Automatic calibration system and method for water quality sensor

Abstract

The embodiment of the present application relates to the technical field of water quality sensors, and discloses an automatic calibration system and method for water quality sensors. The system includes: a data transceiver module, which is used to establish a communication connection with multiple water quality sensors, and respectively distribute the water quality sensors to preset containers; each container is connected to a mixing chamber, and automatically prepares liquid in the mixing chamber according to the configuration scheme to simulate different water quality conditions; there is a corresponding relationship between the configuration scheme and the water quality sensor; the water quality sensor is used to detect the water quality parameters of the liquid in the mixing chamber, and send the water quality parameters to a computer device through the data transceiver module; the computer device is used to establish a parameter model for the water quality sensor according to the water quality parameters, and write the parameter model into the water quality sensor through the data transceiver module to complete the calibration. It can at least be used to solve the technical problem in the related art that the calibration method based on a single production line cannot meet the needs of complex water quality environments in practical applications.

Description

Technical Field

[0001] The present application relates to the technical field of water quality sensors, and in particular to an automatic calibration system and method for water quality sensors. Background Art

[0002] A water quality sensor is a device used to detect and measure various physical, chemical or biological properties in water. It can monitor water quality parameters in real time, such as temperature, pH, dissolved oxygen, conductivity, turbidity, chemical oxygen demand (COD), biochemical oxygen demand (BOD), ammonia nitrogen, nitrate, phosphate, heavy metal ions, etc. Water quality sensors are essential for environmental protection, industrial process control, drinking water safety, sewage treatment, agricultural irrigation, aquariums and aquaculture. Among them, the calibration of water quality sensors refers to the process of achieving the best match between the data output by the water quality sensor and the actual measured value through a series of steps and technical means.

[0003] In the calibration method of water quality sensors in related technologies, a known measured quantity (i.e., a standard quantity) is generally input into the water quality sensor to be calibrated, and the output of the water quality sensor is obtained at the same time. The obtained input and output of the water quality sensor are processed and compared to obtain a series of calibration curves that characterize the corresponding relationship between the two, thereby obtaining the actual measured results of the water quality sensor performance indicators.

[0004] However, the inventors have discovered that there are at least the following technical problems in the related art: in the related art, the calibration process of the water quality sensor basically relies on a single production line calibration method. Since the design and application of water quality sensors need to take into account the complexity and variability of water bodies, as well as the specific needs of different application scenarios, the calibration method based on a single production line in the related art cannot meet the needs of complex water quality environments in actual applications. Summary of the invention

[0005] One purpose of the present application is to provide an automatic calibration system and method for a water quality sensor, at least to solve the technical problem in the related art that the calibration method based on a single production line cannot meet the requirements of complex water quality environments in actual applications.

[0006] To achieve the above objectives, some embodiments of the present application provide the following aspects:

[0007] In the first aspect, some embodiments of the present application also provide an automatic calibration system for a water quality sensor, the system comprising: a data transceiver module, used to establish a communication connection with multiple water quality sensors, and respectively assign the water quality sensors to preset containers; each container is connected to a mixing chamber, and automatic liquid preparation is performed in the mixing chamber according to a configuration scheme to simulate different water quality conditions; there is a corresponding relationship between the configuration scheme and the water quality sensor; the water quality sensor is used to detect the water quality parameters of the liquid in the mixing chamber, and send the water quality parameters to a computer device through the data transceiver module; the computer device is used to establish a parameter model for the water quality sensor based on the water quality parameters, and write the parameter model into the water quality sensor through the data transceiver module to complete the calibration.

[0008] In a second aspect, some embodiments of the present application further provide a method for automatic calibration of a water quality sensor, the method being applied to the system as described above, the method comprising: a roll call process, a liquid preparation process, a temperature control and data acquisition process, and a parameter model deployment process; in the roll call process, the computer device sends instructions to the data transceiver module, and the data transceiver module establishes a communication connection with a plurality of water quality sensors; if the water quality sensors all respond normally, the roll call process is completed and the liquid preparation process is entered; otherwise, the system issues an alarm prompt; in the liquid preparation process, the data transceiver module assigns the water quality sensors to the preset The device comprises containers, each of which is connected to a mixing chamber, and automatically prepares liquid in the mixing chamber according to the configuration scheme to simulate different water quality conditions; in the temperature control and data acquisition process, the computer device adjusts the temperature of the liquid in the mixing chamber according to the temperature level, and collects water quality parameters detected by the water quality sensor at different temperature levels to send the water quality parameters to the computer device; in the parameter model deployment process, the water quality parameters are fitted by an algorithm, a parameter model is established for each water quality sensor, and the parameter model is written into the corresponding water quality sensor through the data transceiver module to complete the calibration.

[0009] In a third aspect, some embodiments of the present application further provide a computer device, comprising: one or more processors; and a memory storing computer program instructions, wherein the computer program instructions, when executed, cause the processor to perform the steps of the method described above.

[0010] In a fourth aspect, some embodiments of the present application further provide a computer-readable medium having computer program instructions stored thereon, wherein the computer program instructions can be executed by a processor to implement the method as described above.

[0011] Compared with the related art, in the solution provided by the embodiment of the present application, in the process of producing water quality sensors, there are often differences in the calibration process of water quality sensors of different production lines, and water quality sensors manufactured by traditional single production methods are often difficult to meet the needs of actual complex water quality scenarios. In this regard, the present application proposes a universal standard production line solution. Specifically, the system includes: a data transceiver module, which is used to establish a communication connection with multiple water quality sensors and distribute the water quality sensors to preset containers respectively; each container is connected to a mixing chamber, and automatic liquid is prepared in the mixing chamber according to the configuration scheme to simulate different water quality conditions; there is a corresponding relationship between the configuration scheme and the water quality sensor; the water quality sensor is used to detect the water quality parameters of the liquid in the mixing chamber, and send the water quality parameters to the computer device through the data transceiver module; the computer device is used to establish a parameter model for the water quality sensor according to the water quality parameters, and write the parameter model into the water quality sensor through the data transceiver module to complete the calibration. It can be seen that the present application forms an automated system by combining hardware equipment and software control. In terms of hardware, multiple water quality sensors can be managed, and the water quality sensors to be mass-produced can be automatically distributed to the container, and different standard liquids can be injected into the container to calibrate the data signal read by the water quality sensor with the known labeled concentration. In terms of software, the system can accurately control the mixing ratio of different types of standard liquids, and can also simulate various water quality conditions such as temperature, so as to calibrate the water quality sensor, and can establish a corresponding parameter model based on the data signal read by the calibrated water quality sensor and the known labeled concentration. The system can not only realize the mass calibration processing of water quality sensors, but also reduce the interference and mistakes of human operation because the system can be realized automatically, so it can also improve the production efficiency of the water quality sensor and ensure the quality reliability of the water quality sensor. Moreover, the system of the present application is universal. It only needs to select the standard parameters of the target object and set them accordingly in the water quality sensor system, so that more water quality sensors can be mass-produced according to this method, which is applicable to any type of water quality sensor. Therefore, the technical problem that the calibration method based on a single production line in the related art cannot meet the needs of complex water quality environments in practical applications can be solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0013] Figure 1An exemplary schematic diagram of an automatic calibration system for a water quality sensor provided according to some embodiments of the present application;

[0014] Figure 2 This is an exemplary schematic diagram of a data transceiver module in an automatic calibration system for a water quality sensor provided according to some embodiments of the present application;

[0015] Figure 3 This is an exemplary schematic diagram of the connection between a data transceiver module and the computer device in an automatic calibration system for a water quality sensor provided according to some embodiments of the present application;

[0016] Figure 4 An exemplary schematic diagram of a verification process based on a verification module in an automatic calibration system for a water quality sensor provided according to some embodiments of the present application;

[0017] Figure 5 An exemplary flow chart of the data analysis module based on concentration points in an automatic calibration system for a water quality sensor provided according to some embodiments of the present application;

[0018] Figure 6 An exemplary flow chart of an automatic calibration method for a water quality sensor provided according to some embodiments of the present application;

[0019] Figure 7 The present invention is a schematic diagram of an exemplary structure of a computer device provided according to some embodiments of the present application. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] First embodiment

[0022] The first embodiment of the present application relates to an automatic calibration system for a water quality sensor. The system may include:

[0023] A data transceiver module is used to establish a communication connection with a plurality of water quality sensors and allocate the water quality sensors to preset containers respectively; each container is connected to a mixing chamber, and liquid is automatically dispensed in the mixing chamber according to a configuration scheme to simulate different water quality conditions; the configuration scheme corresponds to the water quality sensor;

[0024] The water quality sensor is used to detect the water quality parameters of the liquid in the mixing chamber and send the water quality parameters to the computer device through the data transceiver module;

[0025] The computer device is used to establish a parameter model for the water quality sensor according to the water quality parameters, and write the parameter model into the water quality sensor through the data transceiver module to complete calibration.

[0026] In some application examples, see Figure 1 , Figure 1 The basic schematic diagram of the hardware in the system is shown. The data transceiver module is responsible for realizing the automatic allocation and data communication of the water quality sensor during the calibration and production process of large quantities of water quality sensors. Exemplarily, the computer device may include a database and a computing unit, the database is used to receive and store the water quality parameters, the computing unit is connected to the database, and is used to establish a parameter model for the water quality sensor according to the water quality parameters; the data transceiver module is used to communicate with the computer device and write the parameter model into the water quality sensor to complete the calibration. Exemplarily, the computer device may also include a user interface, through which relevant personnel can send instructions and display relevant data in the database.

[0027] Optionally, in some embodiments, see Figure 2 As shown, the data transceiver module may specifically include a CAN bus interface, a micro control unit, a power circuit unit, an RS485 circuit unit, a data selector and an interface array unit.

[0028] The CAN bus interface is used for communication interaction to allow the data transceiver module to interact with other components in the system.

[0029] The microcontroller unit, namely, MCU, is used to process data and control the operation of the data transceiver module.

[0030] Wherein, the power circuit unit is used to provide power to the inside of the data transceiver module and the connected water quality sensor.

[0031] The RS485 circuit unit is used to realize the communication between the 485 signal of the water quality sensor and the serial port of the microcontroller unit MCU.

[0032] The data selector may be, but is not limited to, a 16-to-1 data selector. Since the signal transmission of the water quality sensor can be performed through the 485 interface, it is generally necessary to connect two signal lines, 485A and 485B, for normal communication. Four 16-to-1 data selectors can realize the simultaneous switching of 32 signals A and B, so that a single data transceiver module can simultaneously access 32 water quality sensors.

[0033] The interface array unit may specifically be, but is not limited to, a 32-way water quality sensor interface array unit; the interface array unit may provide a plurality of interfaces for connecting the water quality sensors.

[0034] Specifically, in some examples, see Figure 3 As shown, multiple data transceiver modules, such as data transceiver module 1, data transceiver module 2, ... data transceiver module N, can be connected through a bus, so as to achieve access to more water quality sensors. Multiple data transceiver modules are connected to the computer device, and the computer device is used to monitor and control the entire system. It can be seen that the data transceiver module can send and receive data such as circuits and buses at the hardware level in the system, and can also send and receive data from multiple water quality sensors.

[0035] It can be understood that in the calibration production of small batches of water quality sensors, due to the limited number of the water quality sensors, each water quality sensor can be individually numbered. If problems are encountered during the calibration process, the abnormal water quality sensor can be quickly located and detected by numbering. However, in the mass and standardized production of related technologies, this method will lead to increased labor costs and reduced efficiency. Therefore, in this embodiment, the hardware address of the water quality sensor can be automatically assigned by using a data transceiver module to achieve large-scale production. Since multiple data transceiver modules can be connected through the bus, a large number of water quality sensors can be connected at the same time, and a large number of water quality sensors can be efficiently calibrated, which can reduce manual operations and improve production efficiency. Exemplary, after the water quality sensor is calibrated, the water quality sensor can also be tested. In some examples, each data transceiver module can handle 32 water quality sensors. By increasing the number of data transceiver modules, the system capacity can be expanded to flexibly adapt to different production scales.

[0036] Specifically, in some examples, the mixing chamber is specifically used to automatically mix different standard liquids according to the configuration scheme to simulate different water quality conditions. The configuration scheme may be issued by relevant personnel through the computer device. Water quality sensors used in different scenarios generally correspond to different configuration schemes.

[0037] Specifically, in some examples, the water quality sensor is used to detect the water quality parameters of the liquid in the mixing chamber. It is understood that the liquid in the mixing chamber is the liquid flowing into the container. Exemplarily, the water quality parameters may include but are not limited to: chemical oxygen demand COD, turbidity, ammonia nitrogen, etc. Exemplarily, the container may be a water pool with a temperature control function, which is used to represent the calibration area of ​​the water quality sensor.

[0038] Optionally, in some embodiments, the mixing chamber may be connected to a plurality of mass flow meters, and different mass flow meters are respectively connected to standard liquid containers containing standard liquids of different standards; the mass flow meters are used to control the amount of liquid flowing into the mixing chamber.

[0039] Specifically, in some examples, the number of the mass flow meters may be n, and the number of the standard liquids of different standards may correspond to n, where n is an integer greater than or equal to 1; for example, the standard liquids of different standards include standard liquid 1, standard liquid 2, and standard liquid n. The mass flow meter may be used to accurately control the amount of liquid flowing into the mixing chamber.

[0040] Further optionally, in some embodiments, the system may also include a circuit control module, which is used to control valves between the mass flow meter and standard liquids of different standards, so as to control the amount of liquid flowing into the mixing chamber, thereby controlling the mixing relationship of the liquid in the mixing chamber.

[0041] Specifically, in some examples, the circuit control module can be responsible for the electrical control of the entire system, including starting, stopping and adjusting flow, etc., which is not specifically limited in the embodiments of the present application.

[0042] Specifically, in some examples, the parameter model may include at least one of a temperature compensation model, a linear correlation model, a nonlinear correlation model, and an interferent correction model, and different parameter models may integrate different optimization algorithms. For example, the least squares method, neural network, and other methods may be used to process a nonlinear parameter model or an interferent parameter model. In addition, the computer device may determine a suitable area in the computer device to establish a corresponding parameter model according to the instruction information issued by the relevant personnel, and after generating the corresponding parameter model, the corresponding parameter model may be redeployed back to the water quality sensor.

[0043] Specifically, in some examples, the system can perform correlation analysis based on the signal value output by the water quality sensor, including calculating indicators such as mean square error, to evaluate the difference between the actual measured value of the standard substance and the expected marked value. In some embodiments, when the linear correlation is good, a linear correlation model is used; and in areas where the correlation is poor, a nonlinear correlation model is used to achieve more accurate calibration.

[0044] Optionally, in some embodiments, the computer device is specifically used to establish a parameter model for the water quality sensor based on the water quality parameters, and write the parameter model into the water quality sensor in a preset order through the data transceiver module to complete calibration.

[0045] Specifically, in some examples, the data transceiver module writes the parameter model into the water quality sensor, and the relevant personnel can flexibly choose to use the parameter model through the computer device according to the actual situation. This application does not impose any restrictions on the type of the parameter model and the calibration order based on the parameter model. For example, it can be calibrated based on the interference correction model first, then calibrated based on the temperature compensation model, then calibrated based on the nonlinear parameter model, and finally calibrated based on the linear parameter model. However, it should be understood that in the production process of water quality sensors, calibration is usually carried out in a top-to-bottom order. This is because if the temperature continues to change during the calibration process, the signal of the water quality sensor itself will become unstable. For example, in the temperature compensation model, the rise in temperature will affect the signal of the detector in the water quality sensor. If temperature compensation is not performed first and interference correction is performed directly, the collected signal may be inaccurate in itself, because the signal without temperature compensation cannot establish a good model correlation with the interference. In addition, if the signal stability has been guaranteed, the interference that causes signal drift or offset is likely to be caused by the interference. Therefore, in actual operation, although the calibration order of the parameter model can be flexibly adjusted, considering the impact of temperature on the stability of the water quality sensor signal, the temperature compensation model is usually calibrated first, and then the calibration of other parameter models is performed. This can ensure that the signal collected during the calibration process is accurate, thereby improving the relevance and accuracy of the parameter model. In other words, under normal circumstances, the temperature compensation model can be calibrated first to ensure the signal stability of the water quality sensor, and then the data calibration of the linear correlation model and the nonlinear correlation model can be carried out in turn, and finally the data calibration of the interferent model can be carried out.

[0046] It should be noted that the parameter model constructed in this application is not only applicable to chemical oxygen demand COD sensors, but can also be used for other types of water quality sensors, because the goal of this application is to establish a set of universal processes. That is, other types of water quality sensors can also use the system provided by this application to enter a standardized production line for production. In other words, as long as the standards for the liquid and the system settings of the water quality sensor are determined, more water quality sensors can be produced according to the system. It can be seen that this application incorporates versatility into the system design, which has significant advantages over the system in the related art that can only manufacture a single production line.

[0047] It is not difficult to find that compared with the related art, in the solution provided by the embodiment of the present application, corresponding to the process of producing water quality sensors, there are often differences in the calibration process of water quality sensors of different production lines, and water quality sensors manufactured by traditional single production methods are often difficult to meet the needs of actual complex water quality scenarios. In this regard, the present application proposes a universal standard production line solution. Specifically, the system includes: a data transceiver module, which is used to establish a communication connection with multiple water quality sensors and distribute the water quality sensors to preset containers respectively; each container is connected to a mixing chamber, and automatic liquid is prepared in the mixing chamber according to the configuration scheme to simulate different water quality conditions; the configuration scheme has a corresponding relationship with the water quality sensor; the water quality sensor is used to detect the water quality parameters of the liquid in the mixing chamber, and send the water quality parameters to the computer device through the data transceiver module; the computer device is used to establish a parameter model for the water quality sensor according to the water quality parameters, and write the parameter model into the water quality sensor through the data transceiver module to complete the calibration. It can be seen that the present application forms an automated system by combining hardware equipment and software control. In terms of hardware, multiple water quality sensors can be managed, and the water quality sensors to be mass-produced can be automatically distributed to the container, and different standard liquids can be injected into the container to calibrate the data signal read by the water quality sensor with the known labeled concentration. In terms of software, the system can accurately control the mixing ratio of different types of standard liquids, and can also simulate various water quality conditions such as temperature, so as to calibrate the water quality sensor, and can establish a corresponding parameter model based on the data signal read by the calibrated water quality sensor and the known labeled concentration. The system can not only realize the mass calibration processing of water quality sensors, but also reduce the interference and mistakes of human operation because the system can be realized automatically, so it can also improve the production efficiency of the water quality sensor and ensure the quality reliability of the water quality sensor. Moreover, the system of the present application is universal. It only needs to select the standard parameters of the target object and set them accordingly in the water quality sensor system, so that more water quality sensors can be mass-produced according to this method, which is applicable to any type of water quality sensor. Therefore, the technical problem that the calibration method based on a single production line in the related art cannot meet the needs of complex water quality environments in practical applications can be solved.

[0048] Second embodiment

[0049] The second embodiment of the present application relates to an automatic calibration system for a water quality sensor. The second embodiment is an improvement on the first embodiment, and the specific improvement is: in the second embodiment of the present application, the container also includes a temperature control module, and the temperature control module is connected to the computer device.

[0050] Specifically, in some embodiments, the temperature control module is used to control the temperature of the liquid in the container according to the preset temperature level and preset time sent by the computer device after the computer device detects that the automatic liquid preparation based on the mixing chamber is completed, so that the water quality sensor can detect the water quality parameters of the liquid at each temperature level and send the water quality parameters at each temperature level to the computer device.

[0051] Specifically, in some examples, the computer device can detect whether the automatic liquid preparation process in the mixing chamber is completed. Once the liquid preparation process is completed, the computer device can send two preset values ​​to the temperature control module: a preset temperature level and a preset time. The preset values ​​are used to enable the system to simulate different environmental conditions and test requirements. The temperature control module can accurately control the temperature of the solution according to the received preset temperature level and time to simulate different temperature conditions. It can be understood that temperature is crucial to the measurement of certain water quality parameters. In this way, at the set temperature, the water quality sensor will be used to detect the water quality parameters of the liquid. The water quality parameters detected at each temperature level will be sent back to the computer device. In this way, the system can record and analyze water quality changes under different temperature conditions.

[0052] Further optionally, in some embodiments, the system may further include a waste liquid collection device; the waste liquid collection device is connected to the container and is used to collect the liquid in the container.

[0053] That is, the waste liquid collecting device is used to collect and process the waste liquid generated by the container during the test process to prevent environmental pollution.

[0054] Further optionally, in some embodiments, at least one mass flow meter is connected to a pure water container containing pure water; the circuit control module is further used to control the valve after the computer device obtains the water quality parameters of the liquid at each temperature level, so that the pure water container cleans: the water path, the mixing chamber and the cavity of the container. In this way, preparation can be made for the preparation of the next standard liquid or the deployment of the water quality sensor parameter model.

[0055] Specifically, when changing the sample solution for calibration, for example, calibrating the chemical oxygen demand COD this time and calibrating the ammonia nitrogen next time, it is necessary to ensure that the pipeline, mixing chamber and container are clean to prevent the current solution from mixing with the previous solution to form a contaminated solution, thereby generating impurities and interference, affecting the next round of detection. Therefore, the system can automatically clean the pipelines, mixing chambers and containers in the entire water circuit, especially to avoid the previous residual high-concentration solution from causing incorrect calibration of the water quality sensor, ensuring the accuracy of the signal during data collection.

[0056] Optionally, in some embodiments, a liquid level water quality sensor may be further provided in the container; the liquid level water quality sensor is used to monitor the liquid level of the liquid in the container.

[0057] Specifically, in some examples, the liquid level water quality sensor is used to ensure that the amount of liquid in the container is within an appropriate range.

[0058] It is not difficult to find that in the embodiment of the present application, the container may also include a temperature control module, and the temperature control module is connected to the computer device; the temperature control module is used to control the temperature of the liquid in the container according to the preset temperature level and preset time sent by the computer device after the computer device detects that the automatic liquid preparation based on the mixing chamber is completed, so that the water quality sensor can detect the water quality parameters of the liquid at each temperature level, and send the water quality parameters at each temperature level to the computer device, which is conducive to improving the accuracy of the marking results.

[0059] Third embodiment

[0060] The third embodiment of the present application relates to an automatic calibration system for a water quality sensor. The third embodiment is an improvement on the first embodiment, and the specific improvement is that in the third embodiment of the present application, the computer device also includes a verification module and a data analysis module, providing a water quality sensor production calibration verification and optimization process.

[0061] Specifically, in some embodiments, the computer device further includes a verification module, the verification module is used to verify the water quality sensor; the verification module includes at least one of the following: an aging test module, a temperature compensation experiment module, a standard solution calibration experiment module, and a mixture standard solution calibration experiment module;

[0062] The aging test module is used to continuously operate the water quality sensor for a preset time to obtain a first verification result;

[0063] The temperature compensation experiment module is used to perform a temperature compensation experiment on the water quality sensor to calibrate the performance of the water quality sensor at different temperatures and obtain a second verification result;

[0064] The standard solution calibration experiment module is used to perform a standard solution calibration experiment on the water quality sensor to obtain a third verification result;

[0065] The mixture standard solution calibration experiment module is used to perform a mixture standard solution calibration experiment on the water quality sensor to obtain a fourth verification result;

[0066] The computer device also includes a data analysis module;

[0067] The data analysis module is used to perform data analysis based on at least one of the first verification result, the second verification result, the third verification result and the fourth verification result to obtain an analysis result for reducing concentration points in the calibration process.

[0068] See also Figure 4 As shown, an exemplary schematic diagram of the verification process based on the verification module is provided.

[0069] Exemplarily, for the aging test module, the preset time can be but is not limited to: 12 to 48 hours. That is to say, the water quality sensor can be allowed to run continuously for several days under constant temperature conditions to ensure its stability. It can be understood that in the initial stage of a newly produced water quality sensor, since the internal circuit components, light sources, detectors, etc. are in a brand new state, the water quality sensor has an unstable period. In order to ensure that the response signal of the water quality sensor is stable, an aging test can be performed by allowing the water quality sensor to run continuously for 12 to 48 hours so that its various components reach a stable state. In other words, the stability of the measured value can also be determined within a staged time. For example, within a staged time, if the measured value fluctuates greatly, it can be considered that the stability is poor and there is a problem with the water quality sensor.

[0070] Exemplarily, for the temperature compensation experiment module, it is considered that the measurement results of the water quality sensor are generally affected by temperature changes. In order to solve the corresponding problem, the performance of the water quality sensor under different temperature conditions can be calibrated by conducting a temperature compensation experiment, thereby improving the accuracy of the measurement results of the water quality sensor. For example, in order to compensate for accuracy, a mixed solution of chemical oxygen demand COD and turbidity can be prepared. For example, when calibrating an ammonia nitrogen water quality sensor, since ammonia nitrogen is affected by temperature and pH value in actual applications, an acidic or alkaline solution can be mixed with a standard ammonia nitrogen solution to simulate an actual application scenario. Then, the water quality sensor is used to perform irregular sensing measurements on this mixed solution.

[0071] Optionally, the temperature compensation experiment module can also be used to perform a temperature shock experiment to test the anti-interference performance of the water quality sensor. For example, the water quality sensor can be rapidly cooled from a maximum temperature of 50 degrees to observe its reaction to temperature changes. This phenomenon during the temperature change process is the so-called shock experiment.

[0072] In this embodiment, the system performs aging tests and impact tests on the water quality sensor, which is beneficial to improving the accuracy and reliability of the calibration results.

[0073] Exemplarily, for the standard solution calibration experiment module, the water quality sensor may be preliminarily calibrated using a standard solution to complete the standard solution calibration experiment.

[0074] Exemplarily, for the mixed standard solution calibration experiment module, further calibration work can be performed by using a mixed standard solution to improve the accuracy of the water quality sensor.

[0075] The performance of the water quality sensor can be verified by randomly preparing the concentration points of the standard solution and / or the mixed standard solution under the non-calibration scheme, and performing temperature control and data acquisition.

[0076] Exemplarily, for the data analysis module, data analysis technology can be used to perform data analysis on at least one of the first verification result, the second verification result, the third verification result, and the fourth verification result, to optimize the selection of concentration points in the calibration process, thereby reducing unnecessary use of calibration solutions and time consumption, thereby reducing overall costs. It can be understood that the analysis and reduction of data concentration points can achieve the purpose of reducing the production time and production cost of water quality sensors, and improving production efficiency and economic benefits.

[0077] For example, the aging test is intended to simulate the state of a water quality sensor running for a long time. The data analysis module can focus on observing the stability and accuracy of the water quality parameters output by the water quality sensor during the continuous operation of the preset time. For example, if it is found that the fluctuation range of the detection results of the key water quality indicators (such as COD, ammonia nitrogen, etc.) of the water quality sensor in the first 80% of the aging time is always within a very small allowable error range, it means that the corresponding water quality sensor has stable performance. This means that the purpose of the aging experiment is to allow the water quality sensor to reach a stable state and ensure that its response is stable. If the response of the water quality sensor is unstable, for example, when measuring solution A at time t0, the response value is 100, and when measuring the same solution at time t1, the response value drops to 50, and then rises to 80 at time t2, in this case, the unstable response of the water quality sensor makes it impossible to achieve effective calibration whether it is multi-concentration point calibration or repeated calibration to achieve applicable standards. Therefore, the role of the aging experiment is to make the water quality sensor enter a stable state, so that in subsequent calibration experiments, the water quality sensor can provide a stable response. The model established by calibrated data is only meaningful when the response of the water quality sensor is stable.

[0078] For example, the temperature compensation experiment gives a second verification result, reflecting the performance of the water quality sensor under different temperature scenarios. The data analysis module can sort out the detection deviation data of the water quality sensor under multiple temperature conditions such as high temperature, normal temperature, and low temperature. Assuming that a water quality sensor is in the common water temperature range of -10℃-40℃, the error between the detection result and the standard temperature can be controlled within a very small range, thanks to the effective temperature compensation mechanism. Based on this, the calibration process can reduce the concentration points that are adapted to the temperature gradient. In other words, there is no need to set up multiple sets of high-concentration and low-concentration samples for calibration for extreme temperatures, which reduces the complexity of calibration and the number of concentration points.

[0079] For example, the standard solution calibration experiment provides a third verification result, which accurately reflects the detection accuracy of the water quality sensor for standard solutions of known concentrations. When the data analysis module finds that the water quality sensor has an extremely low measurement error rate for standard solutions of different concentrations and good repeatability (multiple measurements of the same concentration of standard solution with extremely small deviations), this indicates that the water quality sensor itself has met the accuracy standards. Subsequent calibration does not require densely arranged concentration points, but instead selects key concentration nodes, such as nodes near the detection limit and nodes in the common concentration range of daily water bodies, discards redundant concentration points, and ensures a balance between calibration efficiency and accuracy.

[0080] For example, the mixture standard solution calibration experiment is more in line with the actual water body complex component scenario. The fourth verification result covers the water quality sensor's detection ability for mixed solutions of multiple substances. If the water quality sensor is faced with a mixture standard solution containing complex components such as organic matter, inorganic matter, and microorganisms, it can still accurately distinguish and detect the water quality parameters corresponding to each component, and the error can be controlled. The data analysis module can reduce the concentration points of complex components used for calibration accordingly. For example, it was originally planned to configure 10 mixed solutions with different component proportions as concentration points, but now the most representative 3-5 can be screened out to achieve the purpose of streamlining the calibration process and reducing concentration points.

[0081] In actual operation, the data analysis module can comprehensively consider the above multiple verification results. For example, if a water quality sensor has stable response performance, strong temperature adaptability, and high detection accuracy for standard solution and mixed standard solution, the data analysis module can make a comprehensive trade-off and formulate a highly streamlined concentration point calibration plan, remove unnecessary redundant concentration points, improve calibration efficiency and quality, and save time and reagent costs.

[0082] Optionally, in some embodiments, the data analysis module may be specifically used to: update the configuration scheme according to the analysis result, and update the parameter model according to the updated configuration scheme.

[0083] Exemplarily, the configuration scheme is updated according to the concentration point selection to obtain a target scheme, and then a parameter model of the water quality sensor is constructed according to the target scheme. After the parameter model is deployed to the water quality sensor, the water quality sensor is re-verified through the verification module and the data analysis module according to the above process until the verification result meets the preset requirements. After the verification result meets the preset requirements, the parameter model is deployed to the water quality sensor, and the cycle of the above workflow of the verification module and the data analysis module is stopped.

[0084] It should be noted that, in some embodiments, before the calibration, the water quality sensor can be subjected to an aging test through the aging test module, and after the response signal of the water quality sensor remains stable, the water quality sensor can be calibrated, that is, the parameter model is deployed to the water quality sensor. In some embodiments, after the parameter model is deployed to the water quality sensor, the calibration of the water quality sensor is generally completed at this time. In this embodiment, in order to ensure the quality reliability of the water quality sensor, a verification process for the water quality sensor is added. Specifically, the temperature of the liquid in the container can be controlled to reach different temperature points through the temperature compensation experiment module, the concentration point of the standard liquid or mixed standard liquid in the standard liquid calibration experiment module and the mixture standard liquid calibration experiment module in the non-calibration scheme randomly configured by the computer device, the measured value of the water quality sensor and the known value input in the computer device are read, the measured value and the known value are compared, the relative error between the measured value and the known value is calculated, and it is determined whether the relative error meets the preset requirements.

[0085] It should be noted that this embodiment may also be an improvement based on the second embodiment.

[0086] It is not difficult to find that in the embodiment of the present application, the computer device also includes a verification module and a data analysis module, which provides a water quality sensor production calibration verification and optimization process. In this way, the system is a comprehensive system integrating production, testing and verification, which can not only realize the mass production of the water quality sensor, but also improve the quality of the water quality sensor.

[0087] Fourth embodiment

[0088] The fourth embodiment of the present application relates to an automatic calibration system for a water quality sensor. The fourth embodiment is an improvement on the third embodiment, and the specific improvement is that: in the third embodiment of the present application, a data analysis and optimization calibration solution is provided.

[0089] Optionally, in some embodiments, the data analysis module obtains analysis results for reducing concentration points in the calibration process by the following method:

[0090] Preset step: preset thresholds of correlation coefficient R2 and mean square error RMSE of the training set and the test set respectively, wherein the correlation coefficient R2 is used to measure the degree of fit of the established parameter model to the observed data;

[0091] Initial modeling step: Start by selecting n=3 concentration points and perform permutations and combinations to establish a parameter model, and set a test set containing Nn concentration points, where N is the total number of concentration points collected;

[0092] Training set verification step: After completing the training set modeling using the selected n concentration points to obtain the parameter model, these n concentration points are substituted into the parameter model, the correlation coefficient R2 and the mean square error RMSE are calculated, and the calculation results are checked to see whether they meet the preset threshold;

[0093] Test set verification step: If the calculation result of the training set meets the preset threshold, substitute the Nn concentration points of the test set into the parameter model, calculate the correlation coefficient R2 and the mean square error RMSE again, and check whether the calculation result meets the preset threshold;

[0094] Record sorting steps: If the calculation results of the test set still meet the preset threshold, record the concentration information of the current n concentration points, the correlation coefficient R2 and the mean square error RMSE corresponding to the test set, and add them to the analysis optimization table for sorting;

[0095] Cyclic screening steps: Repeat the above permutation and combination test process for 3 concentration points, list the combinations that meet the preset threshold requirements in the analysis optimization table, compare the correlation coefficient R2 and mean square error RMSE of each combination, and select the optimal combination as the initial optimized concentration point; if the combination of 3 concentration points does not meet the preset threshold, automatically switch to permutation and combination of 4 concentration points, and repeat the entire analysis process until the analysis result of the optimal concentration point is screened out.

[0096] It can be understood that whether it is an optical or electrochemical type of water quality sensor, its response tends to saturate when measuring substances of different concentrations. In many existing water quality sensors, a common practice is to use two concentration points to determine a straight line and assume that the response of the sensor is consistent with this straight line, that is, to adopt a linear correlation model. However, in reality, the signal changes of water quality sensors are not always linear, but may show nonlinear changes such as parabolas. Therefore, in order to more accurately describe this relationship, the present application can use multiple concentration points to construct a curve. This nonlinear correlation model can more accurately reflect the response characteristics of water quality sensors at different concentrations.

[0097] However, in the calibration process of water quality sensors, it is generally believed that increasing the calibration concentration points can improve the accuracy of water quality sensors, that is, the more calibration concentration points, the higher the accuracy of water quality sensors. However, doing so will waste more calibration solutions and calibration time during the calibration process. Therefore, by analyzing the database obtained during the calibration process, it is possible to scientifically reduce the necessary concentration points, optimize the calibration scheme, and thus reduce costs.

[0098] See also Figure 5 As shown in the figure, the relationship between the concentration (mg / L) of chemical oxygen demand COD and the AD value is shown. There are two sets of data and two fitting curves in the chart, showing the response of the water quality sensor at different concentration points. Specifically, the blue squares and blue curves: represent data using 6 concentration points. The red dots and red curves: represent data using 3 concentration points. It can be seen from the figure that as the AD value increases, the concentration of chemical oxygen demand COD shows a downward trend. Both the blue curve (6 concentration points) and the red curve (3 concentration points) show this trend. The curves in the chart are obtained by fitting data from different numbers of concentration points. Generally, fitting with more data points may result in a more accurate or smoother curve. However, although traditional technology requires the analysis of multiple data points, we found that the same effect as the original 6 data point model can be achieved by analyzing only 3 data points. Therefore, data from 3 concentration points can be selected. Furthermore, by selecting the optimal concentration point, the purpose of improving calibration efficiency can be achieved.

[0099] For example, in the process of optimizing the concentration point data, Figure 5 Take the data analysis chart of as an example, for the calibration of COD (chemical oxygen demand), the data of N=8 different concentration points are collected for COD calibration. Generally speaking, two points can determine a straight line, and three points can determine a curve; when facing a nonlinear model, the n concentration points in the database (3<=n<8) can be arranged and combined respectively, and these combinations can be used as training sets for modeling. At the same time, the remaining (Nn) concentration points are used as test sets to verify the model effect. Among them, N represents the total number of concentration points collected; the minimum value of n is 3 and the maximum value is N.

[0100] Exemplarily, when the parameter model is the nonlinear model, the basic analysis optimization logic may include:

[0101] 1. Pre-set the correlation coefficient R of the training set and test set 2 and the threshold of the mean square error RMSE, as the preset threshold, that is, the preset threshold includes both the correlation coefficient R of the training set 2 and mean square error RMSE, including the correlation coefficient R of the test set 2And mean square error RMSE. Among them, the correlation coefficient R 2 It is used to measure the degree of fit of the established parameter model to the observed data, and its value is between 0 and 1. 2 =0, it means that the parameter model established based on the corresponding concentration point is of no help in predicting the target variable (such as the actual measured value of COD). 2 The closer it is to 1, the more accurate the parameter model is in predicting the actual measured value of COD, and the combination of concentration points and the parameter model can well reflect the real relationship.

[0102] 2. The parameter model is established starting from the permutations and combinations of n=3 concentration points, and the corresponding test set contains Nn=8-3=5 concentration points.

[0103] 3. After completing the training set modeling using these three concentration points to obtain the parameter model, substitute these three concentration points into the established parameter model to calculate the correlation coefficient R 2 The calculation result of the mean square error RMSE is checked to see whether it meets the preset threshold.

[0104] 4. If the calculation result meets the preset threshold, the remaining Nn=5 concentration points of the test set are used for verification, and the test set data is also substituted into the parameter model to calculate the correlation coefficient R 2 The calculation result of the mean square error RMSE is checked to see whether the calculation result meets the preset threshold.

[0105] 5. If the calculation result still meets the preset threshold, the system will record the concentration information of the three concentration points at this time, as well as the correlation coefficient R corresponding to the test set. 2 and mean square error RMSE, and add them to the analysis optimization table for sorting.

[0106] 6. Repeat the above steps and test all permutations and combinations consisting of 3 concentration points one by one according to this process. All those that meet the requirements of the preset threshold are included in the analysis optimization table. Then compare the correlation coefficients R of each permutation and combination. 2 and mean square error RMSE, and select the best group as the concentration points for the final data analysis. At this point, the preliminary optimization of the concentration point scheme is completed.

[0107] If the combination of 3 concentration points cannot meet the requirements of the preset threshold, the system will automatically switch to 4 concentration points, continue to arrange and combine the 4 concentration points, and repeat the above analysis process until the optimal concentration point solution is screened out.

[0108] It can be understood that the system can accumulate a large amount of data through the management of the database, so that the relevant data of the water quality sensor can be effectively organized and stored, and the later use of the water quality sensor can be more effectively maintained. Specifically, by accumulating a large amount of data, the production cost of the water quality sensor can be effectively reduced and the quality of the water quality sensor can be improved. Without sufficient data support, it is impossible to build a data analysis module to optimize the core parameters of the parameter model. Through data analysis, the process can be simplified and the quality of the water quality sensor can be ensured. In traditional technology, a lot of redundant work is often required, such as using 5 or 7 concentration points to calibrate a curve. But now, if there is enough data support, only 4 concentration points may be needed to calibrate a high-precision curve to ensure the high accuracy of water quality sensors in different ranges.

[0109] It should be noted that the third embodiment of the present application may also be an improvement based on the second embodiment.

[0110] It is not difficult to find that in the embodiment of the present application, a scheme for data analysis and optimization calibration is provided. A high-precision curve can be calibrated with fewer concentration points, which reduces the use of chemical solutions in production, not only reduces waste liquid treatment, but also reduces production costs, and is more environmentally friendly. Therefore, in this way, the present application can optimize the production process, reduce costs, and improve the quality and stability of water quality sensors.

[0111] It is worth mentioning that all modules involved in this embodiment are logic modules. In practical applications, a logic unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed by this application, but this does not mean that there are no other units in this embodiment.

[0112] Fifth embodiment

[0113] The fifth embodiment of the present application relates to an automatic calibration method for a water quality sensor. The method can be applied to the system described in any one or more of the first to fourth embodiments. Figure 6 As shown, the method may include a roll call process, a liquid preparation process, a temperature control and data collection process, and a parameter model deployment process, see Figure 6 shown.

[0114] In the roll call process, the computer device sends instructions to the data transceiver module, and the data transceiver module establishes a communication connection with multiple water quality sensors; if the water quality sensors all respond normally, the roll call process is completed and enters the liquid preparation process; otherwise, the system issues an alarm prompt.

[0115] In the liquid preparation process, the data transceiver module distributes the water quality sensors to preset containers respectively, each container is connected to a mixing chamber, and automatically prepares liquid in the mixing chamber according to the configuration scheme to simulate different water quality conditions;

[0116] In the temperature control and data collection process, the computer device adjusts the temperature of the liquid in the mixing chamber according to the temperature level, and collects water quality parameters detected by the water quality sensor at different temperature levels to send the water quality parameters to the computer device;

[0117] In the parameter model deployment process, the water quality parameters are fitted with an algorithm, a parameter model is established for each water quality sensor, and the parameter model is written into the corresponding water quality sensor through the data transceiver module to complete the calibration.

[0118] Specifically, in some examples, in the roll call process, the data transceiver module can establish communication connections with multiple water quality sensors in sequence, or can establish communication connections with multiple water quality sensors at the same time, which is not specifically limited in this embodiment. If there is a water quality sensor that cannot respond, the computer device can record the corresponding water quality sensor and issue an alarm prompt through the user interface of the computer device for relevant personnel to check.

[0119] For example, if there are 200 water quality sensors that need to be calibrated, the roll call process can be used to confirm two things: first, whether the water quality sensors have been successfully connected to the network system; second, because each water quality sensor has different characteristics, even in the same solution, the signal values ​​they measure may be different. Therefore, it is necessary to ensure that each water quality sensor can accurately match its corresponding signal value, and the matching relationship between the water quality sensor and its corresponding signal value can be obtained through the roll call process.

[0120] Specifically, in some examples, in the liquid preparation process, the valve can be opened by the circuit control module, and the standard liquid can be pumped into the mixing chamber for mixing by a pump, and the flow rate can be controlled by a mass flow meter to achieve quantitative configuration. The liquid in the mixing chamber flows into the container, and when the liquid level water quality sensor detects that the liquid level in the container reaches a certain height, the system stops preparing the liquid.

[0121] Exemplarily, the injection function may include two main steps, including automatic liquid preparation and automated measurement. According to the calibration requirements, the computer device may specify the required solution type, such as a standard solution of a specific concentration, such as a CAD (cadmium) standard solution, a lead (Pb) standard solution, or a mixed solution. Through the liquid preparation process, the corresponding solution can be prepared according to these requirements, and the prepared solution can be transported to the container in the calibration area where the water quality sensor is located. In this way, it can be ensured that the required solution is provided for the water quality sensor for effective calibration production.

[0122] Specifically, in some examples, in the temperature control and data collection process, considering that temperature changes may affect the accuracy of the water quality sensor, relevant personnel can set different temperature levels for different types of water quality sensors through the computer device. For example, in some examples, after the liquid preparation process is completed, the solution in the container can be controlled to cool down to a preset temperature and maintain a constant temperature, and then the data collected by the water quality sensor can be collected and stored in the database of the computer device. Afterwards, the computer device can issue instructions to the temperature control module to control the solution in the container to heat up to the next temperature point in the temperature level and repeat the data collection operation.

[0123] Specifically, in some examples, in the parameter model deployment process, the computer device can use the water quality parameters in the database for algorithm fitting to establish a parameter model for each water quality sensor. There are differences in the temperature control curves of different water quality sensors. This is because the characteristics of each water quality sensor are different, resulting in differences in their signal values ​​at different temperatures. Therefore, for different water quality sensors, it is necessary to integrate the data of all water quality sensors into the system through data acquisition, generate a separate parameter model for each water quality sensor, and then deploy these parameter models back to their respective water quality sensors. This ensures that although there are specific differences between water quality sensors, the system generates corresponding parameter models according to their respective characteristics, and when measuring the same solution, the results will be consistent. In other words, although the signal values ​​of different water quality sensors may be different during the production process, since the system establishes the relationship between these signal values, ultimately, whether a standard water quality sensor or an actual water quality sensor is used, as long as they are placed in the same test solution, the data they output should be consistent, so that the consistency requirements can be met.

[0124] In some embodiments, after the temperature control and data collection process, the method may also enter a cleaning process. In the cleaning process, the computer device may control the opening of a valve, and use pure water to clean the waterway, the mixing chamber, and the cavity of the container, in preparation for the preparation of the next standard solution or the deployment of the parameter model.

[0125] It can be seen that the system communicates with the water quality sensor through the data transceiver module, and can realize a fully automated process from roll call to parameter model deployment.

[0126] It is not difficult to find that in the embodiment of the present application, the method provides a set of control automation processes, which can realize automatic roll call, control liquid preparation, and mixing of different standard solutions or mixed solutions in the process of managing multiple water quality sensors. In addition, the system can also have functions such as sampling, data acquisition, parameter model generation and backwriting. The whole process constitutes a complete large system, which can cover the cleaning of the water quality sensor and other links according to actual needs. Different from the previous manual operation or the calibration line that can only handle a small number of points, the system can conduct experiments on multiple mixed solutions, including various tests including temperature compensation. Therefore, it can achieve the purpose of reducing manual operation and improving the efficiency and accuracy of water quality sensor calibration.

[0127] It is not difficult to find that this embodiment is a method embodiment corresponding to the first embodiment, and this embodiment can be implemented in conjunction with the first embodiment. The relevant technical details mentioned in the first embodiment are still valid in this embodiment, and in order to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied in the first embodiment.

[0128] The step division of the above methods is only for the purpose of clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent; adding insignificant modifications to the algorithm or process or introducing insignificant designs without changing the core design of the algorithm and process are all within the scope of protection of this patent.

[0129] In addition, some embodiments of the present application also provide a computer device. The computer device can be various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, etc. The computer device can also be various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices.

[0130] The computer device includes: one or more processors; and a memory storing computer program instructions, wherein when the computer program instructions are executed, the processor executes the steps of the method provided in any one or more of the above embodiments. Figure 7 An exemplary structural diagram of the computer device is disclosed. Figure 7 As shown, the computer device includes: one or more processors 1101, a memory 1102, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some other embodiments, if necessary, multiple processors and / or multiple buses can be used with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Among them, the components shown in this article, their connections and relationships, and their functions are only used as examples, and are not intended to limit the implementation of the present application described and / or required herein.

[0131] The computer device may further include: an input device 1103 and an output device 1104. The processor 1101, the memory 1102, the input device 1103 and the output device 1104 may be connected via a bus or other means. Figure 7 The example of connecting through bus is taken in the following.

[0132] The input device 1103 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator rod, one or more mouse buttons, a trackball, a joystick and other input devices. The output device 1104 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The display device may include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display and a plasma display. In some embodiments, the display device may be a touch screen.

[0133] To provide interaction with a user, the computer device may be a computer. The computer has: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball), through which the user can provide input to the computer. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0134] In the embodiments of the present application, a computer program / instruction is stored on a computer-readable medium, and when the computer program / instruction is executed by a processor, the steps of the method provided in any one or more of the above embodiments are implemented. The computer-readable medium may be included in the computer device described in the above embodiments; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more computer-readable instructions.

[0135] The memory 1102 can be used as a non-transient computer-readable storage medium, which can be used to store non-transient software programs, non-transient computer executable programs and modules. The processor 1101 executes various functional applications and data processing of the server by running the non-transient software programs, instructions and modules stored in the memory 1102, so as to implement the program instructions / modules corresponding to the method provided by any one or more embodiments in the embodiments of the present application.

[0136] The memory 1102 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 1102 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage devices. In some embodiments, the memory 1102 may optionally include a memory remotely arranged relative to the processor 1101, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0137] It should be noted that the computer-readable medium described in this application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0138] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, modules of programs or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0139] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0140] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. For example, an application specific integrated circuit (ASIC), a general-purpose computer or any other similar hardware device may be used to implement the embodiments. In some embodiments, the software program of the present application may be executed by a processor to implement the above steps or functions. Similarly, the software program of the present application (including related data structures) may be stored in a computer-readable recording medium, such as a RAM memory, a magnetic or optical drive or a floppy disk and the like. In addition, some steps or functions of the present application may be implemented by hardware, for example, as a circuit that cooperates with a processor to perform various steps or functions.

[0141] The computer program product provided in the embodiment of the present application includes one or more computer programs / instructions, which, when executed by the processor, generate in whole or in part the process or function described in the embodiment of the present application. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a server, or a data center to another website site, a computer, a server, or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line DSL) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk, i.e., an SSD), etc.

[0142] The flow chart or block diagram in the accompanying drawings shows the possible architecture, function and operation of the equipment, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated system for hardware that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0143] The scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present application. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device claim may also be implemented by one unit or device through software or hardware. The words "first", "second", etc. are only used to distinguish the description, and do not indicate any particular order, nor can they be understood as indicating or implying relative importance.

[0144] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims, and the above embodiments should be regarded as exemplary and non-restrictive.

Claims

1. An automatic calibration system for a water quality sensor, characterized in that: The system comprises: A data transceiver module is used to establish a communication connection with a plurality of water quality sensors and allocate the water quality sensors to preset containers respectively; each container is connected to a mixing chamber, and liquid is automatically dispensed in the mixing chamber according to a configuration scheme to simulate different water quality conditions; the configuration scheme corresponds to the water quality sensor; The water quality sensor is used to detect the water quality parameters of the liquid in the mixing chamber and send the water quality parameters to the computer device through the data transceiver module; The computer device is used to establish a parameter model for the water quality sensor according to the water quality parameters, and write the parameter model into the water quality sensor through the data transceiver module to complete calibration; The computer device further includes a verification module, which is used to verify the water quality sensor; the verification module includes at least one of the following: an aging test module, a temperature compensation test module, a standard liquid calibration test module and a mixture standard liquid calibration test module; the aging test module is used to continuously run the water quality sensor for a preset time to obtain a first verification result; the temperature compensation test module is used to perform a temperature compensation experiment on the water quality sensor to calibrate the performance of the water quality sensor at different temperatures to obtain a second verification result; the standard liquid calibration test module is used to perform a standard liquid calibration experiment on the water quality sensor to obtain a third verification result; the mixture standard liquid calibration test module is used to perform a mixture standard liquid calibration experiment on the water quality sensor to obtain a fourth verification result; the computer device further includes a data analysis module; the data analysis module is used to perform data analysis based on at least one of the first verification result, the second verification result, the third verification result and the fourth verification result to obtain an analysis result for reducing concentration points in the calibration process; The data analysis module specifically obtains analysis results for reducing concentration points in the calibration process by the following method: Preset step: preset thresholds of correlation coefficient R2 and mean square error RMSE of the training set and the test set respectively, wherein the correlation coefficient R2 is used to measure the degree of fit of the established parameter model to the observed data; Initial modeling step: Start by selecting n=3 concentration points and perform permutations and combinations to establish a parameter model, and set a test set containing Nn concentration points, where N is the total number of concentration points collected; Training set verification step: After completing the training set modeling using the selected n concentration points to obtain the parameter model, these n concentration points are substituted into the parameter model, the correlation coefficient R2 and the mean square error RMSE are calculated, and the calculation results are checked to see whether they meet the preset threshold; Test set verification step: If the calculation result of the training set meets the preset threshold, substitute the Nn concentration points of the test set into the parameter model, calculate the correlation coefficient R2 and the mean square error RMSE again, and check whether the calculation result meets the preset threshold; Record sorting steps: If the calculation results of the test set still meet the preset threshold, record the concentration information of the current n concentration points, the correlation coefficient R2 and the mean square error RMSE corresponding to the test set, and add them to the analysis optimization table for sorting; Cyclic screening steps: Repeat the above permutation and combination test process for 3 concentration points, list the combinations that meet the preset threshold requirements in the analysis optimization table, compare the correlation coefficient R2 and mean square error RMSE of each combination, and select the optimal combination as the initial optimized concentration point; if the combination of 3 concentration points does not meet the preset threshold, automatically switch to permutation and combination of 4 concentration points, and repeat the entire analysis process until the analysis result of the optimal concentration point is screened out.

2. The system according to claim 1, characterized in that The mixing chamber is connected to a plurality of mass flow meters, and different mass flow meters are respectively connected to standard liquid containers containing standard liquids of different standards; The mass flow meter is used to control the amount of liquid flowing into the mixing chamber.

3. The system according to claim 2, characterized in that The system also includes a circuit control module; The circuit control module is used to control the valves between the mass flow meter and the standard liquids of different standards, so as to control the amount of liquid flowing into the mixing chamber, thereby controlling the mixing relationship of the liquids in the mixing chamber.

4. The system according to claim 3, characterized in that The container also includes a temperature control module, and the temperature control module is connected to the computer device; The temperature control module is used to control the temperature of the liquid in the container according to the preset temperature level and preset time sent by the computer device after the computer device detects that the automatic liquid preparation based on the mixing chamber is completed, so that the water quality sensor can detect the water quality parameters of the liquid at each temperature level and send the water quality parameters at each temperature level to the computer device.

5. The system according to claim 4, characterized in that at least one mass flow meter is connected to a pure water container containing pure water; The circuit control module is also used to control the valve after the computer device obtains the water quality parameters of the liquid at each temperature level, so that the pure water container cleans: the water path, the mixing chamber and the cavity of the container.

6. The system according to claim 1, characterized in that The system also includes a waste liquid collecting device; the waste liquid collecting device is connected to the container and is used to collect the liquid in the container.

7. A method for automatic calibration of a water quality sensor, characterized in that: The method is applied to the system as claimed in any one of claims 1 to 6, and the method includes: a roll call process, a liquid preparation process, a temperature control and data collection process, and a parameter model deployment process; In the roll call process, the computer device sends instructions to the data transceiver module, and the data transceiver module establishes a communication connection with multiple water quality sensors; if the water quality sensors all respond normally, the roll call process is completed and the liquid preparation process is entered; otherwise, the system issues an alarm prompt; In the liquid preparation process, the data transceiver module distributes the water quality sensors to preset containers respectively, each container is connected to a mixing chamber, and automatically prepares liquid in the mixing chamber according to the configuration scheme to simulate different water quality conditions; In the temperature control and data collection process, the computer device adjusts the temperature of the liquid in the mixing chamber according to the temperature level, and collects water quality parameters detected by the water quality sensor at different temperature levels to send the water quality parameters to the computer device; In the parameter model deployment process, the water quality parameters are fitted with an algorithm, a parameter model is established for each water quality sensor, and the parameter model is written into the corresponding water quality sensor through the data transceiver module to complete the calibration.

8. A computer device, characterized in that: The computer device comprises: one or more processors; and A memory storing computer program instructions which, when executed, cause the processor to perform the steps of the method of claim 7.

9. A computer readable medium having a computer program / instructions stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method of claim 7 are implemented.

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