Operation management analysis system and method for water enterprises

Abstract

The invention discloses a water enterprise-oriented operation management analysis system and method, and the system comprises a data collection module which obtains the production data and production cost data of each unit in real time; the basic platform module is used for providing basic operation for a user; the data platform module is used for processing, classifying and controlling the production data and the production cost data; the intelligent analysis module is used for carrying out deep mining and multi-dimensional analysis on the production data and the production cost data based on big data analysis and an artificial intelligence algorithm; and the visual display module is used for visually displaying the analysis result of the intelligent analysis module through a graphical interface. The defects of a traditional production data management system in the aspects of data collection, processing and analysis are overcome, and comprehensive intelligent management of the sewage treatment process is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the information management technology of sewage treatment, more particularly to a management and analysis system and method for water enterprises. BACKGROUND

[0002] The production management system in the prior art is mainly applied to collecting, storing, and visually displaying various production data in the sewage treatment process through basic charts to support daily operation and decision management. In terms of data collection, the system mainly obtains data through sensors, instrument devices deployed in various links of sewage treatment, and manual sampling records. The data collected by sensors and devices are transmitted to a local server or a central control room, and some data are manually input into the system after being recorded by manual records, forming a complete record of production process data. In terms of data storage, the system usually adopts a local database or file storage mode to store the collected water quality data, device operation data, energy consumption data, etc. to form structured and unstructured data sets. These data provide a basis for subsequent statistical analysis. In terms of data processing, the traditional system mainly processes data through simple statistical analysis and report generation tools. For example, the system performs trend analysis on water quality data to generate daily, monthly, and other statistical reports to help management personnel understand water quality changes; analyzes device operation data to generate device operation efficiency reports to provide a reference for device maintenance; and summarizes energy consumption data to form energy consumption statistical reports. In terms of data visualization, the traditional system usually provides basic chart display functions such as line charts, column charts, and pie charts to visually display water quality change trends, device operation states, and energy consumption distribution. Management personnel can quickly understand the overall situation of production operation through these visualization tools and make decisions based on data. Overall, the existing sewage company production data management system provides basic data support for the daily operation of sewage treatment plants through data collection, transmission, storage, processing, and visualization, helping management personnel to master production dynamics, optimize operation efficiency, and ensure that the effluent water quality meets the standards.

[0003] The dispersion and discontinuity of the data management of the prior art lead to a serious information island phenomenon, and the sewage production data of different links are difficult to integrate, which affects subsequent data analysis and decision support. Secondly, in terms of data storage, the existing system usually adopts a dispersed storage mode, and the data is stored in different devices or systems, lacking a unified data management platform. For example, water quality data, equipment operation data, energy consumption data, etc. may be stored in different databases or files, and the data format is not unified, which is difficult to effectively integrate and analyze, and the saving and calling efficiency of historical data is low. In terms of data processing, the existing system lacks intelligent analysis tools and algorithms, and the data processing mode is simple and extensive, and the data is usually only used to generate basic statistical reports or trend charts, lacking deep mining and multi-dimensional analysis capability. Management personnel can only rely on experience judgment and simple data analysis results to make decisions, and it is difficult to find the potential rules and problems behind the data. For example, if there are abnormal indicators (such as increased unit consumption, reduced sewage treatment capacity, etc.) in the sewage treatment process, manual identification and processing are usually required, lacking an automatic warning and analysis mechanism, which leads to late problem discovery and low processing efficiency. In addition, the data analysis function of the traditional system is single, and cannot realize cross-system data fusion and comprehensive analysis. For example, there is a lack of correlation analysis between sewage sludge treatment capacity, production cost and energy consumption data, making it difficult to comprehensively evaluate the overall efficiency and optimization potential of the production process. Finally, in terms of data visualization, the existing system can usually only provide simple charts and reports, lacking intuitive and dynamic visualization display methods. Management personnel need to go through complex operations to obtain the required information, and the decision-making efficiency is low. For example, the running state, energy consumption trend, production cost, etc. of the sewage treatment plant are usually presented in the form of static reports, which cannot be dynamically displayed in real time, and it is difficult to support rapid decision-making. In summary, the existing technology has limitations in data collection, transmission, storage, processing and visualization, and it is difficult to meet the needs of modern sewage treatment plants for efficient, accurate and intelligent data analysis. These limitations not only affect the efficiency of production management, but also restrict the optimized operation and sustainable development of the sewage treatment plant. SUMMARY

[0004] In view of the defects in the prior art, the purpose of the present application is to provide a water enterprise-oriented management analysis system and method, to solve the deficiencies of traditional production data management systems in data collection and analysis, and to realize comprehensive intelligent management of the sewage treatment process.

[0005] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0006] The present application provides a water enterprise-oriented management analysis system in the first aspect, comprising:

[0007] The data acquisition module acquires real-time production data and production cost data of each unit.

[0008] a basic platform module providing basic operations for users;

[0009] a data platform module processing, classifying and managing the production data and the production cost data;

[0010] an intelligent analysis module based on big data analysis and artificial intelligence algorithm, performing deep mining and multi-dimensional analysis on the production data and the production cost data;

[0011] a visual display module displaying the analysis results of the intelligent analysis module through a graphical interface.

[0012] Preferably, the production data includes water quantity data, water quality data, equipment operation data and reagent dosing data.

[0013] The production cost data includes energy consumption data, reagent cost data, equipment maintenance cost data and other operating cost data.

[0014] Preferably, the basic platform module includes:

[0015] log management for log recording and analysis of the management and analysis system;

[0016] account management for user identity and permission management;

[0017] permission management for controlling access permissions of different users to data and functions.

[0018] Preferably, the data platform module includes:

[0019] a data access unit accessing various data from different systems to the data platform module;

[0020] a data management and control unit managing and controlling data in the data platform module;

[0021] a data classification unit classifying and managing data in the data platform module;

[0022] a data governance unit cleaning, integrating and maintaining data;

[0023] a data sharing unit sharing data between different systems;

[0024] a special database for a database for a theme or business requirement.

[0025] Preferably, the intelligent analysis module includes:

[0026] The production cost automatic analysis unit automatically analyzes the cost composition and its trend by integrating the production cost data.

[0027] The production efficiency analysis unit evaluates the sewage treatment efficiency by analyzing the production data and identifies factors affecting the efficiency.

[0028] The cost optimization analysis unit analyzes the relationship between the production cost data and the sewage treatment efficiency using optimization algorithms and proposes optimization suggestions.

[0029] The abnormal cost early warning unit identifies production cost abnormalities through machine learning algorithms and triggers early warnings to help managers take timely measures.

[0030] The second aspect of the present application provides a water enterprise-oriented management analysis method, which uses the water enterprise-oriented management analysis system provided by the first aspect of the present application to perform the following steps:

[0031] S1, the production data and the production cost data of each unit are obtained in real time through the data acquisition module;

[0032] S2, the production data and the production cost data are processed, classified and controlled through the data platform module;

[0033] S3, the production data and the production cost data are deeply mined and multi-dimensionally analyzed based on big data analysis and artificial intelligence algorithms using the intelligent analysis module;

[0034] S4, finally, the analysis results of the intelligent analysis module are intuitively displayed through the visual display module.

[0035] Preferably, the production data includes water quantity data, water quality data, equipment operation data and reagent addition data, which are automatically collected and transmitted through sensors, instrument devices and Internet of Things terminals.

[0036] The production cost data includes energy consumption data, reagent cost data, equipment maintenance cost data and other operating cost data, which are automatically obtained through interfaces with financial systems and equipment management systems.

[0037] Preferably, the step S3 specifically includes the following analysis:

[0038] 1) production cost automatic analysis, the cost composition and its trend are automatically analyzed by integrating the production cost data and the production cost data;

[0039] 2) production efficiency analysis, the sewage treatment efficiency is evaluated by analyzing the water quantity data, the water quality data and the equipment operation data, and factors affecting the efficiency are identified.

[0040] 3) Cost optimization analysis, using optimization algorithms to analyze the relationship between production cost data and wastewater treatment efficiency, and make optimization recommendations;

[0041] 4) Abnormal cost warning, through machine learning algorithm, identify production cost anomalies, and trigger warning to help managers take timely measures;

[0042] 5) Predictive algorithm, using prophet model, combined with enterprise business situation, by inputting historical reagents, cost, equipment maintenance data, water quantity data, equipment operation data, output the production comprehensive cost in the future for a period of time.

[0043] Preferably, the production cost automatic analysis calculates the cost change trend of unit water treatment through time series analysis;

[0044] First, by decomposing the cost composition, the total unit cost is obtained:

[0045]

[0046] Then, through SQL window function and python groupby+resample dynamic calculation, time series is formed.

[0047] Preferably, the production efficiency analysis first extracts the water quantity data and the water quality data from the data storage module through data processing and feature extraction;

[0048] Then, the data is feature extracted, the efficiency evaluation model is established, and the factors affecting the efficiency are identified through correlation analysis;

[0049] Finally, based on the analysis results, optimization suggestions are put forward.

[0050] Preferably, the cost optimization analysis uses genetic algorithm to find the optimal solution, that is, to minimize the production cost under the premise of meeting the production quantity;

[0051] First, model the problem and perform genetic algorithm to finally realize optimization decision output, the problem modeling needs to clarify the objective function and constraint condition in the production cost, the objective function is:

[0052] Minimize F(x)=dynamic energy consumption+reagent consumption+maintenance fee

[0053] The constraint condition is that each wastewater treatment plant is configured according to the production index;

[0054] According to the above objective function and constraint condition, genetic algorithm is used to simulate biological evolution to gradually reach the optimal solution;

[0055] The genetic algorithm needs to encode and initialize the population, and evaluate the advantages and disadvantages of each parameter through the fitness function:

[0056]

[0057] Through selection, crossover and mutation, the optimal solution of the optimization cost is generated after several iterations.

[0058] Preferably, the abnormal cost early warning extracts historical production cost data from the data storage module through data preprocessing and feature engineering, and performs feature engineering on the data to extract key features.

[0059] An abnormal detection model is trained using a machine learning algorithm, and the model is trained using historical data so that it can identify normal data patterns and abnormal data patterns.

[0060] Real-time data is accessed to the data model to detect whether there is an anomaly, and if an anomaly is detected, the system triggers an early warning and prompts possible causes.

[0061] The water enterprise-oriented operation and management analysis system and method provided by the application first realizes real-time collection and intelligent analysis of multi-dimensional production data in the sewage treatment process by integrating advanced Internet of Things technology and big data analysis algorithms, and can accurately identify key problems and optimization potential in the production process. Secondly, the system uses machine learning, deep learning and other artificial intelligence technologies to deeply mine and analyze production data, automatically generate performance evaluation reports, cost optimization suggestions and abnormal warning information, help management personnel quickly make scientific decisions, and significantly improve production efficiency and management level. In terms of visual display, the system presents complex production data and analysis results in an easy-to-understand way through an intuitive graphical interface (such as a dashboard, trend chart, heat map, etc.), supports management personnel to monitor production status in real time, view analysis reports and perform interactive operations. This efficient data analysis and visual display capability not only reduces the difficulty of data understanding, but also significantly improves the timeliness and accuracy of decision-making, providing strong technical support for intelligent and fine management of sewage treatment plants. BRIEF DESCRIPTION OF DRAWINGS

[0062] Figure 1 is the architecture schematic diagram of the operation and management analysis system of the application;

[0063] Figure 2 is the schematic diagram of the data transmission network deployed and built in the embodiment of the operation and management analysis method of the application;

[0064] Figure 3 is the schematic diagram of the visual display module in the embodiment of the operation and management analysis method of the application. DETAILED DESCRIPTION

[0065] In order to better understand the above technical solutions of the present application, the technical solutions of the present application are further described below in combination with the drawings and examples.

[0066] In combination with Figure 1 The water enterprise-oriented operation and management analysis system provided by the present application comprises:

[0067] The data acquisition module 1 acquires the production data and production cost data of each unit (such as the company headquarters, subordinate sewage treatment plants, sludge treatment plants, etc.) in real time through the API mode.

[0068] The basic platform module 2 provides basic operations for users.

[0069] The data platform module 3 processes, classifies and controls the production data and production cost data.

[0070] The intelligent analysis module 4 performs deep mining and multi-dimensional analysis on the production data and production cost data based on big data analysis and artificial intelligence algorithms.

[0071] The visualization display module 5 visually displays the analysis results of the intelligent analysis module 4 through a graphical interface.

[0072] The production data is collected in real time by the data acquisition module during the sewage treatment process, including water quantity data (such as inflow, outflow, and return flow), water quality data (such as pH value, dissolved oxygen, COD, and ammonia concentration), equipment operation data (such as pump station operation status, aeration quantity, and sludge treatment equipment parameters), and reagent addition data (such as flocculant and disinfectant dosage).

[0073] The data acquisition module realizes automatic data collection and transmission through sensors, instrument equipment and Internet of Things terminals deployed at each key node of the sewage treatment plant.

[0074] The production cost data includes energy consumption data (such as power consumption and fuel consumption), reagent cost data (such as reagent unit price and addition amount), equipment maintenance cost data (such as spare parts replacement cost and manual maintenance cost), and other operating cost data (such as labor cost and management cost).

[0075] The production cost data is automatically acquired through the interface with the financial system and the equipment management system.

[0076] The company plans to automatically obtain data related to operation through the report system. All collected data uses distributed database or cloud storage technology for efficient storage and management of production data and cost data. Data is classified and stored by type (such as water quantity data, water quality data, energy consumption data, cost data, etc.) and time sequence, supporting fast retrieval and historical data calling.

[0077] The basic platform module 2 includes:

[0078] Log management for log recording and analysis of the operation management analysis system;

[0079] Account management for user identity and permission management;

[0080] Permission management for controlling access permissions of different users to data and functions.

[0081] The data platform module 3 includes:

[0082] Data access unit to access various data from different systems to the data platform module;

[0083] Data management and control unit for managing and controlling data in the data platform module;

[0084] Data classification unit for classified management of data in the data platform module;

[0085] Data governance unit for data cleaning, integration, and maintenance;

[0086] Data sharing unit to realize data sharing between different systems;

[0087] Special database for databases related to themes or business needs.

[0088] The intelligent analysis module 4 is based on big data analysis and artificial intelligence algorithms to deeply mine and multi-dimensionally analyze production data and cost data, specifically including:

[0089] Production cost automatic analysis unit to automatically analyze cost composition and its trend through integrated production cost data;

[0090] Production efficiency analysis unit to evaluate wastewater treatment efficiency (such as treatment efficiency and compliance rate) through analysis of production data and identify key factors affecting efficiency;

[0091] Cost optimization analysis unit to analyze the relationship between production cost data and wastewater treatment efficiency using optimization algorithms and propose optimization suggestions (such as adjusting reagent dosage and optimizing equipment operation parameters) to reduce production cost;

[0092] The abnormal cost early warning unit identifies production cost abnormalities (such as sudden increase in energy consumption, reagent waste, etc.) through a machine learning algorithm and triggers an early warning to help managers take timely measures.

[0093] The visualization display module 5 visually displays the analysis results in the form of charts, dashboards, etc. through a graphical interface (such as a web or mobile interface) to support managers in real-time monitoring of production costs, viewing analysis reports, and making decisions, such as Figure 3

[0094] The present application also provides a water enterprise-oriented operation and management analysis method, which adopts the operation and management analysis system to perform the following steps:

[0095] S1, real-time acquisition of production data and production cost data of each unit through the data acquisition module 1;

[0096] S2, processing, classification, and control of production data and production cost data through the data platform module 2;

[0097] S3, deep mining and multi-dimensional analysis of production data and production cost data based on big data analysis and artificial intelligence algorithms using the intelligent analysis module 4;

[0098] S4, finally, the analysis results of the intelligent analysis module 4 are visually displayed through the visualization display module 5.

[0099] The production data is collected in real time by the data acquisition module during the sewage treatment process, including water quantity data (such as inflow, outflow, and return flow), water quality data (such as pH value, dissolved oxygen, COD, and ammonia concentration), equipment operation data (such as pump station operation status, aeration quantity, and sludge treatment equipment parameters), and reagent dosage data (such as flocculant and disinfectant dosage).

[0100] The data acquisition module realizes automatic data collection and transmission through sensors, instrument devices, and Internet of Things terminals deployed at key nodes of the sewage treatment plant.

[0101] The production cost data includes energy consumption data (such as power consumption and fuel consumption), reagent cost data (such as reagent unit price and dosage), equipment maintenance cost data (such as spare parts replacement cost and manual maintenance cost), and other operating cost data (such as labor cost and management cost).

[0102] The production cost data is automatically obtained through the interface with the financial system and the equipment management system.

[0103] Step S3 specifically includes the following analysis:

[0104] ​1) Production cost automatic analysis, by integrating production cost data, production cost data automatic analysis cost composition and its trend.

[0105] 2) Production efficiency analysis, by analyzing water quantity data, water quality data and equipment operation data to evaluate the efficiency of wastewater treatment and identify factors affecting efficiency.

[0106] 3) Cost optimization analysis, using optimization algorithms to analyze the relationship between production cost data and wastewater treatment efficiency, and make optimization recommendations.

[0107] 4) Abnormal cost warning, through machine learning algorithm, identify production cost anomalies, and trigger warning to help managers take timely measures.

[0108] 5) Predictive algorithm, using the prophet model, combining enterprise business scenarios, by inputting historical reagents, cost, equipment maintenance data, water quantity data, equipment operation data, output the production comprehensive cost in the future for a certain period of time.

[0109] The goal of the production cost automatic analysis of the above 1) aspect is to automatically calculate the unit water treatment cost by integrating energy consumption data, reagent cost data, equipment maintenance cost, etc., and analyze the cost composition and its trend. After data integration and preprocessing, the cost change trend of unit water treatment is calculated through time series analysis;

[0110] First, the total unit cost is obtained by decomposing the cost composition:

[0111]

[0112] Then, through SQL window function and python groupby+resample dynamic calculation to form time series, which can support multi-dimensional data analysis.

[0113] According to the above results, time series analysis, cost composition analysis and abnormal data analysis of production cost are carried out respectively. Time series analysis is to observe long-term trend and identify periodicity through moving average (MA) and seasonal decomposition (SARIMA), which supports same period or period analysis. Cost composition analysis is to show the proportion of each energy consumption, reagent cost, etc. in production through visualization tools. Abnormal data analysis uses Isolation Forest technology route through warning model, which isolates abnormal points (abnormal point path is shorter) by randomly dividing feature space. This technology route is suitable for data distribution is relatively scattered, and abnormal data is not fixed. It is mainly used to detect outliers of production cost, such as the following abnormal examples:

[0114] from sklearn.ensemble import IsolationForest

[0115] model = IsolationForest(contamination = 0.01) # Assuming an anomaly rate of 1%.

[0116] model.fit(data[['energy consumption','pharmaceutical cost']])

[0117] data['anomaly flag'] = model.predict(data[['energy consumption','pharmaceutical cost']]) #-1 indicates an anomaly

[0118] After detecting abnormal data, the system can discover the correlation between the anomaly and potential factors based on business data.

[0119] The goal of the production efficiency analysis in aspect 2) above is to evaluate wastewater treatment efficiency (such as treatment efficiency and effluent compliance rate) and identify key factors affecting efficiency by analyzing water quantity and water quality data. First, water quantity data (such as influent and effluent volume) and water quality data are extracted from the data storage module through data processing and feature extraction.

[0120] Then, feature extraction is performed on the data to establish a performance evaluation model, and correlation analysis is used to identify factors that affect performance.

[0121] Finally, based on the analysis results, optimization suggestions are proposed.

[0122] First, correlation analysis was used to analyze the influencing factors based on key production energy efficiency indicators, examining the correlation between each energy consumption level and each factor. Then, a multiple linear regression model was used to quantify the weight of each factor's impact on production indicators.

[0123] from sklearn.linear_model import LinearRegression

[0124] model = LinearRegression()

[0125] model.fit(X[['COD','DO','Temperature']],y['Unit Energy Consumption'])

[0126] print("Coefficient:", model.coef_) # For example: COD coefficient = 0.5 → For every 1 mg / L of COD, energy consumption increases by 0.5 kWh / m³ 3

[0127] Based on the quantitative indicators derived above, the importance of each factor is output.

[0128] The goal of the cost optimization analysis in aspect 3) above is to propose optimization suggestions to reduce production costs by analyzing the relationship between production costs and processing efficiency. A genetic algorithm is used to find the optimal solution, that is, to minimize production costs while meeting production volume requirements.

[0129] First, the problem is modeled, and a genetic algorithm is used to ultimately achieve the optimized decision output. Problem modeling requires clearly defining the objective function and constraints in the production cost. The objective function is:

[0130] Minimize F(x) = Energy consumption + Medicine consumption + Maintenance cost

[0131] Constraints are configured by each wastewater treatment plant according to its production targets; for example:

[0132] Production volume constraint: Processing volume ≥ Design minimum flow rate

[0133] Water quality requirements: Effluent COD ≤ 50 mg / L, ammonia nitrogen ≤ 5 mg / L

[0134] Based on the above objective function and constraints, a genetic algorithm is used to simulate biological evolution and gradually reach the optimal solution.

[0135] Genetic algorithms require encoding (process parameters) and initializing the population, and then evaluating the merits of each parameter through a fitness function.

[0136]

[0137] Through selection, crossover, and mutation, and after several iterations, the optimal solution with optimized cost is generated.

[0138] The goal of the abnormal cost early warning in aspect 4) above is to identify production cost anomalies (such as a sudden increase in production energy consumption, waste of reagents, and increased production costs) and trigger an early warning through machine learning algorithms. Historical production cost data (such as unit consumption, reagent costs, and maintenance costs) is extracted from the data storage module through data preprocessing and feature engineering. Feature engineering is then performed on the data to extract key features (such as energy consumption change rate and reagent dosage deviation).

[0139] The anomaly detection model is trained using machine learning algorithms and historical data, enabling it to distinguish between normal and anomalous data patterns.

[0140] Real-time data is fed into this data model to detect any anomalies. If an anomaly is detected, the system triggers an alert and indicates the possible cause.

[0141] Example

[0142] This embodiment provides an operational management and analysis system for water utilities, the specific technical features of which include:

[0143] 1) Frontend Technology Stack

[0144] React.js: Used for building user interfaces and data visualizations.

[0145] H5 (HTML5): Supports web application development for both mobile and desktop.

[0146] Ant Design: Used for building enterprise-level UI component libraries to improve development efficiency.

[0147] ECharts or D3.js: Used for complex data visualization and dynamic chart display.

[0148] 2) Backend Technology Stack

[0149] Spring Cloud Alibaba: Adopted as the foundation framework for microservices architecture, supporting service discovery, configuration management, load balancing, service gateway, etc.

[0150] Spring Boot: Used for quickly building independent microservices applications.

[0151] Spring Cloud Gateway: Serves as an API gateway, responsible for request routing, authentication, rate limiting, etc.

[0152] Nacos: Serves as a service discovery and configuration center, managing the registration, discovery, and configuration management of microservices.

[0153] Seata: Used for distributed transaction management to ensure data consistency across services.

[0154] RabbitMQ: Used for asynchronous message processing to ensure reliable data transmission and service decoupling.

[0155] MyBat is or JPA: Used for data persistence layer operations to facilitate interaction with the database.

[0156] Spring Security: Implements identity authentication and permission control to protect API security.

[0157] 3) Data Storage and Processing

[0158] MySQL / PostgreSQL: Relational database used for storing core business data.

[0159] MongoDB: Used for storing unstructured data, such as logs and documents.

[0160] Redis: Serves as a cache layer to improve system response speed and processing capacity.

[0161] Elasticsearch: Used for log and search functions, supporting full-text search and data analysis.

[0162] Hadoop / Spark: Batch processing and analysis of big data.

[0163] 4) Middleware and Integration

[0164] Kafka: Replaces or complements RabbitMQ for handling large-scale real-time data streams.

[0165] Docker: Containerizes applications to ensure consistent development and production environments.

[0166] Real-time steps are as follows:

[0167] 1) Deploy a data transmission network

[0168] Develop data interface to support multiple communication protocols and data formats, and verify the accessed data to ensure accuracy and integrity, as shown in Figure 2

[0169] 2) Build a data storage platform

[0170] Use distributed databases to store data. Design storage structures based on data types (such as real-time data and historical data) to support efficient retrieval and analysis.

[0171] 3) Develop data processing and analysis algorithms

[0172] Develop production cost automatic analysis algorithms, production efficiency analysis algorithms, cost optimization analysis algorithms, abnormal cost early warning algorithms, and predictive maintenance algorithms. Use machine learning, deep learning, and big data analysis techniques to implement algorithm functions. Train algorithm models using historical data and optimize model performance through cross-validation and hyperparameter tuning. Integrate the developed algorithms into the platform to support real-time data analysis and prediction.

[0173] 4) Design a visualization interface

[0174] The results of the intelligent analysis module are presented to management personnel through the visualization display module, supporting their quick understanding of production status and decision-making. Use visualization tools to generate charts, dashboards, etc. Support real-time display of production cost, processing performance, abnormal warning, etc. Provide an interactive interface for management personnel to view detailed analysis results, adjust parameters, and generate optimization schemes.

[0175] 5) System testing and optimization

[0176] Function testing, testing the correctness of data collection, transmission, storage, and analysis functions. Performance testing,​

[0177] The response speed, data processing capacity and concurrent performance of the test system are tested.According to the test results, the hardware device deployment, communication network configuration and platform performance are optimized.

[0178] In summary, the present application aims to solve the shortcomings of traditional production data management systems in data collection, processing and analysis, and to realize comprehensive intelligent management of the wastewater treatment process.By integrating advanced Internet of Things technology, big data analysis and artificial intelligence algorithms, real-time production data, energy consumption data and production costs can be deeply mined and analyzed in multiple dimensions, thereby achieving precise monitoring and optimal control of the wastewater treatment process.Meanwhile, the system has intelligent early warning and predictive feedback functions, which can timely detect abnormalities and automatically generate solutions, significantly improving production efficiency and resource utilization.It also has the following characteristics:

[0179] (1) High degree of automation: the system can automatically complete the data collection, processing and analysis process, greatly reducing manual intervention.

[0180] (2) High analysis accuracy: advanced data analysis algorithms are used to ensure the accuracy of the analysis results.

[0181] (3) Strong real-time performance: the system can reflect the production status of the wastewater company in real time, providing support for timely decision-making.

[0182] (4) Good scalability: the system architecture is flexible and easy to extend, upgrade or access data according to actual needs.

[0183] Those skilled in the art of the present technology should realize that the above embodiments are only used to illustrate the present application, but not as a limitation on the present application, as long as the variations and modifications of the above described embodiments are within the scope of the present application.

Claims

1. A water enterprise-oriented operation and management analysis system, characterized by, The system comprises: a data acquisition module that acquires real-time production data and production cost data of each unit; a basic platform module that provides basic operations for users; a data platform module that processes, classifies, and controls the production data and the production cost data; an intelligent analysis module that performs deep mining and multi-dimensional analysis on the production data and the production cost data based on big data analysis and artificial intelligence algorithms; a visualization display module that visually displays the analysis results of the intelligent analysis module through a graphical interface.

2. The water utility business management analysis system according to claim 1, characterized by: The production data includes water quantity data, water quality data, equipment operation data, and reagent dosing data. The production cost data includes energy consumption data, reagent cost data, equipment maintenance cost data, and other operating cost data.

3. The waterworks-oriented business management analysis system according to claim 1, characterized by, The basic platform module includes: log management for log recording and analysis of the management and analysis system; account management for user identity and permission management; permission management for controlling access permissions of different users to data and functions.

4. The waterworks-oriented business management analysis system according to claim 1, characterized by, The data platform module includes: a data access unit that accesses various data from different systems to the data platform module; a data control unit that manages and controls data in the data platform module; a data classification unit that classifies and manages data in the data platform module; a data governance unit that cleanses, integrates, and maintains data; a data sharing unit that enables data sharing between different systems; a special database for a theme or business requirement.

5. The waterworks-oriented business management analysis system according to claim 1, characterized by, The intelligent analysis module includes: a production cost automatic analysis unit that automatically analyzes cost composition and its trend through integrating the production cost data; a production efficiency analysis unit that evaluates wastewater treatment efficiency by analyzing the production data and identifies factors affecting the efficiency; a cost optimization analysis unit that analyzes the relationship between the production cost data and the wastewater treatment efficiency using optimization algorithms and proposes optimization suggestions; an abnormal cost early warning unit that identifies abnormal production costs through machine learning algorithms and triggers an early warning to help managers take timely measures.

6. A method for analyzing the operation and management of a water enterprise, characterized by, The water enterprise-oriented management and analysis system of any one of claims 1-5 performs the following steps: S1, acquiring real-time production data and production cost data of each unit through the data acquisition module; S2, processing, classifying, and controlling the production data and the production cost data through the data platform module; S3, using the intelligent analysis module to perform deep mining and multi-dimensional analysis on the production data and the production cost data based on big data analysis and artificial intelligence algorithms; S4, finally visually displaying the analysis results of the intelligent analysis module through the visualization display module.

7. The water utility business management analysis method according to claim 6, characterized by: The production data includes water quantity data, water quality data, equipment operation data, and reagent dosing data, which are automatically collected and transmitted through sensors, instrument devices, and Internet of Things terminals. The production cost data includes energy consumption data, reagent cost data, equipment maintenance cost data, and other operating cost data, which are automatically acquired through interfaces with financial systems and equipment management systems.

8. The waterworks-oriented business management analysis method according to claim 7, characterized by, The step S3 specifically includes the following analysis: 1) Automatic production cost analysis, by integrating the production cost data, the production cost data automatic analysis cost composition and its trend; 2) Production efficiency analysis, by analyzing the water quantity data, the water quality data and the equipment operation data to evaluate the sewage treatment efficiency and identify the factors affecting the efficiency; 3) Cost optimization analysis, using optimization algorithm to analyze the relationship between the production cost data and the sewage treatment efficiency, and put forward optimization suggestions; 4) Abnormal cost early warning, through machine learning algorithm, identify production cost anomalies, and trigger early warning to help managers take timely measures; 5) Predictive algorithm, using prophet model, combined with enterprise business situation, by inputting historical reagents, cost, equipment maintenance data, water quantity data, equipment operation data, output the production comprehensive cost in the future for a period of time.

9. The waterworks-oriented business management analysis method according to claim 8, characterized by, The production cost automatic analysis calculates the cost trend of unit water treatment through time series analysis; First, the total unit cost is obtained by decomposing the cost composition: Then, through SQL window function and python's groupby+resample dynamic calculation to form time series.

10. The waterworks-oriented business management analysis method according to claim 8, characterized by, The production efficiency analysis first extracts the water quantity data and the water quality data from the data storage module through data processing and feature extraction; Then, the data is feature extracted, the efficiency evaluation model is established, and the factors affecting the efficiency are identified through correlation analysis; Finally, based on the analysis results, optimization suggestions are put forward.

11. The waterworks-oriented business management analysis method according to claim 8, characterized by, The cost optimization analysis uses genetic algorithm to find the optimal solution, that is, to minimize the production cost under the premise of meeting the production capacity; First, model the problem and perform genetic algorithm to finally realize the output of optimization decision, the problem modeling needs to clarify the objective function and constraint conditions in the production cost, the objective function is: Minimize F(x) = power consumption + reagent consumption + maintenance fee The constraint condition is to configure according to the production index of each sewage plant; According to the above objective function and constraint condition, genetic algorithm is used to simulate biological evolution to gradually reach the optimal solution; The genetic algorithm needs to be coded and initialized population, and the advantages and disadvantages of each parameter are evaluated by fitness function: Through selection, crossover, mutation, the optimal solution of optimization cost is finally generated after several iterations.

12. The waterworks-oriented business management analysis method according to claim 8, characterized by, The abnormal cost early warning extracts historical production cost data from the data storage module through data preprocessing and feature engineering, and extracts key features; Use machine learning algorithm to train anomaly detection model, and train model through historical data to make it can identify normal data pattern and abnormal data pattern; Real-time data is accessed to the data model, detect whether there is an abnormality, if an abnormality is detected, the system triggers an early warning and prompts the possible cause.

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