An integrated system for automated processing and intelligent management of laboratory water sample records

Abstract

The present invention proposes an integrated system for automated processing and intelligent management of laboratory water sample records, which relates to the technical field of management systems. The present invention uses a high-precision robotic arm to achieve non-destructive grasping of water samples and dynamic coding of RFID tags, combined with multi-parameter sensors to collect physical and chemical parameters in real time, ensuring the traceability of samples throughout their life cycle; the liquid-dispensing robot uses microfluidics technology and intelligent scheduling algorithms to control detection errors, and edge computing nodes synchronously complete data cleaning and standardization; the decision-making unit relies on a convolutional neural network model to intelligently determine the type and level of pollutants, and locates microbial particles through the YOLOv5 model to generate a structured report containing a pollution map heat map; the management unit dynamically monitors water sample inventory and triggers automatic replenishment, and combines multiple monitoring mechanisms for TOC, heavy metal concentration, and biological toxicity to achieve standard discharge of purified water samples. The system replaces manual operations with mechanization and intelligence, significantly improving detection efficiency and data reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of management systems, and in particular to an integrated system for automated processing and intelligent management of laboratory water sample records. Background Art

[0002] Laboratory water samples are critical for testing, analyzing, and verifying water quality characteristics in experimental research. They come in a wide variety of types and are used in a wide range of scenarios. Based on their source and purpose, water samples can be divided into three categories: environmental water samples (such as surface water, wastewater, and groundwater), laboratory water samples (such as ultrapure water and deionized water), and biologically relevant water samples (such as cell culture water and dialysate). Environmental monitoring requires analysis of heavy metals, organic matter, and microbial indicators in environmental water samples.

[0003] With the maturity of the Internet of Things (IoT), artificial intelligence (AI), and big data technologies, laboratory water sample processing is transforming from traditional manual methods to automation and intelligence. By integrating sensor networks, automatic packaging equipment, and cloud management platforms, closed-loop control of the entire process of water sample collection, labeling, storage, and analysis can be achieved. For example, the patent with announcement number CN114677033B discloses an intelligent monitoring and analysis management system for the entire cycle of laboratory cultivation data. The system contains multiple modules such as cultivation sample screening, which monitors and analyzes growth information at each cultivation stage, solving the problems of single monitoring and low reliability, realizing the analysis of the impact of single environmental factors, and improving the scientific nature and reference value of monitoring and analysis. However, the existing system only automatically collects and manages information on samples before the experiment and lacks subsequent processing. After the experiment is completed, the samples in stock still need to be cleared manually, which is relatively inconvenient to use.

[0004] Therefore, an integrated system for automated processing and intelligent management of laboratory water sample records was introduced. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the defects in the existing technology that the existing system only automatically collects and manages information of samples before the experiment but lacks subsequent processing, and mainly relies on manual clearing after the experiment is completed. The present invention proposes an integrated system for automated processing and intelligent management of laboratory water sample records.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: an automated processing method for laboratory water sample records

[0007] Integrated with intelligent management system, including:

[0008] The sampling unit is used to complete the storage and grabbing of water samples through an automated robotic arm, simultaneously record metadata including sampling time and geographic location, and use sensors to obtain basic physical and chemical indicators of water samples in real time;

[0009] The processing unit is used to distribute the collected water samples to the testing instruments, automatically generate experimental data of chemical oxygen demand and heavy metal content, and output the data after local cleaning and standardization to support subsequent analysis;

[0010] The decision-making unit identifies pollutant types and assesses pollution levels based on detection data, generates risk warning reports, and synchronizes the results to the cloud database;

[0011] The management unit is used to dynamically monitor the inventory of reagents and consumables, trigger the automatic replenishment process, record operation logs and data flow records throughout the process, and provide visual reports and abnormal alarm functions, including:

[0012] The intelligent inventory management module is used to monitor the inventory balance and consumption status of water samples in real time. When the inventory falls below the threshold, the automatic refill process is triggered and a refill reminder is generated and pushed to the management terminal. Specifically:

[0013] The classification module is used to dynamically match the classification rule library based on the pollution type and pollution degree identification results of different water samples, realize the intelligent classification and labeling of polluted water samples, and provide accurate classification basis for subsequent processing;

[0014] Intelligent storage module, based on classification results and using automated storage equipment, to partition and store water samples of different pollution categories;

[0015] The automated processing module is used to automatically dispatch the corresponding purification equipment based on the classification data, complete the unmanned operation of the entire process from water sample distribution to purification, and simultaneously record the processing parameters and feed them back to the system.

[0016] Furthermore, the management unit also includes a compliance audit module and a user-side management module:

[0017] Compliance audit module, used to record full-process operation logs, permission change information, and report approval status, supports standard audit tracking, and meets laboratory certification requirements;

[0018] The user-side management module is used to provide dual entrances on the web and mobile terminals, supporting real-time monitoring data dashboards, historical data comparison and analysis, and abnormal alarm push.

[0019] Furthermore, the intelligent inventory management module also includes a solution planning module and an emission monitoring module:

[0020] The program planning module is used to combine the regional water quality characteristics of different water samples with historical purification data, and generate purification and remediation plans for polluted water bodies in different regions based on the automatic purification results of purification equipment;

[0021] The emission monitoring module is used to monitor the indicators of the purification process of different types of laboratory inventory water samples in the purification equipment. After the emission standards are met, the purified contaminated water samples are allowed to be discharged to clear the inventory.

[0022] Furthermore, the sampling unit specifically includes:

[0023] The robotic arm sampling module is used to perform automatic grabbing operations for target water sample collection.

[0024] Grab water samples and transfer them to designated sampling containers, supporting compatible grabbing of bottles and cans of different specifications;

[0025] RFID tag writer, used to automatically attach electronic tags to sampling containers, write timestamps, geographic location, sample numbers, and sampling personnel information in real time, and the tag data supports subsequent full-process traceability; and

[0026] Multi-parameter sensing module, integrated pH probe, turbidity meter, conductivity sensor and temperature sensor,

[0027] The physical and chemical parameters of the water body are collected step by step, and the data are transmitted to the processing unit through the edge gateway.

[0028] Furthermore, the processing unit specifically includes:

[0029] The liquid dispensing robot station is used to distribute the collected water samples to different

[0030] In the detection equipment, multi-channel parallel operation is supported;

[0031] Online detector module, including spectrophotometer, atomic absorption spectrometer, biological toxicity detection module, through

[0032] The standardized interface automatically executes the water sample testing process and outputs the original experimental data; and

[0033] Edge computing node module, used to deploy lightweight data cleaning algorithms, remove outliers in real time, and format data

[0034] After being unified into JSON structure, it is pushed to the decision-making unit.

[0035] Furthermore, the decision-making unit specifically includes:

[0036] The pollutant identification module is used in a deep learning model based on a convolutional neural network. After inputting the test data, it automatically determines the type of pollutants in the water sample and assesses the pollution level of the water sample;

[0037] Intelligent analysis module, used to automatically generate a data set containing polluted water samples based on the big data analysis results and preset templates.

[0038] Structured reports with contamination heat maps, risk level identification, and treatment recommendations for managers to review; and

[0039] Cloud data synchronization gateway, used to encrypt and upload raw data and analysis results to the cloud data center through the MQTT protocol

[0040] The database supports historical data storage, cross-platform access and third-party system docking.

[0041] Furthermore, the pollutant identification module uses a deep learning model to calculate the pollutant type of the water sample as follows:

[0042] ,

[0043] in: It represents the scalar value generated by the convolutional layer after scanning the input data, which quantifies the presence intensity of the k-th type of pollutant in the current test sample; k represents the analysis channel number of the pollutant, k=1, 2, ..., M, where M is the total number of channels; Represents the i-th dimension data of the input feature matrix, including the spectral intensity or heavy metal concentration value of a specific band; i represents the position index of the input feature matrix in the time or space dimension, i=1, 2, ..., N, where N is the total number of positions; j represents the offset index within the convolution window, j=1, 2, ..., H, where H is the convolution window radius; represents the feature significance factor, , It is the sensitivity adjustment parameter, the default value is 0.8; Represents the cross-dimensional interaction coefficient matrix, which is adaptively generated by feature covariance; Represents the temporal / spatial attenuation coefficient, which is used to control the weight of historical and future feature influences. ; represents the feature normalization order, , dynamically adjusted according to input data; Indicates the offset of adjacent features, the default , used to capture the correlation patterns between features with fixed intervals; Represents the feature dimension summation operator.

[0044] Furthermore, the pollutant identification module uses a deep learning model to evaluate the pollutant level of water samples using the following calculation formula:

[0045] ,

[0046] in: Indicates the maximum response value of the feature convolution result; Represents the set of all convolution channel output values; Indicates the pollution intensity amplification factor, the default ; represents the baseline adjustment parameter, , dim is the number of input dimensions; Indicates the steepness coefficient of the grade division, the default .

[0047] Furthermore, the cloud data synchronization gateway includes a time series database, a relational database, and a data lake:

[0048] Time series database, used to store real-time sensor data, generate water quality parameter trend charts and assist in anomaly detection, supporting

[0049] Support high-frequency data writing and fast query;

[0050] A relational database used to manage sample metadata including source, collection time, and responsible person, and to store package

[0051] Experimental records including test items, instrument parameters and results;

[0052] Data lake is used to archive raw data including mass spectra, microscopic images and original test reports.

[0053] Hot and cold data are now stored in tiers to support big data analysis.

[0054] Furthermore, the pollutant identification module takes water sample images based on a microscope camera to identify the microorganisms in the water sample.

[0055] The YOLOv5 model is used to locate the position of pollutant particles.

[0056] Compared with the existing technology, the beneficial effects of the present invention include: through the deep integration of precise grasping of robotic arms, full-process RFID traceability, deep learning intelligent analysis and automated purification technology, a full-link closed-loop management system from sampling to discharge is constructed. The system adopts the collaborative operation of high-precision robotic arms and multi-parameter sensors to achieve zero-error positioning of water sample collection and real-time collection of physical and chemical parameters. Combined with the dynamic coding capability of RFID electronic tags, it ensures that each sample is traceable throughout its life cycle from source to terminal. The liquid dispensing robot controls errors through microfluidic technology and intelligent scheduling algorithms. At the same time, the edge computing node performs real-time cleaning and standardization of the raw data to provide a high-quality data basis for subsequent analysis. The decision-making unit relies on the convolutional neural network model. Combining spectral characteristics with multimodal data fusion technology, water samples are used to identify pollution, and partition storage and purification strategies are dynamically triggered according to the pollution level. The management unit ensures that the discharged water quality meets the standards in real time through the dynamic allocation of the intelligent inventory system and unmanned linkage with the purification equipment, combined with multiple monitoring mechanisms for TOC, heavy metal concentration and biological toxicity. At the same time, blockchain technology is used to achieve tamper-proof storage of operation logs to meet the compliance requirements of laboratory certification. The system replaces traditional manual operations with mechanization and intelligence, which not only improves experimental efficiency, but also solves the industry pain points of data silos, cross-contamination and low efficiency of manual clearing. It provides a full-process digital solution from sample recording to purification and discharge for fields such as environmental monitoring and industrial wastewater treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The disclosure of the present invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of the present invention. In the accompanying drawings, the same reference numerals are used to refer to the same components. Among them:

[0058] Figure 1 The overall architecture of an integrated system for automated processing and intelligent management of laboratory water sample records according to one embodiment of the present invention is schematically shown;

[0059] Figure 2 A diagram schematically shows an intelligent inventory management module of an integrated system for automated processing and intelligent management of laboratory water sample records according to one embodiment of the present invention;

[0060] Figure 3 The following schematically shows the overall operation flow chart of an integrated system for automated processing and intelligent management of laboratory water sample records according to one embodiment of the present invention;

[0061] Figure 4 The following schematically shows the architecture of a cloud data synchronization gateway for an integrated system for automated processing and intelligent management of laboratory water sample records according to one embodiment of the present invention. DETAILED DESCRIPTION

[0062] It is easy to understand that according to the technical solution of the present invention, without changing the essential spirit of the present invention, a person skilled in the art can propose a variety of interchangeable structural modes and implementation modes. Therefore, the following specific embodiments and drawings are only exemplary descriptions of the technical solution of the present invention and should not be regarded as the entire invention or as a limitation or restriction of the technical solution of the present invention.

[0063] According to one embodiment of the present invention, Figures 1-4 An automated processing and recording system for laboratory water samples

[0064] Intelligent management integrated system, including four modules: sampling unit, processing unit, decision unit and management unit;

[0065] The sampling unit is used to complete the storage and grabbing of water samples through an automated robotic arm, simultaneously record metadata such as sampling time and geographic location, and use sensors to obtain basic physical and chemical indicators of water samples in real time. It is specifically composed of the following modules:

[0066] The robotic arm sampling module is used to perform automatic grabbing operations for target water sample collection.

[0067] Grab water samples and transfer them to designated sampling containers, supporting compatible grabbing of bottles and cans of different specifications; the water sample grabbing action is performed by a high-precision robotic arm, supporting multi-degree-of-freedom motion control and force feedback adjustment to ensure the sampling process is contamination-free and the sample integrity is guaranteed;

[0068] RFID tag writer, used to automatically attach electronic tags to sampling containers, write timestamps, geographic location (GPS / Beidou positioning), sample numbers, and sampling personnel information in real time, and the tag data supports subsequent full-process traceability; and

[0069] The multi-parameter sensing module integrates a pH probe, turbidity meter, conductivity sensor and temperature sensor to synchronously collect the physical and chemical parameters of the water during sampling, and the data is transmitted to the processing unit through the edge gateway.

[0070] The processing unit distributes the collected water samples to the testing instruments, automatically generates experimental data for chemical oxygen demand and heavy metal content, and outputs the data after local cleaning and standardization to support subsequent analysis. It specifically includes the following modules:

[0071] The liquid dispensing robot station uses a high-precision peristaltic pump and microfluidics technology to distribute collected water samples to detection units (such as heavy metal detection cells and organic matter reaction vessels) according to preset proportions. It has a built-in machine learning algorithm that dynamically allocates liquid dispensing channels based on the priority of the detection items. It supports 8-channel parallel operation, with a liquid dispensing error of less than 0.5% and a cross-contamination rate of less than 0.01%;

[0072] Online detector modules, including spectrophotometers (for COD and ammonia nitrogen), atomic absorption spectrometers (for heavy metals), and biological toxicity detection modules, automatically execute detection processes through standardized interfaces and output raw experimental data; and

[0073] The edge computing node module is used to deploy lightweight data cleaning algorithms, remove outliers (such as sensor drift interference) in real time, and unify the data format into a JSON structure and push it to the decision module.

[0074] The decision-making unit identifies pollutant types and assesses pollution levels based on test data, generates risk warning reports, and synchronizes the results to the cloud database. It specifically includes the following modules:

[0075] The pollutant identification module, based on a deep learning model based on a convolutional neural network (CNN), takes test data (such as spectral characteristics and heavy metal concentrations) as input, automatically determines the type of pollutant (such as lead, mercury, and organic pollutants) and assesses the pollution level (mild / moderate / severe). The pollutant identification module uses a microscope camera to capture water sample images to identify microbial forms (such as algae and bacteria) in the water samples, extract corresponding test data, and use the YOLOv5 model to locate the location of pollutant particles.

[0076] Intelligent analysis module, used to automatically generate a data set containing polluted water samples based on the big data analysis results and preset templates.

[0077] Structured reports with contamination heat maps, risk level identification, and disposal recommendations for managers to review, with support for one-click export to PDF / Excel format; and

[0078] Cloud data synchronization gateway, used to encrypt and upload raw data and analysis results to the cloud data center through the MQTT protocol

[0079] The database supports historical data storage, cross-platform access and third-party system docking.

[0080] Cloud data synchronization gateways include time series databases, relational databases, and data lakes:

[0081] Time series database, used to store real-time sensor data, generate water quality parameter trend charts and assist in anomaly detection, supporting

[0082] Support high-frequency data writing and fast query;

[0083] A relational database used to manage sample metadata including source, collection time, and responsible person, and to store package

[0084] Experimental records including test items, instrument parameters and results;

[0085] Data lake is used to archive raw data including mass spectra, microscopic images and original test reports.

[0086] Hot and cold data are now stored in tiers to support big data analysis.

[0087] The management unit is used to dynamically monitor the inventory of reagents and consumables, trigger the automatic replenishment process, record operation logs and data flow records throughout the process, and provide visual reports and abnormal alarm functions, including:

[0088] The intelligent inventory management module is used to monitor the inventory balance and consumption status of water samples in real time. When the inventory falls below the threshold, the automatic refill process is triggered and a refill reminder is generated and pushed to the management terminal. Specifically:

[0089] The classification module is used to dynamically match the classification rule library based on the pollution type and pollution degree identification results of different water samples, realize the intelligent classification and labeling of polluted water samples, and provide accurate classification basis for subsequent processing;

[0090] Intelligent storage module, based on classification results and using automated storage equipment, to partition and store water samples of different pollution categories;

[0091] The automated processing module is used to automatically dispatch the corresponding purification equipment based on the classification data, complete the unmanned operation of the entire process from water sample distribution to purification, and simultaneously record the processing parameters and feed them back to the system.

[0092] The pollutant identification module uses a deep learning model to calculate the pollutant type of water samples as follows:

[0093] ,

[0094] in: It represents the scalar value generated by the convolutional layer after scanning the input data, which quantifies the presence intensity of the k-th type of pollutant in the current test sample; k represents the analysis channel number of the pollutant, k=1, 2, ..., M, where M is the total number of channels; Represents the i-th dimension data of the input feature matrix, including the spectral intensity or heavy metal concentration value of a specific band; i represents the position index of the input feature matrix in the time or space dimension, i=1, 2,…, N, where N is the total number of positions; j represents the offset index within the convolution window, j=1, 2,…, H, where H is the convolution window radius; represents the feature significance factor, , It is the sensitivity adjustment parameter, the default value is 0.8; Represents the cross-dimensional interaction coefficient matrix, which is adaptively generated by feature covariance; Represents the temporal / spatial attenuation coefficient, which is used to control the weight of historical and future feature influences. ; represents the feature normalization order, , dynamically adjusted according to the input data; represents the offset of adjacent features, the default , used to capture the correlation patterns between features with fixed intervals; Represents the feature dimension summation operator.

[0095] The input feature matrix X is the core input of the model, and its structure design needs to be compatible with multimodal data such as spectral characteristics and heavy metal concentrations. The specific definition is as follows:

[0096] X∈R T×D ,in:

[0097] T: time step or number of spatial sampling points (e.g. monitoring once per hour for 24 hours, then T=24).

[0098] D: The number of characteristic dimensions, including the number of spectral bands (such as 400-700nm divided into 30 bands), types of heavy metals (such as lead, mercury, etc.) and other pollutant indicators.

[0099] Element definition:

[0100] The i-th row Xi represents the multimodal detection data at the i-th time / space point:

[0101] , Represents the value of the kth feature at point i.

[0102] The cross-dimensional interaction coefficient matrix Γ is used to quantify the dynamic correlation strength between different feature dimensions. It generates the covariance matrix and adaptive normalization based on the input features. The formula is as follows:

[0103]

[0104] Where: C represents the covariance matrix of the input features, reflecting the statistical correlation of the features; Represents the Frobenius norm of the matrix, used to normalize the covariance matrix; Indicates row normalization to ensure that the sum of elements in each row is 1, that is, , Indicates the kth feature pair The interaction weight of the features, the value range is (0,1)

[0105] The pollutant identification module uses a deep learning model to evaluate the pollutant level of water samples. The calculation formula is as follows:

[0106] ,

[0107] in: Indicates the maximum response value of the feature convolution result; Represents the set of all convolution channel output values; Indicates the pollution intensity amplification factor, the default ; represents the baseline adjustment parameter, , dim is the number of input dimensions; Indicates the steepness coefficient of the grade division, the default .

[0108] In the prior art, laboratory samples are generally processed by using an automated system to collect and store information, and then placed in a warehouse for storage until laboratory personnel can access them. However, due to the lack of automatic sample processing, the stored samples are generally cleaned manually after the test is completed. For example, as in the system in the prior art CN114677033B, after completing the initial processing of the sample experiment, the sample cannot be subsequently processed, which greatly affects the detection efficiency.

[0109] In this embodiment, the system first uses a robotic arm to capture water samples, simultaneously recording metadata such as sampling time and geographic location, and uniquely identifying them via RFID tags to ensure sample traceability. A dispensing robot then precisely distributes the water samples to different testing instruments according to a preset program. Using online equipment, it automatically performs tests for indicators such as chemical oxygen demand and heavy metal content, generating raw data. A decision-making unit uses a deep learning model to analyze the test data, automatically identifying pollutant types and levels. Using a classification rule library, the water samples are partitioned and stored according to the degree of contamination. After testing, the system dispatches the corresponding equipment (such as a chemical precipitation device or membrane filtration system) for unmanned processing based on purification needs. Water quality indicators are monitored in real time during the purification process, and water samples that meet standards are automatically discharged. This system replaces traditional manual operations by linking robotic arms with automated equipment, effectively reducing human error and the risk of cross-contamination. It also establishes a closed data loop from sampling to discharge, enabling real-time, linked storage of sample information, test results, and processing records, meeting laboratory information management and compliance requirements. Its core advantage lies in significantly improving detection efficiency and data reliability through intelligent scheduling and automated control, solving the technical bottleneck of relying on manual collection before the experiment and cumbersome and inefficient clearing of the inventory after the experiment in the traditional model, and providing efficient and reliable solutions for environmental monitoring, industrial wastewater treatment and other fields.

[0110] like Figure 1-Figure 2As shown, the intelligent inventory management module also includes a program planning module and an emission monitoring module:

[0111] The program planning module is used to combine the regional water quality characteristics of different water samples with historical purification data, and generate purification and remediation plans for polluted water bodies in different regions based on the automatic purification results of purification equipment;

[0112] The emission monitoring module is used to monitor the indicators of the purification process of different types of laboratory inventory water samples in the purification equipment. After the emission standards are met, the purified contaminated water samples are allowed to be discharged to clear the inventory.

[0113] The management unit also includes a compliance audit module and a user-side management module:

[0114] Compliance audit module, used to record full-process operation logs, permission change information, and report approval status, supports standard audit tracking, and meets laboratory certification requirements;

[0115] The user-side management module is used to provide dual entrances on the web and mobile terminals, supporting real-time monitoring data dashboards, historical data comparison and analysis, and abnormal alarm push.

[0116] In this embodiment, the solution planning module deeply integrates regional water quality characteristics with historical purification data, uses machine learning algorithms to dynamically analyze the purification results of different polluted water bodies, and combines regional environmental parameters (such as water flow rate and pollutant diffusion patterns) to generate customized remediation solutions. For example, it automatically matches the adsorption-precipitation combined process for areas with severe heavy metal pollution, while areas dominated by organic pollutants give priority to advanced oxidation technology. The model parameters are continuously optimized through the cloud database to improve the adaptability of the solution. The emission monitoring module collects multi-dimensional indicators such as TOC, conductivity, and biological toxicity in real time during the operation of the purification equipment. It dynamically compares national and industry emission standards through edge computing nodes. When the purified water sample meets the standards for three consecutive tests, the automatic emission command is triggered. If it does not meet the standards, the alarm is activated and the water is linked back to the treatment unit for re-purification, ensuring zero risk in the emission process. The compliance audit module of the management unit uses blockchain technology to record the entire process operation log, permission changes and report approval status, and supports tracing the responsible person and modification traces of any link along the timeline, meeting the audit tracking requirements of ISO / IEC17025 certification; the user-side management module integrates the web and mobile data dashboards through a three-dimensional visualization interface, and displays the water sample inventory distribution heat map, purification progress Gantt chart and pollutant trend warning in real time. Managers can receive abnormal alarm push notifications through mobile terminals and remotely call historical data for multi-dimensional comparative analysis. The system's built-in intelligent reporting engine automatically generates monthly purification efficiency evaluation reports that meet the requirements of the environmental protection department, significantly improving management decision-making efficiency. Each module is deeply integrated with the cloud hub through a standardized API interface, forming a full-link closed loop from intelligent generation of purification solutions to emission compliance verification, solving the technical bottlenecks of poor regional adaptability of traditional laboratory purification processes, uncontrollable emission risks, and low management and traceability efficiency.

[0117] The technical scope of the present invention is not limited to the contents of the above description. Those skilled in the art can make various deformations and modifications to the above embodiments without departing from the technical idea of ​​the present invention, and these deformations and modifications should all fall within the protection scope of the present invention.

Claims

1. An integrated system for automated processing and intelligent management of laboratory water sample records, characterized in that: include: The sampling unit is used to complete the storage and grabbing of water samples through an automated robotic arm, simultaneously record metadata including sampling time and geographic location, and use sensors to obtain basic physical and chemical indicators of water samples in real time; The processing unit is used to distribute the collected water samples to the testing instruments, automatically generate experimental data of chemical oxygen demand and heavy metal content, and output the data after local cleaning and standardization to support subsequent analysis; The decision-making unit identifies pollutant types and assesses pollution levels based on detection data, generates risk warning reports, and synchronizes the results to the cloud database; The management unit is used to dynamically monitor the inventory of reagents and consumables, trigger the automatic replenishment process, record operation logs and data flow records throughout the process, and provide visual reports and abnormal alarm functions, including: The intelligent inventory management module is used to monitor the inventory balance and consumption status of water samples in real time. When the inventory falls below the threshold, the automatic refill process is triggered and a refill reminder is generated and pushed to the management terminal. Specifically: The classification module is used to dynamically match the classification rule library based on the pollution type and pollution degree identification results of different water samples, realize the intelligent classification and labeling of polluted water samples, and provide accurate classification basis for subsequent processing; Intelligent storage module, based on classification results and using automated storage equipment, to partition and store water samples of different pollution categories; The automated processing module is used to automatically dispatch the corresponding purification equipment based on the classified data, complete the unmanned operation of the entire process from water sample distribution to purification, and simultaneously record the processing parameters and feed them back to the system; The decision-making unit specifically includes: The pollutant identification module is used to automatically determine the type of pollutants in water samples and assess the pollution level of water samples after inputting the test data based on the deep learning model of the convolutional neural network. The pollutant identification module uses the deep learning model to calculate the pollutant type of water samples using the following formula: ， in: It represents the scalar value generated by the convolutional layer after scanning the input data, which quantifies the presence intensity of the k-th type of pollutant in the current test sample; k represents the analysis channel number of the pollutant, k=1, 2, ..., M, where M is the total number of channels; Represents the i-th dimension data of the input feature matrix, including the spectral intensity or heavy metal concentration value of a specific band; i represents the position index of the input feature matrix in the time or space dimension, i=1, 2, ..., N, where N is the total number of positions; j represents the offset index within the convolution window, j=1, 2, ..., H, where H is the convolution window radius; represents the feature significance factor, It is the sensitivity adjustment parameter, the default value is 0.8; Represents the cross-dimensional interaction coefficient matrix, which is adaptively generated by feature covariance; Represents the temporal / spatial attenuation coefficient, which is used to control the weight of historical and future feature influences. ; represents the feature normalization order, , dynamically adjusted according to input data; Indicates the offset of adjacent features, the default , used to capture the correlation patterns between features with fixed intervals; represents the feature dimension summation operator; The pollutant identification module uses a deep learning model to evaluate the pollutant level of water samples. The calculation formula is as follows: ， in: Represents the maximum response value of the feature convolution result; represents the set of output values ​​of all convolution channels; Indicates the pollution intensity amplification factor, the default ; represents the baseline adjustment parameter, , dim is the number of input dimensions; Indicates the steepness coefficient of the grade division, the default .

2. The integrated system for automated processing and intelligent management of laboratory water sample records according to claim 1, characterized in that: The management unit also includes a compliance audit module and a user-side management module: Compliance audit module, used to record full-process operation logs, permission change information, and report approval status, supports standard audit tracking, and meets laboratory certification requirements; The user-side management module is used to provide dual entrances on the web and mobile terminals, supporting real-time monitoring data dashboards, historical data comparison and analysis, and abnormal alarm push.

3. The integrated system for automated processing and intelligent management of laboratory water sample records according to claim 1, characterized in that: The intelligent inventory management module also includes a program planning module and an emission monitoring module: The program planning module is used to combine the regional water quality characteristics of different water samples with historical purification data, and generate purification and remediation plans for polluted water bodies in different regions based on the automatic purification results of purification equipment; The emission monitoring module is used to monitor the indicators of the purification process of different types of laboratory inventory water samples in the purification equipment. After the emission standards are met, the purified contaminated water samples are allowed to be discharged to clear the inventory.

4. The integrated system for automated processing and intelligent management of laboratory water sample records according to claim 1, characterized in that: The sampling unit specifically includes: The robotic arm sampling module is used to perform automatic grabbing operations for target water sample collection, with precise positioning through the multi-axis robotic arm. Grab water samples and transfer them to designated sampling containers, supporting compatible grabbing of bottles and cans of different specifications; RFID tag writer, used to automatically attach electronic tags to sampling containers, write timestamps, geographic location, sample numbers, and sampling personnel information in real time, and the tag data supports subsequent full-process traceability; and Multi-parameter sensing module, integrated pH probe, turbidity meter, conductivity sensor and temperature sensor, The physical and chemical parameters of the water body are collected step by step, and the data are transmitted to the processing unit through the edge gateway.

5. The integrated system for automated processing and intelligent management of laboratory water sample records according to claim 4, characterized in that: The processing unit specifically includes: The liquid dispensing robot station is used to distribute the collected water samples to different In the detection equipment, multi-channel parallel operation is supported; Online detector module, including spectrophotometer, atomic absorption spectrometer, biological toxicity detection module, through The standardized interface automatically executes the water sample testing process and outputs the original experimental data; and Edge computing node module, used to deploy lightweight data cleaning algorithms, remove outliers in real time, and format data After being unified into JSON structure, it is pushed to the decision-making unit.

6. The integrated system for automated processing and intelligent management of laboratory water sample records according to claim 5, characterized in that: The decision-making unit also includes: Intelligent analysis module, used to automatically generate a data set containing polluted water samples based on the big data analysis results and preset templates. Structured reports with contamination heat maps, risk level identification, and treatment recommendations for managers to review; and Cloud data synchronization gateway, used to encrypt and upload raw data and analysis results to the cloud data center through the MQTT protocol The database supports historical data storage, cross-platform access and third-party system docking.

7. The integrated system for automated processing and intelligent management of laboratory water sample records according to claim 6, characterized in that: The cloud data synchronization gateway includes a time series database, a relational database, and a data lake: Time series database, used to store real-time sensor data, generate water quality parameter trend charts and assist in anomaly detection, supporting Support high-frequency data writing and fast query; A relational database used to manage sample metadata including source, collection time, and responsible person, and to store package Experimental records including test items, instrument parameters and results; Data lake is used to archive raw data including mass spectra, microscopic images and original test reports. Hot and cold data are now stored in tiers to support big data analysis.

8. The integrated system for automated processing and intelligent management of laboratory water sample records according to claim 1, characterized in that: The pollutant identification module uses a microscope camera to capture water sample images to identify the morphology of microorganisms in the water sample and extract corresponding detection data, and locates the position of pollutant particles in combination with the YOLOv5 model.

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