Visual monitoring and remote control system of microorganism photosynthetic purification system

Abstract

The invention discloses a visual monitoring and remote control system of a microbial photosynthetic purification system. The visual monitoring and remote control system comprises a data acquisition module, a data processing module, a parameter adjustment module, a visual module and a data storage module, a sensor monitoring sub-module is designed, the state of a sensor can be evaluated according to abnormal data, and the abnormal state of the sensor is analyzed and warned, so that the situation that a monitoring result is lack of accuracy due to sensor faults is prevented; the intelligent decision-making sub-module is designed, control parameters are generated according to a control target and collected data, and optimal control parameters are obtained through optimization algorithm processing, so that the optimal wastewater treatment effect is achieved; an error compensation sub-module is designed, the response time is predicted by analyzing the predicted equipment state and the system control strategy, a compensation value is generated according to the response time, the optimal control parameter is compensated, and therefore the wastewater treatment effect is further improved.

Description

Technical Field

[0001] The invention relates to the technical field of wastewater treatment, in particular to a visual monitoring and remote control system for a microbial photosynthetic purification system. Background Art

[0002] The microbial photosynthetic purification system is a system that uses microorganisms to treat wastewater. Its principle is to use the photosynthesis of photosynthetic bacteria to ferment nutrient-type wastewater, thereby effectively removing carbon, nitrogen and phosphorus from the wastewater. In the prior art, in order to detect the operating status of the microbial photosynthetic purification system, it is necessary to monitor the treated wastewater and adjust the parameters of the fermentation tank according to the monitoring results. However, the existing monitoring and control system has the following defects: First, the visual monitoring of the microbial photosynthetic purification system depends on the data obtained by the sensor. Once the sensor fails, the reliability of the system will decrease, making the monitoring results inaccurate; second, the existing control system makes decisions based on preset rules or thresholds, lacks intelligent learning and optimization capabilities, and cannot achieve the best wastewater treatment effect; third, the actuator of the existing control system has a certain response time due to the equipment's own precision and control strategy, which will cause a certain error between the actual adjusted parameters and the ideal parameters, thereby affecting the wastewater treatment effect. Summary of the invention

[0003] The purpose of the present invention is to provide a visual monitoring and remote control system for a microbial photosynthetic purification system to solve the problems raised in the above background technology.

[0004] To achieve the above-mentioned purpose, the present invention provides the following technical scheme: a visual monitoring and remote control system of a microbial photosynthetic purification system, comprising a data acquisition module, a data processing module, a parameter adjustment module, a visualization module and a data storage module, wherein the data processing module establishes data connections with the data acquisition module, the parameter adjustment module, the visualization module and the data storage module respectively.

[0005] Preferably, the data acquisition module includes a temperature acquisition submodule, a light intensity acquisition submodule, a dissolved oxygen concentration acquisition submodule and a water quality acquisition submodule.

[0006] Preferably, the data processing module includes a sensor monitoring submodule, an intelligent decision-making submodule and an error compensation submodule.

[0007] Preferably, the sensor monitoring submodule includes an abnormal data extraction unit, a stability evaluation unit, an abnormal type analysis unit and an abnormal alarm unit, and the stability evaluation unit establishes data connections with the abnormal data extraction unit and the abnormal type analysis unit respectively, and the abnormal type analysis unit establishes a data connection with the abnormal alarm unit.

[0008] Preferably, the stability evaluation unit includes frequency analysis, duration analysis and trend analysis, and the abnormal alarm unit includes environmental abnormality alarm, equipment status abnormality alarm and data transmission abnormality alarm.

[0009] Preferably, the intelligent decision-making submodule includes a control parameter generation unit, a result optimization unit and an online learning unit, and the control parameter generation unit establishes data connections with the result optimization unit and the online learning unit respectively.

[0010] Preferably, the error compensation submodule includes a response time prediction unit and a compensation value generation unit, and the response time prediction unit establishes a data connection with the compensation value generation unit.

[0011] Preferably, the parameter adjustment module includes a temperature adjustment submodule, a light intensity adjustment submodule, a dissolved oxygen concentration adjustment submodule, a pH value adjustment submodule and a carbon-nitrogen ratio adjustment submodule.

[0012] Preferably, the visualization module includes a data display interface, a user interface and a remote control interface, and the data display interface includes a real-time data monitoring sub-interface and a historical data query sub-interface.

[0013] Preferably, the data storage module includes an algorithm library and a system database.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention is designed with a sensor monitoring submodule, which can evaluate the sensor status according to abnormal data, and analyze and alarm the abnormal status of the sensor, so as to prevent the lack of accuracy of the monitoring result due to sensor failure; the present invention is designed with an intelligent decision-making submodule, which generates control parameters according to the control target and the collected data, and obtains the optimal control parameters through optimization algorithm processing, so as to achieve the best wastewater treatment effect; the present invention is designed with an error compensation submodule, which predicts the response time by analyzing the predicted equipment status and the system control strategy, and generates a compensation value accordingly to compensate for the optimal control parameters, thereby further improving the wastewater treatment effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is the overall structural diagram of the present invention;

[0016] Figure 2 It is a structural block diagram of the data acquisition module of the present invention;

[0017] Figure 3 This is a structural block diagram of the sensor monitoring submodule of the present invention;

[0018] Figure 4 It is a structural block diagram of the intelligent decision-making submodule of the present invention;

[0019] Figure 5This is a structural block diagram of the error compensation submodule of the present invention;

[0020] Figure 6 It is a structural block diagram of the parameter adjustment module of the present invention;

[0021] Figure 7 It is a structural block diagram of the visualization module of the present invention;

[0022] Figure 8 It is a system flow chart of the present invention.

[0023] In the figure: 1. Data acquisition module; 11. Temperature acquisition submodule; 12. Light intensity acquisition submodule; 13. Dissolved oxygen concentration acquisition submodule; 14. Water quality acquisition submodule; 2. Data processing module; 21. Sensor monitoring submodule; 211. Abnormal data extraction unit; 212. Stability evaluation unit; 2121. Frequency analysis; 2122. Duration analysis; 2123. Trend analysis; 213. Abnormal type analysis unit; 214. Abnormal alarm unit; 2141. Environmental abnormal alarm; 2142. Equipment status abnormal alarm; 2143. Data transmission abnormal alarm; 22. Intelligent decision submodule; 221. Control parameters Generation unit; 222, result optimization unit; 223, online learning unit; 23, error compensation submodule; 231, response time prediction unit; 232, compensation value generation unit; 3, parameter adjustment module; 31, temperature adjustment submodule; 32, light intensity adjustment submodule; 33, dissolved oxygen concentration adjustment submodule; 34, pH value adjustment submodule; 35, carbon-nitrogen ratio adjustment submodule; 4, visualization module; 41, data display interface; 411, real-time data monitoring subinterface; 412, historical data query subinterface; 42, user interface; 43, remote control interface; 5, data storage module; 51, algorithm library; 52, system database. DETAILED DESCRIPTION

[0024] 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.

[0025] See also Figure 1-8The present invention provides an embodiment: a visual monitoring and remote control system for a microbial photosynthetic purification system, comprising a data acquisition module 1, a data processing module 2, a parameter adjustment module 3, a visualization module 4 and a data storage module 5, wherein the data processing module 2 establishes data connections with the data acquisition module 1, the parameter adjustment module 3, the visualization module 4 and the data storage module 5 respectively, the data acquisition module 1 is used to collect data generated by the microbial photosynthetic purification system, the data processing module 2 is used to analyze and process the collected data and generate parameter adjustment instructions, the parameter adjustment module 3 is used to execute the parameter adjustment instructions, the visualization module 4 is used to visualize the collected data, and also For human-computer interaction, the data storage module 5 is used to store the collected data and the data generated by the system operation; the data acquisition module 1 includes a temperature acquisition submodule 11, a light intensity acquisition submodule 12, a dissolved oxygen concentration acquisition submodule 13 and a water quality acquisition submodule 14, the temperature acquisition submodule 11 is used to collect the temperature data of the environment where the photosynthetic bacteria are located, the light intensity acquisition submodule 12 is used to collect the light intensity data of the environment where the photosynthetic bacteria are located, the dissolved oxygen concentration acquisition submodule 13 is used to collect the dissolved oxygen content data in the water body, and the water quality acquisition submodule 14 is used to collect the content data of chemical substances such as carbon, nitrogen and phosphorus in the water body; the data processing module 2 includes a sensor monitoring submodule 2 1. Intelligent decision-making submodule 22 and error compensation submodule 23. The sensor monitoring submodule 21 is used to monitor the state of the sensor, the intelligent decision-making submodule 22 is used to generate optimal control parameters, and the error compensation submodule 23 is used to compensate for the error of the optimal control parameters; the sensor monitoring submodule 21 includes an abnormal data extraction unit 211, a stability evaluation unit 212, an abnormal type analysis unit 213 and an abnormal alarm unit 214, and the stability evaluation unit 212 establishes data connections with the abnormal data extraction unit 211 and the abnormal type analysis unit 213 respectively, the abnormal type analysis unit 213 establishes data connections with the abnormal alarm unit 214, and the abnormal data extraction unit 211 and the abnormal type analysis unit 213 respectively establish data connections with the abnormal alarm unit 214. Unit 211 is used to extract abnormal data from the collected data, stability evaluation unit 212 evaluates the stability of the sensor according to the extracted abnormal data, and analyzes whether the sensor is abnormal, abnormal type analysis unit 213 is used to analyze the abnormal type of the sensor, and abnormal alarm unit 214 is used to issue different types of abnormal alarms; stability evaluation unit 212 includes frequency analysis 2121, duration analysis 2122 and trend analysis 2123, frequency analysis 2121 is used to analyze the frequency of occurrence of abnormal data, duration analysis 2122 is used to analyze the duration of abnormal data, and trend analysis 2123 is used to analyze the occurrence trend of abnormal data;The abnormal alarm unit 214 includes an environmental abnormality alarm 2141, an equipment state abnormality alarm 2142 and a data transmission abnormality alarm 2143. The environmental abnormality alarm 2141 indicates that the sensor abnormality is caused by environmental conditions, the equipment state abnormality alarm 2142 indicates that the sensor abnormality is caused by its own poor state, and the data transmission abnormality alarm 2143 indicates that the sensor abnormality is caused by communication interruption and data loss during data transmission. The intelligent decision-making submodule 22 includes a control parameter generation unit 221, a result optimization unit 222 and an online learning unit 223, and the control parameter generation unit 221 establishes data with the result optimization unit 222 and the online learning unit 223 respectively. According to the connection, the control parameter generation unit 221 generates multiple control parameters by using a machine learning algorithm according to the control target and the collected data, the result optimization unit 222 obtains the optimal control parameters by using an optimization algorithm, and the online learning unit 223 optimizes the control parameter generation unit 221 by using the feedback results obtained after adjustment; the error compensation submodule 23 includes a response time prediction unit 231 and a compensation value generation unit 232, and the response time prediction unit 231 establishes a data connection with the compensation value generation unit 232, the response time prediction unit 231 predicts the response time by comprehensively evaluating the device status and the control strategy, and the compensation value generation unit 232 generates the response time according to the predicted response time. The compensation value of each parameter is calculated and sent to the intelligent decision-making submodule 22, so that the optimal control parameter is combined with the compensation value; the parameter adjustment module 3 includes a temperature adjustment submodule 31, a light intensity adjustment submodule 32, a dissolved oxygen concentration adjustment submodule 33, a pH value adjustment submodule 34 and a carbon-nitrogen ratio adjustment submodule 35. The temperature adjustment submodule 31 is used to adjust the living environment temperature of photosynthetic bacteria, the light intensity adjustment submodule 32 is used to adjust the light intensity of the living environment of photosynthetic bacteria, the dissolved oxygen concentration adjustment submodule 33 is used to adjust the dissolved oxygen concentration of the water body, the pH value adjustment submodule 34 is used to adjust the pH value of the water body, and the carbon-nitrogen ratio adjustment submodule 35 is used to adjust the pH value of the water body. The carbon-nitrogen ratio of the water-saving body; the visualization module 4 includes a data display interface 41, a user interface 42 and a remote control interface 43. The data display interface 41 includes a real-time data monitoring sub-interface 411 and a historical data query sub-interface 412. The data display interface 41 is used to graphically display the operating data of the microbial photosynthetic purification system. The user interface 42 is used for user login. The remote control interface 43 is used for manual remote control of the microbial photosynthetic purification system; the data storage module 5 includes an algorithm library 51 and a system database 52. The algorithm library 51 is used to store the algorithm programs required for monitoring and control, and the system database 52 is used to store the data generated by the system operation and the collected data. ;

[0026] Working principle: When the present invention is used for visual monitoring and remote control of a microbial photosynthetic purification system, the operating data of the microbial photosynthetic purification system is first collected by the data acquisition module 1, and then sent to the data processing module 2. The data processing module 2 analyzes the data through the sensor monitoring submodule 21 to monitor whether the state of the sensor is abnormal. Specifically, the abnormal data extraction unit 211 extracts abnormal data from the collected data, and uses the stability evaluation unit 212 to analyze the abnormal data to evaluate whether the sensor is abnormal. If the evaluation result is abnormal, the abnormal type analysis unit 213 is used to further analyze the cause of the abnormality, and the abnormal alarm unit 214 sends a corresponding alarm reminder. The intelligent decision submodule 22 generates the optimal control strategy based on the control target and the collected data. Control parameters, i.e., control parameters are generated by the control parameter generation unit 221, and optimal control parameters are generated from the control parameters using the result optimization unit 222. The control parameter generation unit 221 is optimized by the online learning unit 223. The error compensation submodule 23 generates compensation values ​​according to the equipment state prediction results and the control strategy, and compensates for the optimal control parameters, i.e., the response time is predicted by the response time prediction unit 231, and the compensation value is generated by the compensation value generation unit 232. The data processing module 2 sends the compensated optimal control parameters to the parameter adjustment module 3, so as to realize the automated remote control of the microbial photosynthetic purification system. In this process, the visualization module 4 provides data visualization display, and the data storage module 5 provides data storage service;Among them, the temperature acquisition submodule 11 is used to collect temperature data of the environment in which photosynthetic bacteria are located, the light intensity acquisition submodule 12 is used to collect light intensity data of the environment in which photosynthetic bacteria are located, the dissolved oxygen concentration acquisition submodule 13 is used to collect dissolved oxygen content data in the water body, the water quality acquisition submodule 14 is used to collect data on the content of chemical substances such as carbon, nitrogen and phosphorus in the water body, the sensor monitoring submodule 21 is used to monitor the state of the sensor, the intelligent decision-making submodule 22 is used to generate optimal control parameters, the error compensation submodule 23 is used to compensate for the error of the optimal control parameters, the abnormal data extraction unit 211 is used to extract abnormal data from the collected data, and the stability evaluation unit 212 is used to extract abnormal data according to the proposed The obtained abnormal data evaluates the stability of the sensor and analyzes whether the sensor is abnormal. The abnormal type analysis unit 213 is used to analyze the abnormal type of the sensor. The abnormal alarm unit 214 is used to issue different types of abnormal alarms. The frequency analysis 2121 is used to analyze the frequency of occurrence of abnormal data. The duration analysis 2122 is used to analyze the duration of abnormal data. The trend analysis 2123 is used to analyze the occurrence trend of abnormal data. The environmental abnormality alarm 2141 indicates that the sensor abnormality is caused by environmental conditions. The device status abnormality alarm 2142 indicates that the sensor abnormality is caused by its own poor state. The data transmission abnormality alarm 2143 indicates that the sensor abnormality is caused by the data transmission process. The communication interruption and data loss in the process are caused by the control parameter generation unit 221 using a machine learning algorithm to generate multiple control parameters according to the control target and the collected data. The result optimization unit 222 uses an optimization algorithm to obtain the optimal control parameters. The online learning unit 223 optimizes the control parameter generation unit 221 using the feedback results obtained after adjustment. The response time prediction unit 231 predicts the response time by comprehensively evaluating the device status and the control strategy. The compensation value generation unit 232 generates compensation values ​​for each parameter according to the predicted response time, and sends the compensation values ​​to the intelligent decision-making submodule 22, so that the optimal control parameters are combined with the compensation values. The temperature adjustment submodule 31 is used to adjust the photosynthetic The living environment temperature of the bacteria, the light intensity adjustment submodule 32 is used to adjust the light intensity of the living environment of the photosynthetic bacteria, the dissolved oxygen concentration adjustment submodule 33 is used to adjust the dissolved oxygen concentration of the water body, the pH value adjustment submodule 34 is used to adjust the pH value of the water body, the carbon-nitrogen ratio adjustment submodule 35 is used to adjust the carbon-nitrogen ratio of the water body, the data display interface 41 is used to graphically display the operating data of the microbial photosynthetic purification system, the user interface 42 is used for user login, the remote control interface 43 is used for manual remote control of the microbial photosynthetic purification system, the algorithm library 51 is used to store the algorithm programs required for monitoring and control, and the system database 52 is used to store the data generated by the system operation and the collected data. ;

[0027] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A visual monitoring and remote control system for a microbial photosynthetic purification system, comprising a data acquisition module (1), a data processing module (2), a parameter adjustment module (3), a visualization module (4) and a data storage module (5), Features: The data processing module (2) establishes data connections with the data acquisition module (1), the parameter adjustment module (3), the visualization module (4) and the data storage module (5) respectively.

2. The visual monitoring and remote control system of the microbial photosynthetic purification system according to claim 1, Features: The data acquisition module (1) comprises a temperature acquisition submodule (11), a light intensity acquisition submodule (12), a dissolved oxygen concentration acquisition submodule (13) and a water quality acquisition submodule (14).

3. The visual monitoring and remote control system of the microbial photosynthetic purification system according to claim 1, Features: The data processing module (2) comprises a sensor monitoring submodule (21), an intelligent decision-making submodule (22) and an error compensation submodule (23).

4. The visual monitoring and remote control system of the microbial photosynthetic purification system according to claim 3, Features: The sensor monitoring submodule (21) comprises an abnormal data extraction unit (211), a stability evaluation unit (212), an abnormal type analysis unit (213) and an abnormal alarm unit (214), and the stability evaluation unit (212) respectively establishes data connections with the abnormal data extraction unit (211) and the abnormal type analysis unit (213), and the abnormal type analysis unit (213) establishes a data connection with the abnormal alarm unit (214).

5. The visual monitoring and remote control system of the microbial photosynthetic purification system according to claim 4, Features: The stability evaluation unit (212) includes frequency analysis (2121), duration analysis (2122) and trend analysis (2123), and the abnormal alarm unit (214) includes an environmental abnormality alarm (2141), a device status abnormality alarm (2142) and a data transmission abnormality alarm (2143).

6. The visual monitoring and remote control system of the microbial photosynthetic purification system according to claim 3, Features: The intelligent decision-making submodule (22) comprises a control parameter generation unit (221), a result optimization unit (222) and an online learning unit (223), and the control parameter generation unit (221) establishes data connections with the result optimization unit (222) and the online learning unit (223), respectively.

7. The visual monitoring and remote control system of the microbial photosynthetic purification system according to claim 3, Features: The error compensation submodule (23) comprises a response time prediction unit (231) and a compensation value generation unit (232), and the response time prediction unit (231) establishes a data connection with the compensation value generation unit (232).

8. The visual monitoring and remote control system of the microbial photosynthetic purification system according to claim 1, Features: The parameter adjustment module (3) comprises a temperature adjustment submodule (31), a light intensity adjustment submodule (32), a dissolved oxygen concentration adjustment submodule (33), a pH value adjustment submodule (34) and a carbon-nitrogen ratio adjustment submodule (35).

9. The visual monitoring and remote control system of the microbial photosynthetic purification system according to claim 1, Features: The visualization module (4) comprises a data display interface (41), a user interface (42) and a remote control interface (43); the data display interface (41) comprises a real-time data monitoring sub-interface (411) and a historical data query sub-interface (412).

10. The visual monitoring and remote control system of the microbial photosynthetic purification system according to claim 1, Features: The data storage module (5) comprises an algorithm library (51) and a system database (52).

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