CN118052660B

CN118052660B - Hot water management method and system for smart campus

Info

Publication number: CN118052660B
Application number: CN202410451262.3A
Authority: CN
Inventors: 刘坤峰; 刘乾峰; 林进航; 刘升阳
Original assignee: Guangzhou Gaode Environmental Protection Technology Co ltd
Current assignee: Guangzhou Gaode Environmental Protection Technology Co ltd
Priority date: 2024-04-16
Filing date: 2024-04-16
Publication date: 2024-06-11
Anticipated expiration: 2044-04-16
Also published as: CN118052660A

Abstract

The application relates to a hot water management method for an intelligent campus, which comprises the following steps: collecting hot water use data in the intelligent campus, and analyzing the hot water use data to generate a hot water demand trend in the intelligent campus; the method comprises the steps that a preset hot water demand prediction model collects all hot water demand trend data, predicts the hot water service condition and generates expected hot water demand in each time period; formulating a hot water regulation strategy according to the expected hot water demand, wherein the hot water regulation strategy is used for regulating the number of water heaters required for heating hot water so as to regulate the hot water supply temperature and flow; acquiring school table information of an intelligent campus, and dividing a water consumption time period according to the school table information; and formulating a hot water storage strategy according to the water consumption time period, wherein the hot water storage strategy is used for adjusting the number of water heaters required by hot water circulation so as to adjust the storage amount of standby hot water. The intelligent campus energy-saving system has the effect of improving the overall energy conservation of the intelligent campus.

Description

Hot water management method and system for smart campus

Technical Field

The application relates to the technical field of hot water intelligent management, in particular to a hot water management method and system for an intelligent campus.

Background

Along with the improvement of teaching quality, the concept of intelligent campus is gradually popularized, and the intelligent campus is a modern campus which utilizes advanced information technology means to realize intelligent management and optimized operation of various facilities, resources and services in the campus. In smart campuses, hot water management is an important area that affects the life and learning experience of teachers and students.

Existing hot water management methods typically utilize a water heater for heating and hot water circulation. The hot water is then transported through a pipe to a place where the hot water is needed, such as a bathroom, kitchen, etc. In the existing hot water management method, a hot water circulation system keeps hot water at a certain temperature circulating in a pipeline so as to ensure that a user can obtain hot water immediately when needed.

With respect to the related art in the above, there are the following drawbacks: the long-term heating and hot water circulation mode leads to continuous consumption of energy and waste of resources, and reduces the overall energy conservation of the intelligent campus.

Disclosure of Invention

In order to improve the overall energy conservation of the smart campus, the application provides a hot water management method and a hot water management system for the smart campus.

In a first aspect, the above object of the present application is achieved by the following technical solutions:

a method for hot water management for smart campuses, comprising the steps of:

collecting hot water use data in the intelligent campus, and analyzing the hot water use data to generate a hot water demand trend in the intelligent campus;

the preset hot water demand prediction model collects all the hot water demand trend data, predicts the hot water use condition and generates the expected hot water demand of each time period;

formulating a hot water regulation strategy according to the expected hot water demand, wherein the hot water regulation strategy is used for regulating the number of water heaters required for heating hot water so as to regulate the hot water supply temperature and flow;

Acquiring school table information of an intelligent campus, and dividing a water consumption time period according to the school table information, wherein the school table information comprises a class time period, a rest time period between classes and a rest time period;

and formulating a hot water storage strategy according to the water consumption time period, wherein the hot water storage strategy is used for adjusting the number of water heaters required by hot water circulation so as to adjust the storage amount of standby hot water.

By adopting the technical scheme, the quantity of the water heaters required by hot water heating, the hot water supply temperature and the flow rate can be dynamically adjusted according to actual demands through the data-driven hot water demand prediction and the optimization of the hot water regulation strategy, so that unnecessary energy consumption is reduced, the optimized hot water supply strategy can furthest reduce the energy consumption under the condition of not influencing the comfort of users, the effects of energy conservation and emission reduction are realized, and the overall energy conservation of the intelligent campus is improved; the method is based on data acquisition and analysis, intelligent and fine management of hot water supply is realized through continuous optimization of a prediction model and the hot water regulation and control strategy, data-driven optimization can better adapt to the change of hot water demand in the campus, energy waste is reduced to the greatest extent, and the aim of environmental protection and energy saving is fulfilled.

The present application may be further configured in a preferred example to: the method for collecting the hot water usage data in the intelligent campus and analyzing the hot water usage data to generate the hot water demand trend in the intelligent campus comprises the following steps:

collecting hot water point position information of hot water supply in an intelligent campus, and constructing a hot water point data set according to the hot water point position information;

Acquiring hot water point use information in the hot water point data set based on a preset big data acquisition model, wherein the hot water point use information comprises hot water use time and water consumption, and correlating the hot water use time with the water consumption in the corresponding time to construct a hot water use time-water consumption list;

inputting all the hot water point use information and the hot water use time-water consumption list into a preset data processing model to form a hot water point use information data set;

Analyzing the hot water point use information data set by a preset hot water demand trend model, and calculating the data of the hot water use time-water consumption list to obtain the hot water demand of each time period;

and the hot water demand trend model generates hot water demand trends in the intelligent campus according to the hot water demand of each time period.

By adopting the technical scheme, based on the hot water point use information data set, the preset hot water demand trend model can analyze and calculate the hot water demand of each time period, and the hot water point use information data set comprises the data of a hot water use time-water consumption list, so that the hot water demand trend analysis can better know the hot water demand conditions of different time periods in a campus, and provide basis for the subsequent hot water regulation and supply strategy formulation; the hot water demand trend in the intelligent campus can help a campus manager to better know the change trend of the future hot water demand, so that a corresponding hot water regulation strategy is formulated, and compared with a traditional method based on experience and fixed setting, the intelligent prediction model of the scheme can more accurately predict the hot water demand, and improves the efficiency and stability of hot water supply.

The present application may be further configured in a preferred example to: the step of collecting all the hot water demand trend data in a preset hot water demand prediction model, predicting the hot water use condition and generating the expected hot water demand of each time period comprises the following steps:

acquiring all the hot water demand trend data, and sequentially sequencing the hot water demand trend data according to a time axis sequence;

the hot water demand prediction model calculates fluctuation values of adjacent hot water demand trend data, and generates a hot water reserve compensation value according to the fluctuation values of the same hot water use time;

the hot water demand prediction model calculates the expected hot water demand for each time period based on the hot water demand trend data and the hot water reserve compensation value.

By adopting the technical scheme, all hot water demand trend data are acquired and sequenced according to the time axis sequence, the hot water demand prediction model can better understand the change trend of the hot water demand, compared with a traditional fixed prediction method, the dynamic prediction capability can better adapt to the actual change of the hot water demand in a campus, the prediction accuracy and reliability are improved, the hot water demand prediction model considers the fluctuation factor of the hot water demand by calculating the fluctuation value of adjacent hot water demand trend data and generating the hot water reserve compensation value according to the fluctuation value of the same hot water use time, the prediction method considering the fluctuation factor can better cope with the uncertainty of the hot water demand, the prediction robustness and accuracy are improved, the hot water demand prediction model can calculate the expected hot water demand quantity of each time period based on the hot water demand trend data and the hot water reserve compensation value, the hot water reserve compensation strategy can better cope with the hot water supply in each time period because of the supply deficiency or the energy caused by the sudden demand or fluctuation factor, and the hot water reserve compensation strategy can more flexibly cope with the actual supply condition and the stability.

The present application may be further configured in a preferred example to: after the step of acquiring the school timetable information of the smart campus and dividing the water consumption time period according to the school timetable information, wherein the school timetable information comprises the time period of the class, the rest time period between classes and the rest time period, the method comprises the following steps:

According to the class list information, associating with the hot water user, and constructing a water consumption schedule of the hot water user;

The big data acquisition model acquires a hot water usage record of a hot water user in the intelligent campus, and builds a hot water usage habit portrait of the hot water user according to the hot water usage record;

Analyzing according to the water consumption schedule of the hot water user and the hot water use image based on a preset hot water measuring and calculating model so as to calculate the expected hot water consumption of the hot water user;

and the preset hot water control module controls the water heater and the hot water supply pipeline to work according to the hot water using habit image of the hot water user and the expected hot water consumption amount of the user.

Through adopting above-mentioned technical scheme, the water schedule of hot water user is constructed according to the schedule information to combine hot water use record to construct hot water use custom portrait of hot water user, hot water management system can realize the individualized water management to every user, for traditional fixed water supply mode, individualized water management can satisfy different user's actual demand better, avoid the problem of energy waste and hot water supply inadequately, through individualized water management and intelligent control, this technical scheme can realize the fine regulation and control to hot water supply, avoid unnecessary energy consumption, thereby reach energy-concerving and environment-protective effect, simultaneously, because the system supplies water according to actual demand, can also avoid the user experience problem that leads to because of the water supply is not enough.

The present application may be further configured in a preferred example to: the water usage habit representation of the water-heater comprises average water consumption, usage preference and activity factor, wherein the activity factor is positively related to the stability of the water usage habit of the water-heater;

The method comprises the following steps that in the preset hot water control module, the water heater and a hot water supply pipeline are controlled to work according to the hot water usage habit image of a hot water user and the expected hot water consumption amount of the user, and the method comprises the following steps:

Acquiring an activity factor in the hot water use habit representation of the hot water user, and judging the stability of the hot water use habit of the hot water user according to the activity factor;

If the activity factor is in a stable interval of a preset value, namely the judging result of the stability of the hot water use habit of the corresponding hot water user is stable, controlling the hot water to be supplied to the corresponding hot water point according to the use preference of the hot water user;

if the activity factor is in an unstable interval of a preset value, that is, if the corresponding judgment result of the stability of the hot water usage habit of the hot water user is unstable, the hot water supply to the corresponding hot water point is not required to be controlled according to the usage preference of the hot water user, and only the water consumption is required to be ensured to meet the water consumption requirement of the hot water user.

By adopting the technical scheme, the stability of the hot water use habit of the hot water user is judged, the system can judge the stability according to the preset value interval of the activity factor, the water supply strategy is correspondingly determined, and for the user with stable hot water use habit, the system can control the hot water supply according to the use preference of the user; and to the unstable user of hot water use habit, the system can guarantee that the water consumption satisfies its demand, and need not carry out water supply control according to the use preference, this kind of differentiation of water supply strategy can adapt to different users' hot water service conditions better, improve efficiency and the accuracy of water supply, through drawing the hot water use habit of hot water user and using personnel to expect hot water consumption to carry out intelligent hot water control, the work of water heater and hot water supply pipeline can be regulated and control more accurately to the system, the control of the scheme is refined can avoid unnecessary energy consumption, reduce the condition that hot water supply is excessive or insufficient, thereby realize the effect of energy saving.

The present application may be further configured in a preferred example to: after the preset hot water control module controls the water heater and the hot water supply pipeline to work according to the hot water usage habit image of the hot water user and the expected hot water consumption amount of the user, the method comprises the following steps:

Acquiring energy consumption data of hot water equipment in an intelligent campus, and constructing an energy consumption database according to the energy consumption data;

The hot water demand trend model is analyzed and self-learned according to the energy consumption data of the energy consumption database, and a prediction strategy of the hot water demand trend model is optimized;

and the hot water control module optimizes the hot water control model according to the analysis result of the energy consumption data so as to reduce the energy consumption and optimize the hot water supply efficiency.

Through adopting above-mentioned technical scheme, based on the result of energy consumption data analysis, hot water control module can optimize hot water control model, through analysis energy consumption mode and peak period information, the system can adjust the operating strategy of hot water equipment, for example optimize the start-stop time, adjust water supply temperature, this optimization can reduce the energy consumption, improve hot water supply efficiency, thereby realize energy-conserving effect, this scheme can predict hot water demand more accurately with current hot water management method, optimize the operating strategy of hot water equipment, reduce energy consumption, improve hot water supply efficiency, realize smart campus hot water management's refinement, high efficiency and energy-concerving and environment-protective.

In a second aspect, the above object of the present application is achieved by the following technical solutions:

the hot water management device for the intelligent campus comprises a hot water usage data acquisition module, a hot water management module and a hot water management module, wherein the hot water usage data acquisition module is used for acquiring hot water usage data in the intelligent campus and analyzing the hot water usage data to generate a hot water demand trend in the intelligent campus;

the hot water demand prediction model is used for collecting all the hot water demand trend data, predicting the hot water use condition and generating the expected hot water demand of each time period;

The hot water regulation and control strategy generation module is used for formulating a hot water regulation and control strategy according to the expected hot water demand, and the hot water regulation and control strategy is used for adjusting the number of water heaters required by hot water heating so as to adjust the hot water supply temperature and flow;

the intelligent campus information acquisition module is used for acquiring school table information of an intelligent campus and dividing a water consumption time period according to the school table information, wherein the school table information comprises a class time period, a rest time period between classes and a rest time period;

and the hot water storage strategy generation module is used for formulating a hot water storage strategy according to the water use time period, and the hot water storage strategy is used for adjusting the number of water heaters required by hot water circulation so as to adjust the storage amount of standby hot water.

Optionally, the system further comprises a big data acquisition module, a big data acquisition module and a data processing module, wherein the big data acquisition module is used for presetting a big data acquisition model to obtain hot water point use information in the hot water point data set, the hot water point use information comprises hot water use time and water consumption, and the hot water use time and the water consumption in the corresponding time are associated to construct a hot water use time-water consumption list;

the hot water point use information acquisition module is used for inputting all the hot water point use information and the hot water use time-water consumption list into a preset data processing model to form a hot water point use information data set;

the hot water demand trend generation module is used for presetting a hot water demand trend model to analyze the hot water point use information data set, calculating the hot water demand of each time period by calculating the data of the hot water use time-water consumption list, and generating the hot water demand trend in the intelligent campus according to the hot water demand of each time period.

In a third aspect, the above object of the present application is achieved by the following technical solutions:

An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of a hot water management method for a smart campus as described above when the computer program is executed.

In a fourth aspect, the above object of the present application is achieved by the following technical solutions:

A computer readable storage medium storing a computer program which when executed by a processor performs the steps of a hot water management method for a smart campus as described above.

In summary, the present application includes at least one of the following beneficial technical effects:

Through data-driven hot water demand prediction and optimization of a hot water regulation strategy, the number of water heaters, hot water supply temperature and flow required by hot water heating can be dynamically adjusted according to actual demands, so that unnecessary energy consumption is reduced, the optimized hot water supply strategy can furthest reduce the energy consumption under the condition that user comfort is not affected, the effects of energy conservation and emission reduction are realized, and the overall energy conservation of an intelligent campus is improved; the method is based on data acquisition and analysis, the intelligent and fine management of hot water supply is realized through continuous optimization of a prediction model and the hot water regulation strategy, the data-driven optimization can better adapt to the change of hot water demand in the campus, the energy waste is reduced to the greatest extent, and the aim of environmental protection and energy saving is fulfilled;

Based on a hot water point use information data set, a preset hot water demand trend model can analyze and calculate the hot water demand of each time period, the hot water point use information data set comprises data of a hot water use time-water consumption list, the hot water demand trend analysis can better know the hot water demand conditions of different time periods in a campus, and a basis is provided for subsequent hot water regulation and supply strategy formulation; the intelligent prediction model can more accurately predict the hot water demand and improve the efficiency and stability of hot water supply compared with the traditional method based on experience and fixed setting;

The hot water demand prediction model can better understand the variation trend of the hot water demand, and can better adapt to the actual variation of the hot water demand in a campus, so that the accuracy and reliability of prediction are improved, the fluctuation value of adjacent hot water demand trend data is calculated, and a hot water reserve compensation value is generated according to the fluctuation value of the same hot water use time, the fluctuation factor of the hot water demand is considered by the hot water demand prediction model, the uncertainty of the hot water demand can be better dealt with by the prediction method considering the fluctuation factor, the robustness and accuracy of prediction are improved, the expected hot water demand quantity in each time period can be calculated and generated by the hot water demand prediction model based on the hot water demand trend data and the hot water reserve compensation value, the hot water supply of each time period in the campus can be better ensured by the hot water reserve compensation strategy, the hot water reserve compensation strategy can more flexibly cope with the actual demand situation and the stability and reliability are improved compared with the traditional fixed prediction and supply strategy;

Judging the stability of the hot water using habit of the hot water using personnel, wherein the system can judge the stability according to the preset value interval of the activity factor and correspondingly determine the water supply strategy, and for the user with stable hot water using habit, the system can control the hot water supply according to the using preference of the user; and to the unstable user of hot water use habit, the system can guarantee that the water consumption satisfies its demand, and need not carry out water supply control according to the use preference, this kind of differentiation of water supply strategy can adapt to different users' hot water service conditions better, improve efficiency and the accuracy of water supply, through drawing the hot water use habit of hot water user and using personnel to expect hot water consumption to carry out intelligent hot water control, the work of water heater and hot water supply pipeline can be regulated and control more accurately to the system, the control of the scheme is refined can avoid unnecessary energy consumption, reduce the condition that hot water supply is excessive or insufficient, thereby realize the effect of energy saving.

Drawings

FIG. 1 is a flow chart illustrating a hot water management method for a smart campus according to an embodiment of the present application;

FIG. 2 is a flowchart of a hot water management method for smart campus in step S10 according to an embodiment of the present application;

FIG. 3 is a flowchart of a hot water management method for smart campus in step S20 according to an embodiment of the present application;

FIG. 4 is a flowchart illustrating a hot water management method for smart campus according to an embodiment of the present application after step S40;

FIG. 5 is a flowchart illustrating a hot water management method for smart campus according to an embodiment of the present application after step S404;

FIG. 6 is a flowchart of a hot water management method for smart campus in step S404 according to an embodiment of the present application;

FIG. 7 is a schematic block diagram of a hot water management apparatus for a smart campus according to an embodiment of the present application;

FIG. 8 is a schematic diagram of an electronic device in an embodiment of the application;

Reference numerals illustrate:

1. A hot water usage data acquisition module; 2. a hot water demand prediction model; 3. a hot water regulation strategy generation module; 4. the intelligent campus information acquisition module; 5. a hot water storage strategy generation module; 6. a big data acquisition module; 7. the hot water point use information acquisition module; 8. and the hot water demand trend generation module.

Detailed Description

The present application will be described in further detail with reference to the accompanying drawings.

In the embodiment, as shown in fig. 1-6, the application discloses a hot water management method for an intelligent campus, which specifically comprises the following steps:

s10, acquiring hot water use data in the intelligent campus, and analyzing the hot water use data to generate a hot water demand trend in the intelligent campus;

Specifically, intelligent hot water metering equipment is deployed in the intelligent campus, hot water usage data in the intelligent campus is monitored in real time, and the intelligent campus comprises key information of hot water usage data such as hot water flow rate, flow rate and temperature. Analyzing the collected hot water usage data to generate a hot water demand trend, wherein the hot water demand trend refers to analyzing hot water usage data in the smart campus to determine a change trend of the hot water demand, for example, the hot water flow is increased or decreased with time within a certain time period, and the change rate of the hot water flow is analyzed.

Through monitoring and analysis of hot water usage data, peaks and valleys of hot water demands in different time periods can be identified, and further, the change trend of the hot water demands in the future is predicted.

S20, acquiring all hot water demand trend data by a preset hot water demand prediction model, predicting the hot water use condition, and generating expected hot water demand in each time period;

specifically, an LSTM (long short term memory network) model is used as a predictive model in the embodiment of the present application. By inputting the historical hot water demand data into the LSTM model for training, the LSTM model can learn the mode and the change rule in the hot water demand data and can effectively predict the future hot water demand. The LSTM model can provide accurate hot water demand prediction according to different time periods and water consumption conditions, and provides important references for a hot water supply regulation strategy.

In the embodiment of the application, the LSTM model is established as follows:

data acquisition and preparation: first, historical hot water demand data in the smart campus is collected and consolidated, including time stamps and corresponding hot water demand. The quality and the integrity of the data are ensured, and missing values and abnormal values are processed.

Data preprocessing: preprocessing the acquired data, including data cleaning, feature selection and feature engineering. The data is converted into a time-series data format suitable for LSTM model input, for example, the history data is divided into an input sequence and an output sequence in time order.

Establishing an LSTM model: the LSTM model is built using a deep learning framework (e.g., tensorFlow, pyTorch). In the model design, super parameters such as the number of layers of LSTM, the number of hidden units and the like are determined, and model training is performed.

Model training and verification: historical hot water demand data is input into the LSTM model for training, and model parameters are updated through a back propagation algorithm. During the training process, cross-validation and other methods can be used to evaluate the performance and generalization ability of the model.

Hot water demand prediction: future hot water demand is predicted using a trained LSTM model. According to actual demands, the hot water demand in different time periods can be predicted, and a confidence interval or visual display of a prediction result is provided.

Model optimization and adjustment: and optimizing and adjusting the LSTM model according to the accuracy of the prediction result and the actual application effect. The super parameters of the model can be adjusted, more features can be added or regularization can be introduced to improve the performance of the model.

Real-time prediction and application: the optimized LSTM model is applied to an actual hot water management system, so that prediction and regulation of real-time hot water demand are realized. And the intelligent control system is combined, the hot water supply strategy is adjusted according to the prediction result, and the intelligent management of hot water supply is realized.

S30, formulating a hot water regulation strategy according to the expected hot water demand, wherein the hot water regulation strategy is used for regulating the number of water heaters required by hot water heating so as to regulate the hot water supply temperature and flow;

Specifically, an intelligent hot water supply regulation strategy is formulated by combining with the LSTM model prediction result, and dynamic adjustment is performed according to real-time requirements and system states. In the embodiment of the application, the hot water supply regulation strategy needs to consider the following indexes: expected hot water demand, meteorological data, campus activity information and system operating state data (equipment operating conditions, energy consumption conditions) predicted by the LSTM model.

Through the comprehensive judgment of a plurality of indexes, and corresponding hot water regulation and control strategies are formulated according to the change of the indexes, the intelligent campus can achieve more intelligent, efficient and user-friendly hot water management, the energy utilization efficiency is improved, the cost is reduced, and better use experience is provided for users.

S40, acquiring school timetable information of the smart campus, and dividing water consumption time periods according to the school timetable information, wherein the school timetable information comprises a class time period, a rest time period and a rest time period;

Specifically, accurate and complete school table information is obtained from a school educational administration system or related systems, wherein the school educational administration system comprises a school time period, a rest time period between the school and a rest time period. The time period is then analyzed and divided according to the schedule information, and the rest time between classes is considered to predict the possible water consumption of students and coaches. An intelligent hot water supply scheduling scheme is formulated by an intelligent scheduling algorithm in combination with the school timetable information and the historical water consumption data, and a water supply strategy is monitored and adjusted in real time so as to keep supply and demand balance.

According to user behavior analysis and personalized requirements, a personalized water supply scheme is formulated, a strategy is continuously evaluated and optimized, data-driven decision making is achieved, efficiency is improved, energy is saved, user experience is improved, and intelligent hot water management is achieved.

S50, formulating a hot water storage strategy according to the water consumption time period, wherein the hot water storage strategy is used for adjusting the number of water heaters required by hot water circulation so as to adjust the storage amount of standby hot water;

Specifically, through deep water use time period analysis and hot water storage strategy formulation, dynamic adjustment and intelligent control are combined, and more flexible and efficient hot water management is realized. The optimized strategy comprises peak-valley analysis to determine peak-valley time periods, dynamic memory adjustment, supply priority setting and intelligent control system, so as to achieve the aims of energy saving and environmental protection. Meanwhile, by combining green energy utilization and energy efficiency optimization, the system can better adapt to actual demands, improves the energy utilization efficiency, and builds a smart campus green environment-friendly demonstration with assistance.

In the embodiment of the application, according to the water consumption time period data of the student dormitory, the peak water consumption time period is determined to be the washing time in the morning and evening, and the valley water consumption time period is determined to be the midday and the night by combining the school table information and the historical water consumption data. The storage capacity is dynamically adjusted, the supply priority is set, the system can store enough hot water in advance in the peak time period, and the supply stability is ensured; and the storage capacity is flexibly controlled in the low-peak period, so that the energy waste is avoided. The intelligent control system monitors the hot water circulation state in real time, and adjusts the quantity and the operation time length of the water heater according to the requirements so as to realize supply and demand balance.

Such an optimization would bring significant benefits: the energy consumption is saved, the operation cost is reduced, and the stability and the efficiency of the system are improved. By optimizing the hot water storage strategy, the hot water supply is dynamically adjusted according to actual demands, green energy can be utilized to the greatest extent, and carbon emission is reduced. The intelligent control system has the real-time monitoring and adjusting capability, so that the system has more intelligence and flexibility, the energy utilization efficiency is improved, and new power is injected for the green environment-friendly target of the intelligent campus.

At S10: the steps of collecting hot water usage data in the smart campus and analyzing the hot water usage data to generate a hot water demand trend in the smart campus include the steps of:

s11: collecting hot water point position information of hot water supply in the intelligent campus, and constructing a hot water point data set according to the hot water point position information;

Specifically, the internet of things technology or the sensor network is used for collecting the position information of the hot water supply equipment, or the installation position of the hot water supply equipment is recorded through an engineering design drawing of the intelligent campus, so that the hot water point position information of hot water supply in the intelligent campus is collected, and a hot water point data set is constructed according to the hot water point position information.

S12: acquiring hot water point use information in a hot water point data set based on a preset big data acquisition model, wherein the hot water point use information comprises hot water use time and water consumption, and correlating the hot water use time with the water consumption in the corresponding time to construct a hot water use time-water consumption list;

Specifically, a sensor or an intelligent water meter and other devices are used for collecting the use data of the hot water point, the use time and the water consumption of the hot water point are recorded, the time and the water consumption are in one-to-one correspondence, the water consumption of hot water users each time is collected, and a hot water use time-water consumption list is constructed.

S13: inputting all hot water point use information and a hot water use time-water consumption list into a preset data processing model to form a hot water point use information data set;

specifically, the collected hot water usage data is input into a data processing model for cleaning, sorting and analyzing, and the abnormal data is marked and processed, so that the accuracy of the hot water usage data is improved.

The method for identifying the abnormal data comprises a data statistics method, a clustering method and a time sequence analysis method, and in the embodiment of the application, the data statistics method selects a Z-score statistics method, and when the hot water usage data is not within the set threshold value, the data is marked as the abnormal data by calculating the Z-score and setting the threshold value; the clustering method uses a K-means clustering algorithm to divide the data points into different clusters, so that the abnormal data with larger difference with other data points can be identified; the time series analysis analyzes the time series data and identifies data points that deviate greatly from the historical data.

S14: analyzing a hot water point use information data set by a preset hot water demand trend model, and calculating data of a hot water use time-water consumption list to obtain hot water demand in each time period;

specifically, in the embodiment of the application, the hot water demand trend model is a neural network model, hot water usage data is divided into training data and test data, and the hot water demand trend model carries out self-learning of the hot water demand trend model through learning and analysis of the training data and the test data, so that the hot water demand trend model has a hot water demand prediction function in each time period;

and predicting the hot water demand in a future time period by using a trained machine learning model, comparing the predicted result with the actual water consumption to analyze the model predicted result, evaluating the accuracy and reliability of the model, and adjusting model parameters or selecting other models according to the analyzed result so as to improve the prediction accuracy.

S15: the hot water demand trend model generates a hot water demand trend in the intelligent campus according to the hot water demand of each time period;

specifically, a hot water demand trend chart or report in the smart campus is generated according to the hot water demand in each time period. The generated hot water demand trend can help school administrators to reasonably arrange hot water supply, optimize energy utilization, reduce energy waste and improve the stability and efficiency of the system.

The step of collecting all hot water demand trend data and predicting the hot water use condition in the preset hot water demand prediction model in S20 to generate the expected hot water demand of each time period includes the following steps:

s21: acquiring all hot water demand trend data, and sequentially sequencing the hot water demand trend data according to a time axis sequence;

Specifically, historical hot water demand data is collected, wherein the historical hot water demand data comprises a timestamp and a hot water demand corresponding to the hot water demand data, all the hot water demand trend data are ordered according to a time axis and are grouped according to a preset period.

S22: the hot water demand prediction model calculates the fluctuation value of adjacent hot water demand trend data, and generates a hot water reserve compensation value according to the fluctuation value of the same hot water use time;

Specifically, two adjacent hot water demand trend data in a period are compared, an actual water consumption difference value of the adjacent hot water demand trend data is calculated, and the actual water consumption difference value is used as a fluctuation value.

S23: the hot water demand prediction model calculates and generates expected hot water demand of each time period based on the hot water demand trend data and the hot water reserve compensation value;

Specifically, the hot water demand prediction model calculates the expected hot water demand for each time period using the hot water demand trend data and the reserve compensation value. It should be noted that, the calculation of the expected hot water demand should consider the historical data, the volatility and other influencing factors to improve the accuracy and the reliability of the prediction.

S40, acquiring school table information of the smart campus, and dividing water consumption time periods according to the school table information, wherein the school table information comprises the following steps after the step of getting in class time periods, rest time periods among the classes and rest time periods:

S401, associating with hot water users according to the schedule information to construct a water consumption schedule of the hot water users;

Specifically, school table information is obtained from the intelligent campus system, including a class time period, a rest time period between classes and a rest time period. According to the information, each time period is marked as a water using time period or a non-water using time period so as to facilitate subsequent hot water management and regulation, the schedule information is associated with hot water users, a water using schedule of each hot water user is established, the water using schedule records the condition that each hot water user needs hot water in different time periods, and customization can be carried out according to the schedule information and personal requirements. In the embodiment of the application, hot water users in the intelligent campus comprise students, teachers and campus staff, and for the teachers and the students, the time period of taking class is marked as a non-water time period, and the rest time period between classes are used as water time periods; for campus staff, the water use time period and the non-water use time period are divided according to the working time of the campus staff.

S402, acquiring a hot water usage record of a hot water user in an intelligent campus by a big data acquisition model, and constructing a hot water usage habit portrait of the hot water user according to the hot water usage record;

Specifically, a big data acquisition model is utilized to acquire a hot water usage record of hot water users in the smart campus, and in the embodiment of the application, the hot water usage record comprises the following steps:

The water consumption time is as follows: the time stamp of each hot water use, including date and specific time, is recorded.

Water consumption: the amount of water used per hot water is recorded, typically in liters or milliliters.

Duration with water: the duration of each hot water use is recorded in minutes or seconds.

Water use site: the specific location of the hot water use, e.g., bathroom, toilet, etc., is recorded.

Using the person information: personnel related information, such as students, teaching staff, etc., using the hot water are recorded.

Using device information: information about the hot water apparatus used, such as the model of the water heater, the water supply pipe, etc., is recorded.

Usage pattern: the pattern or purpose of hot water usage, such as bathing, laundry, etc., is recorded.

According to the hot water usage record, analyzing the water usage habit of the hot water user, including statistical information on water consumption, water consumption frequency, water consumption duration and the like, and constructing a hot water usage habit portrait of the hot water user, for example: the hot water usage of one student was recorded as follows: the water consumption time is as follows: 2024, 3 months 1 day, 8:30 am; water consumption: 15 liters; duration with water: 10 minutes; water use site: a bathroom; using the person information: student A; using device information: water heater model ABC123, water supply line A1; usage pattern: bathing. From this record, it was found that student A used 15 liters of hot water in the bathroom for 10 minutes at 2024, 3 months, 1 am, 8:30, using water heater model ABC123 and water supply line A1. These data can be used to analyze student a's hot water usage habits, calculate hot water usage, and formulate corresponding hot water regulation strategies. By analyzing a plurality of hot water usage records of student A, the following hot water usage habit representation can be obtained:

average water consumption: the average water usage is calculated from the water usage recorded several times, for example, the average water usage is 12 liters.

Water frequency: the average daily or weekly water usage frequency is calculated from the recorded number of times and time span, e.g. 4 average weekly use of hot water.

Preference period: based on the statistics of the time of use, it was determined that student a was more inclined to use hot water in the morning.

Preference places: based on the statistics of the water usage site, it was determined that student a was using hot water mainly in the bathroom.

Preference mode: based on statistics of the use mode, it is determined that student A mainly uses hot water for bathing

S403, analyzing according to a water consumption schedule and a water consumption image of a water heater based on a preset water heating measuring and calculating model to calculate the expected water consumption of the water heater;

specifically, based on a preset hot water measuring and calculating model, analysis and calculation are performed by combining a water consumption schedule of hot water users and a hot water use habit image. By counting and predicting the demand of the person in the water using period, the expected water consumption of the hot water by each hot water user can be obtained.

For example, in an embodiment of the present application, student A's hot water usage habit representation and water usage schedule may calculate his expected hot water usage.

Suppose that student a's water schedule is as follows:

Weekday (monday to friday): 8:00-8:30 in the morning and 9:00-9:30 in the evening

Weekend (Saturday and sunday): 9:00-9:30 in the morning and 8:00-8:30 in the evening

By combining the hot water usage habit image of the student A, the hot water consumption of the student A can be predicted as follows:

Morning of weekday: based on the average water usage and the water usage period, the morning water usage was estimated to be 12 liters. Weekday evening: the water consumption at night was expected to be 12 liters based on the average water consumption and the water consumption period. Weekend morning: based on the average water usage and the water usage period, the morning water usage was estimated to be 12 liters. Evening on weekend: the water consumption at night was expected to be 12 liters based on the average water consumption and the water consumption period.

Through a preset hot water measuring and calculating model and a water consumption schedule of the student A and combining with a hot water use habit portrait, the expected water consumption time and the expected hot water consumption (12L) of the student A can be calculated. According to the predicted values, corresponding hot water regulation strategies can be formulated, namely, the hot water requirement of the student A is met.

S404, a preset hot water control module controls the water heater and a hot water supply pipeline to work according to hot water usage habit images of hot water users and expected hot water consumption of the users;

Specifically, the hot water measuring and calculating model calculates the expected water consumption time and the expected hot water consumption amount for each hot water user, calculates the expected hot water consumption amount of each time period according to time periods, and the hot water control module controls the water heater to work according to the expected hot water consumption amount so as to meet the hot water requirement of the intelligent campus; according to the scheme, the use habit of a user is analyzed, when the user needs to use hot water, sufficient hot water is provided, the water heater in the campus is controlled to be started and stopped according to the expected hot water consumption, the water heater is not required to be started for a long time, the use of users in the campus is enough, the energy consumption of hot water production is reduced, resources for hot water management are saved, and the overall energy conservation performance of the intelligent campus is improved.

It should be further noted that the representation of the hot water usage habit of the hot water user includes an average water consumption, a usage preference, and an activity factor, the activity factor being positively correlated to the stability of the hot water usage habit of the hot water user;

Specifically, the activity factor is related to the number of hot water points used by a hot water user and the hot water use time, in the embodiment of the present application, the activity factor is U, the number of hot water points used by the hot water user is Q1, the number of hot water points in the smart campus is Q2, if the number of Q1 of the student a is 3, the difference value of the frequencies used by each hot water point of the student a is calculated, the frequency of each hot water point is calculated as (p=the number of times of using the hot water point/the total number of times of using the hot water point), wherein Pmax is the corresponding frequency of the hot water point with the largest number of times of use, pmin is the corresponding frequency of the hot water point with the smallest number of times of use, u= (Q2/Q1) + (Pmax-Pmin), wherein a is the first duty factor, B is the second duty factor, and a+b=1, the set value of a, B can be optimized according to the actual situation, in the embodiment of the present application, the basic value of a is 0.1, the value of B is calculated as the basic value of the Pmax, the value of the hot water point is 0.9, the value of the hot water point is calculated as the basic value of the hot water point, and the value of the hot water point is calculated as the difference value of the hot water point with the largest number of times of use, and the hot water point is more stable in the hot water use, and the hot water point is more than the different than the number of the hot water point in the hot water use, and the application; on the contrary, the hot water use point of the hot water user has small bias, and is difficult to predict and judge which hot water point is used, so that in the embodiment of the application, the activity factor is positively related to the stability of the hot water use habit of the hot water user.

At S404: the preset hot water control module controls the water heater and the hot water supply pipeline to work according to the hot water usage habit image of the hot water user and the expected hot water consumption amount of the user, and comprises the following steps:

S4041: acquiring an activity factor in the hot water use habit representation of the hot water user, and judging the stability of the hot water use habit of the hot water user according to the activity factor;

S4042: if the activity factor is in a stable interval of a preset value, namely the judging result of the stability of the hot water use habit of the corresponding hot water user is stable, controlling the hot water to be supplied to the corresponding hot water point according to the use preference of the hot water user;

S4043: if the activity factor is in an unstable interval of a preset value, namely the judgment result of the stability of the hot water use habit of the corresponding hot water user is unstable, the hot water supply to the corresponding hot water point is not required to be controlled according to the use preference of the hot water user, and only the water consumption is required to be ensured to meet the water consumption requirement of the hot water user;

Specifically, in the embodiment of the present application, all the activity factors of the hot water users are ranked, and the first 70% of the hot water users are selected to be stable, and the second 30% of the hot water users are selected to be unstable, i.e. the preset value of the stable interval is the first 70% of the activity factor value.

Through activity factor screening, can screen the great hot water user of volatility, this type (unstable) hot water user is difficult to the accurate hot water service point of prediction, consequently avoids for this type (unstable) hot water user input too much resource to improve the effective utilization ratio of resource, further improve the whole energy-conserving nature of wisdom campus.

S405: acquiring energy consumption data of hot water equipment in an intelligent campus, and constructing an energy consumption database according to the energy consumption data;

s406: the hot water demand trend model is analyzed and self-learned according to the energy consumption data of the energy consumption database, and a prediction strategy of the hot water demand trend model is optimized;

s407: the hot water control module optimizes the hot water control model according to the result of the energy consumption data analysis, and adjusts the working strategy of the hot water equipment according to the information of the energy consumption mode, the peak period and the like, such as optimizing the starting and stopping time, adjusting the water supply temperature and the like, so as to reduce the energy consumption and optimize the hot water supply efficiency.

Through adopting the technical scheme, based on the result of energy consumption data analysis, the hot water control module can optimize the hot water control model, through analyzing information such as energy consumption mode and peak period, the system can adjust the operating strategy of hot water equipment, for example optimize the start-stop time, adjust water supply temperature etc., this optimization can reduce the energy consumption, improve hot water supply efficiency, thereby realize energy-conserving effect, this scheme can predict hot water demand more accurately than current hot water management method, optimize the operating strategy of hot water equipment, reduce energy consumption, improve hot water supply efficiency, realize smart campus hot water management's refinement, high efficiency and energy-concerving and environment-protective.

It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.

In an embodiment, a hot water management device for a smart campus is provided, where the hot water management device for a smart campus corresponds to a hot water management method for a smart campus in the foregoing embodiment one by one. As shown in fig. 7, the hot water management apparatus for an intelligent campus includes:

The hot water use data acquisition module 1 is used for acquiring hot water use data in the intelligent campus and analyzing the hot water use data to generate a hot water demand trend in the intelligent campus;

The hot water demand prediction model 2 is used for collecting all hot water demand trend data, predicting the hot water use condition and generating expected hot water demand in each time period;

The hot water regulation and control strategy generation module 3 is used for formulating a hot water regulation and control strategy according to the expected hot water demand, wherein the hot water regulation and control strategy is used for adjusting the number of water heaters required by hot water heating so as to adjust the hot water supply temperature and flow;

The intelligent campus information acquisition module 4 is used for acquiring school table information of the intelligent campus and dividing water consumption time periods according to the school table information, wherein the school table information comprises a class time period, a rest time period between classes and a rest time period;

The hot water storage strategy generation module 5 is used for formulating a hot water storage strategy according to the water use time period, wherein the hot water storage strategy is used for adjusting the number of water heaters required by hot water circulation so as to adjust the storage amount of standby hot water;

the big data acquisition module 6 is used for presetting a big data acquisition model to acquire hot water point use information in a hot water point data set, wherein the hot water point use information comprises hot water use time and water consumption, and the hot water use time and the water consumption in the corresponding time are associated to construct a hot water use time-water consumption list;

the hot water point use information acquisition module 7 is used for inputting all hot water point use information and a hot water use time-water consumption list into a preset data processing model to form a hot water point use information data set;

The hot water demand trend generating module 8 is configured to preset a hot water demand trend model to analyze the hot water point usage information data set, calculate the data of the hot water usage time-water consumption list, obtain the hot water demand of each time period, and generate the hot water demand trend in the smart campus according to the hot water demand of each time period.

The preset hot water demand trend model can analyze and calculate the hot water demand of each time period based on the hot water point use information data set, wherein the hot water point use information data set comprises the data of a hot water use time-water consumption list, and the hot water demand trend analysis can better know the hot water demand conditions of different time periods in a campus and provide basis for the subsequent hot water regulation and supply strategy formulation; the hot water demand trend in the intelligent campus can help a campus manager to better know the change trend of the future hot water demand, so that a corresponding hot water regulation strategy is formulated, and compared with a traditional method based on experience and fixed setting, the intelligent prediction model of the scheme can more accurately predict the hot water demand, and improves the efficiency and stability of hot water supply.

For specific limitations of a hot water management apparatus for a smart campus, reference should be made to the above limitation of a hot water management method for a smart campus, and detailed descriptions thereof are omitted herein. The above-mentioned modules in a hot water management device for smart campus may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or independent of a processor in the electronic device, or may be stored in software in a memory in the electronic device, so that the processor may call and execute operations corresponding to the above modules.

In one embodiment, an electronic device is provided, which may be a server, and the internal structure thereof may be as shown in fig. 8. The electronic device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the electronic device is used for communicating with an external terminal through a network connection. The computer program when executed by the processor implements a method for hot water management for smart campuses.

In one embodiment, an electronic device is provided that includes a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:

In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.

The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.

Claims

1. A hot water management method for intelligent campus is characterized in that: the method comprises the following steps:

A hot water storage strategy is formulated according to the water consumption time period, and the hot water storage strategy is used for adjusting the number of water heaters required by hot water circulation so as to adjust the storage amount of standby hot water;

Among the steps of collecting hot water usage data in the smart campus and analyzing the hot water usage data to generate a hot water demand trend in the smart campus, the method comprises the following steps:

The hot water demand trend model generates a hot water demand trend in the intelligent campus according to the hot water demand of each time period;

The method comprises the steps of collecting all the hot water demand trend data in a preset hot water demand prediction model, predicting the hot water use condition and generating the expected hot water demand of each time period, and comprises the following steps:

The hot water demand prediction model calculates and generates the expected hot water demand of each time period based on the hot water demand trend data and the hot water reserve compensation value;

The method comprises the following steps of after the steps of acquiring the school timetable information of the smart campus and dividing the water consumption time period according to the school timetable information, wherein the school timetable information comprises a class time period, a rest time period between classes and a rest time period, and the method comprises the following steps of:

according to the class schedule information, correlating with hot water users, and constructing a water consumption schedule of the hot water users;

The preset hot water control module controls the water heater and the hot water supply pipeline to work according to the hot water using habit image of the hot water user and the expected hot water consumption of the user;

wherein the representation of the hot water usage habit of the hot water user comprises average water consumption, usage preference and activity factor, and the activity factor is positively correlated with the stability of the hot water usage habit of the hot water user;

If the activity factor is in an unstable interval of a preset value, that is, if the judging result of the stability of the hot water usage habit of the corresponding hot water user is unstable, the hot water is not required to be controlled to be supplied to the corresponding hot water point according to the usage preference of the hot water user, and only the water consumption is required to be ensured to meet the water consumption requirement of the hot water user;

after the preset hot water control module controls the water heater and the hot water supply pipeline to work according to the hot water usage habit image of the hot water user and the expected hot water consumption amount of the user, the method comprises the following steps:

The hot water control module optimizes the hot water control model according to the analysis result of the energy consumption data, and adjusts the working strategy of the hot water equipment according to the energy consumption mode and the information of the peak period so as to reduce the energy consumption and optimize the hot water supply efficiency.

2. A hot-water management device for wisdom campus, its characterized in that: a hot water management method for a smart campus as claimed in claim 1, comprising a hot water usage data acquisition module (1) for acquiring hot water usage data in the smart campus and analyzing the hot water usage data to generate a hot water demand trend in the smart campus;

The hot water demand prediction model (2) is used for collecting all the hot water demand trend data, predicting the hot water use condition and generating the expected hot water demand of each time period;

A hot water regulation strategy generation module (3) for formulating a hot water regulation strategy according to the expected hot water demand, the hot water regulation strategy being used for adjusting the number of water heaters required for hot water heating to adjust the hot water supply temperature and flow;

the intelligent campus information acquisition module (4) is used for acquiring school table information of an intelligent campus and dividing a water consumption time period according to the school table information, wherein the school table information comprises a class time period, a rest time period between classes and a rest time period between classes;

And the hot water storage strategy generation module (5) is used for formulating a hot water storage strategy according to the water consumption time period, and the hot water storage strategy is used for adjusting the number of water heaters required by hot water circulation so as to adjust the storage amount of standby hot water.

3. A hot water management apparatus for a smart campus as claimed in claim 2, wherein: the system further comprises a big data acquisition module (6) which is used for presetting a big data acquisition model to obtain hot water point use information in the hot water point data set, wherein the hot water point use information comprises hot water use time and water consumption, and the hot water use time and the water consumption in the corresponding time are associated to construct a hot water use time-water consumption list;

The hot water point use information acquisition module (7) is used for inputting all the hot water point use information and the hot water use time-water consumption list into a preset data processing model to form a hot water point use information data set;

the hot water demand trend generation module (8) is used for presetting a hot water demand trend model to analyze the hot water point use information data set, calculating the hot water demand of each time period by calculating the data of the hot water use time-water consumption list, and generating the hot water demand trend in the intelligent campus according to the hot water demand of each time period.

4. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of a hot water management method for a smart campus as claimed in claim 1 when the computer program is executed by the processor.

5. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of a hot water management method for a smart campus as claimed in claim 1.