Method and related device for diagnosis and regulation of industrial water based on multi-source data fusion

Abstract

The application relates to the technical field of intelligent management decision, in particular to a method for multi-source data fusion diagnosis and regulation based on industrial water and related equipment. The method comprises the following steps: acquiring multi-source heterogeneous data; performing data cleaning and fusion on the multi-source heterogeneous data to obtain standardized data; analyzing the standardized data through a dynamic evaluation model to obtain efficiency insight and problem diagnosis report; dynamically simulating the efficiency insight and problem diagnosis report through a causal reasoning and scenario simulation model to obtain rules and pre-play results; and solving the rules and pre-play results through a cooperative game and a distributed optimization algorithm to obtain a water resource quota allocation scheme. The application can break through the data barriers of multiple departments, realize dynamic perception and intelligent diagnosis of the whole process of industrial water, accurately identify the key path of regulation and control, and improve the efficiency of regional water resource overall planning and recycling.

Description

Technical Field

[0001] This application relates to the field of intelligent management and decision-making technology, and in particular to a method and related equipment for diagnosis and control based on multi-source data fusion of industrial water. Background Technology

[0002] Industrial water use is a significant component of water resource consumption. In regions experiencing rapid industrialization, the total water consumption is large, the intensity is high, and the structure is complex. Water use efficiency and sustainability are directly related to regional water resource security and high-quality economic and social development. Currently, industrial water management still faces several problems: First, data is fragmented and scattered. Information on water intake, use, drainage, and reuse is collected separately by multiple departments, including industry and information technology, water resources, environmental protection, and statistics. Data standards and formats are inconsistent, making cross-departmental integration and analysis difficult, and lacking a systematic diagnosis of regional water use efficiency, structural evolution, and influencing factors. Second, management models are relatively outdated, relying heavily on post-event statistics and static indicators for supervision, lacking real-time perception, dynamic assessment, and intelligent early warning capabilities. Furthermore, industrial water systems are highly coupled and influenced by multiple factors such as processes, industries, policies, and climate, making it difficult for traditional methods to accurately identify key control paths, thus limiting the effectiveness of policy implementation. Third, water-saving technologies and management perspectives are fragmented, focusing on single enterprises or processes, lacking regional-level overall optimization and cross-industry and cross-enterprise collaborative configuration, making it difficult to achieve efficient water resource recycling.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this application is to propose a method and related equipment for diagnosis and control based on multi-source data fusion in industrial water use. This method can break down data barriers between multiple departments, achieve dynamic perception and intelligent diagnosis of the entire industrial water use process, accurately identify key control paths, and improve the efficiency of regional water resource allocation and recycling.

[0005] To achieve the above objectives, one aspect of this application proposes a method for diagnosis and control based on multi-source data fusion of industrial water, the method comprising the following steps: Acquire multi-source heterogeneous data; The multi-source heterogeneous data is cleaned and fused to obtain standardized data; The standardized data is analyzed using a dynamic evaluation model to obtain efficiency insights and problem diagnosis reports. The efficiency insights and problem diagnosis reports are dynamically simulated using causal reasoning and scenario simulation models to obtain patterns and preliminary results. By solving the aforementioned patterns and simulation results using cooperative game theory and distributed optimization algorithms, a water resource quota allocation scheme is obtained.

[0006] In some embodiments, the multi-source heterogeneous data includes statistical and reporting data, IoT monitoring data, remote sensing meteorological data, and economic and social data; The statistical and reporting data are obtained periodically via API or STL, and include the total industrial water consumption in the region, water intake and discharge by industry, water consumption per 10,000 yuan of industrial added value, and reuse rate. The IoT monitoring data is collected in real time from online instruments at the water intake or drainage outlets of key enterprises via MQTT or HTTP protocols. The IoT monitoring data includes instantaneous flow rate, cumulative water volume, pH, and COD. The remote sensing meteorological data includes precipitation data, evaporation grid data, and surface water area or soil moisture data retrieved from satellites; The economic and social data include industrial added value of various industries, enterprise size distribution, and water price policy documents.

[0007] In some embodiments, the process of cleaning and fusing the multi-source heterogeneous data to obtain standardized data includes the following steps: The multi-source heterogeneous data is associated with a unified enterprise-level model, industry-level model, and administrative division-level model through entity parsing and linking technology. Among them, the enterprise-level model focuses on water-using units and process links, the industry-level model focuses on efficiency indicators and structural characteristics, and the administrative division-level model focuses on the integration of macro indicators of water resources, economy and environment. The rule engine performs field range checks and logical verifications on the multi-source heterogeneous data, and machine learning methods are used to detect outliers.

[0008] In some embodiments, the step of analyzing the standardized data through a dynamic evaluation model to obtain an efficiency insight and problem diagnosis report includes the following steps: The standardized data is analyzed using data envelopment analysis to obtain efficiency scores for industrial added value and wastewater discharge. The standardized data were analyzed using stochastic frontier analysis to obtain the industry average efficiency score; The efficiency scores of industrial added value and wastewater discharge are compared with the industry average efficiency score to obtain efficiency insights. Anomaly scores are obtained by using the isolated forest algorithm to score real-time water usage data anomalies in the standardized data. The anomaly score is compared with the anomaly threshold to obtain problem diagnosis data; The efficiency insights and problem diagnosis data are combined to obtain an efficiency insights and problem diagnosis report.

[0009] In some embodiments, the dynamic simulation of the efficiency insight and problem diagnosis report using causal reasoning and scenario simulation models to obtain patterns and pre-simulation results includes the following steps: The core causal path is obtained by analyzing the data in the efficiency insight and problem diagnosis report using a PC algorithm. The core causal path is quantified using structural equation modeling to obtain water use efficiency results; Based on the water use efficiency results, the water use scenario was simulated to obtain the patterns and simulation results.

[0010] In some embodiments, the analysis of data in the efficiency insight and problem diagnosis report using the PC algorithm can also be performed using the NOTEARS method. The calculation formula for analyzing the data in the efficiency insight and problem diagnosis report using the NOTEARS method is as follows: ; Where W represents the weighted adjacency matrix, X represents the observed data in the efficiency insight and problem diagnosis report, and n represents the number of samples. Denotes the squared Frobenius norm of a matrix. Represents the regularization parameter. Let h(W) denote the L1 norm of the matrix, and h(W) = 0 denote the acyclic constraint function.

[0011] In some embodiments, the step of solving the patterns and simulation results using cooperative game theory and distributed optimization algorithms to obtain a water resource quota allocation scheme includes the following steps: The aforementioned patterns and simulation results are solved using a multi-objective weighted optimization model to obtain preliminary allocation ratios; The Shapley value is calculated based on the preliminary allocation ratio to obtain the allocated water volume; The annual water use plan is dynamically fine-tuned by periodically combining the latest monitoring data, reservoir water storage forecasts, and efficiency and anomaly information from the efficiency insights and problem diagnosis reports through a rolling optimization mechanism. When an extreme anomaly or sudden pollution event is detected in the efficiency insight and problem diagnosis report, a water restriction or water outage sequence plan is obtained. The water quota allocation scheme is obtained by summarizing the water restriction or water outage sequence scheme.

[0012] In some embodiments, the calculation formula of the multi-objective weighted optimization model is:

[0013] in, This represents the weighting coefficients for economic, resource, and environmental objectives, where n represents the total number of industrial sectors within the region. Let represent the industrial added value of the i-th industry under decision variable xi. This represents the water consumption of the i-th industry under the decision variable xi. Let represent the main water pollutant emissions of the i-th industry under the decision variable xi, where xi represents the decision variable of the i-th industry.

[0014] To achieve the above objectives, another aspect of this application proposes a system for diagnosis and control based on multi-source data fusion of industrial water, the system comprising: A multi-source data acquisition and fusion module is used to acquire multi-source heterogeneous data; and to perform data cleaning and fusion on the multi-source heterogeneous data to obtain standardized data. The industrial water intelligent diagnostic module is used to analyze the standardized data through a dynamic evaluation model to obtain efficiency insights and problem diagnosis reports; The causal reasoning and scenario simulation module is used to dynamically simulate the efficiency insight and problem diagnosis report through the causal reasoning and scenario simulation model to obtain patterns and pre-simulation results. The collaborative regulation and optimization module is used to solve the aforementioned patterns and simulation results through cooperative game theory and distributed optimization algorithms to obtain a water resource quota allocation scheme.

[0015] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0016] The embodiments of this application include at least the following beneficial effects: This application provides a method and related equipment for diagnosis and control based on multi-source data fusion of industrial water use. This solution acquires multi-source heterogeneous data and performs data cleaning and fusion, transforming the raw data into a knowledge network with clear semantic relationships, laying a solid foundation for in-depth analysis; by analyzing standardized data through a dynamic evaluation model, it not only improves the overall efficiency and sustainability of water resource allocation, but also helps to balance the demands of different stakeholders and promote consensus formation; by dynamically simulating efficiency insights and problem diagnosis reports through causal reasoning and scenario simulation models, it not only improves the overall efficiency and sustainability of water resource allocation, but also helps to balance the demands of different stakeholders and promote consensus formation; by solving the patterns and pre-simulation results through cooperative game theory and distributed optimization algorithms, a water resource quota allocation scheme is obtained, which can quickly adapt to the differentiated industrial water management needs of different regions or different development stages, and supports continuous iterative evolution with the advancement of technology and management concepts. Attached Figure Description

[0017] Figure 1 This is a flowchart of a method for diagnosis and control based on multi-source data fusion of industrial water provided in an embodiment of this application; Figure 2 This is a flowchart illustrating a method for diagnosis and control based on multi-source data fusion in industrial water use. Figure 3 This is a schematic diagram of the structure of a system for diagnosis and control of industrial water based on multi-source data fusion provided in an embodiment of this application; Figure 4 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0019] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0020] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0022] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0023] Data Envelopment Analysis (DEA): It measures the relative efficiency of decision-making units through linear programming, taking into account both desirable and undesirable outputs. It is used for horizontal comparison of water efficiency among enterprises and outputs an efficiency score in the range of 0-1.

[0024] Stochastic Frontier Analysis (SFA): Constructs a production frontier, decomposes random error and technical inefficiency terms, and is used for macro-level industry / regional average efficiency assessment to quantify technical efficiency levels.

[0025] PC algorithm: Based on conditional independence test, redundant edges between variables are gradually eliminated, and a directed acyclic causal graph is learned from the observation data. It is a classic method for discovering causal structures.

[0026] NOTEARS: Transforms causal graph learning into a continuous optimization problem with acyclic constraints, and solves the weighted adjacency matrix using least squares and L1 regularization, making it suitable for high-dimensional data.

[0027] ETL tools: These tools extract, transform, and load data for cleaning, standardizing, and merging heterogeneous data from multiple sources, providing unified data for analysis.

[0028] COD: Chemical Oxygen Demand, a core indicator for measuring the degree of organic pollution in water bodies, reflects the intensity of the environmental impact of industrial wastewater discharge.

[0029] The Malmquist index measures changes in total factor productivity based on panel data, decomposing them into changes in technical efficiency and technological progress, and dynamically tracking the evolution trend of water efficiency.

[0030] Isolation Forest: Isolates samples by random trees and calculates anomaly scores based on path length. It is suitable for real-time detection of abnormal patterns in industrial water use (such as leaks and overproduction).

[0031] Water-saving potential: The difference between a company's actual water consumption and its efficiency benchmarks, which can be quantified to theoretically save water, is the core basis for setting water-saving targets and regulating quotas.

[0032] Efficiency score: A value between 0 and 1 output by methods such as DEA / SFA, which measures the relative efficiency of water resource utilization by the decision-making unit. The closer the score is to 1, the higher the efficiency.

[0033] DAG: Directed Acyclic Graph, a mathematical expression of causal relationships, with no loop paths, used to characterize unidirectional causal dependencies between variables.

[0034] Cause-effect graph: A graph structure consisting of nodes (variables) and directed edges (causal relationships) that intuitively reveals the interaction paths between variables and is the core carrier of causal reasoning.

[0035] In related technologies, industrial water use data is scattered and heterogeneous in format, with inconsistent data definitions and update frequencies across different departments, making it difficult to achieve multi-source data fusion analysis and real-time updates. This can easily lead to decision-making delays and insufficient supporting evidence. Water use efficiency assessments often use static annual statistical indicators, lacking dynamic monitoring and real-time diagnosis throughout the entire process, making it impossible to accurately identify water use anomalies, efficiency shortcomings, and water-saving potential. Diagnosis and regulation are disconnected, with most providing only descriptive statistics and simple early warnings. A causal transmission mechanism from intelligent diagnosis to collaborative regulation has not been established, making it difficult to systematically reveal the causal relationship between water use efficiency and factors such as industrial structure, technological level, and policy intervention. As a result, diagnostic results cannot be transformed into precise and operable regulatory measures, leading to poor policy targeting and implementation effectiveness. At the same time, there is a lack of cross-enterprise and cross-industry collaborative regulation mechanisms, failing to consider the network effects and global optimization of industrial water use systems, and lacking the ability for regional-level water resource coordination and collaborative utilization.

[0036] In view of this, this application provides a method and related equipment for diagnosis and control based on multi-source data fusion of industrial water use. This solution acquires heterogeneous data from multiple sources, performs data cleaning and fusion, integrates data from statistical yearbooks, enterprise direct reports, IoT monitoring, remote sensing inversion, and other sources, establishes a unified data model and quality verification mechanism, and achieves real-time access and dynamic updates of industrial water use data. It analyzes standardized data through a dynamic evaluation model, constructs a dynamic evaluation model for industrial water use efficiency based on machine learning and causal inference methods, and identifies abnormal water use patterns, efficiency bottlenecks, and water-saving potential areas in real time. It dynamically simulates efficiency insights and problem diagnosis reports through causal reasoning and scenario simulation models, further supporting control scenario simulation based on diagnostic results, and achieving causal connection from problem identification to measure evaluation. It solves for patterns and pre-simulation results through cooperative game theory and distributed optimization algorithms, using the diagnosed water-saving potential, abnormal areas, and key influencing factors as input, and constructs a cross-enterprise and cross-industry collaborative model for water resource allocation and recycling based on multi-objective optimization and game theory methods, achieving balanced optimization of economic, environmental, and social benefits, and ensuring that the control scheme is both targeted and systematic.

[0037] Figure 1 This is an optional flowchart of the method for diagnosis and control based on multi-source data fusion of industrial water provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S110 to S150.

[0038] Step S110: Obtain multi-source heterogeneous data; Step S120: Perform data cleaning and fusion on the multi-source heterogeneous data to obtain standardized data; Step S130: Analyze the standardized data using a dynamic evaluation model to obtain an efficiency insight and problem diagnosis report; Step S140: Dynamically simulate the efficiency insight and problem diagnosis report through causal reasoning and scenario simulation model to obtain patterns and pre-simulation results; Step S150: Solve the patterns and simulation results through cooperative game theory and distributed optimization algorithms to obtain the water resource quota allocation scheme.

[0039] Steps S110 to S150 as shown in the embodiments of this application first involve acquiring multi-source heterogeneous data in the field of industrial water use, performing unified data cleaning and fusion processing to form standardized and usable data; then, based on a dynamic evaluation model, performing in-depth analysis on the standardized data to output a water use efficiency insight and problem diagnosis report; next, using causal reasoning and scenario simulation models to conduct dynamic simulation of the diagnosis results, revealing the evolution law of water use and completing the pre-drilling of control scenarios; finally, using cooperative game theory and distributed optimization algorithms to solve the laws and pre-drilling results, generating a scientific and reasonable water resource quota allocation scheme.

[0040] like Figure 2As shown, to address the needs of intelligent management and collaborative control of industrial water use, a comprehensive platform integrating data aggregation, intelligent analysis, simulation, and optimization decision-making is constructed through a layered decoupling and modular design approach. It is divided into four core functional layers according to functional logic and data flow, forming a complete closed loop of "data-information-knowledge-decision": First, the multi-source data acquisition and fusion layer accesses heterogeneous data from statistical reporting, IoT monitoring, and remote sensing meteorology. Through semantic alignment, entity linking, and data quality assessment technologies, data cleaning, alignment, and fusion are completed, constructing a unified standardized data model and a high-quality industrial water use data resource system, providing reliable data support for upper-layer applications. Next, relying on the intelligent industrial water use diagnosis layer, models such as Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) are used based on the fused data to dynamically evaluate the water use efficiency of enterprises, industries, and regions, conduct water use anomaly pattern detection and early warning, and identify water-saving potential by benchmarking against industry leaders, forming a practical solution. The system provides efficiency insights and problem diagnosis reports. Subsequently, through causal reasoning and scenario simulation layers, it uses PC algorithms or NOTEARS to learn the causal relationships between industrial water use and key factors such as industrial structure, technological upgrading, and water pricing policies. It quantifies the degree of impact using structural equation modeling and sets policy intervention parameters through a scenario simulation engine to dynamically predict the impact of policies on total water consumption, efficiency, and structure. Finally, in the collaborative regulation and optimization layer, it constructs a multi-objective optimization model that maximizes economic output and minimizes total water consumption and pollutant emissions, taking into account regional water resource constraints such as water resource red lines, environmental capacity, and economic costs. It uses cooperative game theory and distributed optimization algorithms to solve for cross-enterprise and cross-industry water resource quota allocation schemes. This can be combined with real-time monitoring to achieve dynamic rolling optimization and emergency dispatch, thus realizing intelligent collaborative regulation throughout the entire process from multi-source data acquisition, cleaning and fusion, efficiency diagnosis, causal simulation to water resource quota allocation and water resource quota allocation scheme generation.

[0041] In some embodiments, in steps S110 to S120, firstly, through multi-source heterogeneous data access and data cleaning and fusion, the dispersed, heterogeneous, and multimodal industrial water-related data are uniformly processed into high-quality, standardized data that can be directly used for analysis, providing a reliable data foundation for subsequent intelligent diagnosis and collaborative control.

[0042] In the data access phase, a standardized and scalable multi-source data access channel is established to achieve comprehensive data collection across the entire industrial water supply chain. For statistical and reporting data, data is collected periodically via API interfaces or ETL tools, including structured statistical indicators such as total regional industrial water consumption, industry-specific water intake and discharge, water consumption per 10,000 yuan of industrial added value, and water reuse rate. For IoT online monitoring data, the system connects to online monitoring instruments at key enterprise water intakes, discharge points, and critical process nodes, utilizing communication protocols such as MQTT and HTTP to achieve second-level real-time streaming data acquisition, obtaining real-time operating parameters such as instantaneous flow rate, cumulative water volume, pH, and COD. Simultaneously, remote sensing and meteorological data are integrated, including precipitation, evaporation grid data, satellite-derived surface water area, and soil moisture products, to support regional water resource endowment assessment. Furthermore, economic and social data such as industrial added value, enterprise size distribution, and water pricing policies from various industries are integrated, and in-depth information such as enterprise water use audit data, water balance test reports, process flow diagrams, and equipment lists are imported through standardized templates, forming a comprehensive data system covering resources, production, environment, and management.

[0043] In the data fusion and modeling phase, the transformation and governance of raw, multi-source data into standardized data assets are completed. Addressing issues such as inconsistent naming, inconsistent units, and incompatible definitions across different data sources, an industrial water use ontology is constructed, providing unified semantic definitions for core concepts, indicator attributes, and relationships. Based on this, entity parsing and entity linking technologies are used to map data scattered across different departments and systems onto standardized entities such as "enterprise," "industry," and "administrative division," achieving semantic alignment and logical association across data sources. Based on the aligned unified entities, a three-tiered thematic data model of "enterprise-industry-region" is constructed. The enterprise-level model focuses on water-using units, process steps, and equipment information; the industry-level model emphasizes efficiency indicator aggregation and industrial structure characteristics; and the regional-level model integrates macro-level factors such as water resources, economy, and environment. The model supports temporal dimension expansion, fully recording the historical changes and dynamic evolution of indicators.

[0044] To ensure data availability, reliability, and traceability, a comprehensive data quality control system has been established, evaluating data from multiple dimensions, including completeness, consistency, accuracy, and timeliness. A built-in rule engine automatically verifies field value ranges and data logical relationships, while machine learning algorithms identify outliers, missing values, and conflicting data. A data quality reporting and data traceability mechanism is also provided, automatically triggering alerts for abnormal or low-quality data and fully recording the entire process of data cleaning, transformation, and correction. This ensures that the data governance process is supervised and traceable, significantly improving the reliability and transparency of standardized data and laying a high-quality data foundation for subsequent dynamic evaluation, intelligent diagnosis, and optimization.

[0045] In some embodiments, in step S130, dynamic efficiency assessment involves a combined approach of SFA and DEA. Specifically, the dynamic efficiency assessment stage in the intelligent diagnostic process for industrial water use utilizes a combination of Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA) to achieve efficiency diagnosis from both macro and micro perspectives. At the macro level, Stochastic Frontier Analysis (SFA) is used to assess the average technical efficiency of an industry or region, and its basic form is as follows: ; in, Let i represent the desired output of the i-th decision-making unit in period t. This represents the corresponding input vector. This represents the vector of parameters to be estimated. This represents a random error term that follows a normal distribution and is used to capture uncontrollable external random shocks. The non-negative technical inefficiency term reflects the gap between actual output and the theoretical frontier; the larger the value, the lower the technical efficiency. By estimating the maximum likelihood of this model, an average efficiency score at the industry or regional level can be obtained, providing a benchmark for macroeconomic decision-making.

[0046] At the micro level, Data Envelopment Analysis (DEA) is used to evaluate the relative efficiency among firms. It obtains each firm's efficiency score by solving the following linear programming problem. : ; Where X and Y represent the input and output matrices of all decision-making units, respectively. and Let represent the input-output vector of the k-th firm. Data Envelopment Analysis (DEA) considers both industrial value added (desired output) and wastewater discharge (undesired output) to provide a more comprehensive evaluation of a firm's overall efficiency in water resource utilization and environmental protection. The closer the efficiency score is to 1, the higher the firm's relative efficiency.

[0047] To dynamically track efficiency evolution trends, the Malmquist index is calculated based on panel data: ; Where M represents the total factor productivity index. and Let these represent the input vectors for period t and period t+1, respectively. and Let represent the output vectors for period t and period (t+1), respectively. This represents the distance function of the production point in period t, with the production technology in period t as a reference. This represents the distance function between the production point in period t+1 and the production technology in period t. This represents the distance function of the production point in period t, with the production technology in period t+1 as a reference. This represents the distance function of the production point in period t+1 when the production technology in period t+1 is taken as a reference.

[0048] The Malmquist index can be further decomposed into two components: change in technical efficiency (EC) and technological progress (TC). Change in technical efficiency reflects improvements in management and resource allocation, while technological progress represents innovations in production processes and water-saving technologies. By analyzing the contributions of these two components, the key factors driving efficiency increases or decreases can be precisely identified.

[0049] In intelligent anomaly detection, a dual-engine approach combining Isolation Forest and autoencoder is employed. Specifically, the intelligent anomaly detection process utilizes machine learning algorithms to monitor real-time water usage data and promptly identify abnormal water usage behavior. The Isolation Forest algorithm isolates each sample by constructing multiple random binary trees. Due to their uniqueness, anomalous samples are isolated earlier in the trees, meaning their path length is shorter. The formula for calculating the anomaly score is as follows: ; Where h(x) represents the average path length of sample x in the random forest, and c(n) represents the average path length of n samples. The closer the score s(x,n) is to 1, the greater the probability that the sample is an outlier. When the score exceeds a preset outlier threshold... When an enterprise is identified as abnormal, its production plan, equipment operation, and other data are automatically linked to perform preliminary attribution for leaks, overproduction, etc., and graded alarms are issued according to the severity of the problem.

[0050] In addition, an autoencoder is used for anomaly detection. The autoencoder works by minimizing the reconstruction error. This method learns the feature representation of normal water use patterns. When new water use data is input, if its reconstruction error is significantly higher than the normal level, i.e., the deviation is high, it is judged as an anomaly. This method can capture more complex nonlinear anomaly patterns, complementing the isolation forest approach and improving the accuracy and robustness of anomaly detection.

[0051] Water-saving potential is identified through benchmarking. After efficiency assessment and anomaly detection, benchmarking is used to quantify a company's water-saving potential. First, a multi-dimensional water efficiency benchmark database is established, including benchmark values ​​at different levels, such as national water-saving benchmarks, industry-leading standards, and international advanced levels. These benchmarks are finely categorized by industry, product type, and company size. Then, for each company e, its actual efficiency score obtained through DEA ​​is calculated. (e) Compare its theoretical water-saving potential with the industry benchmark efficiency in its category, using the following formula: ; in, This indicates the amount of water that company e can save when it reaches the benchmark efficiency level. In this way, a structured efficiency insight and problem diagnosis report can be generated for each company, clearly identifying its efficiency shortcomings, abnormal risks, and specific water-saving potential, providing precise and quantitative decision-making basis for subsequent coordinated control.

[0052] In the algorithm flow and output process, the industrial water intelligent diagnostic algorithm (Algorithm 1) is the core carrier of the above functions. It takes a set of enterprises E and multi-source fused data D as input, and outputs structured diagnostic results through a modular process. In stage 1, the algorithm performs DEA evaluation on each enterprise e∈E to obtain its efficiency score η(e). In stage 2, the algorithm uses an isolation forest to detect anomalies in the water use data of each enterprise, calculates anomaly scores s(e), and identifies those exceeding a threshold. The companies were categorized into anomaly set A. In phase 3, the algorithm used a benchmarking method to calculate the water-saving potential of each company. Ultimately, the algorithm outputs a set of efficiency scores for all companies. Abnormal enterprise set A and the water-saving potential of each enterprise. This will generate a comprehensive efficiency insight and problem diagnosis report, providing support for subsequent coordinated regulation.

[0053] The processing procedure of the intelligent diagnostic algorithm for industrial water use is as follows: Input: Set of firms E, multi-source data D Output: Efficiency Score Exception set A, water-saving potential

[0054] 1: / / Phase 1: Water Use Efficiency Assessment (Data Envelopment Analysis (DEA)) 2: Perform the following for each enterprise e∈E: 3:η(e) Perform a DEA assessment (data D(e)) 4: / / Phase 2: Abnormal Water Use Detection (Isolation Forest Algorithm) 5: Perform the following for each enterprise e∈E: 6: Abnormal score s(e) Isolated forest detection (data D(e)) 7: If s(e) > abnormal threshold but: 8: Add e to the exception set A 9: / / Phase 3: Identifying Water-Saving Potential (Benchmarking Method) 10: Perform the following for each enterprise e∈E: 11: ( (e) Industry benchmark efficiency) × Firm output (e) 12: return A,

[0055] In some embodiments, in step S140, the core starting point for causal reasoning and scenario simulation is the accurate construction of the causal network. This process employs the PC algorithm or the NOTEARS method in tandem, and integrates expert knowledge to uncover the causal relationships between variables, laying the foundation for subsequent mechanistic analysis. Specifically, the PC algorithm uses conditional independence testing as its core logic. By gradually eliminating redundant connections between variables, it ultimately identifies the core causal path, with conditional independence being its core test. That is, to verify whether variables X and Y have an independent relationship under the premise of controlling the set of variables Z, so as to determine whether the causal connection between them is valid.

[0056] To address the need for causal structure learning in high-dimensional data, the NOTEARS method can be used as a supplement. This method solves the least squares problem through continuous optimization, completely avoiding the discrete search problem of traditional causal discovery algorithms. Its optimization model is as follows: In the formula, W represents the weighted adjacency matrix characterizing the strength of the causal relationship between variables, X represents the observation data matrix containing core indicators such as water consumption, water use efficiency, industrial structure, and water price, and n represents the sample size. The squared Frobenius norm of a matrix is ​​used to measure the error in data reconstruction. Represents the regularization parameter. The L1 regularization term is used to achieve sparsity optimization of the causal network. The constraint h(W)=0 is the core acyclic constraint, ensuring that the learned weighted adjacency matrix W corresponds to a directed acyclic graph (DAG), which conforms to the logical essence of causality. In actual implementation, expert knowledge K in the field of industrial water management is combined to modify the initial causal network generated by the algorithm, eliminating connections that do not conform to industry logic and supplementing key implicit paths, significantly improving the credibility and practicality of the causal graph G.

[0057] For the numerical analysis of causal paths in structural equation modeling, specifically, after constructing the causal graph G, the impact of the core causal paths is precisely quantified using structural equation modeling, transforming qualitative causal relationships into quantitative effect values ​​E, providing numerical support for policy simulation. The effect quantification process is based on the paths in the causal graph G, focusing on measuring the direct, indirect, and total effects of each macroeconomic variable on core diagnostic indicators such as water use efficiency and water consumption.

[0058] For complex causal paths with mediating variables, such as "water price" Production process Water efficiency The algorithm calculates the "water consumption" by multiplying the coefficients of each segment along the intermediate path according to the path coefficient calculation rules of structural equation modeling. This yields the indirect effect, which is then combined with the direct impact coefficients of the variables on the result, and summed to obtain the total effect. This process clarifies the influence weights of different factors, such as the direct improvement effect of industrial structure optimization on water efficiency and the indirect water-saving effect of water pricing policies that drive technological upgrades. The quantified effect value E not only reveals the core regulatory levers of the industrial water system but also provides precise numerical parameters for model extrapolation in subsequent scenario simulations, ensuring the scientific validity and reliability of the patterns and simulation results.

[0059] During policy scenario simulation, water use trends are pre-simulated based on causal mechanisms. Specifically, scenario simulation is a crucial link between causal analysis and regulatory decision-making. Its core is to dynamically extrapolate industrial water use trends under different policy scenarios P ​​based on the constructed causal graph G and quantified effect values ​​E. The algorithm traverses all preset policy scenarios, including typical intervention schemes such as water price adjustments, industrial restructuring, and the promotion of water-saving technologies, and applies these scenarios accordingly. By adjusting the parameter values ​​of the corresponding policy variables in the causal network, the model is driven to complete the transmission calculation of the entire path, and finally outputs the pattern and prediction results of water use changes under this scenario. It covers the medium- and long-term trends of core indicators such as total regional water consumption, industry water use efficiency, and water-saving potential of key enterprises.

[0060] For example, when simulating a policy scenario of a 10% increase in water prices, the algorithm calculates the extent of process improvements required by enterprises due to the price increase based on the path coefficient of "water price - technology upgrade - water consumption" in the effect value E. It then further extrapolates the effect of these improvements on water efficiency, ultimately obtaining the projected decrease in total regional water consumption. This process enables a forward-looking simulation of the policy's implementation effects, allowing for a direct comparison of the advantages and disadvantages of different control measures. It provides decision-makers with a visual reference and accumulates core data for subsequent parameter conversion.

[0061] In the parameter transformation that connects causal conclusions to optimization constraints, the ultimate goal of the causal reasoning and scenario simulation module is to transform diagnostic results, causal mechanisms, and simulation conclusions into optimization parameters that the collaborative regulation optimization module can directly use. This establishes a closed loop of "diagnosis-analysis-regulation." The transformation process is completed through three core steps, realizing the implementation of practical solutions from theoretical analysis.

[0062] The first step is standardization; the algorithm converts the efficiency score output by the intelligent diagnostic module. Water-saving potential Combined with the causal effect value E, differentiated industrial water efficiency access standards are formulated for the core causal paths of different industries. For example, in water-intensive industries where "technological level" is a core influencing factor, higher efficiency thresholds will be set based on the efficiency improvement resulting from technological advancements in the causal relationship. The second step is constraint quantification, based on the water-saving potential at the enterprise level. Based on the patterns and preliminary results of policy scenario simulations (S), a comprehensive assessment of the region's water resource carrying capacity is conducted to ultimately determine the upper limit constraints on total water consumption for each industry. This sets the boundaries for subsequent water resource quota allocation. The third step is weight adjustment, which dynamically configures the weight coefficients α of the three major objectives of economy, resources, and environment in the multi-objective optimization model based on the magnitude and direction of the causal effect value E. For example, policy variables that have a significant impact on water use efficiency will have their resource objectives weighted accordingly.

[0063] Ultimately, , , Integrate into optimized parameters This is output together with the causal graph G and the effect value E. This process transforms the theoretical conclusions of causal reasoning into specific optimization constraints and target weights, becoming a key bridge connecting the intelligent diagnostic module and the collaborative regulation and optimization module, ensuring the accuracy and operability of subsequent water resource quota allocation schemes.

[0064] The aforementioned functions are executed sequentially in a modular fashion through the causal reasoning and parameter transformation algorithm (Algorithm2), which serves as the core logical carrier of the causal reasoning and scenario simulation layer. The algorithm uses diagnostic indicators... , The calculation is performed in four stages, using macroeconomic variables V, expert knowledge K, and policy scenarios P ​​as inputs: The first stage uses the PC algorithm or NOTEARS method combined with expert knowledge to learn the causal network and output a causal graph G; the second stage calculates the impact of each path based on structural equation modeling to obtain the effect value E; the third stage traverses all policy scenarios to complete the model extrapolation of water use change trends, generating patterns and preliminary results S; the fourth stage generates optimized parameters through standard transformation, constraint quantification, and weight adjustment. The final algorithm outputs the causal graph G, the effect size E, and the optimized parameters. This not only completed the mechanistic analysis and pattern prediction of the industrial water system, but also provided scientific and clear input conditions for subsequent cooperative game theory and distributed optimization algorithm solutions.

[0065] Input: Diagnostic indicators , Macroeconomic variable set V, expert knowledge K, policy scenario P Output: Cause-and-effect diagram G, effect size E, optimized parameters

[0066] 1: / / Stage 1: Causal Discovery 2:G Learning causal networks based on variable V and expert knowledge K 3: / / Phase 2: Effect Quantification 4:E Calculate the impact of each path in the cause-effect graph G. 5: / / Phase 3: Policy Scenario Simulation 6: For each policy scenario p∈P, execute: 7: Water usage change S[p] Model derivation based on G and E (p) 8: / / Phase 4: Parameter Conversion 9: _std based on And E sets standard efficiency 10:W_max The upper limit of water volume is determined based on ΔW, the pattern, and the prediction result S. 11:α Adjusting and optimizing weights based on causal mechanisms 12: { _std,W_max,α} 13: return G, E,

[0067] In some embodiments, in step S150, the multi-objective optimization model is applied to the synergistic trade-off between economy, resources, and environment. Specifically, the core of the synergistic regulation optimization module is to construct and solve a multi-objective weighted optimization model, whose objective function directly responds to the conclusions of intelligent diagnosis and causal simulation: while pursuing the maximization of regional industrial added value (economic objective), it is necessary to minimize the total industrial water consumption (resource objective, corresponding to the diagnosed water-saving potential) and the total discharge of major water pollutants (environmental objective). The mathematical expression is as follows: ; in, This represents the economic output (such as industrial value added) of industry i. Indicates its water consumption. This represents the amount of its main water pollutants discharged, and xi represents the decision variables (such as production scale, technological level, etc.). Weighting coefficients. The causal reasoning module dynamically adjusts the parameters to balance the priorities of the three major objectives: economy, resources, and environment. The constraints directly incorporate the efficiency benchmarks (such as the upper limit of enterprise unit consumption) from the intelligent diagnostic module and the policy paths (such as the upper limit of total water consumption) from the scenario simulation module, ensuring that the optimized solution is both scientific and feasible.

[0068] Cooperative game theory and Shapley values ​​are used for the unified allocation of water resources that balance efficiency and fairness. Specifically, in solving complex multi-objective optimization problems, an advanced cooperative allocation algorithm is employed. Different industries or enterprises are considered as game participants, and water resource quotas and pollution discharge rights are treated as allocable resources. Using the concept of equal solutions based on Shapley values, a water resource quota allocation scheme is found that satisfies both optimal overall water resource utilization efficiency and is considered fair and acceptable by all participants, thereby incentivizing collaborative water conservation and emission reduction. The formula for calculating the Shapley value is shown below: ; Where v(S) represents the water use efficiency of the alliance S. This represents the fair share that industry or firm i should receive in the cooperative game. The total water volume obtained through multi-objective optimization is calculated using the Shapley value. The water is allocated fairly to various industries or enterprises to obtain an initial quota. This ensured the acceptability and incentive of the plan.

[0069] For complex water recycling networks involving numerous enterprises, distributed optimization algorithms (such as ADMM) are employed. This algorithm, while protecting enterprise privacy data, coordinates inter-enterprise water use and reclaimed water reuse through limited information exchange, achieving system-level water conservation goals. Simultaneously, to ensure the accuracy and adaptability of the water resource allocation scheme, a dynamic control mechanism is established. The rolling optimization mechanism periodically (e.g., monthly or quarterly) dynamically fine-tunes the annual water use plan based on the latest monitoring data, reservoir storage forecasts, and real-time efficiency and anomaly information updated by the intelligent diagnostic module, ensuring that the water resource allocation scheme remains synchronized with the actual system status. When the intelligent diagnostic module detects extreme anomalies or sudden pollution events, the system automatically triggers an emergency dispatch mode, quickly generating a water restriction and shutdown sequence scheme with safety as its core priority.

[0070] To strengthen the constraints on water use behavior, the collaborative regulation and optimization algorithm introduces a penalty mechanism based on anomaly detection results. If the anomaly set A of the intelligent diagnosis module is not empty, the algorithm will penalize each anomalous enterprise. Implementing quota reductions (e.g., halving the allocated water volume) incentivizes businesses to regulate water use and improve efficiency. Ultimately, the water allocation quotas calculated using Shapley values, along with the priority schemes for water restriction and outages under emergency conditions, are aggregated to form a complete water resource quota allocation scheme, Salloc. This scheme serves as both the endpoint of the decision-making logic and the starting point for implementation, transforming "current situation and potential assessment" and "intervention path and effect prediction" into concrete actions that balance efficiency and fairness. This forms a complete decision-making loop, from "intelligent diagnosis of the current situation" to "clarifying the path through causal reasoning" and finally to "collaborative optimization to generate a solution."

[0071] The collaborative regulation optimization algorithm (Algorithm3) is the core carrier of the above functions, used to optimize parameters. and total available water volume The process, using this as input, involves three core steps: First, a multi-objective weighted optimization model is constructed and solved to achieve synergistic optimization of the three objectives while satisfying constraints such as total water consumption, unit water consumption efficiency, and potential upper limit. Second, based on cooperative game theory, the Shapley value is calculated for each industry or enterprise, and the initial water volume is allocated accordingly. Finally, quota penalties are imposed on enterprises with abnormal performance, resulting in the final water resource quota allocation scheme, Salloc. This algorithm ensures the scientific validity, fairness, and incentive effect of the final water resource quota allocation scheme, providing solid decision-making support for the sustainable utilization of regional industrial water resources.

[0072] Input: Optimization parameters Total available water volume W_total Output: Water resource quota allocation scheme S_alloc 1: / / Step 1: Construct and solve the multi-objective weighted optimization model 2: Solving the optimization problem: 3: Objective: Maximize 1×Total Output 2×Total water consumption 3× Total Emissions 4: Constraints: 5: Total water consumption ≤ W_total 6: Each enterprise's water consumption per unit is less than or equal to its standard efficiency η_std 7: The water allocated to each enterprise is less than or equal to its potential upper limit W_max 8: / / Step 2: Fair allocation based on cooperative game theory (Shapley value) 9: Perform the following for each industry i: 10: _i Calculate the Shapley value (game v, participant i) 11:W_alloc(i) _i×W_total 12: / / Step 3: Penalty Mechanism for Abnormal Enterprises 13: If the exception set A is not empty, then: 14: For each abnormal enterprise e∈A, execute: 15: W_alloc(e) W_alloc(e)×0.5 / / Water volume halved 16: Water resource quota allocation scheme S_alloc {W_alloc} 17:returnS_alloc Below, we will provide a detailed introduction and explanation of the water resource quota allocation scheme of this invention, using a specific application example from an industrial cluster. This area covers four leading industries: chemical, electronics, textile, and equipment manufacturing, and includes 320 large-scale industrial enterprises. In recent years, the total industrial water consumption has fluctuated between 800 million and 1.2 billion cubic meters per year. There are prominent problems such as data incompatibility across departments, low water efficiency of some enterprises, and uneven distribution of water resources among industries. This system is well-suited to achieve intelligent management and control of the entire process, helping to solve existing management pain points.

[0073] Steps S110 to S120 constitute the basic data support layer, with inputs covering four types of data: Statistical reporting data: By connecting to the statistical systems of the Provincial Department of Industry and Information Technology, the Department of Water Resources and the Department of Environmental Protection via API, annual / quarterly data such as industrial added value, water consumption and reuse rate of 320 enterprises were obtained. Among them, in 2023, the water consumption per 10,000 yuan of industrial added value in the industrial cluster was 45 cubic meters / 10,000 yuan, and the COD emissions from the chemical industry accounted for 62%. IoT monitoring data: 1,200 sets of online monitoring instruments were installed at the water intake and drainage outlets of key enterprises. The data was collected in seconds using the MQTT protocol, covering dynamic parameters such as instantaneous water intake, drainage pH value (standard 6-9), and COD concentration (threshold ≤80mg / L for key enterprises). Remote sensing meteorological data: By accessing satellite-retrieved regional surface water area, soil moisture, and precipitation data from meteorological departments, the surface water area in the summer of 2023 decreased by 15% compared to the spring, and precipitation during the dry season was 40% less than normal. Supplementary data: Integrating economic and social data such as the output value ratio of various industries (chemicals 35%, electronics 28%, textiles 22%, equipment manufacturing 15%), as well as water use audit data such as water balance test reports of textile enterprises and process flow diagrams of chemical enterprises.

[0074] These multi-source heterogeneous data enter the "data fusion and modeling" stage: construct an ontology for the industrial water sector, unify the definitions and units of core concepts such as "water intake" and "reuse rate"; associate data from different sources with three-level entities of "enterprise-industry-region" through entity linking technology; use a rule engine to verify logical consistency, and use the isolated forest algorithm to detect outliers (such as an electronics company whose daily water intake suddenly increased by 300% being judged as an instrument failure and triggering an alarm).

[0075] Ultimately, a standardized data resource library with 98% data integrity and 99% accuracy is formed, outputting a standardized dataset covering the entire chain of "source-supply-use-discharge-reuse" (including enterprise-level real-time water use data, industry-level efficiency indicators, and regional-level macro data), providing standardized data input for the next layer of modules.

[0076] In step S130, building upon the standardized data from the previous layer, the core is Algorithm1 (Intelligent Diagnostic Algorithm for Industrial Water Use), and three analytical tasks are performed based on the data: Efficiency Assessment (DEA / SFA): The DEA model was used to assess the micro-level relative efficiency of 320 enterprises. Input indicators included water intake, fixed assets, and number of employees; output indicators included industrial added value (desired output) and wastewater discharge (undesired output). Simultaneously, the SFA model was used to track macro-level efficiency, decomposing changes in technical efficiency and technological progress. Results showed that the average efficiency score was 0.89 for the electronics industry, 0.82 for equipment manufacturing, 0.75 for textiles, and 0.68 for chemicals. One chemical company, e1, had a DEA efficiency score of only 0.65, far below the industry benchmark of 0.92.

[0077] Anomaly Detection (Isolated Forest): Setting anomaly thresholds =0.8, anomaly score of 0.87 is used to score the real-time water consumption data of a certain textile company (e2) for three consecutive days due to equipment leakage. The company was included in the abnormal set A; a total of 9 abnormal companies were identified, of which 7 had leakage problems and 2 had excessive water consumption.

[0078] Potential Identification (Benchmarking Method): Based on an industry-specific benchmark database, using formulas... = ( (e) The water-saving potential is calculated by multiplying the industry benchmark efficiency by the enterprise output (e). For example, the water-saving potential of enterprise e1 is 32.4 million cubic meters, and the total water-saving potential of the entire industrial cluster is 280 million cubic meters.

[0079] This layer ultimately outputs an efficiency insight and problem diagnosis report, including efficiency scores for each enterprise. Anomaly set A (9 companies) clearly identifies the chemical industry as an area with efficiency shortcomings and equipment leakage as the main anomaly type, providing diagnostic results for the next step.

[0080] In step S140, based on the diagnostic results of the previous step, the core is Algorithm2 (causal reasoning and parameter transformation algorithm), which mainly completes three tasks: Causal discovery (PC algorithm): A set of macroeconomic variables V, such as industrial structure proportion, water price, technology input, and precipitation, is selected. This is combined with water conservancy expert knowledge K, and a causal network G is learned through the PC algorithm to discover "water price increases..." Increased corporate investment in technology "Improving water efficiency" and "Too high proportion of chemical industry" The core causal pathways include "the increase in total regional water consumption".

[0081] Effect Quantification (Structural Equation Model): Based on the structural equation model, the causal effect was quantified, and the results showed that for every 10% increase in water price, the water efficiency of enterprises improved by 3.2%; the proportion of the chemical industry decreased by 1 percentage point, and the total regional water consumption decreased by 0.8%.

[0082] Scenario simulation (policy extrapolation): Three policy scenarios were set up - Scenario P1 (water price increases by 15%), Scenario P2 (chemical industry share decreases by 3 percentage points), and Scenario P3 (water price increases by 10% + technology subsidies). The extrapolation showed that P1 could reduce the total water consumption of the agglomeration area by 4.8%, P2 by 2.4%, and P3 by 6.5%, and P3 had the least impact on economic output (only a decrease of 0.3%).

[0083] After completing the above analysis, this layer performs parameter transformation: setting the standard efficiency for the chemical and textile industries to ≥0.8, and for the electronics and equipment manufacturing industries to ≥0.9; setting the total water consumption limit for the industrial cluster to 900 million cubic meters per year, and the water consumption limit for a single enterprise in the chemical industry to ≤500,000 cubic meters per month; and adjusting and optimizing the weights. (Economic output 0.4, total water consumption 0.35, pollutant emissions 0.25), final output optimized parameters. ={ _std,W_max, This provides control parameters for the next step.

[0084] In step S150, the control parameters from the previous layer are received. The core of this step is Algorithm3 (a collaborative control optimization algorithm), which, combined with the total available water volume W_total = 900 million cubic meters, performs three control functions: Multi-objective optimization (economic / resource / environment): Construct a multi-objective weighted optimization model to "maximize 0.4 × total output". 0.35 × Total Water Consumption The target is "0.25 × total emissions", while simultaneously meeting the requirements of total water consumption ≤ 900 million cubic meters and water consumption per enterprise ≤ corresponding Given constraints such as _std and monthly water consumption of chemical enterprises ≤ 500,000 cubic meters, the preliminary allocation ratio is obtained as follows: electronics industry 32%, equipment manufacturing 28%, textiles 20%, and chemicals 20%.

[0085] Collaborative Allocation (Shapley Value): Treating the four major industries as game participants, the Shapley value (electronics) is calculated. 1=0.33, Equipment Manufacturing 2=0.29, Textiles 3=0.19, Chemical Industry 4=0.19), corresponding to the following total water allocations: electronics 297 million cubic meters, equipment manufacturing 261 million cubic meters, textiles 171 million cubic meters, and chemicals 171 million cubic meters.

[0086] Dynamic regulation (rolling optimization / emergency dispatch): Implement a penalty mechanism of halving the water quota for the 9 enterprises in the abnormal set A (e.g., the original quota of a certain abnormal chemical enterprise was 400,000 cubic meters / month, which was adjusted to 200,000 cubic meters / month, and rectification was required to be completed within 1 month); establish a rolling optimization mechanism to dynamically fine-tune the quota every month based on real-time data; set up an emergency dispatch plan, and when the precipitation is 50% less than normal during the dry season, priority will be given to ensuring water supply for electronic and equipment manufacturing enterprises, and the quota for chemical and textile enterprises will be temporarily reduced by 10%.

[0087] This layer ultimately outputs a water resource quota allocation scheme, S_alloc, which includes monthly water quotas for various industries and enterprises, supporting policies (water price adjustments, technology subsidies, and guidelines for the transformation and upgrading of the chemical industry), a list of abnormal enterprises requiring rectification, and dynamic scheduling rules. After implementation, the scheme is expected to control the total water consumption in the industrial cluster to 880 million cubic meters per year, reduce water consumption per 10,000 yuan of industrial added value to 40 cubic meters per 10,000 yuan, save 280 million cubic meters of water, and reduce COD emissions by 8%, achieving a balanced optimization of economic, resource, and environmental benefits.

[0088] This application achieves multi-dimensional innovation and breakthroughs in the field of industrial water management: First, it realizes the integration and knowledge-based management of the entire industrial water supply chain, completely breaking down "data silos" and constructing an industrial water data resource system covering the entire chain of "source-supply-use-discharge-reuse" and connecting multiple levels of "enterprise-industry-region". Through ontology and data models, it transforms raw data into a knowledge network with clear semantic relationships, laying a solid foundation for in-depth analysis. Second, it drives industrial water management from "perceptual description" to "cognitive decision-making". The intelligent diagnostic module enables accurate efficiency profiling and rapid problem location, while the causal reasoning module deeply reveals "why" and "what will happen", completing the leap from descriptive analysis to explanatory and predictive analysis, and promoting management measures from experience-driven "extensive regulation" to mechanism- and data-based "precise targeted regulation". Third, it enhances the system's ability to coordinate and regulate regional industrial water use under complex constraints. By constructing an optimization model that considers multiple objectives and constraints related to economy, resources, and environment, and applying advanced algorithms such as game theory and distributed optimization, it provides scientific, fair, and operable cross-stakeholder collaborative solutions. This not only improves the overall efficiency and sustainability of water resource allocation but also balances the demands of different stakeholders, promoting consensus-building. Fourth, it significantly enhances the interactivity and collaborative efficiency of the decision-making process. Relying on an intuitive interactive "decision-making sandbox," it lowers the professional threshold, supports online collaborative consultations and solution simulations by multiple departments, effectively shortens the decision-making chain, and improves consultation efficiency and the scientific nature, transparency, and consensus of decisions. Fifth, it ensures the system's long-term viability and broad applicability. The modular and service-oriented architecture design allows for independent upgrades and replacements of data access, analysis models, optimization algorithms, and interactive interfaces. The system has strong configurability and can quickly adapt to the differentiated industrial water management needs of different regions and development stages, such as water-rich and water-scarce areas, incremental control and stock optimization, by adjusting parameters, indicators, models, and rules. It also supports continuous iterative evolution with advancements in technology and management concepts.

[0089] Please see Figure 3 This application also provides a system for diagnosis and control based on multi-source data fusion of industrial water, which can implement the above method. The system includes: The multi-source data acquisition and fusion module is used to acquire multi-source heterogeneous data; to clean and fuse the multi-source heterogeneous data to obtain standardized data; The industrial water intelligent diagnostic module is used to analyze standardized data through a dynamic evaluation model to obtain efficiency insights and problem diagnosis reports; The causal reasoning and scenario simulation module is used to dynamically simulate efficiency insights and problem diagnosis reports through causal reasoning and scenario simulation models to obtain patterns and pre-simulation results. The collaborative regulation and optimization module is used to solve the patterns and simulation results through cooperative game theory and distributed optimization algorithms to obtain a water resource quota allocation scheme.

[0090] It is understood that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0091] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0092] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0093] Please see Figure 4 , Figure 4 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 401 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 402 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 402 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 402 and is called and executed by the processor 401 using the methods described in the embodiments of this application. Input / output interface 403 is used to implement information input and output; The communication interface 404 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 405 transmits information between various components of the device (e.g., processor 401, memory 402, input / output interface 403, and communication interface 404); The processor 401, memory 402, input / output interface 403 and communication interface 404 are connected to each other within the device via bus 405.

[0094] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0095] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0096] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0097] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0098] Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, such that a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or apparatus.

[0099] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0100] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0101] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0102] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0103] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for diagnosis and control of industrial water based on multi-source data fusion, characterized in that, The method includes the following steps: Acquire multi-source heterogeneous data; The multi-source heterogeneous data is cleaned and fused to obtain standardized data; The standardized data is analyzed using a dynamic evaluation model to obtain efficiency insights and problem diagnosis reports. The efficiency insights and problem diagnosis reports are dynamically simulated using causal reasoning and scenario simulation models to obtain patterns and preliminary results. By solving the aforementioned patterns and simulation results using cooperative game theory and distributed optimization algorithms, a water resource quota allocation scheme is obtained.

2. The method according to claim 1, characterized in that, The multi-source heterogeneous data includes statistical and reported data, IoT monitoring data, remote sensing meteorological data, and economic and social data; The statistical and reporting data are obtained periodically via API or STL, and include the total industrial water consumption in the region, water intake and discharge by industry, water consumption per 10,000 yuan of industrial added value, and reuse rate. The IoT monitoring data is collected in real time from online instruments at the water intake or drainage outlets of key enterprises via MQTT or HTTP protocols. The IoT monitoring data includes instantaneous flow rate, cumulative water volume, pH, and COD. The remote sensing meteorological data includes precipitation data, evaporation grid data, and surface water area or soil moisture data retrieved from satellites; The economic and social data include industrial added value of various industries, enterprise size distribution, and water price policy documents.

3. The method according to claim 1, characterized in that, The process of cleaning and fusing the multi-source heterogeneous data to obtain standardized data includes the following steps: The multi-source heterogeneous data is associated with a unified enterprise-level model, industry-level model, and administrative division-level model through entity parsing and linking technology. Among them, the enterprise-level model focuses on water-using units and process links, the industry-level model focuses on efficiency indicators and structural characteristics, and the administrative division-level model focuses on the integration of macro indicators of water resources, economy and environment. The rule engine performs field range checks and logical verifications on the multi-source heterogeneous data, and machine learning methods are used to detect outliers.

4. The method according to claim 1, characterized in that, The process of analyzing the standardized data using a dynamic evaluation model to obtain an efficiency insight and problem diagnosis report includes the following steps: The standardized data is analyzed using data envelopment analysis to obtain efficiency scores for industrial added value and wastewater discharge. The standardized data were analyzed using stochastic frontier analysis to obtain the industry average efficiency score; The efficiency scores of industrial added value and wastewater discharge are compared with the industry average efficiency score to obtain efficiency insights. Anomaly scores are obtained by using the isolated forest algorithm to score real-time water usage data anomalies in the standardized data. The anomaly score is compared with the anomaly threshold to obtain problem diagnosis data; The efficiency insights and problem diagnosis data are combined to obtain an efficiency insights and problem diagnosis report.

5. The method according to claim 1, characterized in that, The process of dynamically simulating the efficiency insights and problem diagnosis reports using causal reasoning and scenario simulation models to obtain patterns and preliminary results includes the following steps: The core causal path is obtained by analyzing the data in the efficiency insight and problem diagnosis report using a PC algorithm. The core causal path is quantified using structural equation modeling to obtain water use efficiency results; Based on the water use efficiency results, the water use scenario was simulated to obtain the patterns and simulation results.

6. The method according to claim 5, characterized in that, The analysis of data in the efficiency insight and problem diagnosis report using the PC algorithm can also be performed using the NOTEARS method. The calculation formula for analyzing the data in the efficiency insight and problem diagnosis report using the NOTEARS method is as follows: ； Where W represents the weighted adjacency matrix, X represents the observed data in the efficiency insight and problem diagnosis report, and n represents the number of samples. Denotes the squared Frobenius norm of a matrix. Represents the regularization parameter. Let h(W) denote the L1 norm of the matrix, and h(W) = 0 denote the acyclic constraint function.

7. The method according to claim 1, characterized in that, The process of solving the patterns and simulation results using cooperative game theory and distributed optimization algorithms to obtain a water resource quota allocation scheme includes the following steps: The aforementioned patterns and simulation results are solved using a multi-objective weighted optimization model to obtain preliminary allocation ratios; The Shapley value is calculated based on the preliminary allocation ratio to obtain the allocated water volume; The annual water use plan is dynamically fine-tuned by periodically combining the latest monitoring data, reservoir water storage forecasts, and efficiency and anomaly information from the efficiency insights and problem diagnosis reports through a rolling optimization mechanism. When an extreme anomaly or sudden pollution event is detected in the efficiency insight and problem diagnosis report, a water restriction or water outage sequence plan is obtained. The water quota allocation scheme is obtained by summarizing the water restriction or water outage sequence scheme.

8. The method according to claim 7, characterized in that, The calculation formula for the multi-objective weighted optimization model is as follows: in, This represents the weighting coefficients for economic, resource, and environmental objectives, where n represents the total number of industrial sectors within the region. Let represent the industrial added value of the i-th industry under decision variable xi. This represents the water consumption of the i-th industry under the decision variable xi. Let represent the main water pollutant emissions of the i-th industry under the decision variable xi, where xi represents the decision variable of the i-th industry.

9. A system for diagnosis and control based on multi-source data fusion in industrial water use, characterized in that, The system includes: A multi-source data acquisition and fusion module is used to acquire multi-source heterogeneous data; and to perform data cleaning and fusion on the multi-source heterogeneous data to obtain standardized data. The industrial water intelligent diagnostic module is used to analyze the standardized data through a dynamic evaluation model to obtain efficiency insights and problem diagnosis reports; The causal reasoning and scenario simulation module is used to dynamically simulate the efficiency insight and problem diagnosis report through the causal reasoning and scenario simulation model to obtain patterns and pre-simulation results. The collaborative regulation and optimization module is used to solve the aforementioned patterns and simulation results through cooperative game theory and distributed optimization algorithms to obtain a water resource quota allocation scheme.

10. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 8.

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