Mineral water resource pollution detection system

Abstract

The invention discloses a mineral water resource pollution detection system, which belongs to the field of water pollution detection, and comprises a sample collection module, a preprocessing module, a detection module, a data processing module and a result feedback module, wherein the sample collection module is used for collecting a water sample of a mineral spring water source based on an automatic sampling device; the pretreatment module is used for pretreating a collected water sample to obtain a pretreated water sample; the detection module is used for carrying out multi-pollutant detection on the pretreated water sample; the data processing module is used for processing the multi-pollutant detection data in real time to obtain a detection result and generate a detection report; and the result feedback module is used for feeding back the detection result to a manager.

Description

Technical Field

[0001] The invention belongs to the technical field of water pollution detection, and in particular relates to a mineral water resource pollution detection system. Background Art

[0002] With the acceleration of industrialization and the continuous growth of population, the protection and rational use of water resources have become a major global issue. Mineral water, as a precious resource given by nature, has become an indispensable source of drinking water in people's daily lives with its unique mineral composition and pure quality.

[0003] Traditional methods for detecting mineral water pollution often rely on laboratory analysis. Although these methods are accurate, they have disadvantages such as long detection cycles, high costs, and complex operations. First, traditional sampling methods are mostly manual operations, which makes it difficult to ensure the timeliness and representativeness of sampling, especially in remote or difficult-to-reach water sources. Secondly, the laboratory analysis process is cumbersome and requires professional technicians and expensive instruments and equipment, which makes the detection cost high. Furthermore, traditional methods can usually only detect single or a few pollutants, making it difficult to achieve multi-parameter, comprehensive water quality assessment.

[0004] In addition to common heavy metals and organic pollutants, new types of pollution such as microbial pollution and radioactive pollution are also gradually emerging. These pollutants have a profound impact on water quality and are often hidden and cumulative, making it difficult for traditional detection methods to effectively deal with them.

[0005] Therefore, it is particularly important to develop an efficient, convenient and economical mineral water resource pollution detection system. The system needs to be able to achieve rapid sampling, multi-parameter detection, real-time data processing and feedback of mineral water sources, so as to comprehensively and accurately evaluate water quality conditions and promptly discover and deal with pollution problems. At the same time, the system should also have the characteristics of low cost, easy operation and scalability to meet the needs of large-scale and normalized water quality monitoring.

[0006] Based on this technical background, the present invention proposes a new mineral water resource pollution detection system. The system realizes comprehensive, rapid and accurate detection of mineral water resources by integrating advanced automatic sampling technology, multi-parameter detection technology, data processing and analysis technology. Summary of the invention

[0007] In order to solve the above technical problems, the present invention proposes a mineral water resource pollution detection system to solve the problems existing in the above prior art.

[0008] To achieve the above-mentioned purpose, the present invention provides a mineral water resource pollution detection system, comprising: a sample collection module, a preprocessing module, a detection module, a data processing module and a result feedback module connected in sequence;

[0009] Wherein, the sample collection module is used to collect water samples from the mineral water source based on the automated sampling device;

[0010] The preprocessing module is used to preprocess the collected water samples to obtain preprocessed water samples;

[0011] The detection module is used to perform multi-pollutant detection on the pretreated water sample;

[0012] The data processing module is used to process the multi-pollutant detection data in real time to obtain the detection results and generate a detection report;

[0013] The result feedback module is used to feed back the detection result to the management personnel.

[0014] Optionally, the sample collection module includes a water source positioning unit, a sampling execution unit, a remote control and data transmission unit, and an energy supply unit;

[0015] Wherein, the water source positioning unit is used to determine the exact location of the mineral water;

[0016] The sampling execution unit is used to automatically collect target water samples and store the water samples;

[0017] The remote control and data transmission unit is used to realize remote control and real-time data transmission based on wireless communication;

[0018] The energy supply unit is used to provide continuous energy supply to the sample sampling module based on a sustainable energy system.

[0019] Optionally, the sampling execution unit includes a sampling subunit and a storage subunit;

[0020] Wherein, the sampling subunit is used to collect water samples from underground or water bodies; the sampling subunit includes a mechanical arm and a drilling device;

[0021] The storage subunit is used to automatically add preservatives to the water sample to ensure the stability of the sample during transportation and storage.

[0022] Optionally, the pre-processing module includes a water sample receiving and registering unit, a filtering unit, a pH value adjusting unit, a dilution and concentration unit and a digestion unit;

[0023] Wherein, the water sample receiving and registering unit is used to receive water samples collected from the site and register them;

[0024] The filtration unit is used to remove suspended particles, silt and microbial impurities in the water sample;

[0025] The pH value adjustment unit is used to adjust the pH value of the water sample according to the pollution detection requirements;

[0026] The dilution and concentration unit is used to dilute water samples with too high a concentration and to concentrate water samples with too low a concentration;

[0027] The digestion unit is used to decompose organic matter in the water sample into inorganic matter.

[0028] Optionally, the detection module includes a chemical detection unit, a physical detection unit and a biological detection unit;

[0029] Wherein, the chemical detection unit is used to identify and quantitatively analyze the chemical components and pollutants in the mineral water;

[0030] The physical detection unit is used to measure the physical properties of mineral water;

[0031] The biological detection unit is used to detect microbial contamination and biological toxicity in mineral water.

[0032] Optionally, the chemical detection unit includes a spectral analysis subunit, a mass spectrometry analysis subunit and a chromatographic analysis subunit;

[0033] The physical detection unit includes a turbidity and colorimetry detection subunit, a dissolved oxygen detection subunit and a conductivity measurement subunit;

[0034] The biological detection unit includes a microorganism detection subunit and a biological toxicity detection subunit.

[0035] Optionally, the data processing module includes a data preprocessing unit, a contrast learning unit and an adaptive target detection unit;

[0036] Wherein, the data preprocessing unit is used to prepare data samples and generate intermediate data sets;

[0037] The contrastive learning unit acquires global information and local information;

[0038] The adaptive target detection unit is used to obtain a detection result based on the global information and the local information.

[0039] Optionally, the result feedback module includes a result display unit and a safety warning unit;

[0040] Wherein, the result display unit is used to display the test results to the management personnel;

[0041] The safety warning unit is used to send a safety warning message to the management personnel when the water sample detection result exceeds the standard threshold.

[0042] Compared with the prior art, the present invention has the following advantages and technical effects:

[0043] This mineral water resource pollution detection system has achieved full automation and intelligence in the entire process from water sample collection to pollution detection, data processing and result feedback through a highly integrated design. The system uses advanced automated sampling devices to ensure the accuracy and timeliness of water sample collection, while combining sustainable energy supply to reduce operating costs. The pretreatment module effectively removes impurities in water samples through fine filtration, pH adjustment, dilution, concentration and digestion, laying a solid foundation for subsequent accurate detection.

[0044] The detection module integrates multiple chemical, physical and biological detection technologies, and can comprehensively and deeply analyze various pollutants and physical properties in mineral water, including trace harmful components, microbial contamination and biological toxicity, ensuring the comprehensiveness and accuracy of the test results. The data processing module uses advanced data preprocessing, comparative learning and adaptive target detection technology to quickly and accurately extract key information from massive data and generate detailed test reports, providing managers with a scientific basis for decision-making.

[0045] The result feedback module conveys the test results and management suggestions to the management personnel in a timely manner through intuitive result display and safety warning functions, effectively improving the pollution response speed and governance efficiency. The entire system not only improves the accuracy and efficiency of mineral water resource pollution detection, but also ensures the safety and sustainable use of water resources through intelligent management methods, with significant technical innovation and practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0047] Figure 1 2 is a diagram of a mineral water resource pollution detection system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0049] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0050] Embodiment 1

[0051] like Figure 1As shown, in this embodiment, a mineral water resource pollution detection system is provided, including: a sample collection module, a pretreatment module, a detection module, a data processing module and a result feedback module. First, the system is equipped with an advanced automated sampling device, which can automatically collect water samples from the mineral water source according to a preset schedule or specific needs. This process not only ensures the representativeness of the sample, but also ensures that the collected water sample has the best freshness, providing a reliable basis for subsequent detection. Next, the water sample will enter the pretreatment module. Here, the water sample will undergo a series of pretreatment steps, including filtration, dilution and concentration, which are designed to remove impurities that may affect the test results, thereby improving the accuracy and reliability of the test. Subsequently, the water sample will be sent to the multi-parameter detection module. This module integrates a variety of high-precision detection instruments, such as inductively coupled plasma mass spectrometer (ICP-MS), high performance liquid chromatograph (HPLC) and bioluminescence detector. These instruments can simultaneously accurately detect a variety of pollutant indicators such as heavy metals, organic pollutants, microorganisms and radioactive substances in water samples, and comprehensively evaluate the water quality. After the detection is completed, the data will be transmitted to the data processing module. Advanced data processing and analysis software is used here to process and analyze the test data in real time and store the results. The system can generate detailed water quality test reports and has an abnormal warning function. Once abnormal water quality is detected, an alarm can be issued immediately. Finally, the result feedback module will ensure that the test results can be communicated to the management personnel in a timely manner. Based on the test report and analysis results, the management personnel can make corresponding treatment suggestions or early warning information, so as to take necessary measures to protect and maintain the purity and safety of mineral water resources.

[0052] Furthermore, the sample collection module includes a water source positioning unit, an automatic sampling unit, a remote control and data transmission unit, and an energy supply unit. The specific implementation of this embodiment includes the following steps:

[0053] The control unit receives the sampling instruction from the host computer or the user, and parses it to determine the sampling target, location, quantity and other parameters. The water source positioning unit uses the geographic information system to accurately determine the source of the mineral water according to the parameters in the instruction to identify and locate the sampling target, ensuring that the sampling execution unit can accurately reach the sampling point. Driven by the control unit, the sampling execution unit performs sampling actions, such as moving the robotic arm to the sampling point and inserting the sampling needle into the sample container. After the sampling is completed, the sample processing unit performs preliminary processing on the sample, including but not limited to packaging into different containers, diluting or mixing, etc. The data in the entire sampling process (such as sampling time, location, quantity, etc.) is stored by the data storage and transmission unit, and the data is transmitted to the host computer or cloud server through wireless communication for subsequent analysis and query.

[0054] Furthermore, the pretreatment module includes a water sample receiving and registration unit, a filtration unit, a pH value adjustment unit, a dilution and concentration unit, and a digestion unit; the specific implementation process is as follows: First, the water sample receiving and registration unit is responsible for safely receiving the water samples collected from the site, and recording the relevant information in detail, such as the sampling time, location, water sample type and personnel, to ensure the traceability and accuracy of the water samples. At the same time, the water sample receiving and registration unit prints the label QR code on the water sample based on the received water sample information, and prints the QR code on the water sample container. Next, the filtration unit performs preliminary treatment on the water sample, removing impurities such as suspended particles, sediment and microorganisms by physical methods. This step is the key to reducing interference in subsequent analysis and providing clearer water samples for subsequent processing. Subsequently, the pH value adjustment unit adjusts the pH of the water sample according to the needs of the analysis project. By using an accurate pH meter and an appropriate amount of acid and alkali solution, the water sample is ensured to reach the optimal analytical pH range, thereby improving the accuracy and sensitivity of the detection. For water samples with too high or too low concentrations, the dilution and concentration unit will play an important role. The dilution unit reduces the concentration of high-concentration water samples by adding pure water to make them meet the detection range of the analytical instrument; while the concentration unit uses evaporation, extraction and other methods to increase the concentration of low-concentration water samples to enhance the sensitivity of detection. In some cases, the digestion unit is introduced to treat water samples containing organic matter. By heating or adding chemical reagents, organic matter is decomposed into inorganic matter, which facilitates subsequent inorganic analysis. This step is crucial to ensure the accuracy of the analysis results. Finally, the data recording and quality control unit runs through the entire pretreatment process, is responsible for recording all key parameters and results, and performs strict quality control. The accuracy and reliability of the pretreatment results are ensured through regular calibration, parallel sample analysis and other means.

[0055] Furthermore, the detection module includes a chemical detection unit, a physical detection unit and a biological detection unit. The specific implementation of this embodiment includes the following steps:

[0056] The chemical detection unit integrates a variety of advanced analytical techniques to identify and measure the chemical composition and potential contaminants in mineral water. Specifically, it consists of the following subunits: Spectral analysis subunit: Uses UV-visible spectroscopy and infrared spectroscopy to perform qualitative and quantitative analysis of organic and inorganic substances in water samples. Mass spectrometry subunit: Utilizes the high-resolution characteristics of the mass spectrometer to perform precise mass analysis of compounds in complex mixtures to identify unknown contaminants. Chromatographic analysis subunit: Separates and determines organic compounds in water samples, including volatile organic compounds and semi-volatile organic compounds, through gas chromatography and liquid chromatography techniques.

[0057] The physical detection unit includes: Turbidity and color detection subunit: uses optical sensors to measure the turbidity and color of water samples, reflecting the content of suspended particles. Dissolved oxygen detection subunit: uses electrochemical sensors to directly measure the dissolved oxygen level in water samples. Key indicators are used to evaluate the oxidation state of water bodies. Conductivity measurement subunit: uses a conductivity meter to measure the conductivity of water samples and evaluate the ion concentration and purity of water samples.

[0058] The biological detection unit is responsible for evaluating the biological safety of mineral water, including microbial contamination and biological toxicity. Specifically, it includes the following: Microbial detection subunit: Detect and identify bacteria, viruses and algae in water samples using microbial culture technology and molecular biology methods such as PCR. Biological toxicity detection subunit: Evaluate the potential toxic effects of water samples on organisms through tests such as luminescent bacteria.

[0059] Furthermore, the data processing module includes a data preprocessing unit, a contrastive learning unit and an adaptive target detection unit; wherein the data preprocessing unit is used to prepare data samples and generate an intermediate data set; the contrastive learning unit obtains global information and local information; and the adaptive target detection unit is used to obtain a detection result based on the global information and local information. As a preferred solution of this embodiment, the specific implementation process includes:

[0060] In the initial stage, two sets of data samples are created: one set is the labeled source domain data samples, i.e., the standard mineral water sample data, and the other set is the unlabeled target domain data samples, i.e., the test data of the collected target mineral water samples after chemical, physical, and biological tests. Using the attention mechanism, we combine the source domain and target domain datasets to generate an intermediate domain dataset. Then, through domain adaptation training, we first train the transformation from the source domain to the intermediate domain, and then use this training result to further train the transformation from the intermediate domain to the target domain; by introducing the global-local contrastive learning module, the two discriminators are trained. This module captures the global features and local details of the image at the same time. After multi-level feature extraction, the image data of the source domain and the target domain are simultaneously fed into the joint training network, which shares weights during the training process. For the source domain images, labeled data is used for training; while for the target domain images, unlabeled prediction is performed to optimize the adaptive performance of the model. Finally, the domain adaptive object detection model is optimized by combining the global-local contrastive learning feature alignment module and the collaborative training network to obtain the detection results.

[0061] Among them, the global-local contrastive learning module includes Conv1 convolution layer, the first discriminator, Conv2 convolution layer and the second discriminator. The specific implementation process includes: in the process of feature alignment, the data of the source domain and the target domain are first taken as input and sent to the Conv1 convolution layer for preliminary processing to capture the global features in the data. Subsequently, these global features are sent to the first discriminator for feature recognition through the global contrastive learning mechanism. This process aims to identify and distinguish the differences in global features between different domains, and then calculate the global contrastive learning loss. Next, in order to analyze the data more carefully, the Conv2 convolution layer is used to further extract local features from the data of the source domain and the target domain. These local features, as subtle manifestations of data characteristics, are sent to the second discriminator for local contrastive learning. In this process, by identifying and quantifying the differences in local features between domains, we obtain the local contrastive learning loss. Finally, the global contrastive learning loss and the local contrastive learning loss are combined to jointly optimize the loss function, so that the module can more effectively align the features of the source domain and the target domain at both the global and local levels, thereby achieving more accurate and comprehensive cross-domain feature adaptation.

[0062] Among them, the loss function is:

[0063]

[0064] Where M represents the number of global feature vectors input, y G i represents the category label of the i-th global feature vector, s i Represents the similarity between the i-th global feature vector and the target. L i represents the category label of the i-th local area, h i represents the category label of the target in the i-th local area, t i represents the similarity between the i-th local region and the target, [y L j =h i ] is an indicator function; Represents the similarity between the i-th local region and the target.

[0065] Furthermore, the result feedback module consists of two main parts: the result display unit and the safety warning unit. The result display unit displays detailed test data and analysis results to managers through an intuitive interface, making the water quality status clear at a glance. At the same time, in order to strengthen safety management, the safety warning unit will immediately send a warning message to managers when it detects that the water quality indicators are abnormal or exceed the preset safety threshold.

[0066] The above are only preferred specific implementations 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.

Claims

1. A mineral water resource pollution detection system, characterized in that: include: Sample collection module, preprocessing module, detection module, data processing module and result feedback module; Wherein, the sample collection module is used to collect water samples from the mineral water source based on the automated sampling device; The preprocessing module is used to preprocess the collected water samples to obtain preprocessed water samples; The detection module is used to perform multi-pollutant detection on the pretreated water sample; The data processing module is used to process the multi-pollutant detection data in real time to obtain the detection results and generate a detection report; The result feedback module is used to feed back the detection result to the management personnel.

2. The mineral water resource pollution detection system according to claim 1, characterized in that: The sample collection module includes a water source positioning unit, a sampling execution unit, a remote control and data transmission unit and an energy supply unit; Wherein, the water source positioning unit is used to determine the exact location of the mineral water; The sampling execution unit is used to automatically collect target water samples and store the water samples; The remote control and data transmission unit is used to realize remote control and real-time data transmission based on wireless communication; The energy supply unit is used to provide continuous energy supply to the sample sampling module based on a sustainable energy system.

3. The mineral water resource pollution detection system according to claim 2, characterized in that: The sampling execution unit includes a sampling subunit and a storage subunit; Wherein, the sampling subunit is used to collect water samples from underground or water bodies; the sampling subunit includes a mechanical arm and a drilling device; The storage subunit is used to automatically add preservatives to the water sample to ensure the stability of the sample during transportation and storage.

4. The mineral water resource pollution detection system according to claim 1, characterized in that: The pretreatment module includes a water sample receiving and registration unit, a filtering unit, a pH value adjustment unit, a dilution and concentration unit, and a digestion unit; Wherein, the water sample receiving and registering unit is used to receive water samples collected from the site and register them; The filtration unit is used to remove suspended particles, silt and microbial impurities in the water sample; The pH value adjustment unit is used to adjust the pH value of the water sample according to the pollution detection requirements; The dilution and concentration unit is used to dilute water samples with too high a concentration and to concentrate water samples with too low a concentration; The digestion unit is used to decompose organic matter in the water sample into inorganic matter.

5. The mineral water resource pollution detection system according to claim 1, characterized in that: The detection module includes a chemical detection unit, a physical detection unit and a biological detection unit; Wherein, the chemical detection unit is used to identify and quantitatively analyze the chemical components and pollutants in the mineral water; The physical detection unit is used to measure the physical properties of mineral water; The biological detection unit is used to detect microbial contamination and biological toxicity in mineral water.

6. The mineral water resource pollution detection system according to claim 5, characterized in that: The chemical detection unit includes a spectrum analysis subunit, a mass spectrum analysis subunit and a chromatography analysis subunit; The physical detection unit includes a turbidity and colorimetry detection subunit, a dissolved oxygen detection subunit and a conductivity measurement subunit; The biological detection unit includes a microorganism detection subunit and a biological toxicity detection subunit.

7. The mineral water resource pollution detection system according to claim 6, characterized in that: The data processing module includes a data preprocessing unit, a contrast learning unit and an adaptive target detection unit; Wherein, the data preprocessing unit is used to prepare data samples and generate intermediate data sets; The contrastive learning unit acquires global information and local information; The adaptive target detection unit is used to obtain a detection result based on the global information and the local information.

8. The mineral water resource pollution detection system according to claim 1, characterized in that: The result feedback module includes a result display unit and a safety warning unit; Wherein, the result display unit is used to display the test results to the management personnel; The safety warning unit is used to send a safety warning message to the management personnel when the water sample detection result exceeds the standard threshold.

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