Intelligent water quality monitoring equipment based on multi-parameter fusion sensing

Abstract

The invention discloses intelligent water quality monitoring equipment based on multi-parameter fusion sensing, and particularly relates to the technical field of water quality monitoring. Water quality data is monitored on line, whether water quality pollution occurs or not is judged based on a water quality fusion data set and a water quality standard data set, the pollution type is identified after the water quality pollution is confirmed, and the pollution coefficient is calculated; when the water quality pollution coefficient reaches a set value or the water storage reaches a set value, a water process treatment instruction is triggered, so that early warning of water quality pollution is realized; according to the invention, a regulation and control instruction is generated based on a fusion data set of each process of water treatment, the water quality fusion data set of each process is compared with an expected index of effluent quality to determine whether the water quality treated by the process reaches the standard, and after the water quality treated by the previous process reaches the standard, the next process is allowed to be treated. Process chain step-by-step standard verification is realized, blind adjustment of process parameters is avoided, and the cost can be effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of water quality monitoring technology, and more specifically, to an intelligent water quality monitoring device based on multi-parameter fusion sensing. Background Technology

[0002] In water treatment scenarios, water quality monitoring is crucial. While traditional chemical analysis methods offer high accuracy, they are cumbersome, time-consuming, and unable to reflect real-time water quality changes. They often require collecting water samples and performing multiple complex chemical reactions and instrumental analyses in a laboratory, which can take several days from sampling to obtaining results, making it difficult to respond promptly to sudden water quality emergencies. Therefore, it is necessary to optimize water quality monitoring methods to improve their intelligence, real-time capabilities, and the timeliness of water treatment.

[0003] A water quality monitoring method is available that involves installing various types of sensors at different stages of water treatment processes. Data collected by these sensors is transmitted wirelessly to a central control system in real time. The central control system employs advanced data analysis algorithms to analyze and process large amounts of data in real time. By establishing a water quality model, the method predicts water quality change trends and adjusts water treatment process parameters promptly based on the prediction results. This significantly improves the timeliness and accuracy of water quality monitoring, enabling rapid response to and adjustment of water quality changes.

[0004] However, existing methods still have some problems: they can only identify which water quality parameters exceed limits in the water body, ignoring water pollution early warning and pipeline water supply compensation. Sudden water pollution cannot be detected in advance, leaving only a reactive response; fluctuations in water quality at the end of the pipeline lack control measures. While relying on water quality models to predict and adjust process parameters, they lack a real-time verification mechanism for effluent compliance. Prediction errors may cause process parameter adjustments to fail, failing to prevent substandard water from entering subsequent processes. Therefore, existing water quality monitoring methods need further optimization to achieve early warning of water pollution and avoid blind adjustments to process parameters. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a water quality intelligent monitoring device based on multi-parameter fusion sensing to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a water quality intelligent monitoring device based on multi-parameter fusion sensing, comprising:

[0007] Online water quality monitoring module: Multiple monitoring sites are set up in the water treatment process and water transmission network to deploy sensors for online monitoring of water quality data;

[0008] Water quality data fusion module: performs temperature compensation on some of the collected water quality data, then aligns the monitoring timestamps and performs data cleaning and fusion in sequence;

[0009] Water pollution identification module: Based on the water quality fusion dataset and water quality standard dataset, it determines whether water pollution has occurred. After confirming that water pollution has occurred, it identifies the pollution type and calculates the pollution coefficient.

[0010] Water treatment process control module: Generates process control schemes and generates control instructions based on the fusion dataset of various water treatment processes based on feedback. Allows the next process to proceed after determining that the water quality treated by the previous process meets the standards.

[0011] Pipeline control module: Based on the fusion dataset of the water transmission pipeline network, it determines whether compensation and control of the water transported by the pipeline network is needed, and continues to transport water after all water quality parameters meet the standards;

[0012] Water quality monitoring effect index integration module: collects water quality monitoring effect index, treatment process monitoring effect index and pipeline transportation monitoring effect index within a preset period and integrates them to obtain the water quality monitoring effect compliance coefficient, treatment process monitoring effect compliance coefficient and pipeline transportation monitoring effect compliance coefficient.

[0013] Water quality monitoring effect evaluation module: Based on the water quality monitoring effect compliance coefficient, the treatment process monitoring effect compliance coefficient, and the pipeline transportation monitoring effect compliance coefficient, calculate the overall water quality monitoring effect compliance index and determine whether the overall water quality monitoring effect meets expectations.

[0014] The technical effects and advantages of this invention are as follows:

[0015] 1. This invention sets up an online water quality data monitoring module, deploying sensors at multiple monitoring sites in the water treatment process and water transmission network for online monitoring of water quality data; it also sets up a water quality data fusion module to perform temperature compensation on some of the collected water quality data, then aligns the monitoring timestamps and performs data cleaning and fusion in sequence. Based on the water quality fusion dataset and the water quality standard dataset, it determines whether water pollution has occurred. After confirming that water pollution has occurred, it identifies the pollution type and calculates the pollution coefficient. When the water pollution coefficient reaches a set value or the water storage reaches a set value, it triggers a water process treatment command, thus realizing early warning of water pollution.

[0016] 2. This invention sets up a water treatment process control module that generates control commands based on the fusion dataset of each water treatment process to regulate each process. The fusion dataset of water quality for each process is compared with the expected effluent water quality indicators. If the fusion value of the water quality parameters for each process is greater than the lower limit of the corresponding expected effluent water quality indicator and less than the upper limit of the corresponding expected effluent water quality indicator, the water quality of the process is determined to meet the standard. After determining that the water quality of the previous process meets the standard, the process is allowed to proceed to the next process. This realizes the step-by-step verification of the process chain, avoids blind adjustment of process parameters, and can effectively reduce chemical consumption.

[0017] 3. This invention sets up a water quality monitoring effect index integration module to collect water quality monitoring effect indicators, treatment process monitoring effect indicators, and pipeline transportation monitoring effect indicators within a preset period and integrates them to obtain water quality monitoring effect compliance coefficient, treatment process monitoring effect compliance coefficient, and pipeline transportation monitoring effect compliance coefficient; and sets up a water quality monitoring effect evaluation module to calculate the whole process water quality monitoring effect compliance index based on the water quality monitoring effect compliance coefficient, treatment process monitoring effect compliance coefficient, and pipeline transportation monitoring effect compliance coefficient. When the whole process water quality monitoring effect compliance index is greater than or equal to 0, it is determined that the whole process water quality monitoring effect meets expectations; otherwise, it is determined that the whole process water quality monitoring effect does not meet expectations. This provides a novel method for evaluating the whole process water quality monitoring effect. Attached Figure Description

[0018] Figure 1 This is a structural block diagram of the present invention.

[0019] Figure 2 This is a diagram illustrating the method steps of the present invention. Detailed Implementation

[0020] 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.

[0021] like Figure 1This embodiment provides a water quality intelligent monitoring device based on multi-parameter fusion sensing, including an online water quality data monitoring module, a water quality data fusion module, a water pollution identification module, a water treatment process control module, a pipeline network regulation module, a water quality monitoring effect index integration module, a water quality monitoring effect evaluation module, and a database. The online water quality data monitoring module, water quality data fusion module, water pollution identification module, water treatment process control module, and pipeline network regulation module are connected sequentially. The water quality data fusion module is connected to the water pollution identification module, water treatment process control module, and pipeline network regulation module. The water pollution identification module, water treatment process control module, and pipeline network regulation module are connected to the water quality monitoring effect index integration module. The water quality monitoring effect index integration module is connected to the water quality monitoring effect evaluation module. Each module is connected to the database.

[0022] The online water quality monitoring module deploys sensors at multiple monitoring sites in the water treatment process and water transmission network for online monitoring of water quality data.

[0023] Furthermore, the online water quality data monitoring module includes a water quality data monitoring unit, a water treatment process water quality data monitoring unit, a water transmission network water quality data monitoring unit, and a data output unit. The water quality data monitoring unit is used to monitor water quality data at different locations; the water treatment process water quality data monitoring unit is used to monitor water quality data and process control data at different locations in the water treatment process; the water transmission network water quality data monitoring unit is used to monitor water quality data at different locations in the water transmission network; and the data output unit is used to transmit the monitored data to the water quality data fusion module.

[0024] In this embodiment, it should be specifically noted that the water quality data includes basic physicochemical data, pollutant data, and environmental auxiliary data. The basic physicochemical data includes pH value, turbidity, conductivity, and dissolved oxygen concentration. The pollutant data includes UV254 absorbance, ammonia nitrogen concentration, heavy metal concentration, residual chlorine concentration, and chlorophyll a concentration. The environmental auxiliary data includes water temperature and flow rate. The water quality data for the water treatment process includes dissolved oxygen saturation, UV254 absorbance, and chlorophyll a concentration for the pretreatment process; pH, turbidity, and water temperature for the coagulation process; and... Turbidity, turbidity of filtered water from filtration processes, residual ozone concentration, UV254 absorbance, and bromate concentration from ozone-activated carbon processes, conductivity and turbidity from membrane treatment processes, residual chlorine and disinfection byproduct concentrations from disinfection processes, process control data including floc size from coagulation processes, floc density, sludge interface, and surface loading rate from sedimentation processes, particulate matter concentration and head loss from filtration processes, and transmembrane pressure differential and membrane flux from membrane treatment processes; water quality data for water transmission networks include pH, turbidity, conductivity, dissolved oxygen concentration, residual chlorine concentration, and water temperature.

[0025] In this embodiment, it should be specifically noted that the UV254 absorption value is the ultraviolet absorption value.

[0026] The water quality data fusion module performs temperature compensation on some of the collected water quality data, then aligns the monitoring timestamps and performs data cleaning and data fusion in sequence.

[0027] Furthermore, the water quality data fusion module includes a data receiving unit, a temperature compensation unit, a timestamp alignment unit, a data cleaning unit, a data fusion unit, and a data transmission unit. The data receiving unit receives monitoring data; the temperature compensation unit performs temperature compensation on pH, conductivity, dissolved oxygen saturation, and UV254 absorbance based on pH-temperature compensation formulas, conductivity-temperature compensation formulas, dissolved oxygen-temperature compensation formulas, and UV254 absorbance value-temperature compensation formulas; the timestamp alignment unit integrates the water quality monitoring timestamps and arranges the monitoring data in chronological order, marking monitoring data that does not exist under a timestamp as missing; the data cleaning unit performs outlier removal and missing value filling on the received data based on the 3σ principle and physical value range constraints to obtain a clean dataset; the data fusion unit fuses the clean datasets from different monitoring sites of various water treatment processes and water transmission networks at the same timestamp to obtain a fused dataset; the data transmission unit transmits the fused water quality dataset to the water pollution identification module, the fused datasets from various water treatment processes to the water treatment process control module, and the fused datasets from the water transmission network to the network regulation module.

[0028] In this embodiment, it should be specifically noted that the temperature compensation formula includes pH value compensation formula, conductivity compensation formula, dissolved oxygen compensation formula, and UV254 absorbance value compensation formula. Temperature compensation can be applied to pH value, conductivity, dissolved oxygen saturation, and UV254 absorbance value based on these formulas. The specific formulas are existing technology and are not detailed here. Assuming the data collection times for pH values ​​(PH1, PH2, PH3, PH4, PH5, PH6) in the pH data set are 12:00:00, 12:00:05, 12:00:10, 12:00:15, 12:00:20, and 12:00:25 respectively, and the data collection times for turbidity values ​​(a1, a2, a3) in the turbidity data set are 12:00:00, 12:00:10, 12:00:15, 12:00:20, and 12:00:25 respectively, then... 20. After merging the pH and turbidity collection timestamps, the pH and turbidity data are arranged in chronological order as follows: 12:00:00, PH1, a1; 12:00:05, PH2, missing; 12:00:10, PH3, a2; 12:00:15, PH4, missing; 12:00:20, PH5, a3; 12:00:05, PH6, missing. The monitoring data are aligned based on the example rules.

[0029] In this embodiment, the specific steps for data cleaning are as follows:

[0030] A1. Group the collected data according to the type of water quality data;

[0031] A2. Fixed time window and window overlap rate;

[0032] A3. Calculate the window mean μ a The specific formula is as follows: In the formula, n1 is the number of sampling points within the time window, and x i For the temperature compensation value of the i-th sampling point, calculate the window standard deviation σ. a The specific formula is as follows: The threshold range is [μ] a -3σ a ,μ a +3σ a ];

[0033] A4. Data points of water quality parameters that exceed the physical value range constraints are directly marked as abnormal data points. Data points that do not exceed the physical value range constraints but exceed the water quality parameter threshold range in A3 are marked as abnormal data points. Physical value range constraints include pH value range of 0–14, concentration cannot be negative, turbidity cannot be negative, and dissolved oxygen saturation value range of 0–100%.

[0034] A5. Three consecutive points exceed 4σ a Data points are deleted directly within the specified range; a single point exceeding 3σ is excluded. a But less than 4σ a The replacement value x at time t is obtained by replacing the exponentially weighted moving average. t The specific calculation formula for ' is: x t '=a1×x t +(1-a1)×x t-1 ', where a1 is the exponential weighting coefficient, and the specific calculation formula is as follows: N eff For the effective observation window length, x t x is the temperature compensation value at time t. t-1 'This is the replacement value of the exponentially weighted moving average at time t-1, which is numerically equivalent to the normal temperature compensation value at time t-1;

[0035] A6. Locate the missing point. For single-point missing points, linear interpolation is used. The specific formula is as follows: x t * x t+1 x t-1 The values ​​are, in order, the missing point filling value at time t, the valid point compensation value at time t+1, and the valid point compensation value at time t-1. For two consecutive missing points, quadratic polynomial interpolation is used. The specific formula is: xt * = a2×(t-t0) 2 +a3×(t-t0)+a4, (t0,x0)(t1,x1)(t2,x2) are the three nearest valid points before and after the missing point, a4=x0, For three to five consecutive missing points, cubic spline interpolation is used; for more than five missing points, historical pattern matching is used; and for boundary missing points, nearest neighbor copying is used.

[0036] In this embodiment, it should be specifically noted that, assuming the collected water volumes G1=1kg, G2=5kg, G3=10kg, G4=3kg, G5=7kg, and G6=8kg correspond to the following pH value data sets: PH1=5.6, PH2=5.9, PH3=7.2, PH4=6.9, PH5=6.3, and PH6=7.8 respectively, the collected water volumes are mixed to obtain the target extracted water volume. The pH value of the target collected water volume is the combined pH value in the combined water quality dataset. It is assumed that the density of water is 1g / cm³. 3 Mass (kg) can be considered as volume (liters, dm). 3 Then the pH value will be blended. r The specific calculation formula is as follows:

[0037] c H+,r The hydrogen ion concentration in the target volume of water to be extracted.

[0038] The water pollution identification module determines whether water pollution has occurred based on the water quality fusion dataset and the water quality standard dataset. After confirming that water pollution has occurred, it identifies the type of pollution and calculates the pollution coefficient.

[0039] Furthermore, the water pollution identification module includes a data receiving unit, a pollution determination unit, a historical pollution data retrieval unit, a pollution type identification unit, a pollution coefficient calculation unit, an instruction trigger determination unit, and an information transmission unit. The data receiving unit receives the fused data set of water quality data. The pollution determination unit compares the fused data set with the water quality standard data set. If any fused data value of a water quality parameter is greater than the corresponding upper limit or less than the corresponding lower limit, the water quality parameter is determined to be out of limit, i.e., water pollution has occurred. Conversely, if the fused data value is less than the corresponding lower limit, the water quality parameter is determined to be normal, i.e., no water pollution has occurred. The historical pollution data retrieval unit is used for... The system retrieves historical pollution event occurrence counts, the number of events with each water quality parameter exceeding the limit in each type of pollution event, the total number of occurrences of each water quality parameter exceeding the limit in pollution events, and the total number of events of each type of pollution. The pollution type identification unit is used to identify water pollution types. The pollution coefficient calculation unit is used to calculate water pollution coefficients. The instruction triggering judgment unit triggers water process treatment instructions when the water pollution coefficient reaches a set value or the water storage reaches a set value. The information transmission unit transmits the fused dataset of water pollution type and water quality to the water treatment process control module when the water process treatment instruction is triggered.

[0040] In this embodiment, the specific steps for identifying the type of contamination are as follows:

[0041] B1. Retrieve the number of historical pollution incidents (N) az The number of events, a, in which the j-th water quality parameter exceeds the limit in the i-th type of pollution event. ij The total number of times the j-th water quality parameter exceeds the limit in the pollution incident, a zj b, the total number of occurrences of pollution type i zi ;

[0042] B2. Calculate the correlation coefficient between the j-th type of water quality parameter exceeding the limit and the i-th type of pollution. xij The specific formula is as follows:

[0043]

[0044] B3. Calculate the weighting coefficient A for the j-th water quality parameter exceeding the limit for the i-th pollution type. qij The specific formula is as follows: n1 represents the number of water quality parameter types in the water quality standard dataset;

[0045] B4. Record the water quality parameters that exceed the limits, and retrieve the weighting coefficients A corresponding to the water quality parameters exceeding the limits in each type of pollution. qik Calculate the deviation coefficient α of the water quality parameters exceeding the limit. pk Then, the matching coefficient A between the current pollution event and each type of pollution is... piThis can be expressed as the sum of the products of the weighting coefficient and the corresponding deviation coefficient for each water quality parameter exceeding the limit in each pollution type. The specific formula is as follows: B xk B yk,max B yk,min The values ​​are, in order, the fused data value of the kth water quality parameter that exceeds the limit, the upper limit of the standard, and the lower limit of the standard, and n1 is the number of water quality parameter types in the water quality standard dataset.

[0046] B5. Extract the maximum value of the pollution type matching coefficient, and mark the corresponding pollution type as the target pollution type.

[0047] In this embodiment, it should be specifically noted that the pollution coefficient W... X The specific calculation formula is as follows: B xi B yi,max B yi,min The values ​​are, in order, the fused data value of the i-th water quality parameter exceeding the limit, the upper limit value of the standard, and the lower limit value of the standard, where n1 is the number of water quality parameter types in the water quality standard dataset.

[0048] The water treatment process control module generates a process control scheme and generates control instructions based on the fusion dataset of the water treatment processes. After determining that the water quality of the previous process meets the standards, it allows the process to proceed to the next process.

[0049] Furthermore, the water treatment process control module includes an information receiving unit, a process control scheme generation unit, a process treatment information feedback unit, a dynamic control unit, a water quality compliance determination unit, and a process flow switching unit. The information receiving unit receives a fusion dataset of water pollution types and water quality. The process control scheme generation unit generates expected effluent water quality indicators, expected process control indicators, and theoretical consumables for pretreatment, coagulation, sedimentation, filtration, ozone-activated carbon, membrane treatment, and disinfection processes based on the fusion dataset of water pollution types and water quality. The process treatment information feedback unit receives feedback fusion datasets of various water treatment processes. The dynamic control unit generates control instructions based on the fusion datasets of various water treatment processes. The water quality compliance determination unit compares the fusion datasets of water quality for each process with the expected effluent water quality indicators. If the fusion value of the water quality parameters for each process is greater than the lower limit of the corresponding expected effluent water quality indicator and less than the upper limit of the corresponding expected effluent water quality indicator, the process treatment water quality is determined to be compliant. The process flow switching unit allows the next process to proceed after determining that the water quality of the previous process has met the compliance criteria.

[0050] Specifically, in this embodiment, the theoretical consumables for the pretreatment process include theoretical aeration volume, theoretical activated carbon dosage, electrical energy, and theoretical pre-oxidant dosage; the theoretical consumables for the coagulation process include theoretical acid and alkali dosage, electrical energy, theoretical coagulant dosage, and theoretical coagulant aid dosage; the theoretical consumable for the sedimentation process is electrical energy; the theoretical consumable for the filtration process is electrical energy; the theoretical consumables for the ozone-activated carbon process are ozone dosage and electrical energy; the theoretical consumables for the membrane treatment process are electrical energy; and the theoretical consumables for the disinfection process are sodium hypochlorite dosage and electrical energy.

[0051] The pipeline control module determines whether compensation and control of the water transported by the pipeline network are needed based on the fusion dataset of the water transport network, and continues to transport water after all water quality parameters meet the standards.

[0052] The water quality monitoring effect index integration module collects water quality monitoring effect indexes, treatment process monitoring effect indexes, and pipeline transportation monitoring effect indexes within a preset period and integrates them to obtain the water quality monitoring effect compliance coefficient, treatment process monitoring effect compliance coefficient, and pipeline transportation monitoring effect compliance coefficient.

[0053] Furthermore, the water quality monitoring data integration module includes a monitoring effect index collection unit, a monitoring effect index integration unit, and a data output unit. The monitoring effect index collection unit is used to collect water quality monitoring effect indicators, treatment process monitoring effect indicators, and pipeline transportation monitoring effect indicators within a preset period. The water quality monitoring effect indicators include the average monitoring coverage rate coefficient and the pollution type identification accuracy rate. The treatment process monitoring effect indicators include the average process control parameter compliance rate coefficient and the average process consumable error coefficient. The pipeline transportation monitoring effect indicators include the average transportation water quality stability coefficient and the average water quality compliance rate coefficient at the end of the pipeline. The monitoring effect index integration unit is used to calculate the water quality monitoring effect compliance coefficient, the treatment process monitoring effect compliance coefficient, and the pipeline transportation monitoring effect compliance coefficient. The data output unit is used to transmit the water quality monitoring effect compliance coefficient, the treatment process monitoring effect compliance coefficient, and the pipeline transportation monitoring effect compliance coefficient to the water quality monitoring effect evaluation module.

[0054] In this embodiment, it is specifically necessary to explain the average monitoring coverage coefficient X. fa The specific calculation formula is as follows: n1, t az t ai The parameters are, in order: the number of water quality parameter types in the water quality standard dataset, the total water quality monitoring time within the preset period, and the cumulative monitoring time of the i-th type of water quality parameter; the pollution type identification accuracy X. sa The specific calculation formula is as follows: m az m acThe numbers represent, in order, the number of times the pollution type was correctly identified and the number of times the pollution type was incorrectly identified within the preset period; the average process control parameter compliance rate coefficient X. ga The specific calculation formula is as follows: m ci m bi N g The following are, in order: the cumulative values ​​of the compliant water quality indicators and compliant process control indicators for the i-th process control scheme within the preset period; the cumulative values ​​of the expected water quality indicators and expected process control indicators for the i-th process control scheme; the number of process control schemes; and the average process consumable error coefficient X. ha The specific calculation formula is as follows: C xij C yij n ai N g The values ​​are, in order: actual value of the j-th process consumable for the i-th process control scheme within the preset period; theoretical value of the j-th process consumable for the i-th process control scheme; number of process consumable types for the i-th process control scheme; and number of process control schemes; average water quality stability coefficient X. wa The specific calculation formula is as follows: n1, N b B aij μ Bi The parameters are, in order: the number of water quality parameter types in the water quality standard dataset, the number of end-point water quality parameter monitoring data sets, the monitored value of the i-th water quality parameter in the j-th data set, and the average value of the i-th water quality parameter; the average water quality compliance rate coefficient X at the end of the pipe network. ma The specific calculation formula is as follows: n bj n1, N b The numbers represent, in order, the number of compliant water quality parameters in the j-th data group of the end-point water quality monitoring, the number of water quality parameter types in the water quality standard dataset, and the number of end-point water quality parameter monitoring data groups.

[0055] In this embodiment, it is specifically necessary to explain the water quality monitoring effectiveness compliance coefficient Y. A The specific calculation formula is as follows: X fb X sb The following are, in order: monitoring coverage coefficient, pollution type identification accuracy setpoint; and treatment process monitoring effectiveness compliance coefficient Y. B The specific calculation formula is as follows: X gb X hb The following are, in order: the compliance rate coefficient of process control parameters, the set value of process consumable error coefficient; and the compliance coefficient Y of pipeline transportation monitoring effect. C The specific calculation formula is as follows: X wb Xmb The values ​​are, in order, the stability coefficient of the transported water quality and the compliance rate coefficient of the water quality at the end of the pipeline network.

[0056] The water quality monitoring effect evaluation module calculates the overall water quality monitoring effect compliance index based on the water quality monitoring effect compliance coefficient, the treatment process monitoring effect compliance coefficient, and the pipeline transportation monitoring effect compliance coefficient, and determines whether the overall water quality monitoring effect meets expectations.

[0057] Furthermore, the water quality monitoring effect evaluation module includes a data receiving unit, a full-process water quality monitoring effect compliance index calculation unit, a full-process water quality monitoring effect judgment unit, and a judgment result feedback unit. The data receiving unit is used to receive the water quality monitoring effect compliance coefficient, the treatment process monitoring effect compliance coefficient, and the pipeline transportation monitoring effect compliance coefficient. The full-process water quality monitoring effect compliance index calculation unit is used to calculate the full-process water quality monitoring effect compliance index QY, with the specific formula being: Q Y =Y A +Y B +Y C The whole-process water quality monitoring effect judgment unit determines that the whole-process water quality monitoring effect meets expectations when the whole-process water quality monitoring effect compliance index is greater than or equal to 0, and otherwise determines that the whole-process water quality monitoring effect does not meet expectations; the judgment result feedback unit is used to feed back the whole-process water quality monitoring effect judgment result and the whole-process water quality monitoring effect compliance index to the water quality monitoring center.

[0058] The database is used to store data information for all modules.

[0059] In this embodiment, it should be specifically noted that the set values, standard values, and expected values ​​used are all selected based on actual needs, and no specific value limit is imposed here. The process theory consumables used are obtained based on chemical equilibrium and loss prediction.

[0060] like Figure 2 The embodiment shown provides a water quality intelligent monitoring method based on multi-parameter fusion sensing, including the following steps:

[0061] S1: Set up multiple monitoring sites and deploy sensors to monitor water quality data of water bodies online;

[0062] S2: Temperature compensation is applied to some of the collected water quality data, then the monitoring timestamps are aligned and the data is cleaned and fused in sequence to finally obtain the water quality fusion dataset.

[0063] S3: Determine whether water pollution has occurred based on the water quality fusion dataset and water quality standard dataset. After confirming that water pollution has occurred, identify the pollution type and calculate the pollution coefficient. Trigger water process treatment instructions when the water pollution coefficient reaches the set value or the water storage volume reaches the set value.

[0064] S4: Based on water pollution types and water quality fusion datasets, generate expected effluent water quality indicators, expected process control indicators, and theoretical process consumables for pretreatment processes, coagulation processes, sedimentation processes, filtration processes, ozone-activated carbon processes, membrane treatment processes, and disinfection processes.

[0065] S5: Set up multiple monitoring points in each water treatment process and deploy sensors to monitor water quality data and process control data at different points in the water treatment process online;

[0066] S6: Temperature compensation is applied to some of the collected water quality data, then the monitoring timestamps are aligned and the data is cleaned and fused in sequence to finally obtain the fused dataset of each water treatment process.

[0067] S7: Based on the fusion dataset of each water treatment process, generate control instructions to control each water treatment process. Compare the fusion dataset of water quality of each process with the expected indicators of effluent water quality. If the fusion value of water quality parameters of each process is greater than the lower limit of the corresponding expected indicators of effluent water quality and less than the upper limit of the corresponding expected indicators of effluent water quality, it is determined that the water quality of the process meets the standards. After determining that the water quality of the previous process meets the standards, it is allowed to enter the next process.

[0068] S8. Set up multiple monitoring points in the water transmission network and deploy sensors to monitor the water quality data of the water transported by the network online;

[0069] S9. Temperature compensation is applied to some of the collected water quality data, then the monitoring timestamps are aligned and the data is cleaned and fused in sequence to finally obtain the fused dataset of the water transmission network.

[0070] S10. Based on the fusion dataset of the water supply network, determine whether compensation and regulation of the water transported by the network are required, and continue to transport water after all water quality parameters meet the standards.

[0071] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A water quality intelligent monitoring device based on multi-parameter fusion sensing, characterized in that: include: Online water quality monitoring module: Multiple monitoring sites are set up in the water treatment process and water transmission network to deploy sensors for online monitoring of water quality data; Water quality data fusion module: performs temperature compensation on some of the collected water quality data, then aligns the monitoring timestamps and performs data cleaning and fusion in sequence; Water pollution identification module: Based on the water quality fusion dataset and water quality standard dataset, it determines whether water pollution has occurred. After confirming that water pollution has occurred, it identifies the pollution type and calculates the pollution coefficient. Water treatment process control module: Generates process control schemes and generates control instructions based on the fusion dataset of various water treatment processes based on feedback. Allows the next process to proceed after determining that the water quality treated by the previous process meets the standards. Pipeline control module: Based on the fusion dataset of the water transmission pipeline network, it determines whether compensation and control of the water transported by the pipeline network is needed, and continues to transport water after all water quality parameters meet the standards; Water quality monitoring effect index integration module: collects water quality monitoring effect index, treatment process monitoring effect index and pipeline transportation monitoring effect index within a preset period and integrates them to obtain the water quality monitoring effect compliance coefficient, treatment process monitoring effect compliance coefficient and pipeline transportation monitoring effect compliance coefficient. Water quality monitoring effect evaluation module: Based on the water quality monitoring effect compliance coefficient, the treatment process monitoring effect compliance coefficient, and the pipeline transportation monitoring effect compliance coefficient, calculate the overall water quality monitoring effect compliance index and determine whether the overall water quality monitoring effect meets expectations.

2. The intelligent water quality monitoring device based on multi-parameter fusion sensing according to claim 1, characterized in that: The online water quality data monitoring module includes a water quality data monitoring unit, a water treatment process water quality data monitoring unit, a water transmission network water quality data monitoring unit, and a data output unit. The water quality data monitoring unit is used to monitor water quality data at different locations. The water quality data monitoring unit for water treatment processes is used to monitor water quality data and process control data at different points in the water treatment process. The water quality data monitoring unit for the water transmission network is used to monitor water quality data at different locations within the water transmission network. The data output unit is used to transmit the monitored data to the water quality data fusion module.

3. The intelligent water quality monitoring device based on multi-parameter fusion sensing according to claim 1, characterized in that: The water quality data fusion module includes a data receiving unit, a temperature compensation unit, a timestamp alignment unit, a data cleaning unit, a data fusion unit, and a data transmission unit. The data receiving unit is used to receive monitoring data. The temperature compensation unit performs temperature compensation for pH, conductivity, dissolved oxygen, and UV254 absorbance based on the pH-temperature compensation formula, conductivity-temperature compensation formula, dissolved oxygen-temperature compensation formula, and UV254 absorbance value-temperature compensation formula. Timestamp After integrating the water quality data monitoring timestamps in the alignment unit, the monitoring data are arranged in chronological order, and monitoring data that does not exist under a timestamp are marked as missing. The data cleaning unit performs outlier removal and missing value imputation on the received data based on the 3σ principle and physical value range constraints to obtain a clean dataset. The data fusion unit is used to fuse clean datasets from various water treatment processes and different monitoring sites in the water transmission network at the same time stamp to obtain a fused dataset; The data transmission unit transmits the fused dataset to the water pollution identification module, the fused dataset of each water treatment process to the water treatment process control module, and the fused dataset of the water transmission network to the network regulation module.

4. The intelligent water quality monitoring device based on multi-parameter fusion sensing according to claim 1, characterized in that: The water pollution identification module includes a data receiving unit, a pollution determination unit, a historical pollution data retrieval unit, a pollution type identification unit, a pollution coefficient calculation unit, an instruction trigger determination unit, and an information transmission unit. The data receiving unit receives a water quality fusion dataset. The pollution determination unit compares the fusion dataset with a water quality standard dataset. If any water quality parameter fusion data value is greater than the corresponding standard upper limit or less than the corresponding standard lower limit, the water quality parameter is determined to be out of limit, i.e., water pollution has occurred. Otherwise, the water quality parameter is determined to be normal, i.e., no water pollution has occurred. The historical pollution data retrieval unit retrieves the number of historical pollution events, the number of events with each type of water quality parameter exceeding the limit in each type of pollution event, the total number of times each type of water quality parameter exceeding the limit occurs in pollution events, and the total number of events of each type of pollution event. The pollution type identification unit is used to identify the type of water pollution; The pollution coefficient calculation unit is used to calculate the water pollution coefficient; The instruction triggering judgment unit triggers a water process treatment instruction when the water pollution coefficient reaches a set value or the water storage volume reaches a set value. When a water treatment process command is triggered, the information transmission unit transmits a fused dataset of water pollution type and water quality to the water treatment process control module.

5. The intelligent water quality monitoring device based on multi-parameter fusion sensing according to claim 1, characterized in that: The water treatment process control module includes an information receiving unit, a process control scheme generation unit, a process treatment information feedback unit, a dynamic control unit, a water quality compliance determination unit, and a process flow switching unit. The information receiving unit is used to receive a fusion dataset of water pollution types and water quality. The process control scheme generation unit generates expected effluent water quality indicators, expected process control indicators, and theoretical process consumables for pretreatment processes, coagulation processes, sedimentation processes, filtration processes, ozone-activated carbon processes, membrane treatment processes, and disinfection processes based on a fusion dataset of water pollution types and water quality. The process information feedback unit is used to receive the fusion dataset of various water treatment processes; the dynamic control unit generates control commands based on the fusion dataset of various water treatment processes. The water quality compliance determination unit compares the water quality fusion dataset of each process with the expected effluent water quality indicators. If the fusion value of the water quality parameters of each process is greater than the lower limit of the corresponding expected effluent water quality indicator and less than the upper limit of the corresponding expected effluent water quality indicator, the water quality of the process is determined to meet the standards. The process flow switching unit allows the process to proceed to the next process flow after determining that the water quality of the previous process flow meets the standards.

6. The intelligent water quality monitoring device based on multi-parameter fusion sensing according to claim 1, characterized in that: The water quality monitoring data integration module includes a monitoring effect index collection unit, a monitoring effect index integration unit, and a data output unit. The monitoring effect index collection unit is used to collect water quality monitoring effect indexes, treatment process monitoring effect indexes, and pipeline transportation monitoring effect indexes within a preset period. The water quality monitoring effect indexes include the average monitoring coverage rate coefficient and the pollution type identification accuracy rate. The treatment process monitoring effect indexes include the average process control parameter compliance rate coefficient and the average process consumable error coefficient. The pipeline transportation monitoring effect indexes include the average transportation water quality stability coefficient and the average water quality compliance rate coefficient at the end of the pipeline. The monitoring effect index integration unit is used to calculate the water quality monitoring effect compliance coefficient, the treatment process monitoring effect compliance coefficient, and the pipeline transportation monitoring effect compliance coefficient; the data output unit is used to transmit the water quality monitoring effect compliance coefficient, the treatment process monitoring effect compliance coefficient, and the pipeline transportation monitoring effect compliance coefficient to the water quality monitoring effect evaluation module.

7. The intelligent water quality monitoring device based on multi-parameter fusion sensing according to claim 1, characterized in that: The water quality monitoring effect evaluation module includes a data receiving unit, a full-process water quality monitoring effect compliance index calculation unit, a full-process water quality monitoring effect judgment unit, and a judgment result feedback unit. The data receiving unit is used to receive the water quality monitoring effect compliance coefficient, the treatment process monitoring effect compliance coefficient, and the pipeline transportation monitoring effect compliance coefficient. The full-process water quality monitoring effect compliance index calculation unit is used to calculate the full-process water quality monitoring effect compliance index. The full-process water quality monitoring effect judgment unit determines that the full-process water quality monitoring effect meets expectations when the full-process water quality monitoring effect compliance index is greater than or equal to 0, and otherwise determines that the full-process water quality monitoring effect does not meet expectations. The judgment result feedback unit is used to feed back the full-process water quality monitoring effect judgment result and the full-process water quality monitoring effect compliance index to the water quality monitoring center.

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