Electroplating wastewater treatment decision-making system based on multi-modal data analysis

Abstract

The invention discloses an electroplating wastewater treatment decision-making system based on multi-modal data analysis, relates to the technical field of wastewater treatment, and aims to solve the problems that in the prior art, due to the lack of synchronous acquisition and analysis capability for multi-source heterogeneous data, the system is often difficult to find water quality fluctuation, equipment abnormity, process deviation and the like in time. A processing decision-making platform is taken as a core, and a multi-modal data acquisition and analysis unit, a multi-factor dynamic analysis unit and a resource scheduling analysis unit are linked to form a closed-loop decision-making process of data acquisition-dynamic analysis-resource scheduling; the processing decision platform is used as a center, triggers each unit to execute a task by sending a control signal, receives an analysis signal fed back by each unit, and outputs a final decision (such as parameter adjustment and process regulation and control) according to the signal content; the three units are clear in division of labor, data synchronous verification, medicament dynamic optimization and resource load matching are achieved in sequence, and it is ensured that the processing process is accurate, efficient and stable.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, specifically to a decision-making system for electroplating wastewater treatment based on multimodal data analysis. Background Technology

[0002] Electroplating wastewater treatment is an important environmental protection step in industrial production. The treatment effect directly affects the ecological environment and the operating costs of enterprises. For example, the patent application number 2025107500590 discloses an electroplating wastewater treatment system. Specifically, the pretreatment tank removes large particulate suspended solids from the water and adjusts the pH to neutral. The electrochemical tank uses modified carbon fiber as the anode plate to oxidize the recalcitrant organic pollutants in the water. After passing through the flocculation tank, flocs are formed. The clear water is filtered into the adsorption tank for further adsorption and removal of heavy metal ions. The adsorption tank is equipped with a backwashing device to rinse the adsorption layer at any time to ensure that the effluent quality after the second rinsing meets the national discharge standards.

[0003] Currently, traditional electroplating wastewater treatment systems mainly rely on single-dimensional water quality monitoring data, which is difficult to fully reflect the complex changes in operating conditions during the treatment process. In actual operation, due to the lack of ability to synchronously collect and analyze multi-source heterogeneous data, it is often difficult to detect problems such as water quality fluctuations, equipment abnormalities, and process deviations in a timely manner.

[0004] Meanwhile, the dynamic impact assessment of reagent addition is insufficient, and the addition strategy cannot be adjusted according to real-time operating conditions, which can easily lead to reagent waste or substandard treatment effect. In addition, conventional systems lack intelligent decision support in resource scheduling, making it difficult to balance treatment efficiency and energy consumption costs.

[0005] These problems lead to defects in the electroplating wastewater treatment process, such as slow response, inaccurate control, and low resource utilization, which seriously restrict the improvement of treatment efficiency. Therefore, a solution is proposed. Summary of the Invention

[0006] The purpose of this invention is to solve the problems mentioned above by proposing a decision system for electroplating wastewater treatment based on multimodal data analysis.

[0007] The objective of this invention can be achieved through the following technical solution: a decision system for electroplating wastewater treatment based on multimodal data analysis, comprising a treatment decision platform, wherein the treatment decision platform is communicatively connected to a multimodal data acquisition and analysis unit, a multi-factor dynamic analysis unit, and a resource scheduling analysis unit;

[0008] The processing and decision-making platform is used to generate multimodal data acquisition and analysis signals and send them to the multimodal data acquisition and analysis unit;

[0009] The multimodal data acquisition and analysis unit is used to respond to the multimodal data acquisition and analysis signal, perform multimodal data acquisition and analysis on the electroplating wastewater treatment process, and send a data synchronization status signal to the processing decision platform based on the analysis results;

[0010] The multi-factor dynamic analysis unit is used to perform dynamic impact analysis on the addition of reagents in the electroplating wastewater treatment process when the treatment decision platform determines that the electroplating wastewater treatment process is operating efficiently, and to send dynamic analysis signals to the treatment decision platform based on the analysis results.

[0011] The resource scheduling analysis unit is used to respond to the resource scheduling analysis signal generated by the processing decision platform, perform resource scheduling analysis on the electroplating wastewater treatment process, and send a resource scheduling signal to the processing decision platform based on the analysis results.

[0012] In a preferred embodiment of the present invention, the multimodal data acquisition and analysis unit performs multimodal data acquisition and analysis on the electroplating wastewater treatment process, including:

[0013] Water quality monitoring data, equipment monitoring data, and process monitoring data are acquired through sensors.

[0014] Based on the water quality monitoring data, equipment monitoring data, and process monitoring data, corresponding data sets are constructed, and timestamps are attached to subsets of each set.

[0015] During the data acquisition process, the acquisition data delay buffer duration and the value fluctuation period of the currently selected acquisition time are obtained for each type of data set at any acquisition time.

[0016] Based on the comparison results of the delay buffer duration and the buffer duration threshold, and the comparison results of the numerical fluctuation period and the current buffer duration, the data synchronization status is determined, and a corresponding data synchronization status signal is generated and sent to the processing decision platform.

[0017] In a preferred embodiment of the present invention, the multimodal data acquisition and analysis unit is further configured to:

[0018] If an anomaly in data synchronization is detected, a synchronization analysis is performed on subsequent timestamp subsets, and the data values ​​of the subset records are adjusted by tracing the source and the fluctuation range of timestamp values ​​to ensure that data of all types at the same timestamp are synchronized.

[0019] In a preferred embodiment of the present invention, the multimodal data acquisition and analysis unit is further configured to perform numerical analysis on various types of data sets, including:

[0020] Based on the proportional relationship between data type and the efficiency of process steps, data types are marked as positively correlated or negatively correlated.

[0021] By comparing the trends of various types of data within adjacent timestamp intervals, the current time period is marked as a high-efficiency processing period, a low-efficiency processing period, or a period requiring adjustment. The marking results are then sent to the processing decision platform so that the platform can adjust the processing workload, perform process self-checks, or implement targeted adjustments accordingly.

[0022] In a preferred embodiment of the present invention, the multi-factor dynamic analysis unit performs dynamic impact analysis on the drug addition process, including:

[0023] Obtain the treatment parameters for electroplating wastewater and mark the time of reagent addition;

[0024] Based on the reaction type between the reagent and the electroplating wastewater, the preset fluctuation trend of the post-treatment parameters after reagent addition is obtained;

[0025] Based on historical processing data, the fluctuation range of processing parameters and the reaction buffer time of the reagent are obtained;

[0026] After the current reagent is added, obtain the fluctuation range of the processing parameters within the reaction buffer time, and the cumulative time during which the processing parameters do not fluctuate according to the preset trend outside the reaction buffer time;

[0027] Based on whether the floating span is within the floating range of the processing parameters and whether the cumulative duration exceeds the set cumulative duration threshold, a corresponding dynamic analysis signal is generated and sent to the processing decision platform.

[0028] In a preferred embodiment of the present invention, the processing decision platform responds to the addition deviation signal or addition strategy adjustment signal sent by the multi-factor dynamic analysis unit to adjust the amount of reagent added or the addition strategy of reagent in the current electroplating wastewater treatment process.

[0029] In a preferred embodiment of the present invention, the resource scheduling analysis unit performs resource scheduling analysis on the electroplating wastewater treatment process, including:

[0030] Obtain the current electroplating wastewater transport volume and the effluent volume after wastewater treatment in the current electroplating wastewater treatment process, and calculate the water volume ratio;

[0031] Based on the changing trend of the water volume ratio, the electroplating wastewater treatment process is marked as either overloaded or unloaded.

[0032] Obtain the percentage of qualified water volume during the overload trend period, and the speed deviation between the treatment speed adjustment value and the required set speed during the idle trend period;

[0033] Based on whether the percentage of qualified water exceeds the threshold for qualified water, and whether the speed deviation value exceeds the set speed deviation threshold, a corresponding resource scheduling signal is generated and sent to the processing decision platform.

[0034] In a preferred embodiment of the present invention, the processing decision platform responds to the resource scheduling anomaly signal sent by the resource scheduling analysis unit, controls the influent and effluent flow of the current wastewater treatment production line, and adjusts the execution speed of the treatment process at different stages.

[0035] In a preferred embodiment of the present invention, the water quality monitoring data includes online monitoring sensor detection data and laboratory offline detection data, the equipment monitoring data is data generated by the PLC system of the treatment equipment, and the process monitoring data is data generated by the operation of the reagent dosing system and the process control software.

[0036] In a preferred embodiment of the present invention, all types of data sets are constructed using synchronous data acquisition to facilitate the collection of multimodal data and the synchronous management of electroplating wastewater treatment.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] 1. This invention uses a multimodal data acquisition and analysis unit to collect and synchronize multi-source data in real time, combined with a multi-factor dynamic analysis unit to optimize the agent dosing strategy, and a resource scheduling analysis unit to balance processing efficiency and energy consumption, in order to solve the problems of system response lag, inaccurate control and low resource utilization. It has the advantages of comprehensive monitoring of operating conditions, dynamic adjustment of strategies and optimization of resource scheduling.

[0039] 2. This invention effectively solves the problem of time synchronization of multi-source heterogeneous data in the electroplating wastewater treatment process; by real-time monitoring of data acquisition delay and numerical fluctuation characteristics, it can promptly identify situations such as sensor anomalies, network transmission failures, or sudden changes in equipment status, avoiding misjudgments of the treatment process due to data asynchrony; for example, when the reagent dosage data fails to match the real-time water quality data due to transmission delay, the system can immediately trigger data synchronization verification to prevent adjustments to the dosage strategy based on incorrect timing relationships, ensuring the accuracy and timeliness of treatment process control.

[0040] 3. This invention effectively identifies treatment anomalies at different stages after reagent addition, avoiding unstable treatment effects caused by changes in reaction conditions. By monitoring the fluctuation range of parameters and the duration of anomalies in real time, it can quickly distinguish different types of problems such as reagent dosage deviation and abnormal reaction conditions, providing an accurate basis for subsequent dosage correction or process adjustment, ensuring the accuracy and timeliness of reagent use in the electroplating wastewater treatment process. In other words, through multimodal data fusion analysis, it accurately captures the delayed effects and trend anomalies of parameter changes after reagent addition, realizing closed-loop dynamic optimization of the addition strategy. Attached Figure Description

[0041] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0042] Figure 1 This is a block diagram illustrating the system architecture principle of the present invention;

[0043] Figure 2 This is a schematic diagram of the system architecture of the present invention. Detailed Implementation

[0044] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0046] In existing technologies, the treatment of electroplating wastewater involves complex multi-parameter control. Traditional methods usually rely on manual experience for process adjustments, which suffers from response lag and decision-making bias. Furthermore, they often use single-dimensional monitoring data, which is difficult to accurately reflect the dynamic relationship between water quality changes, equipment status, and reagent reactions in the treatment process. For example, in the heavy metal ion removal stage of a certain electroplating plant, the failure to capture the nonlinear relationship between pH fluctuations and flocculant dosage in real time led to insufficient neutralization reaction and frequent blockage of subsequent membrane treatment equipment.

[0047] To address the aforementioned issues, the inventors discovered that the root cause of the low efficiency in electroplating wastewater treatment lies in the lack of effective collaborative analysis of multi-source heterogeneous data. By studying the time synchronization mechanism of multimodal data, they proposed establishing a unified data acquisition and processing framework. Further analysis revealed that the dynamic impact of reagent addition requires trend prediction by combining historical data and real-time parameters, while resource scheduling needs to consider the dynamic matching of processing load and equipment capacity. This led to a collaborative control approach centered on a decision-making platform that integrates data acquisition, dynamic analysis, and resource scheduling.

[0048] Therefore, please refer to Figures 1-2As shown, this invention proposes a decision-making system including a processing decision-making platform, which is connected to a multimodal data acquisition and analysis unit, a multi-factor dynamic analysis unit, and a resource scheduling analysis unit. The processing decision-making platform generates data acquisition signals and receives feedback signals from each unit. The multimodal data acquisition and analysis unit performs multi-dimensional data acquisition and synchronization judgment. The multi-factor dynamic analysis unit performs reagent addition impact analysis when the process is operating efficiently. The resource scheduling analysis unit performs resource optimization configuration according to platform instructions.

[0049] Among them, the processing and decision-making platform refers to the central control system that coordinates the work of multiple units. Specifically, it can be implemented by using an industrial control computer equipped with a distributed communication module to realize signal distribution and status monitoring.

[0050] A multimodal data acquisition and analysis unit refers to a data acquisition device that integrates a sensor network. Specifically, it can be implemented using an edge computing gateway equipped with a time synchronization protocol to eliminate data acquisition latency differences.

[0051] The multi-factor dynamic analysis unit refers to the process parameter analysis module, which can be implemented by combining machine learning models with reaction kinetic equations to evaluate the impact of reagent dosing on the treatment effect.

[0052] The resource scheduling and analysis unit refers to the resource optimization module, which can be implemented by using dynamic programming algorithms combined with real-time load data to balance processing capacity and energy consumption indicators.

[0053] Specifically, the treatment decision platform first triggers the multimodal data acquisition unit to start the synchronous acquisition of water quality, equipment and process data. After the data synchronization verification is passed, the platform activates the multi-factor dynamic analysis unit based on the treatment efficiency evaluation results to compare the trend of parameter changes after the addition of reagents. If an abnormal deviation in the reaction is detected, the platform will trigger the resource scheduling unit to reallocate the water pump power and membrane module working pressure. The whole process achieves dynamic optimization of the treatment process through a closed-loop feedback mechanism, avoiding response delays caused by manual intervention.

[0054] Compared with existing technologies, traditional systems use independently operating monitoring and control modules, making it difficult to achieve cross-modal data correlation analysis. For example, most traditional technologies only monitor pH value and flow parameters without considering the impact of mixer speed on reagent diffusion efficiency. In contrast, this invention establishes a time alignment mechanism for multi-source data to accurately identify the coupling relationship between equipment status and treatment effect, thereby improving the accuracy of process adjustment.

[0055] The above technical solution can identify data acquisition anomalies in the electroplating wastewater treatment process in real time, dynamically correct the reagent dosing strategy, and automatically adjust the equipment operating parameters according to changes in the treatment load. This effectively solves the problem of misjudgment caused by data asynchrony in traditional methods, as well as the phenomenon of treatment efficiency fluctuation caused by slow response of manual scheduling.

[0056] The decision-making platform generates multimodal data acquisition and analysis signals and sends them to the multimodal data acquisition and analysis unit.

[0057] After receiving the multimodal data acquisition and analysis signal, the multimodal data acquisition and analysis unit performs multimodal data acquisition and analysis on the electroplating wastewater treatment process;

[0058] Data is collected from each stage of the electroplating wastewater treatment process using sensors, and water quality monitoring data, equipment monitoring data, and process monitoring data are obtained based on water quality monitoring, equipment monitoring, and process monitoring.

[0059] Among them, water quality monitoring data includes online monitoring sensor (pH / ORP / heavy metal) detection data and laboratory offline detection data; equipment monitoring data includes data generated by the PLC system of the treatment equipment (pump / reactor / filter press); and process monitoring data includes data generated by the operation of the reagent dosing system and process control software.

[0060] Construct corresponding data sets based on water quality monitoring data, equipment monitoring data, and process monitoring data, and attach timestamps to subsets of the sets according to the collection time of the data collected within the sets.

[0061] Furthermore, all types of data sets constructed are synchronous data acquisitions to facilitate the collection of multimodal data and the synchronous management of electroplating wastewater treatment;

[0062] During the data acquisition process, the acquired data is synchronously detected to obtain the data delay buffer duration corresponding to any acquisition time for each type of data set, and at the same time, the numerical fluctuation period of the acquired data corresponding to the currently selected acquisition time is obtained.

[0063] The analysis also examines the data acquisition delay buffer duration at any acquisition time for each type of data set and the numerical fluctuation period of the data acquired at the currently selected acquisition time.

[0064] If the data delay buffer duration for any data collection time exceeds the buffer duration threshold, and the fluctuation period of the data value for the currently selected data collection time is lower than the current buffer duration, then it is inferred that the data synchronization is abnormal, a data synchronization deviation signal is generated and sent to the processing decision platform.

[0065] After receiving the data from the decision-making platform, the next subset after the current timestamp is analyzed for synchronization, that is, the time delay is compared. This process is repeated to ensure that the adjacent subsets after the current timestamp are synchronized and qualified. If they are not qualified, the data is collected for synchronization with the subset of the current timestamp. This is done by tracing the source and adjusting the data values ​​of the subset records based on the corresponding timestamp value fluctuation range, so as to ensure that the data of all types at the same timestamp are synchronized.

[0066] If the data delay buffer duration at any collection time for any type of data set exceeds the buffer duration threshold, and the fluctuation period of the data value at the currently selected collection time is higher than the current buffer duration, then a data synchronization risk is inferred, a current data synchronization lag signal is generated and sent to the processing decision platform.

[0067] After receiving the data, the decision-making platform performs numerical fluctuation monitoring and analysis on sets other than the current type. If the fluctuation is within the current buffer duration, the fluctuating values ​​in sets other than the current type are synchronized with the recorded data of the timestamp of the current monitored set.

[0068] If the data delay buffer duration corresponding to any acquisition time of each type of data set does not exceed the buffer duration threshold, and the numerical fluctuation period of the data corresponding to the currently selected acquisition time is higher than the current buffer duration, then the data synchronization risk is inferred, a data synchronization lag signal is generated and sent to the processing decision platform.

[0069] After receiving the data, the decision-making platform monitors and analyzes the numerical fluctuation period of sets other than the current type. If the fluctuation period deviation for all types is below a set range, then subsets of each set at any timestamp are synchronized. Conversely, if the fluctuation period deviation is above the set range, the subset data corresponding to the relatively lower fluctuation period is updated, along with its timestamp, while the subset data corresponding to the relatively higher fluctuation period is updated.

[0070] If the data delay buffer duration corresponding to any acquisition time of each type of data set does not exceed the buffer duration threshold, and the numerical fluctuation period of the data corresponding to the currently selected acquisition time is lower than the current buffer duration, then it is inferred that the data synchronization is small, a data synchronization stability signal is generated and sent to the processing decision platform.

[0071] Numerical analysis of various types of data sets:

[0072] Obtain the data type of each data set, and limit the proportional relationship between the fluctuation trend of the corresponding values ​​of each data type and the influence of the corresponding set type. That is, if the value of the corresponding data type increases, the process link of the set type will be executed more efficiently. Then, this data type is marked as positively correlated data, and vice versa.

[0073] The numerical trend of each type of data set is compared. If the trend of each type of positively correlated data is increasing and the trend of negatively correlated data is decreasing within the interval of each adjacent timestamp, the current time period is marked as a high-efficiency processing period and sent to the processing decision platform. After receiving the data, the processing decision platform increases the processing workload of the current electroplating wastewater treatment process.

[0074] If the trend of each type of positively correlated data is decreasing or the trend of negatively correlated data is increasing within the interval of each adjacent timestamp, then the current time period is marked as an inefficient processing period and sent to the processing decision platform. After receiving the data, the processing decision platform performs a process self-check on the current electroplating wastewater treatment process and performs traceability detection on the execution logs of each link. If there is a deviation in the execution, the execution is adjusted.

[0075] If the trend of each type of positively correlated or negatively correlated data within the interval between adjacent timestamps is not of a single type, then numerical comparisons are performed on any type of data. When the data is within the set fault red line range, the processing decision platform does not make process adjustments. Conversely, when the data is not within the set fault red line range, the processing decision platform makes adjustments based on the specific data type and the corresponding specific execution process.

[0076] Compared with existing technologies, traditional electroplating wastewater monitoring systems typically focus only on the collection of a single type of data, such as monitoring only water quality parameters or equipment status. When there are differences in the collection frequency or transmission delays of multi-source data, it is impossible to effectively determine the temporal consistency of the data, resulting in distorted dynamic analysis results. In contrast, this invention establishes a timestamp association mechanism and combines the dual judgment of delay buffer duration and numerical fluctuation period to accurately identify multimodal data asynchrony problems caused by transmission delays or equipment malfunctions, providing a reliable data foundation for subsequent processing decisions.

[0077] Through the above technical solution, this invention effectively solves the problem of time synchronization of multi-source heterogeneous data in the electroplating wastewater treatment process. By monitoring the data acquisition delay and numerical fluctuation characteristics in real time, it can promptly identify situations such as sensor anomalies, network transmission failures, or sudden changes in equipment status, avoiding misjudgments of the treatment process due to data asynchrony. For example, when the reagent dosage data fails to match the real-time water quality data due to transmission delay, the system can immediately trigger data synchronization verification to prevent adjustments to the dosage strategy based on incorrect timing relationships, thereby ensuring the accuracy and timeliness of the treatment process control.

[0078] Compared with existing technologies, traditional methods usually only use a fixed timestamp alignment strategy, which cannot dynamically handle timing misalignment caused by sensor delay or system failure. In contrast, this invention accurately locates abnormal nodes through a source tracing mechanism and combines quantitative analysis of timestamp fluctuation span to achieve dynamic correction of data values, effectively solving the problem of collaborative analysis failure caused by asynchronous acquisition of multimodal data.

[0079] Through the above technical solution, the present invention can eliminate timestamp misalignment and numerical deviation of multimodal data, ensure the synchronization of water quality, equipment and process data, avoid misjudgment of processing decisions caused by data asynchrony, and improve the control accuracy and execution efficiency of electroplating wastewater treatment process.

[0080] Compared with existing technologies, traditional electroplating wastewater treatment systems typically rely on a single data threshold for alarm control, failing to correlate the dynamic trends of multimodal data with the relationship between treatment efficiency and efficiency. In contrast, this invention establishes a model of the proportional relationship between data and efficiency, combined with time series trend analysis, which can accurately identify inflection points in the process operation status and enable predictive adjustments to the treatment strategy.

[0081] Through the above technical solution, the present invention dynamically classifies the process operation period categories according to the collaborative change trend of multimodal data, enabling the processing decision platform to proactively increase the processing load during high-efficiency periods and promptly initiate self-checking procedures during low-efficiency periods, avoiding efficiency fluctuations caused by data delays or isolated judgments, and significantly improving the operational stability and resource utilization of the electroplating wastewater treatment system.

[0082] After completing multimodal data acquisition and analysis, and when the current electroplating wastewater treatment process is operating efficiently;

[0083] The multi-factor dynamic analysis unit performs dynamic impact analysis on various wastewater treatment stages.

[0084] Based on the treatment of different types of electroplating wastewater, reagents are added, and dynamic impact analysis is conducted on the reagent addition process.

[0085] The treatment parameters for electroplating wastewater are obtained from the sensors. These parameters are expressed as pH, conductivity, and heavy metal concentration. The time of reagent addition is also marked. Based on the reaction type between the reagent and the electroplating wastewater, the preset fluctuation trend of the corresponding treatment parameters for reagent addition is obtained.

[0086] Based on the historical treatment process, the fluctuation range of the treatment parameters of the corresponding electroplating wastewater after the addition of the same type of reagents was completed, and the fluctuation range of the treatment parameters was obtained according to the reagent addition at each treatment time. The reaction buffer time of the reagents was obtained by combining the growth trend of the fluctuation rate of the treatment parameters. It should be noted that the reaction buffer time is calculated and averaged based on multiple time points.

[0087] After the current reagent addition is completed, the fluctuation range of the treatment parameters within the corresponding reaction buffer time of the electroplating wastewater being treated is obtained. At the same time, the cumulative duration during which the treatment parameters of the electroplating wastewater did not fluctuate according to the preset fluctuation trend outside the reaction buffer time is obtained.

[0088] The analysis included the fluctuation range of treatment parameters within the corresponding reaction buffer time for the electroplating wastewater under treatment, and the cumulative duration during which the treatment parameters of the electroplating wastewater outside the reaction buffer time did not fluctuate according to the preset fluctuation trend.

[0089] If the fluctuation range of the treatment parameters within the corresponding reaction buffer time of the electroplating wastewater being treated is not within the fluctuation range of the treatment parameters, it is inferred that the amount of reagent added has not reached the actual requirement range, and a dosage deviation signal is generated and sent to the treatment decision platform. After receiving the signal, the treatment decision platform adjusts the amount of reagent added in the current electroplating wastewater treatment process.

[0090] If the cumulative duration for which the treatment parameters of electroplating wastewater do not fluctuate according to the preset fluctuation trend exceeds the set cumulative duration threshold outside the reaction buffer period, it is inferred that the reagent addition strategy in the electroplating wastewater treatment process is abnormal, and an addition strategy adjustment signal is generated and sent to the treatment decision platform. After receiving the signal, the treatment decision platform adjusts the reagent addition strategy in the electroplating wastewater treatment process, i.e., changes the reagent type or addition time, etc.

[0091] If the fluctuation range of the treatment parameters within the corresponding reaction buffer time of the electroplating wastewater being treated is within the fluctuation range of the treatment parameters, and the cumulative time during which the treatment parameters of the electroplating wastewater do not fluctuate according to the preset fluctuation trend outside the reaction buffer time does not exceed the set cumulative time threshold, then it is inferred that the dynamic impact analysis during the treatment of electroplating wastewater is normal, and a dynamic safety signal is generated and sent to the treatment decision platform.

[0092] Compared with existing technologies, traditional methods only rely on parameter detection results at fixed time points to adjust the reagents, which cannot distinguish the differences in reaction stages and the persistence of abnormalities. However, this invention, by dividing the monitoring intervals inside and outside the reaction buffer period and combining them with the floating range benchmark established by historical data, can accurately identify the deviation of the treatment effect at each stage after reagent addition. For example, if the COD value does not decrease sufficiently within the reaction buffer period after a certain addition, the system immediately identifies it as insufficient reagent dosage. If the COD value continues to fluctuate after the buffer period, it is judged that the reaction conditions are abnormal and process parameters need to be adjusted, thereby achieving precise control by grade and category.

[0093] Through the above technical solution, the present invention can effectively identify treatment anomalies at different stages after the addition of reagents, avoid unstable treatment effects caused by changes in reaction conditions, and quickly distinguish different types of problems such as reagent dosage deviation and abnormal reaction conditions by real-time monitoring of parameter fluctuation range and duration of anomalies, providing an accurate basis for subsequent dosage correction or process adjustment, and ensuring the accuracy and timeliness of reagent use in the electroplating wastewater treatment process.

[0094] Furthermore, since traditional electroplating wastewater treatment systems typically employ fixed dosing modes or single-parameter feedback mechanisms, which cannot identify the mismatch between reagent dosage and the dynamic reaction process in real time, this invention uses multimodal data fusion analysis to accurately capture the delayed effects and trend anomalies of parameter changes after reagent dosing, thereby achieving closed-loop dynamic optimization of the dosing strategy.

[0095] Through the above technical solution, the present invention effectively solves the problem of treatment efficiency fluctuation caused by deviation in reagent dosage or mismatch in dosage sequence, avoids resource waste caused by excessive dosage or the risk of substandard treatment caused by insufficient dosage, and ensures that the electroplating wastewater treatment process maintains a stable and efficient operating state under dynamic changing conditions.

[0096] When it is determined that the dynamic impact of electroplating wastewater treatment is normal, the treatment decision platform generates a resource scheduling analysis signal and sends it to the resource scheduling analysis unit.

[0097] After receiving the resource scheduling analysis signal, the resource scheduling analysis unit performs resource scheduling analysis on the electroplating wastewater treatment process to ensure the operational feasibility of the electroplating wastewater treatment.

[0098] The system obtains the current electroplating wastewater delivery volume and the effluent volume after treatment during the current electroplating wastewater treatment process, and compares the values ​​to obtain the water volume ratio. If the water volume ratio shows an increasing trend, the electroplating wastewater treatment process is marked as an overload trend; if the water volume ratio shows a decreasing trend, the electroplating wastewater treatment process is marked as an idle trend. In this case, a decreasing trend indicates a low delivery volume, while an unchanged output volume indicates an idle trend.

[0099] The proportion of qualified water volume treated by electroplating wastewater during the treatment period when the trend is increasing is obtained. At the same time, the speed adjustment value of electroplating wastewater treatment during the treatment period when the trend is no load is obtained and the actual set speed required for the production line to run continuously is obtained. The speed deviation value is calculated. It should be explained that in order to prevent the treatment production line from running continuously, the production line speed is controlled when the amount to be treated decreases, so as to ensure the operation of the entire production line and avoid unnecessary start-up energy consumption when subsequent treatment tasks are executed.

[0100] If the proportion of qualified water volume in the electroplating wastewater treatment exceeds the threshold for qualified water volume during the treatment period when the trend is increasing, and the calculated speed deviation value during the electroplating wastewater treatment process does not exceed the set speed deviation threshold during the treatment period when the trend is not running, then it is inferred that the resource scheduling analysis is normal during the electroplating wastewater treatment stage, and a qualified resource scheduling signal is generated and sent to the treatment decision platform.

[0101] If the proportion of qualified water volume in the electroplating wastewater treatment does not exceed the qualified water volume proportion threshold during the treatment period when the trend is increasing, or if the calculated speed deviation value in the electroplating wastewater treatment process exceeds the set speed deviation threshold during the treatment period when the trend is not running, it is inferred that the resource scheduling analysis is abnormal in the electroplating wastewater treatment stage. A qualified resource scheduling signal is generated and sent to the treatment decision platform. After receiving the signal, the treatment decision platform controls the inflow and outflow of water in the current wastewater treatment production line and adjusts the execution speed of the treatment process at different stages.

[0102] Compared with existing technologies, traditional electroplating wastewater treatment systems typically rely on fixed thresholds for resource scheduling, failing to dynamically identify trends in water-to-volume ratio changes. This leads to uncontrolled treatment quality during overload or energy waste during idle periods. In contrast, this invention monitors water-to-volume ratio changes in real time and combines this with dual analysis of the percentage of qualified water volume and speed deviation. This allows for precise identification of system operating status, prioritizing treatment quality during overload and proactively optimizing treatment efficiency during idle periods, thereby improving the responsiveness of resource scheduling.

[0103] Through the above technical solution, the present invention solves the problem of rigid resource allocation in traditional electroplating wastewater treatment systems under dynamic load, realizes an active scheduling mechanism based on real-time water volume ratio trend, and avoids the risk of substandard treatment under overload conditions by jointly judging the qualified water volume ratio and speed deviation value, and reduces the energy loss caused by equipment idling when unloaded, ensuring that the treatment system maintains efficient and stable operation under different load conditions.

[0104] Thresholds, preset values, preset ranges, etc. are set for result comparison and analysis to determine whether they are good or bad. The value of these thresholds is determined by a combination of large-scale model analysis of sample data and human experience. They can also be adjusted appropriately based on seasonal or common-sense influences.

[0105] Furthermore, the settings for weighting ratios, influence factors, etc., are based on the magnitude of each parameter's influence on the results. The specific values ​​are allocated to ultimately reflect the impact on the results. The settings for input and storage are also determined by a combination of large-scale model analysis of sample data and human experience. Appropriate adjustments can also be made based on seasonal or rational influence conditions.

[0106] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A decision-making system for electroplating wastewater treatment based on multimodal data analysis, characterized in that, It includes a processing and decision-making platform, which is communicatively connected to a multimodal data acquisition and analysis unit, a multi-factor dynamic analysis unit, and a resource scheduling and analysis unit; The processing and decision-making platform is used to generate multimodal data acquisition and analysis signals and send them to the multimodal data acquisition and analysis unit; The multimodal data acquisition and analysis unit is used to respond to the multimodal data acquisition and analysis signal, perform multimodal data acquisition and analysis on the electroplating wastewater treatment process, and send a data synchronization status signal to the processing decision platform based on the analysis results; The multi-factor dynamic analysis unit is used to perform dynamic impact analysis on the addition of reagents in the electroplating wastewater treatment process when the treatment decision platform determines that the electroplating wastewater treatment process is operating efficiently, and to send dynamic analysis signals to the treatment decision platform based on the analysis results. The resource scheduling analysis unit is used to respond to the resource scheduling analysis signal generated by the processing decision platform, perform resource scheduling analysis on the electroplating wastewater treatment process, and send a resource scheduling signal to the processing decision platform based on the analysis results.

2. The electroplating wastewater treatment decision system based on multimodal data analysis according to claim 1, characterized in that, The multimodal data acquisition and analysis unit performs multimodal data acquisition and analysis on the electroplating wastewater treatment process, including: Water quality monitoring data, equipment monitoring data, and process monitoring data are acquired through sensors. Based on the water quality monitoring data, equipment monitoring data, and process monitoring data, corresponding data sets are constructed, and timestamps are attached to subsets of each set. During the data acquisition process, the acquisition data delay buffer duration and the value fluctuation period of the currently selected acquisition time are obtained for each type of data set at any acquisition time. Based on the comparison results of the delay buffer duration and the buffer duration threshold, and the comparison results of the numerical fluctuation period and the current buffer duration, the data synchronization status is determined, and a corresponding data synchronization status signal is generated and sent to the processing decision platform.

3. The electroplating wastewater treatment decision system based on multimodal data analysis according to claim 2, characterized in that, The multimodal data acquisition and analysis unit is also used for: If an anomaly in data synchronization is detected, a synchronization analysis is performed on subsequent timestamp subsets, and the data values ​​of the subset records are adjusted by tracing the source and the fluctuation range of timestamp values ​​to ensure that data of all types at the same timestamp are synchronized.

4. The electroplating wastewater treatment decision system based on multimodal data analysis according to claim 2, characterized in that, The multimodal data acquisition and analysis unit is also used to perform numerical analysis on various types of data sets, including: Based on the proportional relationship between data type and the efficiency of process steps, data types are marked as positively correlated or negatively correlated data; By comparing the trends of various types of data within adjacent timestamp intervals, the current time period is marked as a high-efficiency processing period, a low-efficiency processing period, or a period requiring adjustment. The marking results are then sent to the processing decision platform so that the platform can adjust the processing workload, perform process self-checks, or implement targeted adjustments accordingly.

5. The electroplating wastewater treatment decision system based on multimodal data analysis according to claim 1, characterized in that, The multi-factor dynamic analysis unit performs dynamic impact analysis on the drug addition process, including: Obtain the treatment parameters for electroplating wastewater and mark the time of reagent addition; Based on the reaction type between the reagent and the electroplating wastewater, the preset fluctuation trend of the post-treatment parameters after reagent addition is obtained; Based on historical processing data, the fluctuation range of processing parameters and the reaction buffer time of the reagent are obtained; After the current reagent is added, obtain the fluctuation range of the processing parameters within the reaction buffer time, and the cumulative time during which the processing parameters do not fluctuate according to the preset trend outside the reaction buffer time; Based on whether the floating span is within the floating range of the processing parameters and whether the cumulative duration exceeds the set cumulative duration threshold, a corresponding dynamic analysis signal is generated and sent to the processing decision platform.

6. The electroplating wastewater treatment decision system based on multimodal data analysis according to claim 5, characterized in that, The processing decision platform responds to the dosage deviation signal or dosage strategy adjustment signal sent by the multi-factor dynamic analysis unit, and adjusts the dosage or dosage strategy of the current electroplating wastewater treatment process.

7. The electroplating wastewater treatment decision system based on multimodal data analysis according to claim 1, characterized in that, The resource scheduling analysis unit performs resource scheduling analysis on the electroplating wastewater treatment process, including: Obtain the current electroplating wastewater transport volume and the effluent volume after wastewater treatment in the current electroplating wastewater treatment process, and calculate the water volume ratio; Based on the changing trend of the water volume ratio, the electroplating wastewater treatment process is marked as either overloaded or unloaded. Obtain the percentage of qualified water volume during the overload trend period, and the speed deviation between the treatment speed adjustment value and the required set speed during the idle trend period; Based on whether the percentage of qualified water exceeds the threshold for qualified water, and whether the speed deviation value exceeds the set speed deviation threshold, a corresponding resource scheduling signal is generated and sent to the processing decision platform.

8. The electroplating wastewater treatment decision system based on multimodal data analysis according to claim 7, characterized in that, The processing decision platform responds to the resource scheduling anomaly signal sent by the resource scheduling analysis unit, controls the influent and effluent flow of the current wastewater treatment production line, and adjusts the execution speed of the treatment process at different stages.

9. The electroplating wastewater treatment decision system based on multimodal data analysis according to claim 2, characterized in that, The water quality monitoring data includes online monitoring sensor detection data and laboratory offline detection data. The equipment monitoring data is generated by the PLC system of the treatment equipment, and the process monitoring data is generated by the operation of the reagent dosing system and process control software.

10. The electroplating wastewater treatment decision system based on multimodal data analysis according to claim 2, characterized in that, All types of datasets constructed are synchronous data acquisitions to facilitate the collection of multimodal data and the synchronous management of electroplating wastewater treatment.

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