Polluted water online monitoring method and device for sewage treatment

Abstract

The invention discloses a polluted water body online monitoring method and device for sewage treatment, and particularly relates to the technical field of sewage treatment monitoring. Monitoring equipment is deployed in a sewage pretreatment stage, a physical and chemical treatment stage, a biological treatment stage, an advanced treatment stage and a discharge stage and used for monitoring characteristic parameters of a polluted water body; an intelligent regulation and control instruction is generated by analyzing monitoring data of each stage, and processing process parameters are dynamically adjusted, so that process optimization is realized; according to the method, a data monitoring stability coefficient and a comprehensive pollution over-limit coefficient are calculated, a sewage treatment monitoring quality index is calculated in combination with an intelligent regulation and control reliability coefficient and a data monitoring reliability coefficient, and a method for quantitatively evaluating a monitoring effect is provided; by collecting process data in a sewage treatment log and combining with a monitoring result, the monitoring effect standard-reaching rate is dynamically updated, and a'monitoring-regulation-evaluation-optimization 'closed loop is formed.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment monitoring, and more specifically, to an online monitoring method and device for polluted water bodies used in sewage treatment. Background Art

[0002] Sewage treatment is the core link of water pollution control, and the elimination of pollutants is directly related to the degree of water pollution reduction. The monitoring method of pollutants in polluted water bodies during sewage treatment is an important tool to ensure the efficient operation of sewage treatment technology.

[0003] The existing sewage treatment monitoring method deploys monitoring equipment at the sewage discharge outlet to monitor the pH value, COD concentration, BOD5 concentration, DO concentration, TP concentration and TN concentration of the discharged sewage. If the monitoring results do not meet expectations, an alarm is issued, and the process flow is optimized based on the alarm information to ensure that the characteristic pollutant parameters of the next sewage discharge meet the indicators, which can guarantee the elimination rate of water pollutants to a certain extent.

[0004] However, the existing methods still have some problems: only deploying monitoring equipment at the discharge port makes it difficult to fully reflect the changes in pollution loads in each link of the process stage, and it is easy to miss abnormal fluctuations in the intermediate process; relying on fixed threshold alarms, the response is delayed and it is difficult to achieve adaptive water quality fluctuations, and the sewage treatment monitoring method still needs to be further optimized; the characteristic pollutant parameter indicators monitored are relatively single, which makes it difficult to meet the requirements of coordinated control of multiple pollutants. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention deploys monitoring equipment in the sewage pretreatment stage, physicochemical treatment stage, biological treatment stage, deep treatment stage and discharge stage to monitor the characteristic parameters of polluted water bodies, thereby realizing real-time monitoring of the entire sewage treatment process; by analyzing the monitoring data of each stage, intelligent control instructions are generated, and the treatment process parameters are dynamically adjusted to achieve process optimization; the monitored characteristic pollutant parameter indicators are more diverse and can better meet the requirements of coordinated control of multiple pollutants.

[0006] To achieve the above object, the present invention provides the following technical solution: an online monitoring method for polluted water for sewage treatment, comprising the following steps: S1. Deployment of monitoring equipment: Deploy monitoring equipment in the sewage pretreatment stage, physical and chemical treatment stage, biological treatment stage, deep treatment stage and discharge stage to monitor the characteristic parameters of polluted water bodies; S2. Setting monitoring standards: Setting the corresponding characteristic parameter monitoring standards for each process stage of sewage treatment; S3. Collect monitoring data of sewage treatment process stages: obtain monitoring data of polluted water bodies at each process stage of sewage treatment through the deployed monitoring equipment, and transmit the monitoring data to the database based on ZigBee and data transmission protocol; S4. Analyze monitoring data: Analyze the monitoring data collected in the pretreatment stage, physical and chemical treatment stage, biological treatment stage, and deep treatment stage, generate intelligent control instructions for the process stage, analyze the monitoring data collected in the emission stage, and calculate the data monitoring stability coefficient and the comprehensive pollution excess coefficient; S5. Intelligent control of sewage treatment process stage: generating an intelligent control plan for the process stage based on the intelligent control instructions for the process stage and controlling the sewage treatment process stage according to the generated intelligent control plan for the process stage; S6. Collecting sewage treatment process data: Collecting sewage treatment process intelligent control data and monitoring equipment application data from sewage treatment logs; S7. Analyze the sewage treatment process data: Analyze the collected sewage treatment process intelligent control data and monitoring equipment application data, and calculate the reliability coefficient of intelligent control and the reliability coefficient of data monitoring; S8. Evaluate monitoring results: calculate the sewage treatment monitoring quality index, evaluate whether the monitoring results meet expectations and update the monitoring results compliance rate.

[0007] To achieve the above object, the present invention provides the following technical solution: an online monitoring device for polluted water for sewage treatment, implementing the above-mentioned online monitoring method for polluted water for sewage treatment, comprising: Monitoring equipment deployment module: Deploy monitoring equipment in sewage pretreatment, physical and chemical treatment, biological treatment, deep treatment and discharge stages to monitor characteristic parameters of polluted water bodies; Monitoring standard setting module: set the corresponding characteristic parameter monitoring standards for polluted water bodies in the sewage pretreatment stage, physical and chemical treatment stage, biological treatment stage, deep treatment stage and discharge stage; Sewage treatment process stage monitoring data acquisition module: obtains polluted water monitoring data at each stage of sewage treatment through deployed monitoring equipment, and transmits the monitoring data to the database based on ZigBee and data transmission protocol; Process stage intelligent control instruction generation module: analyzes the collected monitoring data of the pretreatment stage, physical and chemical treatment stage, biological treatment stage and deep treatment stage, and generates process stage intelligent control instructions; Emission stage monitoring data analysis module: analyzes the collected emission stage monitoring data, calculates the data monitoring stability coefficient and the comprehensive pollution excess coefficient; Intelligent control module for sewage treatment process stage: generates intelligent control scheme for process stage based on intelligent control instructions for process stage and controls sewage treatment process stage according to the generated intelligent control scheme for process stage; Sewage treatment process data collection module: collects sewage treatment process intelligent control data and monitoring equipment application data from sewage treatment logs; Sewage treatment process data analysis module: analyzes the collected sewage treatment process intelligent control data and monitoring equipment application data, and calculates the reliability coefficient of intelligent control and data monitoring; Sewage treatment monitoring effect evaluation module: Calculate the sewage treatment monitoring quality index based on the data monitoring stability coefficient, comprehensive pollution excess coefficient, intelligent control reliability coefficient and data monitoring reliability coefficient, evaluate whether the monitoring effect meets expectations and update the monitoring effect compliance rate, and output the evaluation results, total evaluation times and updated monitoring effect compliance rate to the sewage treatment management center.

[0008] Technical effects and advantages of the present invention: 1. The present invention deploys monitoring equipment in the sewage pretreatment stage, physical and chemical treatment stage, biological treatment stage, deep treatment stage and discharge stage to monitor the characteristic parameters of polluted water bodies, thereby realizing real-time monitoring of the entire sewage treatment process; by analyzing the monitoring data of each stage, intelligent control instructions are generated, and the treatment process parameters are dynamically adjusted to achieve process optimization; the characteristic pollutant parameter indicators monitored are more diverse and can better meet the requirements of coordinated control of multiple pollutants.

[0009] 2. The present invention calculates the data monitoring stability coefficient and the comprehensive pollution exceeding limit coefficient, combines the intelligent control reliability coefficient and the data monitoring reliability coefficient, and constructs a monitoring quality index from four aspects: long-term reliability of monitoring equipment, risk of pollutant exceeding the standard, effectiveness of control measures, and operating status of monitoring equipment, to quantitatively evaluate the monitoring effect.

[0010] 3. The ZigBee protocol and data transmission protocol are used to achieve low-power, multi-node wireless networking to adapt to the complex environment of sewage treatment plants. By collecting process data in sewage treatment logs and combining monitoring results to dynamically update the monitoring effect compliance rate, a "monitoring-control-evaluation-optimization" closed loop is formed. All monitoring data, control instructions and evaluation results are stored in the database and uploaded to the management center, supporting historical data backtracking and auditing, in line with the transparency requirements of environmental protection supervision. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a diagram of the method steps of the present invention.

[0012] Figure 2 This is a connection diagram of the device modules of the present invention. DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0014] like Figure 1 The present embodiment provides an online monitoring method for polluted water for sewage treatment, comprising the following steps: S1. Deployment of monitoring equipment: Deploy monitoring equipment in the sewage pretreatment stage, physical and chemical treatment stage, biological treatment stage, deep treatment stage and discharge stage to monitor the characteristic parameters of polluted water bodies; Furthermore, the characteristic parameters of polluted water bodies in the sewage pretreatment stage include SS concentration and pH value; the characteristic parameters of polluted water bodies in the sewage physicochemical treatment stage include SS concentration, COD concentration, BOD5 concentration and sludge interface height; the characteristic parameters of polluted water bodies in the sewage biological treatment stage include DO concentration, ammonia nitrogen concentration, nitrate nitrogen concentration, MLSS and temperature; the characteristic parameters of polluted water bodies in the sewage deep treatment stage include TP concentration, residual chlorine concentration, ozone concentration and fecal coliform concentration; the characteristic parameters of polluted water bodies in the sewage discharge stage include effluent SS concentration, effluent pH value, effluent COD concentration, effluent BOD5 concentration, effluent DO concentration, effluent MLSS, effluent TP concentration, effluent TN concentration, effluent residual chlorine concentration, effluent ozone concentration and effluent fecal coliform concentration.

[0015] In this embodiment, it should be specifically explained that the values ​​of the same characteristic pollutant parameters in different process stages are different. Among the characteristic pollutant parameters, SS refers to suspended solids, COD refers to chemical oxygen demand, BOD5 refers to biochemical oxygen demand, DO refers to dissolved oxygen, MLSS refers to sludge concentration, TP refers to total phosphorus, and TN refers to total nitrogen.

[0016] In this embodiment, it should be specifically explained that the equipment for monitoring the SS concentration and pH value of the polluted water body in the pretreatment stage is a turbidity meter and a pH sensor in sequence; the equipment for monitoring the SS concentration, COD concentration, BOD5 concentration and sludge interface height of the polluted water body in the physicochemical treatment stage is a turbidity meter, a UV spectrometer online analyzer, a BOD5 meter and an ultrasonic sludge interface meter in sequence; the equipment for monitoring the DO concentration, ammonia nitrogen concentration, nitrate nitrogen concentration, MLSS and temperature of the polluted water body in the biological treatment stage is a fluorescence DO sensor, an ion selective electrode, a UV spectrometer online analyzer, an infrared absorption sludge concentration meter and a temperature sensor in sequence; the equipment for monitoring the TP concentration, residual chlorine concentration and ozone concentration of the polluted water body in the deep treatment stage is The equipment for monitoring the effluent SS concentration, pH value, effluent COD concentration, effluent BOD5 concentration, effluent DO concentration, effluent MLSS, effluent TP concentration, effluent TN concentration, effluent residual chlorine concentration, effluent ozone concentration and effluent fecal coliform concentration in turn are ammonium molybdate photometric analyzer, membrane electrode residual chlorine analyzer, ultraviolet absorption ozone detector and UV sensor array; the equipment for monitoring the effluent SS concentration, pH value, effluent COD concentration, effluent BOD5 concentration, effluent DO concentration, effluent MLSS, effluent TP concentration, effluent TN concentration, effluent residual chlorine concentration, effluent ozone concentration and effluent fecal coliform concentration in turn are turbidity meter, pH sensor, UV spectrum online analyzer, BOD5 meter, fluorescence DO sensor, infrared absorption sludge concentration meter, ammonium molybdate photometric analyzer, TN analyzer, membrane electrode residual chlorine analyzer, ultraviolet absorption ozone detector and UV sensor array in turn.

[0017] In this embodiment, it is specifically necessary to explain that the monitoring equipment turbidity meter used in the pretreatment stage is deployed behind the grid, and the pH sensor is deployed in the outlet pipe of the regulating tank; the monitoring equipment turbidity meter and UV spectrum online analyzer used in the physical and chemical treatment stage are deployed at the outlet of the coagulation tank, the BOD5 meter is deployed in the water outlet trough of the sedimentation tank, and the ultrasonic sludge interface meter is deployed on the middle wall of the sedimentation tank; the monitoring equipment used in the biological treatment stage, the fluorescence DO sensor, ion selective electrode and infrared absorption sludge concentration meter are deployed in the aerobic tank, the UV spectrum online analyzer is deployed at the bottom of the anoxic tank, and the temperature sensor is deployed at the bottom of the anoxic tank. The sensor is deployed in the anaerobic tank; in the deep treatment stage, the ammonium molybdate photometric analyzer is deployed at the filter tank outlet, the membrane electrode residual chlorine analyzer and the UV sensor array are deployed at the disinfection tank outlet, and the ultraviolet absorption ozone detector is deployed in the gas phase space of the ozone contact tank; the monitoring equipment used in the discharge stage, including turbidity meter, pH sensor, UV spectrum online analyzer, BOD5 meter, fluorescence DO sensor, infrared absorption sludge concentration meter, ammonium molybdate photometric analyzer, TN analyzer, membrane electrode residual chlorine analyzer, ultraviolet absorption ozone detector and UV sensor array, are deployed 1m upstream of the discharge port.

[0018] S2. Setting monitoring standards: Setting the corresponding characteristic parameter monitoring standards for each process stage of sewage treatment; Furthermore, the monitoring standards for polluted water bodies set in the pretreatment stage include the standard upper limit of effluent SS concentration and the standard range of pH value; the monitoring standards for polluted water bodies set in the physicochemical treatment stage include the standard upper limit of effluent SS concentration, the standard upper limit of effluent COD concentration, the standard upper limit of effluent BOD5 concentration and the upper limit of sludge interface height; the monitoring standards for polluted water bodies set in the biological treatment stage include the standard range of effluent DO concentration, the standard upper limit of effluent ammonia nitrogen concentration, the standard upper limit of effluent nitrate nitrogen concentration, the standard range of effluent MLSS and the standard range of temperature; the monitoring standards for polluted water bodies set in the deep treatment stage include the standard upper limit of effluent TP concentration, the standard upper limit of effluent residual chlorine concentration, the standard upper limit of effluent ozone concentration and the standard upper limit of effluent fecal coliform concentration.

[0019] What needs to be specifically explained in this embodiment is that the set discharge stage polluted water body monitoring standards include the standard upper limit value of effluent SS concentration, the standard upper limit value of effluent pH value, the standard upper limit value of effluent COD concentration, the standard upper limit value of effluent BOD5 concentration, the standard range of effluent DO concentration, the standard range of effluent MLSS, the standard upper limit value of effluent TP concentration, the standard upper limit value of effluent TN concentration, the standard upper limit value of effluent residual chlorine concentration, the standard upper limit value of effluent ozone concentration and the standard upper limit value of effluent fecal coliform concentration.

[0020] What needs to be specifically explained in this embodiment is that, because the physical and chemical treatment stage of sewage also involves the removal of suspended solids, the SS concentration of the polluted water body at the end of the physical and chemical treatment stage is lower than the SS concentration at the end of the pretreatment stage. Therefore, the set upper limit value of the standard SS concentration of the effluent of the polluted water body in the pretreatment stage is greater than the set upper limit value of the standard SS concentration of the effluent of the polluted water body in the physical and chemical treatment stage.

[0021] S3. Collect monitoring data of sewage treatment process stages: obtain monitoring data of polluted water bodies at each process stage of sewage treatment through the deployed monitoring equipment, and transmit the monitoring data to the database based on ZigBee and data transmission protocol; S4. Analyze monitoring data: Analyze the monitoring data collected in the pretreatment stage, physical and chemical treatment stage, biological treatment stage, and deep treatment stage, generate intelligent control instructions for the process stage, analyze the monitoring data collected in the emission stage, and calculate the data monitoring stability coefficient and the comprehensive pollution excess coefficient; Specifically, in this embodiment, the steps for generating the intelligent control instructions in the process stage are as follows: A1. Compare the numerical relationship between the SS concentration monitoring value and the standard value of the polluted water body in the pretreatment stage. If the monitoring value is greater than the standard value, an instruction to reduce the SS concentration of the polluted water body is generated. Compare the pH value monitoring value of the polluted water body in the pretreatment stage with the standard interval. If the monitoring value is greater than the maximum value of the standard interval, an instruction to reduce the pH value of the polluted water body is generated. If the monitoring value is less than the standard interval, an instruction to increase the pH value of the polluted water body is generated. If both the SS concentration and pH value of the polluted water body meet the standards, an instruction to transfer the polluted water body in the pretreatment stage is generated to transfer the polluted water body in the physical and chemical treatment stage. A2. Compare the numerical relationship between the SS concentration, COD concentration, BOD5 concentration and sludge interface height monitoring values ​​of the polluted water body in the physical and chemical treatment stage and the corresponding standard values. If the SS concentration monitoring value is greater than the standard value, an SS concentration reduction instruction is generated; if the COD concentration monitoring value is greater than the standard value, an COD concentration reduction instruction is generated; if the BOD5 concentration monitoring value is greater than the standard value, an BOD5 concentration reduction instruction is generated; if the sludge interface height monitoring value is greater than the standard value, an sludge interface height reduction instruction is generated; if the SS concentration, COD concentration, BOD5 concentration and sludge interface height monitoring values ​​of the polluted water body are all less than the corresponding standard values, a polluted water body transmission instruction is generated to transmit the polluted water body in the physical and chemical treatment stage to the biological treatment stage; A3. Compare the numerical relationship between the DO concentration, ammonia nitrogen concentration, nitrate nitrogen concentration, MLSS and temperature monitoring values ​​of the polluted water body in the sewage biological treatment stage and the corresponding standard value or standard interval. If the DO concentration monitoring value is greater than the maximum value of the standard interval, an instruction to reduce the DO concentration of the polluted water body is generated; if the DO concentration monitoring value is less than the minimum value of the standard interval, an instruction to increase the DO concentration of the polluted water body is generated; if the ammonia nitrogen concentration monitoring value is greater than the standard value, an instruction to reduce the ammonia nitrogen concentration is generated; if the nitrate nitrogen concentration monitoring value is greater than the standard value, an instruction to reduce the nitrate nitrogen concentration is generated; if the MLSS monitoring value is greater than the maximum value of the standard interval, an instruction to reduce the MLSS is generated; if the MLSS monitoring value is less than the minimum value of the standard interval, an instruction to increase the MLSS is generated; if the temperature monitoring value is greater than the maximum value of the standard interval, an instruction to reduce the temperature is generated; if the temperature monitoring value is less than the minimum value of the standard interval, an instruction to increase the temperature is generated; if the DO concentration, ammonia nitrogen concentration, nitrate nitrogen concentration, MLSS and temperature of the polluted water body meet the standards, an instruction to transfer the polluted water body in the biological treatment stage to the deep treatment stage is generated; A4. Compare the numerical relationship between the TP concentration, residual chlorine concentration, ozone concentration and fecal coliform concentration monitoring values ​​of the polluted water body in the sewage deep treatment stage and the corresponding standard values. If the TP concentration monitoring value is greater than the standard value, an instruction to reduce the TP concentration is generated; if the residual chlorine concentration monitoring value is greater than the standard value, an instruction to reduce the residual chlorine concentration is generated; if the ozone concentration monitoring value is greater than the standard value, an instruction to reduce the ozone concentration is generated; if the fecal coliform concentration monitoring value is greater than the standard value, an instruction to reduce the fecal coliform concentration is generated; if the polluted TP concentration, residual chlorine concentration, ozone concentration and fecal coliform concentration monitoring values ​​are all less than the corresponding standard values, a polluted water body transmission instruction is generated to transmit the polluted water body in the sewage deep treatment stage to the discharge stage.

[0022] Furthermore, the calculation steps of the data monitoring stability coefficient are as follows: B1. Calculate the jth monitoring data value C of the i-th characteristic parameter during sewage discharge ij and the j-1th monitoring data value C of the i-th characteristic parameter during sewage discharge ij-1 The fluctuation coefficient α ij-1 , the specific formula is: ; B2. Compare the calculated fluctuation coefficient with the preset fluctuation threshold. If the calculated value is greater than the preset fluctuation threshold, the jth monitoring data value of the i-th characteristic parameter and the j-1th fluctuation coefficient of the i-th characteristic parameter are marked as invalid. Otherwise, the jth monitoring data value of the i-th characteristic parameter and the j-1th fluctuation coefficient of the i-th characteristic parameter are marked as valid. B3. Extract the effective fluctuation coefficient, sum it up and find the average value to get the average fluctuation coefficient α of data monitoring r , the specific formula is: , α ik is the kth effective fluctuation coefficient of the ith characteristic parameter, n ai is the number of effective fluctuation coefficients of the i-th characteristic parameter, m e is the number of types of characteristic parameters of discharge of polluted water bodies; B4. Record the number of valid monitoring data values ​​and the number of invalid monitoring data values, using m ay and m aw The monitoring data efficiency coefficient α y The specific calculation formula is: ; B5. Calculate the data monitoring stability coefficient X d , the specific formula is: , c α To avoid the formula calculation result being 0, set the data monitoring stability compensation constant value, c α >0.

[0023] In this embodiment, it should be specifically explained that, for the convenience of calculation, c is often taken α=1.

[0024] Furthermore, the calculation steps of the comprehensive pollution excess coefficient are as follows: C1. Calculate the average value of effective data of the i-th characteristic parameter monitored during sewage discharge C ri , mark the calculated average value of effective data of the i-th characteristic parameter as the final monitoring result of the i-th characteristic parameter; In this embodiment, it should be specifically explained that the average value C of the effective data of the i-th characteristic parameter monitored during the sewage discharge period is ri The calculation can be performed based on any existing average value calculation formula, so the specific calculation formula is not given here.

[0025] C2. Compare the monitoring results of the i-th characteristic parameter of the polluted water body during the sewage discharge stage with the numerical value relationship of the corresponding characteristic parameter standard value or standard range. The characteristic parameters that are less than the standard value and in the standard range are marked as compliant characteristic parameters, and the remaining characteristic parameters are marked as out-of-limit characteristic parameters. Record the number of compliant characteristic parameters m by , then m by ≤m e ; C3, m by =m e When the monitoring results of each characteristic parameter of the polluted water body meet the set standards, the over-limit coefficient value of each characteristic parameter is 0, and the comprehensive pollution over-limit coefficient is the cumulative sum of the over-limit coefficients of each characteristic parameter, and its value is 0; C4, m by <m e When the over-limit coefficient X of the i-th over-limit characteristic parameter is Ci There are two calculation formulas: , , C ei is the set standard upper limit value corresponding to the i-th over-limit characteristic parameter, C uimax is the maximum value of the set standard interval corresponding to the i-th over-limit characteristic parameter, C uimin The minimum value of the set standard interval corresponding to the i-th over-limit characteristic parameter. When the set standard of the i-th over-limit characteristic parameter is the standard upper limit value, the first formula is automatically referenced to calculate the over-limit coefficient. When the set standard of the i-th over-limit characteristic parameter is the standard interval, the second formula is automatically referenced to calculate the over-limit coefficient. C5. Calculate the comprehensive pollution excess coefficient X w , the specific formula is: .

[0026] S5. Intelligent control of sewage treatment process stage: generating an intelligent control plan for the process stage based on the intelligent control instructions for the process stage and controlling the sewage treatment process stage according to the generated intelligent control plan for the process stage; S6. Collecting sewage treatment process data: Collecting sewage treatment process intelligent control data and monitoring equipment application data from sewage treatment logs; Furthermore, the intelligent control data includes the time t at which the i-th intelligent control scheme is generated. ai , the i-th intelligent control response time t bi , Expected response time of intelligent control t y , the number of control instructions generated by errors m cx And the number of control instructions m generated correctly cy The monitoring device application data includes the fault-free time t of the i-th monitoring device wci , Expected time between failures t wsi And the fault clearing time t qi .

[0027] S7. Analyze the sewage treatment process data: Analyze the collected sewage treatment process intelligent control data and monitoring equipment application data, and calculate the reliability coefficient of intelligent control and the reliability coefficient of data monitoring; Furthermore, the reliability coefficient X of the intelligent control t The specific calculation formula is: , where n b is the number of times the intelligent control scheme is generated; the reliability coefficient of the data monitoring is X c The specific calculation formula is: , where n c To monitor the number of devices.

[0028] S8. Evaluate monitoring results: calculate the sewage treatment monitoring quality index, evaluate whether the monitoring results meet expectations and update the monitoring results compliance rate.

[0029] Further, the specific steps of evaluating the monitoring effect are as follows: D1. Calculate the sewage treatment monitoring quality index YR. The specific formula is: ; D2. Compare the calculated sewage treatment monitoring quality index with the expected value. If the calculated value is greater than or equal to the expected value, the monitoring effect meets expectations; if the calculated value is less than the expected value, the monitoring effect does not meet expectations; D3. Update the monitoring effect compliance rate. The monitoring effect compliance rate coefficient is expressed as the ratio of the number of monitoring effect evaluations that meet expectations to the total number of monitoring effect evaluations; D4. Output the evaluation results, total evaluation times and updated monitoring effect compliance rate to the sewage treatment management center.

[0030] It should be specifically noted in this embodiment that the preset values, expected values, and set standard values ​​used are all selected based on actual needs and are not limited to specific values ​​here.

[0031] like Figure 2 The present embodiment shown provides an online monitoring device for polluted water bodies for sewage treatment, including a monitoring equipment deployment module, a monitoring standard setting module, a sewage treatment process stage monitoring data acquisition module, a process stage intelligent control instruction generation module, a discharge stage monitoring data analysis module, a sewage treatment process stage intelligent control module, a sewage treatment process data acquisition module, a sewage treatment process data analysis module, a sewage treatment monitoring effect evaluation module and a database.

[0032] The monitoring equipment deployment module, the monitoring standard setting module, the sewage treatment process stage monitoring data acquisition module, the process stage intelligent control instruction generation module, the sewage treatment process stage intelligent control module, the sewage treatment process data acquisition module, the sewage treatment process data analysis module, and the sewage treatment monitoring effect evaluation module are connected in communication sequence. The sewage treatment process stage monitoring data acquisition module is communicated with the discharge stage monitoring data analysis module, and the discharge stage monitoring data analysis module is communicated with the sewage treatment monitoring effect evaluation module. Each module in the device is communicated with the database.

[0033] The monitoring equipment deployment module deploys monitoring equipment in the sewage pretreatment stage, the physical and chemical treatment stage, the biological treatment stage, the deep treatment stage and the discharge stage to monitor the characteristic parameters of the polluted water body; The monitoring standard setting module sets the corresponding characteristic parameter monitoring standards of polluted water bodies in the sewage pretreatment stage, the physical and chemical treatment stage, the biological treatment stage, the deep treatment stage and the discharge stage; The sewage treatment process stage monitoring data acquisition module obtains the polluted water body monitoring data of each sewage treatment process stage through the deployed monitoring equipment, and transmits the monitoring data to the database based on ZigBee and data transmission protocol; The process stage intelligent control instruction generation module analyzes the collected monitoring data of the pretreatment stage, the physical and chemical treatment stage, the biological treatment stage and the deep treatment stage to generate the process stage intelligent control instructions; The emission phase monitoring data analysis module analyzes the collected emission phase monitoring data and calculates the data monitoring stability coefficient and the comprehensive pollution excess coefficient; The sewage treatment process stage intelligent control module generates a process stage intelligent control scheme based on the process stage intelligent control instruction and controls the sewage treatment process stage according to the generated process stage intelligent control scheme; The sewage treatment process data collection module collects sewage treatment process intelligent control data and monitoring equipment application data from the sewage treatment log; The sewage treatment process data analysis module analyzes the collected sewage treatment process intelligent control data and monitoring equipment application data, and calculates the intelligent control reliability coefficient and the data monitoring reliability coefficient; The sewage treatment monitoring effect evaluation module calculates the sewage treatment monitoring quality index based on the data monitoring stability coefficient, the comprehensive pollution over-limit coefficient, the intelligent control reliability coefficient and the data monitoring reliability coefficient, evaluates whether the monitoring effect meets expectations and updates the monitoring effect compliance rate, and outputs the evaluation results, the total number of evaluations and the updated monitoring effect compliance rate to the sewage treatment management center; The database is used to store data information of each module in the device.

[0034] Finally: 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 in the protection scope of the present invention.

Claims

1. A method for online monitoring of polluted water for sewage treatment, characterized in that: The following steps are involved: S1. Deployment of monitoring equipment: Deploy monitoring equipment in the sewage pretreatment stage, physical and chemical treatment stage, biological treatment stage, deep treatment stage and discharge stage to monitor the characteristic parameters of polluted water bodies; S2. Setting monitoring standards: Setting the corresponding characteristic parameter monitoring standards for each process stage of sewage treatment; S3. Collect monitoring data of sewage treatment process stages: obtain monitoring data of polluted water bodies at each process stage of sewage treatment through the deployed monitoring equipment, and transmit the monitoring data to the database based on ZigBee and data transmission protocol; S4. Analyze monitoring data: Analyze the monitoring data collected in the pretreatment stage, physical and chemical treatment stage, biological treatment stage, and deep treatment stage, generate intelligent control instructions for the process stage, analyze the monitoring data collected in the emission stage, and calculate the data monitoring stability coefficient and the comprehensive pollution excess coefficient; S5. Intelligent control of sewage treatment process stage: generating an intelligent control plan for the process stage based on the intelligent control instructions for the process stage and controlling the sewage treatment process stage according to the generated intelligent control plan for the process stage; S6. Collecting sewage treatment process data: Collecting sewage treatment process intelligent control data and monitoring equipment application data from sewage treatment logs; S7. Analyze the sewage treatment process data: Analyze the collected sewage treatment process intelligent control data and monitoring equipment application data, and calculate the reliability coefficient of intelligent control and the reliability coefficient of data monitoring; S8. Evaluate monitoring results: calculate the sewage treatment monitoring quality index, evaluate whether the monitoring results meet expectations and update the monitoring results compliance rate.

2. The method for online monitoring of polluted water for sewage treatment according to claim 1, characterized in that: The characteristic parameters of the polluted water body in the sewage pretreatment stage in step S1 include SS concentration and pH value; the characteristic parameters of the polluted water body in the sewage physicochemical treatment stage include SS concentration, COD concentration, BOD5 concentration and sludge interface height; the characteristic parameters of the polluted water body in the sewage biological treatment stage include DO concentration, ammonia nitrogen concentration, nitrate nitrogen concentration, MLSS and temperature; the characteristic parameters of the polluted water body in the sewage deep treatment stage include TP concentration, residual chlorine concentration, ozone concentration and fecal coliform concentration; the characteristic parameters of the polluted water body in the sewage discharge stage include effluent SS concentration, effluent pH value, effluent COD concentration, effluent BOD5 concentration, effluent DO concentration, effluent MLSS, effluent TP concentration, effluent TN concentration, effluent residual chlorine concentration, effluent ozone concentration and effluent fecal coliform concentration.

3. The method for online monitoring of polluted water for sewage treatment according to claim 1, characterized in that: The monitoring standards for polluted water bodies in the pretreatment stage set in step S2 include the standard upper limit of effluent SS concentration and the standard range of pH value; the monitoring standards for polluted water bodies in the physical and chemical treatment stage set include the standard upper limit of effluent SS concentration, the standard upper limit of effluent COD concentration, the standard upper limit of effluent BOD5 concentration and the upper limit of sludge interface height; the monitoring standards for polluted water bodies in the biological treatment stage set include the standard range of effluent DO concentration, the standard upper limit of effluent ammonia nitrogen concentration, the standard upper limit of effluent nitrate nitrogen concentration, the standard range of effluent MLSS and the standard range of temperature; the monitoring standards for polluted water bodies in the deep treatment stage set include the standard upper limit of effluent TP concentration, the standard upper limit of effluent residual chlorine concentration, the standard upper limit of effluent ozone concentration and the standard upper limit of effluent fecal coliform concentration.

4. The method for online monitoring of polluted water for sewage treatment according to claim 1, characterized in that: The step S4 data monitoring stability coefficient X d The specific calculation formula is: , c α To avoid the formula calculation result being 0, set the data monitoring stability compensation constant value, c α >0,α r is the average fluctuation coefficient of data monitoring, α y is the monitoring data efficiency coefficient; comprehensive pollution excess coefficient X w The specific calculation formula is: , m by is the number of compliance characteristic parameters, X Ci is the excess coefficient of the i-th excess characteristic parameter, m e is the number of types of emission characteristic parameters of polluted water bodies.

5. The method for online monitoring of polluted water for sewage treatment according to claim 1, characterized in that: The intelligent control data in step S6 includes the time t at which the i-th intelligent control scheme was generated ai , the i-th intelligent control response time t bi , Expected response time of intelligent control t y , the number of control instructions generated by errors m cx And the number of control instructions m generated correctly cy The monitoring device application data includes the fault-free time t of the i-th monitoring device wci , Expected time between failures t wsi And the fault clearing time t qi .

6. The method for online monitoring of polluted water for sewage treatment according to claim 1, characterized in that: The step S7 intelligently controls the reliability coefficient X t The specific calculation formula is: , where n b is the number of times the intelligent control scheme is generated; the reliability coefficient of the data monitoring is X c The specific calculation formula is: , where n c To monitor the number of devices.

7. The method for online monitoring of polluted water for sewage treatment according to claim 1, characterized in that: The specific steps of step S8 for evaluating the monitoring effect are as follows: D1. Calculate the sewage treatment monitoring quality index YR. The specific formula is: ; D2. Compare the calculated sewage treatment monitoring quality index with the expected value. If the calculated value is greater than or equal to the expected value, the monitoring effect meets expectations; if the calculated value is less than the expected value, the monitoring effect does not meet expectations; D3. Update the monitoring effect compliance rate. The monitoring effect compliance rate coefficient is expressed as the ratio of the number of monitoring effect evaluations that meet expectations to the total number of monitoring effect evaluations; D4. Output the evaluation results, total evaluation times and updated monitoring effect compliance rate to the sewage treatment management center.

8. An online monitoring device for polluted water for sewage treatment, implementing an online monitoring method for polluted water for sewage treatment as claimed in any one of claims 1 to 7, characterized in that: include: Monitoring equipment deployment module: Deploy monitoring equipment in sewage pretreatment, physical and chemical treatment, biological treatment, deep treatment and discharge stages to monitor characteristic parameters of polluted water bodies; Monitoring standard setting module: setting the corresponding characteristic parameter monitoring standards for polluted water bodies in the sewage pretreatment stage, physical and chemical treatment stage, biological treatment stage, deep treatment stage and discharge stage; Sewage treatment process stage monitoring data acquisition module: obtains polluted water monitoring data at each stage of sewage treatment through deployed monitoring equipment, and transmits the monitoring data to the database based on ZigBee and data transmission protocol; Process stage intelligent control instruction generation module: analyzes the collected monitoring data of the pretreatment stage, physical and chemical treatment stage, biological treatment stage and deep treatment stage, and generates process stage intelligent control instructions; Emission stage monitoring data analysis module: analyzes the collected emission stage monitoring data, calculates the data monitoring stability coefficient and the comprehensive pollution excess coefficient; Intelligent control module for sewage treatment process stage: generates intelligent control scheme for process stage based on intelligent control instructions for process stage and controls sewage treatment process stage according to the generated intelligent control scheme for process stage; Sewage treatment process data collection module: collects sewage treatment process intelligent control data and monitoring equipment application data from sewage treatment logs; Sewage treatment process data analysis module: analyzes the collected sewage treatment process intelligent control data and monitoring equipment application data, and calculates the reliability coefficient of intelligent control and data monitoring; Sewage treatment monitoring effect evaluation module: Calculate the sewage treatment monitoring quality index based on the data monitoring stability coefficient, comprehensive pollution excess coefficient, intelligent control reliability coefficient and data monitoring reliability coefficient, evaluate whether the monitoring effect meets expectations and update the monitoring effect compliance rate, and output the evaluation results, total evaluation times and updated monitoring effect compliance rate to the sewage treatment management center.

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