Dual water supply management system and method based on user data analysis

Abstract

The invention discloses a dual water supply management system based on user data analysis, and the system comprises a mode determination module which obtains the water use data of a park user, and determines the final water use behavior mode of the user; the group division module is used for extracting feature data from the final water use behavior modes of the users and dividing the user groups according to the water use behavior modes through a clustering algorithm; the water consumption dividing module is used for dividing the water consumption levels of all users in each user group according to the water consumption of each user in the user group; and the water supply adjustment module is used for carrying out water supply adjustment on the divided user groups according to the cost requirements, the resource allocation requirements, the water consumption requirements and the water consumption levels of the users in the park. By analyzing the user data, the water demands and habits of different user groups can be known more finely, so that more accurate water resource management is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of water supply management, and in particular to a quality-based water supply management system and method based on user data analysis. Background Art

[0002] With the acceleration of urbanization and the continuous improvement of people's living standards, the requirements for water quality are also gradually increasing. The traditional unified standard water supply method can no longer meet the different water quality requirements of different user groups. In addition, with the increasing scarcity of water resources, how to reasonably and efficiently use limited water resources has become an urgent problem to be solved. In this context, the quality-based water supply management system based on user data analysis came into being.

[0003] In recent years, with the development of information technology, especially the application of technologies such as the Internet of Things (IoT), big data, and cloud computing, technical support has been provided for the realization of differentiated water supply. By collecting real-time data through various sensors installed at the user end, the user's specific water use situation can be monitored, including water use time, water volume, water quality requirements and other information. After processing, these data can reflect the user's personalized needs, and adjust the water supply strategy accordingly to achieve the purpose of saving water resources and reducing water supply costs.

[0004] The Chinese invention patent with publication number CN117993834A discloses a management system for a smart water supply network, including: multiple fixed-point detection workstations, set at various preset locations of the water supply network; a management platform, which is respectively connected to each fixed-point detection workstation in communication; the management platform performs the following operations: constructing a three-dimensional network model of the water supply network; obtaining water quality parameter information detected by each fixed-point detection workstation; generating first monitoring indication information based on the water quality parameter information; mapping the first monitoring indication information to the three-dimensional network model to form a water quality management model. The management system for the smart water supply network of this invention realizes the management of the water quality transported by the network.

[0005] Although the above-mentioned and similar management systems can manage the water quality of the water supply network based on the analysis of user data, they can only divide the regional groups and directly manage the water quality based on the large-scale division. For different enterprises in the industrial park, such a large-scale water use division will not only increase the water treatment cost of the water supply department, but also increase the unnecessary water use expenditure of the enterprises. Summary of the invention

[0006] The object of the present invention is to provide a quality-based water supply management system and method based on user data analysis to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: a quality-based water supply management system based on user data analysis, comprising:

[0008] The mode determination module obtains the water use data of the park users, determines the primary water use behavior pattern of the users, and determines the final water use behavior pattern of the users based on the water use preference information of the park users;

[0009] A group segmentation module extracts characteristic data from the final water use behavior pattern of the user, and divides the user group according to the water use behavior pattern through a clustering algorithm;

[0010] A water use classification module, which classifies the water use levels of all users in each user group according to the water use of each user in the user group;

[0011] The water supply adjustment module adjusts the water supply to the divided user groups according to the cost requirements, resource allocation requirements, water demand and water use level of the park users. Specifically:

[0012] Divide the user groups after multi-level classification according to the cost requirements of park users;

[0013] Adjust the total water supply and water quality standards in different time periods according to resource allocation needs and water demand;

[0014] Through the division of user groups, the adjustment of the total water supply and water quality standards, and according to the optimization goals and standard water consumption of the park user groups, the water supply demand is adjusted as follows:

[0015]

[0016] in: is the water demand of the i-th user group in the t-th time period, is the standard water consumption of the i-th user group, is the optimization target weight of the i-th user group, is the optimization target value of the i-th user group, The index number of the user group.

[0017] Furthermore, the mode determination module includes:

[0018] The data unit sets sensors on the water supply pipelines to obtain and process the water consumption data of park users, and verifies the sensor data through the intelligent diagnosis system;

[0019] A primary unit, based on the water use data of the park users, performs primary classification of the water use behaviors of the park users through a data analysis algorithm;

[0020] An interactive unit, wherein the park user sets water use preference information through an interactive interface, and the interactive interface includes but is not limited to a mobile application, a web page port, an intelligent voice assistant, and a touch screen;

[0021] The ultimate unit determines the user's final water use behavior pattern through the primary classification of water use behavior and water use preference information.

[0022] Furthermore, the methods for verifying sensor data through the intelligent diagnostic system include:

[0023] M1: Determine the model, location and installation specifications of the sensor according to the external environment of the water supply pipeline, collect sensor data in real time, and clean, transform and standardize the sensor data;

[0024] M2: Establishing a time series distribution model, adjusting the model parameters of the time series distribution model by means of a grid search method, and determining the long-term trend and seasonal fluctuation in the processed sensor data according to the time series distribution model;

[0025] M3: Extract characteristic data from the long-term trend and seasonal fluctuations, and establish an anomaly detection model based on the characteristic data. At the same time, verify the sensor data acquired in real time through the anomaly detection model. When the real-time sensor data verification passes, the real-time sensor data is correct. Otherwise, the sensor corresponding to the real-time sensor data is tested.

[0026] Furthermore, the mode determination module determines the water use behavior mode, including:

[0027] SA1: Real-time acquisition and processing of water consumption data of park users, and verification of the water consumption data through anomaly detection models. If the verification is passed, the next step SA2 is directly executed. Otherwise, the sensor corresponding to the abnormal data is tested.

[0028] SA2: Classify the verified sensor data through cluster analysis, and divide the user's water use behavior into primary categories according to water use time period, water use frequency, water use amount, periodicity and seasonal changes of water use;

[0029] SA3: Set the user's water use preference information through the interactive interface, the water use preference information includes but is not limited to water use time, water temperature and water volume, and integrate the water use preference information with the primary water use behavior to finally classify the user's water use behavior.

[0030] Furthermore, the water use division module includes:

[0031] Category unit, dividing users by job category according to the industry characteristics and job nature of each user in the user group;

[0032] A supply unit, which determines the water supply priority of each user according to the average daily water consumption of each user;

[0033] The adjustment unit determines the standard water consumption corresponding to each user group according to the water consumption of each user, and adjusts the water supply of the user group under the same water use mode according to the water supply priority and the standard water consumption of the user, wherein the standard water consumption is specifically:

[0034]

[0035] in: is the standard water consumption of the i-th user group, For the The water consumption of each user, is the number of users in the user group, The index number of the user.

[0036] Furthermore, the water supply to the user groups is adjusted by the water use division module, specifically:

[0037] SB1: Determine the type of work of each user in the user group based on the industry characteristics and nature of work of each user, and determine the primary water supply priority of all users in each user group based on the type of work;

[0038] SB2: Obtain the average daily water consumption of all users in each user group, match the average daily water consumption with the primary water supply priority, and determine the final water supply priority of the user;

[0039] SB3: Determine the standard water consumption for each user group based on the water consumption of all users in each user group, and arrange each user in the user group in order from large to small. At the same time, determine the users in each user group who exceed the standard water consumption, and adjust the water supply for all users in each user group based on the order of user arrangement and the number of users who exceed the standard water consumption.

[0040] Furthermore, according to the order of the users and the number of excesses, the water supply to all users in each user group is adjusted, specifically:

[0041] SB3.1: Based on the standard water consumption corresponding to each user group, determine the excess and surplus of each user's water consumption relative to the standard water consumption;

[0042] SB3.2: According to the final water supply priority of the user, all users with excess water will be sorted in descending order, and the remaining water supply of all users with surplus water will be adjusted to the users with excess water in sequence according to the sorting order of all users with excess water.

[0043] Furthermore, the relationship between the water supply demand, the total water supply, and the water quality standard is specifically as follows:

[0044]

[0045] in: is the total water supply in the tth time period, is the water quality standard in the tth time period, The highest water quality standards, is the water demand in the tth time period, is the total water resources of the park, is the total number of time periods, The index number of the time period.

[0046] Furthermore, the water supply demand is adjusted by water price, specifically:

[0047]

[0048] in: is the water demand of the i-th user group in the t-th time period after water price adjustment, is the standard water consumption of the i-th user group, is the optimization target weight of the i-th user group, is the optimization target value of the i-th user group, is the index number of the user group. is the water price coefficient in the tth time period, is the sensitivity of the i-th user group to water prices, The index number of the time period.

[0049] A quality-based water supply management method based on user data analysis uses any one of the quality-based water supply management systems described above.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] First, by analyzing user data, the present invention can more precisely understand the water demand and habits of different user groups, thereby achieving more accurate water resource management. At the same time, the user groups can be divided and the water supply and water quality standards can be adjusted according to their water demand, cost demand and optimization goals, thereby effectively optimizing resource allocation and avoiding resource waste.

[0052] Second, the present invention can ensure the accuracy and timeliness of data through real-time monitoring and intelligent diagnosis of sensor data, thereby improving water use efficiency and reducing unnecessary water resource losses. At the same time, the sensor data is verified through anomaly detection models, and abnormal data can be discovered and processed in the first place, thereby ensuring the stable operation of the system.

[0053] Third: The present invention can be divided into multiple levels according to the cost requirements of different user groups, so as to control costs in a targeted manner and reduce unnecessary expenses. At the same time, the water supply and water quality standards can be dynamically adjusted according to the changes in user water consumption, thereby adapting to changes in water demand in different time periods and seasons. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a system block diagram of the quality-based water supply management system of the present invention;

[0055] Figure 2 A schematic diagram of a process of verifying sensor data through an intelligent diagnostic system according to the present invention;

[0056] Figure 3 A schematic diagram of a process of determining a water use behavior pattern through a pattern determination module of the present invention;

[0057] Figure 4 A schematic diagram of a process of adjusting the water supply amount of a user group through a water use division module of the present invention;

[0058] Figure 5 It is a schematic diagram of the flow of adjusting water supply to divided user groups according to the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Although the traditional centralized water supply method can meet the large-scale water supply demand, it has many shortcomings in terms of flexibility, accuracy and efficiency. On the one hand, due to the lack of refined management methods, it is easy to have excess or insufficient water during the water supply process; on the other hand, the unified standard water quality supply cannot meet the differentiated needs of different user groups, resulting in a waste of resources, thus proposing the concept of differentiated water supply. Differentiated water supply refers to a new model of on-demand water supply that puts forward different requirements for water quality according to different uses and adopts corresponding treatment processes accordingly. This model can not only reduce unnecessary water treatment costs, but also effectively alleviate the pressure of high-quality water sources and improve the efficiency of water resource utilization. Especially in some high-end service industries (such as hotels and hospitals) and specific industrial fields (such as food processing and pharmaceuticals), the use of differentiated water supply can significantly improve service levels and reduce costs.

[0061] Example 1

[0062] refer to Figure 1-Figure 5 , this embodiment provides a quality-based water supply management system based on user data analysis, and the quality-based water supply management system includes a mode determination module, a group division module, a water use division module and a water supply adjustment module. In this embodiment, the water use data of all enterprise users in a certain industrial park is taken as an example to specifically explain the quality-based water supply management system in this embodiment. Specifically, the quality-based water supply management system in this embodiment obtains the water use data of all users in the park through the mode determination module, and determines the water use behavior pattern of all users in the park based on this. At the same time, through the group division module, feature data is extracted from the water use behavior pattern of all users in the park, and the user group is divided according to the water use behavior pattern based on the extracted feature data. After that, the standard water consumption of the user group is obtained through the water use division module, and the water use level of all users in the user group is divided. It is worth noting that the water use behavior pattern division result obtained by the group division module, the standard water consumption and water use level obtained by the water use division module will be sent to the water supply adjustment module, so that the water supply adjustment module can adjust the water supply to the divided user group based on the data.

[0063] In this embodiment, the mode determination module obtains the water use data of the park users by using, for example, an Internet of Things device, and pre-processes the water use data of the park users. Furthermore, the mode determination module in this embodiment can determine the primary water use behavior pattern of the user based on the pre-processed water use data of the park users, and can also fuse the primary water use pattern with the water use preference information based on the water use preference information of the park users, thereby achieving the user's final water use behavior pattern. The details are as follows:

[0064] Step SA1: Obtain water consumption data of park users in real time through IoT devices (such as LoRaWAN, NB-IoT, etc.), and pre-process the obtained water consumption data of park users. It is worth noting that the pre-processing of data here includes operations such as removing duplicate records and filling missing values. Since the pre-processing of data is a conventional technical means, it will not be explained in detail in this embodiment.

[0065] Furthermore, in order to ensure the accuracy of the water usage data obtained, the water usage data of the park users is verified through an anomaly detection model (such as Isolation Forest, One-Class SVM, etc.). When the verification passes, the next step SA2 is directly executed. Otherwise, the sensor corresponding to the abnormal data is checked.

[0066] During the implementation process, multiple smart water meters were installed in the three areas A, B, and C in the industrial park. The water usage data collected in real time is as follows:

[0067]

[0068] Specifically, after the water consumption data in the above three areas are verified by the anomaly detection model in this embodiment, it can be found that the water consumption data in area A is normal, the water consumption data in area B is abnormal, and the water consumption data in area C is normal. In other words, the sensors in area B need to be tested to correct the accuracy of the data.

[0069] Step SA2: Extract key features from the water use data obtained in the above-mentioned area A and area B, such as water use time period, water use frequency and water use amount, etc. At the same time, the extracted key features are clustered by cluster analysis (such as K-means, DBSCAN, etc.), that is, the user's water use behavior is initially divided according to water use time period, water use frequency, water use amount, periodicity and seasonal changes of water use.

[0070] In the process of specific implementation, the data in areas A and C in the above industrial park are processed to obtain the key features in the following table:

[0071]

[0072] After the above cluster analysis, two cluster centers can be obtained, specifically:

[0073] Cluster 1: Low water consumption and low frequency of water use, mainly concentrated in the morning, and the periodicity of water use is daily water use.

[0074] Cluster 2: The water consumption is high, the water use frequency is medium, mainly concentrated in the morning, and the water use periodicity is daily water use.

[0075] Step SA3: Set the user's water use preference information through an interactive interface (such as a mobile application, a web portal, or a physical terminal, etc.), where the water use preference information includes but is not limited to water use time, water temperature, and water volume. Further, the water use preference information set in step SA3 is merged with the primary water use behavior divided in step SA2 to finally divide the user's water use behavior.

[0076] In the process of specific implementation, the user preference information that can be collected through mobile applications is shown in the following table:

[0077]

[0078] After integrating the above user preference information with the clustering results, the results are as follows:

[0079] User 001: belongs to cluster 1, prefers to use water in the morning, the water temperature is 30℃, and the water consumption is 100 liters each time.

[0080] User 002: belongs to cluster 2, prefers to use water in the afternoon, the water temperature is 25℃, and the water consumption is 150 liters each time.

[0081] In this embodiment, the group segmentation module extracts characteristic data from the user's final water use behavior pattern, and the characteristic data includes but is not limited to water consumption (total water use per day, week or month), water use frequency (number of times water is used per day or the number of times water is used within a specific time period), water use time period (the main time period for water use in a day, such as morning, noon, and evening), water use periodicity (periodic changes in water use patterns, such as the difference between weekdays and weekends), seasonal changes (changes in water use patterns in different seasons), and water use preferences (user preferred water use time, water temperature, etc.).

[0082] Furthermore, a corresponding clustering algorithm (such as K-means, DBSCAN, hierarchical clustering and spectral clustering) is selected, and the user groups are divided according to the water use behavior patterns through the selected clustering algorithm.

[0083] In the process of specific implementation, there is a set of user data of the industrial park, which includes information such as user number, water use time period, water use frequency, water use amount, etc. The following table is a simplified data example:

[0084]

[0085] From the above table, we can extract the following features:

[0086]

[0087] In this embodiment, clustering is performed by using the K-means algorithm, wherein the number of clusters in the K-means algorithm is set to 2, and the clustering results are as follows:

[0088] Cluster 1: Water use in the morning, frequency of water use 3 times / day, water consumption of about 95 L / day.

[0089] Cluster 2: Water use at night, with a frequency of 2 times / day and a water consumption of about 145 L / day.

[0090] In this embodiment, the water use classification module obtains the standard water use of each user group according to the water use of each user in the user group, and classifies the water use levels of all users in each user group, as follows:

[0091] Step SB1: According to the industry characteristics and work nature of each user in the user group, determine the work type of each user, that is, the characteristics of water demand. For example, the manufacturing industry requires a large amount of industrial water, while the office area requires a large amount of domestic water. At the same time, according to the work type, determine the primary water supply priority of all users in each user group.

[0092] In the process of specific implementation, there are several types of user groups in the industrial park:

[0093] Manufacturing users: large water consumption, mainly used in the production process.

[0094] Office users: moderate water consumption, mainly used in daily life.

[0095] Catering industry users: They use a large amount of water, mainly for food processing and cleaning.

[0096] The water supply priorities for the above types of user groups are as follows:

[0097] Manufacturing users: high priority (production water is important).

[0098] Office area users: medium priority (domestic water use is important but not urgent).

[0099] Catering industry users: medium priority (domestic water is important but not urgent).

[0100] Step SB2: Count the average daily water consumption of all users in each user group, match the average daily water consumption with the primary water supply priority obtained in step SB1, adjust the water supply priority, and determine the final water supply priority of the user.

[0101] In the process of specific implementation, the average daily water consumption of the above types of user groups is as follows:

[0102] Manufacturing users: The average daily water consumption is 1,000 liters.

[0103] Office users: Average daily water consumption is 300 liters.

[0104] Catering industry users: The average daily water consumption is 500 liters.

[0105] After adjusting the water supply priority according to the average daily water consumption, the water supply priority is as follows:

[0106] Manufacturing users: Highest priority (large and important water users).

[0107] Office area users: medium priority (moderate water consumption, domestic water use).

[0108] Catering industry users: medium priority (large water consumption, domestic water use).

[0109] Step SB3: According to the water consumption of all users in each user group, determine the standard water consumption corresponding to each user group, and arrange each user in the user group in order from large to small, and at the same time determine the users in each user group who exceed the standard water consumption, and adjust the water supply of all users in each user group according to the order of user arrangement and the number of users who exceed the standard water consumption. The details are as follows:

[0110] Step SB3.1: Determine the standard water consumption of each user group based on the water consumption history data and water demand of all users in each user group. The standard water consumption is as follows:

[0111]

[0112] in: is the standard water consumption of the i-th user group, For the The water consumption of each user, is the number of users in the user group, The index number of the user.

[0113] At the same time, the water consumption of each user is compared with the standard water consumption to determine the excess and surplus water consumption of each user relative to the standard water consumption.

[0114] In the process of specific implementation, the standard water consumption of the above types of user groups is as follows:

[0115] Manufacturing users: standard water consumption is 1000 liters / day.

[0116] Office users: standard water consumption is 300 liters / day.

[0117] Catering industry users: standard water consumption is 500 liters / day.

[0118] According to the standard water consumption of the above users, the excess and surplus are calculated as follows:

[0119] Manufacturing users:

[0120] User A: Average daily water consumption is 1,200 liters / day, exceeding the limit by 200 liters / day.

[0121] User B: Average daily water consumption is 1000 liters / day, and the excess water consumption is 0 liters / day.

[0122] Office users:

[0123] User C: Average daily water consumption is 350 liters / day, exceeding the limit by 50 liters / day.

[0124] User D: Average water consumption is 250 liters / day, with a surplus of 50 liters / day.

[0125] Catering industry users:

[0126] User E: Average daily water consumption is 550 liters / day, exceeding the limit by 50 liters / day.

[0127] User F: Average water consumption is 450 liters / day, with a surplus of 50 liters / day.

[0128] Step SB3.2: According to the final water supply priority of the users determined in step SB2, all users with excess water are sorted in descending order, and at the same time, the remaining water supply of all remaining users is adjusted to the users with excess water in sequence according to the sorting order of all users with excess water.

[0129] In the specific implementation process, the final water supply priorities for the above types of user groups are as follows:

[0130] Manufacturing users: highest priority.

[0131] Office users: medium priority.

[0132] Catering industry users: medium priority.

[0133] Sort by superscalar, specifically:

[0134] Manufacturing users:

[0135] User A: Exceeding the limit by 200 liters / day.

[0136] User B: Exceeded volume 0 L / day.

[0137] Office users:

[0138] User C: Exceeding the limit by 50 liters / day.

[0139] User D: Remaining amount 50 liters / day.

[0140] Catering industry users:

[0141] User E: Exceeding the limit by 50 liters / day.

[0142] User F: Remaining amount 50 liters / day.

[0143] According to the above sorting results of excess water, the water supply is adjusted as follows:

[0144] Manufacturing users:

[0145] User A: The volume exceeds the limit by 200 liters / day and cannot be adjusted temporarily.

[0146] User B: Over-limit 0 L / day, no adjustment required.

[0147] Office users:

[0148] User C: Exceeding the limit by 50 liters / day.

[0149] User D: The remaining amount is 50 liters / day, and the remaining amount will be adjusted to user C.

[0150] After the adjustment, the excess volume of user C is reduced to 0 L / day.

[0151] Catering industry users:

[0152] User E: Exceeding the limit by 50 liters / day.

[0153] User F: The remaining amount is 50 liters / day, and the remaining amount will be adjusted to user E.

[0154] After the adjustment, the excess volume of user E is reduced to 0 L / day.

[0155] In this embodiment, the water supply adjustment module adjusts the water supply to the divided user groups according to the cost requirements, resource allocation requirements, water demand and water use level of the park users, as follows:

[0156] Step SC1: Divide the user groups after multi-level division according to the cost requirements of the park users. That is, according to the cost sensitivity of the user groups, determine which users are more sensitive to cost, that is, divide the user groups into low-cost demand users and high-cost demand users.

[0157] In the process of specific implementation, there are several types of user groups in the industrial park:

[0158] Manufacturing users: They are more cost-sensitive because production costs directly affect profits.

[0159] Office users: moderately cost sensitive, as office water use is important but not a core cost.

[0160] Catering industry users: The cost sensitivity is moderate, because water costs have a certain impact on operations but are not the main cost.

[0161] Based on cost sensitivity, the above user groups can be divided into:

[0162] Users with low-cost requirements: office users.

[0163] Users with high cost demands: manufacturing users and catering industry users.

[0164] Step SC2: Adjust the total water supply and water quality standards in different time periods according to resource allocation requirements and water demand. That is, analyze the water demand and resource requirements of different user groups in different time periods, and determine the priority of their water demand according to their water use levels, and adjust the total water supply and water quality standards in different time periods according to the analysis results.

[0165] In the process of specific implementation, there are several types of user groups in the industrial park:

[0166] Manufacturing users: large water consumption and high water quality requirements, especially during peak production periods.

[0167] Office users: moderate water consumption, average water quality requirements, mainly on weekdays.

[0168] Catering industry users: They consume large amounts of water and have high requirements for water quality, especially during business hours.

[0169] The total water supply and water quality standards for the above user groups are adjusted as follows:

[0170] During the day on weekdays: Increase water supply to users in the manufacturing and catering industries and improve water quality standards.

[0171] Nighttime and weekends: Reduced water supply and lower water quality standards because most users have less demand.

[0172] Step SC3: Adjust the water supply demand by dividing the user groups, adjusting the total water supply and water quality standards, and according to the optimization goals and standard water consumption of the park user groups. In other words, by determining the overall optimization goals of the park, such as minimizing costs and maximizing resource utilization, and according to the standard water consumption of the user groups, ensure that the water supply of each user group meets the standards, and then adjust the water supply demand by combining the division of user groups, the total water supply and the adjustment of water quality standards. The water supply demand is specifically:

[0173]

[0174] in: is the water demand of the i-th user group in the t-th time period, is the standard water consumption of the i-th user group, is the optimization target weight of the i-th user group, is the optimization target value of the i-th user group, The index number of the user group.

[0175] It is worth noting that in this embodiment, the relationship between water supply demand, total water supply, and water quality standard is specifically as follows:

[0176]

[0177] in: is the total water supply in the tth time period, is the water quality standard in the tth time period, The highest water quality standards, is the water demand in the tth time period, is the total water resources of the park, is the total number of time periods, The index number of the time period.

[0178] At the same time, the water supply demand can also be adjusted appropriately according to the water price, specifically:

[0179]

[0180] in: is the water demand of the i-th user group in the t-th time period after water price adjustment, is the standard water consumption of the i-th user group, is the optimization target weight of the i-th user group, is the optimization target value of the i-th user group, is the index number of the user group. is the water price coefficient in the tth time period, is the sensitivity of the i-th user group to water prices, The index number of the time period.

[0181] In the process of specific implementation, the park's optimization goal is to minimize costs and maximize resource utilization. The standard water consumption is as follows:

[0182] Manufacturing users: standard water consumption is 1000 liters / day.

[0183] Office users: standard water consumption is 300 liters / day.

[0184] Catering industry users: standard water consumption is 500 liters / day.

[0185] Adjust water supply demand according to the division of user groups, total water supply and water quality standards, specifically:

[0186] Manufacturing users: Ensure that water supply during peak periods is not lower than the standard water consumption and provide high-standard water quality.

[0187] For users in office areas: ensure that the water supply during working days is not lower than the standard water consumption, and appropriately reduce the water supply during non-peak periods.

[0188] Catering industry users: ensure that the water supply during business hours is not lower than the standard water consumption and provide high-standard water quality.

[0189] Example 2

[0190] This embodiment provides a smart park big data integrated management and control system, and its basic composition is the same as that of embodiment 1, except that the mode determination module in this embodiment includes a data unit, a primary unit, an interactive unit, and an ultimate unit. The data unit obtains the water use data of park users and makes judgments, the primary unit makes a primary classification of the water use behavior of park users based on the water use data of park users after judgment, the interactive unit can set the user's water use preference information, and the ultimate unit combines the water use preference information with the primary classification results of water use behavior to determine the user's final water use behavior pattern.

[0191] It is worth noting that in the data unit of this embodiment, the sensor data can be verified by the intelligent diagnosis system, as follows:

[0192] Step M1: Determine the model, location and installation specifications of the sensor according to the external environment of the water supply pipeline (such as temperature, humidity, electromagnetic interference, etc.), install the sensor and collect sensor data in real time on the water supply pipeline, and clean, convert and standardize the sensor data.

[0193] Step M2: Establish a time series distribution model (such as ARIMA, Holt-Winters, etc.), and adjust the model parameters of the time series distribution model through the grid search method. At the same time, according to the time series distribution model, determine the long-term trend and seasonal fluctuation in the processed sensor data.

[0194] In the process of specific implementation, the ARIMA model was selected, and the optimal parameter combination was determined to be (2, 1, 0) through the grid search method. The analysis results are as follows:

[0195] Long-term trend: Water use has been slowly increasing over time.

[0196] Seasonal fluctuations: Water consumption in the morning is significantly higher than at other times of the day.

[0197] Step M3: Extract characteristic data (such as mean, variance, etc.) from long-term trends and seasonal fluctuations, and establish an anomaly detection model (such as Isolation Forest, One-Class SVM, etc.) based on the characteristic data. At the same time, verify the sensor data obtained in real time through the anomaly detection model. When the real-time sensor data verification passes, the real-time sensor data is correct. Otherwise, the sensor corresponding to the real-time sensor data is tested.

[0198] Example 3

[0199] This embodiment provides a smart park big data comprehensive management and control system, whose basic composition is the same as that of embodiment 1, except that the water use division module in this embodiment includes a category unit, a supply unit, and an adjustment unit. The category unit divides users by job category based on the industry characteristics and job nature of each user in the user group. That is, according to the characteristics of the industry to which the user belongs, it can be divided into manufacturing, catering, and office areas, and according to the characteristics of the industry to which the user belongs, it can be divided into production, office, and service.

[0200] In the process of specific implementation, there are the following types of user groups in the industrial park:

[0201] Manufacturing users: concentrated in production areas, with large water consumption, mainly used in the production process.

[0202] Office area users: distributed in office areas, with moderate water consumption, mainly used in daily life.

[0203] Catering industry users: distributed in restaurants and canteens, they consume large amounts of water, mainly for food processing and cleaning.

[0204] Furthermore, the supply unit counts the average daily water consumption of each user group and determines the user's water supply priority based on the average daily water consumption. The adjustment unit determines the standard water consumption of each user group based on historical data and water demand analysis. At the same time, the water supply under the same water use mode is adjusted based on the user's water supply priority and standard water consumption.

[0205] In the process of specific implementation, the standard water consumption of the above user groups is as follows:

[0206] Manufacturing users: standard water consumption is 1000 liters / day.

[0207] Office users: standard water consumption is 300 liters / day.

[0208] Catering industry users: standard water consumption is 500 liters / day.

[0209] Adjust water supply according to user's water supply priority and standard water consumption:

[0210] Manufacturing users: Ensure that the water supply during peak production is not less than 1,000 liters per day and provide high-standard water quality.

[0211] For users in office areas: ensure that the water supply is no less than 300 liters per day during working days and appropriately reduce the water supply during off-peak periods.

[0212] Catering industry users: Ensure that the water supply during business hours is not less than 500 liters per day and provide high-standard water quality.

[0213] Example 4

[0214] This embodiment provides a quality-based water supply management method based on user data analysis. This quality-based water supply management method is used to use the quality-based water supply management system in the above embodiments and preferred implementations, and what has been explained will not be repeated.

[0215] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is limited by the attached embodiments and their equivalents.

Claims

1. A quality-based water supply management system based on user data analysis, characterized in that: Included are: The mode determination module obtains the water use data of the park users, determines the primary water use behavior pattern of the users, and determines the final water use behavior pattern of the users based on the water use preference information of the park users; A group segmentation module extracts characteristic data from the final water use behavior pattern of the user, and divides the user group according to the water use behavior pattern through a clustering algorithm; A water use classification module, which classifies the water use levels of all users in each user group according to the water use of each user in the user group; The water supply adjustment module adjusts the water supply to the divided user groups according to the cost requirements, resource allocation requirements, water demand and water use level of the park users. Specifically: Divide the user groups after multi-level classification according to the cost requirements of park users; Adjust the total water supply and water quality standards in different time periods according to resource allocation needs and water demand; Through the division of user groups, the adjustment of the total water supply and water quality standards, and according to the optimization goals and standard water consumption of the park user groups, the water supply demand is adjusted as follows: in: is the water demand of the i-th user group in the t-th time period, is the standard water consumption of the i-th user group, is the optimization target weight of the i-th user group, is the optimization target value of the i-th user group, The index number of the user group.

2. A quality-based water supply management system based on user data analysis according to claim 1, characterized in that: The mode determination module includes: The data unit sets sensors on the water supply pipelines to obtain and process the water consumption data of park users, and verifies the sensor data through the intelligent diagnosis system; A primary unit, based on the water use data of the park users, performs primary classification of the water use behaviors of the park users through a data analysis algorithm; An interactive unit, wherein the park user sets water use preference information through an interactive interface, and the interactive interface includes but is not limited to a mobile application, a web page port, an intelligent voice assistant, and a touch screen; The ultimate unit determines the user's final water use behavior pattern through the primary classification of water use behavior and water use preference information.

3. A quality-based water supply management system based on user data analysis according to claim 2, characterized in that: Ways to verify sensor data through the intelligent diagnostic system include: M1: Determine the model, location and installation specifications of the sensor according to the external environment of the water supply pipeline, collect sensor data in real time, and clean, transform and standardize the sensor data; M2: Establishing a time series distribution model, adjusting the model parameters of the time series distribution model by means of a grid search method, and determining the long-term trend and seasonal fluctuation in the processed sensor data according to the time series distribution model; M3: Extract characteristic data from the long-term trend and seasonal fluctuations, and establish an anomaly detection model based on the characteristic data. At the same time, verify the sensor data acquired in real time through the anomaly detection model. When the real-time sensor data verification passes, the real-time sensor data is correct. Otherwise, the sensor corresponding to the real-time sensor data is tested.

4. A quality-based water supply management system based on user data analysis according to claim 1 or 2, characterized in that: The method of determining the water use behavior pattern by the pattern determination module includes: SA1: Real-time acquisition and processing of water consumption data of park users, and verification of the water consumption data through anomaly detection models. If the verification is passed, the next step SA2 is directly executed. Otherwise, the sensor corresponding to the abnormal data is tested. SA2: Classify the verified sensor data through cluster analysis, and divide the user's water use behavior into primary categories according to water use time period, water use frequency, water use amount, periodicity and seasonal changes of water use; SA3: Set the user's water use preference information through the interactive interface, the water use preference information includes but is not limited to water use time, water temperature and water volume, and integrate the water use preference information with the primary water use behavior to finally classify the user's water use behavior.

5. The quality-based water supply management system based on user data analysis according to claim 1 is characterized in that: The water use division module includes: Category unit, dividing users by job category according to the industry characteristics and job nature of each user in the user group; A supply unit, which determines the water supply priority of each user according to the average daily water consumption of each user; The adjustment unit determines the standard water consumption corresponding to each user group according to the water consumption of each user, and adjusts the water supply of the user group under the same water use mode according to the water supply priority and the standard water consumption of the user, wherein the standard water consumption is specifically: in: is the standard water consumption of the i-th user group, For the The water consumption of each user, is the number of users in the user group, The index number of the user.

6. A quality-based water supply management system based on user data analysis according to claim 1 or 5, characterized in that: The water supply to the user groups is adjusted by the water use division module, specifically: SB1: Determine the type of work of each user in the user group based on the industry characteristics and nature of work of each user, and determine the primary water supply priority of all users in each user group based on the type of work; SB2: Obtain the average daily water consumption of all users in each user group, match the average daily water consumption with the primary water supply priority, and determine the final water supply priority of the user; SB3: Determine the standard water consumption for each user group based on the water consumption of all users in each user group, and arrange each user in the user group in order from large to small. At the same time, determine the users in each user group who exceed the standard water consumption, and adjust the water supply for all users in each user group based on the order of user arrangement and the number of users who exceed the standard water consumption.

7. A quality-based water supply management system based on user data analysis according to claim 6, characterized in that: According to the order of users and the number of excesses, adjust the water supply to all users in each user group, specifically: SB3.1: Based on the standard water consumption corresponding to each user group, determine the excess and surplus of each user's water consumption relative to the standard water consumption; SB3.2: According to the final water supply priority of the user, all users with excess water will be sorted in descending order, and the remaining water supply of all users with surplus water will be adjusted to the users with excess water in sequence according to the sorting order of all users with excess water.

8. The quality-based water supply management system based on user data analysis according to claim 1 is characterized in that: The relationship between the water supply demand, the total water supply and the water quality standard is specifically as follows: in: is the total water supply in the tth time period, is the water quality standard in the tth time period, The highest water quality standards, is the water demand in the tth time period, is the total water resources of the park, is the total number of time periods, The index number of the time period.

9. A quality-based water supply management system based on user data analysis according to claim 1 or 8, characterized in that: The water demand is adjusted by water price, specifically: in: is the water demand of the i-th user group in the t-th time period after water price adjustment, is the standard water consumption of the i-th user group, is the optimization target weight of the i-th user group, is the optimization target value of the i-th user group, is the index number of the user group. is the water price coefficient for the tth time period, is the sensitivity of the i-th user group to water prices, The index number of the time period.

10. A quality-based water supply management method based on user data analysis, characterized in that: A quality-based water supply management system based on user data analysis as described in any one of claims 1 to 9 is used.

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