Industrial water demand prediction method based on big data mining and physical mechanism double driving

Abstract

The application relates to the field of water conservancy projects and computer application technologies, and particularly provides an industrial water demand prediction method based on big data mining and physical mechanism double driving, which comprises the following steps: taking industrial water whole cycle elements as indexes, implementing space-time alignment and feature anchoring on a multi-source database; establishing a nonlinear semantic correlation network among indexes; inputting multi-dimensional indexes in the nonlinear semantic correlation network into a dynamic entropy weight redundancy elimination model, eliminating redundant features and generating an industrial water sensitive factor set; combining the physical correlation characteristics of a core control chain to construct an industrial water constitutive equation with explicit interpretability; and bidirectionally embedding the industrial water constitutive equation with the social economic parameter prediction module to form a double-engine prediction architecture with the physical mechanism equation as a core driving force and real-time data flow as a dynamic corrector, so that mechanism-data double driving prediction of industrial water demand evolution is realized. The application solves the pain point that a prediction result of a traditional data driving model cannot be traced in an industrial scene.

Description

Technical Field

[0001] This invention relates to the fields of water conservancy engineering and computer application technology, and in particular to an industrial water demand prediction method based on a dual-drive approach of big data mining and physical mechanisms. Background Technology

[0002] Water demand forecasting is a fundamental and core component of water resource planning, allocation, and management. A scientifically sound understanding of future industrial water demand is a prerequisite for formulating appropriate supply-demand balance policies. Influenced by factors such as climate change, human activities, regional development conditions, and policy adjustments, water demand exhibits complex characteristics including nonlinearity, uncertainty, and dynamism. Changes in the economic and social development landscape make it difficult to accurately predict future water demand. Existing research often attempts to explain the relationship between industrial development and water use from a macro-level perspective using single or limited indicators. The influencing indicators and factors of industrial water use are incomplete, the roles of characteristic elements and key controlling factors in industrial water use changes remain unclear, the contributions of each key controlling factor lack quantification, and the driving mechanisms of industrial water use changes at the micro-level are not clearly revealed. Therefore, these studies cannot effectively guide the refined management of industrial water use.

[0003] Prior art 1, Chinese Patent Application No. 202411156731.5, discloses a water demand forecasting method, electronic device, and storage medium for regional water security. First, it collects influencing factors for industrial water demand, domestic water demand, and agricultural irrigation water demand. Then, it establishes a prediction equation for each influencing factor to reflect its future evolution trend. Next, it establishes industrial, domestic, and agricultural water demand forecasting models and verifies their effectiveness. Finally, it uses the verified water demand forecasting model to predict the water demand of the target area, obtaining the forecast results. Although it fully considers factors such as economic drivers, social impacts, and climate change, and studies the water demand change mechanism from the internal structure of the system, solving the problems of incomplete factor selection and poor simulation results in existing water demand forecasting, it improves the effectiveness and accuracy of forecasting from both model structure and predictive factors. However, it adopts an industry-specific independent modeling approach, failing to address the nonlinear coupling effects across processes and parameters within the industrial system, and relies on static prediction equations, making it unable to adapt to the dynamic calibration requirements driven by real-time data streams.

[0004] Prior art two, Chinese patent application number: 202110751413.3, discloses a method for predicting monthly industrial water demand, specifically including the following steps: constructing an evaluation index system for industrial water demand characteristics; constructing a prediction model set; formulating the optimal prediction method for each industrial water demand evaluation zone; screening key influencing factors of monthly industrial water demand to provide a foundation for multiple regression and feedforward neural network models; and predicting monthly industrial water demand. The method selects industrial water demand characteristic evaluation indicators; formulates evaluation indicator grading thresholds; calculates the total evaluation value, and quantitatively evaluates the industrial water demand situation. Although it has monthly prediction capabilities, considers regional characteristics, and has the ability to screen models based on regional water demand changes, it relies on manual screening of key factors and a fixed model set, lacking the ability to automatically mine implicit rules of industrial water use behavior (such as the correlation between equipment-level water use fluctuations and production rhythm), resulting in insufficient model generalization.

[0005] Prior art three, Chinese patent application number 201910950504.2, discloses a method for predicting production water demand based on system dynamics. It establishes a multi-factor industrial water demand system dynamic prediction model under the influence of human activities, and an agricultural water demand system dynamic prediction model based on water use mechanism prediction under climate change. For the industrial water demand prediction part, it combines the traditional industrial water demand quota method with system dynamics; for the agricultural water demand prediction part, it establishes a system dynamic model based on water use mechanism prediction and incorporates future precipitation under different carbon emission scenarios under climate change. Although it fully considers factors such as economic drivers, social impacts, climate change, and policy constraints, and studies the water demand change mechanism from the internal structure of the system, solving the problems of incomplete factor selection and poor simulation results in existing system dynamics methods for predicting production water demand, and improving the effectiveness and accuracy of prediction from both model structure and predictive factors, it suffers from a disconnect between mechanism modeling and data-driven approaches. The quota method struggles to capture the dynamic impact of technological innovation on water intensity, and climate change parameters are only used as exogenous inputs, not deeply coupled with the industrial water use constitutive equation.

[0006] Current technologies 1, 2, and 3 suffer from insufficient utilization of multi-source heterogeneous data, a disconnect between physical mechanisms and data patterns, and deficiencies in dynamic adaptability. Therefore, this invention provides an industrial water demand forecasting method driven by both big data mining and physical mechanisms. Summary of the Invention

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] One aspect of the present invention provides an industrial water demand forecasting method based on a dual-driven approach of big data mining and physical mechanisms, comprising the following steps:

[0009] By inputting multidimensional indicators from nonlinear semantic association networks into a dynamic entropy weight redundancy elimination model and combining them with the physical association characteristics of the core control chain, an industrial water constitutive equation with explicit interpretability is constructed.

[0010] By bidirectionally embedding the industrial water use constitutive equation with the socio-economic parameter prediction module, and utilizing the parameter sensitivity inversion of the industrial water use constitutive equation to achieve dynamic data calibration under mechanistic constraints, a dual-engine prediction architecture is formed with the physical mechanism equation as the core driving force and real-time data stream as the dynamic corrector, realizing mechanism-data dual-driven prediction of industrial water demand evolution.

[0011] In one optional implementation, after inputting multidimensional indicators into a dynamic entropy weight redundancy elimination model, redundant features are eliminated and an industrial water use sensitive factor set is generated by calculating the information entropy overlap between indicators and using an adaptive pruning strategy; a dynamic identification channel for factor contribution is established based on a group co-evolution mechanism, and the core control chain in the water use evolution path is mined through causal topology analysis.

[0012] In one alternative implementation, the process of constructing an explicit interpretable constitutive equation for industrial water includes the following steps:

[0013] Based on the multidimensional index set output by the nonlinear semantic association network, the information overlap intensity between the indicators in the spatiotemporal evolution process is quantified by the sliding window entropy value; adaptive gradient pruning is adopted to dynamically remove redundant associated edges according to the entropy overlap intensity threshold, thereby generating a redundancy-free industrial water sensitive factor topology network.

[0014] The obtained industrial water-sensitive factor topology network was input into the population cooperative evolution identification system. The robustness of the causal path under different production scenarios was verified by time-series slice analysis, and highly stable causal chains were screened out.

[0015] Based on the extracted core control chain, physical correlation feature embedding is performed. According to the spatial scale of the industrial system, the causal chain is divided into local control units and global control mechanisms. The interaction relationship of factors in the core control chain at the same level is transformed into physical process equivalents. The mass-energy conservation principle of the industrial water system is introduced as the boundary condition to obtain the constitutive equation of industrial water with explicit interpretability.

[0016] In one optional implementation, the process of generating a redundancy-free industrial water-sensitive factor topology network includes the following steps:

[0017] Based on a multidimensional indicator set, the continuous monitoring data stream is divided into dynamic time windows according to the characteristics of the production cycle. The spatial distribution characteristics of the indicators are extracted synchronously within each dynamic time window. An entropy weight coupling topology that reflects the spatiotemporal evolution characteristics is formed. The output multidimensional indicator set is transformed into a spatiotemporally correlated entropy weight topology network through entropy weight analysis.

[0018] The generated entropy weight topology network is input into adaptive gradient pruning. The coefficient of variation of each index with respect to the entropy weight is analyzed along the time axis, and the fluctuation of entropy overlap intensity exceeds the critical threshold. The gradient distribution of the marked associated edges in the spatial dimension is obtained, and a gradient sensitivity heatmap is constructed. The edges are sorted according to the product of gradient sensitivity and entropy weight coefficient of variation, and redundant edges with the least impact on global network connectivity are iteratively removed to generate a redundancy-free sensitive factor topology network.

[0019] The structure of the sensitive factor topology network is mapped to typical production conditions to verify the persistence of the correlation between key factors in different scenarios. The matching degree between the factor transmission path in the sensitive factor topology network and the actual pipeline topology and control logic is compared to correct virtual correlations that violate engineering mechanisms. Based on the control level of the industrial system, the verified factor network is reorganized into a hierarchical topology structure to form a sensitive factor topology network that conforms to engineering understanding.

[0020] In one optional implementation, the joint information entropy value of the index pair is calculated within a single dynamic time window to quantify the information overlap intensity within the spatiotemporal slice; the entropy weight calculation results of each dynamic time window are superimposed on the global index network to form an entropy weight coupling topology that reflects the spatiotemporal evolution characteristics.

[0021] In one optional implementation, after inputting into the population co-evolution identification system, a contribution weight evolution model is established based on the triggering frequency and response amplitude of the industrial water use sensitive factor topology network in historical water use events, and high contribution factors are forward-propagated and backward-traced.

[0022] In one alternative implementation, the process of obtaining an explicit interpretable constitutive equation for industrial water includes the following steps:

[0023] Based on the core control chain set, spatial scale topology mapping is implemented to encapsulate the causal chain involving the operating parameters of a single device into a local control unit with independent physical dimensions; for the causal chain of cross-device linkage, it is reorganized into a production line-level control module according to the material flow-energy flow coupling relationship; based on the plant-wide water network topology, the dispersed control units and control modules are integrated into a plant-level system equation framework.

[0024] For the interaction relationships of factors at the same level, match the typical physical processes of industrial water systems and establish equivalent expressions; use the factor influence coefficients in the contribution weight model to transform the data-driven statistical relationships into a function structure with physically meaningful parameters; embed mass conservation equations and energy conservation equations in each equation module to constrain the physical rationality of the equation structure.

[0025] Under typical operating conditions, the range of values ​​for the heat transfer coefficient is corrected by comparing the degree of agreement between the predicted values ​​of the equation and the actual sensor data.

[0026] In one optional implementation, the process of achieving mechanism-data dual-driven prediction of industrial water demand evolution includes the following steps:

[0027] Using the constitutive equation of industrial water as the physical core, it is bidirectionally embedded with the output of the socio-economic forecasting module through a parameter sensitivity mapping channel;

[0028] Based on real-time monitoring data streams and coupled systems, parameter sensitivity inversion optimization is implemented.

[0029] Real-time data is used to continuously correct the dynamic parameters in the equation through inversion optimization; by considering the deviation between the predicted values ​​from the mechanism and the data correction results, a water demand evolution map with a confidence interval is generated.

[0030] In one optional implementation, the bidirectional embedding includes: decoupling the equation parameter space: identifying sensitive variables in the constitutive equation that are affected by external parameters; injecting socioeconomic parameters: establishing a dynamic response mechanism between sensitive variables and macroeconomic indicators; and constructing a bidirectional feedback loop: allowing the equation prediction results to correct the prediction bias of socioeconomic parameters in reverse.

[0031] In one optional implementation, the entire lifecycle elements of industrial water use, including production processes, resource consumption, and environmental constraints, are used as an index to perform spatiotemporal alignment and feature anchoring on multi-source databases such as equipment logs and environmental monitoring-level economic statistics. An industrial water use feature library with spatiotemporal coupling characteristics is constructed through cross-modal association mining. Based on distributed semantic parsing of mixed data modalities, water use behavior rules implicit in text-level numerical data are extracted from the industrial water use feature library to establish a nonlinear semantic association network between indicators.

[0032] This invention addresses the pain point of "untraceable prediction results" in traditional data-driven models in industrial scenarios. Through explicit expression of the core control chain, it can directly output a distribution map of water-saving potential at each production process node, providing a quantitative basis for water use auditing. On the one hand, it avoids the shortcomings of using fixed indicators for prediction in different regions, clarifying the characteristic indicators of regional industrial water use evolution and demonstrating a clear mechanism for regional industrial water use evolution. On the other hand, it employs a symbolic regression algorithm to quantitatively describe the mathematical relationship between industrial water use and the state of each controlling factor, better aligning with the characteristics of regional industrial water use and improving the scientific rigor, comprehensiveness, and practicality of the industrial water demand calculation method. Attached Figure Description

[0033] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0034] Figure 1 This is a flowchart of the industrial water demand prediction method based on big data mining and physical mechanisms provided in Embodiment 1 of the present invention.

[0035] Figure 2 This is a schematic diagram of the industrial water demand prediction method based on big data mining and physical mechanisms provided in Embodiment 1 of the present invention.

[0036] Figure 3 This is a process diagram of establishing a nonlinear semantic association network between indicators provided in Embodiment 2 of the present invention;

[0037] Figure 4 This is a process diagram of constructing an explicit interpretable constitutive equation for industrial water, as provided in Embodiment 3 of the present invention.

[0038] Figure 5 This is a process diagram illustrating the mechanism-data dual-driven prediction of industrial water demand evolution provided in Embodiment 6 of the present invention.

[0039] Figure 6 This is a schematic diagram of the impact assessment and identification of main control factors of an industrial water use evolution index in a certain city, provided in Embodiment 10 of the present invention.

[0040] Figure 7 This is a schematic diagram of industrial water consumption prediction in a certain city provided in Embodiment 10 of the present invention;

[0041] Figure 8 This is a schematic diagram of water consumption prediction for a certain city's secondary industrial sector, provided in Embodiment 10 of the present invention.

[0042] Figure 9 This is a schematic diagram of water consumption prediction for three industries in a certain city, provided in Embodiment 10 of the present invention.

[0043] Figure 10 This is a schematic diagram of the comprehensive industrial water consumption prediction for a certain city (city 1, city 2, and city 3) provided in Embodiment 10 of the present invention.

[0044] Figure 11 A block diagram of the electronic device provided by the present invention;

[0045] Figure 12 A block diagram of a computer-readable storage medium provided for this invention. Detailed Implementation

[0046] The technical solutions of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0047] Hereinafter, the terms "first," "second," etc., are used for descriptive convenience only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0048] In this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, "connection" can be a fixed mechanical connection, a detachable mechanical connection, or an integral part; or, "connection" can be a direct connection or an indirect connection through an intermediate medium. Furthermore, unless otherwise explicitly specified and limited, the term "coupling" should be interpreted broadly. For example, "coupling" can be a direct electrical connection, such as physical contact and electrical conduction between two components; it can also be understood as an electrical connection between different components in a circuit structure through physical lines capable of transmitting electrical signals, such as copper foil or wires on a printed circuit board (PCB), to transmit electrical signals; or, "coupling" can be an indirect electrical connection between two components through an intermediate medium; or, "coupling" can be an electrical connection between two components in a non-contact manner, such as an electrical connection between two components using capacitive coupling to transmit electrical signals.

[0049] In this embodiment of the invention, directional terms such as "up," "down," "left," and "right" may be defined relative to the orientation of the components shown in the accompanying drawings. It should be understood that these directional terms can be relative concepts, used for relative description and clarification, and can change accordingly depending on the orientation of the components in the accompanying drawings.

[0050] Example 1:

[0051] like Figure 1 As shown, this embodiment of the invention provides an industrial water demand forecasting method based on a dual-driven approach of big data mining and physical mechanisms, comprising the following steps:

[0052] Step S100: Using the full-cycle elements of industrial water use (production process, resource consumption, environmental constraints, etc.) as indexes, perform spatiotemporal alignment and feature anchoring on multi-source databases (equipment logs, environmental monitoring, economic statistics, etc.); construct an industrial water use feature library with spatiotemporal coupling characteristics through cross-modal association mining; based on distributed semantic parsing of mixed data modalities, extract water use behavior rules implicit in text-level numerical data from the industrial water use feature library, and establish a nonlinear semantic association network between indicators;

[0053] Step S200: Input the multidimensional indicators in the nonlinear semantic association network into the dynamic entropy weight redundancy elimination model. Through the calculation of information entropy overlap between indicators and the adaptive pruning strategy, redundant features are eliminated and a set of industrial water use sensitive factors is generated. Based on the group co-evolution mechanism, a dynamic identification channel for factor contribution is established. The core control chain in the water use evolution path is mined through causal topology analysis. Combining the physical correlation characteristics of the core control chain, an industrial water use constitutive equation with explicit interpretability is constructed.

[0054] Step S300: The industrial water use constitutive equation and the socio-economic parameter prediction module are bidirectionally embedded. The parameter sensitivity inversion of the industrial water use constitutive equation is used to achieve dynamic data calibration under the constraint of mechanism. This forms a dual-engine prediction architecture with physical mechanism equation as the core driving force and real-time data stream as the dynamic corrector, realizing mechanism-data dual-driven prediction of industrial water demand evolution.

[0055] In the above embodiments, this embodiment leverages the technological advantages of deeply integrating big data mining and physical mechanism modeling to construct an intelligent industrial water demand prediction system with dynamic evolution capabilities. Through spatiotemporally aligned multi-source heterogeneous data fusion, it simultaneously captures explicit statistical features and implicit physical constraints of the industrial water system during cross-modal correlation mining. This ensures the feature library contains both the temporal continuity of equipment-level real-time monitoring data and retains the spatial correlation of environmental and economic parameters. The establishment of a nonlinear semantic association network breaks through the linear assumption of traditional index analysis, extracting deep coupling patterns between water use behavior and multiple factors from a text-numerical mixed modality. Dynamic interpretability modeling and causal mechanism embedding, along with a dynamic entropy weight redundancy elimination model, achieve physical meaning screening of the feature space through information entropy topology analysis, avoiding the overfitting risk caused by purely data-driven approaches. The group co-evolution mechanism, combined with causal topology analysis, maps statistically significant sensitive factors into control chains with process interpretability, allowing constitutive equation parameters to directly correspond to water-driven mechanisms in actual production (such as the constraint relationship between recycling rate and water intensity). The closed-loop optimization of the dual-drive prediction architecture, the mechanism-data dual-engine architecture, forms positive and negative coupling through parameter sensitivity inversion: the mechanism equation ensures that the prediction results conform to the physical laws of industrial water use (such as material balance constraints), while the real-time data stream dynamically corrects the model's response deviation to emerging influencing factors (such as the application of sudden water-saving technologies); the bidirectional embedding enables the prediction system to have both the robustness of the physical model and the adaptability of the data model, and is particularly suitable for simulating water demand evolution under complex policy intervention and technological change scenarios.

[0056] In summary, this embodiment addresses the pain point of "untraceable prediction results" in traditional data-driven models in industrial scenarios. Through the explicit expression of the core control chain, it can directly output a distribution map of water-saving potential at each production process node, providing a quantitative basis for water use auditing. On the one hand, this embodiment avoids the shortcomings of using fixed indicators for prediction in different regions, clarifying the characteristic indicators of regional industrial water use evolution and demonstrating a clear mechanism for regional industrial water use evolution. On the other hand, it employs a symbolic regression algorithm to quantitatively describe the mathematical relationship between industrial water use and the state of each controlling factor, which better aligns with the characteristics of regional industrial water use and enhances the scientific rigor, comprehensiveness, and practicality of the industrial water demand calculation method.

[0057] Example 2:

[0058] like Figure 3 As shown, based on Example 1, the process of establishing a nonlinear semantic association network between indicators in step S100 of this embodiment of the invention specifically includes the following steps:

[0059] Step S101: Based on the constructed industrial water feature library (including heterogeneous data such as equipment log text fields, environmental monitoring time series data, and economic statistical discrete values), the sampling frequency and dimensional system of different data sources are unified to the same spatiotemporal benchmark framework through spatiotemporal label mapping; then, the feature cross-embedding algorithm is used to jointly encode the text description fields and numerical monitoring sequences to generate a structured feature matrix with spatiotemporal correlation.

[0060] Step S102: Input the generated structured feature matrix into the distributed semantic coupling engine. Use the process description keywords in the text features (such as "cooling circulation rate" and "wastewater reuse intensity") and the water consumption time series data of the corresponding process to perform a sliding window correlation scan to capture the dynamic response relationship between word semantics and water use behavior. Based on the parameters such as water use intensity and resource consumption rate implicit in the feature matrix, identify industrial process clusters with similar water use behavior patterns through adaptive density clustering. Match the clustering results with the process control strategy description in the text field to extract behavioral rules such as "increase the cooling water reuse rate by 12%-15% during high-load production periods"; and obtain the initial semantic relationship set.

[0061] Step S103: Using the obtained initial semantic relation set, the confidence weight of the semantic relations is quantified according to parameters such as the co-occurrence frequency of keywords and water use indicators in the behavioral rules and the response delay time; a threshold adaptive algorithm is used to remove weak association edges with confidence values ​​lower than the dynamic critical value, while retaining highly significant associations such as "changes in equipment start-up and shutdown thresholds → fluctuations in instantaneous cooling water demand"; based on the physical hierarchy of the industrial water system (process unit → production chain → whole plant system), the optimized semantic relations are reorganized hierarchically according to spatial scale and temporal granularity, ultimately forming a nonlinear semantic association network with multi-level response characteristics.

[0062] Among them, the dynamic association strength threshold calculation system, based on the quantification results of the confidence weights described in step S103, constructs the following composite adaptive threshold decision framework:

[0063] Formula Group I: Dynamic synthesis of confidence weights, assuming the semantic association confidence of index node pair (i,j). Dynamic synthesis from multidimensional features:

[0064]

[0065] In the formula, This represents the co-occurrence frequency of indicators i and j within the time window t (the result of the sliding window scan in step S102); This represents the average delay time from the change of index i to the response of j (dynamic response relationship analysis in step S102); The semantic similarity coefficient (based on the cosine of the angle between distributed vectors of process description keywords) represents the text semantic similarity coefficient; Γ F ,Γ τ ,Γ s Represents adaptive weight coefficients for frequency, delay, and semantic dimensions, satisfying ( (where Λ is the standard deviation of frequency within the time window t); F This represents the frequency nonlinear correction index. ( (The average frequency within the window time window t); Ω represents the timing consistency correction factor. (t) This represents the current set of valid associations. Theoretically, this formula integrates three types of evidence: co-occurrence statistics, temporal dynamics, and semantic consistency. Through nonlinear synthesis and dynamic weight adjustment mechanisms, it overcomes the dimensional limitations of traditional single-threshold methods. The exponential term Λ F To suppress the hegemonic effect of high-frequency indicators, the hyperbolic tangent function tanh constrains the dimensional differences of semantic similarity, and the time factor Φ ensures the evolutionary continuity of association strength.

[0066] Adaptive evolution of critical threshold, dynamic critical threshold θ (t) Representation driven by confidence distribution characteristics

[0067]

[0068] In the formula Let ω represent the k-quantile of the current window confidence distribution, where k∈{0.25,0.5,0.75}; k Indicates quantile weight; Ψ (t-1) This represents the set of high-confidence associations retained by the preceding window; Θ (t)This represents the set of abnormal associations where the rate of change of the current window confidence exceeds η. Represents the historical inertia decay coefficient; β = 1.2·(1-exp(-|Θ (t) | / 10)) represents the mutation suppression gain coefficient. Theoretical support indicates that this threshold formula achieves dynamic adjustment through a triple mechanism: the quantile balance term avoids scenario mismatch problems with fixed percentile thresholds; the historical inertia term inherits the stability characteristics of verified reliable associations; the mutation suppression term prevents misjudgments caused by short-term data fluctuations; the coefficient α decays over time to enhance the system's adaptability to new patterns; and β increases nonlinearly with the number of abnormal events to improve robustness.

[0069] The decision function for associated edges ultimately retains the set Ψ of associated edges. (t) The following formula determines the representation:

[0070]

[0071] In the formula, Let represent the set of adjacent nodes of index i; γ represent the trend-sensitive adjustment factor; and sgn(·) represent the second derivative sign function, used to identify the convexity of the confidence trend. Theoretical support indicates that this decision function breaks through the traditional single-threshold judgment mode, introducing a topology normalization factor (denominator) to eliminate bias caused by node degree differences. The trend-sensitive term enhances the ability to identify continuously reinforcing associations; the second derivative sign judgment mechanism can distinguish between random fluctuations and trend changes, avoiding misjudging potentially strong associations in an upward phase as weak associations. It achieves: confidence quantification through multi-evidence fusion (overcoming the one-sidedness of a single index); spatiotemporally adaptive threshold adjustment (adapting to the dynamic evolution characteristics of the production system); and a topology-sensitive decision mechanism (maintaining overall network stability).

[0072] In the above embodiments, this embodiment constructs a dynamic coupling representation system for multi-source parameters of an industrial water system through spatiotemporal alignment of heterogeneous data and cross-modal feature fusion. Specifically, based on spatiotemporal label mapping and feature cross-embedding algorithms, different modal information such as equipment log text, environmental monitoring time-series data, and economic statistical discrete values ​​are encoded into a structured feature matrix with spatiotemporal consistency, solving the obstacle to correlation analysis caused by differences in sampling frequency and dimensional systems of multi-source data. Utilizing a distributed semantic coupling engine, a sliding window correlation scan between process description keywords and water consumption time-series data is used to extract dynamic response patterns of text semantics and water consumption behavior (such as the nonlinear mapping between cooling cycle rate and instantaneous water consumption), realizing explicit association between text rules and numerical indicators. Based on adaptive density clustering, industrial process clusters with similar water consumption behavior characteristics are identified, and combined with text parsing of process control strategies, quantifiable behavioral rules (such as conditional constraints on load changes and reuse rates) are automatically generated, forming a primary semantic relationship set. By using confidence weight quantization and dynamic threshold pruning, redundant associations with low statistical significance are eliminated, while strong coupling relationships supported by physical mechanisms (such as equipment start-up and shutdown and cooling water demand fluctuations) are retained. Semantic relationships are spatially scaled and temporally granularly reorganized according to the physical hierarchy of the industrial system (process → production chain → whole plant), ultimately constructing a nonlinear semantic association network with multi-level response characteristics.

[0073] In summary, this embodiment realizes the automated correlation mining of implicit behavioral rules (text descriptions) and explicit monitoring data (numerical sequences) in industrial water systems, forming an interpretable network structure that simultaneously contains statistical regularities and physical mechanisms, providing structured input for dynamic entropy weight redundancy elimination and mechanism-data dual-driven prediction.

[0074] Example 3:

[0075] like Figure 4 As shown, based on Example 1, the process of constructing an explicit interpretable constitutive equation for industrial water in step S200 of this embodiment of the invention includes the following steps:

[0076] Step S201: Based on the multi-dimensional index set output by the nonlinear semantic association network, the information overlap intensity between the indicators in the spatiotemporal evolution process is quantified by the sliding window entropy value; adaptive gradient pruning is adopted to dynamically remove redundant associated edges according to the entropy overlap intensity threshold, thereby generating a deredundant industrial water sensitive factor topology network.

[0077] Step S202: Input the obtained industrial water-sensitive factor topology network into the population co-evolution identification system. Based on the triggering frequency and response amplitude of the industrial water-sensitive factor topology network in historical water use events, establish a contribution weight evolution model to perform forward propagation and backward tracing of high contribution factors. Verify the robustness of causal paths under different production scenarios through time-series slice analysis, and screen out highly stable causal chains such as "raw material input acceleration rate → evaporative cooling system load → instantaneous water intake".

[0078] Using the sensitive factor topology network as input, the trigger frequency and response amplitude characteristics of historical events are analyzed to generate a spatiotemporal coupling matrix; then, the weight distribution is iteratively optimized through a dynamic collaborative evolution mechanism, incorporating production scenario adaptability and network structure constraints; finally, after multi-scenario stability verification and engineering-level reorganization, a contribution weight model that conforms to the physical mechanism is output.

[0079] Step S203: Based on the extracted core control chain, perform physical correlation feature embedding, divide the causal chain into local control units and global control mechanisms according to the spatial scale of the industrial system (equipment level → production line level → factory area level), perform physical process equivalent transformation on the factor interaction relationship in the core control chain at the same level (such as the nonlinear response of "cooling tower efficiency decay" and "replenishment water volume increase"), introduce the mass-energy conservation principle of industrial water system as boundary conditions, and obtain the constitutive equation of industrial water with explicit interpretability.

[0080] In the above embodiments, step S201 of this embodiment calculates the sliding window entropy value to quantify the information overlap strength between indicators and identify the redundancy of dynamic associations during spatiotemporal evolution; adaptive gradient pruning dynamically removes statistically redundant association edges based on the entropy overlap strength threshold, retains strong coupling associations, and generates a de-redundant industrial water sensitive factor topology network, improving the computational efficiency and robustness of subsequent modeling. Step S202, group cooperative evolution identification: based on the historical triggering frequency and response amplitude of the sensitive factor topology network, a contribution weight evolution model is established to screen high contribution factors; forward propagation and backward tracing: the causal path of factors is analyzed to ensure their stability under different production scenarios; time-series slice analysis: the robustness of the causal chain is verified, and highly stable causal chains are extracted to provide reliable input for the construction of constitutive equations. Step S203: Embedding physical correlation features: Based on the spatial scale of the industrial system, the causal chain is divided into local control units and global control mechanisms to ensure that the modeling granularity matches the actual physical level; Equivalent transformation of factor interaction relationships: The nonlinear interaction relationships in the core control chain at the same level are modeled in a physical process equivalent manner; Conservation principle constraints: Mass-energy conservation is introduced as a boundary condition to ensure that the constitutive equation conforms to the physical laws of the industrial water system and enhances the interpretability of the model.

[0081] In summary, this embodiment constructs an industrial water constitutive equation with explicit interpretability through entropy pruning, causal chain identification, and physical constraint embedding. Based on a redundancy-removed sensitive factor topology network, combined with contribution weight evolution and conservation constraints, it realizes the transformation from data-driven correlation to mechanism-driven modeling, providing a structured and interpretable mathematical expression for the accurate modeling and optimized control of industrial water systems.

[0082] Example 4:

[0083] Based on Example 3, the process of generating a redundancy-free industrial water sensitive factor topology network in step S201 of this embodiment of the invention includes the following steps:

[0084] Step S2011: Based on the multidimensional indicator set, the continuous monitoring data stream is divided into dynamic time windows according to the production cycle characteristics (such as by production batch, equipment start-up and shutdown cycle). The spatial distribution characteristics of the indicators (such as water pressure gradient of the plant area geographical unit) are extracted synchronously within each dynamic time window. The joint information entropy value of the indicator pair (such as "cooling water temperature-steam output") is calculated within a single dynamic time window to quantify the information overlap strength within the spatiotemporal slice. The entropy weight calculation results of each dynamic time window are superimposed on the global indicator network to form an entropy weight coupling topology that reflects the spatiotemporal evolution characteristics. The multidimensional indicator set is transformed into a spatiotemporally correlated entropy weight topology network through entropy weight analysis.

[0085] Step S2012: Input the generated entropy weight topology network into adaptive gradient pruning, analyze the coefficient of variation of each index with respect to the entropy weight along the time axis, and mark the associated edges whose entropy overlap intensity fluctuation exceeds the critical threshold as potential redundant terms; obtain the gradient distribution of the marked associated edges in the spatial dimension and construct a gradient sensitivity heatmap; sort according to the product value of gradient sensitivity and entropy weight coefficient of variation, iteratively remove the redundant edges with the least impact on global network connectivity, and generate a redundancy-free sensitive factor topology network;

[0086] Step S2013: Map the structure of the sensitive factor topology network to typical production conditions, and verify the correlation persistence of key factors (such as "raw material purity → cleaning water consumption") in different scenarios; compare the matching degree of factor transmission paths (such as "air compressor start-up and shutdown → cooling tower water replenishment delay") in the sensitive factor topology network with the actual pipeline topology and control logic, and correct virtual correlations that violate engineering mechanisms; based on the control hierarchy of the industrial system (equipment controller → production line PLC → central dispatch system), reorganize the verified factor network into a hierarchical topology structure to form a sensitive factor topology network that conforms to engineering understanding.

[0087] In the above embodiments, this embodiment constructs a redundancy-free sensitive factor topology network that conforms to the physical mechanism of the industrial system through dynamic time window segmentation, entropy weight coupling analysis, adaptive gradient pruning, and engineering verification correction. Based on the spatiotemporal evolution characteristics of multidimensional indicators, the information overlap intensity is quantified, and redundant associations are screened through the coefficient of variation and gradient sensitivity to ensure that the retained factor relationships are statistically significant and engineering interpretable. Finally, the network structure is mapped to actual production conditions, virtual associations are corrected, and hierarchical recombination is performed to match the industrial control level, forming a high-quality input that can be used for subsequent causal modeling. By combining data-driven analysis and mechanism constraints, the extraction of key factors in the industrial water system is optimized, laying a reliable foundation for the construction of constitutive equations.

[0088] Example 5:

[0089] Based on Example 3, the process of obtaining an explicit interpretable constitutive equation for industrial water in step S203 of this embodiment of the invention includes the following steps:

[0090] Step S2031: Based on the core control chain set, implement spatial scale topology mapping to encapsulate the causal chain involving the operating parameters of a single device into a local control unit with independent physical dimensions; for the causal chain of cross-device linkage, reorganize it into a production line-level control module according to the material flow-energy flow coupling relationship; based on the plant-wide water network topology, integrate the dispersed control units and control modules into a plant-level system equation framework.

[0091] Step S2032: For the interaction relationships of factors at the same level, match the typical physical processes of the industrial water system and establish equivalent expressions; use the factor influence coefficients in the contribution weight model to transform the data-driven statistical relationship into a function structure with physically meaningful parameters; embed the mass conservation equation (input flow = output flow + storage change) and the energy conservation equation (pump work = potential energy change + friction loss) in each equation module to constrain the physical rationality of the equation structure;

[0092] Step S2033: Under typical operating conditions, compare the degree of agreement between the predicted values ​​of the equation and the actual sensor data, and correct the range of values ​​for the heat transfer coefficient.

[0093] In the above embodiments, this embodiment achieves a progressive integration from equipment-level local parameters to production line-level material-energy coupling, and then to the plant-wide network structure through spatial topology mapping, forming a three-layer architecture including micro-control units, meso-level control modules, and macro-level system equations. It maintains a scale correspondence with real water systems through physical dimension consistency. Based on the equivalent expression of typical physical processes, the statistical relationships of data are transformed into functional forms with physical parameters, while embedding mass / energy conservation equations as structural constraints to ensure that all equation terms correspond to mass transfer, heat transfer, or work processes in actual industrial water systems, eliminating the uninterpretability of black-box models. The physical rationality of the equation parameters is verified and iteratively corrected through comparison of operating data, especially optimizing the value range of key parameters such as the heat transfer coefficient, making the model both theoretically rigorous and engineering-applicable. The final constitutive equation system can explicitly express how equipment parameters affect production line performance through material / energy flow transfer, and how local control strategies affect the dynamic response of the entire plant's water system under network topology constraints, providing traceable physical evidence for fault diagnosis and energy efficiency optimization of industrial water systems.

[0094] Example 6:

[0095] like Figure 5 As shown, based on Example 1, the process of realizing mechanism-data dual-driven prediction of industrial water demand evolution in step S300 provided by this embodiment of the invention includes the following steps:

[0096] Step S301: Using the industrial water constitutive equation as the physical kernel, embed it bidirectionally with the output of the socio-economic forecasting module through the parameter sensitivity mapping channel;

[0097] Decoupling of equation parameter space: Identifying sensitive variables in the constitutive equation that are affected by external parameters (such as the adjustment coefficient of the circulating water reuse rate); Injection of socio-economic parameters: Establishing a dynamic response mechanism between sensitive variables and macroeconomic indicators (industrial added value growth rate, environmental policy intensity index); Construction of two-way feedback loop: Allowing the equation prediction results to correct the prediction bias of socio-economic parameters in reverse.

[0098] Step S302: Based on the real-time monitoring data stream and the coupled system, perform parameter sensitivity inversion optimization;

[0099] Spatiotemporal weighting: Dynamic weights are assigned to monitoring data in different regions based on data acquisition frequency and spatial coverage density; Inversion convergence constraints: Constitutive equation conservation conditions (mass balance, energy loss rate) are used as boundary constraints for the inversion process; Abnormal data isolation: Sensor noise and abnormal event interference are filtered out through historical behavior pattern matching algorithms;

[0100] Step S303: Real-time data is continuously used to correct the dynamic parameters in the equation through inversion optimization; the deviation between the predicted value and the data correction result is used to generate a water demand evolution map with a confidence interval.

[0101] In the above embodiments,

[0102] Example 7:

[0103] like Figures 6-10 As shown, based on Examples 1-6, the embodiments of the present invention take a city 1, a city 2 and a city 3 in the Yellow River Basin as examples, and provide a detailed description in conjunction with the accompanying drawings and examples.

[0104] (1) Based on academic databases such as WebofScience, CNKI, and Wanfang, keywords were set, and text mining technology based on TF-IDF (term frequency-inverse document frequency) was used to screen out information databases that are highly related to the evolution of industrial water use;

[0105] (2) The Word2Vec method is used to convert text into vectors to capture the semantic relationship between industrial water use impact indicators and industrial water consumption. Intelligent algorithms are used to select features and define ontology models. A knowledge graph of industrial water use evolution is constructed according to the mapping relationship.

[0106] (3) Considering the characteristics of knowledge graph indicators such as frequency, semantic information overlap, and accessibility, and considering the characteristics of industrial water use in the study area, 18 key industrial water use impact indicators were selected.

[0107] (4) Construct a multi-index impact effect detection algorithm coupled with PSO-SVM (Particle Swarm Optimization Algorithm-Support Vector Machine) to identify the controlling factors of water use evolution through factor contribution, such as... Figure 6 The established PSO-SVM algorithm was used to identify the main controlling factors of industrial water use evolution in a certain city. Positive indicators contributed more than 0.06, and the main driving factors included industrial added value, the proportion of heavy industry, and power generation. Negative indicators contributed less than -0.06, and the main inhibiting factors included industrial water use duplication rate and available water resources. Over the past 35 years, the added value of the city's industrial sector has increased year by year, with an average annual growth rate of 13.9%, making it the most significant driving factor for industrial water use growth, with an impact effect of 0.067. The industrial water price increased from 0.80 yuan / m³. 3 The price has increased to 7.67 yuan / m². 3 The water resource per capita increased nearly tenfold, with an impact effect of -0.079, making it the most significant inhibitory factor. The industrial water reuse rate increased from less than 70% to 90%, significantly reducing the demand for fresh water, with an impact effect of -0.068, also making it a major inhibitory factor. As a typical heavy industrial city, the proportion of heavy industry increased from 79.8% to 97.7%, also a major driving factor. The per capita water resource in this city is less than 500 cubic meters. 3It is far below the national average of 2300m 3 The rigid constraints on water resources force industrial water-saving technology upgrades and limit the expansion of industrial water use, which are the main inhibitory factors.

[0108] Over the past 35 years, the city's raw coal production increased from 0.06 billion tons to 8.04 billion tons, with the largest impact effect at 0.071, making it the primary driving factor. Electricity generation and industrial added value had impact effects of 0.068 and 0.066 respectively, ranking as the second and third driving factors. The city's water pressure index exceeded 1.3, with water resource constraints inhibiting industrial water use growth. This had the largest negative impact effect at -0.065, ranking as the primary inhibiting factor. Industrial water prices and available water resources had impact effects of -0.063 and -0.061 respectively, ranking as the second and third inhibiting factors. The city's three main controlling driving factors included the proportion of the petroleum industry, raw coal production, and industrial added value, with impact effects of 0.069, 0.065, and 0.064 respectively. The main controlling inhibiting factors included reclaimed water reuse and available water resources, with impact effects of -0.067 and -0.061 respectively.

[0109] In summary, due to differences in industrial structure, resource endowment, and water resource conditions, the industrial water use evolution mechanisms in the three typical regions exhibit both industry-specific similarities and regional differences. Industrial added value serves as the driving indicator, while available water resources serve as the inhibiting indicator, with varying degrees of impact due to regional differences. Driving indicators are concentrated in regional resource-based industries, while inhibiting indicators focus on water resource endowment shortcomings (available water resources, water pressure index) and technical / policy regulation (industrial water prices, reclaimed water reuse), reflecting a dual inhibition of "resource constraints + human intervention." Due to differences in resource endowment, the main driving / inhibiting factors affecting water use in the three typical regions also exhibit significant regional characteristics. In one city, industrial water use growth is concentrated in the steel smelting industry; in another city, it is mainly driven by new energy and chemical industries, with significant constraints from available water resources; and in yet another city, water indicators have not reached their upper limits, indicating relatively weak water resource constraints.

[0110] (5) Construct the control factor-water constitutive equation based on the gene expression programming method of symbolic regression, as shown in the following equation;

[0111] In a certain city: a constitutive equation was established between industrial water use and five main controlling factors, as shown in equation (1):

[0112]

[0113] In the formula, Y represents industrial water consumption; X1 represents industrial added value; X2 represents the proportion of heavy industry; X3 represents power generation; X4 represents industrial water duplication rate; and X5 represents water resource development and utilization rate, all of which are standardized values.

[0114] A constitutive equation was established between industrial water use and six main controlling factors in a certain city, as shown in equation (2):

[0115]

[0116] In the formula, Y represents industrial water consumption; X1 represents raw coal production; X2 represents power generation; X3 represents industrial added value; X4 represents water pressure index; X5 represents industrial water price; and X6 represents water resource development and utilization rate, all of which are standardized values.

[0117] In a certain city, the constitutive equation between industrial water use and five main controlling factors is established, as shown in equation (3):

[0118]

[0119] In the formula, Y represents industrial water consumption; X1 represents the proportion of the petroleum industry; X2 represents raw coal production; X3 represents industrial added value; X4 represents reclaimed water reuse; and X5 represents water resource development and utilization rate, all of which are standardized data.

[0120] (6) Combining socioeconomic indicator forecasts, establish a watershed water demand forecasting method driven by both mechanisms and data, such as... Figures 7-10 .

[0121] From 2025 to 2035, the available water resources in a certain city will reach their upper limit. Industrial water use efficiency will gradually improve, and industrial water use inhibition factors will play a dominant role, resulting in a slow downward trend in industrial water use, projected to reach 271 million m³ in 2025. 3 Reduced to 238 million m³ by 2035 3 In a certain city, influenced by the national energy strategy, key indicators such as raw coal production, power generation, and the proportion of added value in the coal industry will play a dominant role. Water resource development and utilization rates, water pressure index, and industrial water prices will also have a restraining effect. Industrial water consumption is predicted to show a fluctuating growth trend, reaching 470 million cubic meters by 2035. 3 The city's three councils further developed local water resources, improving their supporting capacity and reducing their inhibitory effect. The driving effects of the petroleum industry's share, raw coal production, and industrial added value increased, and industrial water consumption is projected to reach 403 million cubic meters by 2035. 3 In summary, the changes in industrial water use in the three typical regions show different patterns. Based on this, it is predicted that industrial water use in the Yellow River Basin will continue to grow slowly in the future, but the growth rate will not be significant.

[0122] In summary, the PSO-SVM identification of key control factors for industrial water use shows that these factors exhibit both similarities and differences in their driving forces. Industrial added value serves as a driving indicator, while available water resources serve as a suppressive indicator. Due to regional differences in the degree of influence, one city is significantly driven by heavy industry, another by coal energy, and yet another by petroleum processing. The constitutive equation for industrial water use established by the symbolic regression algorithm can quantitatively describe the mathematical relationship between industrial water use and the state of each key control factor, further revealing the driving mechanism of changes in industrial water use. A slight increase in industrial water use is predicted for the three cities from 2025 to 2035.

[0123] Figure 11 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present invention is shown.

[0124] Electronic devices may include a central processing unit / microprocessor / main control chip, etc.; a storage medium coupled to the central processing unit / microprocessor / main control chip, etc., and storing computer-executable instructions therein for performing the steps of various methods of embodiments of the present invention when executed by a processor.

[0125] The central processing unit / microprocessor / main control chip, etc., may include, but are not limited to, one or more processors or microprocessors.

[0126] Storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (such as hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).

[0127] In addition, the electronic device may include (but is not limited to) a data bus, an input / output bus / external bus / device bus, a display, and input / output devices (e.g., keyboard, mouse, speaker, etc.).

[0128] The central processing unit / microprocessor / main control chip, etc., can communicate with external devices via the I / O bus through a wired or wireless network (not shown).

[0129] The storage medium may also store at least one computer-executable instruction for performing the steps of various functions and / or methods in the embodiments described herein when run by a central processing unit / microprocessor / main control chip, etc.

[0130] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0131] Figure 12 A schematic diagram of a computer-readable storage medium according to an embodiment of the present invention is shown.

[0132] like Figure 12 As shown, instructions, such as computer-readable instructions, are stored on a non-transitory computer-readable storage medium. When the computer-readable instructions are executed by a processor, the various methods described above can be performed. The non-transitory computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-transitory non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, the non-transitory computer-readable storage medium can be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0133] In the several embodiments provided by this invention, 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 units 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.

[0134] The units described 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.

[0135] Furthermore, the functional units in the various embodiments of the present invention 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.

[0136] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods of the various embodiments of this invention through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0137] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An industrial water demand forecasting method based on a dual-drive approach of big data mining and physical mechanisms, characterized in that, Includes the following steps: By inputting multidimensional indicators from nonlinear semantic association networks into a dynamic entropy weight redundancy elimination model and combining the physical association characteristics of the core control chain, an industrial water constitutive equation with explicit interpretability is constructed. After the multidimensional indicators are input into the dynamic entropy weight redundancy elimination model, redundant features are eliminated and an industrial water sensitivity factor set is generated through the calculation of information entropy overlap between indicators and an adaptive pruning strategy. The process of establishing a dynamic identification channel for factor contribution based on a group co-evolution mechanism, and mining the core control chain in the water use evolution path through causal topology analysis, and constructing an industrial water use constitutive equation with explicit interpretability includes the following steps: Based on the multi-dimensional index set output by the nonlinear semantic association network, the information overlap intensity between indicators in the spatiotemporal evolution process is quantified by the sliding window entropy value; adaptive gradient pruning is adopted to dynamically remove redundant association edges according to the entropy overlap intensity threshold, generating a de-redundant industrial water use sensitive factor topology network; the obtained industrial water use sensitive factor topology network is input into the group co-evolution identification system, and the robustness of the causal path under different production scenarios is verified by time-series slice analysis, and highly stable causal chains are selected; based on the extracted core control chain, physical association feature embedding is performed, and the causal chain is divided into local control units and global control mechanisms according to the spatial scale of the industrial system, and the factor interaction relationship in the same level core control chain is physically equivalently transformed, and the mass-energy conservation principle of the industrial water system is introduced as a boundary condition. By bidirectionally embedding the industrial water use constitutive equation with a socio-economic parameter prediction module, and utilizing parameter sensitivity inversion of the industrial water use constitutive equation to achieve dynamic data calibration under mechanistic constraints, a dual-engine prediction architecture is formed. This architecture uses the physical mechanism equation as the core driving force and real-time data streams as dynamic correctors, enabling mechanism-data dual-driven prediction of industrial water demand evolution. The process of achieving mechanism-data dual-driven prediction of industrial water demand evolution includes the following steps: using the industrial water use constitutive equation as the physical kernel, bidirectionally embedding it with the output of the socio-economic parameter prediction module through a parameter sensitivity mapping channel; implementing parameter sensitivity inversion optimization based on real-time monitoring data streams and the coupling system; continuously correcting the dynamic parameters in the equation through real-time data inversion optimization; and generating a water demand evolution map with a confidence interval by comprehensively considering the deviation between the mechanism prediction value and the data correction result. The bidirectional embedding includes: equation parameter space decoupling: identifying sensitive variables in the constitutive equation affected by external parameters; socio-economic parameter injection: establishing a dynamic response mechanism between sensitive variables and macroeconomic indicators; and bidirectional feedback loop construction: allowing the equation prediction results to correct the prediction deviation of socio-economic parameters.

2. The industrial water demand forecasting method based on big data mining and physical mechanisms as described in claim 1, characterized in that, The process of generating a redundancy-free industrial water sensitivity factor topology network includes the following steps: Based on a multidimensional set of indicators, the continuous monitoring data stream is divided into dynamic time windows according to the characteristics of the production cycle, and the spatial distribution characteristics of the indicators are extracted synchronously within each dynamic time window. An entropy-weighted coupled topology reflecting spatiotemporal evolution characteristics is formed, and the output multidimensional index set is transformed into a spatiotemporally correlated entropy-weighted topology network through entropy-weighted analysis. The generated entropy weight topology network is input into adaptive gradient pruning. The coefficient of variation of each index with respect to the entropy weight is analyzed along the time axis. The associated edges whose entropy overlap intensity fluctuation exceeds the critical threshold are marked as potential redundant terms. The gradient distribution of the marked associated edges in the spatial dimension is obtained, and a gradient sensitivity heatmap is constructed. The edges are sorted according to the product of gradient sensitivity and entropy weight coefficient of variation. The redundant edges with the least impact on the global network connectivity are iteratively removed to generate a redundancy-free sensitive factor topology network. The structure of the sensitive factor topology network is mapped to typical production conditions to verify the persistence of the correlation between key factors in different scenarios. The matching degree between the factor transmission path in the sensitive factor topology network and the actual pipeline topology and control logic is compared to correct virtual correlations that violate engineering mechanisms. Based on the control level of the industrial system, the verified factor network is reorganized into a hierarchical topology structure to form a sensitive factor topology network that conforms to engineering understanding.

3. The industrial water demand forecasting method based on big data mining and physical mechanisms as described in claim 2, characterized in that, The joint information entropy value of the index pair is calculated within a single dynamic time window to quantify the information overlap intensity within the spatiotemporal slice; the entropy weight calculation results of each dynamic time window are superimposed on the global index network to form an entropy weight coupling topology that reflects the spatiotemporal evolution characteristics.

4. The industrial water demand forecasting method based on big data mining and physical mechanisms as described in claim 1, characterized in that, After inputting into the population collaborative evolution identification system, a contribution weight evolution model is established based on the triggering frequency and response amplitude of the industrial water use sensitive factor topology network in historical water use events, and high contribution factors are forward-propagated and backward-traced.

5. The industrial water demand forecasting method based on big data mining and physical mechanisms as described in claim 1, characterized in that, The process of obtaining an explicit and interpretable constitutive equation for industrial water includes the following steps: Based on the core control chain set, spatial scale topological mapping is implemented to encapsulate the causal chain involving the operating parameters of a single device into a local control unit with independent physical dimensions; for the causal chain of cross-device linkage, it is reorganized into a production line-level control module according to the material flow-energy flow coupling relationship. Based on the plant-wide water network topology, the dispersed control units and control modules are integrated into a plant-level system equation framework. For the interaction relationships of factors at the same level, match the typical physical processes of industrial water systems and establish equivalent expressions; use the factor influence coefficients in the contribution weight model to transform the data-driven statistical relationships into a function structure with physically meaningful parameters; embed mass conservation equations and energy conservation equations in each equation module to constrain the physical rationality of the equation structure. Under typical operating conditions, the range of values ​​for the heat transfer coefficient is corrected by comparing the degree of agreement between the predicted values ​​of the equation and the actual sensor data.

6. The industrial water demand forecasting method based on big data mining and physical mechanisms as described in claim 1, characterized in that, Using the full-cycle elements of industrial water use, including production processes, resource consumption, and environmental constraints, as an index, spatiotemporal alignment and feature anchoring are performed on multi-source databases such as equipment logs and environmental monitoring economic statistics. An industrial water use feature database with spatiotemporal coupling characteristics is constructed by cross-modal association mining; based on distributed semantic parsing of mixed data modalities, water use behavior rules implicit in text-level numerical data are extracted from the industrial water use feature database, and a nonlinear semantic association network between indicators is established.

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