Intelligent building construction water management and water saving system

Abstract

The invention discloses an intelligent building construction water management and water saving system. The system comprises a distributed sensing layer, an edge calculation layer, a central decision-making layer and a man-machine interaction layer. A dynamic partition management module automatically adjusts the water utilization priority and the flow threshold value of each construction area according to daily updated construction progress data in combination with weather prediction information obtained by an API of a meteorological department, and a concrete curing area is automatically upgraded to a first-level control area in high-temperature weather and is automatically upgraded to a second-level control area in high-temperature weather. The system is composed of a distributed sensing layer, an edge computing layer, a central decision-making layer and a man-machine interaction layer, and intelligent management and water-saving optimization of building construction water are realized by combining the Internet of Things, edge computing, artificial intelligence and block chain technologies.

Description

Technical Field

[0001] The present invention relates to the technical field of water management for intelligent building construction, and particularly to a water management and water-saving system for intelligent building construction. Background Art

[0002] With the improvement of the efficiency of urbanization development, the number of construction projects is increasing continuously. The construction industry consumes a large amount of water resources during the construction process. For example, a large amount of water is required for the mixing of reinforced concrete and the dilution of cement. The use of energy-saving and water-saving technologies can fully improve the invisible waste of water resources, improve the utilization rate of water resources, reduce the water consumption in building construction, save the investment in building construction costs, and truly put the funds to good use, thus promoting social and economic development.

[0003] Currently, the following technical defects generally exist in the water management of construction sites: (1) Extensive management relying on manual inspections, which cannot grasp the water usage status of each area in real time; (2) The fixed partition management mode is difficult to adapt to the dynamically changing construction requirements; (3) Traditional water-saving devices lack intelligent decision-making capabilities, and situations where water-saving measures affect the construction progress often occur; (4) The water usage data records are scattered, making it difficult to conduct a full-cycle water usage efficiency analysis. Therefore, there is an urgent need for a new type of intelligent water management system that can be deeply integrated with the construction process and has the ability of autonomous optimization. Summary of the Invention

[0004] (1) Technical Problems to be Solved

[0005] Aiming at the deficiencies of the existing technology, the present invention significantly improves the construction water usage efficiency, reduces resource waste, and at the same time ensures the construction progress and quality through dynamic management, intelligent optimization, and real-time monitoring.

[0006] (2) Technical Solutions

[0007] To achieve the above object, the present invention provides the following technical solutions: A water management and water-saving system for intelligent building construction, comprising:

[0008] a) Distributed Sensing Layer

[0009] A multi-modal sensor array deployed in the construction area, including a pipeline pressure sensor with a measuring range of 0 to 2.5 MPa; a high-precision electromagnetic flowmeter with an accuracy of ±0.5%; a soil moisture sensor with a measuring depth of 20 cm; an infrared human activity detector;

[0010] b) Edge Computing Layer

[0011] An embedded controller installed in each partition control box, configured with a LoRaWAN communication module to achieve low-power long-distance communication; a real-time data processing unit, supporting edge computing and local decision-making;

[0012] c) Central Decision-making Layer

[0013] The intelligent management center installed on the cloud server integrates the following modules:

[0014] Dynamic partition management module: Dynamically adjusts the water use priority and flow threshold according to the construction progress data and weather forecast;

[0015] Water use plan generation module: Predicts the water use demand for the next 72 hours based on the LSTM neural network;

[0016] Abnormal diagnosis module: Combines voiceprint detection and machine learning algorithms to achieve pipeline leakage detection and abnormal identification;

[0017] Water saving optimization engine: Uses reinforcement learning algorithm to generate the Pareto optimal solution set of water saving strategies;

[0018] d) Human-computer interaction layer

[0019] Includes the augmented reality (AR) visualization operation interface deployed on the mobile terminal and the WEB management background, supporting gesture control and three-dimensional data visualization.

[0020] As an optimal solution, the dynamic partition management module automatically adjusts the water use priority and flow threshold of each construction area according to the daily updated construction progress data and the weather forecast information obtained through the meteorological department API. Among them, the concrete curing area is automatically upgraded to a first-level control area in high-temperature weather, specifically including:

[0021] Process association unit: Parses the construction progress diagram in the BIM model and establishes a mapping relationship database between the construction process and water use demand;

[0022] Environmental response unit: Connects to the meteorological bureau API to obtain precipitation, temperature, and wind speed forecast data accurate to 1 square kilometer for the next 48 hours;

[0023] Dynamic division algorithm: Uses the fuzzy comprehensive evaluation method to calculate the water use priority index of each construction unit, and automatically upgrades to a first-level control area when the threshold is exceeded;

[0024] Hierarchical control strategy library: Includes the humidity maintenance strategy for the concrete curing area, the intermittent spraying strategy for dust control, and the time-limited flow strategy for the living area.

[0025] As an optimal solution, the intelligent building construction water use management and water saving system also includes:

[0026] Water use metering blockchain node: Builds a consortium chain using the Hyperledger Fabric architecture, and each water use event generates a data block containing a timestamp, geographical location, and digital signature;

[0027] Intelligent contract execution unit: Trigger the preset contract to execute the operations of closing the valve and sending an alarm;

[0028] Cross-project data sharing mechanism: Achieve secure comparison of water usage patterns through zero-knowledge proof technology.

[0029] As an optimal solution, based on the dynamic partition management module, the central decision-making layer automatically executes the following process at 6:00 every day:

[0030] S1. Obtain the daily construction plan from the BIM platform and identify the key water-using processes;

[0031] S2. Call the meteorological API to obtain the weather forecast for the next 24 hours;

[0032] S3. Establish a decision matrix according to the water usage sensitivity of processes such as concrete pouring and curtain wall cleaning;

[0033] S4. Dynamically divide the three-level control area and set differential parameters;

[0034] Among them, for the first-level area: the maximum allowable flow is 30m 3 / h, and the monitoring frequency is 5 minutes / time; for the second-level area: the maximum allowable flow is 15m 3 / h, and the monitoring frequency is 15 minutes / time; for the third-level area: only the basic monitoring function is retained.

[0035] As an optimal solution, the water usage plan generation module includes:

[0036] Water usage demand prediction model: Based on the Attention-LSTM neural network, input the historical water usage curve, construction machinery start-stop records, personnel attendance data, and environmental parameters to predict the water usage demand for the next 24 hours;

[0037] Multi-objective optimization engine: Build a non-linear programming model for water conservation rate, construction progress impact, and equipment energy consumption, and use the NSGA-II algorithm to generate the Pareto front solution set;

[0038] Strategy visualization component: Convert the optimization results into a three-dimensional heat map and mark the recommended water usage time period and flow threshold in the BIM model.

[0039] As an optimal solution, the abnormal diagnosis module includes:

[0040] Pipeline leakage detection unit: Use the wavelet packet decomposition algorithm to process the pipeline vibration signal and detect the energy mutation in the frequency band of 6.5 - 7.2 kHz;

[0041] Water usage anomaly identification model: Build a feature vector based on the isolation forest algorithm, and compare the Mahalanobis distance between the current water usage pattern and the reference curve in real time. When it exceeds the 3σ threshold, an alarm is triggered;

[0042] Emergency handling mechanism: including automatically closing the valves in the faulty section, starting the backup water source, and generating maintenance work orders with GPS coordinates.

[0043] As a preferred solution, the intelligent construction water management and water-saving system includes:

[0044] Water use efficiency evaluation module: calculating the comparison between the actual water consumption and the quota standard, generating the water-saving rate and the top 10 list of abnormal water use;

[0045] Self-learning optimization engine: adopting a federated learning framework to aggregate data from multiple construction sites and continuously update the parameters of the prediction model;

[0046] Green construction certification interface: connecting to the LEED system to generate water management certification materials that meet the green building standards.

[0047] (III) Beneficial effects

[0048] Compared with the prior art, the present invention provides an intelligent construction water management and water-saving system, which has the following beneficial effects:

[0049] First, through the intelligent optimized water use plan and dynamic zoning management, the present invention significantly reduces the waste of construction water, improves the water use efficiency, and combines the sensor network and edge computing to realize the real-time monitoring of construction water, quickly diagnosing pipeline leaks and abnormal water use.

[0050] Second, the present invention also deploys a multi-modal sensor array through the implementation of the distributed sensing layer to collect the water use data and environmental data of the construction site, transmits them to the edge controller through the wireless communication network, the edge controller processes the real-time data and executes local decisions, and through intelligent management, reduces the manual inspection and unnecessary water use costs, and reduces the overall construction expenditure. Description of the drawings

[0051] Figure 1 It is a schematic diagram of the system hierarchical structure of the present invention;

[0052] Figure 2 It is a schematic diagram of the central decision-making layer module and control logic of the system of the present invention. Detailed implementation manners

[0053] In order to better understand the purpose, structure and function of the present invention, the intelligent construction water management and water-saving system of the present invention will be further described below in conjunction with the drawings and specific embodiments.

[0054] Embodiment 1

[0055] Refer to Figure 1-2 , the present invention: the intelligent construction water management and water-saving system, includes:

[0056] a) Distributed Sensing Layer

[0057] A multi-modal sensor array deployed in the construction area, including a pipeline pressure sensor with a range of 0 to 2.5 MPa; a high-precision electromagnetic flowmeter with an accuracy of ±0.5%; a soil moisture sensor with a measurement depth of 20 cm; an infrared human activity detector;

[0058] b) Edge Computing Layer

[0059] An embedded controller installed in each partition control box, configured with a LoRaWAN communication module to achieve low-power long-distance communication; a real-time data processing unit that supports edge computing and local decision-making;

[0060] c) Central Decision-making Layer

[0061] An intelligent management center hosted on a cloud server, integrating the following modules:

[0062] Dynamic Partition Management Module: Dynamically adjusts the water usage priority and flow threshold according to the construction progress data and weather forecast;

[0063] Water Usage Plan Generation Module: Predicts the water demand for the next 72 hours based on the LSTM neural network;

[0064] Abnormal Diagnosis Module: Combines voiceprint detection and machine learning algorithms to achieve pipeline leakage detection and abnormal identification;

[0065] Water Saving Optimization Engine: Adopts a reinforcement learning algorithm to generate a Pareto optimal solution set of water saving strategies;

[0066] d) Human-Machine Interaction Layer

[0067] Includes an augmented reality (AR) visualization operation interface deployed on a mobile terminal and a WEB management background, supporting gesture control and three-dimensional data visualization.

[0068] Specifically, in the present invention, for the pipeline pressure sensor: Install a CYG216 type pressure sensor every 50 meters along the main water supply pipeline in the construction area, with a range of 0 - 2.5 MPa, an IP68 protection level, and a pulse signal output; for the electromagnetic flowmeter: Install an E+H Promag 50 type flowmeter at the inlet of the branch pipeline, with an accuracy of ±0.5%, an in-built self-cleaning electrode, and suitable for water quality containing sediment; for the soil moisture sensor: Deploy TEROS12 sensors in a 10m×10m grid in the concrete curing area, with a probe depth of 20 cm and a measurement range of 0 - 100% volumetric water content; for the infrared human detector: Install HX-03C infrared detectors in the living area and equipment area, with a detection angle of 120°, an induction distance of 10 m, and activate the water usage permission in the area after triggering.

[0069] The present invention uses the Modbus RTU protocol to aggregate sensor data. The sampling frequency is dynamically adjusted according to the regional control level (5 seconds per time in the first-level area and 60 seconds per time in the third-level area). The multi-modal data is uploaded to the edge layer through the LoRaWAN gateway. The channel bandwidth is 125 kHz, and the transmission distance can reach 3 km.

[0070] In the edge computing layer of the present invention, the embedded controller uses the Raspberry Pi CM4 core board, which is equipped with the NXP i.MX 8M processor. The extended DI / DO module supports 12-way digital input and output. The LoRaWAN module selects RAK3172, which supports the Class C communication mode. When the network is disconnected, it automatically switches to the local storage mode. Specifically, the lightweight YOLOv5s model is deployed to identify the wearing status of construction workers' safety helmets and reflective vests, with an accuracy rate of ≥95%. When safety equipment is not worn, it can automatically close the water valves in high-risk areas. The embedded controller is configured with:

[0071] Pressure adaptive adjustment algorithm: Adopt fuzzy PID control technology to maintain the stability of the pipe network flow;

[0072] Edge AI inference unit: Deploy the lightweight YOLOv5 model to identify the wearing situation of construction workers' safety equipment;

[0073] Disconnected network continuous transmission mechanism: Cache 72 hours of data when the network is interrupted and switch to the offline mode to continue implementing the water-saving strategy.

[0074] Embodiment 2

[0075] In the dynamic partition management module of the central decision-making layer of the present invention, according to the daily updated construction progress data and combined with the weather forecast information obtained from the meteorological department API, it automatically adjusts the water use priority and flow threshold of each construction area. Among them, the concrete curing area is automatically upgraded to the first-level control area in high-temperature weather, specifically including:

[0076] Process association unit: Analyze the construction progress diagram in the BIM model and establish a mapping relationship database between construction processes and water use requirements;

[0077] Environmental response unit: Access the meteorological department API to obtain precipitation, temperature, and wind speed forecast data accurate to 1 square kilometer for the next 48 hours;

[0078] Dynamic division algorithm: Use the fuzzy comprehensive evaluation method to calculate the water use priority index of each construction unit, and automatically upgrade to the first-level control area when the threshold is exceeded;

[0079] Hierarchical control strategy library: Includes the humidity maintenance strategy for the concrete curing area, the intermittent spraying strategy for dust control, and the time-limited flow control strategy for the living area.

[0080] Further, based on the dynamic partition management module, the central decision-making layer automatically executes the following process at 6:00 every day:

[0081] S1. Obtain the daily construction plan from the BIM platform and identify the key water-using processes;

[0082] S2. Call the meteorological API to obtain the weather forecast for the next 24 hours;

[0083] S3. Establish a decision matrix based on the water use sensitivity of processes such as concrete pouring and curtain wall cleaning;

[0084] S4. Dynamically divide the three-level control areas and set differential parameters;

[0085] Among them, for the first-level area: the maximum allowable flow is 30 m 3 / h, and the monitoring frequency is 5 minutes / time; for the second-level area: the maximum allowable flow is 15 m 3 / h, and the monitoring frequency is 15 minutes / time; for the third-level area: only the basic monitoring function is retained.

[0086] Specifically, the data input of this embodiment: obtain the daily construction plan from the BIM platform at 6:00 every day (Revit model parsing), synchronize the meteorological bureau API to obtain the weather forecast for 48 hours, and divide the three-level control areas:

[0087] First-level area: concrete curing area (flow threshold of 30 m 3 / h on high-temperature days, monitoring interval of 5 minutes)

[0088] Second-level area: curtain wall cleaning area (threshold of 15 m 3 / h, interval of 15 minutes)

[0089] Third-level area: living area (only basic monitoring).

[0090] Send the parameters to the edge controller through the MQTT protocol, and enable dual-pipeline redundant water supply in the first-level area.

[0091] Specifically, the water use plan generation module of the present invention includes:

[0092] Water use demand prediction model: Based on the Attention-LSTM neural network, input the historical water use curve, construction machinery start-stop records, personnel attendance data, and environmental parameters to predict the water use demand for the next 24 hours;

[0093] Multi-objective optimization engine: Construct a non-linear programming model for water saving rate, construction progress impact, and equipment energy consumption, and use the NSGA-II algorithm to generate the Pareto front solution set;

[0094] Strategy visualization component: Convert the optimization results into a 3D heat map, and mark the recommended water usage periods and flow thresholds in the BIM model.

[0095] Its model input features are: historical water consumption, construction machinery start / stop records (ON / OFF status), environmental temperature and humidity, and construction progress percentage.

[0096] More specifically, the anomaly diagnosis module of the present invention includes:

[0097] Pipeline leakage detection unit: Use the wavelet packet decomposition algorithm to process the pipeline vibration signal and detect the energy mutation in the frequency band of 6.5 - 7.2 kHz;

[0098] Water usage anomaly recognition model: Construct a feature vector based on the isolation forest algorithm, and compare the Mahalanobis distance between the current water usage pattern and the reference curve in real time. When it exceeds the 3σ threshold, an alarm is triggered;

[0099] Emergency handling mechanism: Include automatically closing the valves in the faulty section, starting the backup water source, and generating a maintenance work order with GPS coordinates.

[0100] Example 3

[0101] The intelligent building construction water management and water conservation system of the present invention further includes:

[0102] Water usage metering blockchain node: Build a consortium chain using the Hyperledger Fabric architecture, and each water usage event generates a data block containing a timestamp, geographical location, and digital signature;

[0103] Intelligent contract execution unit: Trigger the preset contract and execute operations such as closing the valve and sending an alarm;

[0104] Cross-project data sharing mechanism: Achieve secure comparison of water usage patterns through zero-knowledge proof technology;

[0105] Water usage efficiency evaluation module: Calculate the comparison between the actual water consumption and the quota standard, and generate a water conservation rate and a top 10 list of abnormal water usage;

[0106] Self-learning optimization engine: Aggregate multi-site data using the federated learning framework and continuously update the parameters of the prediction model;

[0107] Green construction certification interface: Connect to the LEED system and generate water management certification materials that meet the green building standards.

[0108] This system is composed of four layers: a distributed sensing layer, an edge computing layer, a central decision-making layer, and a human-computer interaction layer. Combining Internet of Things, edge computing, artificial intelligence, and blockchain technologies, it realizes the intelligent management and water conservation optimization of building construction water usage.

[0109] It is understood that the present invention is described by way of some embodiments, and those skilled in the art will be aware that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the present invention. Additionally, under the teachings of the present invention, these features and embodiments can be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application belong to the scope protected by the present invention.

Claims

1. Intelligent building construction water management and water saving system, characterized by: include: a) Distributed Perception Layer The multimodal sensor array deployed in the construction area includes a pipeline pressure sensor with a range of 0 to 2.5MPa; a high-precision electromagnetic flow meter with an accuracy of ±0.5%; a soil moisture sensor with a measurement depth of 20cm; and an infrared human activity detector; b) Edge computing layer The embedded controller installed in each partition control box is equipped with a LoRaWAN communication module to achieve low-power long-distance communication; a real-time data processing unit supports edge computing and local decision-making; c) Central decision-making level The intelligent management center installed on the cloud server integrates the following modules: Dynamic zoning management module: dynamically adjust water use priorities and flow thresholds based on construction progress data and weather forecasts; Water use plan generation module: predicts water demand for the next 72 hours based on LSTM neural network; Abnormal diagnosis module: combines voiceprint detection and machine learning algorithms to achieve pipeline leak detection and abnormality identification; Water-saving optimization engine: uses reinforcement learning algorithm to generate the Pareto optimal solution set of water-saving strategies; d) Human-computer interaction layer It includes an augmented reality (AR) visual operation interface and WEB management background deployed on mobile terminals, supporting gesture control and three-dimensional data visualization.

2. The intelligent building construction water management and water saving system according to claim 1 is characterized in that: The dynamic zoning management module automatically adjusts the water use priority and flow threshold of each construction area based on the daily updated construction progress data and the weather forecast information obtained from the meteorological department API. The concrete curing area is automatically upgraded to a first-level control area in high temperature weather, including: Process association unit: Analyze the construction progress chart in the BIM model and establish a mapping database between construction processes and water demand; Environmental response unit: access to the Meteorological Bureau API to obtain precipitation, temperature and wind speed forecast data accurate to 1 square kilometer for the next 48 hours; Dynamic division algorithm: The fuzzy comprehensive evaluation method is used to calculate the water priority index of each construction unit, and it will automatically upgrade to the first-level control area when it exceeds the threshold; Hierarchical control strategy library: including humidity maintenance strategy for concrete curing area, intermittent spray strategy for dust control and time-limited flow strategy for living area.

3. The intelligent building construction water management and water saving system according to claim 2 is characterized in that: The intelligent building construction water management and water saving system also includes: Water metering blockchain node: The consortium chain is built using the Hyperledger Fabric architecture. Each water use event generates a data block containing a timestamp, geographic location, and digital signature. Smart contract execution unit: triggers the preset contract to execute valve closing and alarm sending operations; Cross-project data sharing mechanism: secure comparison of water usage patterns is achieved through zero-knowledge proof technology.

4. The intelligent building construction water management and water saving system according to claim 2 is characterized in that: The embedded controller is configured with: Pressure adaptive regulation algorithm: adopts fuzzy PID control technology to maintain stable flow in the pipe network; Edge AI inference unit: deploys a lightweight YOLOv5 model to identify whether construction workers are wearing safety equipment; Resume transmission after network disconnection: cache data for 72 hours when the network is disconnected, and switch to offline mode to continue to implement water-saving strategies.

5. The intelligent building construction water management and water saving system according to claim 1 is characterized in that: Based on the dynamic partition management module, the central decision-making layer automatically executes the following process at 6:00 every day: S1. Obtain the construction plan for the day from the BIM platform and identify the key water-using processes; S2. Call the weather API to obtain the weather forecast for the next 24 hours; S3. Establish a decision matrix based on the water sensitivity of processes such as concrete pouring and curtain wall cleaning; S4. Dynamically divide the three-level control areas and set differentiated parameters; Among them, the first-level area: the maximum flow allowed is 30m 3 / h, monitoring frequency 5 minutes / time; Secondary area: maximum flow rate allowed is 15m 3 / h, monitoring frequency is 15 minutes / time; Level 3 area: only basic monitoring functions are retained.

6. The intelligent building construction water management and water saving system according to claim 2 is characterized in that: The water use plan generation module comprises: Water demand prediction model: Based on the Attention-LSTM neural network, the historical water consumption curve, construction machinery start and stop records, personnel attendance data and environmental parameters are input to predict the water demand in the next 24 hours; Multi-objective optimization engine: Construct a nonlinear programming model for water saving rate, construction progress impact and equipment energy consumption, and use the NSGA-II algorithm to generate the Pareto frontier solution set; Strategy visualization component: Convert optimization results into 3D heat maps and mark recommended water use periods and flow thresholds in the BIM model.

7. The intelligent building construction water management and water saving system according to claim 2 is characterized in that: The abnormality diagnosis module comprises: Pipeline leakage detection unit: uses wavelet packet decomposition algorithm to process pipeline vibration signals and detect energy mutations in the 6.5-7.2kHz frequency band; Water use anomaly identification model: The model builds feature vectors based on the isolation forest algorithm, compares the Mahalanobis distance between the current water use pattern and the benchmark curve in real time, and triggers an alarm when the 3σ threshold is exceeded; Emergency response mechanism: including automatic closing of valves in faulty sections, activation of backup water sources and generation of maintenance work orders with GPS coordinates.

8. The intelligent building construction water management and water saving system according to claim 3 is characterized in that: The intelligent building construction water management and water saving system includes: Water efficiency evaluation module: Calculate the comparison between actual water consumption and quota standards, and generate a list of water saving rates and abnormal water consumption top 10; Self-learning optimization engine: uses a federated learning framework to aggregate data from multiple construction sites and continuously update prediction model parameters; Green construction certification interface: connect to the LEED system to generate water management certification materials that meet green building standards.

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