A quartz sand filter material operation state on-line monitoring and intelligent diagnosis system

Abstract

The application provides a quartz sand filter material operation state online monitoring and intelligent diagnosis system, and relates to the technical field of water treatment.The system is characterized by comprising a sensing layer, a data transmission layer, a platform processing layer and an application layer.The sensing layer comprises a filter layer pressure sensor group and a capacitive moisture content sensor group.The sensing layer is provided with multiple sensor groups at three positions, i.e., upper, middle and lower positions of the filter material layer, for identifying data information.The data transmission layer comprises a data signal collection module.The data signal collection module is used for collecting data and transmitting the data to the platform processing layer through wireless signals.The platform processing layer calls corresponding model algorithms from an algorithm model library to process data.The application layer receives decision evaluation processing results of the platform processing layer, performs real-time information early warning and supervision on corresponding administrators, and assists in remote control processing.The application has the advantages of facilitating remote management, ensuring wastewater treatment quality and reducing resource waste.

Description

Technical Field

[0001] This invention relates to the field of water treatment technology, and in particular to an online monitoring and intelligent diagnostic system for the operating status of quartz sand filter media. Background Technology

[0002] Among existing wastewater treatment technologies, quartz sand filter media is a highly efficient, economical, and reliable filtration medium. Its core technology lies in utilizing quartz sand particles of different sizes to form pore channels, which, through mechanical sieving, sedimentation, and adsorption, trap suspended solids and impurities in the water. This allows for the treatment of corresponding impurities at a low cost, achieving preliminary filtration of wastewater and reducing the operational burden on subsequent filtration structures. However, most common filter systems rely on experience, fixed time intervals, or inlet / outlet pressure differences to determine whether the filter media needs rinsing. This lack of real-time monitoring of the internal contamination status of the filter media can easily lead to untimely or excessive rinsing, resulting in reduced wastewater treatment efficiency or wasted water and electricity, thus failing to meet actual usage requirements. Summary of the Invention

[0003] The purpose of this invention is to provide an online monitoring and intelligent diagnostic system for the operating status of quartz sand filter media, which solves the technical problems of poor wastewater treatment supervision and easy water pollution or waste in the prior art, and achieves the technical effect of improving the supervision of wastewater treatment and reducing water pollution and waste.

[0004] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows:

[0005] An online monitoring and intelligent diagnostic system for the operating status of quartz sand filter media includes a sensing layer, a data transmission layer, a platform processing layer, and an application layer. The sensing layer includes a filter layer pressure sensor group and a capacitive moisture content sensor group. These sensors are installed in three sections (upper, middle, and lower) of the filter media layer, with corresponding groupings for each layer height, to identify the usage status data of the filter media layer at that height. The data transmission layer includes a data signal collection module located near the sensors. This module is connected to the sensors via lines to collect the status data from each sensor in real time and transmits it wirelessly to the platform processing layer. The platform processing layer processes the transmitted data in batches using a data processing engine, calls corresponding model algorithms from an algorithm model library for data processing, and performs decision evaluation. The application layer receives the decision evaluation results from the platform processing layer and, in conjunction with the real-time monitoring module, diagnostic processing module, and visualization processing module, provides real-time information warnings and supervision to the corresponding administrator, assisting in remote control processing.

[0006] As an improvement, the sensor group of the sensing layer can record corresponding pressure data and capacitance water content data respectively. After collecting the corresponding data signals, the data signal collection module of the data transmission layer will clean and filter them, process the redundant and duplicate data, extract the core feature information, and then classify and package them according to the processing device code and sensor code of the data source and upload them to the platform processing layer in a unified manner.

[0007] As an improvement, after receiving the data transmission from the data transmission layer, the platform processing layer will send the data to the data processing engine according to the classification, and perform data analysis on pressure gradient and capacitance moisture content respectively. The relevant formula for calculating pressure data is as follows:

[0008]

[0009] This formula is for calculating the pressure gradient, where The pressure gradient (Pa / m) is from layer i to layer i+1. Let be the reading (Pa) of the pressure sensor at the i-th layer. This represents the reading (Pa) of the pressure sensor at layer i+1. The pressure gradient is used to reflect the pressure loss rate per unit depth, where the sensor spacing is defined.

[0010]

[0011] This formula is the normalized pressure gradient formula, where The normalized gradient exponent is (0-1). The current pressure gradient, As the baseline gradient for cleanliness, The formula represents the saturated pollution state gradient and is used to facilitate unified assessment and threshold determination.

[0012] After obtaining the corresponding value, the current status information of the filter layer can be determined by comparing it with the preset threshold.

[0013] As an improvement, the platform processing layer calls the algorithm model in the library to calculate the capacitance moisture content. The capacitance moisture content is used to determine the dielectric properties of the filter layer and its working status. The relevant formula is as follows:

[0014]

[0015] This formula is the conversion formula for the water content of capacitors, where The volumetric moisture content is expressed as (%). This is the reading of the capacitance sensor (pF). This is the temperature compensation term (°C). , , , The calibration coefficients are obtained by fitting the relationship between capacitance and moisture content using a quadratic polynomial, and temperature compensation is used to eliminate the influence of ambient temperature.

[0016]

[0017] This formula represents the rate of change in relative moisture content, where The relative change rate of moisture content (%) Given the current moisture content, The initial clean state moisture content is calculated. A positive result indicates an increase in moisture content, reflecting pore blockage, while a negative result may indicate the adhesion of hydrophobic contaminants.

[0018]

[0019] This formula is a porosity estimation model, where To estimate the current porosity, The initial porosity is typically 0.35-0.45. This is the porosity attenuation coefficient.

[0020] As an improvement, after the platform processing layer processes the corresponding data, it sends it to the decision evaluation module. The decision evaluation module automatically generates corresponding chart content based on the data content and retrieves the processing plan and risk assessment model based on the feature data content.

[0021] As an improvement, the processing scheme retrieved by the platform processing layer will be directly uploaded to the application layer, and the device will be enabled and disabled according to the preset operation to achieve automated control. After the risk assessment is completed, a corresponding problem form will be summarized and uploaded to the diagnostic processing module of the application layer. After the diagnostic processing module processes the corresponding data, it will generate a corresponding interactive interface through the visualization processing module for manual control operation.

[0022] The beneficial effects of this invention are as follows: By setting a sensing layer, data status can be collected at the location of the quartz sand filter media. By observing different pressure changes in the filter media layer, the current porosity can be determined. Combined with capacitance and moisture content, the overall judgment accuracy can be further improved. By setting a data transmission layer, multiple data sources can be collected, enabling unified and effective management of several devices. By setting a platform layer, the corresponding data can be fed into the model algorithm for accurate judgment, while simultaneously retrieving the corresponding treatment plan and risk assessment. Combined with the application layer, automated management can be achieved, improving management efficiency, ensuring wastewater treatment effect, and reducing resource waste. Attached Figure Description

[0023] Figure 1 This is a structural block diagram of an online monitoring and intelligent diagnostic system for the operating status of quartz sand filter media according to the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0025] It should be noted that the terms "first" and "second" in this application are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0026] like Figure 1 As shown, an online monitoring and intelligent diagnostic system for the operating status of quartz sand filter media includes a sensing layer, a data transmission layer, a platform processing layer, and an application layer. The sensing layer includes a filter layer pressure sensor group and a capacitive moisture content sensor group. These sensors are installed in three sections (upper, middle, and lower) of the filter media layer, with corresponding groupings for each layer height, to identify the usage status data of the filter media layer at that height. The data transmission layer includes a data signal collection module located near the sensors. This module collects the status data from each sensor in real time via a line connection and transmits it wirelessly to the platform processing layer. The platform processing layer processes the transmitted data in batches using a data processing engine, calls corresponding model algorithms from an algorithm model library for data processing, and performs decision evaluation. The application layer receives the decision evaluation results from the platform processing layer and, in conjunction with the real-time monitoring module, diagnostic processing module, and visualization processing module, provides real-time information alerts and supervision to the corresponding administrator, assisting in remote control. The application layer can connect several terminals via the internet, enabling interaction between different data sources and facilitating timely access and adjustments by the administrator.

[0027] The sensor array in the sensing layer can record corresponding pressure data and capacitance-water content data respectively. After collecting the corresponding data signals, the data signal collection module in the data transmission layer cleans and filters them, removing redundant and duplicate data, extracting core feature information, and then classifying and packaging them according to the processing device code and sensor code of the data source before uploading them uniformly to the platform processing layer. The data uploaded by the sensing layer is separately categorized and stored in the database.

[0028] After receiving the data transmission layer, the platform processing layer sends the data to the data processing engine according to the classification. The engine then performs data analysis on pressure gradient and capacitance moisture content, with the relevant formulas for calculating pressure data as follows:

[0029]

[0030] This formula is for calculating the pressure gradient, where The pressure gradient (Pa / m) is from layer i to layer i+1. Let be the reading (Pa) of the pressure sensor at the i-th layer. This represents the reading (Pa) of the pressure sensor at layer i+1. The pressure gradient is used to reflect the pressure loss rate per unit depth, where the sensor spacing is defined.

[0031]

[0032] This formula is the normalized pressure gradient formula, where The normalized gradient exponent is (0-1). The current pressure gradient, As the baseline gradient for cleanliness, The formula represents the saturated pollution state gradient and is used to facilitate unified assessment and threshold determination.

[0033] After obtaining the corresponding value, the current status information of the filter layer can be determined by comparing it with the preset threshold.

[0034] The platform processing layer calls the algorithm model in the library to calculate the capacitance moisture content. The capacitance moisture content is used to determine the dielectric properties of the filter layer and its working status. The relevant formula is as follows:

[0035]

[0036] This formula is the conversion formula for the water content of capacitors, where The volumetric moisture content is expressed as (%). This is the reading of the capacitance sensor (pF). This is the temperature compensation term (°C). , , , The calibration coefficients are obtained by fitting the relationship between capacitance and moisture content using a quadratic polynomial, and temperature compensation is used to eliminate the influence of ambient temperature.

[0037]

[0038] This formula represents the rate of change in relative moisture content, where The relative change rate of moisture content (%) Given the current moisture content, The initial clean state moisture content is calculated. A positive result indicates an increase in moisture content, reflecting pore blockage, while a negative result may indicate the adhesion of hydrophobic contaminants.

[0039]

[0040] This formula is a porosity estimation model, where To estimate the current porosity, The initial porosity is typically 0.35-0.45. This is the porosity attenuation coefficient.

[0041] After processing the corresponding data, the platform processing layer sends it to the decision evaluation module. This module automatically generates corresponding charts based on the data content and retrieves processing solutions and risk assessment models based on the characteristic data. The processing solutions retrieved by the platform processing layer are directly uploaded to the application layer, and devices are enabled and disabled according to preset operations to achieve automated control. After the risk assessment is completed, a corresponding problem form is summarized and uploaded to the diagnostic processing module of the application layer. The diagnostic processing module processes the corresponding data and generates a corresponding interactive interface through the visualization processing module for manual control. The diagnostic processing module allows administrators to input corresponding strategies, which are then executed under different device usage status data, significantly shortening the overall response time and improving the overall processing effect.

[0042] During operation, construction personnel need to install pressure sensors and capacitance sensors at three locations—top, middle, and bottom—of the filter media layer to record and measure the initial baseline values. Subsequently, a data signal acquisition module collects data every five minutes and processes it accordingly. After a period of operation, multiple data changes are collected and processed. The current condition of the filter media layer is determined based on porosity and capacitance conductivity, and corresponding charts are generated in conjunction with the application layer. Simultaneously, the corresponding data information is sent to the administrator, who can then remotely control the treatment equipment through the application layer to rinse the filter media layer with clean water. During rinsing, the sensing layer monitors the corresponding data changes in real time. Once all data reaches the initial preset targets, the application layer provides feedback on the current completion status, stops the equipment cleaning process, and switches to the corresponding wastewater treatment state.

[0043] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An online monitoring and intelligent diagnostic system for the operating status of quartz sand filter media, characterized in that, The system comprises a sensing layer, a data transmission layer, a platform processing layer, and an application layer. The sensing layer includes a filter layer pressure sensor group and a capacitive moisture content sensor group. These sensors are installed in three sections (upper, middle, and lower) of the filter layer, with corresponding groups assigned to each layer height to identify the usage status data of the corresponding filter layer height. The data transmission layer includes a data signal collection module located near the sensors. This module collects the status data from each sensor in real time via a line connection and transmits it wirelessly to the platform processing layer. The platform processing layer processes the transmitted data in batches using a data processing engine, calls corresponding model algorithms from the algorithm model library for data processing, and performs decision evaluation. The application layer receives the decision evaluation results from the platform processing layer and, in conjunction with the real-time monitoring module, diagnostic processing module, and visualization processing module, provides real-time information alerts and supervision to the corresponding administrator, assisting in remote control processing.

2. The online monitoring and intelligent diagnosis system for the operating status of quartz sand filter media according to claim 1, characterized in that, The sensor group in the sensing layer can record corresponding pressure data and capacitance water content data respectively. After collecting the corresponding data signals, the data signal collection module in the data transmission layer will clean and filter them, process the redundant and duplicate data, extract the core feature information, and then classify and package them according to the processing device code and sensor code of the data source and upload them to the platform processing layer in a unified manner.

3. The online monitoring and intelligent diagnosis system for the operating status of quartz sand filter media according to claim 2, characterized in that, After receiving the data transmission layer, the platform processing layer sends the data to the data processing engine according to the classification. The engine then performs data analysis on pressure gradient and capacitance moisture content, with the relevant formulas for calculating pressure data as follows: This formula is for calculating the pressure gradient, where The pressure gradient (Pa / m) is from layer i to layer i+1. Let be the reading (Pa) of the pressure sensor at the i-th layer. This represents the reading (Pa) of the pressure sensor at layer i+1. The pressure gradient is used to reflect the pressure loss rate per unit depth, where the sensor spacing is defined. This formula is the normalized pressure gradient formula, where The normalized gradient exponent is (0-1). The current pressure gradient, As the baseline gradient for cleanliness, The formula represents the saturated pollution state gradient and is used to facilitate unified assessment and threshold determination. After obtaining the corresponding value, the current status information of the filter layer can be determined by comparing it with the preset threshold.

4. The online monitoring and intelligent diagnosis system for the operating status of quartz sand filter media according to claim 3, characterized in that, The platform processing layer calls the algorithm model in the library to calculate the capacitance moisture content. The capacitance moisture content is used to determine the dielectric properties of the filter layer and its working status. The relevant formula is as follows: This formula is the conversion formula for the water content of capacitors, where The volumetric moisture content is expressed as (%). This is the reading of the capacitance sensor (pF). This is the temperature compensation term (°C). , , , The calibration coefficients are obtained by fitting the relationship between capacitance and moisture content using a quadratic polynomial, and temperature compensation is used to eliminate the influence of ambient temperature. This formula represents the rate of change in relative moisture content, where The relative change rate of moisture content (%) Given the current moisture content, The initial clean state moisture content is calculated. A positive result indicates an increase in moisture content, reflecting pore blockage, while a negative result may indicate the adhesion of hydrophobic contaminants. This formula is a porosity estimation model, where To estimate the current porosity, The initial porosity is typically 0.35-0.

45. This is the porosity attenuation coefficient.

5. The online monitoring and intelligent diagnosis system for the operating status of quartz sand filter media according to claim 4, characterized in that, After the platform processing layer processes the corresponding data, it sends it to the decision evaluation module. The decision evaluation module automatically generates corresponding charts based on the data content and retrieves the processing plan and risk assessment model based on the feature data content.

6. The online monitoring and intelligent diagnosis system for the operating status of quartz sand filter media according to claim 5, characterized in that, The processing solution retrieved by the platform processing layer will be directly uploaded to the application layer, and the device will be enabled and disabled according to the preset operation to achieve automated control. After the risk assessment is completed, a corresponding problem form will be summarized and uploaded to the diagnostic processing module of the application layer. After the diagnostic processing module processes the corresponding data, it will generate a corresponding interactive interface through the visualization processing module for manual control operation.

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