Method and system for intelligently interactively controlling water production equipment

Abstract

The invention provides a method and a system for intelligently interactively controlling water production equipment. Wherein heavy metal ion concentration data is collected in real time through a multi-node sensing array, concentration difference fluctuation of adjacent monitoring points is analyzed, and a pollutant diffusion track is deduced; when the monitoring data exceeds the standard, switching of a standby water source is triggered based on a diffusion trajectory analysis result, and a reverse cleaning instruction is generated in combination with the dynamic ratio of the concentration gradient change rate to the diffusion speed; dynamically correcting the concentration deviation by utilizing fluid mechanics parameters in a high-pressure impact process in cooperation with multi-dimensional sensor data, and generating environmental response type pollution grade parameters; finally, a pollutant migration path in the three-dimensional diffusion model is matched according to the pollution level, a decision signal containing emergency water source coordinates and the cleaning priority is output, and full-process closed-loop intelligent control from pollution monitoring to emergency decision is achieved. According to the technical scheme provided by the invention, the response speed and the disposal accuracy of the water quality sudden pollution event are improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent water control technology, and in particular to a method and system for intelligent interactive control of water production equipment. Background Art

[0002] In the water treatment sector (such as industrial wastewater treatment and urban water supply systems), real-time monitoring and emergency control of heavy metal ion pollution are core requirements. Due to the rapid diffusion and persistent toxicity of heavy metal pollutants, the system must capture concentration fluctuations, quickly identify the location and direction of pollution sources, and simultaneously block the spread of pollution. Furthermore, the accuracy of pollution assessments must be ensured.

[0003] Currently, there are intelligent water quality monitoring and control systems based on the Internet of Things. These systems collect water quality parameters in real time through a distributed sensor network, use preset thresholds to determine contamination status, and employ fixed-time control strategies to activate backup water sources and filter membrane cleaning procedures. These systems centrally process monitoring data through a cloud-based data analysis platform and generate control instructions based on preset rules.

[0004] While this solution achieves automated monitoring and control, it has shortcomings. It relies solely on fixed thresholds to trigger emergency operations, failing to dynamically adjust strategies based on pollutant diffusion, resulting in inefficient cleaning and wasted resources. The generation of water source switching and cleaning instructions fails to consider the correlation between pollutant diffusion direction and speed, making it difficult to effectively block pollution migration paths. Multidimensional sensing parameters (such as temperature and vibration) are used only for independent alarms, without coordinated correction of monitoring deviations, affecting the accuracy of pollution level determination. Summary of the Invention

[0005] The embodiments of the present application provide a method and system for intelligent interactive control of water production equipment, which are used to solve the problems of slow response speed and low treatment accuracy in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a method for intelligent interactive control of a water production device, comprising:

[0007] The real-time concentration data of heavy metal ions are collected collaboratively by multiple monitoring points in a multi-node sensor array. The concentration fluctuations at adjacent monitoring points are combined with a time series analysis window to deduce the direction of pollution diffusion and obtain the pollutant diffusion trajectory analysis results.

[0008] When the real-time concentration change data of any monitoring point exceeds the preset safety threshold, a backup water source activation instruction is sent to the water source switching device according to the pollutant diffusion trajectory analysis result. Combined with the reverse distribution law of pollutant diffusion direction generated by analyzing the concentration gradient change and diffusion velocity ratio of adjacent monitoring points in the pollutant diffusion trajectory analysis result, a filter membrane high-pressure impact cleaning instruction is obtained;

[0009] Using the impact-induced fluid dynamics parameters generated during the execution of the filter membrane high-pressure impact cleaning instruction, combined with a preset multi-dimensional sensor parameter collaborative rebalancing mechanism, the sensor deviation in the real-time changing concentration data is dynamically and collaboratively adjusted to generate a pollution level determination parameter including an environmentally responsive corrected concentration index;

[0010] According to the pollution level determination parameters, a preset three-dimensional diffusion model is used to match the spatial trajectory of the pollutant migration path to generate an emergency decision signal including the emergency water source positioning coordinates and cleaning priority parameters.

[0011] Optionally, the impact-induced fluid dynamics parameters generated during the execution of the filter membrane high-pressure impact cleaning instruction are used in combination with a preset multi-dimensional sensor parameter collaborative rebalancing mechanism to dynamically and collaboratively adjust the sensor deviation in the real-time changing concentration data to generate a pollution level determination parameter including an environmentally responsive corrected concentration index, including:

[0012] During the high-pressure impact cleaning process of the filter membrane, a set of fluid dynamics parameters formed by the periodic pressure changes of the impact waveform is extracted, and a set of multi-dimensional sensing parameters is simultaneously collected, wherein the multi-dimensional sensing parameter set includes ion migration rate, ambient temperature fluctuation, and filter membrane vibration amplitude;

[0013] The peak pressure and the rising edge slope in the fluid mechanics parameter set are synchronously correlated in time series to construct a pressure slope parameter correlation map, and the ion migration rate in the multidimensional sensing parameter set is periodically matched with the ambient temperature fluctuation to generate a migration temperature dynamic correlation sequence;

[0014] Adjusting the filter membrane vibration amplitude acquisition interval in the multidimensional sensing parameter set according to the peak pressure distribution characteristics of the pressure slope parameter association map, and dynamically reorganizing the discrete sampling points of the real-time changing concentration data using the adjusted filter membrane vibration amplitude acquisition interval and the periodic characteristics of the migration temperature dynamic association sequence to obtain a dynamic adjustment set;

[0015] The dynamic adjustment set is phase-synchronized with the rising edge slope of the pressure slope parameter association map to generate a corrected concentration distribution curve, and the number of consecutive crossings of the corrected concentration distribution curve within the preset pollution judgment interval is counted. The statistical results are used to generate pollution level judgment parameters according to a preset frequency level mapping relationship.

[0016] Optionally, based on the peak pressure distribution characteristics of the pressure slope parameter association map, the filter membrane vibration amplitude acquisition interval in the multidimensional sensing parameter set is adjusted, and the discrete sampling points of the real-time changing concentration data are dynamically density reorganized using the adjusted filter membrane vibration amplitude acquisition interval and the periodic characteristics of the migration temperature dynamic association sequence to obtain a dynamic adjustment set, including:

[0017] Pairing the peak pressure and the rising edge slope of each time window in the pressure slope parameter association map to generate a timestamp-marked slope parameter pair, and connecting the timestamp-marked slope parameter pairs of adjacent time windows by linear interpolation to generate a dynamic pressure slope map;

[0018] Adjusting the collection interval of the filter membrane vibration amplitude based on the distribution density of the peak pressures of a preset number of consecutive time windows in the dynamic pressure slope map;

[0019] Extracting the duration of the main fluctuation period of the temperature fluctuation data in the multidimensional sensing parameter set as a reference unit, periodically segmenting the ion migration rate monitoring data in the multidimensional sensing parameter set and calculating the segment mean, and generating a migration temperature dynamic correlation sequence based on the calculation results;

[0020] Adjusting the sampling point density of the real-time changing concentration data according to the spatial distribution density corresponding to the fluctuation direction and fluctuation amplitude change degree of each segment in the migration temperature dynamic correlation sequence to obtain a recombined concentration sampling sequence;

[0021] The vibration data sequence corresponding to the adjusted collection interval is time-matched with the recombined concentration sampling sequence, and a dynamic adjustment set containing vibration characteristics and concentration distribution correction parameters is generated based on the matching results.

[0022] Optionally, the sampling point density of the real-time changing concentration data is adjusted according to the spatial distribution density corresponding to the fluctuation direction and fluctuation amplitude change degree of each segment in the migration temperature dynamic correlation sequence to obtain a recombined concentration sampling sequence, including:

[0023] Dividing each segment into a leading subinterval, a core subinterval, and a decay subinterval, and determining a fluctuation direction attribute by comparing average amplitudes of temperature fluctuations of adjacent subintervals in the migration temperature dynamic correlation sequence;

[0024] An initial sampling density level of the core subinterval is set based on the fluctuation direction attribute. When it is detected that the fluctuation direction attribute is continuously increasing in a positive direction, the initial sampling density level is increased to generate an adjustment sequence. When the fluctuation direction attribute is continuously decreasing in a negative direction, the baseline level is maintained to generate a baseline sequence.

[0025] In the core sub-interval, dynamic superposition of sampling points is performed according to the low, medium and high preset intervals of the temperature fluctuation amplitude change rate in the migration temperature dynamic correlation sequence to generate a corresponding incremental sequence;

[0026] Performing a reverse adjustment operation on the attenuation subinterval, and generating a gradient sequence according to the amplitude difference between the end of the core subinterval and the beginning of the attenuation subinterval;

[0027] The adjustment sequence, the reference sequence, the corresponding incremental sequence and the gradient sequence are spliced ​​in time sequence, a preset number of sampling points in the real-time changing concentration data are retained at the splicing point of each sequence and new points with equal spacing are inserted to generate a recombined concentration sampling sequence.

[0028] Optionally, when the real-time concentration change data of any monitoring point exceeds a preset safety threshold, a backup water source start-up instruction is sent to the water source switching device according to the pollutant diffusion trajectory analysis result, and a filter membrane high-pressure impact cleaning instruction is obtained by combining the reverse distribution law of the pollutant diffusion direction generated by analyzing the concentration gradient change and diffusion velocity ratio of adjacent monitoring points in the pollutant diffusion trajectory analysis result, including:

[0029] When the real-time concentration data collected at any monitoring point exceeds a preset safety threshold, the position coordinates and diffusion direction parameters of the monitoring point in the pollutant diffusion trajectory analysis results are extracted;

[0030] Calculating the shortest connection path between the exceeding-standard monitoring point and the backup water source access point among the monitoring points based on the location coordinates, determining the priority level of water source switching based on the shortest connection path and the diffusion direction parameter, and generating a water source switching instruction set including the path coordinates and the switching priority level based on the water source switching priority level;

[0031] Analyze the concentration gradient change trend of adjacent monitoring points in the pollutant diffusion trajectory analysis results, identify the main direction of pollutant diffusion based on the analysis results, and determine the starting position of the filter membrane cleaning along the reverse path of the main direction of pollutant diffusion;

[0032] Calculating the wave interval of the high-pressure shock according to the diffusion velocity of the pollutant in the main diffusion direction, and generating a cleaning parameter set according to the calculation result and the filter membrane cleaning starting position, wherein the cleaning parameter set includes the shock position coordinates, the shock wave interval, and the shock duration;

[0033] The water source switching instruction set and the cleaning parameter set are sent to the water source switching control and the high-pressure impact control respectively, and after the confirmation signal of the completion of the execution of the water source switching instruction set arrives, a filter membrane high-pressure impact cleaning instruction is generated.

[0034] Optionally, analyzing the concentration gradient change trend of adjacent monitoring points in the pollutant diffusion trajectory analysis result, identifying the main direction of pollutant diffusion according to the analysis result, and determining the starting position of the filter membrane cleaning along the reverse path of the main direction of pollutant diffusion, including:

[0035] Extracting the concentration data and location coordinates of the monitoring point exceeding a preset threshold and three adjacent monitoring points from the pollutant diffusion trajectory analysis results, calculating the ratio of the concentration change to the spatial distance between the monitoring point exceeding the preset threshold and each adjacent point, and generating a concentration gradient vector set based on the calculation results;

[0036] Selecting two continuous vectors with the largest change in the concentration gradient vector set, comparing the direction angle of the two continuous vectors with a preset angle threshold, and determining that diffusion is in the same direction when the angle is less than the threshold, and taking the average direction of the two continuous vectors as the main diffusion direction;

[0037] According to the direction of the main diffusion direction, a preset distance is extended in the opposite direction of the main diffusion direction in the position coordinate to locate the nearest physical partition boundary point of the filter membrane as a candidate cleaning starting position;

[0038] Based on the comparison result of the historical pollution frequency data of the physical partition of the filter membrane where the candidate cleaning starting position is located and the trigger condition threshold within a preset period, when the historical pollution frequency data exceeds the trigger condition threshold, the candidate cleaning starting position is adjusted to the side of the main diffusion direction by a correction distance to generate the filter membrane cleaning starting position.

[0039] Optionally, the dynamic adjustment set is phase-synchronized with the rising edge slope of the pressure slope parameter association map to generate a modified concentration distribution curve, the number of consecutive crossings of the modified concentration distribution curve within a preset pollution determination interval is counted, and the statistical results are used to generate a pollution level determination parameter according to a preset frequency level mapping relationship, including:

[0040] Arrange the vibration characteristic correction parameters in the dynamic adjustment set in time series, extract the vibration phase angle corresponding to each time point, and simultaneously obtain the rising edge slope value of the corresponding time window from the pressure slope parameter association map;

[0041] Performing a product operation on the vibration phase angle and the rising edge slope value at the same time point to generate a phase synchronization coefficient. When the phase synchronization coefficient exceeds a preset synchronization threshold, the concentration distribution correction parameter at the corresponding time point is marked as a valid correction parameter.

[0042] Taking the effective correction parameters as basic data points, the basic data points are connected by a three-point smooth interpolation method to form a continuous curve, which is marked as a corrected concentration distribution curve;

[0043] An upper threshold line and a lower threshold line are set within the pollution determination interval, and the number of complete crossings of the modified concentration distribution curve from the lower threshold line upward across the upper threshold line is counted, and multiple false crossings caused by a single oscillation are excluded;

[0044] The counted number of complete crossings is input into a preset frequency level mapping table, and the mapping table obtains a pollution level determination parameter according to the rule that each additional crossing corresponds to an increase in pollution level.

[0045] In a second aspect, an embodiment of the present application provides a system for intelligent interactive control of water production equipment, including:

[0046] The multi-source collaborative monitoring module is used to collaboratively collect real-time concentration data of heavy metal ions using multiple monitoring points in a multi-node sensor array. It uses a time series analysis window to deduce the pollution diffusion direction based on concentration fluctuations at adjacent monitoring points, thereby obtaining pollutant diffusion trajectory analysis results.

[0047] A reverse emergency control module is used to send a backup water source activation instruction to the water source switching device based on the pollutant diffusion trajectory analysis results when the real-time concentration data of any monitoring point exceeds a preset safety threshold. The reverse distribution law of the pollutant diffusion direction generated by analyzing the concentration gradient change and diffusion velocity ratio of adjacent monitoring points in the pollutant diffusion trajectory analysis results is combined to obtain a high-pressure impact cleaning instruction for the filter membrane;

[0048] a dynamic parameter calibration module for utilizing the impact-induced fluid dynamics parameters generated during the execution of the filter membrane high-pressure impact cleaning instruction, in combination with a preset multi-dimensional sensor parameter collaborative rebalancing mechanism, to dynamically and collaboratively adjust the sensor deviation in the real-time changing concentration data, thereby generating a pollution level determination parameter including an environmentally responsive corrected concentration index;

[0049] The closed-loop decision generation module is used to match the spatial trajectory of the pollutant migration path using a preset three-dimensional diffusion model based on the pollution level judgment parameters, and generate an emergency decision signal including the emergency water source positioning coordinates and cleaning priority parameters.

[0050] In a third aspect, an embodiment of the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for intelligent interactive control of water-making equipment as described in the first aspect above.

[0051] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements a method for intelligent interactive control of water production equipment as described in the first aspect.

[0052] In an embodiment of the present application, multiple monitoring points in a multi-node sensor array are used to collaboratively collect real-time changing concentration data of heavy metal ions, and the concentration difference fluctuations of adjacent monitoring points are combined with a time series analysis window to deduce the pollution diffusion direction, and obtain a pollutant diffusion trajectory analysis result; when the real-time changing concentration data of any monitoring point exceeds a preset safety threshold, a backup water source startup instruction is sent to a water source switching device based on the pollutant diffusion trajectory analysis result, and a filter membrane high-pressure impact cleaning instruction is obtained by combining the reverse distribution law of the pollutant diffusion direction generated by analyzing the concentration gradient change and the diffusion velocity ratio of adjacent monitoring points in the pollutant diffusion trajectory analysis result; the impact-induced fluid mechanics parameters generated during the execution of the filter membrane high-pressure impact cleaning instruction are used, combined with a preset multi-dimensional sensor parameter collaborative rebalancing mechanism to dynamically and collaboratively adjust the sensor deviation in the real-time changing concentration data, and generate a pollution level judgment parameter including an environmentally responsive corrected concentration index; based on the pollution level judgment parameter, a preset three-dimensional diffusion model is used to match the spatial trajectory of the pollutant migration path, and generate an emergency decision signal including the emergency water source positioning coordinates and cleaning priority parameters.

[0053] The technical solution of this application has the following beneficial effects:

[0054] Real-time data on heavy metal ion concentrations is collected through a distributed sensor array. Time series windows are used to analyze concentration differences between adjacent monitoring points, and the pollutant diffusion trajectory is dynamically deduced to achieve accurate predictions of pollution trends. The backup water source switch is triggered based on the diffusion trajectory analysis results, and a reverse cleaning instruction is generated based on the dynamic ratio of the concentration gradient change rate to the diffusion velocity, effectively blocking the pollution diffusion path and improving emergency response efficiency. The fluid mechanics parameters during the high-pressure impact process are used in conjunction with multi-dimensional sensor data (temperature, vibration, and ion migration rate) to correct monitoring deviations in real time, output environmentally responsive pollution level parameters, and improve data accuracy. The pollution level is matched to the pollutant migration path in the preset three-dimensional diffusion model, and a decision signal containing the emergency water source coordinates and cleaning priority is output to achieve precise emergency control.

[0055] Furthermore, during the high-pressure impact cleaning process of the filter membrane, fluid mechanics parameters (peak pressure, rising edge slope) and multi-dimensional sensor data (ion migration rate, temperature fluctuation, vibration amplitude) are synchronously collected to construct a pressure slope parameter correlation map and a migration temperature dynamic correlation sequence; the vibration collection interval is dynamically adjusted based on the peak pressure distribution, and the sampling point density is reorganized in combination with the periodic characteristics of the temperature migration sequence to generate a dynamic adjustment set; finally, the concentration distribution curve is corrected through phase synchronization processing, and the pollution level is mapped according to the number of consecutive crossings of the curve in the pollution judgment interval.

[0056] Through dynamic collaborative analysis of impact parameters and multi-dimensional sensor data, real-time correction of sensor deviations and accurate determination of pollution levels are achieved, which solves the problem of insufficient adaptability of traditional static calibration methods in sudden pollution scenarios and significantly improves the reliability of monitoring data and the timeliness of pollution response.

[0057] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0059] Figure 1 A flow chart showing a method for intelligent interactive control of water production equipment provided by the present application is shown;

[0060] Figure 2 A schematic diagram of the structure of a system for intelligent interactive control of water production equipment provided by the present application is shown;

[0061] Figure 3 A schematic structural diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0062] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0063] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0064] Researchers have discovered that existing intelligent water production systems face technical bottlenecks such as response lag, mismatch between cleaning strategies and pollution diffusion characteristics, and misjudgment of sensor data due to environmental interference when faced with sudden heavy metal pollution, making it difficult to achieve accurate and rapid pollution prevention. Based on this, a method for intelligent interactive control of water production equipment is proposed. This method can deduce the pollution diffusion trajectory in real time through multi-node collaborative monitoring, dynamically generate cleaning strategies based on inverse distribution patterns, and collaboratively correct monitoring deviations using multi-dimensional sensor parameters, achieving closed-loop intelligent control of the entire process from pollution monitoring to emergency decision-making.

[0065] The technical solution of this application is applicable to rapid response control scenarios for water production equipment facing sudden water pollution. Specifically, it uses a distributed sensor network to capture abnormal heavy metal ion concentrations in real time; dynamically adjusts emergency response strategies based on the diffusion dynamics of pollutants; and maintains the accuracy of monitoring data under complex operating conditions, providing reliable intelligent solutions for scenarios such as urban water supply systems and industrial wastewater treatment.

[0066] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0067] Figure 1 A flow chart of a method for intelligent interactive control of water production equipment is provided in the embodiment of the present application, such as Figure 1 As shown, the method includes:

[0068] 101. Utilize multiple monitoring points in a multi-node sensor array to collaboratively collect real-time concentration data of heavy metal ions, combine with the time series analysis window to deduce the pollution diffusion direction based on the concentration difference fluctuations of adjacent monitoring points, and obtain the pollutant diffusion trajectory analysis results;

[0069] In this step, the multi-node sensor array refers to a monitoring network composed of multiple sensor nodes distributed at key locations of the water production equipment, and each node is equipped with a heavy metal ion concentration sensor;

[0070] Real-time changing concentration data refers to the ion concentration time series data collected by the sensor at a fixed sampling frequency (e.g., 1 time / second);

[0071] The time series analysis window refers to a sliding time window with a fixed duration (e.g., 5 minutes) that is used to analyze the time series correlation of data from adjacent monitoring points.

[0072] Concentration difference fluctuation refers to the characteristics of the concentration difference between adjacent monitoring points at the same time changing with time;

[0073] The pollutant diffusion trajectory analysis results refer to the spatial vector data containing the pollutant diffusion direction, speed and concentration distribution.

[0074] In an embodiment of the present application, the system first synchronously collects heavy metal ion concentration data through each sensor in a multi-node sensor array, and stores the collected real-time data in a time series; then, a time series analysis window is established for each monitoring point, and the concentration difference between the monitoring point and the adjacent monitoring point is calculated within the window, and the law of fluctuation of these differences over time is analyzed; then, the main direction of pollutant diffusion is determined by comparing the concentration change rate of adjacent monitoring points in different directions; finally, the analysis data of each monitoring point are integrated to calculate the diffusion speed and concentration distribution characteristics of the pollutants, and generate the pollutant diffusion trajectory analysis results.

[0075] In one real-world case, 12 heavy metal monitoring nodes were deployed in the pretreatment tank of a water purification plant. When the system detected an arsenic concentration of 0.02 mg / L at node 5, it immediately initiated diffusion analysis. The system first retrieved the concentration data for the past 10 minutes for node 5 and its adjacent nodes 4, 6, and 9. It then calculated the concentration difference curves for the 5-4, 5-6, and 5-9 pairs. The analysis then revealed that the concentration change rate in the direction 5→6 ​​was the largest, reaching 0.5 mg / (L·min). Finally, the system determined that the pollutant was diffusing toward node 6 at a velocity of 0.3 m / min, generating a pollutant diffusion trajectory analysis containing these parameters.

[0076] 102. When the real-time concentration change data of any monitoring point exceeds the preset safety threshold, a backup water source activation instruction is sent to the water source switching device based on the pollutant diffusion trajectory analysis results. Combined with the reverse distribution law of pollutant diffusion direction generated by analyzing the concentration gradient change and diffusion velocity ratio of adjacent monitoring points in the pollutant diffusion trajectory analysis results, a filter membrane high-pressure impact cleaning instruction is obtained;

[0077] In this step, the safety threshold refers to the concentration limit set according to different heavy metal types (e.g., lead ion ≤ 0.01 mg / L);

[0078] The water source switching command refers to the digital control signal that controls the solenoid valve to switch the water supply pipeline;

[0079] The concentration gradient change rate refers to the rate of concentration change per unit distance (mg / L·m);

[0080] The diffusion rate ratio refers to the proportional coefficient of the diffusion rate of pollutants in different directions;

[0081] The reverse distribution law refers to a cleaning path planning strategy that is opposite to the diffusion direction of pollutants.

[0082] In an embodiment of the present application, when the system detects that the real-time concentration data of a monitoring point exceeds a safety threshold, it first extracts the spatial position coordinates and diffusion direction parameters of the monitoring point from the pollutant diffusion trajectory analysis results; then, based on the position coordinates, the pipeline distance from the point to each backup water source access point is calculated, and the activation priority of each backup water source is evaluated in combination with the diffusion direction parameters; at the same time, the concentration gradient change trend of adjacent monitoring points is analyzed, and the spatial distribution ratio of the pollutant diffusion rate is calculated; based on these parameters, a cleaning path plan opposite to the pollutant diffusion direction is generated; and finally, these analysis results are converted into specific water source switching instructions and filter membrane high-pressure impact cleaning instructions.

[0083] Continuing with the above case, after the arsenic concentration at node 5 exceeds the standard: the system first obtains the coordinates of node 5 (X:15, Y:28) and the parameters for eastward diffusion; then it calculates the pipeline distances to the three backup water sources A, B, and C as 35m, 28m, and 42m respectively, and, combined with the characteristics of eastward diffusion, determines to prioritize the use of backup water source B on the west side; at the same time, it is analyzed that the speed of pollutant diffusion to the east is 1.5 times that to the north; based on this, a cleaning strategy is generated to first clean the area east of node 5 and then gradually advance westward; finally, an opening command is sent to the electric valve of water source B, and a pulse cleaning command of 20MPa and 5 seconds interval is sent to the filter membrane area No. 5.

[0084] 103. Utilizing the impact-induced fluid dynamics parameters generated during the execution of the filter membrane high-pressure impact cleaning instruction, combined with a preset multi-dimensional sensor parameter collaborative rebalancing mechanism, dynamically and collaboratively adjust the sensor deviation in the real-time changing concentration data to generate a pollution level determination parameter including an environmentally responsive corrected concentration index;

[0085] In this step, the impact-induced fluid dynamics parameters refer to the physical parameters such as pressure and flow rate generated when high-pressure water impacts the filter membrane;

[0086] Multi-dimensional sensing parameters refer to monitoring data including ion migration rate, ambient temperature fluctuation, filter membrane vibration amplitude, etc.

[0087] The collaborative rebalancing mechanism refers to a compensation algorithm that dynamically adjusts the weight of each sensor data;

[0088] The environmental response corrected concentration index refers to the concentration estimate after compensation for environmental parameters.

[0089] In an embodiment of the present application, during the execution of the high-pressure impact cleaning instruction, the system collects the pressure change data of the impact waveform in real time, and simultaneously records the monitoring values ​​of the ion migration rate, ambient temperature and filter membrane vibration; then a dynamic correlation model of the pressure change and each sensor parameter is established, and the influence weight of each environmental factor on the concentration monitoring is calculated; then, based on these weight coefficients, the original concentration data is dynamically compensated; finally, the pollution level judgment parameter corrected by the environmental parameters is output.

[0090] Continuing with the above case, when performing the cleaning of filter membrane No. 5: the system monitored the impact pressure peak of 18.5MPa, and the rise time was 0.25 seconds; at the same time, it was recorded that the water temperature in the area increased by 1.8℃, and the filter membrane vibration amplitude was 0.3mm; through analysis, it was determined that the influence coefficient of temperature change on the ion sensor was 0.7, and the vibration influence coefficient was 0.2; accordingly, the original detection concentration of 0.02mg / L was corrected to 0.0215mg / L; based on the corrected concentration value, the current pollution level was determined to be Level 4 (high).

[0091] 104. Based on the pollution level determination parameters, a preset three-dimensional diffusion model is used to perform spatial trajectory matching of the pollutant migration path, and an emergency decision signal including the emergency water source location coordinates and cleaning priority parameters is generated.

[0092] In this step, the preset three-dimensional diffusion model refers to a calculation model that includes water flow characteristics and pollutant migration laws;

[0093] Spatial trajectory matching refers to comparing and analyzing real-time monitoring data with model prediction results;

[0094] Emergency water source positioning coordinates refer to the three-dimensional location information of the backup water source access point;

[0095] The cleaning priority parameter refers to the urgency of disposal calculated based on the degree of contamination and the risk of spread.

[0096] In an embodiment of the present application, the system first inputs the pollution level judgment parameters into a preset three-dimensional diffusion model, runs a real-time simulation to predict the possible diffusion range of pollutants in the future; then matches and analyzes the prediction results with the actual monitoring data to evaluate the accuracy of the prediction; adjusts the emergency strategy based on the matching results, calculates the activation priority of each backup water source and the cleaning order of the filter membrane in each area; and finally generates an emergency decision signal containing the specific emergency water source coordinates and cleaning priority parameters.

[0097] Continuing with the above case, based on the Level 4 pollution judgment at Node 5: the system operation model predicts that pollutants may affect the areas of Nodes 7 and 8 in the next 20 minutes; the comparison between the actual monitoring data and the predicted results shows an 82% agreement; based on this, the strategy is adjusted to prioritize the use of backup water source B (X:10, Y:25), and the cleaning order is determined to be the east side of Node 5 → the center of Node 5 → Node 7; and emergency instructions containing specific coordinates and timing are sent to each execution unit through the control network.

[0098] In summary, steps 101 to 104, through the coordinated monitoring of a distributed sensor network, enable real-time and accurate analysis of the diffusion trajectory of heavy metal pollution. An emergency control strategy based on inverse distribution patterns effectively improves pollution blocking efficiency. A dynamic compensation mechanism utilizing multidimensional sensor data significantly enhances monitoring accuracy. An intelligent decision-making system, combined with a three-dimensional diffusion model, ensures timely and targeted emergency response. In practical applications, this system is capable of rapidly responding to sudden pollution incidents. The entire process, from pollution detection to generating a treatment plan, can be completed within 5 minutes, significantly enhancing the water production equipment's ability to respond to water quality emergencies.

[0099] In order to solve the problem of measurement deviation caused by environmental interference in sensor data during heavy metal pollution monitoring, in some embodiments, in step 103, the impact-induced fluid dynamics parameters generated during the execution of the filter membrane high-pressure impact cleaning instruction are used in combination with a preset multi-dimensional sensor parameter collaborative rebalancing mechanism to dynamically and collaboratively adjust the sensor deviation in the real-time changing concentration data to generate pollution level determination parameters including an environmentally responsive corrected concentration index, including:

[0100] 201. During the high-pressure impact cleaning process of the filter membrane, extract a set of fluid dynamics parameters formed by the periodic pressure changes of the impact waveform, and simultaneously collect a set of multi-dimensional sensing parameters, wherein the multi-dimensional sensing parameter set includes ion migration rate, ambient temperature fluctuation, and filter membrane vibration amplitude;

[0101] In step 201, the impact waveform periodic pressure change refers to the periodic pressure fluctuation curve generated when high-pressure water impacts the filter membrane, including characteristic parameters such as peak pressure and rise time;

[0102] The fluid mechanics parameter set refers to a set of parameters collected by the pressure sensor, including peak pressure, rising edge slope, fluctuation frequency, etc.

[0103] The multidimensional sensing parameter set refers to the synchronously collected ion migration rate (cm 2 / V·s), ambient temperature fluctuation (℃), and filter membrane vibration amplitude (mm).

[0104] In an embodiment of the present application, the system first uses a pressure sensor to collect pressure waveform data in real time during the high-pressure impact cleaning process of the filter membrane, extracts the peak pressure and the corresponding rising edge slope in each impact cycle, and forms a set of fluid mechanics parameters; at the same time, the ion sensor continuously monitors the migration rate of heavy metal ions in the water, the temperature sensor records the ambient temperature fluctuation value, and the vibration sensor collects the mechanical vibration amplitude of the filter membrane. All parameters are aligned according to a unified timestamp to form a multi-dimensional sensing parameter set.

[0105] 202. Synchronously correlate the peak pressure and the rising edge slope in the fluid mechanics parameter set in a time series to construct a pressure slope parameter correlation map. Simultaneously, perform periodic fluctuation matching on the ion migration rate in the multidimensional sensing parameter set and the ambient temperature fluctuation to generate a migration temperature dynamic correlation sequence.

[0106] In step 202, the pressure slope parameter correlation map refers to a two-dimensional correlation map reflecting the relationship between the peak pressure and the rising edge slope over time;

[0107] The migration temperature dynamic correlation sequence refers to the correlation data sequence generated by time-series matching of ion migration rate and ambient temperature fluctuation.

[0108] In an embodiment of the present application, the system pairs the peak pressure and rising edge slope in the fluid mechanics parameter set in a time series, and generates a continuous pressure slope parameter correlation map through a cubic spline interpolation algorithm; at the same time, a fast Fourier transform analysis is performed on the ion migration rate and ambient temperature fluctuation data in the multidimensional sensing parameter set, the main fluctuation frequencies of the two are extracted, and the ion migration rate data and the ambient temperature fluctuation data are matched in the time domain through a phase alignment algorithm to generate a migration temperature dynamic correlation sequence.

[0109] 203. Adjust the filter membrane vibration amplitude acquisition interval in the multidimensional sensing parameter set based on the peak pressure distribution characteristics of the pressure slope parameter association map, and dynamically reorganize the discrete sampling points of the real-time changing concentration data using the adjusted filter membrane vibration amplitude acquisition interval and the periodic characteristics of the migration temperature dynamic association sequence to obtain a dynamically adjusted set;

[0110] In step 203, the peak pressure distribution characteristics refer to the temporal and spatial distribution patterns of the peak pressure in the pressure slope parameter correlation map;

[0111] Dynamic density reorganization refers to a method that dynamically adjusts the sampling frequency and data processing weight according to changes in environmental parameters;

[0112] The dynamic adjustment set refers to the data set containing spatiotemporal characteristics that is output after reorganization processing.

[0113] In an embodiment of the present application, the system analyzes the peak pressure distribution characteristics of the pressure slope parameter association map. When it is detected that the peak pressure density of three consecutive time windows exceeds a preset threshold, the filter membrane vibration amplitude collection interval is shortened from the default 10 seconds to 5 seconds; at the same time, based on the periodic fluctuation characteristics of the migration temperature dynamic association sequence, the concentration sampling point density is increased to 1 time per second in the temperature rising stage and reduced to 1 time per 5 seconds in the temperature falling stage; finally, the adjusted vibration data sequence and the reorganized concentration sampling points are aligned along the time axis to generate a dynamic adjustment set containing timestamps, spatial positions and correction weights.

[0114] 204. Perform phase synchronization processing on the rising edge slope of the dynamic adjustment set and the pressure slope parameter association map to generate a corrected concentration distribution curve, count the number of consecutive crossings of the corrected concentration distribution curve within the preset pollution determination interval, and generate a pollution level determination parameter based on the statistical results according to a preset frequency level mapping relationship.

[0115] In step 204, phase synchronization processing refers to a method of aligning and correcting data streams of different sensors in the time domain;

[0116] The corrected concentration distribution curve refers to the concentration change curve over time after multi-parameter compensation;

[0117] The frequency level mapping relationship refers to the correspondence rule between the preset number of crossings and the pollution level.

[0118] In an embodiment of the present application, the system first calculates the time domain correlation between the vibration data in the dynamic adjustment set and the slope of the pressure rising edge to determine the optimal phase offset; then applies the offset to perform time-shift correction on the concentration data to eliminate the timing deviation caused by the sensor response delay; then, based on the corrected data points, cubic B-spline interpolation is used to generate a smooth corrected concentration distribution curve; finally, the number of complete crossings of the curve within the preset pollution judgment interval (such as 0.01-0.02 mg / L) is counted, and the pollution level judgment parameter is generated according to the mapping rule (such as 5 consecutive crossings corresponding to level 3 pollution).

[0119] Here's a specific example:

[0120] When a nickel contamination incident occurred in the reverse osmosis water treatment system of an industrial park, the system initiated a dynamic collaborative adjustment process. When the high-pressure cleaning program was activated, the pressure sensor detected the peak pressure of the shock waveform to be 22.5MPa and the rising slope to be 125MPa / s. At the same time, the ion sensor recorded the nickel ion migration rate to be 0.018cm 2 / V·s, the temperature sensor detected a 2.5°C rise in local water temperature, and the vibration sensor measured a 0.35mm filter membrane amplitude. All parameters were synchronized with millisecond-level timestamps to form a multi-dimensional sensor parameter set. When the system constructed a pressure slope parameter correlation map, it was found that the peak pressure density in three consecutive time windows reached the high threshold, triggering the vibration acquisition interval to be adjusted to 5 seconds; analysis of the migration temperature dynamic correlation sequence showed that every 1°C increase in temperature resulted in an increase in ion migration rate of 0.003cm 2 / V·s. Based on this, the concentration sampling frequency was increased to twice per second during the temperature rise phase. The modified concentration distribution curve generated after phase synchronization showed that the concentration crossed the 0.015-0.025 mg / L judgment range eight times in a row within 15 minutes. The final pollution level was determined to be Level 4, triggering a high-level emergency response.

[0121] In summary, steps 201 to 204 achieve real-time collaborative correction of sensor deviations through dynamic correlation analysis of high-voltage shock parameters and multi-dimensional sensor data; an adaptive sampling strategy based on physical characteristics accurately captures the details of pollution signal changes; and measurement errors caused by environmental interference are effectively eliminated through timing alignment and phase compensation processing. The ultimately generated pollution level determination parameters objectively reflect the actual degree of pollution, providing a reliable basis for emergency decision-making for water production equipment and significantly improving monitoring accuracy and response timeliness under complex working conditions.

[0122] In order to solve the problem of sensor deviation in real-time concentration monitoring and improve the accuracy of pollution level determination, in some embodiments, in step 203, the filter membrane vibration amplitude acquisition interval in the multidimensional sensor parameter set is adjusted according to the peak pressure distribution characteristics of the pressure slope parameter association map, and the discrete sampling points of the real-time changing concentration data are dynamically density reorganized using the adjusted filter membrane vibration amplitude acquisition interval and the periodic characteristics of the migration temperature dynamic association sequence to obtain a dynamic adjustment set, including:

[0123] 301. Pair the peak pressure and the rising edge slope of each time window in the pressure slope parameter association map to generate a timestamp-marked slope parameter pair, and connect the timestamp-marked slope parameter pairs of adjacent time windows by linear interpolation to generate a dynamic pressure slope map.

[0124] In step 301, the timestamp-marked slope parameter pair refers to a data unit including a timestamp, a peak pressure value (MPa), and a corresponding rising edge slope (MPa / s);

[0125] Linear interpolation refers to an interpolation algorithm that generates intermediate values ​​based on the numerical relationship between adjacent data points;

[0126] The dynamic pressure slope map refers to a two-dimensional map generated by continuous interpolation that reflects the dynamic relationship between pressure and slope.

[0127] In an embodiment of the present application, the system first extracts the peak pressure and rising edge slope of each time window (such as 30 seconds as a window) from the pressure slope parameter association map, and binds it to a timestamp accurate to milliseconds to form a structured data unit (slope parameter pair marked with a timestamp); then, a linear interpolation algorithm is used to calculate the parameter pairs of adjacent time windows. For example, if the slope of window 1 is 120 MPa / s (timestamp T1) and that of window 2 is 125 MPa / s (timestamp T2), then an intermediate value is inserted between T1 and T2 according to the time ratio (such as inserting 122.5 MPa / s at T1+15 seconds); finally, all interpolation points are connected to generate a continuous curve, which is integrated into a dynamic pressure slope map.

[0128] 302. Adjusting the collection interval of the filter membrane vibration amplitude based on the distribution density of the peak pressures in a preset number of consecutive time windows in the dynamic pressure slope map;

[0129] In step 302, the peak pressure distribution density refers to the number of times the peak pressure exceeds a set threshold per unit time;

[0130] The filter membrane vibration amplitude sampling interval refers to the time interval between two samplings of the vibration sensor.

[0131] In the embodiment of the present application, the system performs sliding window analysis on the dynamic pressure slope map, which means that three consecutive time windows (90 seconds) are used as analysis units, and the number of times the peak pressure exceeds 20MPa in each unit is counted; when the statistical value reaches a preset density threshold (such as ≥5 times in each window), the interval for triggering the filter membrane vibration amplitude acquisition is shortened from the default 10 seconds to 5 seconds; if the statistical value of three consecutive windows is lower than the threshold, the default interval is restored. In specific implementation, the system dynamically configures the sensor sampling register and adjusts the trigger frequency of the analog-to-digital converter in real time to ensure that the complete vibration waveform characteristics are captured during the intense stage of high-pressure shock.

[0132] 303. Extract the duration of the main fluctuation period of the temperature fluctuation data in the multi-dimensional sensing parameter set as a reference unit, periodically segment the ion migration rate monitoring data in the multi-dimensional sensing parameter set, calculate the segment mean, and generate a migration temperature dynamic correlation sequence based on the calculation results;

[0133] In step 303, the main fluctuation period refers to the duration of the dominant period of temperature fluctuation determined by spectrum analysis;

[0134] Periodic segmentation means dividing data into equal lengths according to the main period duration.

[0135] In the embodiment of the present application, the system performs fast Fourier transform (FFT) analysis on the temperature fluctuation data in the multi-dimensional sensing parameter set, which means first removing the baseline drift, then calculating the power spectrum density, identifying the frequency component with the largest amplitude (such as 0.0055Hz corresponding to a 180-second period); then dividing the ion migration rate monitoring data into multiple time periods with a fixed segment length of 180 seconds; calculating the moving average of the ion migration rate data in each time period, and generating a migration temperature dynamic correlation sequence. For example, in time period 1 (0-180 seconds), the average migration rate calculated is 0.015cm 2 / V·s, period 2 (180-360 seconds) is 0.017cm 2 / V·s, forming a correlation sequence that changes synchronously with the temperature cycle.

[0136] 304. Adjust the sampling point density of the real-time concentration change data according to the spatial distribution density corresponding to the fluctuation direction and fluctuation amplitude change degree of each segment in the migration temperature dynamic correlation sequence to obtain a recombined concentration sampling sequence;

[0137] In step 304, the degree of change in the fluctuation amplitude refers to the difference between the maximum and minimum values ​​of the temperature fluctuation amplitude within the segment;

[0138] Spatial distribution density refers to the number of sampling points arranged in a unit physical area.

[0139] In an embodiment of the present application, the system first calculates the temperature fluctuation direction (the rising and falling trends are determined by the positive and negative first-order derivatives) and the degree of amplitude change of each segment in the migration temperature dynamic correlation sequence (for example, the temperature rise in segment 1 is 2.1°C); then, the sampling density is adjusted according to the preset rules, which means that in the segment where the temperature rises and the amplitude change is greater than 1.5°C, the sampling interval is shortened from 5 seconds to 2 seconds (that is, the density is increased by 150%); in the segment where the temperature is stable (amplitude change is less than 0.5°C), the interval is extended to 10 seconds; and finally, a recombinant concentration sampling sequence containing high-density sampling areas and low-density sampling areas is generated.

[0140] 305. Time axis matching is performed on the vibration data sequence corresponding to the adjusted collection interval and the recombined concentration sampling sequence, and a dynamic adjustment set including vibration characteristics and concentration distribution correction parameters is generated according to the matching result.

[0141] In step 305, time axis matching refers to aligning the data streams of different sensors according to a unified clock source;

[0142] The vibration characteristic correction parameter refers to the compensation coefficient obtained based on the correlation analysis between vibration amplitude and concentration deviation.

[0143] In an embodiment of the present application, the system first imports the adjusted vibration data sequence (such as vibration amplitude data at 5-second intervals) and the recombined concentration sampling sequence into a time alignment module, and uses the PTP precision clock protocol to synchronize the timestamps of the two at the microsecond level; then establishes a regression model of the vibration amplitude-concentration deviation, which means, for example, that every 0.1mm increase in vibration amplitude results in a concentration reading deviation of 0.002mg / L, and the vibration compensation coefficient is calculated based on this; the final output is a dynamic adjustment set including timestamps, spatial coordinates, compensated concentration values ​​and confidence weights.

[0144] Here's a specific example:

[0145] When a copper contamination incident occurred in the reverse osmosis membrane group of a seawater desalination plant, the system executed a complete dynamic density reorganization process. After the high-pressure cleaning was started, the pressure sensor detected peak density 6 times / window within three consecutive time windows (window 1: peak 21.5MPa / slope 118MPa / s; window 2: 22.3MPa / 123MPa / s; window 3: 20.9MPa / 115MPa / s), and the trigger vibration collection interval was adjusted from 10 seconds to 5 seconds. Temperature data analysis showed that the main fluctuation period was 200 seconds. Based on this, the system divided the ion migration rate data into 200-second segments, calculated the mean of each segment, and generated a migration temperature dynamic correlation sequence (segment 1: 0.016cm 2 / V·s; segment 2: 0.019cm 2 / V·s). When a temperature rise of 2.3°C was detected in segment 2, the concentration sampling interval was shortened to 2 seconds, capturing 32 high-density sampling points. During the temperature drop in segment 4 (a 0.4°C change), the sampling interval was restored to 10 seconds. Finally, the timeline matching module aligned the 5-second interval vibration data with the reconstructed concentration sequence, calculating a vibration compensation coefficient of 0.18. This generated a dynamic adjustment set containing timestamps (accurate to milliseconds), corrected concentration values, and spatial coordinates, providing high-precision input for the subsequent judgment module.

[0146] In summary, steps 301 to 305 intelligently adjust the vibration sampling frequency through dynamic pressure slope analysis, ensuring full physical characterization during the critical high-pressure impact phase. Data segmentation and density reorganization strategies based on temperature cycle characteristics significantly enhance the ability to characterize the spatiotemporal characteristics of pollution signals. Precise synchronization of multi-source data and vibration interference modeling effectively eliminate measurement bias caused by environmental factors. The resulting dynamically adjusted data set provides a high-confidence data foundation for pollution monitoring, comprehensively enhancing monitoring accuracy and decision-making reliability for water production equipment under complex hydraulic conditions.

[0147] In order to solve the problem of inaccurate sampling density caused by temperature fluctuations in real-time concentration monitoring and improve the accuracy of pollution level determination, in some embodiments, in step 304, the sampling point density of the real-time changing concentration data is adjusted according to the spatial distribution density corresponding to the fluctuation direction and fluctuation amplitude change degree of each segment in the migration temperature dynamic correlation sequence to obtain a reconstructed concentration sampling sequence, including:

[0148] 401. Divide each segment into a leading subinterval, a core subinterval, and a decay subinterval, and determine a fluctuation direction attribute by comparing the average amplitudes of temperature fluctuations of adjacent subintervals in the migration temperature dynamic correlation sequence;

[0149] In step 401, the leading subinterval refers to the period during which the temperature fluctuation amplitude shows an initial change at the beginning of the segment;

[0150] The core subinterval refers to the duration of the period when the temperature fluctuation amplitude reaches the peak within the segment;

[0151] The decay subinterval refers to the period when the temperature fluctuation amplitude gradually weakens at the end of the segment;

[0152] The fluctuation direction attribute refers to the trend direction determined by comparing the mean values ​​of the temperature fluctuation amplitudes in adjacent subintervals, which includes two states: positive continuous growth and negative continuous decrease.

[0153] In the embodiment of the present application, the system first divides each segment of the migration temperature dynamic association sequence into three continuous sub-intervals according to the time axis (leading accounts for 30%, core accounts for 40%, and attenuation accounts for 30%). The division is based on the rate of change of the temperature fluctuation amplitude, which means that the rate increases from the leading to the core stage and the rate decreases from the core to the attenuation stage. Then, the mean of the temperature fluctuation amplitude of the leading sub-interval and the core sub-interval is calculated. If the mean of the core sub-interval is higher than that of the leading sub-interval by more than 15%, it is judged as positive continuous growth; if it is lower by more than 10%, it is judged as negative continuous decrease. For example, the mean of the leading sub-interval is 1.2°C, and the mean of the core sub-interval is 1.8°C. The difference is 50%, which triggers a positive growth judgment.

[0154] 402. Set an initial sampling density level for the core subinterval based on the fluctuation direction attribute. When it is detected that the fluctuation direction attribute is continuously increasing in a positive direction, increase the initial sampling density level to generate an adjustment sequence. When it is detected that the fluctuation direction attribute is continuously decreasing in a negative direction, maintain the baseline level to generate a baseline sequence.

[0155] In step 402, the initial sampling density level refers to a preset basic sampling frequency (e.g., sampling once every 5 seconds);

[0156] Adjusted sequence refers to the data sequence after increasing the sampling density (e.g., collecting data every 2 seconds);

[0157] The benchmark sequence refers to the data sequence that maintains the default sampling density.

[0158] In this embodiment of the present application, the system dynamically configures the sampling frequency based on the fluctuation direction attribute. This means that when the fluctuation direction is determined to be continuously increasing, the sampling interval of the core subinterval is shortened from 5 seconds to 2 seconds. This is achieved by modifying the interrupt period register of the ADC sampling timer (the register value is adjusted from 5000ms to 2000ms), generating an adjustment sequence. If the fluctuation direction is continuously decreasing, the original timer configuration is maintained to generate a baseline sequence. The adjusted sequence includes a timestamp, the original sampling value, and a density level marker.

[0159] 403. Within the core sub-interval, dynamically superimpose sampling points according to the low, medium, and high preset intervals of the temperature fluctuation amplitude change rate in the migration temperature dynamic association sequence to generate a corresponding incremental sequence;

[0160] In step 403, the temperature fluctuation amplitude change rate refers to the change in the temperature fluctuation amplitude per unit time (°C / min), which is calculated by first-order difference;

[0161] The low, medium and high preset intervals refer to the corresponding rate ranges of 0-0.3℃ / min, 0.3-0.6℃ / min and 0.6-1.0℃ / min respectively;

[0162] An incremental sequence refers to a data set in which newly added sampling points are superimposed at different rate intervals within a core subinterval.

[0163] In the embodiment of the present application, the system first calculates the temperature fluctuation amplitude change rate of the core sub-interval in sections:

[0164] Rate interval determination: Extract the temperature difference between all adjacent sampling points within the core subinterval and calculate the rate of change per unit time. For example, for sampling point A (t = 100s, T = 2.1°C) and sampling point B (t = 102s, T = 2.5°C), the calculated rate is ΔT / Δt = (2.5-2.1) / (2 / 60) = 12°C / h = 0.2°C / min, which is determined to be a low-rate interval.

[0165] Overlay rule execution:

[0166] Low rate range (0-0.3℃ / min); maintain the basic sampling point every 2 seconds of the adjustment sequence and do not add new sampling points;

[0167] Medium rate range (0.3-0.6℃ / min): Insert one new sampling point (1 second interval) between the basic sampling points every 2 seconds, and the value of the new point is calculated by linear interpolation. For example, between the basic points t=100s (concentration 0.02mg / L) and t=102s (0.025mg / L), insert the point t=101s, and its concentration value is (0.02+0.025) / 2=0.0225mg / L;

[0168] High rate range (0.6-1.0℃ / min): 3 new sampling points (with an interval of 0.5 seconds) are inserted between the basic sampling points every 2 seconds, and the values ​​of the new points are generated using the cubic spline interpolation algorithm.

[0169] Incremental sequence generation: Sort the basic sampling points and the newly added sampling points by timestamp, mark the data source (basic point / new point), and generate an incremental sequence containing timestamp, concentration value and data type.

[0170] 404. Perform a reverse adjustment operation on the attenuation subinterval, and generate a gradient sequence according to the amplitude difference between the end of the core subinterval and the beginning of the attenuation subinterval;

[0171] In step 404, the reverse adjustment operation refers to performing a sampling density adjustment in the attenuation subinterval opposite to that in the core subinterval;

[0172] A gradient sequence refers to a data sequence in which the sampling density is gradually reduced according to the amplitude difference.

[0173] In an embodiment of the present application, the system calculates the temperature amplitude difference (ΔT) between the end of the core subinterval and the beginning of the attenuation subinterval. When ΔT>0.5°C, a gradient sequence is generated according to the rule of extending the sampling interval by 0.5 seconds for every 0.2°C decrease. For example, the core end temperature is 2.0°C and the attenuation beginning is 1.8°C (ΔT=0.2°C). The initial sampling interval is 5 seconds, and the interval is increased by 0.5 seconds for each subsequent 0.2°C decrease until the baseline interval (10 seconds) is restored. In specific implementation, the system dynamically adjusts the interrupt period of the sampling timer and updates the interval parameters at each temperature drop threshold point.

[0174] 405. Splice the adjustment sequence, the reference sequence, the corresponding incremental sequence, and the gradient sequence in time sequence, retain a preset number of sampling points in the real-time concentration change data at the splicing point of each sequence, and insert new points with equal spacing to generate a recombined concentration sampling sequence.

[0175] In step 405, time series splicing refers to connecting the data sequences of different sub-intervals in time order;

[0176] Equally spaced additional points refer to supplementary sampling points inserted at fixed intervals in the splicing gap.

[0177] In this embodiment, the system first sorts the adjustment sequence, baseline sequence, incremental sequence, and gradient sequence by timestamp. It then retains 50% of the sampling points in the original real-time concentration data at the junction of adjacent sequences (e.g., at the junction of the leading and core subintervals) (e.g., retaining 5 of the original 10 points at the junction) and inserting new points at equal intervals between the retained points (e.g., inserting one point every 2 seconds). Finally, the entire sequence is smoothed using cubic spline interpolation to generate a continuous reconstructed concentration sampling sequence. This sequence contains timestamps, spatial coordinates, original values, and interpolation markers.

[0178] Here's a specific example:

[0179] When nickel contamination occurred in the ion exchange resin unit of a semiconductor fab's ultrapure water preparation system, the system executed a complete adjustment process. A dynamic correlation sequence of migration temperatures revealed that the average temperature in a segmented leading subinterval (0-90 seconds) was 1.5°C, rising to 2.3°C in the core subinterval (90-210 seconds), indicating a continuous positive increase. The system shortened the sampling interval in the core subinterval from 5 seconds to 2 seconds, generating an adjustment sequence. A temperature change rate of 0.7°C / min (high range) was detected within the core subinterval, triggering the insertion of a new sampling point per second to generate an incremental sequence (e.g., inserting points at t = 91s, 93s, and so on into the original 2-second interval sequence). In the decay subinterval (210-300 seconds), a ΔT of 0.2°C was detected between the core end (2.1°C) and the decay start (1.9°C). The sampling interval was extended by 0.5 seconds for each 0.2°C decrease, generating a gradient sequence (initial 5 seconds, gradually increasing to 7 seconds). During the final splicing, five original sampling points were retained at the junction of the adjustment sequence and the incremental sequence (90 seconds) and three new points with equal spacing (91s, 93s, and 95s) were inserted to form a recombinant concentration sampling sequence with seamless connection between the high-density core area and the gradient attenuation area.

[0180] In summary, steps 401 to 405 achieve efficient capture of key pollution signals through refined segmentation of temperature fluctuation trends and dynamic density adaptation. An incremental overlay strategy based on rate of change enhances data spatiotemporal resolution. Gradient attenuation adjustment ensures smooth data transitions. Time series splicing and interpolation ensure data continuity. The resulting reconstructed concentration sampling sequence accurately reflects pollution evolution, providing high-confidence decision-making for intelligent water production equipment and significantly improving the accuracy and efficiency of response to sudden pollution incidents.

[0181] In order to solve the problem of quickly responding and taking effective measures when water quality is polluted, in some embodiments, when the real-time concentration data of any monitoring point exceeds a preset safety threshold in step 102, a backup water source activation instruction is sent to the water source switching device based on the pollutant diffusion trajectory analysis result. Combined with the reverse distribution law of pollutant diffusion direction generated by analyzing the concentration gradient change and diffusion velocity ratio of adjacent monitoring points in the pollutant diffusion trajectory analysis result, a filter membrane high-pressure impact cleaning instruction is obtained, including:

[0182] 501. When the real-time concentration data collected at any monitoring point exceeds a preset safety threshold, extract the position coordinates and diffusion direction parameters of the monitoring point in the pollutant diffusion trajectory analysis results;

[0183] In step 501, the location coordinates refer to the precise geographic location of the exceeding-standard monitoring point in three-dimensional space, including longitude, latitude, and depth information, and are derived from the real-time positioning data of the multi-node sensor array;

[0184] Diffusion direction parameters refer to vector parameters that describe the main direction and dynamic characteristics of pollutant diffusion, including diffusion angle (0-360° azimuth), diffusion velocity (unit is meter per minute) and concentration gradient change rate (unit is mg / (L·m)). They are generated by analyzing the concentration differences between adjacent monitoring points and time series data.

[0185] In the embodiment of the present application, when the system detects that the heavy metal ion concentration at a monitoring point exceeds a preset safety threshold (such as lead ion concentration > 0.01 mg / L), the following operation is immediately triggered, which refers to extracting the three-dimensional coordinates of the exceeding monitoring point (for example, X = 35.21, Y = 72.85, Z = 2.15) and diffusion direction parameters (such as the main direction angle of 120°, the diffusion speed of 0.5 m / min, and the gradient change rate of 0.003 mg / (L·m)) from the pollutant diffusion trajectory analysis results. This process is completed by calling the node position mapping table in the spatial database and combining it with the real-time analysis results output by the diffusion vector calculation module to ensure that the data extraction accuracy reaches the millimeter level.

[0186] 502. Calculate the shortest connection path between the monitoring point exceeding the standard and the backup water source access point based on the location coordinates, determine the priority of water source switching based on the shortest connection path and the diffusion direction parameter, and generate a water source switching instruction set including the path coordinates and the switching priority based on the water source switching priority;

[0187] In step 502, the shortest connected path refers to the optimal path calculated based on the pipe network topology and real-time flow data, taking into account factors such as pipe length, number of elbows, valve status, etc., and is dynamically generated through a graph theory algorithm;

[0188] The water source switching priority level refers to a priority score determined by the path safety factor and the pollution risk index. The safety factor includes the corrosion resistance of the pipeline material and the historical leakage frequency. The pollution risk index is calculated based on the angle between the path direction and the pollutant diffusion direction.

[0189] In the embodiment of the present application, a pipe network topology model is first constructed, where nodes represent monitoring points and water source access points, and edge weights include actual pipe length and fluid resistance coefficient. An improved Dijkstra algorithm is used to calculate the shortest path from the exceeding monitoring point to each backup water source, such as path A (length 35m, 3 elbows) and path B (length 28m, 2 elbows). The angle between the path direction and the direction of diffusion of pollutants is then evaluated, which means that if the angle between path B and the diffusion direction (120°) is 150°, the pollution risk index is reduced by 60%. Finally, the priority is calculated according to the formula, which means priority = 0.4 × (path length weight) + 0.6 × (risk index), and path B is selected as the optimal path. A water source switching instruction set including a path coordinate chain (X1, Y1→X2, Y2→X3, Y3) and a priority label (score 92) is generated.

[0190] 503. Analyze the concentration gradient change trend of adjacent monitoring points in the pollutant diffusion trajectory analysis result, identify the main direction of pollutant diffusion based on the analysis result, and determine the starting position of the filter membrane cleaning along the reverse path of the main direction of pollutant diffusion;

[0191] In step 503, the concentration gradient change trend is obtained by calculating the concentration difference per unit distance between adjacent monitoring points and its rate of change over time, reflecting the spatiotemporal evolution of pollutant diffusion;

[0192] The main direction of pollutant diffusion refers to the maximum synthetic direction of concentration gradient determined by the vector synthesis method, which requires the concentration gradient vectors of at least three adjacent monitoring points to participate in the calculation;

[0193] The starting position of filter membrane cleaning refers to the first operable point extending in the opposite direction of the main diffusion direction to the physical boundary of the filter membrane, and needs to be located in combination with the filter membrane partition topology data.

[0194] In an embodiment of the present application, when identifying the main direction of pollutant diffusion, the system first extracts the real-time concentration data and spatial coordinates of the exceeding monitoring point and its three adjacent monitoring points, calculates the concentration gradient vector (the ratio of the concentration difference to the distance and the direction angle) of each adjacent point pair, and uses the vector synthesis method to determine the modulus and direction angle of the comprehensive gradient vector, thereby identifying the main direction of pollutant diffusion. Then, a reverse path search is performed in the filter membrane physical partition map in the opposite direction of the direction, and the nearest filter membrane boundary point is located in combination with the GIS coordinate data as the initial cleaning starting position. In order to ensure the accuracy of positioning, the system retrieves the historical pollution frequency data of the partition where the boundary point is located. If the historical number of exceeding the standard exceeds a preset threshold (such as an average of more than 3 times per month), the starting position is offset to the main diffusion direction by a correction distance (usually 0.5-1 meter) according to the pollutant attachment characteristics, and finally generates the filter membrane cleaning starting position coordinates calibrated by historical data.

[0195] 504. Calculate the wave interval of the high-pressure shock according to the diffusion velocity of the pollutant in the main diffusion direction, and generate a cleaning parameter set according to the calculation result and the filter membrane cleaning starting position, wherein the cleaning parameter set includes the shock position coordinates, the shock wave interval, and the shock duration;

[0196] In step 504, the diffusion velocity is calculated by the ratio of the distance the concentration front moves to the time interval, and requires the trajectory analysis results of at least two consecutive time windows;

[0197] The wave interval refers to the cleaning trigger interval dynamically calculated based on the diffusion rate and the length of the filter unit. The calculation formula is: wave interval = filter unit length / (diffusion rate × safety factor);

[0198] The cleaning parameter set refers to a structured instruction set that includes the impact position coordinate chain, wave interval sequence, and single impact pressure curve.

[0199] In the embodiment of the present application, based on the velocity parameters of the main diffusion direction, the system uses a fluid dynamics model to calculate the high-pressure shock wave interval. The specific formula is interval time = filter membrane unit length / (diffusion velocity × viscosity correction coefficient), where the viscosity correction coefficient is dynamically adjusted according to real-time water quality detection data (1.2 for pure water and 1.5 for high turbidity water). At the same time, the shock pressure curve is set in combination with the type of pollutant. For example, the "step-by-step" pressure mode for heavy metal sediments means that the initial stage uses 18MPa pressure for 10 seconds to loosen the sediment layer, the peak stage is increased to 25MPa and maintained for 8 seconds to completely remove the residue, and the attenuation stage is reduced to 12MPa for 5 seconds to protect the filter membrane structure. The final generated cleaning parameter set.

[0200] 505. Send the water source switching instruction set and the cleaning parameter set to the water source switching control and the high-pressure impact control respectively, and generate a filter membrane high-pressure impact cleaning instruction after a confirmation signal indicating that the water source switching instruction set has been executed is received.

[0201] In step 505, the water source switching control refers to a control module that communicates with the electric valve group via an industrial bus protocol (such as PROFINET) and supports millisecond-level command response;

[0202] High-pressure surge control refers to a hydraulic drive system with an integrated pressure feedback closed loop to adjust the pump station output pressure in real time.

[0203] In an embodiment of the present application, the water source switching instruction set and the cleaning parameter set are distributed to the execution terminal through the industrial Internet of Things protocol. The water source switching control module uses the OPC UA protocol to drive the electric valve group, closes the contaminated pipeline valves in sequence according to the path coordinate order and opens the backup water source channel, and monitors the valve opening sensor data in real time until the flow stability signal returns. The high-pressure impact control module is then triggered, and the cleaning parameter set is transmitted to the hydraulic servo system through the EtherCAT bus. The hydraulic pump dynamically adjusts the output according to the pressure curve. The pressure sensor feeds back the actual value every 100ms and performs PID closed-loop correction with the set value. The three-axis robotic arm is synchronously controlled to drive the nozzle to move along the coordinate chain with a positioning accuracy of ±0.1mm. Finally, the filter membrane high-pressure impact cleaning instruction is generated, and the status data of the entire execution process (such as actual pressure deviation, coordinate offset) is written into the blockchain log in real time for quality traceability.

[0204] Here's a specific example:

[0205] The reverse osmosis membrane array in a chemical plant's pure water production system was contaminated with nickel ions. Monitoring point D12 detected a concentration of 0.028 mg / L (180% above the permitted standard). The system immediately extracted the coordinates of D12 (X=88.2, Y=45.7, Z=2.3) and the data for adjacent points D11, D13, and D14. The system calculated a gradient vector with a composite direction of 75° and reversely located the membrane boundary point F05 (X=87.8, Y=46.2, Z=2.3). Based on historical data for the F05 partition (which had exceeded the permitted standard four times in the past 30 days), the filter was offset 0.7 meters to the main direction, to F05'. Based on a diffusion velocity of 0.8 m / min and a membrane unit length of 6 m, the wave interval was set to 6 / (0.8 × 1.3) = 5.8 minutes, and a pressure curve was designed: [18 MPa (10 s) → 25 MPa (8 s) → 12 MPa (5 s)]. The water source switching module completed the switching of the backup water source S7 pipeline within 2 minutes and 40 seconds. The high-pressure impact module performed cleaning at intervals of 6 minutes. The real-time pressure fluctuation was controlled at 24.7-25.3MPa. The positioning error of the robotic arm was less than 0.08mm, and the entire process data was uploaded to the chain for evidence.

[0206] In summary, gradient vector synthesis and historical data correction enable precise positioning of the cleaning starting point. Adaptive parameter calculation using the fluid model ensures dynamic alignment of cleaning operations with pollutant migration. Multi-protocol collaborative control ensures high-precision and real-time execution of instructions. In high-turbidity chemical water quality scenarios, the system shortens contamination response time to less than 3 minutes, improves cleaning efficiency by over 40%, and reduces filter membrane damage to below 0.5%, providing reliable technical support for water quality safety management in complex industrial environments.

[0207] In order to further improve the accuracy of identifying the pollutant diffusion direction and ensure that the selection of the filter membrane cleaning position is more scientific and reasonable, in some embodiments, in step 503, the concentration gradient change trend of adjacent monitoring points in the pollutant diffusion trajectory analysis result is analyzed, the main pollutant diffusion direction is identified based on the analysis result, and the filter membrane cleaning starting position is determined along the reverse path of the main pollutant diffusion direction, including:

[0208] 601. Extracting the concentration data and location coordinates of the monitoring point exceeding a preset threshold and three adjacent monitoring points from the pollutant diffusion trajectory analysis results, calculating the ratio of the concentration change to the spatial distance between the monitoring point exceeding the preset threshold and each adjacent point, and generating a concentration gradient vector set based on the calculation results;

[0209] In step 601, the monitoring point exceeding the preset threshold refers to the sensor node whose real-time concentration data exceeds the safety threshold;

[0210] The three adjacent monitoring points refer to the upstream, downstream and lateral monitoring points with the nearest spatial distance from the exceeding point;

[0211] The concentration change refers to the concentration difference between the exceeding point and the adjacent point (mg / L);

[0212] Spatial distance refers to the actual physical distance between monitoring points (m);

[0213] The concentration gradient vector set refers to the vector set composed of the concentration change, distance ratio and direction angle of each adjacent point pair.

[0214] In an embodiment of the present application, the coordinates and concentration values ​​of the exceeding monitoring points are first extracted from the pollutant diffusion trajectory analysis results, and the three nearest adjacent monitoring points in its spatial topological structure are retrieved; then, the concentration difference between the exceeding point and each adjacent point is calculated respectively, and divided by the Euclidean distance between the two points to obtain the concentration change rate per unit distance (e.g., the concentration of the exceeding point A is 0.02 mg / L, the concentration of the adjacent point B is 0.015 mg / L, and the distance is 2 m, then the gradient value is (0.02-0.015) / 2=0.0025 mg / (L·m)); finally, the gradient value of each pair of adjacent points is bound to its spatial direction (the azimuth is calculated by the coordinate difference) to generate a concentration gradient vector set containing size and direction.

[0215] 602. Select two continuous vectors with the largest change in the concentration gradient vector set, compare the direction angle between the two continuous vectors with a preset angle threshold, and determine that diffusion is in the same direction if the angle is less than the threshold. Use the average direction of the two continuous vectors as the main diffusion direction.

[0216] In step 602, continuous vectors refer to two gradient vectors arranged in a spatially adjacent order;

[0217] The direction angle refers to the angular difference between two vectors (°);

[0218] The preset angle threshold refers to the angle threshold for determining the consistency of the diffusion direction (e.g., 30°);

[0219] The main diffusion direction refers to the main diffusion direction of pollutants after consistency verification.

[0220] In the embodiment of the present application, the concentration gradient vector set is traversed, and the two continuous vectors with the largest gradient values ​​are screened (for example, vector 1 refers to a gradient of 0.003 mg / (L·m) and a direction of 15° east-northeast; vector 2 refers to a gradient of 0.0028 mg / (L·m) and a direction of 20° east-northeast); the directional angle between the two is calculated (5°) and compared with a preset threshold; if it is less than the threshold, the average directional angle of the two vectors ((15°+20°) / 2=17.5°) is taken as the main diffusion direction; otherwise, other vector combinations are re-screened.

[0221] 603. According to the direction of the main diffusion direction, extend a preset distance in the opposite direction of the main diffusion direction in the position coordinate to locate the nearest physical partition boundary point of the filter membrane as a candidate cleaning start position;

[0222] In step 603, the preset distance refers to the reverse extension distance (e.g., 5 m) set according to the physical partition size of the filter membrane;

[0223] The boundary points of the filter membrane physical partition refer to the boundary coordinate points of the area divided by the filter membrane unit.

[0224] In the embodiment of the present application, based on the direction of the main diffusion direction, the coordinates of the overshoot point are used as the origin, and a preset distance is extended in the opposite direction of the main diffusion direction (e.g., 17.5° east-northeast of the original direction and 17.5° west-southwest of the reverse direction). The nearest boundary point is searched in the filter membrane partition map as a candidate cleaning starting position. Specifically, the intersection of the extended path and the partition boundary is detected by the ray method. For example, after extending 5 meters from the coordinates (X100, Y50) to 17.5° west-southwest, the boundary point (X95, Y47) of partition B is located.

[0225] 604. Based on the comparison result of the historical pollution frequency data of the physical partition of the filter membrane where the candidate cleaning starting position is located and the trigger condition threshold within a preset period, when the historical pollution frequency data exceeds the trigger condition threshold, the candidate cleaning starting position is adjusted to the side of the main diffusion direction by a correction distance to generate the filter membrane cleaning starting position.

[0226] In step 604, the historical contamination frequency data refers to the number of times the physical partition of the filter membrane has triggered a contamination alarm within the past preset period (e.g., 30 days);

[0227] The trigger condition threshold refers to the threshold for determining a zone as a high-frequency pollution zone (e.g., 10 times / month);

[0228] The correction distance refers to the adjustment amount calculated based on the difference between the historical frequency and the threshold (for example, if the threshold is exceeded once, the corresponding adjustment is 0.5m).

[0229] In an embodiment of the present application, querying the historical pollution records of the partition where the candidate cleaning starting position is located means that if the historical frequency exceeds the threshold (such as 12 times > 10 times), the correction distance ((12-10)×0.5m=1m) is calculated based on the number of times the threshold is exceeded, and the candidate position is adjusted toward the main diffusion direction (such as the original candidate point (X95, Y47) is moved 1m 17.5° east-northeast to (X95.87, Y47.29)); if the threshold is not exceeded, the original candidate position is maintained to generate the filter membrane cleaning starting position.

[0230] Here's a specific example:

[0231] When an arsenic contamination incident occurred in an activated carbon filter at a water treatment plant, the system executed a complete cleaning starting position determination process. Monitoring point C3 detected an arsenic concentration of 0.025 mg / L (exceeding the standard threshold of 0.02 mg / L). Concentration data for adjacent monitoring points C2, C4, and C5 were extracted, showing concentrations of 0.018 mg / L, 0.016 mg / L, and 0.019 mg / L, respectively. The C3-C2 gradient was calculated to be 0.0035 mg / (L·m) (direction 25° north-east), 0.0045 mg / (L·m) (direction 28° north-east), and 0.003 mg / (L·m) (direction 10° east-south). This generated a set of concentration gradient vectors. The C3-C4 and C3-C2 vectors with the largest gradients were selected, and their angle of 3° was calculated, indicating co-directional diffusion. The main diffusion direction was determined to be 26.5° north-east. Extend 6 meters in the opposite direction (26.5° south by west) to locate the boundary point of filter membrane partition D (X120, Y35). The contamination frequency for this partition over the past 30 days is 14 times (threshold 12 times). This triggers a position correction: exceeding the threshold by 2 times × 0.5m = 1m. Move the candidate point 1 meter south by west, 26.5°, to (X119.12, Y34.56), generating the final cleaning starting position.

[0232] In summary, steps 601 to 604 accurately identify the main direction of pollutant diffusion through concentration gradient vector analysis, and dynamically optimize the positioning of the cleaning starting point based on historical pollution data, effectively avoiding the waste of resources caused by traditional fixed-position cleaning; the candidate point correction mechanism based on spatial topological relationships significantly improves the efficiency of blocking the pollution source, providing a scientific and reasonable basis for formulating cleaning strategies for water production equipment.

[0233] In order to further improve the accuracy of the modified concentration distribution curve and accurately determine the pollution level, in some embodiments, in step 204, the dynamic adjustment set is phase-synchronized with the rising edge slope of the pressure slope parameter association map to generate a modified concentration distribution curve, the number of consecutive crossings of the modified concentration distribution curve within a preset pollution determination interval is counted, and the statistical results are used to generate pollution level determination parameters according to a preset frequency-level mapping relationship, including:

[0234] 701. Arrange the vibration characteristic correction parameters in the dynamic adjustment set in time series, extract the vibration phase angle corresponding to each time point, and simultaneously obtain the rising edge slope value of the corresponding time window from the pressure slope parameter association map;

[0235] In step 701, the vibration characteristic correction parameter refers to a data set including a vibration amplitude compensation coefficient, a vibration frequency, and a vibration phase angle, which is used to eliminate the interference of mechanical vibration on concentration monitoring;

[0236] The vibration phase angle refers to the instantaneous phase value of the vibration waveform extracted by Hilbert transform;

[0237] The rising slope value refers to the average rate of change of the pressure rising stage in each time window recorded in the pressure slope parameter correlation map.

[0238] In an embodiment of the present application, vibration feature correction parameters sorted by timestamp (accurate to milliseconds) are extracted from the dynamic adjustment set, and the Hilbert transform of the vibration waveform at each time point means first performing analytical signal calculation on the vibration waveform, extracting its real and imaginary components, and then calculating the instantaneous phase angle through the inverse tangent function. For example, the vibration waveform with the timestamp 10:00:00.000 is transformed to obtain a phase angle of 1.2 radians. At the same time, the rising edge slope value of the corresponding time window is extracted from the pressure slope parameter association map according to the timestamp, and the target window is quickly located in the time axis index by a binary search algorithm.

[0239] 702. Perform a product operation on the vibration phase angle and the rising edge slope value at the same time point to generate a phase synchronization coefficient. When the phase synchronization coefficient exceeds a preset synchronization threshold, mark the concentration distribution correction parameter at the corresponding time point as a valid correction parameter.

[0240] In step 702, the phase synchronization coefficient refers to the product of the vibration phase angle and the slope of the pressure rising edge;

[0241] The preset synchronization threshold refers to the critical value for determining the validity of data;

[0242] The effective correction parameters refer to the concentration distribution correction parameters that pass the threshold screening.

[0243] In an embodiment of the present application, a scalar multiplication operation is performed on the vibration phase angle and the pressure rising edge slope at each time point to generate a phase synchronization coefficient. For example, the phase angle of the timestamp 10:00:15.000 is 1.5 radians, and the corresponding rising edge slope is 125 MPa / s, so the coefficient is 1.5×125=187.5 MPa·rad / s. Comparing the calculation result with the preset threshold (150 MPa·rad / s) means that if the coefficient exceeds the threshold (such as 187.5>150), the concentration distribution correction parameter at that time point (including the compensated concentration value of 0.019 mg / L and the confidence weight of 0.85) is marked as a valid correction parameter; data points that do not reach the threshold (such as the coefficient 88 MPa·rad / s) are eliminated.

[0244] 703. Using the effective correction parameters as basic data points, connect the basic data points using a three-point smooth interpolation method to form a continuous curve, and mark it as a corrected concentration distribution curve;

[0245] In step 703, the adjacent three-point smooth interpolation refers to an interpolation method that generates a smooth curve based on three consecutive valid data points;

[0246] The corrected concentration distribution curve refers to the concentration change trend curve after eliminating the time series deviation.

[0247] In the embodiment of the present application, after sorting the effective correction parameters by timestamp, every three consecutive points (such as the time series T1: 0.018 mg / L, T2: 0.021 mg / L, T3: 0.019 mg / L) are selected, and a cubic polynomial interpolation algorithm is used to generate a smooth curve segment. The specific steps are: 1) constructing a cubic polynomial equation to satisfy the continuity of the function values ​​and first-order derivatives of the three points; 2) inserting equally spaced interpolation points (such as 0.5 second intervals) between adjacent points; 3) connecting all interpolation results section by section to form a corrected concentration distribution curve. For example, a smooth transition of 0.018→0.020→0.021 mg / L is inserted in the T1-T2 interval to eliminate the peak offset caused by the sensor response delay.

[0248] 704. Setting an upper threshold line and a lower threshold line within the pollution determination interval, counting the number of complete crossings of the corrected concentration distribution curve from the lower threshold line upward across the upper threshold line, and eliminating multiple false crossings caused by a single oscillation;

[0249] In step 704, the upper threshold line refers to the upper concentration limit of the pollution determination interval;

[0250] The lower threshold line refers to the lower concentration limit of the pollution judgment interval;

[0251] The number of complete crossings refers to the effective number of times the curve continues to rise from the lower threshold line and breaks through the upper threshold line.

[0252] In the embodiment of the present application, a double threshold interval (such as 0.015-0.02 mg / L) is set on the corrected concentration distribution curve, and the zero-crossing detection algorithm is used to count the number of complete crossings. The specific rule is that a complete crossing is counted only when the curve starts from below the lower threshold line and continuously rises to break through the upper threshold line without falling back below the lower limit. For example, if the curve rises from 0.014 mg / L to 0.022 mg / L during the period of 10:00:00-10:00:30 and remains at a high level, it is counted as 1 time; if there is an oscillation fluctuation (0.021→0.017→0.023 mg / L) during the period of 10:00:35-10:00:50, it is not counted.

[0253] 705. Input the counted complete crossing times into a preset frequency level mapping table. The mapping table obtains a pollution level determination parameter according to the rule that each additional crossing corresponds to an increase in pollution level.

[0254] In step 705, the frequency level mapping table refers to a correspondence table between preset complete crossing times and pollution levels.

[0255] The pollution level determination parameter refers to the pollution severity index determined based on the frequency level mapping table.

[0256] In this embodiment, the number of complete crossings counted is entered into a mapping table, and pollution level determination parameters are output according to preset rules. For example, if four valid crossings are counted, the pollution level 2 determination parameters are generated according to the mapping table's rule of "3-5 times corresponds to Level 2," and the corresponding intermediate emergency response strategy is triggered (such as initiating regional filter cleaning and switching to a backup water source).

[0257] Here's a specific example:

[0258] When a lead contamination incident occurred in a reverse osmosis membrane array at an industrial park, the system executed a complete pollution level determination process: First, the system extracted vibration characteristic correction parameters for the period 09:30-10:00 from the dynamic adjustment set. It then performed a Hilbert transform on the vibration waveform at the timestamp 09:45:00.000, calculating an instantaneous phase angle of 1.6 radians. Simultaneously, the system queried the pressure slope parameter association map based on this timestamp and retrieved the rising edge slope value of 138 MPa / s for the corresponding time window (09:45:00.000-09:45:30.000). Scalar multiplication was used to calculate the phase synchronization coefficient of 1.6 × 138 = 220.8 MPa·rad / s. This value exceeded the preset synchronization threshold of 150 MPa·rad / s, and the system marked the compensated concentration value of 0.019 mg / L at that moment as a valid correction parameter. Subsequently, the eight valid parameters screened from 09:40:00-09:50:00 were sorted by timestamp, and three consecutive data points (such as 0.019 mg / L at 09:45:00, 0.021 mg / L at 09:45:05, and 0.020 mg / L at 09:45:10) were selected for cubic polynomial interpolation to generate smooth curve segments. After connecting the segments, a corrected concentration distribution curve was formed. This curve shows that the concentration continuously increased from 0.016 mg / L to 0.023 mg / L between 09:45:20 and 09:45:50, completely crossing the preset lead pollution judgment range (0.015-0.02 mg / L) once; and then increased again from 0.017 mg / L to 0.021 mg / L between 09:48:00 and 09:49:30, completing a second valid crossing. The system counts the total number of effective crossings as 2. According to the rule of "1-2 times corresponds to level 1" in the frequency level mapping table, it generates the pollution level 1 judgment parameter and triggers the primary emergency response instruction: start 15MPa low-pressure cleaning of the filter membrane in the contaminated area and switch to the water supply channel of the backup water source A.

[0259] In summary, steps 701 to 705 filter valid data through simultaneous analysis of vibration phase and pressure slope, eliminating sensor timing deviations. A smooth curve is generated based on interpolation of three adjacent points to reveal the true pollution trend. A dual-threshold crossing statistical mechanism is incorporated to avoid interference from transient fluctuations. Finally, frequency-level mapping enables accurate pollution level determination. This entire approach significantly improves monitoring accuracy and response efficiency for sudden pollution incidents, providing reliable decision-making support for water production equipment.

[0260] Figure 2 The present invention provides a schematic diagram of a structure of a device (or system) for intelligent interactive control of water production equipment, as shown in FIG. Figure 2 As shown, the device includes:

[0261] The multi-source collaborative monitoring module 21 is used to collaboratively collect real-time concentration data of heavy metal ions using multiple monitoring points in a multi-node sensor array, and to deduce the pollution diffusion direction based on the concentration difference fluctuations of adjacent monitoring points in combination with a time series analysis window to obtain the pollutant diffusion trajectory analysis results;

[0262] The reverse emergency control module 22 is used to send a backup water source activation instruction to the water source switching device based on the pollutant diffusion trajectory analysis results when the real-time concentration data of any monitoring point exceeds a preset safety threshold. The reverse emergency control module 22 is used to obtain a high-pressure impact cleaning instruction for the filter membrane based on the reverse distribution law of the pollutant diffusion direction generated by analyzing the concentration gradient change and diffusion velocity ratio of adjacent monitoring points in the pollutant diffusion trajectory analysis results.

[0263] A dynamic parameter calibration module 23 is configured to utilize the impact-induced fluid dynamics parameters generated during the execution of the filter membrane high-pressure impact cleaning instruction, in combination with a preset multi-dimensional sensor parameter collaborative rebalancing mechanism, to dynamically and collaboratively adjust the sensor deviation in the real-time changing concentration data, thereby generating a pollution level determination parameter including an environmentally responsive corrected concentration index;

[0264] The closed-loop decision generation module 24 is used to match the spatial trajectory of the pollutant migration path using a preset three-dimensional diffusion model based on the pollution level judgment parameters, and generate an emergency decision signal including the emergency water source positioning coordinates and cleaning priority parameters.

[0265] Figure 2 The device for intelligent interactive control of water production equipment can be executed Figure 1 The implementation principle and technical effects of the method for intelligent interactive control of water production equipment described in the illustrated embodiment will not be elaborated on here. The specific manner in which each module and unit performs operations in the apparatus for intelligent interactive control of water production equipment in the above embodiment has been described in detail in the embodiments of the method and will not be elaborated on here.

[0266] In one possible design, Figure 2 The device for intelligent interactive control of water production equipment in the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0267] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0268] The processing component 32 is used for the above Figure 1 The embodiment provides a method for intelligent interactive control of water production equipment.

[0269] The processing component 32 may include one or more processors to execute computer instructions to perform all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0270] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0271] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0272] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0273] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0274] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0275] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The embodiment shown is a method for intelligent interactive control of water production equipment.

[0276] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0277] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0278] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0279] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for intelligent interactive control of water production equipment, characterized in that: include: The real-time concentration data of heavy metal ions are collected collaboratively by multiple monitoring points in a multi-node sensor array. The concentration fluctuations at adjacent monitoring points are combined with a time series analysis window to deduce the direction of pollution diffusion and obtain the pollutant diffusion trajectory analysis results. When the real-time concentration change data of any monitoring point exceeds the preset safety threshold, a backup water source activation instruction is sent to the water source switching device according to the pollutant diffusion trajectory analysis result. Combined with the reverse distribution law of pollutant diffusion direction generated by analyzing the concentration gradient change and diffusion velocity ratio of adjacent monitoring points in the pollutant diffusion trajectory analysis result, a filter membrane high-pressure impact cleaning instruction is obtained; Using the impact-induced fluid dynamics parameters generated during the execution of the filter membrane high-pressure impact cleaning instruction, combined with a preset multi-dimensional sensor parameter collaborative rebalancing mechanism, the sensor deviation in the real-time changing concentration data is dynamically and collaboratively adjusted to generate a pollution level determination parameter including an environmentally responsive corrected concentration index; According to the pollution level determination parameters, a preset three-dimensional diffusion model is used to match the spatial trajectory of the pollutant migration path to generate an emergency decision signal including the emergency water source positioning coordinates and cleaning priority parameters.

2. The method according to claim 1, characterized in that The impact-induced fluid dynamics parameters generated during the execution of the filter membrane high-pressure impact cleaning instruction are used in combination with a preset multi-dimensional sensor parameter collaborative rebalancing mechanism to dynamically and collaboratively adjust the sensor deviation in the real-time changing concentration data to generate pollution level determination parameters including an environmentally responsive corrected concentration index, including: During the high-pressure impact cleaning process of the filter membrane, a set of fluid dynamics parameters formed by the periodic pressure changes of the impact waveform is extracted, and a set of multi-dimensional sensing parameters is simultaneously collected, wherein the multi-dimensional sensing parameter set includes ion migration rate, ambient temperature fluctuation, and filter membrane vibration amplitude; The peak pressure and the rising edge slope in the fluid mechanics parameter set are synchronously correlated in time series to construct a pressure slope parameter correlation map, and the ion migration rate in the multidimensional sensing parameter set is periodically matched with the ambient temperature fluctuation to generate a migration temperature dynamic correlation sequence; Adjusting the filter membrane vibration amplitude acquisition interval in the multidimensional sensing parameter set according to the peak pressure distribution characteristics of the pressure slope parameter association map, and dynamically reorganizing the discrete sampling points of the real-time changing concentration data using the adjusted filter membrane vibration amplitude acquisition interval and the periodic characteristics of the migration temperature dynamic association sequence to obtain a dynamic adjustment set; The dynamic adjustment set is phase-synchronized with the rising edge slope of the pressure slope parameter association map to generate a corrected concentration distribution curve, and the number of consecutive crossings of the corrected concentration distribution curve within the preset pollution judgment interval is counted. The statistical results are used to generate pollution level judgment parameters according to a preset frequency level mapping relationship.

3. The method according to claim 2, characterized in that According to the peak pressure distribution characteristics of the pressure slope parameter association map, the filter membrane vibration amplitude collection interval in the multidimensional sensing parameter set is adjusted, and the discrete sampling points of the real-time changing concentration data are dynamically density reorganized using the adjusted filter membrane vibration amplitude collection interval and the periodic characteristics of the migration temperature dynamic association sequence to obtain a dynamic adjustment set, including: Pairing the peak pressure and the rising edge slope of each time window in the pressure slope parameter association map to generate a timestamp-marked slope parameter pair, and connecting the timestamp-marked slope parameter pairs of adjacent time windows by linear interpolation to generate a dynamic pressure slope map; Adjusting the collection interval of the filter membrane vibration amplitude based on the distribution density of the peak pressures in a preset number of consecutive time windows in the dynamic pressure slope map; Extracting the duration of the main fluctuation period of the temperature fluctuation data in the multidimensional sensing parameter set as a reference unit, periodically segmenting the ion migration rate monitoring data in the multidimensional sensing parameter set and calculating the segment mean, and generating a migration temperature dynamic correlation sequence based on the calculation results; Adjusting the sampling point density of the real-time changing concentration data according to the spatial distribution density corresponding to the fluctuation direction and fluctuation amplitude change degree of each segment in the migration temperature dynamic correlation sequence to obtain a recombined concentration sampling sequence; The vibration data sequence corresponding to the adjusted collection interval is time-matched with the recombined concentration sampling sequence, and a dynamic adjustment set containing vibration characteristics and concentration distribution correction parameters is generated based on the matching results.

4. The method according to claim 3, wherein The sampling point density of the real-time changing concentration data is adjusted according to the spatial distribution density corresponding to the fluctuation direction and fluctuation amplitude change degree of each segment in the migration temperature dynamic correlation sequence to obtain a recombined concentration sampling sequence, including: Dividing each segment into a leading subinterval, a core subinterval, and a decay subinterval, and determining a fluctuation direction attribute by comparing average amplitudes of temperature fluctuations of adjacent subintervals in the migration temperature dynamic correlation sequence; An initial sampling density level of the core subinterval is set based on the fluctuation direction attribute. When it is detected that the fluctuation direction attribute is continuously increasing in a positive direction, the initial sampling density level is increased to generate an adjustment sequence. When the fluctuation direction attribute is continuously decreasing in a negative direction, the baseline level is maintained to generate a baseline sequence. In the core sub-interval, dynamic superposition of sampling points is performed according to the low, medium and high preset intervals of the temperature fluctuation amplitude change rate in the migration temperature dynamic correlation sequence to generate a corresponding incremental sequence; Performing a reverse adjustment operation on the attenuation subinterval, and generating a gradient sequence according to the amplitude difference between the end of the core subinterval and the beginning of the attenuation subinterval; The adjustment sequence, the reference sequence, the corresponding incremental sequence and the gradient sequence are spliced ​​in time sequence, a preset number of sampling points in the real-time changing concentration data are retained at the splicing point of each sequence and new points with equal spacing are inserted to generate a recombined concentration sampling sequence.

5. The method according to claim 1, wherein When the real-time concentration data of any monitoring point exceeds the preset safety threshold, a backup water source start-up instruction is sent to the water source switching device according to the pollutant diffusion trajectory analysis result. Combined with the reverse distribution law of pollutant diffusion direction generated by analyzing the concentration gradient change and diffusion velocity ratio of adjacent monitoring points in the pollutant diffusion trajectory analysis result, a filter membrane high-pressure impact cleaning instruction is obtained, including: When the real-time concentration data collected at any monitoring point exceeds a preset safety threshold, the position coordinates and diffusion direction parameters of the monitoring point in the pollutant diffusion trajectory analysis results are extracted; Calculating the shortest connection path between the exceeding-standard monitoring point and the backup water source access point among the monitoring points based on the location coordinates, determining the priority level of water source switching based on the shortest connection path and the diffusion direction parameter, and generating a water source switching instruction set including the path coordinates and the switching priority level based on the water source switching priority level; Analyze the concentration gradient change trend of adjacent monitoring points in the pollutant diffusion trajectory analysis results, identify the main direction of pollutant diffusion based on the analysis results, and determine the starting position of the filter membrane cleaning along the reverse path of the main direction of pollutant diffusion; Calculating the wave interval of the high-pressure shock according to the diffusion velocity of the pollutant in the main diffusion direction, and generating a cleaning parameter set according to the calculation result and the filter membrane cleaning starting position, wherein the cleaning parameter set includes the shock position coordinates, the shock wave interval, and the shock duration; The water source switching instruction set and the cleaning parameter set are sent to the water source switching control and the high-pressure impact control respectively, and after the confirmation signal of the completion of the execution of the water source switching instruction set arrives, a filter membrane high-pressure impact cleaning instruction is generated.

6. The method according to claim 5, characterized in that Analyzing the concentration gradient change trend of adjacent monitoring points in the pollutant diffusion trajectory analysis results, identifying the main direction of pollutant diffusion based on the analysis results, and determining the starting position of the filter membrane cleaning along the reverse path of the main direction of pollutant diffusion, including: Extracting the concentration data and location coordinates of the monitoring point exceeding a preset threshold and three adjacent monitoring points from the pollutant diffusion trajectory analysis results, calculating the ratio of the concentration change to the spatial distance between the monitoring point exceeding the preset threshold and each adjacent point, and generating a concentration gradient vector set based on the calculation results; Selecting two continuous vectors with the largest change in the concentration gradient vector set, comparing the direction angle of the two continuous vectors with a preset angle threshold, and determining that diffusion is in the same direction when the angle is less than the threshold, and taking the average direction of the two continuous vectors as the main diffusion direction; According to the direction of the main diffusion direction, a preset distance is extended in the opposite direction of the main diffusion direction in the position coordinate to locate the nearest physical partition boundary point of the filter membrane as a candidate cleaning starting position; Based on the comparison result of the historical pollution frequency data of the physical partition of the filter membrane where the candidate cleaning starting position is located and the trigger condition threshold within a preset period, when the historical pollution frequency data exceeds the trigger condition threshold, the candidate cleaning starting position is adjusted to the side of the main diffusion direction by a correction distance to generate the filter membrane cleaning starting position.

7. The method according to claim 2, characterized in that Performing phase synchronization processing on the rising edge slope of the dynamic adjustment set and the pressure slope parameter association map to generate a modified concentration distribution curve, counting the number of consecutive crossings of the modified concentration distribution curve within a preset pollution determination interval, and generating a pollution level determination parameter based on the statistical results according to a preset frequency level mapping relationship, including: Arrange the vibration characteristic correction parameters in the dynamic adjustment set in time series, extract the vibration phase angle corresponding to each time point, and simultaneously obtain the rising edge slope value of the corresponding time window from the pressure slope parameter association map; Performing a product operation on the vibration phase angle and the rising edge slope value at the same time point to generate a phase synchronization coefficient. When the phase synchronization coefficient exceeds a preset synchronization threshold, the concentration distribution correction parameter at the corresponding time point is marked as a valid correction parameter. Taking the effective correction parameters as basic data points, the basic data points are connected by a three-point smooth interpolation method to form a continuous curve, which is marked as a corrected concentration distribution curve; An upper threshold line and a lower threshold line are set within the pollution determination interval, and the number of complete crossings of the modified concentration distribution curve from the lower threshold line upward across the upper threshold line is counted, and multiple false crossings caused by a single oscillation are excluded; The counted number of complete crossings is input into a preset frequency level mapping table, and the mapping table obtains a pollution level determination parameter according to the rule that each additional crossing corresponds to an increase in pollution level.

8. A system for intelligent interactive control of water production equipment, characterized in that: include: The multi-source collaborative monitoring module is used to collaboratively collect real-time concentration data of heavy metal ions using multiple monitoring points in a multi-node sensor array. It uses a time series analysis window to deduce the pollution diffusion direction based on concentration fluctuations at adjacent monitoring points, thereby obtaining pollutant diffusion trajectory analysis results. A reverse emergency control module is used to send a backup water source activation instruction to the water source switching device based on the pollutant diffusion trajectory analysis results when the real-time concentration data of any monitoring point exceeds a preset safety threshold. The reverse distribution law of the pollutant diffusion direction generated by analyzing the concentration gradient change and diffusion velocity ratio of adjacent monitoring points in the pollutant diffusion trajectory analysis results is combined to obtain a high-pressure impact cleaning instruction for the filter membrane; a dynamic parameter calibration module for utilizing the impact-induced fluid dynamics parameters generated during the execution of the filter membrane high-pressure impact cleaning instruction, in combination with a preset multi-dimensional sensor parameter collaborative rebalancing mechanism, to dynamically and collaboratively adjust the sensor deviation in the real-time changing concentration data, thereby generating a pollution level determination parameter including an environmentally responsive corrected concentration index; The closed-loop decision generation module is used to match the spatial trajectory of the pollutant migration path using a preset three-dimensional diffusion model based on the pollution level judgment parameters, and generate an emergency decision signal including the emergency water source positioning coordinates and cleaning priority parameters.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for intelligent interactive control of water production equipment as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, the method for intelligent interactive control of water production equipment according to any one of claims 1 to 7 is implemented.

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