A community smart sponge control system coupled with thermal comfort optimization objectives

Abstract

The present invention relates to the field of image data processing technology, and specifically to a residential smart sponge control system coupled with a thermal comfort optimization target. The present invention utilizes sensors and computer control, comprehensively considers multiple big data information such as sensor data of downstream rainwater and sewage pipelines, residents' travel, and courier activities, and uses a coupling algorithm based on machine learning to accurately control facilities such as solar pumps and electric control valves in optimized sponge facilities, thereby achieving a 20% improvement in the PMV value of the thermal comfort index of the sponge facility's surrounding environment under common climatic conditions in the north during the hot summer season.

Description

Technical Field

[0001] The present invention relates to the technical field of image data processing, and in particular to a community smart sponge control system coupled with a thermal comfort optimization target. Background Art

[0002] Sponge facilities are an important part of green buildings. Sponge facilities refer to the use of the concept and technology of sponge cities to apply sustainable rainwater management systems in cities, aiming to improve the utilization efficiency of water resources, reduce flood risks, improve the water environment, and enhance the quality of the urban environment.

[0003] Although the sponge facilities in the existing technology have the concept of "heat island mitigation", it is not clear how to quantitatively reduce it. Although they can effectively store rainwater runoff and purify water quality, they have not been carefully designed to regulate the urban thermal environment, so their efficiency is still relatively low. With the acceleration of urbanization, the urban "heat island effect" is becoming increasingly serious. Traditional sponge facilities lack intelligent management. Because of the storage target and the prevention of mosquito and fly pressure, most of them require the stagnant water to be emptied within 24 hours. Therefore, it is impossible to dynamically adjust the ambient temperature and humidity according to the actual situation, resulting in the difficulty in effectively improving the comfort of the urban thermal environment. In addition, the existing sponge facilities are also not intelligent enough in terms of data collection, processing and application, and cannot make full use of modern information technology for precise control and optimized management.

[0004] Therefore, it is necessary to design a community smart sponge control system that is coupled with the thermal comfort optimization goal, which can monitor environmental parameters in real time, dynamically adjust thermal comfort, and deeply integrate with modern information technologies such as big data and cloud computing, so as to improve the overall comfort of the urban environment. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a community smart sponge control system that is coupled with the thermal comfort optimization goal, which can monitor environmental parameters in real time, dynamically adjust thermal comfort, and deeply integrate with modern information technologies such as big data and cloud computing, so as to improve the overall comfort of the urban environment.

[0006] In order to achieve the above object, the present invention provides a community smart sponge control system coupled with thermal comfort optimization objectives, comprising:

[0007] S1, thermal comfort adjustment function module:

[0008] S1-1, real-time monitoring of ambient temperature and humidity through specific sensors, used to adjust according to actual conditions;

[0009] S1-2, adjusted to: automatically start or shut down the solar pump and the electric control valve within the set temperature range to maintain environmental comfort;

[0010] S2, Big Data and Mobile Internet Technology Module:

[0011] S2-1, the system receives and processes data from downstream stormwater pipe sensors and residents’ travel big data information in real time;

[0012] S2-2, data processing should be implemented through mobile Internet technology to achieve remote monitoring and control for intelligent management of facilities;

[0013] S3, coupling algorithm module:

[0014] S3-1, dynamic adjustment based on real-time collected data for precise control of solar pumps and electronically controlled valves;

[0015] S3-2, the algorithm includes a prediction model to predict future thermal comfort and rainfall conditions based on historical data and current environmental parameters, so that adjustments can be made in advance;

[0016] S4, Special Population Module:

[0017] S4-1, the system sets up additional shading and cooling equipment in areas where various groups of people frequently move;

[0018] S4-2, the system includes an alarm system, which automatically initiates cooling measures and notifies relevant personnel when the ambient temperature exceeds a preset safety threshold;

[0019] S5, multifunctional integrated module:

[0020] S5-1, while improving the thermal comfort regulation function, the system's rainwater runoff retention capacity shall be at least equivalent to that of existing sponge facilities, and shall not reduce the water purification capacity.

[0021] The specific formula of the coupling algorithm in S3 is:

[0022] [ F_{control} = f(Q, qd, λ, PMV, e, P_1, P_2, ..., P_{11}) ];

[0023] [F_{control} is the control function of the sponge facility and is dynamically adjusted according to the following parameters:

[0024] Q: Weather forecasts predict future rainfall, which will affect the water storage and drainage needs of sponge facilities;

[0025] qd: The existing water storage capacity of the sponge city facilities, reflecting the current water resource utilization of the facilities;

[0026] λ: Future relative humidity, which affects air humidity and human comfort;

[0027] PMV: Predicted Mean Vote, used to assess human thermal comfort;

[0028] The distribution of activity venues for special vulnerable groups includes couriers, car drivers, and elderly people who actively dance in squares;

[0029] e: Annual runoff volume control rate, which measures the ability of sponge facilities to control urban rainwater runoff.

[0030] The specific formula of the coupling algorithm is:

[0031] P_1: soil moisture, which affects the water infiltration capacity of the sponge facility and plant growth;

[0032] P_2: groundwater level, which is related to the water storage capacity and drainage effect of the sponge facility;

[0033] P_3: Facility aging degree, reflecting the service life and maintenance requirements of sponge facilities, including unexpected situations such as leakage;

[0034] P_4: surrounding vegetation coverage, which affects the evapotranspiration and microclimate of the surface;

[0035] P_5: solar radiation intensity, which is related to the efficiency of the solar pump;

[0036] P_6: wind speed, which affects the evaporation rate and human comfort;

[0037] P_7: Building density around the facility, affecting the heat island effect and ventilation;

[0038] P_8: Water quality index, reflecting the purification effect of sponge facilities on water quality;

[0039] P_9: rainfall change rate, used to predict the dynamic changes of future rainwater runoff;

[0040] P_{10}: The capacity of the urban drainage system, which affects the coordination between sponge facilities and the municipal drainage system;

[0041] P_{11}: Residents can use Bluetooth or Wifi feedback to understand the intelligent operation of the sponge facility through the mobile terminal App, and monitor and control the operation effect of the facility. If any inconsiderate situation occurs, upload the information for correction.

[0042] The control function f in the specific formula of the coupling algorithm is a machine learning model, which is embedded in the single-chip microcomputer control mechanism where the sponge facility is located. It dynamically adjusts the operating status of the sponge facility according to the combined influence of these parameters, so as to enable the intelligent control system to respond to environmental changes in real time, improve the comfort of the urban thermal environment, and maintain the efficient operation of the sponge facility.

[0043] The operating status includes: turning on or off the solar pump and adjusting the opening of the electronically controlled valve.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The thermal comfort regulating function of the present invention focuses on rainwater runoff retention and water purification, and also particularly enhances the ability to regulate the thermal comfort of urban microenvironments, which is not available in traditional facilities.

[0046] The application of big data and mobile Internet technology in the present invention collects sensor data of downstream rainwater pipelines and big data information such as residents' travel, which is more efficient and accurate than traditional collection methods.

[0047] The present invention uses advanced coupling algorithms to utilize the collected big data to precisely control facilities such as solar pumps and electronically controlled valves.

[0048] While enhancing the thermal comfort regulation function, the present invention maintains or even improves the original rainwater runoff retention and water purification functions without affecting key sponge city indicators such as the annual runoff total control rate, thereby achieving multi-functional integration and optimization.

[0049] The present invention takes special considerations for special groups: for example, in areas where special groups such as couriers, car drivers, and elderly people who are active in square dancing often move, additional sunshade and cooling equipment, such as spray cooling awnings, are installed. At the same time, an alarm system is also set up, which automatically starts cooling measures and notifies relevant personnel when the ambient temperature exceeds the preset safety threshold.

[0050] Combined with the above-mentioned innovations in multiple functions, the present invention achieves the goal of improving the PMV value index of human thermal comfort by 20% in the local environment around the facility under the common climatic conditions in the north during the hot summer season. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a schematic diagram of a scenario in which the thermosensitive sponge brain of the present invention senses the thermal comfort of the human body.

[0052] Figure 2 It is a schematic diagram of the thermosensitive sponge brain of the present invention inquiring about the downstream pipe network situation.

[0053] Figure 3 This is a schematic diagram of the thermosensitive sponge brain querying weather forecast of the present invention.

[0054] Figure 4 This is a schematic diagram of the thermosensitive sponge brain of the present invention achieving comfort regulation through comprehensive calculation and control of sponge settings.

[0055] Figure 5 This is a plan view of the initial environment in an embodiment of the present invention.

[0056] Figure 6 This is a plan view after applying the patented technology in an embodiment of the present invention.

[0057] Figure 7 It is a schematic diagram of the simulation results of the current environment model according to an embodiment of the present invention.

[0058] Figure 8 This is a schematic diagram of simulation results after applying the patented technology in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] See also Figures 1 to 8 The present invention is now further described in conjunction with the accompanying drawings. The present invention relates to a community smart sponge control system coupled with a thermal comfort optimization objective:

[0060] S1, thermal comfort adjustment function module:

[0061] S1-1, real-time monitoring of ambient temperature and humidity through specific sensors, used to adjust according to actual conditions;

[0062] S1-2, adjusted to: automatically start or shut down the solar pump and the electric control valve within the set temperature range to maintain environmental comfort;

[0063] S2, Big Data and Mobile Internet Technology Module:

[0064] S2-1, the system receives and processes data from downstream stormwater pipe sensors and residents’ travel big data information in real time;

[0065] S2-2, data processing should be implemented through mobile Internet technology to achieve remote monitoring and control for intelligent management of facilities;

[0066] S3, coupling algorithm module:

[0067] S3-1, dynamic adjustment based on real-time collected data for precise control of solar pumps and electronically controlled valves;

[0068] S3-2, the algorithm includes a prediction model to predict future thermal comfort and rainfall conditions based on historical data and current environmental parameters, so that adjustments can be made in advance;

[0069] S4, Special Population Module:

[0070] S4-1, the system sets up additional shading and cooling equipment in areas where various groups of people frequently move;

[0071] S4-2, the system includes an alarm system, which automatically initiates cooling measures and notifies relevant personnel when the ambient temperature exceeds a preset safety threshold;

[0072] S5, multifunctional integrated module:

[0073] S5-1, while improving the thermal comfort regulation function, the system's rainwater runoff retention capacity shall be at least equivalent to that of existing sponge facilities, and shall not reduce the water purification capacity.

[0074] The specific formula of the coupling algorithm in S3 is:

[0075] [ F_{control} = f(Q, qd, λ, PMV, e, P_1, P_2, ..., P_{11}) ];

[0076] [F_{control} is the control function of the sponge facility and is dynamically adjusted according to the following parameters:

[0077] Q: Weather forecasts predict future rainfall, which will affect the water storage and drainage needs of sponge facilities;

[0078] qd: The existing water storage capacity of the sponge city facilities, reflecting the current water resource utilization of the facilities;

[0079] λ: Future relative humidity, which affects air humidity and human comfort;

[0080] The distribution of activity venues for special vulnerable groups includes couriers, car drivers, and elderly people who actively dance in squares;

[0081] e: Annual runoff volume control rate, which measures the ability of sponge facilities to control urban rainwater runoff.

[0082] The specific formula of the coupling algorithm is:

[0083] P_1: soil moisture, which affects the water infiltration capacity of the sponge facility and plant growth;

[0084] P_2: groundwater level, which is related to the water storage capacity and drainage effect of the sponge facility;

[0085] P_3: Facility aging degree, reflecting the service life and maintenance requirements of sponge facilities, including unexpected situations such as leakage;

[0086] P_4: surrounding vegetation coverage, which affects the evapotranspiration and microclimate of the surface;

[0087] P_5: solar radiation intensity, which is related to the efficiency of the solar pump;

[0088] P_6: wind speed, which affects the evaporation rate and human comfort;

[0089] P_7: Building density around the facility, affecting the heat island effect and ventilation;

[0090] P_8: Water quality index, reflecting the purification effect of sponge facilities on water quality;

[0091] P_9: rainfall change rate, used to predict the dynamic changes of future rainwater runoff;

[0092] P_{10}: The capacity of the urban drainage system, which affects the coordination between sponge facilities and the municipal drainage system;

[0093] P_{11}: Residents can use Bluetooth or Wifi feedback to understand the intelligent operation of the sponge facility through the mobile terminal App, and monitor and control the operation effect of the facility. If any inconsiderate situation occurs, upload the information for correction.

[0094] The control function f in the specific formula of the coupling algorithm is a machine learning model, which is embedded in the single-chip microcomputer control mechanism where the sponge facility is located. It dynamically adjusts the operating status of the sponge facility according to the combined influence of these parameters, so as to enable the intelligent control system to respond to environmental changes in real time, improve the comfort of the urban thermal environment, and maintain the efficient operation of the sponge facility.

[0095] The operating status includes: turning on or off the solar pump and adjusting the opening of the electronically controlled valve.

[0096] The present invention achieves the goal of improving the PMV value index of the local environment thermal comfort around the facility by 20% under the common climate conditions in the north during the hot summer season. The specific examples of the improvement are as follows:

[0097] The Envi-met model is used to evaluate the improvement of thermal comfort in the residential area after the patented technology is applied:

[0098] The "thermal comfort control system" can be generalized as setting up a parking shed on the parking space in the community, and starting the water spray device according to the thermal environment conditions at that time when someone picks up the car;

[0099] like Figure 5 As shown, Envi-met is first used to build a model of the current environmental conditions of the community;

[0100] like Figure 6As shown, the parking shed and the sprinkler device in the present invention are converted into model data, and the thermal comfort simulation results of the initial environmental model and the environmental model after adopting the technology of the present invention are compared and analyzed at 14:00, the highest temperature time on a typical meteorological day, so as to determine whether the thermal comfort of the site after applying the technology of the present invention has been improved by 20%.

[0101] 1. Selection of initial environment model data parameters:

[0102] (1) Initial environment material parameters:

[0103] The initial environment of the site, including roads, buildings, grass-brick parking spaces and trees, are selected from the Envi-met model material library according to the actual situation. The parking space material is replaced by sandy loam material in the Envi-met model material library, as shown in Table 1 below:

[0104] Table 1 Basic parameters of environmental model

[0105]

[0106] (2) Meteorological and soil parameters:

[0107] The soil in the initial environmental model is made of loam. Based on the representative meteorological data of the Beijing-Tianjin-Hebei region in the past 30 years, the typical summer meteorological day (clear and cloudless) was selected to obtain the meteorological data and soil temperature and humidity data. The simpleforcing mode was used to simulate the thermal environment comfort. The meteorological and soil parameters are shown in Table 2 below:

[0108] Table 2 Detailed table of meteorological and soil parameters

[0109]

[0110] 2. Model parameters using the technology of the present invention:

[0111] According to the technical content of the present invention, the parameters are selected: the parking shed is a steel structure sunshade, the roof of which is an organic transparent board, and a nozzle is provided on the top of the shed to start the spray cooling device. The sponge facility clear water tank is a rainwater storage facility located under the parking shed. The corresponding parameters are detailed in Table 3 below:

[0112] Table 3 Detailed table of thermal comfort control system parameters

[0113]

[0114] 3. Analysis of simulation results

[0115] Through the analysis of the thermal comfort simulation results of the initial environmental model and the environmental model after applying the technology of the present invention, it is known that at 14:00, the hottest time of a typical summer meteorological day, the initial environmental thermal comfort PMV value is in the range of 1.8 to 6.08, and the environmental thermal comfort PMV value after applying the technology of the present invention is in the range of 1.79 to 5.87. It can be seen that both the maximum and minimum values ​​are reduced, indicating that the present invention has improved the thermal comfort of the surrounding environment to a certain extent. Figure 7 and Figure 8 .

[0116] Among them, after applying the patented technology, the PMV value of the parking area environment has improved by 22.2%, that is, (4.87~3.79) / 4.87≈22.2%, achieving the expected goal of improving thermal comfort by 20%, as shown in Table 4 below:

[0117] Table 4 Statistics of thermal comfort PMV values

[0118]

[0119] According to the above simulation results, the PMV value of the environmental thermal comfort index has dropped significantly by 20% under hot summer conditions, which should be understood as the reduction of human heat load in the hot summer environment, making the human body feel more comfortable. Since the above simulation uses the meteorological data at 14:00 on a typical summer meteorological day in the Beijing-Tianjin-Hebei region, which is the most unfavorable working condition, and other meteorological conditions are better than this working condition, it can be considered that the 20% thermal comfort improvement goal can be achieved throughout the year.

[0120] Explanation of the PMV value index of environmental thermal comfort:

[0121] The PMV value index is derived from the introduction of the human body heat load that reflects the degree of deviation from the human body's thermal balance. Its theoretical basis is that when the human body is in a steady-state thermal environment, the greater the human body heat load, the further the human body deviates from the state of thermal comfort. That is, the greater the positive value of the human body heat load, the hotter the person feels, and the greater the negative value, the colder the person feels. See Table 5 below for details:

[0122] Table 5 Environmental thermal comfort PMV value index classification table

[0123]

[0124] The above are only preferred embodiments of the present invention, which are only used to help understand the method and core ideas of the present application. The protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

[0125] The above embodiments can be implemented in whole or in part by software or hardware. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium.

[0126] The present invention solves the shortcomings of the sponge facilities in the prior art, such as it is unclear how to quantitatively reduce the thermal environment, the lack of careful design in regulating the urban thermal environment, the relatively low performance, and the lack of intelligent management, which leads to the difficulty in effectively improving the comfort of the urban thermal environment and low data collection efficiency. By utilizing sensors and computer control, and comprehensively considering multiple big data information such as sensor data of downstream rainwater pipelines, residents' travel, and courier activities, the solar pumps, electric control valves and other facilities in the optimized sponge facilities are precisely controlled based on the coupling algorithm of machine learning, achieving the goal of improving the PMV value index of the local microenvironment comfort of the sponge city around the sponge city under the common climatic conditions in the north during the hot summer season by 20%.

Claims

1. A community smart sponge control system coupled with thermal comfort optimization objectives, characterized in that: include: S1, thermal comfort adjustment function module: S1-1, real-time monitoring of ambient temperature and humidity through sensors, used to adjust according to actual conditions; S1-2, the adjustment is: automatically starting or shutting down the solar pump and the electric control valve within the set temperature range to maintain environmental comfort; S2, Big Data and Mobile Internet Technology Module: S2-1, the system receives and processes data from downstream rainwater pipe sensors and residents' travel big data information in real time; S2-2, the data processing should be remotely monitored and controlled through mobile Internet technology for intelligent management of facilities; S3, coupling algorithm module: S3-1, dynamic adjustment based on real-time collected data for precise control of solar pumps and electronically controlled valves; S3-2, the algorithm includes a prediction model for predicting future thermal comfort and rainfall conditions based on historical data and current environmental parameters, so as to make adjustments in advance; S4, Special Population Module: S4-1, the system sets up additional shading and cooling equipment in areas where various groups of people frequently move; S4-2, the system includes an alarm system, which automatically initiates cooling measures and notifies relevant personnel when the ambient temperature exceeds a preset safety threshold; S5, multifunctional integrated module: S5-1, the system shall have a rainwater runoff retention capacity at least equivalent to that of existing sponge facilities while improving the thermal comfort regulation function, and shall not reduce the water purification capacity; The specific formula of the coupling algorithm in S3 is: [ F_{control} = f(Q, qd, λ, PMV, e, P_1, P_2, ..., P_11) ]; The F_{control} is the control function of the sponge facility and is dynamically adjusted according to the following parameters: Q: Weather forecasts predict future rainfall, which will affect the water storage and drainage needs of sponge facilities; qd: The existing water storage capacity of the sponge city facilities, reflecting the current water resource utilization of the facilities; λ: Future relative humidity, which affects air humidity and human comfort; PMV: Predicted Mean Vote, a human thermal comfort index, is used to assess human thermal comfort; The distribution of activity venues that include particularly vulnerable groups; e: Annual runoff control rate, which measures the ability of sponge facilities to control urban rainwater runoff; P_1 to P_11 in the specific formula of the coupling algorithm are specifically: P_1: soil moisture, which affects the water infiltration capacity of the sponge facility and plant growth; P_2: groundwater level, which is related to the water storage capacity and drainage effect of the sponge facility; P_3: Facility aging degree, reflecting the service life and maintenance requirements of sponge facilities; P_4: surrounding vegetation coverage, which affects the evapotranspiration and microclimate of the surface; P_5: solar radiation intensity, which is related to the efficiency of the solar pump; P_6: wind speed, which affects the evaporation rate and human comfort; P_7: Building density around the facility, affecting the heat island effect and ventilation; P_8: Water quality index, reflecting the purification effect of sponge facilities on water quality; P_9: rainfall change rate, used to predict the dynamic changes of future rainwater runoff; P_10: The capacity of the urban drainage system affects the coordination between sponge facilities and the municipal drainage system; P_11: Residents can use Bluetooth or Wifi to provide feedback. Residents can use the mobile terminal App to understand the intelligent operation of the sponge facility, monitor and control the operation effect of the facility, and upload information to correct any inconsiderate situations. The control function f in the specific formula of the coupling algorithm is a machine learning model, which is embedded in the single-chip microcomputer control mechanism where the sponge facility is located. It dynamically adjusts the operating status of the sponge facility according to the comprehensive influence of these parameters, so as to enable the intelligent control system to respond to environmental changes in real time, improve the comfort of the urban thermal environment, and maintain the efficient operation of the sponge facility.

2. The community smart sponge control system coupled with thermal comfort optimization objective according to claim 1 is characterized in that: The operating state includes: turning on or off the solar pump and adjusting the opening of the electronically controlled valve.

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