A method, system, equipment, and medium for integrated management and control of pretreatment and reverse osmosis module equipment.

Abstract

This invention provides an integrated management and control method, system, equipment, and medium for pretreatment and reverse osmosis module equipment, belonging to the field of water pollution treatment pretreatment technology. The invention acquires the initial wastewater treatment dataset returned by the pretreatment-reverse osmosis-concentration evaporation module under preset wastewater treatment process conditions, selects a preset number of target data sets from this dataset, inputs it into a trained detection model to obtain corresponding preset treatment state intervals, compares each group of target data to see if it falls within the interval, and records the results, generates parameter adjustment information for the pretreatment-reverse osmosis-concentration evaporation module based on the wastewater treatment results and the preset wastewater treatment process, and outputs the parameter adjustment information to the control unit of the pretreatment and reverse osmosis module equipment to complete on-site parameter adjustment. This achieves intelligent monitoring and precise control of the water treatment process, improving wastewater treatment efficiency and quality.

Description

Technical Field

[0001] This invention belongs to the field of water pollution treatment pretreatment technology, specifically relating to a method, system, equipment and medium for integrated management and control of pretreatment and reverse osmosis module equipment. Background Technology

[0002] With rapid industrial development and accelerated urbanization, sudden water pollution incidents occur frequently, posing a serious threat to the environment and public health. Pretreatment-reverse osmosis-concentration evaporation integrated technology has emerged as a treatment method capable of meeting water treatment requirements.

[0003] In related technologies, during the integrated operation of pretreatment-reverse osmosis-concentration evaporation, the reverse osmosis membrane pressure is generally monitored while the pretreatment turbidity is ignored. This may lead to a misjudgment that the membrane pressure is too high as membrane fouling, when in fact it is caused by impurities clogging due to pretreatment filtration failure, which in turn leads to incorrect parameter adjustment.

[0004] The monitoring methods used for different wastewater types and reverse osmosis pressures were all set at 0.8-1.2 MPa, without considering the impact of different process conditions on the normal range. For example, when treating high-concentration chemical wastewater, reverse osmosis requires higher pressure to ensure desalination, but the relevant technology may misjudge this pressure as abnormal, leading to pressure reduction and decreased treatment efficiency. Furthermore, directly executing parameter adjustment commands without verifying whether the target values ​​exceed the hardware safety range, and often involving one-time adjustments, can easily cause sudden parameter changes that impact equipment and shorten the lifespan of equipment components. Summary of the Invention

[0005] This invention provides an integrated management and control method for pretreatment and reverse osmosis module equipment, which realizes intelligent monitoring and precise control of the water treatment process, and improves wastewater treatment efficiency and quality.

[0006] The methods include: S101: When the pretreatment and reverse osmosis module equipment is in the start-up state, obtain the initial wastewater treatment dataset returned by the pretreatment-reverse osmosis-concentration evaporation module under the preset wastewater treatment process conditions. The initial wastewater treatment dataset records the actual wastewater treatment status. S102: Select a first preset number of target wastewater treatment data from the initial wastewater treatment dataset; S103: Input the first preset number of target wastewater treatment data into the wastewater treatment data detection model that has been trained to obtain a preset wastewater treatment state interval that corresponds to the first preset number of target wastewater treatment data and has the same number. S104: Compare the target wastewater treatment data group by group to see if it falls into its corresponding preset wastewater treatment state range, and record the comparison results as wastewater treatment results; S105: Based on the wastewater treatment results and the preset wastewater treatment process, generate parameter adjustment information for the pretreatment-reverse osmosis-concentration evaporation module; S106: Output the parameter adjustment information to the control unit of the pretreatment and reverse osmosis module equipment to complete the on-site parameter adjustment.

[0007] Preferably, the wastewater treatment data detection model processing method includes the following steps: Acquire a first preset number of predefined virtual water treatment complete sets of equipment, and wastewater treatment sample data of the virtual equipment under a second preset number of different process conditions, to form the first preset number of wastewater treatment sample data. Obtain wastewater treatment condition data of the first preset quantity of predefined virtual water treatment complete equipment under the second preset quantity of process conditions, and form the first preset quantity of wastewater treatment condition data; The first preset number of wastewater treatment sample data and the first preset number of wastewater treatment condition data are combined one-to-one to obtain the first preset number of complete wastewater treatment data samples. The preset wastewater treatment data detection model is trained using the first preset number of complete wastewater treatment data samples to obtain the initially trained wastewater treatment data detection model. The detection accuracy of the wastewater treatment data detection model after initial training was evaluated using a validation dataset. If the detection accuracy is less than or equal to the preset accuracy threshold, the internal control parameters of the model are adjusted; training and detection accuracy are recalculated; this process is repeated until the detection accuracy of the adjusted model is greater than the preset accuracy threshold, and the finally adjusted model is determined as the wastewater treatment data detection model.

[0008] Preferably, step S102 further includes the following steps: Step S3011: Obtain the preset wastewater treatment data type; Step S3012: Obtain the equipment parameters of the pretreatment and reverse osmosis module equipment; Step S3013: Based on the preset wastewater treatment data type obtained in step S3011, the initial wastewater treatment dataset obtained in step S101 is filtered to obtain multiple first target state data. Step S3014: Based on the equipment parameters of the pretreatment and reverse osmosis module obtained in step S3012, extract background data from the wastewater treatment monitoring terminal associated with the equipment to obtain multiple second target status data. Step S3015: Combine the multiple first target state data obtained in step S3013 with the multiple second target state data obtained in step S3014 to form a number of target wastewater treatment data to be determined. Step S3016: The target wastewater treatment data of the quantity to be determined formed in step S3015 is determined as the first preset quantity of target wastewater treatment data required in step S102.

[0009] Preferably, step S103 specifically includes: The parameter format of the first preset number of target wastewater treatment data selected in step S102 is converted into the preset input format of the wastewater treatment data detection model. The preset input format includes the unit, data precision and field arrangement order of the wastewater treatment parameters. A bidirectional data transmission link is established between the target wastewater treatment data and the wastewater treatment data detection model deployment unit through the industrial control bus of the pretreatment and reverse osmosis module equipment; The wastewater treatment data detection model deployment unit receives the first preset number of target wastewater treatment data after format conversion, verifies the field integrity and value range of each data group, and removes data groups that fail the verification. The wastewater treatment data detection model calls a pre-stored wastewater parameter-preset state interval mapping library. The mapping library is established based on preset wastewater treatment process standards. For each set of target wastewater treatment data that has passed the verification, it matches the preset wastewater treatment state interval of its corresponding parameter type.

[0010] Preferably, step S104 specifically includes the following steps: Step S1041: Retrieve the target data-preset interval correspondence list generated in step S103 in the buffer area of ​​the pretreatment and reverse osmosis module equipment control unit, and associate the first preset number of target wastewater treatment data selected in step S102 with the corresponding preset wastewater treatment status intervals one by one through the data group number in the list. Step S1042: For each set of associated target wastewater treatment data and preset wastewater treatment status interval, read the parameter type identifier in the target data and extract the lower limit and upper limit values ​​of the corresponding preset interval; Step S1043: Read the parameter values ​​of the target wastewater treatment data of the current group, and compare the values ​​with the lower limit and upper limit of the corresponding preset wastewater treatment state range respectively; Step S1044: Based on the comparison results, mark the comparison results of the current group of data in the result recording module of the control unit, and record the data number, parameter type, and the difference between the specific value and the interval boundary; Step S1045: After the first preset number of target wastewater treatment data have been compared, the comparison results of all groups are classified and summarized according to parameter type, and a wastewater treatment result table containing data group number, parameter type, target value, preset range, comparison result, and difference is generated as the input basis for step S105.

[0011] Preferably, step S105 specifically includes: Read the wastewater treatment result table generated in step S104, and classify the target wastewater treatment data in the table that are not qualified into the corresponding modules according to the module division rules of the pretreatment module, reverse osmosis module, and concentration evaporation module, to form a module-unqualified data association table. For the non-conforming parameters of each module in the module-non-conforming data association table, the abnormality level of each non-conforming parameter is determined by combining the preset abnormality level judgment criteria and the frequency of non-conforming data occurrence. Retrieve the preset wastewater treatment process file, identify the current process stage of the pretreatment and reverse osmosis module equipment, and adjust the parameter adjustment strategy according to the characteristics of the process stage. Based on the abnormality level of each non-conforming parameter, the characteristics of the corresponding module, and the current process stage, and in conjunction with the preset parameter adjustment range benchmark table, calculate the specific parameter adjustment value of each non-conforming parameter. Cross-module conflict verification is performed on the parameter tuning values ​​calculated by each module to check whether there are coupling contradictions between the parameter tuning values ​​of different modules. If there is a conflict, the parameter tuning values ​​of the module with the higher anomaly level are retained first, and the parameter tuning values ​​of the lower level module are adjusted to a conflict-free state. Finally, the parameter tuning information of each module is generated.

[0012] Preferably, step S106 specifically includes: The parameter adjustment information of each module of pretreatment-reverse osmosis-concentration evaporation generated in step S105 is processed, and the module unique identifier, parameter type, target parameter adjustment value, adjustment time and hardware safety threshold are added according to the preset format to form a parameter adjustment instruction set that can be directly parsed by the control unit. Retrieve the module-control unit address mapping table pre-stored in the pretreatment and reverse osmosis module equipment, and transmit the corresponding module's parameter adjustment instructions to the dedicated control unit of each module according to the module identifier in the parameter adjustment instruction set, and receive the instruction reception confirmation signal returned by each control unit; After receiving the parameter tuning command, each module control unit extracts the target parameter tuning value from the command and performs a pre-execution verification with its own pre-stored hardware safe operating range. If the target value is within the safe range, an executable signal is returned; if the target value exceeds the safe threshold range, an over-limit signal is returned, the parameter tuning operation is paused, and step S105 is triggered to recalculate the parameter tuning value. After receiving the executable signals from all module control units, the system controls each module control unit to perform parameter adjustment operations step by step according to the preset adjustment priority rules, and collects parameter change data in real time during the adjustment process. After the parameter adjustment operations of each module are completed, the control unit collects the actual parameter values ​​after adjustment and compares them with the target values ​​in the parameter adjustment command. If the deviation value is less than or equal to the preset deviation threshold, the adjustment is deemed qualified and the adjustment result is fed back to the equipment control system. If the deviation value is greater than the threshold, a second fine-tuning is triggered, and the parameter adjustment command, execution process data and results are recorded in the equipment operation database.

[0013] This application also provides an integrated management and control system for pretreatment and reverse osmosis module equipment, the system comprising: The data acquisition module is used to acquire the initial wastewater treatment dataset transmitted back by the pretreatment-reverse osmosis-concentration evaporation module under the preset wastewater treatment process conditions when the pretreatment and reverse osmosis module equipment is in the start-up state. The initial wastewater treatment dataset records the actual wastewater treatment status. The data selection module is used to select a first preset number of target wastewater treatment data from the initial wastewater treatment dataset; The state interval generation module is used to input the first preset number of target wastewater treatment data into the wastewater treatment data detection model that has been trained, and to obtain a preset wastewater treatment state interval that corresponds to and is the same number as the first preset number of target wastewater treatment data. The interval comparison module is used to compare the target wastewater treatment data group by group to see if it falls into its corresponding preset wastewater treatment state interval, and record the comparison results as the wastewater treatment results. The parameter adjustment information generation module is used to generate parameter adjustment information for the pretreatment-reverse osmosis-concentration evaporation module based on the wastewater treatment results and the preset wastewater treatment process. The parameter distribution module is used to output the parameter adjustment information to the control unit of the pretreatment and reverse osmosis module equipment to complete the on-site parameter adjustment.

[0014] According to another embodiment of this application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the pretreatment and reverse osmosis module device integrated management and control method.

[0015] According to another embodiment of this application, a storage medium is also provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the pretreatment and reverse osmosis module equipment integrated management and control method.

[0016] As can be seen from the above technical solutions, the present invention has the following advantages: This application discloses an integrated management and control method for pretreatment and reverse osmosis module equipment. This method involves the targeted collection of data from the entire pretreatment-reverse osmosis-concentration evaporation process while the equipment is running. Utilizing a trained wastewater treatment data detection model, based on the learning patterns from multi-condition virtual samples, it outputs a suitable preset state range for each set of target data, improving the targeted nature of state judgment. By comparing each set of data, the degree of data anomalies is clearly identified, and quantitative parameter adjustment information is generated in conjunction with preset processes, avoiding aimless parameter adjustment. This invention achieves intelligent operation of the complete set of water pollution treatment equipment, enhancing the system's stability and reliability. Attached Figure Description

[0017] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 Flowchart of the integrated management and control method for pretreatment and reverse osmosis module equipment; Figure 2 A schematic diagram of the integrated control system for pretreatment and reverse osmosis modules; Figure 3 This is a schematic diagram of an electronic device. Detailed Implementation

[0019] The following will describe in detail the integrated management and control method for pretreatment and reverse osmosis module equipment involved in this application. Specific details such as particular system structures and technologies are presented for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details.

[0020] It should be understood that, when used in this specification, terms include indicating the presence of a described feature, integral, step, operation, element, and / or component, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof. The terms include, encompass, have, and variations thereof mean including but not limited to, unless otherwise specifically emphasized.

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0022] Please see Figure 1 The diagram shows a flowchart of a pretreatment and reverse osmosis module equipment integration and management method in a specific embodiment. The method includes: Step S101: With the pretreatment and reverse osmosis module equipment in the start-up state, obtain the initial wastewater treatment dataset returned by the pretreatment-reverse osmosis-concentration evaporation module under the preset wastewater treatment process conditions. The initial wastewater treatment dataset records the actual wastewater treatment status.

[0023] In some embodiments, the control unit of the pretreatment and reverse osmosis module equipment reads the status signal fed back by the control unit to confirm that the equipment is in the start-up state. Preset wastewater treatment process condition parameters are retrieved, and data acquisition commands are sent to the filtration device of the pretreatment unit, the reverse osmosis membrane separation unit, and the concentration-evaporation unit, respectively. Each module collects operating data in real time through its built-in turbidity sensor, pressure sensor, temperature sensor, and COD detector, and then transmits the data back to the control unit to form an initial wastewater treatment dataset. The dataset includes fields such as module name, acquisition time, parameter type, parameter value, and sensor number to ensure that the actual wastewater treatment status of the equipment is recorded. Optionally, the parameter type is pretreatment turbidity, reverse osmosis inlet water pressure, and concentration-evaporation temperature.

[0024] Step S102: Select the first preset number of target wastewater treatment data from the initial wastewater treatment dataset.

[0025] In some embodiments, a list of key parameter types is preset, which includes the turbidity and feed water flow rate of the pretreatment unit, the feed water pressure, desalination rate, and membrane flux of the reverse osmosis unit, and the temperature, evaporation rate, and concentrate concentration of the concentration and evaporation unit.

[0026] In this embodiment, parameter data within the list are selected from the initial dataset of wastewater treatment, and non-critical parameters are eliminated; a first preset quantity is set according to the scale of wastewater treatment and the complexity of the process.

[0027] Data is selected based on a uniform time distribution principle, with one group selected every 10 minutes for a total of 10 groups, covering a preset operating cycle. The selected data undergoes preliminary screening to remove outliers, ultimately determining the first preset number of target wastewater treatment data groups.

[0028] Step S103: Input the first preset number of target wastewater treatment data into the wastewater treatment data detection model that has been trained, and obtain a preset wastewater treatment state interval that corresponds to the first preset number of target wastewater treatment data and has the same number.

[0029] In some embodiments, the first preset number of target wastewater treatment data selected in step S102 is converted according to the input format required by the model; the converted target data is input into the trained model. In this embodiment, the model is trained using historical wastewater treatment sample data, such as pretreatment turbidity of 0.5-1.0 NTU and reverse osmosis pressure of 0.8-1.2 MPa under qualified conditions. The model calls the built-in parameter-interval mapping library to match the corresponding preset interval for each set of target data. Optionally, the preset interval can be a pretreatment turbidity of 1.1 NTU matching a 0.5-1.0 NTU interval, and a reverse osmosis feed water pressure of 1.3 MPa matching a 0.8-1.2 MPa interval.

[0030] The final output is a preset wastewater treatment status interval with the same number of target data sets, such as 10 target data sets corresponding to 10 preset intervals. This ensures a one-to-one correspondence between target data sets and intervals, rather than multiple data sets sharing a single interval. The model is trained using historical samples, and the intervals conform to actual wastewater treatment standards, rather than theoretical values, thus ensuring the practicality of the intervals.

[0031] Step S104: Compare the target wastewater treatment data group by group to see if it falls into its corresponding preset wastewater treatment state range, and record the comparison results as the wastewater treatment results.

[0032] In some embodiments, a target data-preset interval correspondence table is extracted from the output of step S103. For example, target data 1: pre-treated turbidity 1.1 NTU, preset interval 0.5-1.0 NTU; target data 2: reverse osmosis feed water pressure 1.3 MPa, preset interval 0.8-1.2 MPa.

[0033] In this embodiment, the parameter values ​​of the target data are read one by one by the control unit and compared with the lower limit and upper limit of the corresponding preset range. The numerical values ​​are compared and the specific difference is recorded. The data number, parameter type, target value, preset range, comparison status and difference of all groups are integrated to form the wastewater treatment result.

[0034] This embodiment's group-by-group comparison ensures that each data set has its own specific standard, recording the differences rather than simply marking it as qualified / unqualified, thus clearly identifying the degree of abnormality. The integrated wastewater treatment results include complete comparison details.

[0035] Step S104 in this embodiment specifically includes the following steps: Step S1041: Retrieve the target data-preset interval correspondence list generated in step S103 from the buffer area of ​​the pretreatment and reverse osmosis module equipment control unit. Using the data group number in the list, associate the first preset quantity group of target wastewater treatment data selected in step S102 with the corresponding preset wastewater treatment status interval one by one.

[0036] This embodiment utilizes the uniqueness of data group numbers to perform a number search in the cache list, finding records with completely identical numbers, thus achieving a one-to-one correspondence between target data and preset intervals. Data group numbers can involve detection time, module identifier, and group sequence number.

[0037] Step S1042: For each set of associated target wastewater treatment data and preset wastewater treatment status interval, read the parameter type identifier in the target data and extract the lower limit and upper limit values ​​of the corresponding preset interval.

[0038] In this embodiment, the parameter type identifier of the target data is stored in the parameter field of the data frame. For example, the field value PRE-TURB represents the turbidity of the pretreatment unit, and RO-P represents the inlet water pressure of the reverse osmosis unit. The field parsing module of the control unit identifies the field value and determines the processing module to which the parameter belongs; at the same time, it reads the value from the preset interval field and converts it into a directly comparable numerical format.

[0039] Step S1043: Read the parameter values ​​of the target wastewater treatment data of the current group, and compare the values ​​with the lower limit and upper limit of the corresponding preset wastewater treatment state range.

[0040] In this embodiment, the specific numerical value of the parameter is read from the numerical field of the target data, and the unit symbol is removed to obtain the pure numerical value. The pure numerical value is then compared with the lower limit pure numerical value and the upper limit pure numerical value of the interval extracted in step S1042.

[0041] For example, if the value is 0.8 and the range is 0.5-1.0, then compare 0.8≥0.5 and 0.8≤1.0; if the value is 1.2 and the range is 0.8-1.2, then compare 1.2≥0.8 and 1.2≤1.2. By directly comparing the values, it is intuitive to determine whether the target data meets the preset process requirements, avoiding subjective judgment errors and meeting the need for rapid judgment.

[0042] Step S1044: Based on the comparison results, mark the comparison results of the current group of data in the result recording module of the control unit, and record the data group number, parameter type, and the difference between the specific value and the interval boundary.

[0043] In this embodiment, the result recording module of the control unit has preset result marking rules. Based on the comparison result of step S1043, the current group result is marked according to the rules, and the difference between the target value and the interval boundary is calculated. For example, a value of 1.1 and an upper limit of 1.0 → difference +0.1; a value of 0.4 and a lower limit of 0.5 → difference -0.1. The data group number, parameter type, target value, preset interval, comparison result, and difference are synchronously stored in the result recording table. This enables the comparison results to be traceable and analyzable.

[0044] Step S1045: After the first preset number of target wastewater treatment data have been compared, the comparison results of all groups are classified and summarized according to parameter type, and a wastewater treatment result table containing data group number, parameter type, target value, preset range, comparison result, and difference is generated as the input basis for step S105.

[0045] After all groups of data are compared, the summary module of the control unit is classified into pretreatment unit, reverse osmosis unit and concentration evaporation unit, and the results of each group are statistically analyzed. At the same time, the pass rate of each group of parameters is calculated, and the classification statistics, pass rate and detailed records of all groups are integrated into a wastewater treatment result table and stored in the result database of the control unit for use in step S105.

[0046] Step S105: Based on the wastewater treatment results and the preset wastewater treatment process, generate parameter adjustment information for the pretreatment-reverse osmosis-concentration evaporation module.

[0047] In some embodiments, the wastewater treatment results are first classified into pretreatment units, reverse osmosis units, and concentration and evaporation units, and the parameter type, abnormal state (exceeding / below the upper limit), and difference of each group of unqualified data are extracted.

[0048] Retrieve the module parameter adjustment benchmarks for the preset wastewater treatment process. For example, for every 0.1 NTU increase in pretreatment turbidity beyond the upper limit, the backwashing frequency of the filter device needs to be increased by 5%; for every 0.1 MPa increase in reverse osmosis inlet pressure beyond the upper limit, the inlet flow rate needs to be reduced by 3%.

[0049] This embodiment considers the current process stage of the equipment. For example, during the first hour after startup (the start-up period), parameter adjustments should be slowed down to avoid module impact. After 8 hours of operation, during the stabilization period, parameters can be adjusted according to the baseline range, and target values ​​for each non-compliant parameter are calculated. Finally, the parameter values ​​of different modules are verified. Verification methods include pre-processing to ensure that increasing the backwash frequency may increase inlet water resistance, and confirming whether the reduced reverse osmosis inlet water pressure remains within a safe range. If there are no conflicts, parameter adjustment information is generated, including the module name, parameter to be adjusted, current value, target value, adjustment range, and adjustment duration. This ensures coordinated operation of all modules and overall stable operation of the equipment after parameter adjustment.

[0050] Step S106: Output the parameter adjustment information to the control unit of the pretreatment and reverse osmosis module equipment to complete the on-site parameter adjustment.

[0051] In some embodiments, the parameter tuning information generated in step S105 is split according to module name, and a targeted parameter tuning instruction is sent to the dedicated control unit of each module through the control unit. After receiving the instruction, the control unit extracts the target parameter tuning value and verifies it with its own pre-stored hardware safety range. If the target value is within the safety range, an executable signal is returned; otherwise, an out-of-limit signal is returned.

[0052] Upon receiving the executable signal, the control unit adjusts the parameters in a step-by-step manner. During the adjustment process, parameter changes are collected in real time. After adjustment, the final actual value is collected and compared with the target value. If the deviation is ≤±2%, the adjustment is deemed qualified, and the adjustment result is fed back to the control unit. Simultaneously, the parameter adjustment command, execution process data, and results are recorded in the equipment operation database, completing the on-site parameter adjustment.

[0053] In some specific embodiments, step S106 specifically includes: Step S1061: Process the parameter adjustment information of each module of pretreatment-reverse osmosis-concentration evaporation generated in step S105, add the module unique identifier, parameter type, target parameter adjustment value, adjustment time and hardware safety threshold according to the preset format, and form a parameter adjustment instruction set that can be directly parsed by the control unit.

[0054] It should be noted that by standardizing the field types and arrangement order of the parameter tuning information, the control unit can extract key information through fixed field positions without the need for recognition logic.

[0055] Step S1062: Retrieve the module-control unit address mapping table pre-stored in the pretreatment and reverse osmosis module equipment, and transmit the corresponding module's parameter adjustment instructions to the dedicated control unit of each module according to the module identifier in the parameter adjustment instruction set, and receive the instruction reception confirmation signal returned by each control unit.

[0056] In this embodiment, the module-control unit address mapping table is pre-stored in the device management system, recording the identifier of each module and the hardware address of the corresponding control unit. During transmission, the parameter adjustment command is encapsulated into a data packet containing the target hardware address. After the receiving control unit returns an acknowledgment signal that the command has been received, the next module command is transmitted. If no acknowledgment is received, the command is resent.

[0057] Step S1063: After receiving the parameter tuning command, each module control unit extracts the target parameter tuning value in the command and performs a pre-execution verification with its own pre-stored hardware safe operating range. If the target value is within the safe range, an executable signal is returned; if the target value exceeds the safe threshold range, an over-limit signal is returned, the parameter tuning operation is paused, and step S105 is triggered to recalculate the parameter tuning value.

[0058] In this embodiment, the module control unit pre-stores the safe operating parameters of the module hardware. During verification, the control unit extracts the target value from the parameter adjustment instruction and compares it with the pre-stored safe range. If the value exceeds the limit, it immediately sends an over-limit signal. After receiving the signal, the control system triggers step S105 to recalculate the parameter adjustment value that meets the safe range.

[0059] Step S1064: After receiving the executable signals from all module control units, control each module control unit to perform parameter adjustment operations step by step according to the preset adjustment priority rules, and collect parameter change data in real time during the adjustment process.

[0060] This embodiment is based on a preset adjustment range-step correspondence rule, combined with the adjustment duration in the parameter adjustment command, to calculate the adjustment amount and interval time for each step; during execution, the control unit adjusts the parameters step by step according to the calculation results, and collects the parameter values ​​after each step of adjustment in real time. If a parameter change occurs, the adjustment is paused, and the process continues after troubleshooting the anomaly.

[0061] Step S1065: After the parameter adjustment operation of each module is completed, the control unit collects the actual parameter value after adjustment and compares it with the target value in the parameter adjustment command. If the deviation value is ≤ the preset deviation threshold, the adjustment is deemed qualified and the adjustment result is fed back to the equipment control system; if the deviation value is > the threshold, a second fine adjustment is triggered, and the parameter adjustment command, execution process data and results are recorded in the equipment operation database.

[0062] This embodiment verifies the parameter tuning effect by comparing the deviation between the actual value and the target value. If the target is not met, a second fine-tuning is triggered to ensure that the parameter tuning result meets the preset requirements. The second fine-tuning makes up for the error of the first adjustment.

[0063] In one embodiment of the present invention, the wastewater treatment data detection model processing method includes the following steps: Step S2011: Obtain a first preset number of predefined virtual water treatment complete sets of equipment, and wastewater treatment sample data of the virtual equipment under a second preset number of different process conditions, to form the first preset number of wastewater treatment sample data.

[0064] In this embodiment, the predefined virtual water treatment system is a digital model constructed using MATLAB simulation software based on the structure, function, and parameter range of actual pretreatment and reverse osmosis modules, including pretreatment, reverse osmosis, and concentration / evaporation modules. The first preset quantity is typically set to 50-100 units to ensure coverage of equipment characteristics of different specifications. The second preset quantity includes different process conditions encompassing different values ​​of key influencing factors in actual wastewater treatment, such as wastewater concentration (COD 100-5000 mg / L), influent flow rate (1-10 m³ / h), reverse osmosis operating pressure (0.5-2.0 MPa), water temperature (15-40℃), and concentration / evaporation vacuum degree (-0.08 to -0.05 MPa), covering a variety of operating conditions.

[0065] Wastewater treatment sample data are quantitative data that directly reflects the treatment status, generated by virtual equipment when it runs under corresponding process conditions. These include influent water quality, intermediate treatment data, final treatment results, and energy consumption data. Sample data for all process conditions corresponding to each virtual equipment must be recorded completely to form a wastewater treatment sample data group that matches the number of virtual equipment.

[0066] Step S2012: Obtain wastewater treatment condition data of the first preset quantity of predefined virtual water treatment equipment under the second preset quantity of process conditions, and form the first preset quantity of wastewater treatment condition data; The wastewater treatment condition data in this embodiment is the operating background data that corresponds one-to-one with the wastewater treatment sample data in S2011, and records the specific input parameters of each virtual device under each process condition.

[0067] In this embodiment, the wastewater treatment conditions data of the first preset number are completely consistent with the number of virtual devices, and the parameter values ​​must be based on industry standards to ensure that they match the process adjustment capabilities of the actual equipment.

[0068] The conditional data in this embodiment is set based on the actual equipment parameter range to ensure that the rules learned by the model are consistent with the actual operating capabilities of the equipment.

[0069] Step S2013: Combine the first preset number of wastewater treatment sample data with the first preset number of wastewater treatment condition data in a one-to-one correspondence to obtain the first preset number of complete wastewater treatment data samples; optionally, each sample contains actual treatment data and corresponding process conditions. In this embodiment, model training is based on complete data pairs of input features and target labels. Individual sample data cannot enable the model to learn how process conditions affect processing results, nor can individual condition data reflect what the normal processing result is under those conditions. By combining data to form complete samples, a mapping relationship between process conditions as input features, processed data, and normal labels can be constructed. This allows the model to learn during training which processed data falls within the normal range under specific process conditions, providing a learning foundation for subsequent practical applications where input process conditions → output normal state range.

[0070] Step S2014: Use the first preset number of complete wastewater treatment data samples to train the preset wastewater treatment data detection model to obtain the initially trained wastewater treatment data detection model. The preset wastewater treatment data detection model in this embodiment is a supervised learning model selected based on the data characteristics. The models include gradient boosting trees and backpropagation neural networks. If the data to be processed contains time-series features, an LSTM model can be selected. The initial parameters of the model need to be set based on experience.

[0071] Before training, the complete sample is divided into a training set and a validation set in a 7:3 or 8:2 ratio, ensuring that the operating conditions of the two sets are consistent. During training, the process condition data from the complete sample is used as the input feature, and the normal state interval label of the processed data is used as the target label. The model parameters are optimized through a neural network to minimize the mean squared error and cross-entropy loss between the model's predicted state interval and the true label. The number of training iterations is set to 100-500 times until the loss function converges or the maximum number of iterations is reached, thus obtaining the initial trained model.

[0072] Step S2015: Evaluate the detection accuracy of the wastewater treatment data detection model after initial training by using a validation dataset, i.e., the proportion of treatment states correctly identified by the model; The validation dataset in this embodiment is an independent dataset partitioned from S2014. Its operating condition distribution and data format are consistent with the training set, but it was not used in model training to ensure that the evaluation results reflect the model's generalization ability. The detection accuracy is calculated as follows: for each validation sample, its process conditions are input into the initial model, and the model outputs a corresponding preset processing state interval. If the actual processing data of the sample falls within this interval, it is considered a correct identification; if the actual data does not fall within the interval, it is considered an incorrect identification. Detection accuracy = (number of correctly identified samples / total number of samples in the validation set) × 100%.

[0073] This embodiment evaluates the generalization ability of the initial model, avoiding the direct use of overfitted or underfitted models; and the verification process is independent of the training process, ensuring the reliability of the evaluation results.

[0074] Step S2016: If the detection accuracy is less than or equal to the preset accuracy threshold, adjust the internal control parameters of the model, such as the learning rate and the number of neural network layers; retrain and recalculate the detection accuracy; repeat this process until the detection accuracy of the adjusted model is greater than the preset accuracy threshold, and finally determine the adjusted model as the wastewater treatment data detection model.

[0075] The preset accuracy threshold in this embodiment is set according to actual application requirements. For example, wastewater treatment requires effluent COD ≤ 50 mg / L, and the corresponding model needs to accurately identify the normal range under this standard. The threshold can be set to above 95%. Adjusting the model's internal control parameters involves selecting parameter types based on the initial model's shortcomings: if the loss function oscillates and does not converge during model training, the learning rate can be adjusted from 0.1 to 0.01 or 0.001; if the model accuracy is low and the training loss is high, the number of neural network layers can be increased; if the training loss is low but the validation loss is high, the regularization parameter can be increased. A grid search strategy can be used to find the optimal parameter combination. Retraining and evaluation are consistent with the S2014 and S2015 processes. The accuracy is recorded after each adjustment until the accuracy exceeds the threshold; the model at this point is the final usable model. This ensures that the final model can accurately output the normal range of the processed data under different actual working conditions.

[0076] As one implementation of this application, step S102 further includes the following steps: Step S3011: Obtain a preset wastewater treatment data type, wherein the preset wastewater treatment data type refers to a data type used to characterize the performance of the pretreatment and reverse osmosis module equipment.

[0077] Step S3012: Obtain the equipment parameters of the pretreatment and reverse osmosis module equipment. The equipment parameters are used to extract the background data of the wastewater treatment monitoring terminal associated with the equipment.

[0078] Step S3013: Based on the preset wastewater treatment data type obtained in step S3011, the initial wastewater treatment dataset obtained in step S101 is filtered to obtain multiple first target state data.

[0079] Step S3014: Based on the equipment parameters of the pretreatment and reverse osmosis module obtained in step S3012, extract background data from the wastewater treatment monitoring terminal associated with the equipment to obtain multiple second target status data.

[0080] Step S3015: Combine the multiple first target state data obtained in step S3013 with the multiple second target state data obtained in step S3014 to form a number of target wastewater treatment data to be determined.

[0081] Step S3016: The target wastewater treatment data of the quantity to be determined formed in step S3015 is determined as the first preset quantity of target wastewater treatment data required in step S102.

[0082] This embodiment achieves highly targeted data filtering. In S3011, the core data types characterizing equipment performance are pre-defined. Then, in S3013, irrelevant and redundant data is removed from the initial dataset, ensuring the data focuses on the core operating status of the equipment. In S3012, equipment parameters are acquired in conjunction with monitoring backend data. In S3014, in-depth data such as historical operating patterns and hardware calibration records are extracted. Finally, in S3015, this data is combined with real-time first target status data, ensuring the target data simultaneously possesses both real-time operating status and historical reference. This embodiment improves data quality and adaptability. In S3012, equipment parameters ensure accurate matching between backend data and the current equipment. In S3016, time-evenly distributed selection and quality filtering determine the first preset number of target data sets, which not only meets the quantity requirements of step S102 but also reflects the stable operating status of the equipment over a period of time.

[0083] In one embodiment of the present invention, based on step S103, the following will provide a possible embodiment and its specific implementation will be described in a non-limiting manner. Step S103 specifically includes: Step S1031: Convert the parameter format of the first preset number of target wastewater treatment data selected in step S102 into the preset input format of the wastewater treatment data detection model. The preset input format includes the unit, data precision and field arrangement order of the wastewater treatment parameters.

[0084] Step S1032: Establish a bidirectional data transmission link between the target wastewater treatment data and the wastewater treatment data detection model deployment unit through the industrial control bus of the pretreatment and reverse osmosis module equipment.

[0085] This embodiment is based on industrial communication protocols and uses the TCP / IP protocol to achieve bidirectional data transmission.

[0086] Step S1033: The wastewater treatment data detection model deployment unit receives the first preset number of target wastewater treatment data after format conversion, verifies the field integrity and value range of each data group, and removes the data groups that fail the verification.

[0087] This embodiment eliminates interference from out-of-range values ​​caused by sensor malfunctions and missing fields caused by data transmission errors on the model output, ensuring that the subsequently generated preset wastewater treatment state intervals are based on valid data and improving the reliability of the intervals.

[0088] Step S1034: The wastewater treatment data detection model calls the pre-stored wastewater parameter-preset state interval mapping library. The mapping library is established based on the preset wastewater treatment process standard. For each set of target wastewater treatment data that has passed the verification, the preset wastewater treatment state interval of its corresponding parameter type is matched.

[0089] In this embodiment, the wastewater parameter-preset state interval mapping library is stored according to wastewater treatment modules, and each parameter interval is labeled with the corresponding process standard. During matching, the parameter name and module of the target data are first identified, and then the preset interval corresponding to the parameter is retrieved from the mapping library. This ensures that the generated preset wastewater treatment state interval is consistent with the preset wastewater treatment process standard, providing a compliant judgment basis for the subsequent comparison in S104.

[0090] Step S1035: Associate and bind the identification information of each group of target wastewater treatment data with the matched preset wastewater treatment state interval to generate a target data-interval correspondence list, which is the same as the preset wastewater treatment state interval output result as the first preset number of groups of target wastewater treatment data.

[0091] This embodiment associates the unique identifier of the target data with a preset interval for storage, ensuring that each interval can accurately correspond to the target data from which it originates, thereby improving the efficiency of the comparison process.

[0092] In one embodiment of the present invention, based on step S105, the following will provide a possible embodiment and its specific implementation will be described in a non-limiting manner. Step S105 specifically includes: Step S1051: Read the wastewater treatment result table generated in step S104, and classify the target wastewater treatment data in the table that are not qualified into the corresponding modules according to the module division rules of the pretreatment module, reverse osmosis module, and concentration evaporation module, to form a module-unqualified data association table. This records the types of non-compliant parameters, their corresponding target values, preset ranges, and differences for each module.

[0093] This embodiment uses a preset parameter type-module mapping table. It reads the parameter type identifier of non-compliant data from the wastewater treatment results table and matches it to the mapping table to determine the module to which it belongs. Non-compliant data within the same module are integrated, and the specific information of all non-compliant parameters within that module is recorded, forming a module-non-compliant data association table. This ensures targeted parameter tuning and improves its effectiveness.

[0094] Step S1052: For the non-conforming parameters of each module in the module-non-conforming data association table, determine the abnormality level of each non-conforming parameter by combining the preset abnormality level judgment criteria and the frequency of non-conforming data occurrence.

[0095] This embodiment pre-defines anomaly level judgment criteria, establishing the correspondence between the absolute value of the difference and the anomaly level. For example, a turbidity difference of +0.1 NTU indicates mild anomaly, +0.3 NTU indicates moderate anomaly, and +0.6 NTU indicates severe anomaly. Frequency judgment thresholds are set; for example, two consecutive sets of non-compliant data for the same parameter are considered low frequency, and three or more consecutive sets are considered high frequency. For each non-compliant parameter, the absolute value of the difference is first calculated to determine the basic level, and then the level is adjusted based on the frequency of occurrence, ultimately outputting a clear anomaly level for each parameter. This level classification clarifies the priority of parameter adjustment, providing a basis for subsequent calculations of the adjustment amplitude.

[0096] Step S1053: Retrieve the preset wastewater treatment process file, identify the current process stage of the pretreatment and reverse osmosis module equipment, and adjust the parameter adjustment strategy according to the characteristics of the process stage.

[0097] This embodiment determines the current stage by reading the equipment running time and feed water load data; it formulates differentiated parameter adjustment strategies for different stages. Optionally, during the start-up period, the parameter adjustment range of the reverse osmosis unit feed water pressure is reduced by 50% to avoid damage to the membrane module due to sudden pressure changes; during the stable operation period, the parameter adjustment range is executed according to the benchmark; during the load fluctuation period, the parameter adjustment frequency is increased, and the parameter adjustment effect is checked every 10 minutes to ensure that the parameter adjustment conforms to the process rules.

[0098] Step S1054: Based on the abnormality level of each non-conforming parameter, the corresponding module characteristics and the current process stage, and in conjunction with the preset parameter adjustment range benchmark table, calculate the specific parameter adjustment value of each non-conforming parameter.

[0099] This embodiment pre-defines a parameter adjustment range benchmark table, specifying the benchmark range according to module, parameter type, and anomaly level. For example, the benchmark range for turbidity in the pretreatment unit is 8% for mild anomalies, 15% for moderate anomalies, and 25% for severe anomalies; for the reverse osmosis unit, the benchmark range for inlet pressure is 5% for mild anomalies, 12% for moderate anomalies, and 20% for severe anomalies. For each non-compliant parameter, the benchmark range is first determined according to the module and parameter type, then the corresponding range value is selected based on the anomaly level, and finally, fine-tuning is performed based on the absolute value of the difference to ensure parameter adjustment accuracy.

[0100] Step S1055: Perform cross-module conflict verification on the parameter tuning values ​​calculated by each module to check whether there is a coupling contradiction between the parameter tuning values ​​of different modules. If there is a conflict, prioritize retaining the parameter tuning values ​​of the module with the higher anomaly level, adjust the parameter tuning values ​​of the lower level module to a conflict-free state, and finally generate the parameter tuning information of each module.

[0101] This embodiment pre-sets a module parameter coupling relationship table to determine the correlation and influence of parameters between different modules. For example, the influent flow rate of the pretreatment unit is positively correlated with the influent pressure of the reverse osmosis unit. If the pretreatment flow rate increases by 10%, the reverse osmosis pressure will increase by about 8%.

[0102] In this embodiment, the parameter tuning values ​​calculated for each module are verified one by one to determine whether they trigger coupling conflicts. If a conflict exists, adjustments are made according to the principle of prioritizing the level of anomaly: specifically, if the reverse osmosis unit has a severe anomaly and the pretreatment unit has a mild anomaly, the reverse osmosis parameter tuning values ​​are retained, the pretreatment flow rate is increased by 5%, and the pressure after coupling is recalculated to a safe range. Finally, the parameter tuning values ​​are integrated to generate the parameter tuning information for each module, ensuring that all modules operate collaboratively after tuning.

[0103] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0104] The following are embodiments of the pretreatment and reverse osmosis module equipment integrated management and control system provided in this disclosure. This system and the pretreatment and reverse osmosis module equipment integrated management and control methods in the above embodiments belong to the same inventive concept. For details not described in detail in the embodiments of the pretreatment and reverse osmosis module equipment integrated management and control system, please refer to the embodiments of the pretreatment and reverse osmosis module equipment integrated management and control methods.

[0105] like Figure 2 As shown, the system includes: Data acquisition module 201 is used to acquire the initial wastewater treatment dataset transmitted back by the pretreatment-reverse osmosis-concentration evaporation module under preset wastewater treatment process conditions when the pretreatment and reverse osmosis module equipment is in the start-up state. The initial wastewater treatment dataset records the actual wastewater treatment status. Data selection module 202 is used to select a first preset number of target wastewater treatment data from the initial wastewater treatment dataset; The state interval generation module 203 is used to input the first preset number of target wastewater treatment data into the wastewater treatment data detection model that has been trained, and to obtain a preset wastewater treatment state interval that corresponds to the first preset number of target wastewater treatment data and has the same number. The interval comparison module 204 is used to compare the target wastewater treatment data group by group to see if it falls into its corresponding preset wastewater treatment state interval, and record the comparison result as the wastewater treatment result. The parameter adjustment information generation module 205 is used to generate parameter adjustment information for the pretreatment-reverse osmosis-concentration evaporation module based on the wastewater treatment results and the preset wastewater treatment process. The parameter distribution module 206 is used to output the parameter adjustment information to the control unit of the pretreatment and reverse osmosis module equipment to complete the on-site parameter adjustment.

[0106] like Figure 3As shown, this application also provides an electronic device, including a display module 103, a memory 102, a processor 101, and a computer program stored in the memory and executable on the processor 101. When the processor 101 executes the program, it implements the steps of the pretreatment and reverse osmosis module equipment integrated management and control method.

[0107] In embodiments of the present invention, electronic devices include, but are not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments described and / or claimed herein.

[0108] In this embodiment, processor 101 may be implemented using at least one of an application-specific integrated circuit, a programmable logic device, a field-programmable gate array, a processor, a controller, a microcontroller, a microprocessor, or an electronic unit designed to perform the functions described herein. In some cases, such an implementation may be implemented within a controller. For software implementation, implementations such as processes or functions may be implemented with separate software modules that allow the performance of at least one function or operation. Software code may be implemented by a software application (or program) written in any suitable programming language, and the software code may be stored in memory and executed by the controller.

[0109] The display module 103 is used to display information input by the user or information provided to the user. The display module 103 may include a display panel, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like.

[0110] The memory 102 can be used to store software programs and various data. The memory 102 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0111] This application also provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the pretreatment and reverse osmosis module equipment integrated management and control method.

[0112] The storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example,, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0113] In a storage medium, a readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying readable program code. This propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0114] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for integrated management and control of pretreatment and reverse osmosis module equipment, characterized in that, The methods include: S101: When the pretreatment and reverse osmosis module equipment is in the start-up state, obtain the initial wastewater treatment dataset returned by the pretreatment-reverse osmosis-concentration evaporation module under the preset wastewater treatment process conditions. The initial wastewater treatment dataset records the actual wastewater treatment status. S102: Select the first preset number of target wastewater treatment data from the initial wastewater treatment dataset; Step S102 includes the following steps: Step S3011: Obtain the preset wastewater treatment data type; Step S3012: Obtain the equipment parameters of the pretreatment and reverse osmosis module equipment; Step S3013: Based on the preset wastewater treatment data type obtained in step S3011, the initial wastewater treatment dataset obtained in step S101 is filtered to obtain multiple first target state data. Step S3014: Based on the equipment parameters of the pretreatment and reverse osmosis module obtained in step S3012, extract background data from the wastewater treatment monitoring terminal associated with the equipment to obtain multiple second target status data. Step S3015: Combine the multiple first target state data obtained in step S3013 with the multiple second target state data obtained in step S3014 to form a number of target wastewater treatment data to be determined. Step S3016: Determine the target wastewater treatment data of the quantity to be determined formed in step S3015 as the first preset quantity group of target wastewater treatment data required in step S102. S103: Input the first preset number of target wastewater treatment data into the wastewater treatment data detection model that has been trained to obtain a preset wastewater treatment state interval that corresponds to the first preset number of target wastewater treatment data and has the same number. S104: Compare each group of target wastewater treatment data to see if it falls into its corresponding preset wastewater treatment state range, and record the comparison results as wastewater treatment results; S105: Based on the wastewater treatment results and the preset wastewater treatment process, generate parameter adjustment information for the pretreatment-reverse osmosis-concentration evaporation module; S106: Output the parameter adjustment information to the control unit of the pretreatment and reverse osmosis module equipment to complete the on-site parameter adjustment.

2. The integrated management and control method for pretreatment and reverse osmosis module equipment according to claim 1, characterized in that, The wastewater treatment data detection model processing method includes the following steps: Acquire a first preset number of predefined virtual water treatment complete sets of equipment, and wastewater treatment sample data of the virtual equipment under a second preset number of different process conditions, to form the first preset number of wastewater treatment sample data. Obtain wastewater treatment condition data of the first preset quantity of predefined virtual water treatment complete equipment under the second preset quantity of process conditions, and form the first preset quantity of wastewater treatment condition data; The first preset number of wastewater treatment sample data and the first preset number of wastewater treatment condition data are combined one-to-one to obtain the first preset number of complete wastewater treatment data samples. The preset wastewater treatment data detection model is trained using the first preset number of complete wastewater treatment data samples to obtain the initially trained wastewater treatment data detection model. The detection accuracy of the wastewater treatment data detection model after initial training was evaluated using a validation dataset. If the detection accuracy is less than or equal to the preset accuracy threshold, the internal control parameters of the model are adjusted; training and detection accuracy are recalculated; this process is repeated until the detection accuracy of the adjusted model is greater than the preset accuracy threshold, and the finally adjusted model is determined as the wastewater treatment data detection model.

3. The integrated management and control method for pretreatment and reverse osmosis module equipment according to claim 1, characterized in that, Step S103 specifically includes: The parameter format of the first preset number of target wastewater treatment data selected in step S102 is converted into the preset input format of the wastewater treatment data detection model. The preset input format includes the unit, data precision and field arrangement order of the wastewater treatment parameters. A bidirectional data transmission link is established between the target wastewater treatment data and the wastewater treatment data detection model deployment unit through the industrial control bus of the pretreatment and reverse osmosis module equipment; The wastewater treatment data detection model deployment unit receives the first preset number of target wastewater treatment data after format conversion, verifies the field integrity and value range of each data group, and removes data groups that fail the verification. The wastewater treatment data detection model calls a pre-stored wastewater parameter-preset state interval mapping library. The mapping library is established based on preset wastewater treatment process standards. For each set of target wastewater treatment data that has passed the verification, it matches the preset wastewater treatment state interval of its corresponding parameter type.

4. The integrated management and control method for pretreatment and reverse osmosis module equipment according to claim 1, characterized in that, Step S104 specifically includes the following steps: Step S1041: Retrieve the target data-preset interval correspondence list generated in step S103 in the buffer area of ​​the pretreatment and reverse osmosis module equipment control unit, and associate the first preset number of target wastewater treatment data selected in step S102 with the corresponding preset wastewater treatment status intervals one by one through the data group number in the list. Step S1042: For each set of associated target wastewater treatment data and preset wastewater treatment status interval, read the parameter type identifier in the target data and extract the lower limit and upper limit values ​​of the corresponding preset interval; Step S1043: Read the parameter values ​​of the target wastewater treatment data of the current group, and compare the values ​​with the lower limit and upper limit of the corresponding preset wastewater treatment state range respectively; Step S1044: Based on the comparison results, mark the comparison results of the current group of data in the result recording module of the control unit, and record the data number, parameter type, and the difference between the specific value and the interval boundary; Step S1045: After the comparison of the target wastewater treatment data of the first preset number of groups is completed, the comparison results of all groups are classified and summarized according to parameter type, and a wastewater treatment result table containing data group number, parameter type, target value, preset range, comparison result, and difference is generated as the input basis for step S105.

5. The integrated management and control method for pretreatment and reverse osmosis module equipment according to claim 1, characterized in that, Step S105 specifically includes: Read the wastewater treatment result table generated in step S104, and classify the target wastewater treatment data in the table that are not qualified into the corresponding modules according to the module division rules of the pretreatment module, reverse osmosis module, and concentration evaporation module, to form a module-unqualified data association table. For the non-conforming parameters of each module in the module-non-conforming data association table, the abnormality level of each non-conforming parameter is determined by combining the preset abnormality level judgment criteria and the frequency of non-conforming data occurrence. Retrieve the preset wastewater treatment process file, identify the current process stage of the pretreatment and reverse osmosis module equipment, and adjust the parameter adjustment strategy according to the characteristics of the process stage. Based on the abnormality level of each non-conforming parameter, the characteristics of the corresponding module, and the current process stage, and in conjunction with the preset parameter adjustment range benchmark table, calculate the specific parameter adjustment value of each non-conforming parameter. Cross-module conflict verification is performed on the parameter tuning values ​​calculated by each module to check whether there are coupling contradictions between the parameter tuning values ​​of different modules. If there is a conflict, the parameter tuning values ​​of the module with the higher anomaly level are retained first, and the parameter tuning values ​​of the lower level module are adjusted to a conflict-free state. Finally, the parameter tuning information of each module is generated.

6. The integrated management and control method for pretreatment and reverse osmosis module equipment according to claim 1, characterized in that, Step S106 specifically includes: The parameter adjustment information of each module of pretreatment-reverse osmosis-concentration evaporation generated in step S105 is processed, and the module unique identifier, parameter type, target parameter adjustment value, adjustment time and hardware safety threshold are added according to the preset format to form a parameter adjustment instruction set that can be directly parsed by the control unit. Retrieve the module-control unit address mapping table pre-stored in the pretreatment and reverse osmosis module equipment, and transmit the corresponding module's parameter adjustment instructions to the dedicated control unit of each module according to the module identifier in the parameter adjustment instruction set, and receive the instruction reception confirmation signal returned by each control unit; After receiving the parameter tuning command, each module control unit extracts the target parameter tuning value from the command and performs a pre-execution verification with its own pre-stored hardware safe operating range. If the target value is within the safe range, an executable signal is returned; if the target value exceeds the safe threshold range, an over-limit signal is returned, the parameter tuning operation is paused, and step S105 is triggered to recalculate the parameter tuning value. After receiving the executable signals from all module control units, the system controls each module control unit to perform parameter adjustment operations step by step according to the preset adjustment priority rules, and collects parameter change data in real time during the adjustment process. After the parameter adjustment operations of each module are completed, the control unit collects the actual parameter values ​​after adjustment and compares them with the target values ​​in the parameter adjustment command. If the deviation value is less than or equal to the preset deviation threshold, the adjustment is deemed qualified and the adjustment result is fed back to the equipment control system. If the deviation value is greater than the threshold, a second fine-tuning is triggered, and the parameter adjustment command, execution process data and results are recorded in the equipment operation database.

7. An integrated control system for pretreatment and reverse osmosis module equipment, characterized in that, The system is used to implement the integrated management and control method for pretreatment and reverse osmosis module equipment as described in any one of claims 1 to 6; The system includes: The data acquisition module is used to acquire the initial wastewater treatment dataset transmitted back by the pretreatment-reverse osmosis-concentration evaporation module under the preset wastewater treatment process conditions when the pretreatment and reverse osmosis module equipment is in the start-up state. The initial wastewater treatment dataset records the actual wastewater treatment status. The data selection module is used to select a first preset number of target wastewater treatment data from the initial wastewater treatment dataset; The state interval generation module is used to input the first preset number of target wastewater treatment data into the wastewater treatment data detection model that has been trained, and to obtain a preset wastewater treatment state interval that corresponds to and is the same number as the first preset number of target wastewater treatment data. The interval comparison module is used to compare the target wastewater treatment data group by group to see if it falls into its corresponding preset wastewater treatment state interval, and record the comparison results as the wastewater treatment results. The parameter adjustment information generation module is used to generate parameter adjustment information for the pretreatment-reverse osmosis-concentration evaporation module based on the wastewater treatment results and the preset wastewater treatment process. The parameter distribution module is used to output the parameter adjustment information to the control unit of the pretreatment and reverse osmosis module equipment to complete the on-site parameter adjustment.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the pretreatment and reverse osmosis module equipment integrated management and control method as described in any one of claims 1 to 6.

9. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the pretreatment and reverse osmosis module equipment integrated management and control method as described in any one of claims 1 to 6.

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