Water sample chloride ion content detection method and system, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a water sample chloride ion content detection method and system, electronic equipment and a storage medium. The method comprises the following steps: acquiring test parameters of a water sample; inputting the water sample test parameters into a pre-established chloride ion content detection model, and outputting to obtain the chloride ion content, wherein the chloride ion content detection model is obtained by training a neural network model based on a water sample test data set; and carrying out visual output on the chlorine ion content. According to the water sample chloride ion content detection method and system, the electronic equipment and the storage medium, the chloride ion content detection model is established by adopting machine learning, and the chloride ion content detection result is obtained by utilizing the model, so that the workload of a laboratory technician is reduced, the experiment efficiency is improved, the error of the detection result is reduced, and the detection accuracy is improved. And a reference basis is provided for the accuracy of a manual operation experiment result.

Description

Technical Field

[0001] The embodiments of the present disclosure belong to the technical field of chloride ion content detection of water samples, and specifically relate to a method, system, electronic device and storage medium for detecting chloride ion content of water samples. Background Art

[0002] In thermal power generation, the laboratory is an indispensable and important link in the production of power plants. The laboratory work is directly related to the normal operation of production equipment and the stability of product quality. The laboratory is responsible for various environmental protection experiments such as water, coal, and oil in the whole plant. For example, the silver nitrate titration method (Mohr method) is used to detect the chloride ion content in water samples. It is necessary to manually operate the burette, measuring cylinder and other experimental instruments, record the data and calculate the test results to determine whether the chloride ion content in the water sample exceeds the standard value.

[0003] In laboratory work, due to the limitations of experimental equipment and manual operation precision, the accuracy and repeatability of experimental results will be affected. Analysts need to take samples and review them multiple times to reduce errors in the experiment. The workload is large, the efficiency is low, and the accuracy of the experimental results is not high enough. Summary of the invention

[0004] The embodiments of the present disclosure aim to solve at least one of the technical problems existing in the prior art, and provide a method, system, electronic device and storage medium for detecting chloride ion content in water samples.

[0005] One aspect of the present disclosure provides a method for detecting chloride ion content in a water sample.

[0006] Methods include:

[0007] Obtain water sample testing parameters;

[0008] Input the water sample test parameters into a pre-established chloride ion content detection model, and output the chloride ion content; wherein the chloride ion content detection model is trained using a neural network model based on a water sample test data set;

[0009] The chloride ion content is output visually.

[0010] Further, the obtaining of water sample test parameters includes:

[0011] Pressure sensors and flow sensors are used to obtain water sample test parameters; among them,

[0012] The water sample test parameters at least include the consumption of silver nitrate standard solution for water sample titration, the consumption of silver nitrate standard solution for blank titration, the concentration of silver nitrate standard solution and the volume of water sample.

[0013] Furthermore, the chloride ion content detection model is pre-established by the following steps:

[0014] Acquire a water sample test data set; wherein the water sample test data set includes water sample titration silver nitrate standard solution consumption, blank titration silver nitrate standard solution consumption, silver nitrate standard solution concentration, water sample volume and chloride ion content;

[0015] The consumption of the silver nitrate standard solution for water sample titration, the consumption of the silver nitrate standard solution for blank titration, the concentration of the silver nitrate standard solution, and the volume of the water sample are used as inputs, and the chloride ion content is used as output to train the neural network model to obtain a chloride ion content detection model.

[0016] Furthermore, the chloride ion content in the water sample test data set is calculated by the following formula:

[0017]

[0018] Where X is the chloride ion content, a is the consumption of silver nitrate standard solution for water sample titration, b is the consumption of silver nitrate standard solution for blank titration, T is the concentration of silver nitrate standard solution, and V is the volume of water sample.

[0019] Another aspect of the present disclosure provides a system for detecting chloride ion content in a water sample, the system comprising:

[0020] A data acquisition module is used to obtain water sample test parameters;

[0021] A data processing module, used for inputting the water sample test parameters into a pre-established chloride ion content detection model, and outputting the chloride ion content; wherein the chloride ion content detection model is obtained by training a neural network model based on a water sample test data set;

[0022] The data output module is used to visually output the chloride ion content.

[0023] Furthermore, the data acquisition module includes a pressure sensor and a flow sensor;

[0024] The water sample test parameters at least include the consumption of silver nitrate standard solution for water sample titration, the consumption of silver nitrate standard solution for blank titration, the concentration of silver nitrate standard solution and the volume of water sample.

[0025] Furthermore, the system further comprises a model building module; the model building module is specifically used for:

[0026] Acquire a water sample test data set; wherein the water sample test data set includes water sample titration silver nitrate standard solution consumption, blank titration silver nitrate standard solution consumption, silver nitrate standard solution concentration, water sample volume and chloride ion content;

[0027] The consumption of the silver nitrate standard solution for water sample titration, the consumption of the silver nitrate standard solution for blank titration, the concentration of the silver nitrate standard solution, and the volume of the water sample are used as inputs, and the chloride ion content is used as output to train the neural network model to obtain a chloride ion content detection model.

[0028] Furthermore, the chloride ion content in the water sample test data set is calculated by the following formula:

[0029]

[0030] Where X is the chloride ion content, a is the consumption of silver nitrate standard solution for water sample titration, b is the consumption of silver nitrate standard solution for blank titration, T is the concentration of silver nitrate standard solution, and V is the volume of water sample.

[0031] Another aspect of the present disclosure provides an electronic device, comprising:

[0032] at least one processor; and,

[0033] The memory communicatively connected to the at least one processor is used to store one or more programs, and when the one or more programs are executed by the at least one processor, the at least one processor can implement the method for detecting chloride ion content in water samples as described above.

[0034] Another aspect of the present disclosure provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for detecting chloride ion content in a water sample as described above.

[0035] A method, system, electronic device and storage medium for detecting chloride ion content in water samples according to an embodiment of the present disclosure establishes a chloride ion content detection model by adopting machine learning, and uses the model to obtain chloride ion content detection results, thereby reducing the workload of laboratory technicians, improving experimental efficiency, reducing the error of detection results, and providing a reference basis for the accuracy of manual experimental results. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic diagram of a process for detecting chloride ion content in a water sample according to an embodiment of the present disclosure;

[0037] Figure 2 This is a schematic structural diagram of a system for detecting chloride ion content in a water sample according to another embodiment of the present disclosure;

[0038] Figure 3 The figure is a schematic diagram of the structure of an electronic device according to another embodiment of the present disclosure. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0040] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the present disclosure.

[0041] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0042] It should be understood that although the terms first, second, third, etc. may be used in this disclosure to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another component. Therefore, the first component discussed below can be referred to as the second component without departing from the teachings of the concepts of the present disclosure. As used in this disclosure, the term "and / or" includes any one of the associated listed items and all combinations of one or more.

[0043] Those skilled in the art will appreciate that the drawings are merely schematic diagrams of example embodiments, and the modules or processes in the drawings are not necessarily necessary for implementing the present disclosure, and therefore cannot be used to limit the protection scope of the present disclosure.

[0044] like Figure 1 As shown, one embodiment of the present disclosure provides a method for detecting chloride ion content in a water sample, comprising:

[0045] Step S1, obtaining water sample testing parameters.

[0046] Specifically, pressure sensors, flow sensors, etc. are used to obtain parameters of water sample testing titration test, and the water sample testing parameters at least include water sample titration silver nitrate standard solution consumption a, blank titration silver nitrate standard solution consumption b, silver nitrate standard solution concentration T and water sample volume V.

[0047] Among them, the consumption a of silver nitrate standard solution for water sample titration refers to the volume of silver nitrate standard solution dripped into the water sample until precipitation is produced, and the consumption b of silver nitrate standard solution for blank titration refers to the volume of silver nitrate standard solution dripped into distilled water with the same volume as the water sample until precipitation is produced, which can be obtained by real-time detection using a flow sensor; the concentration T of the silver nitrate standard solution needs to be calibrated using a sodium chloride standard solution; the water sample volume V refers to the volume of the water sample taken in the titration experiment, which can be obtained by detection using a pressure sensor.

[0048] Step S2: input the water sample test parameters into a pre-established chloride ion content detection model, and output the chloride ion content.

[0049] Specifically, the chloride ion content detection model is pre-established through the following steps:

[0050] 1. Obtain water sample test data set.

[0051] A large number of historical data of chloride ion content detection titration experiments of water samples are obtained, including the consumption of silver nitrate standard solution for water sample titration, the consumption of silver nitrate standard solution for blank titration, the concentration of silver nitrate standard solution, the volume of water samples, and the chloride ion content detection results corresponding to the above data in each experiment, and a data set consisting of a large amount of labeled data is obtained.

[0052] The chloride ion content in the water sample test data set can be calculated by the following formula:

[0053]

[0054] In the formula, X is a chloride ion (Cl - ) content (mg / L), a is the consumption of silver nitrate standard solution for water sample titration (ml), b is the consumption of silver nitrate standard solution for blank titration (ml), T is the concentration of silver nitrate standard solution (mg / L), and V is the volume of water sample (ml).

[0055] 2. The consumption of the silver nitrate standard solution for water sample titration, the consumption of the silver nitrate standard solution for blank titration, the concentration of the silver nitrate standard solution, and the volume of the water sample are taken as input, and the chloride ion content is taken as output to train the neural network model to obtain a chloride ion content detection model.

[0056] Specifically, the water sample testing data set is divided into a training set and a validation set, and parameters such as the learning rate, batch size, and number of iterations are set. The neural network model is trained with the training set, and the accuracy of the model is verified with the validation set. The neural network model that meets the requirements is used as a chloride ion content detection model.

[0057] The water sample test parameters obtained in step S1 are input into the trained chloride ion content detection model, and the model will output the corresponding chloride ion content.

[0058] Step S3, visually outputting the chloride ion content.

[0059] Specifically, the chloride ion content result obtained by the model output in the previous step S2 is displayed by means of a liquid crystal display, printing, recording into a storage medium, etc., so as to facilitate query and comparison by experimenters.

[0060] A method for detecting chloride ion content in water samples according to an embodiment of the present disclosure establishes a chloride ion content detection model by adopting machine learning, and uses the model to obtain chloride ion content detection results, thereby reducing the workload of laboratory technicians, improving experimental efficiency, reducing the error of detection results, and providing a reference basis for the accuracy of manual operation experimental results.

[0061] like Figure 2 As shown, another embodiment of the present disclosure provides a water sample chloride ion content detection system, comprising:

[0062] The data acquisition module 210 is used to obtain the test parameters of the water sample;

[0063] The data processing module 220 is used to input the water sample test parameters into a pre-established chloride ion content detection model, and output the chloride ion content; wherein the chloride ion content detection model is trained by a neural network model based on the water sample test data set;

[0064] The data output module 230 is used to visually output the chloride ion content.

[0065] Exemplarily, the data acquisition module includes a pressure sensor and a flow sensor;

[0066] The water sample test parameters at least include the consumption of silver nitrate standard solution for water sample titration, the consumption of silver nitrate standard solution for blank titration, the concentration of silver nitrate standard solution and the volume of water sample.

[0067] For example, Figure 2 As shown, the system further includes a model building module 240; the model building module 240 is specifically used for:

[0068] Acquire a water sample test data set; wherein the water sample test data set includes water sample titration silver nitrate standard solution consumption, blank titration silver nitrate standard solution consumption, silver nitrate standard solution concentration, water sample volume and chloride ion content;

[0069] The consumption of the silver nitrate standard solution for water sample titration, the consumption of the silver nitrate standard solution for blank titration, the concentration of the silver nitrate standard solution, and the volume of the water sample are used as inputs, and the chloride ion content is used as output to train the neural network model to obtain a chloride ion content detection model.

[0070] Exemplarily, the chloride ion content in the water sample test data set is calculated by the following formula:

[0071]

[0072] Where X is the chloride ion content, a is the consumption of silver nitrate standard solution for water sample titration, b is the consumption of silver nitrate standard solution for blank titration, T is the concentration of silver nitrate standard solution, and V is the volume of water sample.

[0073] Specifically, a water sample chloride ion content detection system of an embodiment of the present disclosure is used to implement the water sample chloride ion content detection method described in the above embodiments. The specific implementation process has been described in detail in the above embodiments and will not be repeated here.

[0074] A water sample chloride ion content detection system according to an embodiment of the present disclosure establishes a chloride ion content detection model by adopting machine learning, and uses the model to obtain chloride ion content detection results, thereby reducing the workload of laboratory technicians, improving experimental efficiency, reducing the error of detection results, and providing a reference basis for the accuracy of manual operation experimental results.

[0075] like Figure 3 As shown, another embodiment of the present disclosure provides an electronic device, including:

[0076] At least one processor 301; and a memory 302 communicatively connected to the at least one processor 301, for storing one or more programs, which, when executed by the at least one processor 301, enable the at least one processor 301 to implement the method for detecting chloride ion content in a water sample as described above.

[0077] The memory 302 and the processor 301 are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors 301 and the memory 302 together. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor 301 is transmitted on a wireless medium through an antenna, and further, the antenna also receives data and transmits the data to the processor 301.

[0078] The processor 301 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management and other control functions. The memory 302 can be used to store data used by the processor 301 when performing operations.

[0079] Yet another embodiment of the present disclosure provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the above-mentioned method for detecting chloride ion content in a water sample.

[0080] The computer-readable storage medium may be included in the system or electronic device of the present disclosure, or may exist independently.

[0081] Computer-readable storage media may be any tangible media that contains or stores a program, which may be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples include, but are not limited to, an electrical connection with one or more wires, a portable computer disk, a hard disk, optical fiber, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0082] The computer-readable storage medium may also include a data signal propagated in baseband or as part of a carrier wave, in which the computer-readable program code is carried. Specific examples include but are not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0083] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present disclosure, but the present disclosure is not limited thereto. For those of ordinary skill in the art, various modifications and improvements can be made without departing from the spirit and substance of the present disclosure, and these modifications and improvements are also considered to be within the scope of protection of the present disclosure.

Claims

1. A method for detecting chloride ion content in a water sample, characterized in that: The method comprises: Obtain water sample testing parameters; Input the water sample test parameters into a pre-established chloride ion content detection model, and output the chloride ion content; wherein the chloride ion content detection model is trained using a neural network model based on a water sample test data set; The chloride ion content is output visually.

2. The method according to claim 1, characterized in that The step of obtaining water sample test parameters comprises: Pressure sensors and flow sensors are used to obtain water sample test parameters; among them, The water sample test parameters at least include the consumption of silver nitrate standard solution for water sample titration, the consumption of silver nitrate standard solution for blank titration, the concentration of silver nitrate standard solution and the volume of water sample.

3. The method according to claim 1, characterized in that The chloride ion content detection model is pre-established by the following steps: Acquire a water sample test data set; wherein the water sample test data set includes water sample titration silver nitrate standard solution consumption, blank titration silver nitrate standard solution consumption, silver nitrate standard solution concentration, water sample volume and chloride ion content; The consumption of the silver nitrate standard solution for water sample titration, the consumption of the silver nitrate standard solution for blank titration, the concentration of the silver nitrate standard solution, and the volume of the water sample are used as inputs, and the chloride ion content is used as output to train the neural network model to obtain a chloride ion content detection model.

4. The method according to claim 3, characterized in that: The chloride ion content in the water sample test data set is calculated by the following formula: Where X is the chloride ion content, a is the consumption of silver nitrate standard solution for water sample titration, b is the consumption of silver nitrate standard solution for blank titration, T is the concentration of silver nitrate standard solution, and V is the volume of water sample.

5. A water sample chloride ion content detection system, characterized in that: The system comprises: A data acquisition module is used to obtain water sample test parameters; A data processing module, used for inputting the water sample test parameters into a pre-established chloride ion content detection model, and outputting the chloride ion content; wherein the chloride ion content detection model is obtained by training a neural network model based on a water sample test data set; The data output module is used to visually output the chloride ion content.

6. The system according to claim 5, characterized in that The data acquisition module includes a pressure sensor and a flow sensor; The water sample test parameters at least include the consumption of silver nitrate standard solution for water sample titration, the consumption of silver nitrate standard solution for blank titration, the concentration of silver nitrate standard solution and the volume of water sample.

7. The system according to claim 5, characterized in that The system also includes a model building module; the model building module is specifically used to: Acquire a water sample test data set; wherein the water sample test data set includes water sample titration silver nitrate standard solution consumption, blank titration silver nitrate standard solution consumption, silver nitrate standard solution concentration, water sample volume and chloride ion content; The consumption of the silver nitrate standard solution for water sample titration, the consumption of the silver nitrate standard solution for blank titration, the concentration of the silver nitrate standard solution, and the volume of the water sample are used as inputs, and the chloride ion content is used as output to train the neural network model to obtain a chloride ion content detection model.

8. The system according to claim 7, characterized in that The chloride ion content in the water sample test data set is calculated by the following formula: Where X is the chloride ion content, a is the consumption of silver nitrate standard solution for water sample titration, b is the consumption of silver nitrate standard solution for blank titration, T is the concentration of silver nitrate standard solution, and V is the volume of water sample.

9. An electronic device, characterized in that: include: at least one processor; as well as, A memory communicatively connected to the at least one processor is used to store one or more programs, and when the one or more programs are executed by the at least one processor, the at least one processor can implement the method for detecting chloride ion content in a water sample as described in any one of claims 1 to 4.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for detecting chloride ion content in a water sample according to any one of claims 1 to 4 is implemented.

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