Method and system for detecting and regulating a water-flooded vehicle repair waste liquid purification treatment device

Abstract

The embodiment of the application discloses a detection and regulation method and system for water-flooded vehicle maintenance waste liquid purification treatment equipment, belongs to the water-flooded vehicle maintenance waste liquid purification technical field, and solves the influence of the hysteresis caused by passive detection on water quality and the safety of waste liquid purification treatment equipment. The method comprises the following steps: acquiring state monitoring data corresponding to different functional units in the water-flooded vehicle maintenance waste liquid purification treatment equipment; performing correlation comparison analysis on the state monitoring data, performing trend analysis on the water quality detection parameters, and determining abnormal information; calling a prediction model matched with the abnormal information to output a predicted purification trend of the current waste liquid impurities in the purification treatment process and a predicted intervention duration corresponding to the time when the waste liquid impurities penetrate to a preset key unit; and performing a hierarchical response operation according to the predicted purification trend and the predicted intervention duration; wherein the hierarchical response operation at least includes early warning, parameter adjustment, automatic backflow and global interlocking shutdown protection.

Description

Technical Field

[0001] This application relates to the field of waste liquid purification technology for water-damaged vehicle repair, and in particular to a detection and control method and system for waste liquid purification treatment equipment for water-damaged vehicle repair. Background Technology

[0002] The repair of flooded vehicles generates a large amount of waste liquid containing complex pollutants such as mud, oil, bacteria and emulsions. Direct discharge of such waste liquid would cause serious environmental pollution. Therefore, it is necessary to purify the waste liquid through waste liquid purification equipment and then recycle it.

[0003] In existing technologies, the detection and control of wastewater purification and treatment equipment for flooded vehicle repairs typically employs a single parameter threshold comparison method for anomaly detection. That is, the system installs water quality detection sensors at the outlet of each functional unit to monitor water quality indicators such as turbidity and oil content in real time. When a certain indicator exceeds a preset fixed threshold, an anomaly is detected, triggering an alarm or shutdown protection.

[0004] However, existing detection and control methods typically respond passively after an anomaly occurs and has consequences. When serious anomalies occur, the failure to respond in a timely manner often leads to the spread of pollutants to downstream units, which has a significant impact on water quality and the safety of waste liquid purification and treatment equipment. Summary of the Invention

[0005] This application provides a detection and control method and system for a waste liquid purification and treatment equipment for flooded vehicle repairs, which solves the following technical problems: Existing detection and control methods usually respond passively after an anomaly occurs and has consequences. When a serious anomaly occurs, the pollutants often spread to downstream units due to untimely handling, which has a significant impact on water quality and the safety of the waste liquid purification and treatment equipment.

[0006] The embodiments of this application adopt the following technical solutions: This application provides a detection and control method for a wastewater purification treatment equipment for flooded vehicle repairs. The method includes: acquiring status monitoring data corresponding to different functional units within the wastewater purification treatment equipment; wherein the status monitoring data includes at least equipment operating parameters and water quality testing parameters; performing correlation and comparison analysis on different parameters in the status monitoring data, and performing trend analysis on the water quality testing parameters to identify abnormal information; calling a prediction model matched with the abnormal information to output the predicted purification trend of impurities in the current wastewater from flooded vehicle repairs during the purification process, and outputting the predicted intervention time before the impurities penetrate to a preset key unit; and executing graded response operations based on the predicted purification trend and predicted intervention time; wherein the graded response operations include at least early warning prompts, parameter adjustment, automatic backflow, and full-domain interlocking shutdown protection.

[0007] In one implementation of this application, correlation and comparison analysis are performed on different parameters in the status monitoring data, and trend analysis is performed on the water quality detection parameters to determine abnormal information. Specifically, this includes: comparing the water quality detection parameters with a preset waste liquid impurity baseline; if the comparison result shows a step change in impurity concentration, a waste liquid medium characteristic mutation event is determined; identifying the target unit associated with the waste liquid medium characteristic mutation event, and comparing the equipment operating parameters corresponding to adjacent target units to determine the load matching degree; if the load matching degree is less than a preset load threshold, a load imbalance state is determined, and the bottleneck unit with excessive load is identified; obtaining the water quality detection parameters corresponding to each functional unit, and determining the performance failure unit based on the changes in the water quality detection parameters; and determining abnormal information based on the waste liquid medium characteristic mutation event, the bottleneck unit, and the performance failure unit.

[0008] In one implementation of this application, a performance fault unit is determined based on the changes in water quality detection parameters. Specifically, this includes: constructing corresponding water quality decay curves for each functional unit based on their water quality detection parameters; determining a first reference fault unit based on the rate of change of the water quality decay curves; acquiring local extreme points of the water quality decay curves within a preset time window; determining the magnitude and direction of change between adjacent extreme points; marking the turning point as a false recovery starting point when the direction of change changes from rising to falling, and the magnitude of change before and after the turning point is greater than a preset magnitude threshold; extracting a false recovery segment starting from the false recovery starting point and ending at the first point after the starting point whose magnitude is within a preset magnitude range; acquiring the duration of the recovery of water quality indicators within the false recovery segment, and acquiring the changes in the operating status of the upstream unit of the current functional unit during the recovery duration; determining the current functional unit as a second reference fault unit when there is a correlation between the changes in the operating status of the upstream unit and the recovery of water quality in the current functional unit; and using the first and second reference fault units as performance fault units.

[0009] In one implementation of this application, a prediction model matching the anomaly information is invoked to output the predicted purification trend of impurities in the current water-damaged vehicle repair wastewater during the purification process, as well as the predicted intervention time before the wastewater impurities penetrate to a preset key unit. Specifically, this includes: determining the attribute category of the water-damaged vehicle repair wastewater impurities based on the anomaly information; invoking an impurity diffusion model matching the impurity attribute category to predict the penetration depth and residual period of the current wastewater impurities in the purification process; obtaining the historical prediction deviation rate of the impurity diffusion model, correcting the penetration depth and residual period based on the historical prediction deviation rate, and determining the predicted purification trend and basic response window period based on the corrected penetration depth and residual period; obtaining the current load status of each functional unit, and using the minimum load margin as a contraction factor to reduce the basic response window period to obtain the predicted intervention time.

[0010] In one implementation of this application, the minimum load margin is used as a contraction factor to reduce the basic response window period to obtain the predicted intervention duration. Specifically, this includes: comparing the load margins of each functional unit to determine the minimum load margin; determining the corresponding impact coefficient based on the current wastewater impurity attribute category of the flooded vehicle repair wastewater; wherein the impact coefficient is positively correlated with the degree of damage to the functional unit by the wastewater impurity attribute category; determining the product of the minimum load margin and the impact coefficient, and using the ratio of the product to the rated treatment capacity as the dynamic contraction coefficient; and obtaining the predicted intervention duration based on the basic response window period and the dynamic contraction coefficient.

[0011] In one implementation of this application, a graded response operation is performed based on the predicted purification trend and the predicted intervention duration. Specifically, this includes: comparing the predicted intervention duration with a preset safety time limit; if the predicted intervention duration is longer than the preset safety time limit, then determining the target control parameter based on the predicted purification trend and sending a control command to the target unit to adjust the operating parameters; if the predicted intervention duration is less than or equal to the preset safety time limit, then determining the key unit where waste liquid impurities are about to penetrate based on the predicted purification trend, cutting off the operating path of the key unit and its associated units based on the full-domain interlock shutdown protection mechanism, and returning the unqualified water to the corresponding functional unit for reprocessing until the water quality meets the standards and is then recycled.

[0012] In one implementation of this application, determining the target control parameters based on the predicted purification trend specifically includes: determining the mutation type and mutation magnitude based on the predicted purification trend, and determining the remaining duration of the predicted intervention time; determining the affected target unit chain based on the mutation type; wherein, the target unit chain is a continuous unit sequence extending downstream along the treatment process from the location of waste liquid impurity intrusion, and the processing function of each unit in the target unit chain matches the impurity treatment requirements corresponding to the mutation type; determining the additional processing load to be borne by each unit in the target unit chain based on the mutation magnitude and the remaining duration, and generating a load allocation scheme with the additional processing load not exceeding the rated redundancy capacity of each functional unit as a constraint; determining the control target parameters of each target unit based on the load allocation scheme, and generating control instructions.

[0013] In one implementation of this application, the additional processing load to be borne by each unit in the target unit chain is determined based on the mutation amplitude and remaining time. Specifically, this includes: obtaining the current load rate and rated processing capacity of each target unit in the target unit chain to determine the available remaining capacity corresponding to each target unit; determining the total additional processing load based on the mutation amplitude, and using the remaining time as a time constraint to determine the rate of additional load to be processed per unit time; and, based on the available remaining capacity and the additional load rate as constraints, sequentially allocating the additional processing load to each unit according to the order of load sensitivity from low to high for each target unit; wherein, the load sensitivity is related to the change in processing efficiency of the target unit when the load changes.

[0014] In one implementation of this application, substandard water is returned to the corresponding functional unit for reprocessing until the water quality meets the standards and is then recycled. Specifically, this includes: determining the expected treatment time for each exceeding impurity based on the predicted concentration of various waste liquid impurities in the predicted purification trend; when a single exceeding impurity exists, initiating the corresponding return path according to a preset return mapping relationship; when multiple exceeding impurities exist, determining the return priority based on the expected treatment time and predicted intervention time for each exceeding impurity; determining the corresponding return path based on the return priority; activating the return pump and corresponding valve on the return path to guide the substandard water into the corresponding functional unit for reprocessing; and continuously acquiring water quality monitoring parameters during the return process, closing the return path when the water quality monitoring parameters meet the preset reuse conditions.

[0015] This application provides a detection and control system for a wastewater purification treatment equipment for flooded vehicle repairs, comprising: a data acquisition and monitoring module for acquiring status monitoring data corresponding to different functional units in the wastewater purification treatment equipment; wherein the status monitoring data includes at least equipment operating parameters and water quality testing parameters; an anomaly detection module connected to the data acquisition and monitoring module for performing correlation comparison analysis on different parameters in the status monitoring data and trend analysis on the water quality testing parameters to determine anomaly information; a predictive analysis module connected to the anomaly detection module for calling a predictive model matching the anomaly information to output the predicted purification trend of impurities in the current wastewater from flooded vehicle repairs in the purification treatment process, and the predicted intervention time before the impurities penetrate to a preset key unit; and a graded response module connected to the predictive analysis module for executing graded response operations based on the predicted purification trend and the predicted intervention time; wherein the graded response module includes at least an alarm prompting module, a safety interlock protection module, an automatic reflux module, and a parameter adjustment module.

[0016] The above-mentioned technical solutions adopted in this application embodiment can achieve the following beneficial effects: This application embodiment, through joint analysis of equipment operating parameters and water quality detection parameters, can identify abnormal information in multiple dimensions, overcoming the limitations of traditional single-parameter detection which is prone to missed or false detection. Secondly, by predicting the penetration depth and residual period of impurities using a predictive model, and combining this with the real-time load status of each functional unit to dynamically tighten the intervention window, a leap from passive response to proactive prevention is achieved, buying time for maintenance and operation. Finally, based on the comparison results of the predicted intervention duration and the safety time limit, a graded response strategy such as early warning prompts, automatic control, or full-domain interlock shutdown is automatically matched to ensure rapid isolation and backflow purification in the event of a serious fault. This improves the operational stability and safety of the flooded vehicle wastewater treatment system under complex working conditions, reducing equipment wear and the risk of unplanned downtime. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 A flowchart illustrating a detection and control method for a waste liquid purification and treatment equipment used in the repair of flooded vehicles, provided in an embodiment of this application; Figure 2 This is a schematic diagram of the detection and control system for a waste liquid purification and treatment equipment for water-damaged vehicle repair, provided in an embodiment of this application. Detailed Implementation

[0018] This application provides a detection and control method and system for a waste liquid purification and treatment equipment used in the repair of flooded vehicles.

[0019] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0020] Figure 1 A flowchart illustrating a detection and control method for a wastewater purification and treatment equipment used in the repair of flooded vehicles, as provided in this application embodiment, is shown below. Figure 1 As shown, the detection and control method for the waste liquid purification and treatment equipment used in the repair of flooded vehicles includes the following steps: S101. Obtain the status monitoring data corresponding to different functional units in the water-damaged vehicle repair waste liquid purification and treatment equipment.

[0021] In one implementation of this application, the wastewater purification and treatment equipment for flooded vehicle repair includes multiple functional units arranged sequentially along the treatment process. These functional units include at least a pre-filtration unit, an oil-water separation unit, a fine filtration unit, an adsorption unit, and a sterilization unit. Each functional unit is equipped with a corresponding sensor group for real-time acquisition of the unit's operating status data.

[0022] The central control module establishes data connections with the sensor groups of each functional unit to acquire status monitoring data from each functional unit. The acquired status monitoring data includes at least two categories: equipment operating parameters and water quality detection parameters. Among them, equipment operating parameters include inlet pressure, outlet pressure, inlet and outlet pressure difference, liquid level, motor current, motor voltage, and operating power of each functional unit; water quality detection parameters include turbidity, oil content, bacterial content, pH value, and particulate matter size distribution.

[0023] S102. Perform correlation and comparison analysis on different parameters in the status monitoring data, and perform trend analysis on water quality detection parameters to identify abnormal information.

[0024] In one implementation of this application, the central control module detects sudden changes in the characteristics of the waste liquid medium by comparing water quality parameters, identifies load imbalance and bottleneck units by comparing parameters of adjacent unit equipment, identifies performance failure units by analyzing water quality time-series trends, and obtains abnormal information by combining the three.

[0025] Specifically, the central control module compares the real-time collected water quality monitoring parameters with a preset waste liquid impurity baseline. This baseline records the benchmark values ​​and allowable fluctuation ranges of various impurity concentrations in the waste liquid from flooded vehicle repairs under normal operating conditions. When the concentration of a certain type of impurity, such as silt, oil, or bacteria, experiences a step increase within a unit of time and lasts for more than a preset period, a sudden change in waste liquid media characteristics is identified. Based on the type of impurity involved in the sudden change in waste liquid media characteristics—for example, silt corresponds to the pre-filtration unit, and oil corresponds to the oil-water separation unit—the functional unit directly related to the treatment of that impurity is identified as the target unit. The central control module extracts the equipment operating parameters of the target unit and its adjacent upstream and downstream units, performs a horizontal comparison of the equipment operating parameters of adjacent units, and calculates the load matching degree between them. This load matching degree reflects the degree of coordination of the processing capabilities between adjacent units.

[0026] When the load matching degree is less than the preset load threshold, the central control module determines that there is a load imbalance in the current processing flow. It further analyzes the current load rate and rated processing capacity of each unit and identifies the unit whose load rate exceeds the rated capacity and causes the upstream and downstream matching degree to decrease as the bottleneck unit with overload.

[0027] In one implementation of this application, the central control module constructs a water quality degradation curve based on water quality detection parameters, and identifies faulty units with directly declining treatment efficiency by analyzing the curve's rate of change. Simultaneously, by analyzing the inflection features in the curve to extract abnormal recovery segments, and combining this with changes in the operating status of upstream units, it identifies hidden performance degradation units that are masked by changes in the upstream status.

[0028] Specifically, the central control module acquires water quality detection parameters from each functional unit within a continuous time window. These parameters include indicators such as turbidity, oil content, and bacterial content. Using time as the horizontal axis and water quality indicator values ​​as the vertical axis, a water quality degradation curve is constructed for each functional unit. The average rate of change of each water quality degradation curve within a preset time interval is calculated. When the rate of change of the water quality degradation curve of a functional unit exceeds a preset degradation threshold, that functional unit is marked as the first reference fault unit.

[0029] Furthermore, on the water quality degradation curve of each functional unit, local extreme points are scanned in units of a preset time window, the difference in the vertical coordinate between adjacent extreme points is calculated as the magnitude of change, and the direction of change is determined according to the order of the extreme points, wherein the direction of change includes the upward direction and the downward direction.

[0030] When the central control module detects a turning point in the water quality degradation curve, indicating a shift from rising to falling, it records the location of this turning point. It further determines whether the changes before and after the turning point are both greater than a preset threshold. If this condition is met, the turning point is marked as the false recovery start point. Starting from the false recovery start point, the system searches backward along the time axis. When the difference between the first water quality index value and the water quality index values ​​before the false recovery start point falls within a preset range, this point is marked as the false recovery end point. The curve segment between the false recovery start point and the false recovery end point is extracted as the false recovery segment.

[0031] The calculation of the rise in water quality indicators from the lowest point to the highest point within the pseudo-recovery segment, along with the corresponding rise duration, is performed. Simultaneously, the operational status changes of the upstream unit within this functional unit during this rise duration are acquired, including changes in load rate, inlet / outlet pressure difference, and flow rate. A correlation analysis is conducted between the rise amplitude within the pseudo-recovery segment and the operational status changes of the upstream unit. If the increase in load rate or the severity of blockage in the upstream unit is positively correlated with the rise in water quality indicators of the current functional unit, it is determined that the current functional unit has a performance degradation fault masked by changes in the operational status of the upstream unit, and this functional unit is marked as the second reference fault unit.

[0032] The central control module merges the first reference fault unit and the second reference fault unit into a single performance fault unit. The first reference fault unit reflects the direct decline in the processing performance of the functional unit itself, while the second reference fault unit reflects the hidden performance degradation masked by changes in the state of upstream units. Together, they constitute a complete performance fault diagnosis result.

[0033] S103. Call the prediction model that matches the abnormal information, so as to output the predicted purification trend of the waste liquid impurities in the current water-damaged vehicle repair process, and output the predicted intervention time before the waste liquid impurities penetrate to the preset key unit.

[0034] In one implementation of this application, the central control module determines the waste liquid impurity attribute category based on abnormal information, calls the corresponding impurity diffusion model to predict the penetration depth and residual cycle of impurities in the purification process, corrects the prediction results using historical prediction deviation rate, obtains the predicted purification trend and basic response window period, and then dynamically reduces the response window period in combination with the current load status of each functional unit to obtain the final predicted intervention duration.

[0035] Specifically, the central control module extracts the impurity type and concentration change data corresponding to the abrupt change in the characteristics of the waste liquid medium from the abnormal information. Based on the impurity type, it determines the impurity attribute category of the waste liquid from the water-damaged vehicle repair. In this embodiment, the impurity attribute categories include at least silt impurities, oil impurities, and emulsion impurities. Impurities of different attribute categories exhibit different diffusion behaviors and penetration characteristics in the purification process. An impurity diffusion model matching the current impurity attribute category is called from a preset model library. This impurity diffusion model is a neural network model trained based on historical experimental data, capable of outputting the penetration depth and residual period of impurities in the purification process based on input parameters such as impurity concentration, flow rate, and temperature. The penetration depth represents the distance the impurity travels along the treatment process, and the residual period represents the remaining time required for the impurity to be completely removed under the current treatment conditions.

[0036] The central control module acquires the historical prediction deviation rate of the impurity diffusion model. This historical prediction deviation rate is automatically calculated and updated by the central control module after each prediction. The calculation method is to compare the previously predicted penetration depth and residual period with the actual measured values, calculate the deviation ratio between the two, and take the average of the deviation ratios of the most recent ten predictions as the current historical prediction deviation rate. The predicted penetration depth is multiplied by one and the difference between the historical prediction deviation rate and the predicted residual period is multiplied by one and added to the sum of the historical prediction deviation rates. When the historical prediction deviation rate is positive, indicating that the predicted value is too large, the corrected penetration depth decreases and the residual period increases. When the historical prediction deviation rate is negative, indicating that the predicted value is too small, the corrected penetration depth increases and the residual period decreases. The corrected penetration depth is used as the predicted purification trend, which is used to represent the change in the spatial position of waste liquid impurities in the purification process over time. The remaining processing time is obtained by subtracting the current processing time consumed by the impurities from the corrected residual period, which serves as the basic response window period.

[0037] Furthermore, the central control module reads the motor current and operating power of each functional unit to calculate the current load rate of each unit. It then subtracts the current actual processing load from the rated processing capacity to obtain the load margin of each functional unit. The load margins of each functional unit are compared one by one, and the smallest value is selected as the minimum load margin. The central control module reads the corresponding impact coefficient from the storage chip according to the impurity attribute category. The impact coefficient is determined by the following rules: smaller impact coefficients for silt-like impurities, medium impact coefficients for oil-like impurities, and larger impact coefficients for emulsion-like impurities. The magnitude of the impact coefficient reflects the different degrees of damage caused by impurities of different attributes to the internal processing medium of the functional unit.

[0038] Multiplying the minimum load margin by the impact factor yields the weighted load margin. Dividing this weighted load margin by the rated processing capacity gives a ratio between zero and one, which is used as the dynamic contraction factor. The dynamic contraction factor reflects the combined impact of the current equipment load status and waste liquid impurity properties on the intervention duration. The central control module multiplies the basic response window period by the dynamic contraction factor to obtain the final predicted intervention duration. This predicted intervention duration represents the actual time available for intervention before impurities penetrate to the preset critical unit under the combined effects of the current equipment load status and the current waste liquid impurity properties.

[0039] S104. Based on the predicted purification trend and the predicted intervention duration, execute graded response operations.

[0040] In one implementation of this application, the central control module compares the predicted intervention duration with a preset safety time limit and executes different response strategies based on the comparison results. When the predicted intervention duration exceeds the preset safety time limit, it indicates sufficient intervention time. The module determines control parameters based on the predicted purification trend and sends control commands to the target unit. When the predicted intervention duration is less than or equal to the preset safety time limit, it indicates urgent intervention time. Based on the predicted purification trend, it identifies the critical unit where impurities are about to penetrate and immediately triggers a full-domain interlock shutdown protection, cutting off the operation path of the critical unit and its associated units. Simultaneously, it initiates a return purification process to return the substandard water to the corresponding functional unit for reprocessing until the water quality meets standards and is then recycled.

[0041] Specifically, the embodiments of this application include a preset safety time limit, which is a fixed time threshold used to determine the urgency of the current abnormal state, and the calculated predicted intervention duration is numerically compared with the preset safety time limit.

[0042] When the predicted intervention duration exceeds the preset safety time limit, it indicates that there is sufficient time for intervention in the current abnormal state. The central control module determines the step amplitude of the impurity concentration based on the multiple relationship between the current concentration and the baseline concentration, and subtracts the consumed time from the predicted intervention duration to obtain the remaining duration. Secondly, the central control module determines the range of affected functional units based on the type of mutation. Specifically, starting from the location of the waste liquid impurity intrusion, the central control module extends downstream along the treatment process, identifying a sequence of consecutive functional units related to the mutation type and whose treatment functions correspond to the same impurity treatment requirements as the target unit chain. For example, when there is a sudden increase in silt-like impurities, the target unit chain extends downstream from the pre-filtration unit to the fine filtration unit; these units all have the function of intercepting or filtering particulate matter. When there is a sudden increase in oil-like impurities, the target unit chain extends downstream from the oil-water separation unit to the adsorption unit; these units all have the function of separating or adsorbing oil.

[0043] The central control module reads the current load rate and rated processing capacity of the first target unit in the target unit chain. The current load rate is acquired in real time by current and power sensors installed on the target unit. The central control module subtracts the current actual processing load from the rated processing capacity of the target unit to obtain the available remaining capacity of the unit. The central control module continues to read the current load rate and rated processing capacity of the second target unit and calculates its available remaining capacity in the same way. The central control module repeats the above process until the available remaining capacity of all target units in the target unit chain has been calculated. The central control module extracts the mutation amplitude data from the predicted purification trend, obtains the remaining duration of the predicted intervention time from the storage chip, and the load sensitivity coefficient of each target unit. The load sensitivity coefficient reflects the rate at which the processing efficiency of the target unit decreases with the increase of load. The target units in the target unit chain are sorted in ascending order of load sensitivity coefficient to generate an allocation order list. The first target unit is taken from the allocation order list, and its available remaining capacity is compared with the additional load rate to be allocated. If the available remaining capacity is greater than or equal to the additional load rate to be allocated, all additional load to be allocated is allocated to that unit, and the allocation is complete. If the available remaining capacity is less than the additional load rate to be allocated, all available remaining capacity of that unit is used to carry the additional load, and the allocated portion is deducted from the additional load rate to be allocated. The next target unit is selected from the allocation order list, and the above allocation process is repeated until all additional loads are allocated to the target units.

[0044] The central control module determines the control target parameters for each target unit according to the load distribution scheme. The control target parameters include the adjusted operating power, water flow rate and operating frequency. The control target parameters of each target unit are encapsulated into control instructions and sent to the corresponding unit controller for execution via industrial Ethernet or RS485 communication bus.

[0045] In one implementation of this application, when the predicted intervention duration is less than or equal to a preset safety time limit, the central control module determines that the current abnormal state is urgent and there is insufficient time for gentle adjustment. At this time, the central control module extracts the processing location where impurities are about to arrive from the predicted purification trend and identifies the functional unit corresponding to that location as a critical unit. Based on the upstream and downstream relationships of the processing flow, it identifies related units that have direct pipeline connections to the critical unit and sends power-off commands to the power supply circuits of the critical unit and related units, while simultaneously sending closing commands to the corresponding pipeline valves to cut off the water flow path.

[0046] Simultaneously, the central control module determines the estimated treatment time for each exceeding impurity standard based on the predicted concentration of various impurities in the predicted purification trend. When a single exceeding impurity exists, the corresponding return path is directly activated according to the preset return mapping relationship. When multiple exceeding impurities exist, the return priority is determined comprehensively based on the estimated treatment time of each exceeding impurity and the predicted intervention time, and the corresponding return path is determined based on the return priority. The central control module activates the return pumps and valves on the return path to introduce the unqualified water into the corresponding functional unit for reprocessing, and continuously monitors water quality parameters during the return treatment process. When the water quality meets the standards, the return path is closed.

[0047] Specifically, the central control module extracts predicted concentration data of various waste liquid impurities from the predicted purification trend. It then compares the predicted concentrations of each impurity with preset reuse standards, filtering out impurities whose concentrations exceed the reuse standards. The module reads historical treatment efficiency data for different impurities from each functional unit and calculates the estimated treatment time for each type of impurity based on the multiple by which the concentration exceeds the standard and the historical treatment efficiency of the corresponding functional unit. The estimated treatment time is calculated by dividing the multiple by which the concentration exceeds the standard by the unit's unit-time treatment efficiency, yielding the time required to reduce the impurity concentration to the reuse standard. For example, when the oil concentration is three times the reuse standard, and the oil-water separation unit's unit-time treatment efficiency is a reduction of one-half the concentration per hour, the estimated treatment time is three hours. When the silt concentration is five times the reuse standard, and the pre-filtration unit's unit-time treatment efficiency is a reduction of two-half the concentration per hour, the estimated treatment time is 2.5 hours.

[0048] If only one type of impurity exceeds the standard in the current substandard water body, a preset reflux mapping table is retrieved. This table records the correspondence between various types of impurities exceeding the standard and the reflux path. For example, when silt particles exceed the standard, the reflux path ends at the pre-filtration unit; when oil pollution exceeds the standard, the reflux path ends at the oil-water separation unit; when bacterial flora exceeds the standard, the reflux path ends at the sterilization unit; and when the water is turbid, the reflux path ends at the fine filtration unit or adsorption unit. The central control module directly determines the corresponding reflux path based on this mapping relationship and initiates it.

[0049] When multiple impurities exceed the standard, the central control module obtains the estimated processing time for each type of impurity and the remaining time of the current predicted intervention time. It compares the estimated processing time with the remaining time for each type of impurity, assigning higher recirculation priority to impurities with shorter estimated processing times. Recirculation paths are allocated to each type of impurity in descending order of recirculation priority. When multiple recirculation paths share the same pipeline resource, the central control module prioritizes the pipeline resource for the impurity with the highest recirculation priority, with the recirculation paths for the remaining impurities following sequentially. Based on the determined recirculation paths, control commands are generated for the recirculation pumps and valves. These commands include starting the corresponding recirculation pump, opening the corresponding recirculation valve, and adjusting the valve opening. The control commands are then sent to the corresponding actuators to extract the substandard water from the current treatment location and guide it to the inlet of the corresponding functional unit, allowing it to undergo a complete purification process again.

[0050] Finally, during the recirculation process, the central control module continuously reads the output data from the water quality sensors installed on the recirculation pipeline, including at least the turbidity sensor, oil content sensor, and bacteria content sensor. The real-time water quality data is compared item by item with preset reuse standards. When the turbidity, oil content, and bacteria content all meet the preset reuse standards, the previously unqualified water is deemed to have been treated successfully. The central control module then sends a shut-off command to the recirculation pump and recirculation valve to stop the recirculation process and switches the treated water to the recycling pipeline, allowing it to re-enter the regular treatment process for reuse.

[0051] Figure 2 This is a schematic diagram of a detection and control system for a wastewater purification and treatment equipment used in the repair of flooded vehicles, provided as an embodiment of this application. Figure 2 As shown, the detection and control system for the waste liquid purification and treatment equipment used in the repair of flooded vehicles includes a data acquisition and monitoring module, an anomaly detection module, a predictive analysis module, a graded response module, and a central control module. The overall dimensions of the machine are 750mm × 500mm × 800mm. The casing is made of 304 thickened stainless steel with a wall thickness of 2.0mm; the overall protection level is IP67, which is dustproof, waterproof, and oil-resistant; the operating voltage is AC220V 50Hz.

[0052] Specifically, the data acquisition and monitoring module in this embodiment is used to acquire status monitoring data corresponding to different functional units in the wastewater purification and treatment equipment for flooded vehicle repairs. The wastewater purification and treatment equipment for flooded vehicle repairs includes multiple functional units arranged sequentially along the treatment process. These functional units include at least a pre-filtration unit, an oil-water separation unit, a fine filtration unit, an adsorption unit, and a sterilization unit. Each functional unit is equipped with a corresponding sensor group for real-time acquisition of its operating status data. The data acquisition and monitoring module establishes a data connection with the sensor groups of each functional unit via an industrial Ethernet or RS485 communication bus. The acquired status monitoring data includes at least two categories: equipment operating parameters and water quality detection parameters. Equipment operating parameters include the inlet pressure, outlet pressure, inlet-outlet pressure difference, liquid level, motor current, motor voltage, and operating power of each functional unit. Water quality detection parameters include turbidity, oil content, bacterial content, pH value, and particulate matter size distribution. The data acquisition and monitoring module stores the acquired sensor data in a structured manner according to the functional unit number and data acquisition timestamp, forming a time-series dataset containing multi-dimensional parameters.

[0053] The anomaly detection module is connected to the data acquisition and monitoring module. It is used to perform correlation and comparison analysis on different parameters in the status monitoring data and to perform trend analysis on water quality detection parameters to identify anomalies. The anomaly detection module includes a mutation detection unit, a load analysis unit, an efficiency analysis unit, and an information fusion unit. The mutation detection unit compares water quality detection parameters with a preset waste liquid impurity baseline. When the impurity concentration shows a step change, it identifies a sudden change event in the waste liquid media characteristics. The load analysis unit compares the operating parameters of equipment corresponding to adjacent target units to determine the load matching degree. When the load matching degree is less than a preset load threshold, it identifies a load imbalance state and identifies the bottleneck unit with excessive load. The efficiency analysis unit constructs water quality decay curves based on the water quality detection parameters of each functional unit. It identifies the first reference fault unit by analyzing the curve change rate and identifies the second reference fault unit by identifying false recovery segments and combining them with changes in the operating status of upstream units. Both are used as the efficiency fault unit. The information fusion unit determines anomalies based on the sudden change event in waste liquid media characteristics, the bottleneck unit, and the efficiency fault unit.

[0054] The predictive analysis module is connected to the anomaly detection module. It calls a predictive model matching the anomaly information and outputs the predicted purification trend of impurities in the wastewater from flooded vehicle repairs during the purification process, as well as the predicted intervention time before the impurities penetrate to a preset key unit. The predictive analysis module includes an attribute identification unit, a model invocation unit, a deviation correction unit, and a time-compression unit. The attribute identification unit determines the attribute category of the impurities in the wastewater based on the anomaly information. The model invocation unit calls an impurity diffusion model matching the impurity attribute category to predict the penetration depth and residual period of the current impurities in the purification process. The deviation correction unit obtains the historical prediction deviation rate of the impurity diffusion model, corrects the penetration depth and residual period, and determines the predicted purification trend and basic response window based on the corrected data. The time-compression unit obtains the current load status of each functional unit, uses the minimum load margin as a compression factor to reduce the basic response window, and obtains the predicted intervention time.

[0055] The tiered response module is connected to the predictive analysis module and is used to execute tiered response operations based on the predicted purification trend and predicted intervention duration. The tiered response module includes at least an alarm notification module, a safety interlock protection module, an automatic backflow module, and a parameter adjustment module. The alarm notification module issues corresponding audible and visual alarm signals during the tiered response operation: a green indicator light indicates normal operation, a yellow indicator light indicates a warning, and a red indicator light indicates a fault interlock alarm. The safety interlock protection module cuts off the operating pathways of key units and their associated units when the predicted intervention duration is less than or equal to a preset safety time limit. The automatic backflow module returns unqualified water to the corresponding functional unit for reprocessing until the water quality meets standards and is then recycled. The parameter adjustment module determines target control parameters based on the predicted purification trend and sends control commands to the target unit to adjust operating parameters when the predicted intervention duration exceeds the preset safety time limit.

[0056] The central control module is connected to the data acquisition and monitoring module, the anomaly detection module, the predictive analysis module, and the hierarchical response module, respectively. It is used to coordinate the data interaction and command transmission between the modules and to execute the detection and control methods for the waste liquid purification and treatment equipment for flooded vehicle repair.

[0057] In addition, the detection and control system for the waste liquid purification and treatment equipment for flooded vehicle repair also includes an intelligent sequential linkage start-stop control module, which automatically starts the front-end pretreatment, mid-stage purification, and back-end circulation return equipment in sequence to prevent water pressure shock and equipment damage caused by disordered start-up; and automatically shuts down in reverse sequence when the machine stops, so as to smoothly release pressure and protect pipeline valves.

[0058] It also includes a full-process operation data ledger archiving module, which automatically records daily running time, start and stop records, number of interlock actions, alarm records, number of return flows, sensor parameters, and equipment operating data. This data is tamper-proof and can be stored for long-term reference.

[0059] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for devices and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0060] The above descriptions are merely embodiments of this application and are not intended to limit the scope of this application. For those skilled in the art, various modifications and variations can be made to the embodiments of this application. These modifications or substitutions do not cause the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions in the embodiments of this application.

Claims

1. A detection and control method for wastewater purification and treatment equipment used in the repair of flooded vehicles, characterized in that, The method includes: Acquire status monitoring data corresponding to different functional units in the waste liquid purification and treatment equipment for repairing flooded vehicles; wherein, the status monitoring data includes at least equipment operating parameters and water quality testing parameters; Correlation and comparison analysis were performed on different parameters in the status monitoring data, and trend analysis was performed on the water quality detection parameters to identify abnormal information; Call the prediction model that matches the abnormal information, so as to output the predicted purification trend of the waste liquid impurities in the current water-damaged vehicle repair process, and output the predicted intervention time before the waste liquid impurities penetrate to the preset key unit. Based on the predicted purification trend and the predicted intervention duration, a tiered response operation is executed; wherein the tiered response operation includes at least early warning prompts, parameter adjustment, automatic backflow, and global interlocking shutdown protection.

2. The detection and control method for the wastewater purification and treatment equipment for flooded vehicle repair as described in claim 1, characterized in that, The process of performing correlation and comparison analysis on different parameters in the status monitoring data, and trend analysis on water quality detection parameters to identify abnormal information, specifically includes: The water quality detection parameters are compared with the preset waste liquid impurity baseline. If the comparison result shows a step change in impurity concentration, it is determined that a sudden change event in waste liquid medium characteristics has occurred. The target units associated with the sudden change event in the characteristics of the waste liquid medium are identified, and the equipment operating parameters corresponding to adjacent target units are compared to determine the load matching degree. If the load matching degree is less than the preset load threshold, it is determined to be a load imbalance state, and the bottleneck unit with excessive load is identified. Obtain the water quality detection parameters corresponding to each of the aforementioned functional units, and determine the performance failure unit based on the changes in the water quality detection parameters. The abnormal information is determined based on the sudden change event in the characteristics of the waste liquid medium, the bottleneck unit, and the performance failure unit.

3. The detection and control method for the wastewater purification and treatment equipment for flooded vehicle repair as described in claim 2, characterized in that, The step of determining the performance failure unit based on the changes in the water quality detection parameters specifically includes: Based on the water quality detection parameters of each functional unit, a corresponding water quality decay curve is constructed, and the first reference fault unit is determined based on the rate of change of the water quality decay curve. The local extreme points of the water quality degradation curve within a preset time window are obtained, and the variation range and direction between adjacent extreme points are determined. When the direction of change changes from rising to falling, and the magnitude of the change before and after the change is greater than a preset magnitude threshold, the turning point is marked as a false recovery starting point. Starting from the false recovery starting point and ending at the first point after the starting point whose amplitude is within a preset amplitude range, a false recovery segment is extracted. The duration of the rise in water quality indicators within the pseudo-recovery segment is obtained, and the changes in the operating status of the upstream unit of the current functional unit during the duration of the rise are obtained. When the change in the operating status of the upstream unit is correlated with the recovery of water quality in the current functional unit, the current functional unit is determined as the second reference fault unit. The first reference fault unit and the second reference fault unit are used as the performance fault unit.

4. The detection and control method for the wastewater purification and treatment equipment for flooded vehicle repair as described in claim 1, characterized in that, The invocation of the prediction model matching the anomaly information, to output the predicted purification trend of impurities in the current water-damaged vehicle repair wastewater during the purification process, and the predicted intervention time before the wastewater impurities penetrate to the preset key unit, specifically includes: Based on the aforementioned abnormal information, the category of impurities in the waste fluid from the repair of flooded vehicles was determined. Call the impurity diffusion model that matches the impurity attribute category to predict the penetration depth and residual cycle of the current waste liquid impurities in the purification process. The historical prediction deviation rate of the impurity diffusion model is obtained, and the penetration depth and the residual period are corrected based on the historical prediction deviation rate. The predicted purification trend and the basic response window period are determined based on the corrected penetration depth and residual period. The current load status of each functional unit is obtained, and the basic response window period is reduced by using the minimum load margin as a shrinkage factor to obtain the predicted intervention duration.

5. The detection and control method for the wastewater purification and treatment equipment for flooded vehicle repair as described in claim 4, characterized in that, The method of using the minimum load margin as a contraction factor to reduce the basic response window period to obtain the predicted intervention duration specifically includes: The load margins of each functional unit are compared to determine the minimum load margin. Based on the current classification of impurities in the waste fluid from repairing flooded vehicles, a corresponding impact coefficient is determined; wherein, the impact coefficient is positively correlated with the degree of damage to functional units caused by the classification of impurities in the waste fluid. The product of the minimum load margin and the impact coefficient is determined, and the ratio of the product to the rated processing capacity is used as the dynamic compression coefficient. The predicted intervention duration is obtained based on the basic response window period and the dynamic contraction coefficient.

6. The detection and control method for the wastewater purification and treatment equipment for flooded vehicle repair as described in claim 1, characterized in that, The step of executing a tiered response operation based on the predicted purification trend and predicted intervention duration specifically includes: The predicted intervention duration is compared with the preset safety time limit. If the predicted intervention duration is longer than the preset safety time limit, the target control parameter is determined based on the predicted purification trend, and a control instruction is sent to the target unit to adjust the operating parameters. If the predicted intervention duration is less than or equal to the preset safety time limit, the key unit that the waste liquid impurities are about to penetrate is determined according to the predicted purification trend. Based on the whole-domain interlocking shutdown protection mechanism, the operation path of the key unit and its related units is cut off, and the unqualified water is returned to the corresponding functional unit for reprocessing until the water quality meets the standards and is recycled.

7. The detection and control method for the wastewater purification and treatment equipment for flooded vehicle repair as described in claim 6, characterized in that, The step of determining the target control parameter based on the predicted purification trend specifically includes: Based on the predicted purification trend, the mutation type and mutation magnitude are determined, as well as the remaining duration of the predicted intervention duration. Based on the mutation type, the affected target unit chain is determined; wherein, the target unit chain is a continuous unit sequence extending downstream along the treatment process at the location of waste liquid impurity intrusion, and the treatment function of each unit in the target unit chain matches the impurity treatment requirements corresponding to the mutation type. Based on the mutation magnitude and the remaining duration, the additional processing load to be borne by each unit in the target unit chain is determined, and a load allocation scheme is generated with the additional processing load not exceeding the rated redundancy capacity of each functional unit as a constraint. Based on the load allocation scheme, the control target parameters for each target unit are determined, and the control instructions are generated.

8. The detection and control method for the wastewater purification and treatment equipment for flooded vehicle repair as described in claim 7, characterized in that, The step of determining the additional processing load required for each unit in the target unit chain based on the mutation magnitude and the remaining duration specifically includes: Obtain the current load rate and rated processing capacity of each target unit in the target unit chain to determine the available remaining capacity corresponding to each target unit; The total additional processing load is determined based on the mutation amplitude, and the rate of additional load to be processed per unit time is determined using the remaining duration as a time constraint. Constrained by the available remaining capacity and the additional load rate, the additional processing load is sequentially allocated to each target unit based on the order of load sensitivity from low to high; wherein, the load sensitivity is related to the change in processing efficiency of the target unit when the load changes.

9. The detection and control method for the wastewater purification and treatment equipment for flooded vehicle repair as described in claim 6, characterized in that, The process of returning substandard water to the corresponding functional unit for reprocessing until the water quality meets the standards and is then recycled includes: Based on the predicted concentrations of various waste liquid impurities in the predicted purification trend, the estimated treatment time for each impurity exceeding the standard is determined. When a single impurity exceeds the standard, the corresponding reflux path is activated according to the preset reflux mapping relationship; When multiple excessive impurities are present, the reflux priority is determined based on the expected processing time of each excessive impurity and the predicted intervention time. Based on the backflow priority, the corresponding backflow path is determined; Start the return pump and corresponding valves on the return path to introduce the unqualified water into the corresponding functional unit for reprocessing; During the reflux process, the water quality detection parameters are continuously acquired. When the water quality detection parameters meet the preset reuse conditions, the reflux path is closed.

10. A detection and control system for wastewater purification and treatment equipment used in the repair of flooded vehicles, characterized in that, The system includes: The data acquisition and monitoring module is used to acquire status monitoring data corresponding to different functional units in the waste liquid purification and treatment equipment for flooded vehicle repairs; wherein, the status monitoring data includes at least equipment operating parameters and water quality testing parameters; An anomaly detection module, connected to the data acquisition and monitoring module, is used to perform correlation comparison analysis on different parameters in the status monitoring data, and to perform trend analysis on water quality detection parameters to determine anomaly information. The predictive analysis module, connected to the anomaly detection module, is used to call the predictive model that matches the anomaly information, so as to output the predicted purification trend of the impurities in the current water-damaged vehicle repair waste liquid in the purification process, and output the predicted intervention time before the waste liquid impurities penetrate to the preset key unit. A graded response module, connected to the predictive analysis module, is used to perform graded response operations based on the predicted purification trend and the predicted intervention duration; wherein, the graded response module includes at least an alarm prompt module, a safety interlock protection module, an automatic backflow module, and a parameter adjustment module; A central control module, the central control module being used to perform the method as described in any one of claims 1-9.

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