow in the working state. How to increase the blade frontal area by automatically adjusting the blade angle is easy to understand for those skilled in the art and will not be elaborated here. The value of the preset state continuity index can be determined by the user according to the actual working scenario. 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 distribution of the self-generated electricity of the target execution device, the smaller the value of the preset state continuity index. A method for determining the value of the preset state continuity index is provided, and the allocation optimization record for increasing the water-facing area of ​​the blade is recorded as the energy storage optimization record. The minimum value of the state continuity 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 electricity of the target execution device is recorded as the preset state continuity index.

[0046] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0047] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. An intelligent temperature monitoring system for hydropower sanitary equipment, characterized in that: include: The power generation evaluation module is used to periodically determine the stage energy storage evaluation coefficient based on the water pressure transmission parameters and the stage water consumption parameters, and determine whether to perform energy storage effective allocation analysis for the bathroom monitoring process based on the stage energy storage evaluation coefficient; An analysis execution module, connected to the power generation assessment module, is used to perform energy storage effective allocation analysis, determine the monitoring demand status of each execution assessment cycle based on the real-time water usage index and the water temperature fluctuation index, and determine whether to perform water use behavior analysis or monitoring management analysis for the bathroom monitoring process based on the monitoring demand status of the target analysis cycle; A behavior analysis module, connected to the analysis execution module, is used to determine an initial temperature monitoring coefficient based on the water temperature fluctuation index and the fluctuation ratio index to determine the initial temperature monitoring parameters, and to determine whether to perform dynamic adaptation analysis based on the operation stability index and the water use continuity index; A dynamic matching module, connected to the behavior analysis module, is used to determine a real-time matching curve and a reference adaptation curve based on bathroom operating parameters to determine a prediction evaluation coefficient, and determine whether to perform an optimization analysis on the initial temperature setting based on the prediction evaluation coefficient; The supervision and analysis module is connected to the analysis execution module and is used to set the temperature monitoring parameters of 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 continuity index.

2. The intelligent temperature monitoring system for hydropower sanitary equipment according to claim 1, characterized in that: 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 allocation analysis for the bathroom monitoring process; The stage energy storage evaluation coefficient is positively correlated with the water pressure transmission parameter and the stage water use parameter.

3. The intelligent temperature monitoring system for hydropower sanitary equipment according to claim 1, characterized in that: The monitoring demand state includes a first-class monitoring demand state and a second-class monitoring demand state, wherein: The execution evaluation period in the first type of monitoring demand state is the execution evaluation period in which the real-time water consumption index is greater than the preset real-time water consumption index or the water temperature fluctuation index is greater than the preset water temperature fluctuation index; The execution evaluation period in the second type of monitoring demand state is the 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; The target analysis cycle is an execution evaluation cycle starting at the current moment.

4. The intelligent temperature monitoring system for hydropower sanitary equipment according to claim 3, characterized in that: If the target analysis period is in a type 1 monitoring demand state, it is determined that the behavior analysis module performs water usage behavior analysis on the bathroom monitoring process.

5. The intelligent temperature monitoring system for hydropower sanitary equipment according to claim 4, characterized in that: The initial temperature monitoring parameters include an enabled monitoring ratio and a temperature acquisition frequency, and both the enabled monitoring ratio and the temperature acquisition frequency are positively correlated with the initial temperature monitoring coefficient.

6. The intelligent temperature monitoring system for hydropower sanitary equipment according to claim 5, characterized in that: If the operation stability index of the target analysis period is greater than the preset operation stability index or the water use continuity index is greater than the preset water use continuity index, it is determined that the dynamic matching module performs dynamic adaptation analysis, wherein, Determine the real-time matching curve based on the bathroom operating parameters of each evaluation cycle during the dynamic analysis phase; The prediction and evaluation coefficient is determined based on the corresponding bathroom operating parameters in the prediction and evaluation phase of the reference adaptation curve; The overlap phase ratio index of any reference adaptation curve for the real-time matching curve is greater than the preset overlap phase ratio index and the trend-related ratio index is greater than the preset trend-related ratio index.

7. The intelligent temperature monitoring system for hydropower sanitary equipment according to claim 6, characterized in that: If the prediction evaluation coefficient is greater than the preset prediction evaluation coefficient, an optimization analysis is performed on the initial temperature setting, and whether to adjust the initial temperature monitoring parameters is determined based on the prediction floating parameters and the change coordination parameters; If the predicted floating parameter is less than the preset predicted floating parameter, the initially set temperature acquisition frequency is reduced according to the predicted floating parameter; If the changed coordination parameter is greater than the preset changed coordination parameter, the initially set enabled monitoring ratio will be reduced according to the changed coordination parameter; The reduction value of the temperature acquisition frequency is positively correlated with the predicted floating parameter, and the reduction value of the enabled monitoring ratio is positively correlated with the changed coordination parameter.

8. The intelligent temperature monitoring system for hydropower sanitary equipment according to claim 7, characterized in that: 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 activation distribution tendency parameter is determined based on the reference water temperature index; The activation distribution tendency parameter is positively correlated with the reference water temperature index.

9. The intelligent temperature monitoring system for hydropower sanitary equipment according to claim 1, characterized in that: If the target analysis cycle is in the second-class monitoring demand state, it is determined that the supervision analysis module performs monitoring management analysis on the bathroom monitoring process; The temperature monitoring parameters include an enabled monitoring ratio and a temperature acquisition frequency, and both the enabled monitoring ratio and the temperature acquisition frequency are positively correlated with the stage energy storage evaluation coefficient.

10. The intelligent temperature monitoring system for hydropower sanitary equipment according to claim 9, characterized in that: If the state continuity index is greater than the preset state continuity index, the blade water-facing area is increased according to the state continuity index; The increase in the water-facing area of ​​the blade is positively correlated with the state continuity index.

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