Scale inhibitor with activity period self-detection function

Abstract

The invention discloses a scale inhibitor with an activity cycle self-detection function, relates to the technical field of scale inhibitor detection, and solves the technical problems that the cycle is not reasonably set according to the correctness and mistake of data corresponding to the cycle, so that large errors exist in subsequent data acquisition, and the overall detection effect is influenced. According to the method, the data corresponding to the preset acquisition period is analyzed, whether the current preset acquisition period meets the requirement or not is judged according to the acquired correct data and the quantity of the abnormal data, and the working state of the scale inhibitor is detected and early warned by analyzing the data for the acquisition period meeting the requirement; and meanwhile, for the acquisition period which does not meet the requirement, the acquisition time periods corresponding to the normal data and the abnormal data are analyzed, and the acquisition period is properly adjusted according to the analysis result, so that the data acquisition error caused by the acquisition period is reduced, and the self-detection accuracy is further improved.

Description

Technical Field

[0001] The invention relates to the technical field of scale inhibitor detection, in particular to a scale inhibitor with active periodic self-detection. Background Art

[0002] Scale inhibitors are used to remove dirt from household utensils. When the concentration of phosphate drinking water treatment agents in water is about 3-5ppm, soluble complexes and insoluble nano-scale membranes are formed to prevent scale and corrosion, effectively preventing secondary pollution.

[0003] Traditional scale inhibitors require regular maintenance and testing to ensure their continued performance, which not only increases operating costs, but may also lead to poor scale inhibition due to human negligence. Scale inhibitors with active cycle self-detection function can monitor the concentration, stability and various parameters related to water quality of the scale inhibitor in real time through the built-in intelligent detection system, and automatically adjust the working state to achieve the best scale inhibition effect.

[0004] However, in actual use, some existing scale inhibitors with self-detection functions have artificial settings for the collection of usage data, and have not made reasonable cycle settings based on changes in corresponding data. Unreasonable cycle settings will result in the collection of multiple sets of erroneous data during the collection cycle. If detection is performed based on erroneous data, there will be a large error between the detection result and the actual effect, which will affect the overall self-detection effect. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a scale inhibitor with active cycle self-detection, which solves the problem that the cycle is not reasonably set according to the correctness or error of the data corresponding to the cycle, thereby causing large errors in subsequent data collection and affecting the overall detection effect.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a scale inhibitor with active cycle self-detection, including: a scale inhibitor body, a data processing center, a self-detection analysis module, and a detection information output module, wherein the data processing center includes multiple functional units, specifically including: a conductivity sensor, a clock chip, a low-power MCU, a turbidity sensor and a host computer.

[0007] Clock chip, this functional unit is used to collect data acquisition cycle information and transmit it to the low-power MCU at the same time.

[0008] Conductivity sensor: This functional unit is used to detect the conductivity of the scale inhibitor and transmit the detected conductivity to the low-power MCU.

[0009] Turbidity sensor, this functional unit is used to collect turbidity data of the scale inhibitor and transmit the collected turbidity data to the low-power MCU.

[0010] The host computer is used to obtain the signal processed by the low-power MCU and generate corresponding instructions according to the signal.

[0011] The data processing center is used to analyze the data collection cycle obtained by the clock chip, analyze the turbidity data and conductivity data within the data collection cycle t, and record the turbidity data as a, record the conductivity data as b, and obtain the abnormal number Ca of turbidity data a and the abnormal number Cb of conductivity data within the data collection cycle t, and calculate the proportion of abnormal data Ca and Cb as Za and Zb; the specific proportion calculated here is expressed as the proportion of all abnormal numbers to the overall number, for example, the abnormal number of turbidity data is 3, and the overall number is 7, so the calculated proportion is 3 / 7, and the proportion of conductivity data is calculated similarly.

[0012] Then, the total proportion value corresponding to the turbidity data and the conductivity data is calculated and recorded as Z1, and Z1 is compared with the preset value Zy. When the total proportion value Z1 is greater than the preset value Zy, it means that the data collected within the data collection period t has a large error, and an error analysis signal is generated at the same time. On the contrary, when the total proportion value Z1 is less than the preset value Zy, it means that the error of the data collected within the data collection period t is within an acceptable range, and a data normal signal is generated, and the data normal signal is transmitted to the self-detection analysis module at the same time; For the generated error analysis signal, the correct data within the data collection period t is collected and labeled as i, and i=1, 2, ..., j. At the same time, the collection time corresponding to the correct data i is obtained and recorded as Ti. Here, one of the groups of data is used as the analysis object, such as turbidity data is collected and analyzed as the analysis object. At the same time, it is assumed in this application that turbidity data and conductivity data are collected at the same time when data is collected. Then, the time continuity corresponding to the collection time Ti of the correct data i is judged. When the collection time is continuous, a continuous time signal is generated. Otherwise, when the collection time is not continuous, an intermittent time signal is generated. The time continuity is judged here in that if there are three or more correct data collected continuously, the corresponding time period is recorded as a continuous time period, otherwise it is an intermittent time period.

[0013] For the generated continuous time signal, the corresponding continuous time period is obtained, and the number of abnormal data after the continuous time period is determined. If there is only one abnormal data, the last normal data after the abnormal data is obtained, and the collection time corresponding to the normal data is obtained. At the same time, the continuous time period and the corresponding collection time are processed together to obtain the secondary collection cycle. On the contrary, when there are two or more abnormal data, the continuous time period is recorded as the secondary collection cycle.

[0014] For the generated intermittent time signal, the number of correct data corresponding to the intermittent time signal is obtained, and the secondary collection period is selected according to the number of correct data, and the intermittent time period with the smallest number of abnormal data between adjacent correct data is obtained, and it is used as the secondary collection period, and the secondary collection period information is generated at the same time, and then the secondary collection period information is transmitted to the self-detection analysis module.

[0015] Self-detection and analysis module, this functional module is used to re-acquire the turbidity data and conductivity data of the scale inhibitor according to the acquired secondary collection cycle information, and perform calculations based on the acquired turbidity data and conductivity data. The working status of the scale inhibitor is determined by calculating the activity percentage of the scale inhibitor. When the activity percentage is greater than the threshold, it indicates that the scale inhibitor is working normally and a monitoring signal is generated. Conversely, when the activity percentage is less than the threshold, it indicates that the scale inhibitor is working abnormally and a replacement signal is generated. The generated replacement signal is transmitted to the detection information output module at the same time.

[0016] According to the generated monitoring signal, the current activity percentage of the scale inhibitor is obtained, and the historical data is obtained at the same time, and the activity percentage change of the scale inhibitor corresponding to the period is calculated. When the activity percentage change is unstable, a data analysis signal is generated. Conversely, when the activity percentage change is stable, an early warning signal is generated.

[0017] For the generated data analysis signal, the corresponding mutation cycle is obtained and recorded as the target cycle. At the same time, the normal data and abnormal data within the target cycle are obtained and recorded as C1 and C2, where C1 represents the number of normal data and C2 represents the number of abnormal data. Then, the specific value of the abnormal data C2 is analyzed to determine the difference between the abnormal data and the normal data. The difference here is expressed as the difference calculated between all abnormal data and normal data. Then, the overall mean difference is calculated based on the obtained difference, and the mean difference is compared with the preset difference. When the mean difference is greater than the preset difference, it means that the acquired abnormal data has a large difference and needs to be re-acquired. When the mean difference is less than the preset difference, it means that the acquired abnormal data can be used for subsequent detection.

[0018] For the generated warning signal, the current activity percentage is calculated, and the difference between the activity percentage and the threshold is calculated, and the time value of the data collection cycle is obtained. Then, the activity percentage change value corresponding to the adjacent data collection cycle is calculated, and the unit time change value is further calculated based on the activity percentage change value and the time value. The unit time change value and the activity percentage difference are calculated to obtain the predicted time, and the predicted time information is generated and transmitted to the detection information output module.

[0019] Detection information output module, this functional module is used to display the acquired prediction time information to the operator.

[0020] The present invention provides a scale inhibitor with active cycle self-detection. Compared with the prior art, it has the following beneficial effects: The present invention analyzes the data corresponding to the preset collection cycle, and determines whether the current preset collection cycle meets the requirements according to the number of correct data and abnormal data collected. For the collection cycle that meets the requirements, the working state of the scale inhibitor is detected and warned by analyzing the data. At the same time, for the collection cycle that does not meet the requirements, the collection time periods corresponding to the normal data and the abnormal data are analyzed, and the collection cycle is appropriately adjusted according to the analysis results, so as to reduce the error of the collection cycle on data collection and further improve the accuracy of self-detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a block diagram of the system principle of the present invention; Figure 2 This is a functional unit diagram of the data processing center of the present invention. DETAILED DESCRIPTION

[0022] 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.

[0023] See also Figure 1 and Figure 2 The present application provides a scale inhibitor with active cycle self-detection, including: a scale inhibitor body, a data processing center, a self-detection analysis module, and a detection information output module, wherein the data processing center includes multiple functional units, specifically including: a conductivity sensor, a clock chip, a low-power MCU, a turbidity sensor, and a host computer.

[0024] Clock chip, this functional unit is used to collect data acquisition cycle information and transmit it to the low-power MCU at the same time.

[0025] Conductivity sensor: This functional unit is used to detect the conductivity of the scale inhibitor and transmit the detected conductivity to the low-power MCU.

[0026] Turbidity sensor, this functional unit is used to collect turbidity data of the scale inhibitor and transmit the collected turbidity data to the low-power MCU.

[0027] The host computer is used to obtain the signal processed by the low-power MCU and generate corresponding instructions according to the signal.

[0028] The data processing center is used to analyze the data collection cycle obtained by the clock chip, analyze the turbidity data and conductivity data within the data collection cycle t, and record the turbidity data as a, record the conductivity data as b, and obtain the abnormal number Ca of turbidity data a and the abnormal number Cb of conductivity data within the data collection cycle t, and calculate the proportion of abnormal data Ca and Cb as Za and Zb; the specific proportion calculated here is expressed as the proportion of all abnormal numbers to the overall number, for example, the abnormal number of turbidity data is 3, and the overall number is 7, so the calculated proportion is 3 / 7, and the proportion of conductivity data is calculated similarly.

[0029] Then, the total proportion value corresponding to the turbidity data and the conductivity data is calculated and recorded as Z1, and Z1 is compared with the preset value Zy. When the total proportion value Z1 is greater than the preset value Zy, it means that the data collected within the data collection period t has a large error, and an error analysis signal is generated at the same time. On the contrary, when the total proportion value Z1 is less than the preset value Zy, it means that the error of the data collected within the data collection period t is within an acceptable range, and a data normal signal is generated, and the data normal signal is transmitted to the self-detection analysis module at the same time; For the generated error analysis signal, the correct data within the data collection period t is collected and labeled as i, and i=1, 2, ..., j. At the same time, the collection time corresponding to the correct data i is obtained and recorded as Ti. Here, one of the groups of data is used as the analysis object, such as turbidity data is collected and analyzed as the analysis object. At the same time, it is assumed in this application that turbidity data and conductivity data are collected at the same time when data is collected. Then, the time continuity corresponding to the collection time Ti of the correct data i is judged. When the collection time is continuous, a continuous time signal is generated. Otherwise, when the collection time is not continuous, an intermittent time signal is generated. The time continuity is judged here in that if there are three or more correct data collected continuously, the corresponding time period is recorded as a continuous time period, otherwise it is an intermittent time period.

[0030] Assume that in an environmental monitoring system, turbidity and conductivity are monitored in real time to evaluate changes in water quality. The data collection period t is set to 1 hour, that is, data is collected once every hour. In a certain data collection period, the following turbidity data are obtained in sequence (conductivity data is also collected at the same time, but in this example we mainly focus on turbidity data): the first data point (i=1) is collected at time T1=10:00 AM, the second data point (i=2) is collected at time T2=11:00 AM, the third data point (i=3) is collected at time T3=12:05 PM, the fourth data point (i=4) is collected at time T4=01:10 PM, and the fifth data point (i=5) is collected at time T5=02:00 PM.

[0031] According to the acquisition time, we can see that the time interval between the first three data points (i=1 to 3) is 1 hour, while the time interval between the third and fourth data points (i=3 and 4) is 55 minutes, and the time interval between the fourth and fifth data points (i=4 and 5) is 50 minutes. In this example, the first three data points (i=1 to 3) show a continuous time interval of 1 hour, so they constitute a continuous time period. After the third data point, the time interval changes, so starting from the third data point (i=3), the time period becomes a discontinuous time period.

[0032] For the generated continuous time signal, the corresponding continuous time period is obtained, and the number of abnormal data after the continuous time period is determined. If there is only one abnormal data, the last normal data after the abnormal data is obtained, and the collection time corresponding to the normal data is obtained. At the same time, the continuous time period and the corresponding collection time are processed together to obtain the secondary collection cycle. On the contrary, when there are two or more abnormal data, the continuous time period is recorded as the secondary collection cycle.

[0033] Suppose that in an environmental monitoring system, a set of continuous time signals and corresponding continuous time periods are determined according to the method mentioned above. The continuous time period starts at 10:00 am and lasts until 12:00 noon. After 12:00, the system detects an abnormal data point, and then collects normal data points in the following time. According to the method, the number of abnormal data after the continuous time period needs to be determined, so it is necessary to obtain the last normal data after this abnormal data and record its corresponding collection time. Assuming that the collection time of this normal data is 1:00 pm, next, we will combine the original continuous time period (from 10:00 am to 12:00 noon) with the newly acquired normal data collection time (1:00 pm) to obtain a new time period, which is the secondary collection cycle. This secondary collection cycle covers the time period from 10:00 am to 1:00 pm.

[0034] If, in this example, there are two or more abnormal data after the continuous time period, we will not obtain the last normal data after the abnormal data, but directly record the original continuous time period as the secondary collection period. This means that the secondary collection period will remain unchanged, still from 10:00 am to 12:00 noon.

[0035] For the generated intermittent time signal, the number of correct data corresponding to the intermittent time signal is obtained, and the secondary collection period is selected according to the number of correct data, and the intermittent time period with the smallest number of abnormal data between adjacent correct data is obtained, and it is used as the secondary collection period, and the secondary collection period information is generated at the same time, and then the secondary collection period information is transmitted to the self-detection analysis module.

[0036] Self-detection analysis module, this functional module is used to re-acquire the turbidity data and conductivity data of the scale inhibitor according to the acquired secondary collection cycle information, and calculate according to the acquired turbidity data and conductivity data, and judge the working state of the scale inhibitor by calculating the activity percentage of the scale inhibitor. When the activity percentage is greater than the threshold, it means that the scale inhibitor is working normally and generates a monitoring signal. On the contrary, when the activity percentage is less than the threshold, it means that the scale inhibitor is working abnormally and generates a replacement signal. At the same time, the generated replacement signal is transmitted to the detection information output module. The threshold here is specifically 65%. According to the built-in turbidity and conductivity sensors in the scale inhibitor, data is collected periodically for one week, and after filtering, it forms the input signal of the expert system. The expert system determines the chip activity percentage through the built-in data table (the data table is obtained through theoretical calculation and experiment). If the activity is lower than 65%, a switch signal is output to inform the host computer system that the scale inhibitor needs to be replaced in time.

[0037] According to the generated monitoring signal, the current activity percentage of the scale inhibitor is obtained, and the historical data is obtained at the same time, and the activity percentage change of the scale inhibitor corresponding to the period is calculated. When the activity percentage change is unstable, a data analysis signal is generated. Conversely, when the activity percentage change is stable, an early warning signal is generated.

[0038] For the generated data analysis signal, the corresponding mutation cycle is obtained and recorded as the target cycle. At the same time, the normal data and abnormal data within the target cycle are obtained and recorded as C1 and C2, where C1 represents the number of normal data and C2 represents the number of abnormal data. Then, the specific value of the abnormal data C2 is analyzed to determine the difference between the abnormal data and the normal data. The difference here is expressed as the difference calculated between all abnormal data and normal data. Then, the overall mean difference is calculated based on the obtained difference, and the mean difference is compared with the preset difference. When the mean difference is greater than the preset difference, it means that the acquired abnormal data has a large difference and needs to be re-acquired. When the mean difference is less than the preset difference, it means that the acquired abnormal data can be used for subsequent detection.

[0039] Specifically, when the data needs to be re-acquired, the corresponding turbidity data and conductivity data are collected according to the secondary collection cycle, and the activity percentage is calculated based on the collected data, and the working state of the scale inhibitor is judged based on the calculated activity percentage.

[0040] For the generated warning signal, the current activity percentage is calculated, and the difference between the activity percentage and the threshold is calculated, and the time value of the data collection cycle is obtained. Then, the activity percentage change value corresponding to the adjacent data collection cycle is calculated, and the unit time change value is further calculated based on the activity percentage change value and the time value. The unit time change value and the activity percentage difference are calculated to obtain the predicted time, and the predicted time information is generated and transmitted to the detection information output module.

[0041] Detection information output module, this functional module is used to display the acquired prediction time information to the operator.

[0042] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0043] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A scale inhibitor with active period self-detection, characterized in that: include: The data processing center is used to analyze the acquired data collection cycle, conductivity data and turbidity data, and to adjust and judge the data collection cycle according to the errors of conductivity data and turbidity data, and to generate a data normal signal and an error analysis signal; For the error analysis signal, the time period of the correct data in the data acquisition cycle is analyzed and a continuous time signal and a discontinuous time signal are generated, and secondary acquisition cycle information is generated according to the number of correct data, and the secondary acquisition cycle information is transmitted to the self-detection analysis module; The self-detection and analysis module is used to collect the conductivity data and turbidity data of the scale inhibitor according to the acquired secondary collection cycle information, and calculate the activity percentage at the same time. The working state of the scale inhibitor is judged according to the activity percentage to generate a replacement signal and a detection signal, and the replacement signal is transmitted to the detection information output module. The monitoring signal is analyzed according to the periodic change of the activity percentage of the scale inhibitor to generate a data analysis signal and a monitoring early warning signal. At the same time, the normal data and the number of abnormal data are combined to determine whether the data needs to be collected again to process the data analysis signal, and the predicted time information is generated by the activity percentage change value, and the predicted time information is transmitted to the detection information output module.

2. The scale inhibitor with active period self-detection according to claim 1, characterized in that: The data processing center includes: a clock chip, a conductivity sensor, a turbidity sensor, a low-power MCU and a host computer; Clock chip, used to collect data collection cycle information and transmit it to low-power MCU at the same time; Conductivity sensor, used to detect the conductivity of the scale inhibitor and transmit the detected conductivity to the low-power MCU; Turbidity sensor, used to collect turbidity data from the scale inhibitor and transmit the collected turbidity data to the low-power MCU; Low-power MCU for comprehensive analysis of acquired conductivity data, turbidity data, and data collection cycle information.

3. The scale inhibitor with active period self-detection according to claim 1, characterized in that: It also includes a detection information output module for displaying the acquired prediction time information and replacement information to the operator.

4. The scale inhibitor with active period self-detection according to claim 1, characterized in that: The specific method for the data processing center to adjust and judge the data collection cycle is: The turbidity data is recorded as a, the conductivity data is recorded as b, and the corresponding proportion values ​​are calculated according to the abnormal number Ca of turbidity data a and the abnormal number Cb of conductivity data within the data collection period t and recorded as Za and Zb; The total proportion value corresponding to the turbidity data and the conductivity data is calculated and recorded as Z1, and Z1 is compared with the preset value Zy. When the total proportion value Z1 is greater than the preset value Zy, an error analysis signal is generated. Conversely, when the total proportion value Z1 is less than the preset value Zy, a data normal signal is generated.

5. The scale inhibitor with active period self-detection according to claim 1, characterized in that: The specific method in which the data processing center analyzes and processes the error analysis signal to generate the secondary acquisition cycle information is: The correct data within the data collection period t is collected and labeled as i, where i=1, 2, ..., j. At the same time, the collection time corresponding to the correct data i is obtained and recorded as Ti. Then, the time continuity corresponding to the collection time Ti of the correct data i is judged. When the collection time is continuous, a continuous time signal is generated. Otherwise, when the collection time is not continuous, an intermittent time signal is generated.

6. The scale inhibitor with active period self-detection according to claim 5, characterized in that: The specific way in which the data processing center analyzes continuous time signals and discontinuous time signals is as follows: For the generated intermittent time signal, the number of correct data corresponding to the intermittent time signal is obtained, and the secondary collection period is selected according to the number of correct data, and the intermittent time period with the smallest number of abnormal data between adjacent correct data is obtained, and it is used as the secondary collection period, and the secondary collection period information is generated at the same time; For the generated continuous time signal, the corresponding continuous time period is obtained, and the number of abnormal data after the continuous time period is determined. If there is only one abnormal data, the last normal data after the abnormal data is obtained, and the collection time corresponding to the normal data is obtained. At the same time, the continuous time period and the corresponding collection time are processed together to obtain the secondary collection cycle. On the contrary, when there are two or more abnormal data, the continuous time period is recorded as the secondary collection cycle.

7. The scale inhibitor with active period self-detection according to claim 1, characterized in that: The specific method of the self-detection analysis module to analyze according to the activity percentage is: The activity percentage of the scale inhibitor is calculated based on the acquired turbidity data and conductivity data, and the activity percentage is compared with the threshold value. When the activity percentage is greater than the threshold value, it indicates that the scale inhibitor is working normally and a monitoring signal is generated. On the contrary, when the activity percentage is less than the threshold value, it indicates that the scale inhibitor is working abnormally and a replacement signal is generated. According to the generated monitoring signal, the current activity percentage of the scale inhibitor is obtained, and the historical data is obtained at the same time, and the activity percentage change of the scale inhibitor corresponding to the period is calculated. When the activity percentage change is unstable, a data analysis signal is generated. Conversely, when the activity percentage change is stable, an early warning signal is generated.

8. The scale inhibitor with active period self-detection according to claim 7, characterized in that: The specific way in which the self-detection analysis module analyzes the data analysis signal is as follows: The corresponding mutation cycle is obtained and recorded as the target cycle. At the same time, the normal data and abnormal data in the target cycle are obtained and recorded as C1 and C2, where C1 represents the number of normal data and C2 represents the number of abnormal data. Then, the specific value of the abnormal data C2 is analyzed to determine the difference between the abnormal data and the normal data. Then, the overall average difference is calculated based on the obtained difference, and the average difference is compared with the preset difference. When the average difference is greater than the preset difference, it needs to be re-acquired. When the average difference is less than the preset difference, it does not need to be re-acquired.

9. The scale inhibitor with active period self-detection according to claim 7, characterized in that: The specific way in which the self-detection analysis module analyzes the warning signal is as follows: Calculate the current activity percentage, calculate the activity percentage difference with the threshold, and obtain the time value of the data collection cycle, then calculate the activity percentage change value corresponding to the adjacent data collection cycle, further calculate the unit time change value based on the activity percentage change value and the time value, calculate the unit time change value and the activity percentage difference to get the predicted time, and generate the predicted time information.

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