Water resource supply management system based on artificial intelligence

Abstract

A water resource supply management system based on artificial intelligence can monitor water quality, water volume and hydrological information in real time through a widely deployed sensor network, and transmit the information to a data center for analysis and processing, thereby realizing comprehensive perception and dynamic monitoring of a water supply system. Through deep mining of mass data, rules and trends hidden behind the data are revealed, and a scientific basis is provided for decision making. Through application of the artificial intelligence technology, the management level of the water supply system is improved to a new height; through an AI algorithm, water supply demand prediction, leakage monitoring and prediction, water quality early warning and the like can be carried out based on historical data and real-time data, and accurate management and quick response are realized. Through machine learning and deep learning, the AI system can continuously optimize a water supply plan, automatically adjust pipe network pressure and optimize equipment operation, so that water saving and maximization of energy benefits are realized while water supply safety is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of technical water resource management, and in particular to a water resource supply management system based on artificial intelligence. Background Art

[0002] In today's context of accelerating urbanization, water supply projects, as an important part of urban infrastructure, are facing unprecedented challenges and opportunities. With the trend of population agglomeration, the rapid development of industrialization and the impact of climate change, the management, distribution and utilization of water resources have become increasingly complex and critical. Traditional water supply systems often rely on manual operation and static management, and have problems such as low efficiency, delayed response, and waste of resources, making it difficult to meet the needs of modern cities for safe, efficient and sustainable water supply. Summary of the invention

[0003] In view of the above problems, the present invention is proposed to provide a water resource supply management system based on artificial intelligence that overcomes the above problems or at least partially solves the above problems.

[0004] In order to solve the above technical problems, the embodiments of the present application disclose the following technical solutions:

[0005] In a first aspect, an embodiment of the present invention discloses a water resource supply management system based on artificial intelligence, comprising: a data acquisition unit, a big data and digital twin unit, an operation management unit and an intelligent decision-making unit; wherein:

[0006] A data collection unit, used to collect water quantity information, water quality information, hydrological information and external information in the area, and send the information to the big data and digital twin unit;

[0007] Big data and digital twin units are used to store water quantity information, water quality information, hydrological information, and external information transmitted by the data acquisition unit in a structured manner and to generate virtual space simulations during digital transformation; at the same time, digital transformation is performed on the information transmitted by the operation management unit and a virtual space simulation is generated; and data is standardized and output to the intelligent decision-making unit for intelligent training and reasoning;

[0008] The operation management unit is used to collect water intake information, water transmission information, water purification information and water supply information of the water project operation in real time; transmit the water intake information, water transmission information, water purification information and water supply information to the big data and digital twin unit; and is also used to receive the instructions issued by the intelligent decision-making unit and execute the operation parameter changes of the equipment and devices according to the instructions;

[0009] The intelligent decision-making unit is used to train and analyze the data transmitted from the big data layer based on the artificial intelligence algorithm, compare it with the operating status and feedback results, obtain the artificial intelligence model, and analyze the stability and safety of the operation of the water resources supply management system based on the artificial intelligence model; it is also used to automatically generate adjustment and optimization instructions based on the analysis results of the stability and safety of the operation of the water resources supply management system, optimize the adjustment and optimization instructions based on expert judgment and auxiliary decision-making, obtain final adjustment and optimization instructions, and send the final adjustment and optimization instructions to the operation management unit for implementation.

[0010] Furthermore, the data acquisition unit includes a water quantity information acquisition subunit, a water quality information subunit, a hydrological information subunit and an external information subunit; wherein: the water quantity information acquisition subunit is composed of a water level meter and a flow meter, and collects the water flow of water resources; the water quality information subunit is composed of a chemical sensor, a physical sensor and a biological sensor, and is used to collect the physical, chemical and biological characteristics of water resources; the hydrological information subunit is composed of a water level meter, a rain level sensor and an industrial camera, and is used to collect the temporal and spatial distribution and change characteristics of water resources.

[0011] Furthermore, the operation management unit includes a water intake subunit, a water delivery subunit, a water purification subunit and a water supply subunit; wherein the water intake subunit is composed of a water source, a water pump and a water pool, water is taken from the water source by the water pump, and the taken water is stored in the water pool; the water delivery subunit is composed of a water pipe, a water pump and a channel, water is taken from the water intake unit by the water pump, and then the water is transported through the water pipe and the channel; the water purification subunit is composed of a coagulation tank, a disinfection tank and a sedimentation tank, water is purified by the coagulation tank, the disinfection tank and the sedimentation tank; the water supply subunit is composed of a water pump, a water pipe and a water tower, the water supply subunit calculates the regional water consumption, and supplies water to the region through the water supply subunit.

[0012] Furthermore, the water supply subunit calculates the regional water consumption, including the sum of the water consumption of domestic water, industrial water, public facilities, etc. supplied by the water supply system in the region; the sum of all water consumption other than the water consumption of domestic water, industrial water, public facilities, etc. supplied by the regional water supply system.

[0013] Furthermore, the specific method for calculating regional water consumption includes:

[0014] Calculate the maximum daily domestic water consumption Q1 in the residential area:

[0015] Q0=Σ(q i N i )→Q1′=f1(w, e, s, p, ....., m)

[0016] q iis the maximum daily domestic water consumption of residential areas with different sanitary equipment. The initial value is determined according to the national quota. The water meter in the residential area monitors the peak water consumption of each residential area, and the weighted average is calculated according to the population size to obtain the actual maximum daily domestic water consumption; N i The number of people who are planned to use water within the design period is obtained by initial value according to the urban planning documents, and then simulated and predicted according to social population changes and migration trends; f1 is a model dynamically generated by massive data sets, and w, e, s, p, and m are model input factors respectively;

[0017] Calculate the water consumption of public buildings Q2:

[0018] Q2=∑(q j N j )

[0019] q j The maximum daily water consumption of each public building. The initial value is determined according to the national quota. The peak water consumption of each building is subsequently monitored by the public building water meter, and the actual water consumption is obtained by weighted average based on the building characteristics and the number of units.

[0020] N j The number of water units used by each public building is not fixed, but is dynamically adjusted according to the city's continuous construction, municipal approval, and construction plans;

[0021] Calculate the production water consumption of industrial enterprises and the living water consumption of staff Q3:

[0022] Q3=∑(Q I +Q II +Q III )

[0023] Q I The production water consumption of each industrial enterprise is determined by the production process requirements. The initial value is reported by each industrial enterprise and is subsequently adjusted dynamically according to the water consumption; Q II The living water consumption of employees in various industrial enterprises is initially calculated at 30-50 L / (person / shift) with an hourly variation coefficient of 1.5-2.5, and is subsequently adjusted dynamically according to actual water consumption; Q III The water consumption for showering of employees in various industrial enterprises is initially calculated as 40-60L / (person / shift), and the shower duration is calculated as 1h. It will be dynamically adjusted later based on actual water consumption.

[0024] Furthermore, the specific method for calculating regional water consumption also includes:

[0025] Calculate the water consumption for watering roads and green spaces Q4:

[0026] Q4=∑(q L N L )

[0027] q L The initial water consumption per unit area is 2.0-3.0L / (m2·d) for watering roads and sites, and 1.0-3.0L / (m2·d) for watering wine and green land. It will be adjusted dynamically according to the actual water consumption of the watering system in the future.

[0028] N L The area of ​​roads and green areas watered daily will be adjusted dynamically according to municipal construction plans and weather conditions;

[0029] Calculate the water loss of the pipe network Q5. The water loss of the pipe network is calculated as a certain proportion δ of the sum of the maximum daily domestic water consumption in residential areas, water consumption in public buildings, water consumption in industrial enterprises, and water consumption for watering roads and green spaces. The initial value is 10% to 12%. The subsequent value is related to the length, size, material, service life and climate of the pipe network according to actual dynamic changes;

[0030] Q5=δ(Q1+Q2+Q3+Q4)

[0031] Calculate the unforeseen water consumption Q6, which is calculated based on a certain proportion σ of the sum of the highest daily domestic water consumption in residential areas, water consumption in public buildings, water consumption in industrial enterprises, water consumption for watering roads and green spaces, and water loss in pipe networks. The initial value is 8% to 12%, and the subsequent value is related to the actual dynamic changes and special events and unexpected situations.

[0032] Q6=σ(Q1+Q2+Q3+Q4+Q5)

[0033] Finally, the maximum daily water consumption Q d ;

[0034] Q d =Q1+Q2+Q3+Q4+Q5+Q6.

[0035] Furthermore, artificial intelligence methods are used to purify water resources. The specific methods include: collecting various water quality indicators of water resources through various sensors, analyzing the required processing modules and processing flows through artificial intelligence large models, simulating the results of water treatment through the processing efficiency of each module, verifying the treatment effectiveness, analyzing the treatment cost and utilization rate, and comprehensively proposing treatment process suggestions. According to the treatment process suggestions, instructions are issued, and the water treatment process flow is established and executed.

[0036] Furthermore, the intelligent decision-making unit is used to train and analyze the data transmitted from the big data layer based on the artificial intelligence big algorithm, compare it with the operating status and feedback results, obtain the artificial intelligence model, and analyze the stability and safety of the operation of the water resources supply management system based on the artificial intelligence model; the specific methods include: the artificial intelligence big model analyzes and simulates the data of the big data layer, and infers the future safe and stable operation trend. When it is judged that there is a risk of safe and stable operation in the future, the artificial intelligence big model generates a sequence of construction optimization solutions; when it is judged that the water supply system is operating safely and stably, the artificial intelligence big model analyzes and simulates the data of the big data layer, and infers the future economically reasonable operation trend. When it is judged that the economically reasonable operation requirements are not met, the artificial intelligence big model generates a sequence of construction optimization solutions.

[0037] Furthermore, the intelligent decision-making unit optimizes the adjustment and optimization instructions based on expert judgment and auxiliary decision-making to obtain final adjustment and optimization instructions, and sends the final adjustment and optimization instructions to the operation management unit for implementation; the specific method includes: whether the construction optimization solution submitted by the artificial intelligence big model needs to be passed after decision-making, the set decision-free execution plan is directly issued, and for the execution plan that requires decision, the manager decides whether to adopt the execution plan, adopts the sequence and execution time of the execution plan, generates a solution after the approval decision and issues it for execution; in the case where the approval decision is not passed, the decision maker outputs the opinion to the artificial intelligence big model, and the artificial intelligence big model learns the reasons for rejection and then tunes the model.

[0038] The beneficial effects of the above technical solution provided by the embodiment of the present invention include at least:

[0039] The present invention discloses a water resource supply management system based on artificial intelligence to improve management efficiency. Through the sensor network deployed by the Internet of Things technology, the system can monitor key parameters such as water quality, water volume, and water pressure in real time, and transmit them to the data center for analysis and processing in real time. This method reduces the tediousness and errors of manual monitoring, significantly improves the efficiency and accuracy of data collection, and thus improves the overall management efficiency. The present invention uses big data technology and machine learning algorithms, and the system can deeply mine and analyze massive water resource data to reveal the temporal and spatial distribution laws, change trends and potential risks of water resources. These analysis results provide a scientific basis for decision-making for governments and enterprises, and help to formulate more reasonable water resource management policies and plans. In the event of natural disasters such as floods and droughts, the water resource supply management system based on artificial intelligence can quickly launch emergency plans and mobilize all parties to carry out rescue and disaster relief. Through real-time monitoring and data analysis, the system can predict the time and intensity of disasters in advance, provide a scientific basis for emergency response, and effectively reduce disaster losses. By establishing a sound data sharing mechanism and collaboration mechanism, the water resource supply management system based on artificial intelligence can achieve cross-regional and cross-departmental collaboration and resource sharing. This helps break down information silos and data barriers and improve overall water resource utilization efficiency.

[0040] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0042] Figure 1 This is a structural diagram of a water resource supply management system based on artificial intelligence in Embodiment 1 of the present invention;

[0043] Figure 2 This is a framework diagram of a water resource supply management system based on artificial intelligence in Embodiment 1 of the present invention;

[0044] Figure 3 This is a schematic diagram of the water purification process of the water purification subunit in Example 1 of the present invention;

[0045] Figure 4 This is a flowchart of intelligent monitoring and operation management of the intelligent decision-making unit in Example 1 of the present invention. DETAILED DESCRIPTION

[0046] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0047] In order to solve the problems existing in the prior art, an embodiment of the present invention provides a water resource supply management system based on artificial intelligence.

[0048] Example 1

[0049] The present invention discloses a water resource supply management system based on artificial intelligence. Figure 1 and 2 , including: data acquisition unit, big data and digital twin unit, operation management unit and intelligent decision-making unit; among which:

[0050] The data acquisition unit is used to collect water quantity information, water quality information, hydrological information and external information in the region, and send the information to the big data and digital twin unit; among them, water quantity, water quality and hydrology are three important aspects in water resources research, there are obvious differences between them, and the collection methods are also different. Water quantity usually refers to the amount of water in a certain water body (such as rivers, lakes, reservoirs, etc.), including parameters such as water level, flow rate, and flow rate. It mainly focuses on the quantitative change of water bodies, that is, the volume and flow rate of water bodies. Water quality refers to the physical, chemical and biological properties of water bodies, including temperature, pH value, dissolved oxygen, conductivity, turbidity, total suspended solids, toxic and harmful substances, and the types and quantities of various microorganisms. It pays more attention to the qualitative change of water bodies, that is, the components and content of water bodies. Hydrology is a broader concept, which covers comprehensive information on water quantity, water quality, water flow, water level, water temperature and other aspects of water bodies. It mainly focuses on the spatial and temporal distribution of water bodies, water cycle laws, hydrological forecasts, and the design and operation of water conservancy projects.

[0051] Specifically, in this embodiment, the data acquisition unit includes a water quantity information acquisition subunit, a water quality information subunit, a hydrological information subunit and an external information subunit; wherein: the water quantity information acquisition subunit is composed of a water level meter and a flow meter, and collects the water flow of water resources; the water quality information subunit is composed of a chemical sensor, a physical sensor and a biological sensor, and is used to collect the physical, chemical and biological characteristics of water resources; the hydrological information subunit is composed of a water level meter, a rain level sensor and an industrial camera, and is used to collect the temporal and spatial distribution and change characteristics of water resources.

[0052] Big data and digital twin units are used to store water quantity information, water quality information, hydrological information, and external information transmitted by the data acquisition unit in a structured manner and to generate virtual space simulations during digital transformation; at the same time, digital transformation is performed on the information transmitted by the operation management unit and a virtual space simulation is generated; and data is standardized and output to the intelligent decision-making unit for intelligent training and reasoning;

[0053] Specifically, the big data layer is the data storage, simulation, and interaction layer of the entire system, which realizes the input of collected data, operation data, and data output to the superior system.

[0054] The big data and digital twin platform structures the water quantity information, water quality information, hydrological information, and external information uploaded by the data collection layer and digitally transforms them into virtual space simulations. At the same time, it digitally transforms the relevant information of the water intake system, water transmission system, water purification system, and water supply system and generates virtual space simulations. The standardized data is output to the artificial intelligence model for intelligent training and reasoning.

[0055] The operation management unit is used to collect water intake information, water transmission information, water purification information and water supply information of the water project operation in real time; transmit the water intake information, water transmission information, water purification information and water supply information to the big data and digital twin unit; and is also used to receive the instructions issued by the intelligent decision-making unit and execute the operation parameter changes of the equipment and devices according to the instructions;

[0056] In this embodiment, the operation management unit includes a water intake subunit, a water delivery subunit, a water purification subunit and a water supply subunit; wherein the water intake subunit is composed of a water source, a water pump and a water pool, water is taken from the water source by the water pump, and the taken water is stored in the water pool; the water delivery subunit is composed of a water pipe, a water pump and a channel, water is taken from the water intake unit by the water pump, and then the water is transported through the water pipe and the channel; the water purification subunit is composed of a coagulation tank, a disinfection tank and a sedimentation tank, and water is purified by the coagulation tank, the disinfection tank and the sedimentation tank; the water supply subunit is composed of a water pump, a water pipe and a water tower, the water supply subunit calculates the water consumption of the area, and supplies water to the area through the water supply subunit.

[0057] In this embodiment, the water supply sub-unit calculates the regional water consumption, specifically including the sum of the water consumption of residential water, industrial water, public facilities, etc. supplied by the water supply system in the region; the sum of all water consumption other than the water consumption of residential water, industrial water, public facilities, etc. supplied by the regional water supply system.

[0058] Among them, the specific methods for calculating regional water consumption include:

[0059] Calculate the maximum daily domestic water consumption Q1 in the residential area:

[0060] Q1=∑(qi N i )→Q1′=f1(w, e, s, p, ....., m)

[0061] q i is the maximum daily domestic water consumption of residential areas with different sanitary equipment. The initial value is determined according to the national quota. The water meter in the residential area monitors the peak water consumption of each residential area, and the weighted average is calculated according to the population size to obtain the actual maximum daily domestic water consumption; N i The number of people who are planned to use water within the design period is obtained by initial value according to the urban planning documents, and then simulated and predicted according to social population changes and migration trends; f1 is a model dynamically generated by massive data sets, and w, e, s, p, and m are model input factors respectively;

[0062] Calculate the water consumption of public buildings Q2:

[0063] Q2=∑(q j N j )

[0064] q j The maximum daily water consumption of each public building. The initial value is determined according to the national quota. The peak water consumption of each building is subsequently monitored by the public building water meter, and the actual water consumption is obtained by weighted average based on the building characteristics and the number of units.

[0065] N j The number of water units used by each public building is not fixed, but is dynamically adjusted according to the city's continuous construction, municipal approval, and construction plans;

[0066] Calculate the production water consumption of industrial enterprises and the living water consumption of staff Q3:

[0067] Q3=∑(Q I +Q II +Q III )

[0068] Q I The production water consumption of each industrial enterprise is determined by the production process requirements. The initial value is reported by each industrial enterprise and is subsequently adjusted dynamically according to the water consumption; Q II The living water consumption of employees in various industrial enterprises is initially calculated at 30-50 L / (person / shift) with an hourly variation coefficient of 1.5-2.5, and is subsequently adjusted dynamically according to actual water consumption; Q III The water consumption for showering of employees in various industrial enterprises is initially calculated as 40-60L / (person / shift), and the shower duration is calculated as 1h. It will be dynamically adjusted later based on actual water consumption.

[0069] Calculate the water consumption for watering roads and green spaces Q4:

[0070] Q4=∑(q L N L )

[0071] q L The initial water consumption per unit area is 2.0-3.0L / (m2·d) for watering roads and sites, and 1.0-3.0L / (m2·d) for watering wine and green land. It will be adjusted dynamically according to the actual water consumption of the watering system in the future.

[0072] N L The area of ​​roads and green areas watered daily will be adjusted dynamically according to municipal construction plans and weather conditions;

[0073] Calculate the water loss of the pipe network Q5. The water loss of the pipe network is calculated as a certain proportion δ of the sum of the maximum daily domestic water consumption in residential areas, water consumption in public buildings, water consumption in industrial enterprises, and water consumption for watering roads and green spaces. The initial value is 10% to 12%. The subsequent value is related to the length, size, material, service life and climate of the pipe network according to actual dynamic changes;

[0074] Q5=δ(Q1+Q2+Q3+Q4)

[0075] Calculate the unforeseen water consumption Q6, which is calculated based on a certain proportion σ of the sum of the highest daily domestic water consumption in residential areas, water consumption in public buildings, water consumption in industrial enterprises, water consumption for watering roads and green spaces, and water loss in pipe networks. The initial value is 8% to 12%, and the subsequent value is related to the actual dynamic changes and special events and unexpected situations.

[0076] Q6=σ(Q1+Q2+Q3+Q4+Q5)

[0077] Finally, the maximum daily water consumption Q d ;

[0078] Q d =Q1+Q2+Q3+Q4+Q5+Q6.

[0079] In some preferred embodiments, artificial intelligence methods are used to purify water resources. The specific methods include: collecting various water quality indicators of water resources through various sensors, analyzing the required processing modules and processing flows through a large artificial intelligence model, simulating the results of water treatment through the processing efficiency of each module, verifying the treatment effectiveness, analyzing the treatment cost and utilization rate, comprehensively proposing treatment process suggestions, issuing instructions based on the treatment process suggestions, and establishing and executing the water treatment process flow.

[0080] Specifically, Figure 3, there may be various pollutants in water resources that may cause health problems to the human body or have adverse effects on production, such as organic matter, suspended matter, oil, inorganic matter, foaming substances, acid, alkali and toxic substances, radioactive substances, etc. The cleanliness of water resources is not guaranteed, and different treatment process combinations are needed for different water sources and water quality to meet water hygiene standards. Usually including conventional treatment process, pretreatment-conventional treatment process, conventional treatment-deep treatment process, pretreatment-conventional treatment-deep treatment process, etc. The traditional method is mainly to adopt different treatment processes according to different water sources and different pollution conditions. The selection of treatment process is highly correlated with the experience of designers and the classification of water sources. It is difficult to dynamically adjust the treatment process according to the characteristics and changes of water resources. Therefore, it is considered to conduct big data analysis based on the data extracted from the Internet of Things, and use AI for simulation reasoning to achieve low-cost, high-efficiency, safe and reliable water resource treatment.

[0081] Various water quality indicators of water resources are collected through various sensors. On the one hand, the required processing modules and processing flows are analyzed through the artificial intelligence large model. On the other hand, the processing efficiency of each module is used to simulate the results of water treatment. After verifying the treatment effectiveness, analyzing the treatment cost and utilization rate, comprehensive treatment process recommendations are put forward.

[0082] After the decision-making system makes a decision, the water supply project operation management system issues instructions to set up and execute the water treatment process. For example, if the water quality monitoring system detects that the ammonia nitrogen, algae and organic matter content in the source water is high, the system recommends the use of biological pretreatment + conventional treatment through reasoning, and the decision-making system issues instructions for process configuration and parameter setting.

[0083] The source water is biologically pretreated, mixed flocculation, sedimentation and filtered into the clear water tank. The water quality detection system monitors the source water, intermediate process tanks (such as flocculation tanks, sedimentation tanks) and clear water tanks, and analyzes the changes in water quality and compares them with the AI ​​predictions. On the one hand, it adjusts the operating parameters and even the operating process in real time when the gap exceeds the control value. On the other hand, it submits the data to the large model for further learning and training.

[0084] The intelligent decision-making unit is used to train and analyze the data transmitted from the big data layer based on the artificial intelligence algorithm, compare it with the operating status and feedback results, obtain the artificial intelligence model, and analyze the stability and safety of the operation of the water resources supply management system based on the artificial intelligence model; it is also used to automatically generate adjustment and optimization instructions based on the analysis results of the stability and safety of the operation of the water resources supply management system, optimize the adjustment and optimization instructions based on expert judgment and auxiliary decision-making, obtain final adjustment and optimization instructions, and send the final adjustment and optimization instructions to the operation management unit for implementation.

[0085] In this embodiment, the intelligent decision-making unit is used to train and analyze the data transmitted from the big data layer based on the artificial intelligence algorithm, compare it with the operating status and feedback results, obtain the artificial intelligence model, and analyze the stability and security of the operation of the water resources supply management system based on the artificial intelligence model; Figure 4 The specific methods include: the artificial intelligence big model analyzes and simulates the data of the big data layer to infer the future safe and stable operation trend. When it is judged that there is a risk of safe and stable operation in the future, the artificial intelligence big model generates a sequence of construction optimization solutions; when the water supply system is judged to be operating safely and stably, the artificial intelligence big model analyzes and simulates the data of the big data layer to infer the future economically reasonable operation trend. When it is judged that the economically reasonable operation requirements are not met, the artificial intelligence big model generates a sequence of construction optimization solutions.

[0086] In this embodiment, the intelligent decision-making unit optimizes the adjustment and optimization instructions based on expert judgment and auxiliary decision-making to obtain final adjustment and optimization instructions, and sends the final adjustment and optimization instructions to the operation management unit for implementation; the specific method includes: whether the construction optimization solution submitted by the artificial intelligence big model needs to be passed after decision-making, the set decision-free execution plan is directly issued, and for the execution plan that requires decision, the manager decides whether to adopt the execution plan, adopts the sequence and execution time of the execution plan, generates a solution after the approval decision and issues it for execution; in the case where the approval decision is not passed, the decision maker outputs the opinion to the artificial intelligence big model, and the artificial intelligence big model learns the reasons for rejection and then tunes the model.

[0087] The water resource supply management system based on artificial intelligence has the following advantages:

[0088] 1. Improve management efficiency

[0089] Automated monitoring and real-time data analysis: Through the sensor network deployed by IoT technology, the system can monitor key parameters such as water quality, water volume, and water pressure in real time, and transmit them to the data center for analysis and processing in real time. This method reduces the tediousness and errors of manual monitoring, significantly improves the efficiency and accuracy of data collection, and thus improves overall management efficiency.

[0090] Rapid response and adjustment: Based on real-time data analysis results, the artificial intelligence system can quickly identify abnormal situations in water supply and automatically adjust management strategies, such as adjusting water supply plans, optimizing equipment operating parameters, etc., to quickly respond to changes in water resources and ensure the stable operation of the water supply system.

[0091] 2. Promote scientific decision-making

[0092] Big data analysis and prediction: Using big data technology and machine learning algorithms, the system can conduct in-depth mining and analysis of massive water resources data, revealing the temporal and spatial distribution patterns, changing trends and potential risks of water resources. These analysis results provide a scientific basis for decision-making for governments and enterprises, and help formulate more reasonable water resources management policies and plans.

[0093] Intelligent decision support: Intelligent decision support systems based on big data analysis results can provide managers with the best decision-making solutions. These solutions take into account multiple factors such as water supply and demand, ecological and environmental needs, and social and economic development goals, and help achieve optimal allocation and efficient use of water resources.

[0094] The intelligent decision support system based on the analysis results can provide managers with the best decision-making solutions. These solutions comprehensively consider multiple factors such as water supply and demand, ecological and environmental needs, and social and economic development goals, and help achieve the optimal allocation and efficient use of water resources.

[0095] 3. Enhance emergency response capabilities

[0096] Rapid warning and response: When natural disasters such as floods and droughts occur, the water resources supply management system based on artificial intelligence can quickly launch emergency plans and mobilize all parties to carry out rescue and disaster relief. Through real-time monitoring and data analysis, the system can predict the time and intensity of disasters in advance, provide a scientific basis for emergency response, and effectively reduce disaster losses.

[0097] Precise scheduling and allocation: During disaster response, the system can accurately schedule and allocate water resources according to actual needs to ensure that the water supply needs of key areas and industries are met.

[0098] 4. Optimize resource allocation

[0099] Cross-regional collaboration and sharing: By establishing a sound data sharing and collaboration mechanism, the AI-based water resource supply management system can achieve cross-regional and cross-departmental collaboration and resource sharing. This helps break down information silos and data barriers and improve the overall efficiency of water resource utilization.

[0100] Intelligent optimization algorithm: Using intelligent optimization algorithm to optimize the allocation and dispatch of water resources, the system can comprehensively consider multiple factors to formulate scientific and reasonable dispatch plans. These plans help to achieve balanced allocation and efficient use of water resources and alleviate water shortage problems.

[0101] 5. Ensure water quality safety

[0102] Real-time monitoring and early warning: The Internet of Things technology is used to monitor water quality indicators in real time, and combined with artificial intelligence algorithms to evaluate water quality and trace pollution sources. Once abnormal water quality or pollution incidents are found, the system can immediately issue an early warning and activate the emergency response mechanism, and take timely measures to protect the water ecological environment and ensure water supply safety.

[0103] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of protection of the present disclosure. The attached method claims present the elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy described.

[0104] In the above detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that the embodiments of the claimed subject matter require more features than are clearly stated in each claim. On the contrary, as reflected in the appended claims, the invention is in a state of having less than all the features of the disclosed individual embodiments. Therefore, the appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0105] Those skilled in the art will also appreciate that the various illustrative logic blocks, modules, circuits, and algorithmic steps described in conjunction with the embodiments herein can all be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate the interchangeability between hardware and software, various illustrative components, blocks, modules, circuits, and steps are generally described above around their functions. Whether such functions are implemented as hardware or software depends on specific applications and the design constraints imposed on the entire system. A skilled person can implement the described functions in an alternative manner for each specific application, but such implementation decisions should not be interpreted as departing from the scope of protection of the present disclosure.

[0106] The steps of the method or algorithm described in conjunction with the embodiments herein may be directly embodied as hardware, a software module executed by a processor, or a combination thereof. The software module may be located in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a mobile disk, a CD-ROM, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor so that the processor can read information from the storage medium and can write information to the storage medium. Of course, the storage medium may also be an integral part of the processor. The processor and the storage medium may be located in an ASIC. The ASIC may be located in a user terminal. Of course, the processor and the storage medium may also be present in a user terminal as discrete components.

[0107] For software implementation, the techniques described in this application can be implemented with modules (e.g., procedures, functions, etc.) that perform the functions described in this application. These software codes can be stored in a memory unit and executed by a processor. The memory unit can be implemented within the processor or outside the processor. In the latter case, it is coupled to the processor in a communication manner via various means, which are well known in the art.

[0108] The above description includes examples of one or more embodiments. Of course, it is impossible to describe all possible combinations of components or methods for the purpose of describing the above embodiments, but it should be recognized by those skilled in the art that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to cover all such changes, modifications and variations that fall within the scope of protection of the appended claims. In addition, with respect to the term "comprising" used in the specification or claims, the word is covered in a manner similar to the term "including", just as "including," is explained as a transitional word in the claims. In addition, any term "or" used in the specification of the claims is intended to mean "non-exclusive or".

Claims

1. A water resource supply management system based on artificial intelligence, characterized in that: include: Data collection unit, big data and digital twin unit, operation management unit and intelligent decision-making unit; among which: A data collection unit, used to collect water quantity information, water quality information, hydrological information and external information in the area, and send the information to the big data and digital twin unit; Big data and digital twin units are used to store water quantity information, water quality information, hydrological information, and external information transmitted by the data acquisition unit in a structured manner and to generate virtual space simulations during digital transformation; at the same time, digital transformation is performed on the information transmitted by the operation management unit and a virtual space simulation is generated; and data is standardized and output to the intelligent decision-making unit for intelligent training and reasoning; The operation management unit is used to collect water intake information, water transmission information, water purification information and water supply information of the water project operation in real time; transmit the water intake information, water transmission information, water purification information and water supply information to the big data and digital twin unit; and is also used to receive the instructions issued by the intelligent decision-making unit and execute the operation parameter changes of the equipment and devices according to the instructions; The intelligent decision-making unit is used to train and analyze the data transmitted from the big data layer based on the artificial intelligence algorithm, compare it with the operating status and feedback results, obtain the artificial intelligence model, and analyze the stability and safety of the operation of the water resources supply management system based on the artificial intelligence model; it is also used to automatically generate adjustment and optimization instructions based on the analysis results of the stability and safety of the operation of the water resources supply management system, optimize the adjustment and optimization instructions based on expert judgment and auxiliary decision-making, obtain final adjustment and optimization instructions, and send the final adjustment and optimization instructions to the operation management unit for implementation.

2. The water resource supply management system based on artificial intelligence as claimed in claim 1, characterized in that: The data acquisition unit includes a water quantity information acquisition subunit, a water quality information subunit, a hydrological information subunit and an external information subunit; wherein: the water quantity information acquisition subunit is composed of a water level meter and a flow meter, and collects the water flow of water resources; the water quality information subunit is composed of a chemical sensor, a physical sensor and a biological sensor, and is used to collect the physical, chemical and biological characteristics of water resources; the hydrological information subunit is composed of a water level meter, a rain level sensor and an industrial camera, and is used to collect the temporal and spatial distribution and change characteristics of water resources.

3. The water resource supply management system based on artificial intelligence as claimed in claim 1, characterized in that: The operation management unit includes a water intake subunit, a water delivery subunit, a water purification subunit and a water supply subunit; wherein the water intake subunit is composed of a water source, a water pump and a water pool, water is taken from the water source by the water pump, and the taken water is stored in the water pool; the water delivery subunit is composed of a water pipe, a water pump and a channel, water is taken from the water intake unit by the water pump, and then the water is transported through the water pipe and the channel; the water purification subunit is composed of a coagulation tank, a disinfection tank and a sedimentation tank, water is purified by the coagulation tank, the disinfection tank and the sedimentation tank; the water supply subunit is composed of a water pump, a water pipe and a water tower, the water supply subunit calculates the regional water consumption, and supplies water to the region through the water supply subunit.

4. The water resource supply management system based on artificial intelligence as claimed in claim 3, characterized in that: The water supply subunit calculates the regional water consumption, including the sum of the water consumption of residential water, industrial enterprises, public facilities, etc. supplied by the water supply system in the region; the sum of all water consumption other than the water consumption of residential water, industrial enterprises, public facilities, etc. supplied by the regional water supply system.

5. The water resource supply management system based on artificial intelligence as claimed in claim 4, characterized in that: The specific methods for calculating regional water consumption include: Calculate the maximum daily domestic water consumption Q1 in the residential area: Q1=∑(q i N i )→Q1′=f1(w、e、s、p、.....、m) q i is the maximum daily domestic water consumption of residential areas with different sanitary equipment. The initial value is determined according to the national quota. The water meter in the residential area monitors the peak water consumption of each residential area, and the weighted average is calculated according to the population size to obtain the actual maximum daily domestic water consumption; N i The number of people who are planned to use water within the design period is obtained by initial value according to the urban planning documents, and then simulated and predicted according to social population changes and migration trends; f1 is a model dynamically generated by massive data sets, and w, e, s, p, and m are model input factors respectively; Calculate the water consumption of public buildings Q2: Q2=∑(q j N j ) q j The maximum daily water consumption of each public building. The initial value is determined according to the national quota. The peak water consumption of each building is subsequently monitored by the public building water meter, and the actual water consumption is obtained by weighted average based on the building characteristics and the number of units. N j The number of water units used by each public building is not fixed, but is dynamically adjusted according to the city's continuous construction, municipal approval, and construction plans; Calculate the production water consumption of industrial enterprises and the living water consumption of staff Q3: Q3=∑(Q I +Q II +Q III ) Q I The production water consumption of each industrial enterprise is determined by the production process requirements. The initial value is reported by each industrial enterprise and is subsequently adjusted dynamically according to the water consumption; Q II The living water consumption of employees in various industrial enterprises is initially calculated at 30-50 L / (person / shift) with an hourly variation coefficient of 1.5-2.5, and is subsequently adjusted dynamically according to actual water consumption; Q III The water consumption for showering of employees in various industrial enterprises is initially calculated as 40-60L / (person / shift), and the shower duration is calculated as 1h. It will be dynamically adjusted later based on actual water consumption.

6. The water resource supply management system based on artificial intelligence as claimed in claim 4, characterized in that: Specific methods for calculating regional water consumption also include: Calculate the water consumption for watering roads and green spaces Q4: Q4=v(q L N L ) q L The initial water consumption per unit area is 2.0-3.0L / (m2·d) for watering roads and sites, and 1.0-3.0L / (m2·d) for watering wine and green land. It will be adjusted dynamically according to the actual water consumption of the watering system in the future. N L The area of ​​roads and green areas watered daily will be adjusted dynamically according to municipal construction plans and weather conditions; Calculate the water loss of the pipe network Q5. The water loss of the pipe network is calculated as a certain proportion δ of the sum of the maximum daily domestic water consumption in residential areas, water consumption in public buildings, water consumption in industrial enterprises, and water consumption for watering roads and green spaces. The initial value is 10% to 12%. The subsequent value is related to the length, size, material, service life and climate of the pipe network according to actual dynamic changes; Q5=δ(Q1+Q2+Q3+Q4) Calculate the unforeseen water consumption Q6, which is calculated based on a certain proportion σ of the sum of the highest daily domestic water consumption in residential areas, water consumption in public buildings, water consumption in industrial enterprises, water consumption for watering roads and green spaces, and water loss in pipe networks. The initial value is 8% to 12%, and the subsequent value is related to the actual dynamic changes and special events and unexpected situations. Q6=σ(Q1+Q2+Q3+Q4+Q5) Finally, the maximum daily water consumption Q d ; Q d =Q1+Q2+Q3+Q4+Q5+Q6。 7. The water resource supply management system based on artificial intelligence as claimed in claim 1, characterized in that: Artificial intelligence methods are used to purify water resources. The specific methods include: collecting various water quality indicators of water resources through various sensors, analyzing the required processing modules and processing flows through large artificial intelligence models, simulating the results of water treatment through the processing efficiency of each module, verifying the treatment effectiveness, analyzing the treatment cost and utilization rate, and comprehensively proposing treatment process suggestions. According to the treatment process suggestions, instructions are issued, and the water treatment process flow is established and executed.

8. The water resource supply management system based on artificial intelligence as claimed in claim 1, characterized in that: The intelligent decision-making unit is used to train and analyze the data transmitted from the big data layer based on the artificial intelligence big algorithm, compare it with the operating status and feedback results, obtain the artificial intelligence model, and analyze the stability and safety of the operation of the water resources supply management system based on the artificial intelligence model; the specific methods include: the artificial intelligence big model analyzes and simulates the data of the big data layer, and infers the future safe and stable operation trend. When it is judged that there is a risk of safe and stable operation in the future, the artificial intelligence big model generates a sequence of construction optimization solutions; when it is judged that the water supply system is operating safely and stably, the artificial intelligence big model analyzes and simulates the data of the big data layer, and infers the future economically reasonable operation trend. When it is judged that the economically reasonable operation requirements are not met, the artificial intelligence big model generates a sequence of construction optimization solutions.

9. The water resource supply management system based on artificial intelligence as claimed in claim 1, characterized in that: The intelligent decision-making unit optimizes the adjustment and optimization instructions based on expert judgment and auxiliary decision-making to obtain final adjustment and optimization instructions, and sends the final adjustment and optimization instructions to the operation management unit for implementation; The specific methods include: whether the construction optimization solution submitted by the artificial intelligence big model needs to be approved after decision-making. The set decision-free execution plan is directly issued. For the execution plan that requires decision-making, the manager decides whether to adopt the execution plan, adopts the sequence and execution time of the execution plan, generates the solution after the approval decision and issues it for execution; in the case that the approval decision is not passed, the decision maker outputs the opinion to the artificial intelligence big model, and the artificial intelligence big model learns the reasons for the rejection and then optimizes the model.

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