Intelligent bathing control system with digital control and multiple foaming functions

Abstract

The invention relates to the technical field of smart homes, in particular to an intelligent bathing control system with a digital control multi-foaming function, and aims to solve the problems that in the prior art, the formula type of bath foam cannot be accurately recognized, operation parameters of bathing facilities cannot be automatically adjusted, and the user experience and the resource utilization rate are reduced. Through the bath foam formula identification module, the bath foam formula type can be accurately identified, manual errors are reduced, characteristic parameters are processed in real time, operation data are rapidly adjusted, user experience is optimized, parameters are intelligently adjusted, resource waste is avoided, efficiency is improved, manual intervention is reduced, the intelligent level and operation efficiency are improved, and operation cost is reduced; the temperature difference influence is reduced; the health of a user is guaranteed; faults are reduced through preventive maintenance; the service life of equipment is prolonged;

Description

Technical Field

[0001] The invention relates to the field of smart home technology, and more particularly to an intelligent bathing control system with digitally controlled multiple foaming. Background Art

[0002] With the advancement of science and technology and the improvement of people's living standards, smart home products have gradually been integrated into people's daily lives. In the field of bathing, traditional bathing methods can no longer meet people's needs for comfort, convenience and personalization. Most of the bathing systems on the market are single-function and lack intelligent control. They cannot provide customized bathing experiences based on users' personal preferences and physical conditions. In addition, the selection and use of shower gels often rely on users' personal experience and feelings, and lack scientific basis and guidance.

[0003] The patent application with reference publication number CN116578006A discloses a household bathing intelligent control method and control system, including a terminal device, a bathtub state detection control unit, a first environment state detection unit, a time recording and processing unit, and an information processing and control unit. In the process of filling water and heating the hot water in the bathtub and the bathroom environment, the user's historical time and usage habits are considered to improve the accuracy of bathtub filling, bathtub heating and temperature heating control, minimize the user's waiting time, reduce energy consumption, reduce the impact of the temperature difference of the bathing environment on the human body, and optimize the user experience;

[0004] However, the above-mentioned reference patent optimizes the control strategy of smart home appliances to achieve intelligent management of bathtub water filling and heating, thereby minimizing energy waste and reducing the potential health risks of temperature differences while ensuring bathing hygiene and comfort. However, it cannot accurately identify the type of bath gel formula, cannot automatically adjust the operating parameters of the bathing facilities, reduces user experience and resource utilization, cannot scientifically and quantitatively evaluate the foaming state of the bathing facilities, cannot quickly discover and respond to abnormal situations, reduces resource utilization efficiency, and cannot accurately determine the system operation status, cannot take protective measures in time to prevent further damage, makes it difficult to ensure user safety, and reduces the operation and maintenance efficiency of the system.

[0005] Therefore, we propose an intelligent bathing control system with digitally controlled multiple foaming to address the above problems. Summary of the invention

[0006] The purpose of the present invention is to provide an intelligent bathing control system with digitally controlled multiple foaming, which solves the problems that the prior art cannot accurately identify the type of bath gel formula, cannot automatically adjust the operating parameters of the bathing facilities, reduces user experience and resource utilization, cannot scientifically and quantitatively evaluate the foaming state of the bathing facilities, cannot quickly discover and respond to abnormal situations, reduces resource utilization efficiency, and cannot accurately determine the system operation status, cannot take protective measures in time to prevent further damage, is difficult to ensure user safety, and reduces the operation and maintenance efficiency of the system.

[0007] The purpose of the present invention is achieved through the following technical solutions:

[0008] An intelligent bathing control system with digitally controlled multiple foaming, comprising a bathing management platform, a data acquisition module, a bathing liquid formula recognition module, a water body monitoring module, a foaming monitoring module and a safety protection module;

[0009] A data acquisition module is used to collect the operation data of the bathing facilities in real time and perform pre-processing operations on the collected operation data;

[0010] The bathing liquid formula recognition module is used to collect historical bathing liquid characteristic parameters, build a bathing liquid formula recognition model, automatically identify the bathing liquid formula type through the model, and adjust the operation data of the bathing facility according to the recognition results;

[0011] The water monitoring module is used to monitor the water parameters of the bathing facilities in real time and to monitor and evaluate the water conditions of the bathing facilities;

[0012] A foaming monitoring module is used to monitor the foaming evaluation parameters of the bathing facilities in real time and monitor and evaluate the foaming status of the bathing facilities;

[0013] The safety protection module is used to comprehensively evaluate the operating status of the bathing facilities based on the water condition and foaming status monitoring results. When an abnormal operation is detected, an alarm is immediately triggered and corresponding protection measures are taken to deal with it.

[0014] As a preferred embodiment of the present invention, the specific process of the bath liquid formula recognition module automatically identifying the formula type of the bath liquid and adjusting the operation data of the bathing facility according to the recognition result is as follows:

[0015] Obtain historical bath liquid characteristic parameters, including bath liquid viscosity, pH value, main component concentration and foam density, generate a collection cycle, and divide the collection cycle SJ into multiple collection time periods {sj1, sj2, ..., sjn}, that is, SJ = {sj1, sj2, ..., sjn};

[0016] Obtain the viscosity change rate of the bath gel in multiple collection periods, where the viscosity change rate represents the ratio between the viscosity change and the length of the corresponding time period, thereby constructing a set A of viscosity change rates, and recording the average of the difference between the largest subset and the smallest subset in set A as the viscosity change rate difference NDC;

[0017] The pH value change rate of the bath gel in multiple collection periods is obtained. The pH value change rate represents the ratio between the pH value change and the duration of the corresponding time period. A set B of pH value change rates is constructed based on this, and the average of the difference between the largest subset and the smallest subset in set B is recorded as the pH value change rate difference PHC.

[0018] As a preferred embodiment of the present invention, the concentration change rate of the main component of the bath liquid in multiple collection time periods is obtained, and the main component concentration change rate represents the ratio between the change amount of the main component concentration and the length of the corresponding time period, so as to construct a set C of the main component concentration change rate, and the mean of the difference between the largest subset and the smallest subset in the set C is recorded as the main component concentration change rate difference CNC;

[0019] The foam density change rate of the bath gel in multiple collection periods is obtained. The foam density change rate represents the ratio between the foam density change and the duration of the corresponding time period. A set D of foam density change rates is constructed based on this, and the mean of the difference between the largest subset and the smallest subset in set D is recorded as the foam density change rate difference PMC.

[0020] As a preferred embodiment of the present invention, the viscosity change rate difference NDC, the pH value change rate difference PHC, the main component concentration change rate difference CNC and the foam density change rate difference PMC are obtained, and the viscosity change rate difference NDC, the pH value change rate difference PHC, the main component concentration change rate difference CNC and the foam density change rate difference PMC are combined to construct a bath gel characteristic feature matrix MT;

[0021] The constructed feature matrix MT is used as the input of the machine learning model, and the label vector w is used as the output of the machine learning model. The label vector w represents the formula type of the shower gel. The output of the label vector w is 0, 1 or 2. 0 indicates that the formula type of the shower gel is moisturizing, 1 indicates that the formula type of the shower gel is refreshing, and 2 indicates that the formula type of the shower gel is medicinal. The label vector w is used as the prediction target, and the training target is to minimize the sum of the prediction errors of all training data. The machine learning model is trained until the sum of the prediction errors converges and the training is stopped to obtain a machine learning model that predicts the label vector w.

[0022] As a preferred embodiment of the present invention, real-time bath liquid characteristic parameters are collected and processed to construct a bath liquid characteristic feature matrix MT. 实时, automatically identify the formula type of bath gel through the trained machine learning model;

[0023] If the label vector w output by the machine learning model is 0, it means that the formula type of the shower gel is moisturizing;

[0024] If the label vector w output by the machine learning model is 1, it means that the formula type of the shower gel is refreshing;

[0025] If the label vector w output by the machine learning model is 2, it means that the formula type of the bath gel is medicinal;

[0026] When the formula type of the bath gel is identified, corresponding measures are immediately taken to adjust the operating data of the bath gel according to the identification result.

[0027] As a preferred embodiment of the present invention, the specific process of the water body monitoring module monitoring and evaluating the water condition of the bathing facility is as follows:

[0028] Obtain water parameters of the bathing facilities, including water temperature, water pressure and water flow, generate a monitoring period ZQ, and divide the monitoring period ZQ into multiple monitoring periods {zq1, zq2, ..., zqn}, that is, ZQ = {zq1, zq2, ..., zqn};

[0029] Obtain the water temperature of the bathing facility in multiple monitoring periods, calculate the arithmetic average of the multiple water temperatures obtained, and record the arithmetic average of the multiple water temperatures as the average water temperature PSW;

[0030] Obtain the water pressure of the bathing facility in multiple monitoring periods, calculate the arithmetic mean of the multiple water pressures obtained, and record the arithmetic mean of the multiple water pressures as the average water pressure PSY;

[0031] The water flow of the bathing facilities during multiple monitoring periods is obtained, and the arithmetic mean of the multiple water flow rates obtained is calculated and recorded as the average water flow PSL.

[0032] As a preferred embodiment of the present invention, the average water temperature PSW, the average water pressure PSY and the average water flow PSL are obtained, and the water monitoring evaluation coefficient SJP is calculated by the following formula:

[0033]

[0034] Among them, r1, r2 and r3 are preset proportional factor coefficients, r3>r2>r1>0, and the water body monitoring assessment coefficient SJP is compared with the preset water body monitoring assessment coefficient threshold:

[0035] If the water monitoring and assessment coefficient SJP is less than the preset water monitoring and assessment coefficient threshold, it indicates that the water condition of the bathing facility is normal and there is no safety hazard in the bathing environment;

[0036] If the water body monitoring assessment coefficient SJP is greater than or equal to the preset water body monitoring assessment coefficient threshold, it indicates that the water condition of the bathing facility is abnormal and there are safety hazards in the bathing environment.

[0037] As a preferred embodiment of the present invention, the specific process of the foaming monitoring module monitoring and evaluating the foaming state of the bathing facility is as follows:

[0038] Obtaining foaming evaluation parameters of the bathing facility, the foaming evaluation parameters including foaming agent injection amount, foam generation rate, water temperature and water flow rate;

[0039] Obtaining the foaming agent injection amount imbalance value of the bathing facility within the monitoring period, the foaming agent injection amount imbalance value represents the ratio between the portion of the foaming agent variation difference greater than the preset foaming agent variation difference threshold value and the foaming agent variation difference within each monitoring period, the foaming agent variation difference represents the difference between the maximum value and the minimum value of the foaming agent injection amount;

[0040] Obtaining a foam generation rate imbalance value of the bathing facility within a monitoring period, the foam generation rate imbalance value representing a ratio between a portion of a foam change difference greater than a preset foam change difference threshold value and a foam change difference value within each monitoring period, the foam change difference value representing a difference between a maximum value and a minimum value of the foam generation rate;

[0041] Obtaining a water temperature imbalance value of the bathing facility within a monitoring period, the water temperature imbalance value indicating the ratio between the portion of the water temperature variation difference greater than a preset water temperature variation difference threshold value and the water temperature variation difference value within each monitoring period, the water temperature variation difference value indicating the difference between the maximum and minimum water temperature values;

[0042] The water flow velocity imbalance value of the bathing facilities during the monitoring period is obtained. The water flow velocity imbalance value represents the ratio between the part of the water flow change difference in each monitoring period that is greater than the preset water flow change difference threshold and the water flow change difference. The water flow change difference represents the difference between the maximum and minimum values ​​of the water flow velocity.

[0043] As a preferred embodiment of the present invention, the foaming agent injection amount imbalance value, foam generation rate imbalance value, water temperature imbalance value and water flow rate imbalance value are obtained, and the foaming agent injection amount imbalance value, foam generation rate imbalance value, water temperature imbalance value and water flow rate imbalance value are marked as FZS, PSS, SWS and SLS respectively, and the foaming state assessment coefficient FPP is calculated by the following formula:

[0044] FPP=z1*FZS+z2*PSS+z3*SWS+z4*SLS;

[0045] Among them, z1, z2, z3 and z4 are all weight coefficients, z1+z2+z3+z4=1, z4>z3>z2>z1>0, and the foaming state assessment coefficient FPP is compared with the preset foaming state assessment coefficient threshold:

[0046] If the foaming state assessment coefficient FPP is less than the preset foaming state assessment coefficient threshold, it indicates that the foaming state of the bathing facility is normal;

[0047] If the foaming state assessment coefficient FPP is greater than or equal to the preset foaming state assessment coefficient threshold, it indicates that the foaming state of the bathing facility is abnormal.

[0048] As a preferred embodiment of the present invention, the specific process of the safety protection module comprehensively evaluating the operating status of the bathing facility and immediately triggering an alarm when an abnormal operation is detected is as follows:

[0049] The water body monitoring evaluation coefficient SJP and the foaming state evaluation coefficient FPP are obtained, and the operation status evaluation coefficient YZP is calculated by the following formula:

[0050]

[0051] Where v1 and v2 are both preset proportional factor coefficients, v2>v1>0, and the operating status evaluation coefficient YZP is compared with the preset operating status evaluation coefficient threshold:

[0052] If the operating status evaluation coefficient YZP is less than the preset operating status evaluation coefficient threshold, it indicates that the bathing facility is operating normally and no signal is generated;

[0053] If the operation status evaluation coefficient YZP is greater than or equal to the preset operation status evaluation coefficient threshold, it indicates that the operation status of the bathing facility is abnormal, and an operation abnormality signal is generated and sent to the bathing management platform;

[0054] Upon receiving an abnormal operation signal, the bathing management platform will immediately sound and light an alarm and send an alarm signal to the operation and maintenance personnel, and take corresponding protective measures to deal with it.

[0055] Compared with the prior art, the advantages of the present invention are:

[0056] (1) In the present invention, the bath liquid formula identification module can accurately identify the type of bath liquid formula, reduce human errors, process characteristic parameters in real time and quickly adjust operating data, optimize user experience, intelligently adjust parameters to avoid resource waste, improve efficiency, reduce human intervention, improve intelligence level and operating efficiency, reduce operating costs, ensure a stable and safe bathing environment, reduce the impact of temperature differences, protect user health, reduce failures through preventive maintenance, extend equipment life, and ensure long-term stable operation of the system;

[0057] (2) In the present invention, the key foaming parameters are monitored in real time by the foaming monitoring module to ensure the accuracy and timeliness of the data, and any abnormal situation can be quickly discovered and responded to. By calculating the imbalance value and the foaming state evaluation coefficient, a scientific and quantitative method is provided to evaluate the foaming state, reducing the subjective error of manual judgment, and automatically adjusting the operating parameters of the bathing facility according to the evaluation results, thereby optimizing the user experience and improving user satisfaction, while avoiding resource waste and improving resource utilization efficiency;

[0058] (3) In the present invention, the water body and foaming state are monitored in real time through the safety protection module, which can quickly identify and respond to abnormal situations. A scientific quantitative evaluation method is used to reduce human errors and ensure the accuracy of the evaluation. Once an abnormality is detected, an audible and visual alarm is immediately triggered and the operation and maintenance personnel are notified. Protective measures are automatically taken to prevent further damage and ensure user safety. At the same time, abnormal information is sent to the management platform to facilitate centralized management and maintenance, improve operation and maintenance efficiency, and reduce operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0060] Figure 2 This is a system block diagram of Embodiment 2 of the present invention;

[0061] Figure 3 This is a logical flow diagram of the first embodiment of the present invention. DETAILED DESCRIPTION

[0062] The following will combine the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all the embodiments. All other embodiments obtained by ordinary technicians in this field without creative work based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0063] Embodiment 1: Figure 1 and Figure 3 As shown, the present invention proposes an intelligent bathing control system with digitally controlled multiple foaming, including a bathing management platform, a data acquisition module, a bath liquid formula recognition module, a water body monitoring module, a foaming monitoring module and a safety protection module;

[0064] The data collection module is used to collect the operation data of the bathing facilities in real time, including water temperature, water pressure, water flow and bathing time, and perform preprocessing operations on the collected operation data, including data cleaning, format conversion and normalization processing;

[0065] The data acquisition module significantly improves system performance through data cleaning, format conversion and normalization: cleaning removes outliers and improves data quality; conversion to a unified format simplifies processing; normalization eliminates dimensional effects and optimizes algorithm performance; these operations together improve user experience, resource efficiency and system reliability, laying the foundation for real-time adjustment, behavior analysis and fault prediction.

[0066] The bathing liquid formula recognition module is used to collect historical bathing liquid characteristic parameters, build a bathing liquid formula recognition model, automatically identify the bathing liquid formula type through the model, and adjust the operation data of the bathing facility according to the recognition results;

[0067] The specific process of the bath formula recognition module automatically identifying the formula type of the bath formula and adjusting the operation data of the bathing facility according to the recognition result is as follows:

[0068] Obtain historical bath liquid characteristic parameters, including bath liquid viscosity, pH value, main component concentration and foam density, generate a collection cycle, and divide the collection cycle SJ into multiple collection time periods {sj1, sj2, ..., sjn}, that is, SJ = {sj1, sj2, ..., sjn};

[0069] Obtain the viscosity change rate of the bath gel in multiple collection periods, where the viscosity change rate represents the ratio between the viscosity change and the length of the corresponding time period, thereby constructing a set A of viscosity change rates, and recording the average of the difference between the largest subset and the smallest subset in set A as the viscosity change rate difference NDC;

[0070] Obtain the pH value change rate of the bath liquid in multiple collection periods, where the pH value change rate represents the ratio between the pH value change amount and the length of the corresponding time period, thereby constructing a set B of pH value change rates, and recording the average of the difference between the largest subset and the smallest subset in set B as the pH value change rate difference PHC;

[0071] Obtain the concentration change rate of the main component of the bath liquid in multiple collection periods. The main component concentration change rate represents the ratio between the change amount of the main component concentration and the length of the corresponding time period, thereby constructing a set C of the main component concentration change rate, and record the average of the difference between the largest subset and the smallest subset in the set C as the main component concentration change rate difference CNC;

[0072] Obtain the foam density change rate of the bath gel in multiple collection periods, where the foam density change rate represents the ratio between the foam density change amount and the length of the corresponding time period, thereby constructing a set D of foam density change rates, and recording the mean of the difference between the largest subset and the smallest subset in the set D as the foam density change rate difference PMC;

[0073] Obtain the viscosity change rate difference NDC, the pH value change rate difference PHC, the main component concentration change rate difference CNC and the foam density change rate difference PMC, and combine the viscosity change rate difference NDC, the pH value change rate difference PHC, the main component concentration change rate difference CNC and the foam density change rate difference PMC to construct the bath gel characteristic feature matrix MT;

[0074] The constructed feature matrix MT is used as the input of the machine learning model, and the label vector w is used as the output of the machine learning model. The label vector w represents the formula type of the shower gel. The output of the label vector w is 0, 1 or 2. 0 represents that the formula type of the shower gel is moisturizing, 1 represents that the formula type of the shower gel is refreshing, and 2 represents that the formula type of the shower gel is medicinal. The label vector w is used as the prediction target, and the sum of the prediction errors of all training data is minimized as the training target. The machine learning model is trained until the sum of the prediction errors reaches convergence and the training is stopped. The machine learning model that predicts the label vector w is obtained.

[0075] Collect real-time bath liquid characteristic parameters, process them and construct bath liquid characteristic feature matrix MT 实时 , automatically identify the formula type of bath gel through the trained machine learning model;

[0076] If the label vector w output by the machine learning model is 0, it means that the formula type of the shower gel is moisturizing;

[0077] If the label vector w output by the machine learning model is 1, it means that the formula type of the shower gel is refreshing;

[0078] If the label vector w output by the machine learning model is 2, it means that the formula type of the bath gel is medicinal;

[0079] When the formula type of the shower gel is identified as moisturizing, corresponding measures are immediately taken to adjust the running data of the shower gel. The specific adjustment measures are as follows:

[0080] Increase the current water temperature, keep the current water pressure fluctuating within a stable range, keep the current water flow fluctuating within a stable range, and extend the current bathing time;

[0081] When the formula type of the shower gel is identified as refreshing, corresponding measures are immediately taken to adjust the running data of the shower gel. The specific adjustment measures are as follows:

[0082] Lower the current water temperature, increase the current water pressure, increase the current water flow, and shorten the current bathing time;

[0083] When the formula type of the bath liquid is identified as medicinal, corresponding measures are immediately taken to adjust the running data of the bath liquid. The specific adjustment measures are as follows:

[0084] Keep the current water temperature within a stable range, reduce the current water pressure, reduce the current water flow, and extend the current bathing time;

[0085] The bath gel formula recognition module constructs a feature matrix by analyzing the change rate of viscosity, pH value, main ingredient concentration and foam density, and uses a machine learning model to accurately identify the type of bath gel formula, reduce manual judgment errors, and adjust the operating parameters of bathing facilities in real time according to the recognition results to optimize the bathing experience and improve user satisfaction. It adjusts the operating data of bathing facilities for different formula types to avoid waste of resources and improve resource utilization efficiency. It realizes automatic recognition and adjustment through machine learning models, reduces manual intervention, improves the intelligence level and operating efficiency of the system, identifies multiple formula types, and provides personalized services according to different needs to meet the diverse needs of users. Through real-time data collection and model prediction, it can quickly respond to changes in bath gel characteristics and ensure the stability and reliability of system operation.

[0086] The water monitoring module is used to monitor the water parameters of the bathing facilities in real time and to monitor and evaluate the water conditions of the bathing facilities;

[0087] The specific process of the water monitoring module to monitor and evaluate the water conditions of bathing facilities is as follows:

[0088] Obtain water parameters of the bathing facilities, including water temperature, water pressure and water flow, generate a monitoring period ZQ, and divide the monitoring period ZQ into multiple monitoring periods {zq1, zq2, ..., zqn}, that is, ZQ = {zq1, zq2, ..., zqn};

[0089] Obtain the water temperature of the bathing facility in multiple monitoring periods, calculate the arithmetic average of the multiple water temperatures obtained, and record the arithmetic average of the multiple water temperatures as the average water temperature PSW;

[0090] Obtain the water pressure of the bathing facility in multiple monitoring periods, calculate the arithmetic mean of the multiple water pressures obtained, and record the arithmetic mean of the multiple water pressures as the average water pressure PSY;

[0091] Obtain the water flow of the bathing facilities in multiple monitoring periods, and calculate the arithmetic average of the multiple water flow obtained as the average water flow PSL;

[0092] The average water temperature PSW, average water pressure PSY and average water flow PSL are obtained, and the water monitoring evaluation coefficient SJP is calculated by the following formula:

[0093]

[0094] Among them, r1, r2 and r3 are preset proportional factor coefficients, r3>r2>r1>0, and the water body monitoring assessment coefficient SJP is compared with the preset water body monitoring assessment coefficient threshold:

[0095] If the water monitoring and assessment coefficient SJP is less than the preset water monitoring and assessment coefficient threshold, it indicates that the water condition of the bathing facility is normal and there is no safety hazard in the bathing environment;

[0096] If the water monitoring and evaluation coefficient SJP is greater than or equal to the preset water monitoring and evaluation coefficient threshold, it indicates that the water condition of the bathing facility is abnormal and there are safety hazards in the bathing environment;

[0097] The water monitoring module acquires and evaluates the water parameters of bathing facilities in real time to promptly identify potential safety hazards. It provides a more comprehensive water condition assessment by integrating multiple parameters such as water temperature, water pressure and water flow. It automatically calculates average values ​​and evaluation coefficients to reduce manual intervention and improve efficiency and accuracy. By setting preset thresholds, it can promptly warn of abnormal situations and ensure the safety of the bathing environment.

[0098] A foaming monitoring module is used to monitor the foaming evaluation parameters of the bathing facilities in real time and monitor and evaluate the foaming status of the bathing facilities;

[0099] The specific process of the foaming monitoring module to monitor and evaluate the foaming status of bathing facilities is as follows:

[0100] Obtaining foaming evaluation parameters of the bathing facility, the foaming evaluation parameters including foaming agent injection amount, foam generation rate, water temperature and water flow rate;

[0101] Obtaining the foaming agent injection amount imbalance value of the bathing facility within the monitoring period, the foaming agent injection amount imbalance value represents the ratio between the portion of the foaming agent variation difference greater than the preset foaming agent variation difference threshold value and the foaming agent variation difference within each monitoring period, the foaming agent variation difference represents the difference between the maximum value and the minimum value of the foaming agent injection amount;

[0102] Obtaining a foam generation rate imbalance value of the bathing facility within a monitoring period, the foam generation rate imbalance value representing a ratio between a portion of a foam change difference greater than a preset foam change difference threshold value and a foam change difference value within each monitoring period, the foam change difference value representing a difference between a maximum value and a minimum value of the foam generation rate;

[0103] Obtaining a water temperature imbalance value of the bathing facility within a monitoring period, the water temperature imbalance value indicating the ratio between the portion of the water temperature variation difference greater than a preset water temperature variation difference threshold value and the water temperature variation difference value within each monitoring period, the water temperature variation difference value indicating the difference between the maximum and minimum water temperature values;

[0104] Obtaining the water flow velocity imbalance value of the bathing facility within the monitoring period, the water flow velocity imbalance value represents the ratio between the portion of the water flow change difference greater than the preset water flow change difference threshold value and the water flow change difference value within each monitoring period, the water flow change difference value represents the difference between the maximum value and the minimum value of the water flow velocity;

[0105] Obtain the imbalance value of foaming agent injection amount, foam generation rate, water temperature and water flow rate, mark them as FZS, PSS, SWS and SLS respectively, and calculate the foaming state assessment coefficient FPP by the following formula:

[0106] FPP=z1*FZS+z2*PSS+z3*SWS+z4*SLS;

[0107] Among them, z1, z2, z3 and z4 are weight coefficients, z1+z2+z3+z4=1, z4>z3>z2>z1>0. By analyzing historical data, we can understand the influence of weight coefficients z1, z2, z3 and z4 on the foaming state of bathing facilities. According to historical data, we can determine the specific values ​​of z1, z2, z3 and z4 to ensure that the calculated foaming state assessment coefficient FPP can accurately reflect the actual foaming state of bathing facilities.

[0108] Compare the foaming state assessment coefficient FPP with the preset foaming state assessment coefficient threshold:

[0109] If the foaming state assessment coefficient FPP is less than the preset foaming state assessment coefficient threshold, it indicates that the foaming state of the bathing facility is normal;

[0110] If the foaming state assessment coefficient FPP is greater than or equal to the preset foaming state assessment coefficient threshold, it indicates that the foaming state of the bathing facility is abnormal;

[0111] The foaming monitoring module monitors key foaming parameters in real time to ensure the accuracy and timeliness of the data, and can quickly detect and respond to any abnormal situation. By calculating the imbalance value and the foaming state assessment coefficient, it provides a scientific and quantitative method to evaluate the foaming state, reducing the subjective error of manual judgment. According to the evaluation results, the operating parameters of the bathing facilities are automatically adjusted to optimize the user experience and improve user satisfaction, while avoiding resource waste and improving resource utilization efficiency.

[0112] Embodiment 2: The technical solution of the embodiment of the present invention is different from that of embodiment 1 in that:

[0113] like Figure 2As shown, the safety protection module is used to comprehensively evaluate the operation status of the bathing facilities according to the water condition and the foaming state monitoring results, and immediately trigger an alarm and take corresponding protection measures to deal with abnormal operation when abnormal operation is detected;

[0114] The specific process of the safety protection module comprehensively evaluating the operating status of the bathing facilities and immediately triggering an alarm when an abnormal operation is detected is as follows:

[0115] The water body monitoring evaluation coefficient SJP and the foaming state evaluation coefficient FPP are obtained, and the operation status evaluation coefficient YZP is calculated by the following formula:

[0116]

[0117] Where v1 and v2 are both preset proportional factor coefficients, v2>v1>0, and the operating status evaluation coefficient YZP is compared with the preset operating status evaluation coefficient threshold:

[0118] If the operating status evaluation coefficient YZP is less than the preset operating status evaluation coefficient threshold, it indicates that the bathing facility is operating normally and no signal is generated;

[0119] If the operation status evaluation coefficient YZP is greater than or equal to the preset operation status evaluation coefficient threshold, it indicates that the operation status of the bathing facility is abnormal, and an operation abnormality signal is generated and sent to the bathing management platform;

[0120] After receiving the abnormal operation signal, the bathing management platform will immediately sound and light alarm and send the alarm signal to the operation and maintenance personnel, and take corresponding protective measures to deal with it. The specific contents of the protective measures are as follows:

[0121] Set up an over-temperature protection mechanism. When the water temperature is detected to be too high, the cooling program will be activated immediately to quickly reduce the water temperature;

[0122] Set up an over-flow protection mechanism. When an abnormal increase in traffic is detected, it automatically switches to protection mode to limit traffic outflow;

[0123] Set up an overpressure protection mechanism. When the water pressure is detected to be too high, the pressure reducing valve will be automatically activated to reduce the water pressure;

[0124] A foam control mechanism is provided to automatically adjust the output of the foam generating device to reduce the foam generating rate when abnormal foaming is detected, and to activate the pressure relief device to prevent excessive foam overflow;

[0125] The safety protection module monitors the water body and foaming status in real time, can quickly identify and respond to abnormal situations, and adopts scientific quantitative evaluation methods to reduce human errors and ensure the accuracy of evaluation. Once an abnormality is detected, it will immediately trigger an audible and visual alarm and notify the operation and maintenance personnel, automatically take protective measures to prevent further damage and ensure user safety. At the same time, abnormal information is sent to the management platform for centralized management and maintenance, improving operation and maintenance efficiency and reducing operating costs.

[0126] The above are only preferred specific implementation modes of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and improved concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An intelligent bathing control system with digitally controlled multiple foaming, characterized in that: It includes bathing management platform, data collection module, bath liquid formula identification module, water body monitoring module, foaming monitoring module and safety protection module; A data acquisition module is used to collect the operation data of the bathing facilities in real time and perform pre-processing operations on the collected operation data; The bathing liquid formula recognition module is used to collect historical bathing liquid characteristic parameters, build a bathing liquid formula recognition model, automatically identify the bathing liquid formula type through the model, and adjust the operation data of the bathing facility according to the recognition results; The water monitoring module is used to monitor the water parameters of the bathing facilities in real time and to monitor and evaluate the water conditions of the bathing facilities; A foaming monitoring module is used to monitor the foaming evaluation parameters of the bathing facilities in real time and monitor and evaluate the foaming status of the bathing facilities; The safety protection module is used to comprehensively evaluate the operating status of the bathing facilities based on the water condition and foaming status monitoring results. When an abnormal operation is detected, an alarm is immediately triggered and corresponding protection measures are taken to deal with it.

2. An intelligent bathing control system with digitally controlled multiple foaming according to claim 1, characterized in that: The specific process of the bath liquid formula recognition module automatically identifying the formula type of the bath liquid and adjusting the operation data of the bathing facility according to the recognition result is as follows: Obtain historical bath liquid characteristic parameters, including bath liquid viscosity, pH value, main component concentration and foam density, generate a collection cycle, and divide the collection cycle SJ into multiple collection time periods {sj1, sj2, ..., sjn}, that is, SJ = {sj1, sj2, ..., sjn}; Obtain the viscosity change rate of the bath gel in multiple collection periods, where the viscosity change rate represents the ratio between the viscosity change and the length of the corresponding time period, thereby constructing a set A of viscosity change rates, and recording the average of the difference between the largest subset and the smallest subset in set A as the viscosity change rate difference NDC; The pH value change rate of the bath gel in multiple collection periods is obtained. The pH value change rate represents the ratio between the pH value change and the duration of the corresponding time period. A set B of pH value change rates is constructed based on this, and the average of the difference between the largest subset and the smallest subset in set B is recorded as the pH value change rate difference PHC.

3. An intelligent bathing control system with digitally controlled multiple foaming according to claim 2, characterized in that: Obtain the concentration change rate of the main component of the bath liquid in multiple collection periods. The main component concentration change rate represents the ratio between the change amount of the main component concentration and the length of the corresponding time period, thereby constructing a set C of the main component concentration change rate, and record the average of the difference between the largest subset and the smallest subset in the set C as the main component concentration change rate difference CNC; The foam density change rate of the bath gel in multiple collection periods is obtained. The foam density change rate represents the ratio between the foam density change and the duration of the corresponding time period. A set D of foam density change rates is constructed based on this, and the mean of the difference between the largest subset and the smallest subset in set D is recorded as the foam density change rate difference PMC.

4. An intelligent bathing control system with digitally controlled multiple foaming according to claim 3, characterized in that: Obtain the viscosity change rate difference NDC, the pH value change rate difference PHC, the main component concentration change rate difference CNC and the foam density change rate difference PMC, and combine the viscosity change rate difference NDC, the pH value change rate difference PHC, the main component concentration change rate difference CNC and the foam density change rate difference PMC to construct the bath gel characteristic feature matrix MT; The constructed feature matrix MT is used as the input of the machine learning model, and the label vector w is used as the output of the machine learning model. The label vector w represents the formula type of the shower gel. The output of the label vector w is 0, 1 or 2. 0 indicates that the formula type of the shower gel is moisturizing, 1 indicates that the formula type of the shower gel is refreshing, and 2 indicates that the formula type of the shower gel is medicinal. The label vector w is used as the prediction target, and the training target is to minimize the sum of the prediction errors of all training data. The machine learning model is trained until the sum of the prediction errors converges and the training is stopped to obtain a machine learning model that predicts the label vector w.

5. An intelligent bathing control system with digitally controlled multiple foaming according to claim 4, characterized in that: Collect real-time bath liquid characteristic parameters, process them and construct bath liquid characteristic feature matrix MT 实时 , automatically identify the formula type of bath gel through the trained machine learning model; If the label vector w output by the machine learning model is 0, it means that the formula type of the shower gel is moisturizing; If the label vector w output by the machine learning model is 1, it means that the formula type of the shower gel is refreshing; If the label vector w output by the machine learning model is 2, it means that the formula type of the bath gel is medicinal; When the formula type of the bath gel is identified, corresponding measures are immediately taken to adjust the operating data of the bath gel according to the identification result.

6. An intelligent bathing control system with digitally controlled multiple foaming according to claim 1, characterized in that: The specific process of the water monitoring module monitoring and evaluating the water conditions of the bathing facilities is as follows: Obtain water parameters of the bathing facilities, including water temperature, water pressure and water flow, generate a monitoring period ZQ, and divide the monitoring period ZQ into multiple monitoring periods {zq1, zq2, ..., zqn}, that is, ZQ = {zq1, zq2, ..., zqn}; Obtain the water temperature of the bathing facility in multiple monitoring periods, calculate the arithmetic average of the multiple water temperatures obtained, and record the arithmetic average of the multiple water temperatures as the average water temperature PSW; Obtain the water pressure of the bathing facility in multiple monitoring periods, calculate the arithmetic mean of the multiple water pressures obtained, and record the arithmetic mean of the multiple water pressures as the average water pressure PSY; The water flow of the bathing facilities during multiple monitoring periods is obtained, and the arithmetic mean of the multiple water flow rates obtained is calculated and recorded as the average water flow PSL.

7. An intelligent bathing control system with digitally controlled multiple foaming according to claim 6, characterized in that: The average water temperature PSW, average water pressure PSY and average water flow PSL are obtained, and the water monitoring evaluation coefficient SJP is calculated by the following formula: Among them, r1, r2 and r3 are preset proportional factor coefficients, r3>r2>r1>0, and the water body monitoring assessment coefficient SJP is compared with the preset water body monitoring assessment coefficient threshold: If the water monitoring and assessment coefficient SJP is less than the preset water monitoring and assessment coefficient threshold, it indicates that the water condition of the bathing facility is normal and there is no safety hazard in the bathing environment; If the water body monitoring assessment coefficient SJP is greater than or equal to the preset water body monitoring assessment coefficient threshold, it indicates that the water condition of the bathing facility is abnormal and there are safety hazards in the bathing environment.

8. An intelligent bathing control system with digitally controlled multiple foaming according to claim 1, characterized in that: The specific process of the foaming monitoring module monitoring and evaluating the foaming state of the bathing facility is as follows: Obtaining foaming evaluation parameters of the bathing facility, the foaming evaluation parameters including foaming agent injection amount, foam generation rate, water temperature and water flow rate; Obtaining the foaming agent injection amount imbalance value of the bathing facility within the monitoring period, the foaming agent injection amount imbalance value represents the ratio between the portion of the foaming agent variation difference greater than the preset foaming agent variation difference threshold value and the foaming agent variation difference within each monitoring period, the foaming agent variation difference represents the difference between the maximum value and the minimum value of the foaming agent injection amount; Obtaining a foam generation rate imbalance value of the bathing facility within a monitoring period, the foam generation rate imbalance value representing a ratio between a portion of a foam change difference greater than a preset foam change difference threshold value and a foam change difference value within each monitoring period, the foam change difference value representing a difference between a maximum value and a minimum value of the foam generation rate; Obtaining a water temperature imbalance value of the bathing facility within a monitoring period, the water temperature imbalance value indicating the ratio between the portion of the water temperature variation difference greater than a preset water temperature variation difference threshold value and the water temperature variation difference value within each monitoring period, the water temperature variation difference value indicating the difference between the maximum and minimum water temperature values; The water flow velocity imbalance value of the bathing facilities during the monitoring period is obtained. The water flow velocity imbalance value represents the ratio between the part of the water flow change difference in each monitoring period that is greater than the preset water flow change difference threshold and the water flow change difference. The water flow change difference represents the difference between the maximum and minimum values ​​of the water flow velocity.

9. An intelligent bathing control system with digitally controlled multiple foaming according to claim 8, characterized in that: Obtain the imbalance value of foaming agent injection amount, foam generation rate, water temperature and water flow rate, mark them as FZS, PSS, SWS and SLS respectively, and calculate the foaming state assessment coefficient FPP by the following formula: FPP=z1*FZS+z2*PSS+z3*SWS+z4*SLS; Among them, z1, z2, z3 and z4 are all weight coefficients, z1+z2+z3+z4=1, z4>z3>z2>z1>0, and the foaming state assessment coefficient FPP is compared with the preset foaming state assessment coefficient threshold: If the foaming state assessment coefficient FPP is less than the preset foaming state assessment coefficient threshold, it indicates that the foaming state of the bathing facility is normal; If the foaming state assessment coefficient FPP is greater than or equal to the preset foaming state assessment coefficient threshold, it indicates that the foaming state of the bathing facility is abnormal.

10. The intelligent bathing control system with digitally controlled multiple foaming according to claim 1, characterized in that: The specific process of the safety protection module comprehensively evaluating the operating status of the bathing facilities and immediately triggering an alarm when an abnormal operation is detected is as follows: The water body monitoring evaluation coefficient SJP and the foaming state evaluation coefficient FPP are obtained, and the operation status evaluation coefficient YZP is calculated by the following formula: Where v1 and v2 are both preset proportional factor coefficients, v2>v1>0, and the operating status evaluation coefficient YZP is compared with the preset operating status evaluation coefficient threshold: If the operating status evaluation coefficient YZP is less than the preset operating status evaluation coefficient threshold, it indicates that the bathing facility is operating normally and no signal is generated; If the operation status evaluation coefficient YZP is greater than or equal to the preset operation status evaluation coefficient threshold, it indicates that the operation status of the bathing facility is abnormal, and an operation abnormality signal is generated and sent to the bathing management platform; Upon receiving an abnormal operation signal, the bathing management platform will immediately sound and light an alarm and send an alarm signal to the operation and maintenance personnel, and take corresponding protective measures to deal with it.

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