Emergency water supply intelligent scheduling method and system for sudden pollution of water source

Abstract

The invention discloses an emergency water supply intelligent scheduling method and system for sudden pollution of a water source, and belongs to the technical field of automatic control, and the method comprises the steps: obtaining multi-dimensional pollution parameters, executing fluid mechanical simulation and geological parameter fusion calculation, and generating a three-dimensional pollution cloud picture; a pollution diffusion boundary is extracted, and pollution blocking boundary parameters are generated; a pipe network control logic decision is made according to the pollution blocking boundary parameters, and a pressure control unit working mode instruction set is generated; calculating a pressure pulse characteristic parameter, generating a pressure buffer wave control signal, dynamically matching the pressure buffer wave control signal with a pipe network pressure threshold parameter, and generating a grading response execution instruction; and controlling the pipe network valve group to switch working modes according to the grading response execution instruction. According to the method, a progressive generation method of a multi-dimensional boundary calculation model fused with underground water medium layering characteristics and a pipe network fluid imitation line characteristic constraint type pulse control signal is adopted, and time-space precise collaborative intervention of pollution isolation pressure compensation and a water quality harmless switching process can be achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic control, in particular to an emergency water supply intelligent scheduling method and system for sudden pollution of a water source. BACKGROUND

[0002] The water treatment scheduling system based on water quality sensing and pipe network model is an important guarantee technology for water supply safety, which often includes remote valve execution and pipe network pressure dynamic compensation function modules. The existing method discriminates the pollution diffusion trend by monitoring the basic parameters and triggers part of the isolation operation, and the execution subject is concentrated in the physical blockage and part of the pressure compensation of a single pollution source.

[0003] The conventional technology at the present stage mostly adopts the mode of threshold triggered valve control to drive the emergency water supply switching decision, and the pressure adjustment amount is set by a fixed gradient, and the elimination measures of water hammer effect depend on the static model of historical test data. The coupling degree of the water quality monitoring system and the valve action logic is subject to the fixed analysis interval division strategy, and the determination of the isolation boundary needs to rely on artificial review calibration. This mechanical grading execution mode cannot respond to the interference factors of the stratified permeation anomaly of the geological medium, causing the risk of incomplete isolation.

[0004] The defects of the prior art mainly manifest that the linearization setting of the dynamic isolation operation pressure parameter does not consider the complex transient disturbance of the underground flow velocity distribution of the porous medium, resulting in that the stress oscillation of the isolation area deviates from the equipment tolerance limit for a long time; in addition, the artificial water quality checking parameter of the standby water source switching stage lags behind the demand timeliness of the valve opening operation, and the flow regulation characteristic curve of the reverse process flushing does not match the trajectory of the pollution front migration path, which reduces the system reliability of the emergency blocking measures. SUMMARY

[0005] To solve the above problems, the present application provides an emergency water supply intelligent scheduling method and system for sudden pollution of a water source, which adopts a multi-dimensional boundary calculation model fused with the stratified characteristics of underground water medium and a progressive generation method of pipe network fluid simulation line feature constrained pulse control signal, which can realize the spatiotemporal precise collaborative intervention of pollution isolation pressure compensation and water quality harmless switching process.

[0006] The above object can be achieved by the following scheme: An emergency water supply intelligent scheduling method for sudden pollution of a water source, comprising: acquiring pH value, heavy metal concentration and organic matter content collected by a movable water quality detection buoy group to generate multi-dimensional pollution parameters; performing fluid mechanics simulation and geological parameter fusion calculation on the multi-dimensional pollution parameters to generate a three-dimensional pollution cloud map; extracting a pollution diffusion boundary based on the three-dimensional pollution cloud map to generate pollution blocking boundary parameters; performing pipe network control logic decision according to the pollution blocking boundary parameters to generate a pressure control unit working mode instruction set; calculating pressure pulse characteristic parameters based on the pressure control unit working mode instruction set to generate a pressure buffer wave control signal; dynamically matching the pressure buffer wave control signal with pipe network pressure threshold parameters to generate a hierarchical response execution instruction; and controlling pipe network valve group switching working modes according to the hierarchical response execution instruction, wherein the working modes include electromagnetic drive mode, mechanical self-locking mode and gravity self-locking mode.

[0007] Optionally, the generating a three-dimensional pollution cloud map comprises: performing data cleaning and standardization processing on the multi-dimensional pollution parameters to generate standardized pollution parameters; acquiring a permeability coefficient of groundwater hydrogeological parameter data; performing spatial superposition calculation on the standardized pollution parameters and the permeability coefficient to generate a vertical direction pollution migration rate distribution map; calculating a horizontal direction pollution diffusion range through a fluid mechanics equation to generate a horizontal direction pollution range prediction map; and fusing the vertical direction pollution migration rate distribution map and the horizontal direction pollution range prediction map according to a weight ratio of the permeability coefficient and the standardized pollution parameters to generate a three-dimensional pollution cloud map.

[0008] Optionally, the generating a pressure control unit working mode instruction set comprises: identifying a pollution concentration gradient mutation area based on the three-dimensional pollution cloud map to generate pollution blocking boundary parameters; dividing the pollution blocking boundary parameters to determine a pollution core area isolation range and a pollution diffusion buffer area boundary; generating an isolation mode control instruction based on the pollution core area isolation range; generating a reverse flushing mode control instruction based on the pollution diffusion buffer area boundary; generating a normal mode control instruction in a non-polluted area; and combining the isolation mode control instruction, the reverse flushing mode control instruction and the normal mode control instruction to generate a pressure control unit working mode instruction set.

[0009] Optionally, the generating a pressure buffer wave control signal comprises: calculating a pipe network flow mutation amplitude based on the pressure control unit working mode instruction set to generate an expected flow mutation gradient parameter; determining a pressure pulse frequency and amplitude according to the expected flow mutation gradient parameter to generate a pressure pulse characteristic parameter; and performing dynamic superposition calculation on the pressure pulse characteristic parameter and a preset pipe network elastic compensation coefficient to generate a pressure buffer wave control signal.

[0010] Optionally, the generating the hierarchical response execution instruction comprises: extracting fluctuation frequency and amplitude characteristics in the pressure buffer wave control signal, generating an emergency level parameter; matching the emergency level parameter with a preset pollution event level division standard, determining a current event level; calling a pipe network pressure threshold parameter corresponding to the current event level, generating a hierarchical response execution instruction; wherein the pipe network pressure threshold parameter includes an upper pressure limit value, a pressure fluctuation tolerance range, and an emergency pressure relief trigger condition.

[0011] Optionally, the determining the current event level comprises: comparing the emergency level parameter with a threshold range in the pollution event level division standard; according to the comparison result, attributing the emergency level parameter to an event level corresponding to the threshold range; determining the attributed event level as the current event level.

[0012] Optionally, the controlling the pipe network valve group switching mode according to the hierarchical response execution instruction comprises: analyzing the hierarchical response execution instruction to identify event level information contained therein; determining the level of the current pollution event according to the event level information; when the level of the current pollution event is a first level, controlling the valve group to switch to an electromagnetic drive mode; when the level of the current pollution event is a second level, controlling the valve group to switch to a mechanical self-locking mode; when the level of the current pollution event is a third level, controlling the valve group to switch to a gravity self-locking mode and starting a backup water source switching process.

[0013] Optionally, the starting the backup water source switching process comprises: obtaining real-time pressure state parameters and real-time water quality parameters of a backup water source pipe network segment; based on the real-time pressure state parameters, adjusting a backup water source booster pump to make the backup water source pressure higher than the pollution area pipe network pressure; when the real-time water quality parameters meet a preset safety threshold, opening a switching valve between the backup water source and the target pipe network segment.

[0014] Optionally, the method further comprises: collecting pollution blocking time, water supply recovery time, and pipe network pressure data to generate pipe network operation parameter records; evaluating system response efficiency according to the pipe network operation parameter records to generate system optimization parameters; adjusting pipe network control logic decision-making processes according to the system optimization parameters.

[0015] Based on the same inventive concept, the application also provides an emergency water supply intelligent scheduling system for sudden pollution of a water source, comprising: a data acquisition module, configured to acquire pH value, heavy metal concentration and organic matter content collected by a movable water quality detection buoy group, and generate multi-dimensional pollution parameters; a pollution simulation module, configured to perform fluid mechanics simulation and geological parameter fusion calculation on the multi-dimensional pollution parameters, and generate a three-dimensional pollution cloud picture; a boundary identification module, configured to extract a pollution diffusion boundary based on the three-dimensional pollution cloud picture, and generate pollution blocking boundary parameters; a control decision module, configured to perform pipe network control logic decision according to the pollution blocking boundary parameters, and generate a pressure control unit working mode instruction set; a pressure feature module, configured to calculate pressure pulse feature parameters based on the pressure control unit working mode instruction set, and generate a pressure buffer wave control signal; an instruction matching module, configured to dynamically match the pressure buffer wave control signal with pipe network pressure threshold parameters, and generate a hierarchical response execution instruction; and an execution control module, configured to control pipe network valve group switching working modes according to the hierarchical response execution instruction, wherein the working modes include an electromagnetic drive mode, a mechanical self-locking mode and a gravity self-locking mode.

[0016] Compared with the prior art, the application has the following advantages: The application realizes high-speed dynamic calibration of pollution blocking boundary conditions by establishing a deep binding between a pollution multi-dimensional dynamic feature identification model and a pressure regulation system. The three-dimensional cloud picture analysis technology accurately represents the spatial hierarchical relationship of the pollution, improves the calculation efficiency and dynamic adaptability of the isolation area, and overcomes the pollution range misjudgment hidden danger caused by the dependence of traditional static parameters.

[0017] The hierarchical pressure regulation instruction combines the double-track control scheme of pollution buffer zone reverse flushing and normal area head optimization to realize non-uniform buffer compensation of the pipe network pressure field from the water flow inertia level. The flow regulation parameter and the elastic wave feature are superimposed and optimized to limit the pollution migration interference in the local space, and to cooperatively ensure the stable pressure of the large-scale water supply and the rapid blocking of the core area.

[0018] The standby water source switching adopts a full-link verification mechanism, synchronously meeting the dual requirements of operation safety and water quality standard. The pressure field transition condition is corrected in real time through a pressure difference compensation function and pipeline resistance simulation parameters, the hydraulic pulse risk at the confluence of the multi-source water network is gradually reduced section by section, and the secondary pollution chain diffusion and the superimposed risk of pipe burst accidents are eliminated.

[0019] Other features and advantages of the application will be set forth in the following description of the application, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the application. The objects and other advantages of the application can be realized and attained by the structure particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings described below are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor under the premise of the drawings.

[0021] Figure 1 is a flowchart of an emergency water supply intelligent scheduling method for sudden pollution of a water source in an embodiment of the present application.

[0022] Figure 2 is a schematic diagram of spatial distribution of three-dimensional pollution cloud in an embodiment of the present application.

[0023] Figure 3 is a pressure buffer wave control signal timing curve in an embodiment of the present application.

[0024] Figure 4 is a blocking efficiency and recovery resilience factor timing change curve in an embodiment of the present application.

[0025] Figure 5 is a structural schematic diagram of an emergency water supply intelligent scheduling system for sudden pollution of a water source in an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0027] With reference to Figure 1 , one embodiment of the present application proposes an emergency water supply intelligent scheduling method for sudden pollution of a water source. A multi-dimensional boundary calculation model with fusion of layered characteristics of groundwater medium and a progressive generation method of pulse control signal with fluid simulation line feature constraints of pipe network are adopted, so that the spatiotemporal precise collaborative intervention of pollution isolation pressure compensation and water quality harmless switching process can be realized.

[0028] The method of the embodiment specifically includes: acquiring pH value, heavy metal concentration and organic matter content collected by a movable water quality detection buoy group to generate multi-dimensional pollution parameters; performing fluid mechanics simulation and geological parameter fusion calculation on the multi-dimensional pollution parameters to generate a three-dimensional pollution cloud map; extract a pollution diffusion boundary based on the three-dimensional pollution cloud map, and generate pollution blocking boundary parameters; perform pipe network control logic decision-making according to the pollution blocking boundary parameters, and generate a pressure control unit working mode instruction set; calculate pressure pulse characteristic parameters based on the pressure control unit working mode instruction set, and generate a pressure buffer wave control signal; dynamically match the pressure buffer wave control signal with pipe network pressure threshold parameters, and generate a hierarchical response execution instruction; control a pipe network valve group switching working mode according to the hierarchical response execution instruction, and the working mode includes an electromagnetic drive mode, a mechanical self-locking mode, and a gravity self-locking mode.

[0029] Specifically, multi-dimensional pollution parameters are monitored by deploying movable water quality detection buoys to obtain diffusion characteristic data of pollution substances. Fluid mechanics modeling and geological parameter integration are used to generate a visual three-dimensional pollution cloud map to accurately depict the three-dimensional migration trend of pollution substances in water and geological media. Based on the diffusion boundary analysis results of the cloud map, the physical range and directional boundary of pollution blocking are determined to trigger a pipe network control decision-making model to generate a corresponding pressure control unit working mode instruction set. Through calculation of the pulse characteristic parameters of water flow dynamics, a pressure buffer wave control signal is formed, and a hierarchical response execution instruction is generated by dynamically matching the pipe network pressure threshold to guide the pipe network valve group to automatically switch the working mode according to the emergency level, realizing the collaborative control of pollution area blocking and normal water supply scheduling. This method systematically integrates pollution space analysis, device multi-state regulation and control, and fluid transient suppression mechanism, providing an adaptive collaborative control scheme for watershed emergency water supply.

[0030] Optionally, the generating a three-dimensional pollution cloud map comprises: performing data cleaning and standardization processing on the multi-dimensional pollution parameters to generate standardized pollution parameters; obtaining a permeability coefficient of groundwater hydrogeological parameter data; performing spatial superposition calculation on the standardized pollution parameters and the permeability coefficient to generate a vertical direction pollution migration rate distribution map; calculating the horizontal direction pollution diffusion range through fluid mechanics equations to generate a horizontal direction pollution range prediction map; fusing the vertical direction pollution migration rate distribution map and the horizontal direction pollution range prediction map according to the weight proportion of the permeability coefficient and the standardized pollution parameters to generate a three-dimensional pollution cloud map.

[0031] Specifically, the multi-dimensional pollution parameters were first cleaned and standardized to generate standardized pollution parameters. Data cleaning employed the Laida criterion and Kalman filtering algorithm for filtering and real-time calibration. Standardization mapped the monitored values ​​of pH, heavy metal concentration, and organic matter content to the [0,1] interval, respectively. Normalization algorithm: , in, The original detection value. This is the lowest historical value for the detection indicator. This is the maximum design threshold for this location. This is the standardized detection value. The permeability coefficient, which has clear hydrological attributes, is obtained from the groundwater geological parameter data. This permeability coefficient is derived from the combined data of ground-penetrating radar and pressure level gauge, and verified by experimental values ​​obtained through comparison with particle sieving experiments of each layer of media in the vertical direction. The vertical migration index and permeability coefficient in the standardized contaminant parameters after cleaning are spatially superimposed to calculate a three-dimensional coordinate system for contaminant transport in each sedimentary layer. The calculation formula is: In the formula, Indicates the first Layer vertical nodes The rate of pollution migration at the location, This is the pollution diffusion potential energy conversion function. The mapping parameters of the artificial neural network model trained on a discrete sample set obtained through groundwater dynamic dispersion experiments are used. The model employs a three-layer fully connected feedforward structure. The input layer nodes correspond to the pollution detection intensity index, equivalent permeability coefficient, and porosity adjustment factor, while the output layer nodes correspond to the predicted pollution migration rate. The activation function uses... The training method is supervised learning, which uses a representative sample set obtained from the experiment (containing different combinations of geological and hydrological parameters and corresponding measured pollutant migration rates) for training and validation. This is a standardized pollution intensity index, which includes solute mass concentration and adsorption reaction rate parameters. The k-th geological body was determined through mapping Equivalent permeability coefficient in the coordinate system The time-varying heterogeneity adjustment factor of the porous medium is used. The horizontal contaminant diffusion range is modeled based on the improved two-dimensional heterogeneous medium mass transfer equation. Darcy's law combined with the solute diffusion equation is used to numerically simulate the contaminant front evolution, and the partial differential equations are solved using the finite difference method. When a horizontal fluid encounters a porous medium, the formula is as follows: Pollutant concentration over time The function of change, and The vertical and horizontal diffusion coefficients of the formation were obtained from isotope tracing experiments, respectively. and The pollutant concentrations are respectively at , The second-order partial derivatives in the direction describe the change in concentration gradient. Finally, multi-field coupling calculations are performed based on the physical weights of the permeability coefficient and standardized pollution parameters. A piecewise linear weighting method is used to allocate the gravitational potential energy index of permeability properties and pollution trends for each region, satisfying the spatial continuity constraint equation for mass flux. ,in, To optimize the weighting coefficients for formation permeability effects matched with an expert knowledge base, The vertical migration rate, The soil adsorption coefficient describes the ease with which pollutants penetrate a weakly permeable layer. , These represent the pollutant concentration entropy and diffusion driving torque at each location. This represents the radius of curvature of the mainstream line of pollution diffusion. This indicates the angle between the mainstream pollution diffusion line and the normal direction of the rock stratum cross-section. For example... Figure 2 As shown in the figure, this diagram visually reflects the migration and distribution of pollutants in three-dimensional space. This method can intuitively display the advancement risk zone at the leading edge of the pollution plume, providing a data-fusion model basis for the boundary delineation of intelligent blocking systems, and ensuring that emergency response decisions are based on a comprehensive understanding of the coupled effects of geology, water flow, and pollution chemistry.

[0032] Optionally, the instruction set for generating the pressure control unit operating mode includes: Based on the three-dimensional pollution cloud map, regions with abrupt changes in pollution concentration gradient are identified, and pollution blocking boundary parameters are generated. The pollution blocking boundary parameters are divided to determine the isolation range of the pollution core area and the boundary of the pollution diffusion buffer zone; Based on the isolation range of the core pollution area, generate isolation mode control instructions; Based on the boundary of the contamination diffusion buffer zone, a reverse flushing mode control command is generated; Generate normal mode control commands in uncontaminated areas; The isolation mode control command, the backflushing mode control command, and the normal mode control command are combined to generate a pressure control unit operating mode command set.

[0033] Specifically, based on the three-dimensional pollution cloud map, regions with abrupt changes in pollution concentration gradients are first identified. Then, a joint detection method combining three-dimensional spatial interpolation and kernel density estimation is used to calculate the intensity modulus of pollutant concentration change along the water flow direction at each coordinate point. : ,in, , , These represent the concentration differences between this point and adjacent points in the horizontal longitudinal, horizontal transverse, and vertical directions of the groundwater flow field. , , Its spacing. When The value exceeds the preset mutation detection threshold. The pollution front area was considered as the location of the pollution front. Based on the connectivity between the pollution front and topographic contour points, pollution blocking boundary parameters were generated from continuous boundary coordinate data. Subsequently, spatial clustering was used to divide these boundary coordinate points and extract them as the isolation range of the pollution core area. The boundary morphology of this range was determined based on the persistent biotoxic adsorption factor in historical samples from monitoring wells. and monitoring value concentration threshold Two-way constraints, using variation functions and Interpolation optimization, when the pollutant detection value exceeds Or the cumulative time exceeds The rigid isolation condition of the core area is triggered at this time. Simultaneously, the periphery is assessed based on the diffusion coefficient and the equivalent pumping displacement delay rate. Conversely, a gradual, stratified design is established as a contamination diffusion buffer zone. Corresponding control commands are generated for different areas: within the contamination core zone, a linked process for pump station shutdown and neutralizer dosing is set up and compiled into isolation mode control commands. The buffer zone then undergoes reverse backwashing through multi-level pump station scheduling, with pressure regulation triggered periodically according to the flushing gradient equation. In the formula represent Section No. The flushing volume flow rate at any given time This represents the median of historical measured values ​​for the maximum pressure-bearing flow rate of the pipe section. This is the backwash correction factor for the section with the lowest friction resistance. For time, Based on the hydraulic sensor sampling values ​​of the pressure regulating unit, all variables are encoded into drive signal parameter fields after online verification by the hydraulic model, and assembled into backflushing mode control commands. In uncontaminated areas, the booster pump station speed is set based on the normal distribution target pressure compensation equation to achieve normal mode regulation. Finally, through the central controller, the three types of control commands—isolation, backflushing, and normal—are integrated according to the pipeline topology and timing to collaboratively generate the pressure control unit's operating mode command set. This method precisely implements zoned measures, dynamically decouples intervention needs, improves the linkage efficiency of local contamination blocking and overall pressure safety, and achieves efficient and safe emergency dispatch.

[0034] Optionally, the control signal for generating the pressure buffer wave includes: The pipeline flow rate fluctuation amplitude is calculated based on the pressure control unit's operating mode instruction set, and expected flow rate fluctuation gradient parameters are generated. determining a pressure pulse frequency and amplitude according to the expected flow mutation gradient parameter, and generating a pressure pulse characteristic parameter; superimposing the pressure pulse characteristic parameter and a preset pipe network elastic compensation coefficient dynamically to generate a pressure buffer wave control signal.

[0035] Specifically, based on the pressure control unit operation mode instruction set, the fluid mechanical action time sequence of each group of operation instructions is analyzed, and the pipe network flow transient mutation amplitude caused by the working mode switching is calculated in combination with the water supply partition topology and the pipe segment initial parameters. The mutation amplitude is modeled by using a cumulative flow step function: , wherein, represents the first The total flow variation gradient parameter of the pipe network segment in the time window , is the single flow change increment caused by the sub-action in the instruction queue, is the time response coefficient of the sub-action trigger time and the total time window . The expected flow mutation gradient parameter is obtained by superimposing the Bayesian decision tree. According to the parameter, a characteristic relationship set of pressure pulse frequency and amplitude is generated. In this step, a three-dimensional propagation compensation model of pressure wave along different flow directions in the pipe network is established according to the fluid damping wave transmission equation based on the fluid transient theory. The specific pressure pulse frequency The calculation formula is: , wherein is the maximum allowable flow rate set value of the pipe segment, is a hyperbolic tangent function, is the longitudinal coordinate of the current calculation position, represents the correction coefficient of the product of the volumetric elastic modulus of the corresponding pipe material and the corresponding water hammer wave velocity; is the normalized longitudinal coordinate difference of the fluid disturbance source position, is the fluid-structure coupling measurement parameter generated according to the pipe connection shape coefficient, and the numerical solution is obtained by FTA Fourier transform method iteration, represents the equivalent diameter influence factor of the pipe segment, which is calculated by combining the pipe segment bending angle and flow rate gradient weight. The construction of the pressure pulse amplitude A follows the constraint condition of pipe wall stress distribution optimization, and the optimal solution of the superposition effect of the two-dimensional wave field is obtained by solving the following equation group: , wherein is the pressure pulse amplitude, is the deviation ratio of the residual pressure bearing degree of the pipe segment to the nominal value; is the phase attenuation item set parameter of the environmental temperature and the aging coefficient; represents the fluid cumulative variation and the stress wave fluctuation rate a correlation regression analysis equation of the relatedness, The hydraulic load back-calculation verification curve is determined according to historical leakage detection events. As shown in Figure 3 , the frequency and amplitude of the pressure buffer wave control signal present dynamic changes during the emergency switching process. The time series of the pressure pulse frequency and amplitude are summarized as a pressure pulse characteristic parameter group, and then parameter correction is performed using a pipeline network elastic compensation coefficient. The pipeline network elastic compensation coefficient is calculated in combination with the displacement absorption amount of the pipeline expansion joint in the area where each valve is located and the elbow stress release effect coefficient : , wherein is the fitting feedback factor of the least squares residual of the intrinsic vibration frequency measured by the transient strain sensor and the least squares residual of the inherent frequency matrix of the pipeline. The corrected pressure buffer wave control signal can be expressed as: , is a function of the change of the pressure pulse frequency with time, is the maximum pressure pulse amplitude change amount, is a step function, indicating that compensation is applied after time , is the time of elastic compensation, is the current time. By real-time correlating the expected flow impact characteristics of the mechanical operation mode change with the dynamic response compensation characteristics of the pipeline network, the multi-objective balance of continuous impact load decomposition and reduction caused by pipeline network emergency stop and emergency start and low load shock stabilization is achieved. Optionally, the generated hierarchical response execution instruction includes: extracting the fluctuation frequency and amplitude characteristics in the pressure buffer wave control signal to generate an emergency level parameter; matching the emergency level parameter with the preset pollution event level division standard to determine the current event level; calling the pipeline network pressure threshold parameter corresponding to the current event level to generate a hierarchical response execution instruction; wherein the pipeline network pressure threshold parameter includes a pressure upper limit value, a pressure fluctuation tolerance range, and an emergency pressure relief trigger condition.

[0036] Specifically, first, the fluctuation frequency parameter of the periodic fluctuation and the amplitude variable between the wave peaks and troughs are extracted from the pressure buffer wave control signal. The amplitude-frequency analysis of the signal is performed by fast Fourier transform, and the mean value of the first harmonic period is taken: , wherein Let be the absolute value of the amplitude of the k-th harmonic obtained from frequency domain decomposition. This refers to the frequency value at the location of the harmonic, obtained through signal processing of at least eight cycle samples acquired from a digital sensor. Amplitude variable. The extreme value bounding line fitting method is adopted. By taking the half-width at half-maximum value of the interval between adjacent maxima and minima in the time-domain waveform sequence and performing moving average optimization, the optimized dynamic amplitude characterization coefficients are obtained. ,in and Represents the first in a continuous waveform A maximum and minimum value measurement point, To analyze the number of peak-valley pairs captured within the period. This is the damping coefficient of signal fluctuation under time-varying load, taken from the torque deviation correlation factor in the variable frequency drive's operating log. The fluctuation frequency... and amplitude Substitute into the quantile weight classification model to generate urgency parameters. : ,in, It is a type of benchmark fluctuation frequency constant set according to the safety threshold of the annual maximum operating frequency of the pipeline system; It is an empirically corrected reference value of 90% of the allowable alternating stress amplitude obtained from the hydrostatic fatigue test of the pipe. and These are the frequency influence coefficient and the amplitude sensitivity coefficient, respectively, and their values ​​are allocated through statistical regression analysis of the destructive pressure feedback in accident cases. Take the average frequency sample standard deviation of the monitored values ​​during the calibration phase. This is the inverse proportionality factor of the inverse logarithmic distribution function of the cumulative number of impact pressures in failure cases. The preset pollution event level classification standard adopts the discrete interval constraint principle, including three level determinations with emergency response parameters for levels one to three. The pollution event level determination model uses the membership degree interval weighted method to calculate... Establish a correlation between the value and the interval limits of the dividing criteria. In specific calculations, if... Matching three levels of response, Matching secondary responses in time, Matching first-level response in time, and To extract the optimal distinguishing boundary value from a set of historically successful pressure oscillations and pollution diffusion events, the calculation method uses... - Mean clustering analysis was used to segment the emergency recovery time series and cumulative damage data of abnormal normal operating pressure after the pipeline network was subjected to disturbances, resulting in a classification baseline value. During the process of calling the pipeline pressure threshold parameters corresponding to the event level, the first-level response corresponds to the first safety threshold directory group, which includes pressure upper limits designed with highly sensitive characteristics. , represented as: ,in This is the reliability limit parameter for the standard ultimate pressure bearing coefficient of the pipe material. This is the maximum permissible working pressure value allowed by the pipeline engineering specifications. The second safety threshold catalog group stores the pressure fluctuation tolerance range. The parameters are constructed as follows: ,in As the benchmark working pressure, It is the normalized value of the standard deviation of the flow velocity. It is the empirical tolerance amplification ratio parameter, which represents the cumulative impact of continuous oscillations at the pressure detection point within the allowable range. This is the deviation angle correction coefficient between the main direction of water flow and the axial support structure of the pipeline, calculated from the torsional stress amplitude reading output by the distributed piezoelectric sensor. The pressure relief triggering condition for the third-level emergency response. : ,in Get the set response time period The mean rate of dynamic evolution of internal pressure difference. It is the safety margin threshold at the current stage corresponding to the critical safety pressure calibration point where the pipeline rupture risk is approaching. The impact buffer gradient calibration coefficients are used for different material aging levels in different regions. All the above threshold parameters are uniformly stored in the parameter relationship feature library in the emergency control module via the main station. By adaptively matching the action sensitivity range of the pressure buffer valve through standardized response rules, the existing water flow transport pressure is guaranteed to maintain a resilience safety margin under sudden pollution situations, ensuring that the valve switching operation simultaneously follows the principles of water quality protection and pipeline operation supply guarantee.

[0037] Optionally, determining the current event level includes: Compare the urgency parameter with the threshold range in the pollution event classification standard; Based on the comparison results, the urgency parameter is assigned to the event level within the corresponding threshold range; The event level to which it belongs is determined as the current event level.

[0038] Specifically, the database of pipeline operation in historical pollution control events is analyzed to establish parameters for the urgency of typical pollution events. A chart showing the distribution characteristics of the value range was used to delineate interval boundary values ​​applicable to various emergency responses. Principal component analysis was applied to analyze the damage rate caused by pressure shock. And pipeline restoration efficiency factor The combined data matrix is ​​used to calculate the interval segmentation criteria and construct a threshold space corresponding to three event levels. The processed historical data forms a four-dimensional scatter plot vector Z= , The input represents the actual duration of each event's handling, after normalization and fuzzy processing. The mean clustering algorithm yields... Optimal cut point under various pressure threshold constraints and The objective function during clustering. The following settings must be met: In the formula Representative sample data points right Membership degree of the center point To smooth out the coefficients and prevent overclassification, This is the center vector for each cluster. The algorithm iterates until all parameters converge and reach a stable state, then extracts... The classification interval points corresponding to the axis, and the upper margin of the intervals corresponding to each emergency level sorted by probability density, are used as the boundary threshold for determining the level of pollution events. of and of During real-time system operation, the urgency level parameter is determined based on the currently generated parameters. The values ​​are sequentially compared with the event level classification thresholds. and The value range matching and comparison is performed, with the following three-level judgment rules: when the calculated value range is... The value is in the range When it is between, it is identified as a Level 3 emergency event; when it belongs to The time is divided into the second level; when it exceeds The event is forcibly classified into the highest level of urgency (Level 1). After classification confirmation, the level identifier, combined with the event coordinate geocode, is mapped to the topology map of the pollution containment control master station, marking the regional activity attribute of this pollution event. Subsequently, the corresponding pressure safety control mode envelope function is called from the pipeline stress model library to update the operating rules of the current equipment group. This method utilizes the parameter optimization and adaptation capabilities of machine learning data interval-driven judgment benchmarks to achieve scientific classification logic settings, improving the scenario fit and timeliness of emergency self-control strategies.

[0039] Optionally, the step of controlling the pipeline valve group to switch operating modes according to the graded response execution command is as follows: Parse the hierarchical response execution instructions to identify the event level information contained therein; Based on the event level information, determine the level of the current pollution event; When the level of the current pollution event is Level 1, control the valve group to switch to electromagnetic drive mode; When the level of the current pollution event is Level 2, control the valve group to switch to mechanical self-locking mode; When the level of the current pollution event is the third level, the valve group is switched to the gravity self-locking mode, and the standby water source switching process is started.

[0040] Specifically, the central control unit parses the hierarchical response execution instruction, extracts the event level identifier , which uses a three-bit binary number to represent the emergency level of the pollution event, with the highest bit indicating whether there is a cross-regional spread characteristic. By calling the event level mapping library through the instruction parsing engine, the corresponding emergency disposal plan number is matched . If the event level is determined to be the first level, the controller issues a solenoid drive mode activation instruction, and the valve is quickly driven by the ampere force generated by the coil, with a response time , the valve opening adjustment precision is controlled within an error band of ±2%, and its dynamic positioning algorithm uses the following formula: , where is the real-time output valve rotation angle instruction value, and are the proportional coefficient and integral coefficient, respectively, which are obtained by fitting the flow characteristic curve in the valve model database; derived from the target pressure value set in the hierarchical response execution instruction; is the measured pressure value collected by the vibration compensation type pressure transmitter on the pipeline, with a sampling period of 10ms. During electromagnetic driving, the coil temperature rise state is continuously monitored, and when it exceeds the tolerance threshold of the insulation material, it is automatically switched to the standby driving source. If the event level is determined to be the second level, the valve group is switched to the mechanical self-locking mode. The valve rod is precisely adjusted by a worm gear and locked by a ratchet. The locking process verifies the locking reliability according to the balance equation of the valve rod axial stress and the sealing specific pressure : , where is the minimum cross-sectional area of the valve rod taken from the valve design drawing; and are the effective action diameter and contact width of the sealing ring, respectively; and are dynamically adjusted according to the compression permanent deformation test data of the sealing material. After mechanical locking, the valve position offset is checked every 5 minutes by a displacement sensor, and when is detected, the self-locking compensation process is started. When the event level rises to the third level, the control system performs double operations: first, it sends a hydraulic control switching instruction to the gravity self-locking mode dedicated valve, which uses the gravity moment generated by the counterweight to overcome the fluid impact force, and the counterweight mass calculation formula is: , where is the maximum fluid impact force under the third level calculated according to the pipe network simulation model; is the design length of the counterweight arm; is the measured value of the local gravitational acceleration; Gearbox transmission efficiency, calibrated online by torque sensor; is a safety factor, taken as an interval value between 1.5 and 2.0 according to ASME B31.1 specification. At the same time, the standby water source switching process is started, and the pressure state parameters of the standby water source inlet main pipe are first obtained and the water quality parameter group , wherein at least contains turbidity NTU value and residual chlorine concentration CLR value. When the value is 10% higher than the contaminated area pipe network pressure for 30 seconds and meets the GB5749 Drinking Water Health Standard, the control valve executes the opening operation, and the opening rate follows: , wherein is the valve diameter, is the pressure difference between the standby water source and the target pipe section, is the measured value of the water flow density. This method realizes a hierarchical defense system from millisecond electromagnetic regulation to mechanical rigid locking to gravity failure protection by establishing a strong correlation mechanism between event level and valve driving mode.

[0041] Optionally, the standby water source switching process includes: obtaining real-time pressure state parameters and real-time water quality parameters of the standby water source pipe network section; adjusting the standby water source booster pump based on the real-time pressure state parameters, so that the standby water source pressure is higher than the contaminated area pipe network pressure; when the real-time water quality parameters meet the preset safety threshold, opening the switching valve between the standby water source and the target pipe network section.

[0042] Specifically, first, the inlet pipe pressure parameters and the water quality parameter group of the booster pump are obtained, wherein the latter includes turbidity and residual chlorine concentration . The real-time pressure state parameters of the standby water source pipe network section are verified by three-point method to improve data confidence, and the formula is: , wherein is the real-time pressure state parameter of the standby water source pipe network section, , , respectively taken from the instantaneous sampling mean values of the three-point distributed pressure transmitters at the water source inlet main pipe, branch pipe cross node and end user access point, eliminating local turbulent pulsation distortion. Then, the frequency output of the frequency converter controller of the standby water source booster pump is adjusted to make meet the pressure connection constraint: , wherein The upper limit value of the steady-state pressure fluctuation envelope line of the pollution pipe network segment calculated in real time for the front-end monitoring module; is the preset pressure difference compensation allowance to overcome the flow resistance difference at the pipe connection. Then, the double water source water quality state mutual verification algorithm is executed every 5 seconds, and when the continuous 3 times and the time lock qualified state of twice are met, the steady flow buffering process is started. The duration is calculated as follows: , wherein is the topological adjustment factor after the nominal value of the standard diameter of the main pipe of the switching pipe network node is divided by the equivalent diameter; represents the minimum allowable operating pressure calculated synchronously at the key pressure control point of the pipe network in the original pollution area, is the minimum duration; and when the adjustment to each sensor of the standby water source meets is in the qualified interval of the national standard and the remaining last three pressure difference stable time periods, the switching valve starts the incremental opening program, and the valve opening rate is gradually increased to the maximum safe opening limit point based on the linear interpolation function, and the formula is as follows: , wherein, is the damping control constant, is the pressure difference between the standby water source and the supplied pipe network, , wherein is the average gauge pressure reading of the standby water source within the first 5 seconds before starting, is the maximum impact pressure peak value allowed by the supplied pipe network. The entire process maintains the reverse pressure gradient of the pollution area pipe segment to the opening of the sewage valve group, and the locking joint debugging confirmation signal is removed synchronously only in the last 0.5 seconds before the opening is completed, so as to realize the peak-shifting blocking of the pollution flow. This method ensures that the pipe network pressure regulation process does not destroy the existing hydraulic balance, and simultaneously realizes the reliability of the water source switching action and the irreversible compliance characteristics of the water source safety standard.

[0043] Optionally, the method further comprises: collecting pollution blocking time, water supply recovery time and pipe network pressure data to generate pipe network operation parameter records; evaluating system response efficiency according to the pipe network operation parameter records to generate system optimization parameters; adjusting the pipe network control logic decision process according to the system optimization parameters.

[0044] Specifically, the pollution blocking effective time parameter (the time required for sending instructions to the core area turbidity recovery of 95%), the water supply recovery stable time parameter (the time period from the opening of the standby water source valve to the pressure stabilization of ±5%), and the pipe network pressure fluctuation variance matrix during the pollution blocking period are collected. , The discrete variance formula is used for processing: In the formula For the variance of pressure fluctuation, Let i be the instantaneous pressure value at the i-th pressure sampling point. For target pressure, This represents the total number of pressure sampling points. A sample is collected every 10ms by default, and the sampling duration for each data set is equal to the corresponding... or Time period. System response efficiency as a whole blocking efficiency. and recovery resilience factor As an indicator. The process efficiency decay model is used for calculation: ,in The reference value for the nominal blocking time is obtained from the maximum propagation delay characteristics of the pipeline network in a static laboratory test. It is a time-domain compensation coefficient calculated in real time based on the pollution concentration and penetration depth. The value is affected by the fracture rate parameter of the three-dimensional geological block model. The impact is calculated as follows: , The linear adjustment constant is , For the test pressure baseline, This is the actual pressure difference during mining. The geometric effect weighting factor is obtained by converting fluid transport characteristics; This is a condition-corrected parameter for the creep delay characteristics of pipeline materials. (Recovery toughness factor) Based on a comprehensive evaluation of pressure fluctuation distribution characteristics and steady-state recovery rate, the following results were obtained: ,in, The actual damping ratio in the water pipe network is obtained by separating the frequency response relationship of the damping resonance peak from the discrete vibration acceleration signal of the pressure sensor. It is the magnitude of the cumulative phase offset of the top three amplitude values ​​of the time-varying pressure trajectory; The integral result is taken from the time-space integral value of the water supply recovery phase in the regulated area; The fatigue strengthening inhibition factor for pipe sections is determined by calibrating the pressure loss curve of cyclic load test according to the half-life reversal coefficient. This is the temperature correlation correction coefficient for the hysteresis characteristics of the pipeline material, obtained by weighting the mean of the temperature field in the underground passage during the data collection process. For example... Figure 4 As shown, the trend curves of blocking efficiency and recovery resilience factor with event number reflect the system's operational efficiency and resilience after multiple contamination blocking and water source switching operations. Based on efficiency indicators... and Parameter calculation feedback system optimizes suggestion parameters , specifically using a genetic algorithm to solve: , weight and The initial setting of the Delphi expert interview method is assigned, and the running case is increased after iteration optimization. is the network management network coordination control response parameter increment that needs to be optimized, and the corresponding adjustment item is mapped to the output valve response period and the acceleration ratio parameter of the reverse flushing gradient of the isolation mode control instruction. After the optimization result priority sorting, the control logic parameter library is automatically updated, which is used for subsequent emergency process execution adjustment conditions. The system parameter adjustment amount generated by the method drives the intelligent algorithm update iteration of the key decision step, forms a self-upgrading mechanism of the actual operation and maintenance effect optimization strategy set, overcomes the global adjustment failure phenomenon caused by the single parameter library rule update post, and finally constructs the self-adaptive strengthening mechanism of the emergency system execution efficiency in multiple events.

[0045] Based on the same inventive concept, as shown in Figure 5 , the application also provides an emergency water supply intelligent scheduling system for water source pollution, which comprises: A data acquisition module is used to acquire the pH value, heavy metal concentration and organic matter content collected by the movable water quality detection buoy group, and generate multi-dimensional pollution parameters; A pollution simulation module is used to perform fluid mechanics simulation and geological parameter fusion calculation on the multi-dimensional pollution parameters, and generate a three-dimensional pollution cloud map; A boundary identification module is used to extract a pollution diffusion boundary based on the three-dimensional pollution cloud map, and generate a pollution blocking boundary parameter; A control decision module is used to make pipe network control logic decision according to the pollution blocking boundary parameter, and generate a pressure control unit working mode instruction set; A pressure feature module is used to calculate pressure pulse feature parameters based on the pressure control unit working mode instruction set, and generate a pressure buffer wave control signal; An instruction matching module is used to dynamically match the pressure buffer wave control signal with the pipe network pressure threshold parameter, and generate a hierarchical response execution instruction; An execution control module is used to control the pipe network valve group to switch the working mode according to the hierarchical response execution instruction, and the working mode includes an electromagnetic drive mode, a mechanical self-locking mode and a gravity self-locking mode.

[0046] To verify the feasibility of the application in implementation, the application is applied to the urban water supply emergency scheduling system of a certain city. The water source of the city is an upstream river, and there is a risk of sudden pollution caused by industrial accidents. The application aims to quickly and accurately perform emergency scheduling when a sudden pollution occurs at the water source, block the spread of pollution, and ensure the safety of the pipe network and the stability of water supply to downstream users.

[0047] To verify the effectiveness of the application, a sudden heavy metal pollution event upstream of a certain city's water source is simulated as an example to test the whole process of the emergency water supply intelligent scheduling method and system of the application. Data of multiple links such as pollution detection, diffusion simulation, decision response and valve control are recorded during monitoring.

[0048] In the embodiment, the system of the application monitors the pollution event at 10:00 on September 5, 2024. First, the movable water quality detection buoy group deployed at the river section collects abnormal pH value, heavy metal concentration and organic matter content, and the data acquisition module of the system immediately generates multi-dimensional pollution parameters. The pollution simulation module immediately performs fluid mechanics simulation and geological parameter fusion calculation on these parameters, and generates a three-dimensional pollution cloud map at 10:03. The cloud map accurately shows the migration trend of the pollutants to the aquifer in the vertical direction and the diffusion range in the horizontal direction, and predicts that the front edge of the pollution plume will reach the first water intake in 45 minutes.

[0049] Based on the three-dimensional pollution cloud map, the boundary identification module extracts the pollution diffusion boundary at 10:04 and identifies the pollution concentration gradient mutation area, and generates pollution blocking boundary parameters including the isolation range of the pollution core area and the buffer area of the pollution diffusion. The control decision module makes pipe network control logic decision according to the boundary parameters, and generates a pressure control unit working mode instruction set at 10:05, which clearly specifies that the core area is executed in isolation mode, the buffer area is executed in reverse flushing mode, and the non-polluted area is maintained in normal mode.

[0050] To prevent the pipe network from being damaged by the destructive water hammer effect caused by the rapid switching of the valve, the pressure feature module calculates that the pipe network flow will have a large mutation based on the instruction set, and combines the elastic compensation coefficient of the pipe network to generate a pressure buffer wave control signal at 10:06 to smooth the pressure impact. Subsequently, the instruction matching module matches the fluctuation frequency and amplitude characteristics in the signal with the preset pollution event level division standard, determines that the current event level is the second level, and calls the corresponding pipe network pressure threshold parameter to generate a hierarchical response execution instruction at 10:07.

[0051] Finally, at 10:08, the execution control module controls the pipe network valve group to switch to the mechanical self-locking mode according to the instruction, forcibly closes the pipe section of the pollution core area, and starts the reverse flushing process, effectively completing the isolation before the pollution group reaches the water intake. The entire automatic emergency response process takes only 8 minutes from detection to completion, compared with the traditional manual intervention mode which takes 2-3 hours, and the efficiency is greatly improved.

[0052] In a more serious simulated pollution event, the emergency level parameter generated by the system The value exceeds the threshold , the event level is determined to be the first level. The system immediately controls the valve group to switch to the electromagnetic drive mode with the fastest response speed (response time less than 150 ms), and synchronously starts the standby water source switching process. After the system monitors that the standby water source pipe network pressure is 10% higher than the pollution area and the water quality parameters (turbidity , residual chlorine mg / L) meet the standards continuously, the switching valve is smoothly opened to ensure uninterrupted water supply to downstream key users and successfully avoids pollution transfer.

[0053] By collecting pollution blocking time, water supply recovery time and pipe network pressure data, the system evaluates the response efficiency and generates system optimization parameters. For example, after a response, the system analyzes that the start-up pressure of the reverse flushing mode can be appropriately increased, and accordingly adjusts the pipe network control logic through the feedback mechanism, so that the recovery resilience factor of the next similar event is improved by about 12%.

[0054] Table 1 Comparison table of emergency pollution event response efficiency

[0055] Table 2 Pipe network valve group graded response mode data table

[0056] Table 3 Key parameters of standby water source switching process

[0057] From the data recorded in Tables 1 to 3, it can be seen that the present application has significant technical advantages in emergency water supply scheduling.

[0058] The data in Table 1 clearly shows that the present application greatly shortens the total emergency response time from 180 minutes of the traditional method to 8 minutes, and the response speed is improved by more than 20 times. At the same time, through pressure buffer wave control, the maximum pressure fluctuation of the pipe network is controlled at 0.08 MPa, which is much lower than the 0.45 MPa that may cause pipe damage in the traditional method, greatly ensuring the safe operation of the pipe network.

[0059] Table 2 demonstrates the effectiveness of the system hierarchical response mechanism. The system can automatically match the optimal valve operation mode according to the calculated emergency degree parameter value, achieving a balance between response speed and system energy consumption and mechanical loss, and ensuring that the most appropriate measures are taken under different emergency degrees.

[0060] The data in Table 3 verifies the safety and reliability of the standby water source switching process. The system strictly follows the principle of "first pressure measurement, then quality measurement, and then switching", and only after confirming that the standby water source pressure and water quality both meet the standards, does it smoothly execute the switching operation, fundamentally eliminating the risk of secondary pollution and the pipe network impact caused by pressure surges, and achieving safe and seamless water supply protection.

[0061] It should be noted that the electrical connection between the above-mentioned units does not necessarily mean direct connection, and indirect connection methods can also be used as long as the purpose of the application is achieved. The above-described embodiments are only exemplary embodiments of the application and cannot limit the scope of the application.

[0062] That is, any equivalent changes and modifications made in accordance with the teachings of the present application are still within the scope of the present application. Other embodiments of the present application will be readily apparent to those skilled in the art upon considering the description and practice of the principles disclosed herein. The present application is intended to cover any variations, uses, or adaptive changes to the present application that follow the general principles of the present application and include commonly known or customary techniques in the art not described herein.

Claims

1. An intelligent emergency water supply scheduling method for sudden pollution of a water source, characterized in that, The method includes: The system acquires pH values, heavy metal concentrations, and organic matter content from a group of mobile water quality monitoring buoys, generating multi-dimensional pollution parameters. Fluid dynamics simulation and geological parameter fusion calculation are performed on the multi-dimensional pollution parameters to generate a three-dimensional pollution cloud map; Based on the three-dimensional pollution cloud map, the pollution diffusion boundary is extracted, and pollution blocking boundary parameters are generated. Based on the pollution blocking boundary parameters, pipeline control logic decisions are made to generate a pressure control unit operating mode instruction set. Based on the pressure control unit's operating mode instruction set, pressure pulse characteristic parameters are calculated, and a pressure buffer wave control signal is generated. The pressure buffer wave control signal is dynamically matched with the pipeline pressure threshold parameter to generate a graded response execution command; According to the graded response execution command, the pipeline valve group is controlled to switch the working mode, which includes electromagnetic drive mode, mechanical self-locking mode and gravity self-locking mode.

2. The emergency water supply intelligent scheduling method for sudden pollution of a water source as described in claim 1, characterized in that, The generation of the three-dimensional pollution cloud map includes: The multi-dimensional pollution parameters are cleaned and standardized to generate standardized pollution parameters; Permeability coefficient for obtaining groundwater hydrogeological parameters; The standardized pollution parameters and the permeability coefficient are spatially superimposed to generate a vertical pollution migration rate distribution map. The horizontal pollution diffusion range is calculated using fluid dynamics equations, and a horizontal pollution range prediction map is generated. Based on the weight ratio of the permeability coefficient and the standardized pollution parameters, the vertical pollution migration rate distribution map and the horizontal pollution range prediction map are fused to generate a three-dimensional pollution cloud map.

3. The emergency water supply intelligent scheduling method for sudden pollution of a water source as described in claim 2, characterized in that, The instruction set for the operating mode of the pressure control unit includes: Based on the three-dimensional pollution cloud map, regions with abrupt changes in pollution concentration gradient are identified, and pollution blocking boundary parameters are generated. The pollution blocking boundary parameters are divided to determine the isolation range of the pollution core area and the boundary of the pollution diffusion buffer zone; Based on the isolation range of the core pollution area, generate isolation mode control instructions; Based on the boundary of the contamination diffusion buffer zone, a reverse flushing mode control command is generated; Generate normal mode control commands in uncontaminated areas; The isolation mode control command, the backflushing mode control command, and the normal mode control command are combined to generate a pressure control unit operating mode command set.

4. The emergency water supply intelligent scheduling method for sudden pollution of a water source as described in claim 3, characterized in that, The control signal for generating the pressure buffer wave includes: The pipeline flow rate fluctuation amplitude is calculated based on the pressure control unit's operating mode instruction set, and expected flow rate fluctuation gradient parameters are generated. The pressure pulse frequency and amplitude are determined based on the expected flow rate mutation gradient parameters, and pressure pulse characteristic parameters are generated. The pressure pulse characteristic parameters are dynamically superimposed with the preset pipeline elastic compensation coefficient to generate a pressure buffer wave control signal.

5. The emergency water supply intelligent scheduling method for sudden pollution of a water source as described in claim 4, characterized in that, The instructions for generating hierarchical responses include: Extract the fluctuation frequency and amplitude characteristics from the pressure buffer wave control signal to generate an emergency level parameter; The urgency parameter is matched with a preset pollution event level classification standard to determine the current event level; Invoke the pipeline pressure threshold parameter corresponding to the current event level to generate a graded response execution command; The pipeline pressure threshold parameters include the upper pressure limit, the pressure fluctuation tolerance range, and the emergency pressure relief triggering conditions.

6. The emergency water supply intelligent scheduling method for sudden pollution of a water source as described in claim 5, characterized in that, Determining the current event level includes: Compare the urgency parameter with the threshold range in the pollution event classification standard; Based on the comparison results, the urgency parameter is assigned to the event level within the corresponding threshold range; The event level to which it belongs is determined as the current event level.

7. The emergency water supply intelligent scheduling method for sudden pollution of a water source as described in claim 5, characterized in that, The control network valve group switches its operating mode according to the graded response execution command: Parse the hierarchical response execution instructions to identify the event level information contained therein; Based on the event level information, determine the level of the current pollution event; When the level of the current pollution event is Level 1, control the valve group to switch to electromagnetic drive mode; When the level of the current pollution event is Level 2, control the valve group to switch to mechanical self-locking mode; When the current pollution event is classified as Level 3, the valve assembly is switched to gravity self-locking mode, and the backup water source switching process is initiated.

8. The emergency water supply intelligent scheduling method for sudden pollution of a water source as described in claim 7, characterized in that, The process for switching to the backup water source includes: Obtain real-time pressure status parameters and real-time water quality parameters of the backup water source pipeline section; Adjust the backup water source booster pump based on the real-time pressure status parameters to make the backup water source pressure higher than the pipeline pressure in the polluted area. When the real-time water quality parameters meet the preset safety threshold, the switching valve between the backup water source and the target pipeline segment is opened.

9. The emergency water supply intelligent scheduling method for sudden pollution of a water source according to claim 1, characterized in that, The method further includes: Collect data on pollution interruption time, water supply restoration time, and pipeline pressure, and generate pipeline operation parameter records; The system response efficiency is evaluated based on the recorded pipeline operation parameters, and system optimization parameters are generated. The pipeline control logic decision-making process is adjusted based on the system optimization parameters.

10. An intelligent emergency water supply scheduling system for sudden pollution of a water source, applied to the intelligent emergency water supply scheduling method for sudden pollution of a water source as described in any one of claims 1-9, characterized in that, The system includes: The data acquisition module is used to acquire pH values, heavy metal concentrations, and organic matter content collected by the mobile water quality monitoring buoy group, and generate multi-dimensional pollution parameters; The pollution simulation module is used to perform fluid dynamics simulation and geological parameter fusion calculation on the multi-dimensional pollution parameters to generate a three-dimensional pollution cloud map. The boundary recognition module is used to extract the pollution diffusion boundary based on the three-dimensional pollution cloud map and generate pollution blocking boundary parameters; The control decision module is used to make pipeline control logic decisions based on the pollution blocking boundary parameters and generate a pressure control unit operating mode instruction set. The pressure characteristic module is used to calculate pressure pulse characteristic parameters based on the working mode instruction set of the pressure control unit and generate a pressure buffer wave control signal. The instruction matching module is used to dynamically match the pressure buffer wave control signal with the pipeline pressure threshold parameter to generate graded response execution instructions; The execution control module is used to control the pipeline valve group to switch working modes according to the graded response execution command. The working modes include electromagnetic drive mode, mechanical self-locking mode and gravity self-locking mode.

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