Automatic control system for concentrated dosing based on cloud computing

Abstract

The invention discloses a concentrated dosing automatic control system based on cloud computing, relates to the technical field of dosing control, and is used to solve the problems of unstable operation, difficult data acquisition and slow response speed in the traditional manual dosing process. The invention comprises a data acquisition module, a data preprocessing module, a concentration control module, a concentration fine-tuning module, a data monitoring module and a state detection module; the invention automatically realizes concentrated dosing management by collecting data of various parameters in concentrated dosing equipment, and can accurately control key parameters such as concentration, and can adjust the situation when concentration error occurs to avoid increasing errors, improve production quality and efficiency, and monitor the data of the equipment in real time through sensors, analyze and process in real time, and provide scientific basis and decision support for management personnel.

Description

Technical Field

[0001] The present invention relates to the technical field of dosing control, and in particular to a concentrated dosing automatic control system based on cloud computing. Background Art

[0002] With the rapid development of science and technology and the increasing requirements of modern production, many industries are seeking to use new technologies to improve production efficiency and quality. Among them, the cloud computing-based concentrated dosing automatic control system is one of the most popular research directions in recent years. This technology uses cloud computing and automatic control technology to achieve intelligent management and automatic control of the concentrated dosing process, greatly improving production efficiency and quality, and reducing manual operation and management costs.

[0003] However, traditional manual operations have the following problems:

[0004] Unstable manual operation: Due to the influence of human factors, manual operation often has unstable and inaccurate problems, resulting in the inability to guarantee the efficiency and stability of the concentration and dosing process;

[0005] Difficulty in data acquisition: In the traditional concentrated dosing process, there is often a lack of real-time monitoring and data aggregation of key parameters, making it difficult to accurately understand the real-time status and data information of the production process;

[0006] Slow response speed: When abnormal situations occur during the concentration and dosing process or parameters need to be adjusted, the response speed of manual operation is relatively slow, and measures cannot be taken in time to intervene and adjust, which can easily cause greater losses. Summary of the invention

[0007] The purpose of the present invention is to solve the problems of unstable operation, difficult data acquisition and slow response speed in the traditional manual dosing process, and to propose a concentrated dosing automatic control system based on cloud computing.

[0008] The purpose of the present invention can be achieved through the following technical solutions:

[0009] A concentrated dosing automatic control system based on cloud computing, comprising:

[0010] The data acquisition module is used to collect various parameters of the concentrated dosing equipment during the dosing process;

[0011] A data preprocessing module, used for preprocessing the data collected by the collection module;

[0012] A concentration control module, used to flexibly control the concentration of the target solution;

[0013] The specific control process is as follows:

[0014] First, the collected data is processed preliminarily in the data preprocessing module; the real-time concentration of the target solution is obtained from the data, and the required dosage of the drug is analyzed and calculated;

[0015] Then, the drug content in the target solution is calculated according to the volume and concentration of the target solution;

[0016] and calculating the total amount of drug in the required volume of solution based on the required concentration and the required volume of solution;

[0017] The total amount of drug is subtracted from the amount of drug in the target solution to obtain the amount of drug to be added. After the required amount of drug is added, water is added to make the volume of the target solution reach the required solution volume so that the target solution reaches the required concentration;

[0018] The concentration fine-tuning module is used to fine-tune the concentration of the target solution to reduce the error between the real-time concentration and the preset concentration;

[0019] The specific concentration fine-tuning steps include:

[0020] S1: First, verify the concentration of the target solution and detect the volume of the target solution, and then calculate the drug content in the target solution;

[0021] S2: Calculate the error between the measured reagent content and the standard reagent content; multiply the error by the proportional coefficient to obtain the proportional control component;

[0022] S3: Set a verification cycle, and add up the reagent errors of each verification to obtain the total reagent error, and multiply it by the integral coefficient to obtain the integral control air volume;

[0023] S4: Subtract the error measured in the previous verification cycle from the error measured in this verification cycle and obtain the difference in the verification cycle market. Use this difference to determine the error change rate. Multiply the error change rate obtained by the differential coefficient to obtain the differential control amount.

[0024] S5: summing the calculated proportional control component, integral control component and differential control component to obtain the additional amount of drug added to the target solution, so as to fine-tune the concentration of the target solution to reach the standard solution concentration.

[0025] Furthermore, it also includes a data monitoring module;

[0026] The data monitoring module is used to monitor abnormal data of the system;

[0027] Specific monitoring steps include:

[0028] First, a target solution concentration range is preset, and the obtained real-time concentration of the solution is compared with the target solution concentration range;

[0029] When the real-time concentration is outside the target solution concentration range, a concentration abnormality signal is generated;

[0030] After receiving the abnormal concentration signal, the concentration sensor in the data acquisition module is self-checked;

[0031] The specific self-test process includes:

[0032] First, determine the difference between the real-time concentration of the solution and the preset concentration of the solution. When the real-time concentration is higher than the preset concentration of the solution, add water to the target solution in a preset amount of water. When the real-time concentration is lower than the preset concentration of the solution, add a preset amount of medicine to the target solution.

[0033] After adding a preset amount of water or medicine to the target solution, the concentration of the target solution is detected in real time to determine whether the concentration of the target solution changes accordingly, and the change amount is compared with the preset change amount interval. When the change amount is outside the change amount interval, a concentration sensor abnormal signal is generated;

[0034] When receiving the abnormal signal of the concentration sensor, the location of the abnormal concentration sensor and the time of the abnormality are obtained, and the backup concentration sensor is started to receive the concentration data detected by the backup sensor. Before using the backup concentration sensor, the backup concentration sensor is verified to determine whether the concentration detection is accurate. When the result is qualified, the backup concentration sensor is used to perform the replacement operation to maintain the operation of the entire system;

[0035] At the same time, the acquired abnormal concentration sensor location and abnormal time are sent to the management terminal, and a buzzer and light are used to form an audible and visual alarm.

[0036] Furthermore, it also includes a state detection module;

[0037] The state detection module is used to analyze the system data and determine the system state level;

[0038] The specific system status level judgment process includes:

[0039] By acquiring the real-time concentration, flow rate and temperature in the acquisition module, an acquisition cycle is preset, and the mean values ​​of concentration, flow rate and temperature are calculated according to the concentration, flow rate and temperature acquired in each cycle, and the concentration standard deviation, flow rate standard deviation and temperature standard deviation are calculated according to the concentration mean, flow rate mean and temperature mean respectively; the obtained concentration standard deviation, flow rate standard deviation and temperature standard deviation are calculated and analyzed to obtain the parameter value;

[0040] Obtain the number of data with abnormal values ​​and outliers exceeding the preset value in the data preprocessing module, and calculate the ratio of this part of abnormal data to all data;

[0041] Then obtain the number of abnormal concentration sensor signals in the data monitoring module and the time it takes for the management terminal to receive the information and generate an audible and visual alarm after each abnormal concentration sensor signal. Then average the response time after multiple abnormal concentration sensor signals appear, and analyze and calculate to obtain the abnormal response value.

[0042] The calculated parameter values, abnormal data proportions and abnormal response values ​​are calculated and analyzed to obtain the status value;

[0043] Three continuous intervals of status values ​​are preset, each of which corresponds to a status level, and the three levels are excellent, good, and fair. The calculated status value is compared with the three status value intervals to determine the status value interval to which the status value belongs, and the system status level is determined.

[0044] Further, when the system status level is determined to be excellent in the status detection module;

[0045] Increase the cycle of status value calculation, thereby reducing the analysis load of the entire system, and at the same time double the original scheduled maintenance time, reducing the workload of maintenance personnel;

[0046] When it is determined that the system status level is good;

[0047] The calculated parameter values, abnormal data proportions and abnormal response values ​​are individually verified to determine the optimizable values, and the corresponding optimizable parameters are determined for the optimizable values, and the corresponding optimizable parameters are optimized to reduce the overall state value;

[0048] When the system status is determined to be normal;

[0049] First, the status value calculation cycle and the original scheduled maintenance cycle are shortened by half, and the system status is continuously monitored;

[0050] The sensors in the data acquisition module are calibrated, the sensors that do not meet the accuracy standards are screened, and a replacement signal is sent to the maintenance terminal, thereby reducing the calculated concentration standard deviation, flow standard deviation and temperature standard deviation, and reducing the overall parameter value;

[0051] Then, the data collection and transmission channels are tested to determine whether there is a fault. If there is a fault, a repair or replacement signal is generated and sent to the maintenance terminal. At the same time, the backup outlier determination algorithm and outlier identification method are replaced to reduce the total proportion of abnormal data.

[0052] Furthermore, the data collected by the data acquisition module from the concentrated dosing equipment includes:

[0053] Different sensors are used to collect the real-time concentration of the solution, the flow rate of the solution and the temperature during the dosing process.

[0054] Furthermore, the specific operation steps of the data preprocessing module to preprocess the data collected by the collection module are as follows:

[0055] First, after acquiring the parameter data collected by the acquisition module, outliers are removed and basic data cleaning is performed;

[0056] Then analyze the cleaned data to see if there is any noise, and use frequency domain analysis to detect the noise;

[0057] When the outliers in the data set exceed the preset value, the median filter algorithm is used to suppress the noise; and the sliding window or recursive filtering method is used to adjust and optimize the parameters of the median filter algorithm to improve the noise suppression effect;

[0058] Then detect the outliers of the initially processed data;

[0059] Delete, replace or correct the abnormal values ​​in the data to make the abnormal data tend to be normal;

[0060] Interpolation methods are used to fill in missing values ​​in the data. Specific interpolation methods include linear interpolation, polynomial interpolation or spline interpolation;

[0061] Finally, the processed data is standardized and normalized.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] (1) The present invention automatically realizes concentrated dosing management by collecting data of various parameters in the concentrated dosing equipment, and can accurately control key parameters such as concentration, and can adjust the situation when concentration errors occur to avoid increasing errors, improve production quality and efficiency, and monitor the data of the equipment in real time through sensors, analyze and process in real time, and provide scientific basis and decision support for managers;

[0064] (2) The present invention utilizes a status detection module to detect the operating status of the system. When a problem occurs, early warning can be implemented and better optimization strategies can be provided to management personnel, thereby providing better services for the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0066] Figure 1 This is the overall system block diagram of the present invention. DETAILED DESCRIPTION

[0067] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.

[0068] It should be understood that the terms "include" and "comprising" used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0069] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and claims, the singular forms of "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations.

[0070] like Figure 1 As shown, the concentrated dosing automatic control system based on cloud computing includes a data acquisition module, a data preprocessing module, a concentration control module, a concentration fine-tuning module, a data monitoring module and a state detection module;

[0071] The data acquisition module is used to collect various parameters of the concentrated dosing equipment during the dosing process;

[0072] Different sensors are used to collect the real-time concentration of the solution, the flow rate of the solution and the temperature during the dosing process;

[0073] The data preprocessing module is used to preprocess the data collected by the collection module;

[0074] After acquiring the parameter data collected by the acquisition module, outliers are removed and basic data cleaning is performed; the cleaned data is then analyzed for noise, and noise is detected using frequency domain analysis; when the outliers in the data set exceed the preset value, the median filter algorithm is used to suppress the noise; and the parameters of the median filter algorithm are adjusted and optimized using a sliding window or recursive filtering method to achieve a better noise suppression effect; the outliers in the initially processed data are then detected, where the outliers are caused by factors such as sensor failure or signal interference in the acquisition module; the outliers in the data are deleted, replaced or corrected to make the abnormal data normal; missing values ​​in the data are filled using interpolation methods, and specific interpolation methods include linear interpolation, polynomial interpolation or spline interpolation; finally, the processed data is standardized and normalized;

[0075] The concentration control module is used to flexibly control the concentration of the target solution;

[0076] According to the collected data that has been preliminarily processed in the data preprocessing module; the real-time concentration of the target solution is obtained from the data, and the required amount of the drug to be added is analyzed and calculated; the drug content in the target solution is calculated according to the volume and concentration of the target solution; and the total amount of the drug in the required solution volume is calculated according to the required concentration and the required solution volume;

[0077] The total amount of medicine calculated is subtracted from the amount of medicine in the target solution to obtain the amount of medicine required to be added. After the required amount of medicine is calculated, water is added to make the volume of the target solution reach the required solution volume so that the target solution reaches the required concentration.

[0078] The concentration fine-tuning module is used to fine-tune the concentration of the target solution to reduce the error from the preset concentration;

[0079] Verify the concentration of the target solution and measure the volume of the target solution to calculate the reagent content in the target solution; calculate the error between the measured reagent content and the standard reagent content; multiply the error by the proportional coefficient to obtain the proportional control component; set a verification cycle, and add the reagent errors of each verification to obtain the total reagent error, and multiply it by the integral coefficient to obtain the integral control air volume; subtract the error measured in the previous verification cycle from the error measured in this verification cycle and obtain the difference in the verification cycle market, use this difference to determine the error change rate, and multiply the error change rate obtained by the differential coefficient to obtain the differential control amount;

[0080] The calculated proportional control component, integral control component and differential control component are summed to obtain the additional amount of drug added to the target solution, so as to fine-tune the concentration of the target solution to reach the standard solution concentration;

[0081] The role of proportional control is to generate a control quantity proportional to the error according to the size of the error. Properly increasing the proportional coefficient can enhance the response speed of the controller. The role of integral control is to eliminate the steady-state error, so that the error that cannot be eliminated by proportional control is gradually reduced. Properly increasing the integral coefficient can reduce the steady-state error. The role of differential control is to predict the future error trend according to the rate of error change, and generate a control quantity proportional to the error change rate. Properly increasing the differential coefficient can enhance the sensitivity of the controller to error changes.

[0082] By continuously adjusting the proportional coefficient, integral coefficient and differential coefficient, the system can reach a stable state and achieve precise control of the concentrated dosing process.

[0083] The data monitoring module is used to monitor abnormal data of the system;

[0084] Preset a target solution concentration range; compare the obtained real-time concentration of the solution with the target solution concentration range; when the real-time concentration is outside the target solution concentration range, generate a concentration abnormality signal; after receiving the concentration abnormality signal, perform a self-check on the concentration sensor in the data acquisition module;

[0085] Specifically:

[0086] First, determine the difference between the real-time concentration of the solution and the preset concentration of the solution. When the real-time concentration is higher than the preset concentration of the solution, add water to the target solution in a preset amount of water. When the real-time concentration is lower than the preset concentration of the solution, add a preset amount of medicine to the target solution.

[0087] After adding a preset amount of water or medicine to the target solution, the concentration of the target solution is detected in real time to determine whether the concentration of the target solution changes accordingly, and the change amount is compared with the preset change amount interval. When the change amount is outside the change amount interval, a concentration sensor abnormal signal is generated;

[0088] When receiving the abnormal signal of the concentration sensor, the location of the abnormal concentration sensor and the time of the abnormality are obtained, and the backup concentration sensor is started to receive the concentration data detected by the backup sensor. Before using the backup concentration sensor, the backup concentration sensor is verified to determine whether the concentration detection is accurate. When the result is qualified, the backup concentration sensor is used to perform the replacement operation to maintain the operation of the entire system;

[0089] At the same time, the acquired abnormal concentration sensor location and abnormal time are sent to the management terminal, and a buzzer and light are used to form an audible and visual alarm;

[0090] The status detection module is used to analyze the system data and determine the system status level;

[0091] By acquiring the real-time concentration, flow rate and temperature in the acquisition module, an acquisition cycle is preset, and the mean values ​​of concentration, flow rate and temperature are calculated according to the concentration, flow rate and temperature acquired in each cycle, and the concentration standard deviation, flow rate standard deviation and temperature standard deviation are calculated according to the concentration mean, flow rate mean and temperature mean, and calibrated as DB, LB and WB respectively;

[0092] The concentration standard deviation DB, flow standard deviation LB and temperature standard deviation WB are normalized and then entered into the formula: To obtain the parameter value CDZ, where α, β, and χ are the preset weight coefficients of the concentration standard deviation DB, flow standard deviation LB, and temperature standard deviation WB, and their values ​​are 1.023, 0.965, and 0.982, respectively;

[0093] Obtain the number of data with abnormal values ​​and outliers exceeding the preset value in the data preprocessing module, calculate the proportion of this part of abnormal data to all data, and mark it as YZ;

[0094] Then obtain the number of abnormal concentration sensor signals in the data monitoring module and the time it takes for the management terminal to receive the information and generate an audible and visual alarm after each abnormal concentration sensor signal. Then find the average of the reaction times after multiple abnormal concentration sensor signals appear, and substitute into the formula: XZ = (SL*1.132+XS*0.996) / 0.9635 to obtain the abnormal response value XZ, where XS and SL are the number of abnormal concentration sensor signals and the average reaction time, respectively.

[0095] The calculated parameter value CDZ, abnormal data proportion YZ and abnormal response value XZ are normalized and then entered into the formula: To obtain the status value ZTZ; preset three status value continuous intervals, each status value interval corresponds to a status level, and the three levels are excellent, good and general; compare the calculated status value ZTZ with the three status value intervals to determine the status value interval to which the status value ZTZ belongs, thereby determining the status level. When the status value ZTZ is higher, the corresponding status level is lower;

[0096] When the system status level is determined to be excellent, the status value calculation cycle is increased to reduce the analysis load of the entire system. At the same time, the original scheduled maintenance time is doubled to reduce the workload of maintenance personnel.

[0097] When the system status is determined to be good, the calculated parameter value CDZ, abnormal data proportion YZ and abnormal response value XZ are individually verified to determine which values ​​can be optimized, and the corresponding optimizable parameters are determined for the optimizable values, and the corresponding optimizable parameters are optimized, thereby reducing the overall status value;

[0098] When the system status is determined to be normal, first shorten the status value calculation cycle and the originally set regular maintenance cycle by half, and continuously monitor the system status;

[0099] The sensors in the data acquisition module are calibrated, and sensors that do not meet the accuracy standards are screened. A replacement signal is sent to the maintenance terminal. At the same time, other components are screened for aging or leakage, thereby reducing the calculated concentration standard deviation, flow standard deviation, and temperature standard deviation, and reducing the overall parameter value CDZ.

[0100] Then, the data collection and transmission channels are tested to determine whether there is a fault. If there is a signal distortion or packet loss problem, a repair or replacement signal is generated and sent to the maintenance terminal. At the same time, the backup outlier determination algorithm and outlier identification method are replaced to reduce the total proportion of abnormal data by YZ.

[0101] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A cloud computing-based concentrated dosing automatic control system, characterized in that: include: The data acquisition module is used to collect various parameters of the concentrated dosing equipment during the dosing process; A data preprocessing module, used for preprocessing the data collected by the collection module; A concentration control module, used to flexibly control the concentration of the target solution; Status detection module, used to analyze system data and determine the system status level; The specific control process is as follows: First, the collected data is processed preliminarily in the data preprocessing module; the real-time concentration of the target solution is obtained from the data, and the required dosage of the drug is analyzed and calculated; Then, the drug content in the target solution is calculated according to the volume and concentration of the target solution; and calculating the total amount of drug in the required volume of solution based on the required concentration and the required volume of solution; The total amount of drug is subtracted from the amount of drug in the target solution to obtain the amount of drug to be added. After the required amount of drug is added, water is added to make the volume of the target solution reach the required solution volume so that the target solution reaches the required concentration; The concentration fine-tuning module is used to fine-tune the concentration of the target solution to reduce the error between the real-time concentration and the preset concentration; The specific concentration fine-tuning steps include: S1: First, verify the concentration of the target solution and detect the volume of the target solution, and then calculate the drug content in the target solution; S2: Calculate the error between the measured reagent content and the standard reagent content; multiply the error by the proportional coefficient to obtain the proportional control component; S3: Set a verification cycle, and add up the reagent errors of each verification to obtain the total reagent error, and multiply it by the integral coefficient to obtain the integral control air volume; S4: Subtract the error measured in the previous verification cycle from the error measured in this verification cycle and obtain the difference in the verification cycle market. Use this difference to determine the error change rate. Multiply the error change rate obtained by the differential coefficient to obtain the differential control amount. S5: summing the calculated proportional control component, integral control component and differential control component to obtain the additional amount of drug added to the target solution, so as to fine-tune the concentration of the target solution to reach the standard solution concentration; The status detection module is used to analyze and judge the system data. The system status level judgment process includes: By acquiring the real-time concentration, flow rate and temperature in the acquisition module, an acquisition cycle is preset, and the mean values ​​of concentration, flow rate and temperature are calculated according to the concentration, flow rate and temperature acquired in each cycle, and the concentration standard deviation, flow rate standard deviation and temperature standard deviation are calculated according to the concentration mean, flow rate mean and temperature mean respectively; the obtained concentration standard deviation, flow rate standard deviation and temperature standard deviation are calculated and analyzed to obtain the parameter value; Obtain the number of data with abnormal values ​​and outliers exceeding the preset value in the data preprocessing module, and calculate the ratio of this part of abnormal data to all data; Then obtain the number of abnormal concentration sensor signals in the data monitoring module and the time it takes for the management terminal to receive the information and generate an audible and visual alarm after each abnormal concentration sensor signal. Then average the response time after multiple abnormal concentration sensor signals appear, and analyze and calculate to obtain the abnormal response value. The calculated parameter values, abnormal data proportions and abnormal response values ​​are calculated and analyzed to obtain the status value; Three continuous intervals of status values ​​are preset, each of which corresponds to a status level, and the three levels are excellent, good, and fair. The calculated status value is compared with the three status value intervals to determine the status value interval to which the status value belongs, and the system status level is determined. When the status detection module determines that the system status level is excellent; increase the status value calculation cycle, thereby reducing the analysis load of the entire system, and at the same time increase the original scheduled maintenance time by two times, reducing the workload of maintenance personnel; When the system status level is determined to be good, the calculated parameter values, abnormal data proportions and abnormal response values ​​are individually verified to determine the optimizable values, and the corresponding optimizable parameters are determined for the optimizable values, and the corresponding optimizable parameters are optimized to reduce the overall status value; When the system status is determined to be general, the status value calculation cycle and the originally set regular maintenance cycle are first shortened by half, and the system status is continuously monitored; The sensors in the data acquisition module are calibrated, the sensors that do not meet the accuracy standards are screened, and a replacement signal is sent to the maintenance terminal, thereby reducing the calculated concentration standard deviation, flow standard deviation and temperature standard deviation, and reducing the overall parameter value; Then, the data collection and transmission channels are tested to determine whether there is a fault. If there is a fault, a repair or replacement signal is generated and sent to the maintenance terminal. At the same time, the backup outlier determination algorithm and outlier identification method are replaced to reduce the total proportion of abnormal data.

2. According to the cloud computing-based concentrated dosing automatic control system of claim 1, it is characterized in that: Also includes data monitoring module; The data monitoring module is used to monitor abnormal data of the system; Specific monitoring steps include: First, a target solution concentration range is preset, and the obtained real-time concentration of the solution is compared with the target solution concentration range; When the real-time concentration is outside the target solution concentration range, a concentration abnormality signal is generated; After receiving the abnormal concentration signal, the concentration sensor in the data acquisition module is self-checked; The specific self-test process includes: First, determine the difference between the real-time concentration of the solution and the preset concentration of the solution. When the real-time concentration is higher than the preset concentration of the solution, add water to the target solution in a preset amount of water. When the real-time concentration is lower than the preset concentration of the solution, add a preset amount of medicine to the target solution. After adding a preset amount of water or medicine to the target solution, the concentration of the target solution is detected in real time to determine whether the concentration of the target solution changes accordingly, and the change amount is compared with the preset change amount interval. When the change amount is outside the change amount interval, a concentration sensor abnormal signal is generated; When receiving the abnormal signal of the concentration sensor, the location of the abnormal concentration sensor and the time of the abnormality are obtained, and the backup concentration sensor is started to receive the concentration data detected by the backup sensor. Before using the backup concentration sensor, the backup concentration sensor is verified to determine whether the concentration detection is accurate. When the result is qualified, the backup concentration sensor is used to perform the replacement operation to maintain the operation of the entire system; At the same time, the acquired abnormal concentration sensor location and abnormal time are sent to the management terminal, and a buzzer and light are used to form an audible and visual alarm.

3. According to the cloud computing-based concentrated dosing automatic control system of claim 1, it is characterized in that: The data collected by the data acquisition module from the concentrated dosing equipment includes: Different sensors are used to collect the real-time concentration of the solution, the flow rate of the solution and the temperature during the dosing process.

4. The cloud computing-based concentrated dosing automatic control system according to claim 1 is characterized in that: The specific operation steps of the data preprocessing module to preprocess the data collected by the collection module are as follows: First, after acquiring the parameter data collected by the acquisition module, outliers are removed and basic data cleaning is performed; Then analyze the cleaned data to see if there is any noise, and use frequency domain analysis to detect the noise; When the outliers in the data set exceed the preset value, the median filtering algorithm is used to suppress the noise; And use sliding window or recursive filtering method to adjust and optimize the parameters of median filtering algorithm to improve the noise suppression effect; Then detect the outliers of the initially processed data; Delete, replace or correct the abnormal values ​​in the data to make the abnormal data tend to be normal; Interpolation methods are used to fill in missing values ​​in the data. Specific interpolation methods include linear interpolation, polynomial interpolation or spline interpolation; Finally, the processed data is standardized and normalized.

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