Automatic processing and intelligent management integrated system for laboratory water sample recording piece

Abstract

The invention provides an automatic processing and intelligent management integrated system for laboratory water sample recording, and relates to the technical field of management systems.Water sample lossless grabbing and RFID tag dynamic tagging are achieved through a high-precision mechanical arm, physical and chemical parameters are collected in real time in combination with a multi-parameter sensor, and it is ensured that samples are traceable in the whole life cycle; the liquid separation robot adopts a microfluidic technology and an intelligent scheduling algorithm to control detection errors, and edge computing nodes synchronously complete data cleaning and standardization; the decision-making unit is used for intelligently judging the pollutant type and grade by virtue of a convolutional neural network model, positioning microbial particles by virtue of a YOLOv5 model, and generating a structured report containing a pollution map thermodynamic diagram; the management unit dynamically monitors water sample stock and triggers automatic replenishment, standard discharge of purified water samples is achieved in combination with TOC, heavy metal concentration and biotoxicity multiple monitoring mechanisms, and the system replaces manual operation through mechanization and intelligentization, so that the detection efficiency and data reliability are remarkably improved.

Description

Technical Field

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

[0002] Laboratory water samples are key samples used for detecting, analyzing, and verifying water quality characteristics in experimental research. They are diverse in type and have a wide range of application scenarios. According to their sources and uses, water samples can be divided into three categories: environmental water samples (such as surface water, wastewater, groundwater), experimental water (such as ultrapure water, deionized water), and biologically related water samples (such as cell culture water, dialysate). Among them, heavy metals, organic substances, and microbial indicators in environmental water samples need to be analyzed in environmental monitoring.

[0003] With the maturity of Internet of Things (IoT), artificial intelligence (AI), and big data technologies, the processing of laboratory water samples is transforming from traditional manual mode to automation and intelligence. By integrating sensor networks, automatic dispensing equipment, and cloud management platforms, a full-process closed-loop control of water sample collection, identification, preservation, and analysis can be achieved. For example, the patent with publication number CN114677033B discloses an intelligent monitoring and analysis management system for the full life cycle process of laboratory cultivation data. The system includes multiple modules such as cultivation sample screening, monitors and analyzes the growth information at each cultivation stage, solves the problems of existing single monitoring and low reliability, realizes the analysis of the influence of a single environmental factor, and improves the scientificity and reference of monitoring and analysis. However, the existing systems only automatically collect and manage the information of samples before the experiment and lack 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 automatic processing and intelligent management of laboratory water sample counting is introduced. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the defect in the prior art that the existing systems only automatically collect and manage the information of samples before the experiment and lack subsequent processing, and mainly rely on manual inventory clearance after the experiment is completed. The present invention proposes an integrated system for automatic processing and intelligent management of laboratory water sample counting.

[0006] To solve the above technical problem, the technical solution adopted by the present invention is: An integrated system for automatic processing and intelligent management of laboratory water sample counting, comprising: A sampling unit, configured to complete the grasping of water samples for storage in the warehouse through an automatic robotic arm, synchronously record metadata including sampling time and geographical location, and use sensors to obtain the basic physical and chemical indicators of the water samples in real time; A processing unit for distributing the collected water samples to the detection instruments, automatically generating experimental data on chemical oxygen demand and heavy metal content, and outputting the data after local cleaning and standardization processing to support subsequent analysis; A decision-making unit for identifying pollutant types and evaluating pollution levels based on the detection data, generating a risk warning report, and synchronizing the results to the cloud database; A management unit for dynamically monitoring the inventory of reagents and consumables, triggering an automatic replenishment process, recording operation logs and data transfer records throughout the process, and providing visualization reports and exception alarm functions, specifically including: An intelligent inventory management module for real-time monitoring of the remaining quantity and consumption status of water samples in stock. When the inventory is below the threshold, it triggers an automatic sample replenishment process and generates a replenishment reminder to be pushed to the management terminal. Specifically: A classification module for dynamically matching the classification rule library according to the identification results of the pollution types and degrees of different water samples, realizing the intelligent classification and marking of polluted water samples, and providing an accurate classification basis for subsequent processing; An intelligent storage module for partitioning and storing and managing water samples of different pollution categories based on the classification results and using automated warehousing equipment; An automated processing module for automatically scheduling the corresponding purification equipment according to the classification data, completing the unmanned operation of the whole process from water sample distribution to purification, synchronously recording the processing parameters and feeding them back to the system.

[0007] Furthermore, the management unit also includes a compliance audit module and a user terminal management module: A compliance audit module for recording the operation logs, permission change information, and report approval status of the whole process, supporting standard audit tracking, and meeting the requirements of laboratory certification; A user terminal management module for providing dual entrances of the Web end and the mobile end, supporting real-time monitoring of data dashboards, historical data comparison analysis, and exception alarm pushing.

[0008] Furthermore, the intelligent inventory management module also includes a solution planning module and an emission monitoring module: A solution planning module for generating purification and repair solutions for polluted water bodies in different regions based on the automatic purification results of the purification equipment by combining the regional water quality characteristics and historical purification data of different water samples; An emission monitoring module for monitoring indicators during the purification process of different types of water samples in stock in the laboratory by the purification equipment, and allowing the purified polluted water samples to be discharged to clear the inventory after meeting the emission standards.

[0009] Furthermore, the sampling unit specifically includes: A robotic arm sampling module for performing automatic grasping operations for collecting target water samples and accurately positioning through a multi-axis robotic arm Collect water samples and transfer them to the designated sampling containers, supporting compatible collection for different specifications of bottles and cans; An RFID tag writer for automatically attaching electronic tags to sampling containers, writing timestamps, geographical locations, sample numbers, and sampler information in real time, and the tag data supports full-process traceability in the subsequent process; and A multi-parameter sensing module integrating a pH probe, a turbidimeter, a conductivity sensor, and a temperature sensor, simultaneously collecting the physical and chemical parameters of the water body during sampling, and the data is transmitted to the processing unit through an edge gateway.

[0010] Furthermore, the processing unit specifically includes: A liquid separation robot station for using peristaltic pumps and microfluidic technology to distribute the collected water samples to different detection devices according to a preset ratio, supporting multi-channel parallel operation; An on-line detector module, including a spectrophotometer, an atomic absorption spectrometer, and a biotoxicity detection module, automatically executes the detection process of water samples through a standardized interface and outputs the original experimental data; and An edge computing node module for deploying a lightweight data cleaning algorithm, real-time removing outliers, and unifying the data format into a JSON structure and then pushing it to the decision-making unit.

[0011] Furthermore, the decision-making unit specifically includes: A pollutant identification module for automatically determining the pollutant type of the water sample and evaluating the pollution level of the water sample based on a deep learning model of a convolutional neural network after inputting the detection data; An intelligent analysis module for automatically generating a structured report including a pollution map heat map, a risk level identifier, and disposal suggestions according to the big data analysis results of polluted water samples and a preset template for managers to consult; and A cloud data synchronization gateway for encrypting and uploading the original data and analysis results to the cloud data library through the MQTT protocol, supporting historical data storage, cross-platform access, and third-party system docking.

[0012] Furthermore, the formula for calculating the pollutant type of the water sample by the pollutant identification module using the deep learning model is as follows: , where: represents the scalar value generated after the convolutional layer scans the input data, quantifying the presence intensity of the kth type of pollutant in the current detection sample; K represents the analysis channel number of the pollutant, k = 1, 2,..., M, and M is the total number of channels; ​​Represents the data of the $i$-th dimension of the input feature matrix, including the spectral intensity of a specific band or the value of heavy metal concentration; $i$ represents the position index of the input feature matrix in the time or space dimension, and $i = 1, 2, \ldots, N$ is the total number of positions; $j$ represents the offset index within the convolution window, and $j = 1, 2, \ldots, H$, where $H$ is the radius of the convolution window; Represents the feature significance factor, , is the sensitivity adjustment parameter, with a default value of 0.8; Represents the cross-dimensional interaction coefficient matrix, which is adaptively generated through feature covariance; Represents the temporal / spatial attenuation coefficient, used to control the influence weights of historical and future features, with a default ; Represents the order of feature normalization, , which is dynamically adjusted according to the input data; Represents the offset of adjacent features, with a default , used to capture the association pattern at a fixed interval between features; Represents the feature dimension summation operator.

[0013] Furthermore, the pollutant identification module uses a deep learning model to calculate the pollutant level assessment formula for water samples as follows: , where: Represents the maximum response value of the feature convolution result; Represents the set of output values of all convolution channels; Represents the pollution intensity amplification factor, with a default ; Represents the baseline adjustment parameter, , where dim is the number of input dimensions; Represents the steepness coefficient of level division, with a default .

[0014] Furthermore, the cloud data synchronization gateway includes a time-series database, a relational database, and a data lake: The time-series database is used to store real-time sensor data, generate water quality parameter trend charts, and assist in anomaly detection, and supports high-frequency data writing and fast querying; The relational database is used to manage sample metadata including source, collection time, and responsible person, and store experimental records including detection items, instrument parameters, and results; The data lake is used to archive raw data including mass spectrometry diagrams, microscopic images, and original test reports, implement hierarchical storage of hot and cold data, and support big data analysis.

[0015] ​​​Further, the pollutant identification module captures the water sample image based on a microscopic camera to identify the microbial form in the water sample, extract corresponding detection data, and combines the YOLOv5 model to locate the positions of pollutant particles. Compared with the prior art, the beneficial effects of the present invention include: by deeply integrating the precise grasping of the robotic arm, the whole-process traceability of RFID, the intelligent analysis of deep learning, and the automated purification technology, a full-link closed-loop management system from sampling to discharge is constructed. The system adopts the collaborative operation of a high-precision robotic arm and multi-parameter sensors to achieve zero-error positioning of water sample collection and real-time collection of physicochemical parameters. Combining the dynamic coding ability of RFID electronic tags, it ensures the full life cycle traceability of each sample from the source to the terminal. The liquid separation robot controls the error through microfluidic technology and intelligent scheduling algorithms. At the same time, the edge computing node performs real-time cleaning and standardization processing on the original data to provide a high-quality data basis for subsequent analysis. The decision-making unit relies on the convolutional neural network model, combines spectral features and multi-modal data fusion technology to identify water sample pollution, and dynamically triggers zoning storage and purification strategies according to the pollution level. The management unit realizes the dynamic allocation of the intelligent inventory system and the unmanned linkage of the purification equipment, combines the multiple monitoring mechanisms of TOC, heavy metal concentration, and biological toxicity to ensure that the discharged water quality meets the standards in real time. At the same time, the blockchain technology is used to realize the tamper-proof storage of operation logs, meeting the compliance requirements of laboratory certification; this system replaces traditional manual operations with mechanization and intelligence, not only improving the experimental efficiency, but also solving the industry pain points of data islands, cross-contamination, and low efficiency of manual inventory cleaning, providing a full-process digital solution from sample counting to purification and discharge for fields such as environmental monitoring and industrial wastewater treatment.

[0016] Compared with the prior art, the beneficial effects of the present invention include: by deeply integrating the precise grasping of the robotic arm, the whole-process traceability of RFID, the intelligent analysis of deep learning, and the automated purification technology, a full-link closed-loop management system from sampling to discharge is constructed. The system adopts the collaborative operation of a high-precision robotic arm and multi-parameter sensors to achieve zero-error positioning of water sample collection and real-time collection of physicochemical parameters. Combining the dynamic coding ability of RFID electronic tags, it ensures the full life cycle traceability of each sample from the source to the terminal. The liquid separation robot controls the error through microfluidic technology and intelligent scheduling algorithms. At the same time, the edge computing node performs real-time cleaning and standardization processing on the original data to provide a high-quality data basis for subsequent analysis. The decision-making unit relies on the convolutional neural network model, combines spectral features and multi-modal data fusion technology to identify water sample pollution, and dynamically triggers zoning storage and purification strategies according to the pollution level. The management unit realizes the dynamic allocation of the intelligent inventory system and the unmanned linkage of the purification equipment, combines the multiple monitoring mechanisms of TOC, heavy metal concentration, and biological toxicity to ensure that the discharged water quality meets the standards in real time. At the same time, the blockchain technology is used to realize the tamper-proof storage of operation logs, meeting the compliance requirements of laboratory certification; this system replaces traditional manual operations with mechanization and intelligence, not only improving the experimental efficiency, but also solving the industry pain points of data islands, cross-contamination, and low efficiency of manual inventory cleaning, providing a full-process digital solution from sample counting to purification and discharge for fields such as environmental monitoring and industrial wastewater treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The disclosure of the present invention will be described with reference to the accompanying drawings. It should be understood that the drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present invention. In the drawings, the same reference numerals are used to refer to the same components. Among them: Figure 1 Schematically shows an overall architecture diagram of an integrated system for automated processing and intelligent management of laboratory water sample counting according to an embodiment of the present invention; Figure 2 Schematically shows an intelligent inventory management module diagram of an integrated system for automated processing and intelligent management of laboratory water sample counting according to an embodiment of the present invention; Figure 3 Schematically shows an overall operation flowchart of an integrated system for automated processing and intelligent management of laboratory water sample counting according to an embodiment of the present invention; Figure 4Schematically shows the cloud data synchronization gateway architecture diagram of an integrated system for automated processing and intelligent management of laboratory water sample records proposed according to an embodiment of the present invention. Detailed implementation manners

[0018] It is easy to understand that according to the technical solution of the present invention, without changing the essence of the present invention, those of ordinary skill in the art can propose various interchangeable structural ways and implementation manners. Therefore, the following detailed implementation manners and the accompanying drawings are only exemplary descriptions of the technical solution of the present invention, and should not be regarded as the whole of the present invention or as a limitation or restriction on the technical solution of the present invention.

[0019] Combined with an embodiment of the present invention Figures 1-4 Shown. An integrated system for automated processing and intelligent management of laboratory water sample records, including: four modules: a sampling unit, a processing unit, a decision-making unit, and a management unit; The sampling unit is used to complete the grasping of water samples into the warehouse through an automated robotic arm, synchronously record metadata including sampling time and geographical location, and use sensors to obtain the basic physical and chemical indicators of the water samples in real time; it is specifically composed of the following modules: The robotic arm sampling module is used to perform the automatic grasping operation of target water sample collection, accurately position and grasp the water sample and transfer it to a designated sampling container through a multi-axis robotic arm, supporting compatible grasping of different specifications of bottles and cans; perform the water sample grasping action through a high-precision robotic arm, support multi-degree-of-freedom motion control and force feedback adjustment to ensure that the sampling process is pollution-free and the sample integrity; The RFID tag writer is used to automatically attach an electronic tag to the sampling container, and write the timestamp, geographical location (GPS / Beidou positioning), sample number, and sampling personnel information in real time. The tag data supports full-process traceability in the follow-up; and The multi-parameter sensing module integrates a pH probe, a turbidimeter, a conductivity sensor, and a temperature sensor to simultaneously collect the physical and chemical parameters of the water body during sampling, and the data is transmitted to the processing unit through the edge gateway.

[0020] The processing unit is used to distribute the collected water samples to the detection instruments, automatically generate experimental data such as chemical oxygen demand and heavy metal content, and output the data after local cleaning and standardization processing to support subsequent analysis; it specifically includes the following component modules: The liquid separation robot station is used to adopt a high-precision peristaltic pump and microfluidic technology to distribute the collected water samples to the detection units (such as heavy metal detection pools, organic matter reaction dishes) according to a preset ratio. It has a built-in machine learning algorithm, dynamically distributes the liquid separation channels according to the priority of the detection items, supports 8-channel parallel operation, with a liquid separation error <0.5% and a cross-contamination rate <0.01%; On-line detector module, including spectrophotometer (for measuring COD and ammonia nitrogen), atomic absorption spectrometer (for measuring heavy metals), biotoxicity detection module, etc., automatically executes the detection process through a standardized interface and outputs the original experimental data; and Edge computing node module, used to deploy lightweight data cleaning algorithms, real-time eliminate outliers (such as sensor drift interference), and unify the data format into a JSON structure and push it to the decision-making module.

[0021] Decision-making unit, based on the detection data, conducts pollutant type identification and pollution level assessment, generates a risk warning report, and synchronizes the results to the cloud database; specifically includes the following component modules: Pollutant identification module, used for a deep learning model based on convolutional neural network (CNN), inputs detection data (such as spectral features, heavy metal concentrations), automatically determines the pollutant type (such as lead, mercury, organic pollutants) and evaluates the pollution level (mild / moderate / severe). The pollutant identification module takes pictures of the water sample image based on a microscopic camera to identify the microbial morphology (such as algae, bacteria) in the water sample and extract the corresponding detection data, and combines the YOLOv5 model to locate the position of pollutant particles; Intelligent analysis module, used to automatically generate a structured report containing pollution map heat map, risk level identification and disposal suggestions according to the big data analysis results of polluted water samples and preset templates for managers to consult, and supports one-key export in PDF / Excel format; and Cloud data synchronization gateway, used to encrypt and upload the original data and analysis results to the cloud database through the MQTT protocol, supporting historical data storage, cross-platform access and third-party system docking. The cloud data synchronization gateway includes a time series database, a relational database and a data lake: Time series database, used to store sensor real-time data, generate water quality parameter trend charts and assist in anomaly detection, supporting high-frequency data writing and fast query;

[0022] Relational database, used to manage sample metadata including source, collection time and responsible person, and store experimental records including detection items, instrument parameters and results; Data lake, used to archive the original data including mass spectrometry diagrams, microscopic images and original test reports, realize hierarchical storage of hot and cold data, and support big data analysis. Support high-frequency data writing and fast query; Relational database, used to manage sample metadata including source, collection time and responsible person, and store experimental records including detection items, instrument parameters and results; including detection items, instrument parameters and results; Data lake, used to archive the original data including mass spectrometry diagrams, microscopic images and original test reports, realize hierarchical storage of hot and cold data, and support big data analysis. Realize hierarchical storage of hot and cold data, support big data analysis.

[0023] The management unit is used to dynamically monitor the inventory of reagents and consumables, trigger the automatic replenishment process, record the operation logs and data flow records throughout the process, and provide visualization reports and abnormal alarm functions, specifically including: The intelligent inventory management module is used to real-time monitor the remaining amount and consumption status of water sample inventory. When the inventory is lower than the threshold, it triggers the automatic water sample replenishment process and generates a replenishment reminder to be pushed to the management terminal. Specifically: The classification module is used to dynamically match the classification rule library according to the recognition results of the pollution types and degrees of different water samples, realize the intelligent classification and marking of polluted water samples, and provide an accurate classification basis for subsequent processing; The intelligent storage module, based on the classification results, uses automated warehousing equipment to manage the partitioned storage of water samples of different pollution categories; The automated processing module is used to automatically schedule the corresponding purification equipment according to the classification data, complete the unmanned operation of the entire process from water sample allocation to purification, record the processing parameters synchronously and feedback them to the system.

[0024] The pollutant identification module uses a deep learning model to calculate the pollutant types of water samples as follows: , Where: represents the scalar value generated after the convolutional layer scans the input data, quantifying the presence intensity of the k-th type of pollutant in the current detection sample; K represents the analysis channel number of the pollutant, k = 1, 2,..., M, and M is the total number of channels; represents the i-th dimensional data of the input feature matrix, including the spectral intensity of a specific band or the heavy metal concentration value; i represents the position index of the input feature matrix in the time or space dimension, i = 1, 2,..., N is the total number of positions; j represents the offset index within the convolutional window, j = 1, 2,..., H, and H is the radius of the convolutional window; represents the feature significance factor, , is the sensitivity adjustment parameter, with a default value of 0.8; represents the cross-dimensional interaction coefficient matrix, which is adaptively generated through feature covariance; represents the temporal / spatial decay coefficient, used to control the influence weights of historical and future features, with a default ; represents the feature normalization order, , which is dynamically adjusted according to the input data; represents the adjacent feature offset, with a default , used to capture the correlation pattern at a fixed interval between feature parts; represents the feature dimension summation operator.

[0025] The input feature matrix X is the core input of the model, and its structural design needs to be compatible with multi-modal data such as spectral features and heavy metal concentrations. The specific definition is as follows: X ∈ R T×D , where: T: The time step or the number of spatial sampling points (for example, if monitored once per hour for a total of 24 hours, then T = 24).

[0026] D: The number of feature dimensions, including the number of spectral bands (such as 30 bands in the range of 400 - 700 nm), the types of heavy metals (such as lead, mercury, etc.), and other pollutant indicators.

[0027] Element definition: The i-th row Xi represents the multi-modal detection data at the i-th time / space point: , representing the value of the k-th feature at point i. The cross-dimensional interaction coefficient matrix Γ is used to quantify the dynamic association strength between different feature dimensions, and its generation is based on the covariance matrix and adaptive normalization of the input features. The formula is as follows: , where: C represents the covariance matrix of the input features, reflecting the statistical correlation between features; represents the Frobenius norm of the matrix, which is used to normalize the covariance matrix; represents row-wise normalization to ensure that the sum of elements in each row is 1, that is , represents the interaction weight of the k-th feature with respect to the -th feature, and the value range is (0, 1) The pollutant identification module uses a deep learning model to calculate the pollutant level assessment formula for water samples as follows: ,

[0028] where: represents the maximum response value of the feature convolution result; represents the set of output values of all convolution channels; represents the pollution intensity amplification factor, default ; represents the baseline adjustment parameter, , dim is the input dimension number; represents the steepness coefficient for level division, default .

[0029] In the prior art, when dealing with laboratory samples, generally, an automated system is used to collect and store their information, and then they are placed in a warehouse for storage, waiting for laboratory personnel to retrieve them. However, in the absence of an automatic processing part for the samples, after the detection is completed, the stored samples are generally cleaned manually. Just like the system in the prior art CN114677033B, after the pre-experiment processing of the samples is completed, subsequent processing of the samples cannot be carried out, which greatly affects the detection efficiency.

[0030] In this embodiment, the system first uses a robotic arm to complete the grabbing of water samples, synchronously records metadata such as sampling time and geographical location, and uniquely identifies them through RFID tags to ensure the traceability of sample identity; then the liquid distribution robot accurately distributes the water samples to different detection instruments according to a preset program, and automatically completes the detection of indicators such as chemical oxygen demand and heavy metal content through on-line equipment to generate raw data; the decision-making unit analyzes the characteristics of the detection data through a deep learning model, automatically identifies the types and pollution levels of pollutants, and stores the water samples in different zones according to the pollution degree in combination with the classification rule library; after the detection is completed, the system schedules corresponding equipment (such as chemical precipitation devices, membrane filtration systems) for unmanned treatment according to the purification requirements, and monitors the water quality indicators in real time during the purification process, and the qualified water samples are automatically discharged. This system replaces traditional manual operations through the linkage of the robotic arm and automated equipment, effectively reducing human errors and the risk of cross-contamination. At the same time, it constructs a full-link data closed-loop from sampling to discharge, realizing the real-time associated storage of sample information, detection results and treatment records, and meeting the requirements of laboratory informatization management and compliance. Its core advantage lies in significantly improving the detection efficiency and data reliability through intelligent scheduling and automated control, solving the technical bottleneck of relying on manual collection before the experiment and the cumbersome and inefficient inventory cleaning after the experiment in the traditional mode, and providing an efficient and reliable solution for fields such as environmental monitoring and industrial wastewater treatment.

[0031] As Figures 1-2 shown, the intelligent inventory management module also includes a scheme planning module and an emission monitoring module: The scheme planning module is used to generate purification and repair schemes for polluted water bodies in different regions based on the regional water quality characteristics and historical purification data of different water samples and the automatic purification results of purification equipment; The emission monitoring module is used to monitor the indicators during the purification process of different types of inventory water samples in the laboratory by the purification equipment, and allow the purified polluted water samples to be discharged to clean the inventory after reaching the emission standard.

[0032] The management unit also includes a compliance audit module and a user terminal management module: The compliance audit module is used to record the full-process operation logs, permission change information and report approval status, support standard audit tracking, and meet the requirements of laboratory certification; The client management module is used to provide dual entrances for the Web side and the mobile side, and supports real-time monitoring of data dashboards, comparative analysis of historical data, and abnormal alarm push.

[0033] In this embodiment, the solution planning module deeply integrates regional water quality characteristics and historical purification data, dynamically analyzes the purification results of different polluted water bodies using machine learning algorithms, and generates customized repair solutions in combination with regional environmental parameters (such as water body flow rate, pollutant diffusion law). For example, it automatically matches the combined adsorption-precipitation 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 biotoxicity in real time during the operation of the purification equipment, and dynamically compares with national and industry emission standards through edge computing nodes. When the purified water sample passes the detection three times in a row, it triggers an automatic emission instruction. If it fails to meet the standard, it starts an alarm and links back to the treatment unit for re-purification to ensure zero risk during the emission process. The compliance audit module of the management unit records the full-process operation logs, permission changes, and report approval status using blockchain technology, supports tracing the operation responsible person and modification traces of any link according to the time axis, and meets the audit tracking requirements of ISO / IEC17025 certification; the client management module integrates the data dashboards of the Web side and the mobile side through a three-dimensional visualization interface, and real-time displays the heat map of the water sample inventory distribution, the Gantt chart of the purification progress, and the pollutant trend warning. Managers can receive abnormal alarm pushes through the mobile terminal and remotely call historical data for multi-dimensional comparative analysis. The intelligent report engine built into the system automatically generates a monthly purification efficiency evaluation report that meets the requirements of the environmental protection department, significantly improving the management decision-making efficiency. Each module is deeply integrated with the cloud center through standardized API interfaces, forming a full-link closed loop from intelligent generation of purification solutions to emission compliance verification, solving the technical bottlenecks of poor regional adaptability, uncontrollable emission risks, and low management traceability efficiency in the traditional laboratory purification process.

[0034] The technical scope of the present invention is not limited to the content described above. 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 automatic processing and intelligent management of laboratory water sample records, characterized in that, Including: A sampling unit, which is used to complete the grasping of water samples when they are put into storage by an automated robotic arm, synchronously record metadata including sampling time and geographical location, and use sensors to obtain the basic physical and chemical indicators of water samples in real time; A processing unit, which is used to allocate the collected water samples to detection instruments, automatically generate experimental data on chemical oxygen demand and heavy metal content, and output the data after local cleaning and standardization processing to support subsequent analysis; A decision-making unit, which identifies pollutant types and evaluates pollution levels based on the detection data, generates a risk warning report, and synchronizes the results to the cloud database; A management unit, which is used to dynamically monitor the inventory of reagents and consumables, trigger an automatic replenishment process, record operation logs and data flow records throughout the process, and provide visualization reports and abnormal alarm functions. Specifically, it includes: An intelligent inventory management module, which is used to monitor the remaining amount and consumption status of water sample inventory in real time. When the inventory is lower than the threshold, it triggers an automatic water sample replenishment process and generates a replenishment reminder to be pushed to the manager's terminal. Specifically: A classification module, which is used to dynamically match the classification rule library according to the identification results of the pollution types and degrees of different water samples, realize the intelligent classification and marking of polluted water samples, and provide an accurate classification basis for subsequent processing; An intelligent storage module, which is based on the classification results and uses automated warehousing equipment to manage the partitioned storage of water samples of different pollution categories; An automated processing module, which is used to automatically schedule corresponding purification equipment according to the classification data, complete the unmanned operation of the whole process from water sample allocation to purification, synchronously record the processing parameters and feedback them to the system.

2. The integrated system for automatic processing and intelligent management of laboratory water sample counting according to claim 1, wherein, The management unit further includes a compliance audit module and a user terminal management module: A compliance audit module, which is used to record the operation logs of the whole process, permission change information and report approval status, support standard audit tracking, and meet the requirements of laboratory certification; A user terminal management module, which is used to provide dual entrances for the Web side and the mobile side, support real-time monitoring of data dashboards, historical data comparative analysis and abnormal alarm pushing.

3. The integrated system for automatic processing and intelligent management of laboratory water sample counting according to claim 1, characterized in that, The intelligent inventory management module further includes a scheme planning module and an emission monitoring module: A scheme planning module, which is used to combine the regional water quality characteristics and historical purification data of different water samples, and generate purification and repair schemes for polluted water bodies in different regions according to the automatic purification results of the purification equipment; An emission monitoring module, which is used to monitor the indicators during the purification process of different types of inventory water samples in the laboratory by the purification equipment, and allow the purified polluted water samples to be discharged after reaching the emission standard to clean the inventory.

4. The integrated system for automated processing and intelligent management of laboratory water sample counting according to claim 1, wherein, The sampling unit specifically includes: A robotic arm sampling module, which is used to perform the automatic grasping operation of target water sample collection, and accurately locate through a multi-axis robotic arm Grasp the water sample and transfer it to a specified sampling container, and support the compatible grasping of bottles and cans of different specifications; An RFID tag writer, which is used to automatically attach an electronic tag to the sampling container, and write the timestamp, geographical location, sample number and sampling personnel information in real time. The tag data supports the subsequent full-process traceability; and A multi-parameter sensing module, which integrates a pH probe, a turbidimeter, a conductivity sensor and a temperature sensor, and simultaneously Collects the physical and chemical parameters of the water body during sampling, and the data is transmitted to the processing unit through the edge gateway.

5. The integrated system for automated processing and intelligent management of laboratory water sample counting according to claim 4, characterized in that The processing unit specifically includes: A liquid separation robot station, which uses a peristaltic pump and microfluidic technology to distribute the collected water samples to different detection devices according to a preset ratio and supports multi-channel parallel operation; An on-line detector module, including a spectrophotometer, an atomic absorption spectrometer, and a biotoxicity detection module, automatically executes the detection process of water samples through a standardized interface and outputs the original experimental data; and An edge computing node module, which is used to deploy a lightweight data cleaning algorithm, real-time eliminate outliers, and unify the data format into a JSON structure and then push it to the decision-making unit.

6. The integrated system for automatic processing and intelligent management of laboratory water sample counting according to claim 5, characterized in that, The decision-making unit specifically includes: A pollutant identification module, which is used to automatically determine the pollutant type of the water sample and evaluate the pollution level of the water sample based on a deep learning model of a convolutional neural network after inputting the detection data; An intelligent analysis module, which is used to automatically generate a structured report including a pollution map heat map, a risk level identifier, and disposal suggestions according to the big data analysis results of polluted water samples and a preset template for managers to consult; and A cloud data synchronization gateway, which is used to encrypt and upload the original data and analysis results to the cloud data library through the MQTT protocol, supports historical data storage, cross-platform access, and third-party system docking. The pollutant identification module uses the deep learning model to calculate the formula for the pollutant type of the water sample as follows:

7. The integrated system for automated processing and intelligent management of laboratory water sample counting according to claim 6, characterized in that The pollutant identification module uses the deep learning model to calculate the formula for evaluating the pollutant level of the water sample as follows: ; Wherein: represents the scalar value generated after the convolutional layer scans the input data, quantifying the presence intensity of the k-th type of pollutant in the current detection sample; K represents the analysis channel number of the pollutant, k = 1, 2, …, M, and M is the total number of channels; represents the i-th dimensional data of the input feature matrix, including the spectral intensity of a specific band or the heavy metal concentration value; i represents the position index of the input feature matrix in the time or space dimension, i = 1, 2, ..., N is the total number of positions; j represents the offset index within the convolutional window, j = 1, 2, …, H, and H is the convolutional window radius; represents the feature significance factor, , is the sensitivity adjustment parameter, with a default value of 0.8; represents the cross-dimensional interaction coefficient matrix, which is adaptively generated through feature covariance; represents the temporal / spatial decay coefficient, used to control the influence weights of historical and future features, default ; represents the feature normalization order, , which is dynamically adjusted according to the input data; represents the adjacent feature offset, default , used to capture the correlation pattern at a fixed interval between features; represents the feature dimension summation operator.

8. The integrated system for automatic processing and intelligent management of laboratory water sample counting according to claim 7, wherein, The cloud data synchronization gateway includes a time series database, a relational database, and a data lake: ； Wherein: represents the maximum response value of the feature convolution result; represents the set of output values of all convolution channels; represents the pollution intensity amplification factor, default ; represents the baseline adjustment parameter, , where dim is the number of input dimensions; represents the steepness coefficient of level division, default .

9. The integrated system for automated processing and intelligent management of laboratory water sample counting according to claim 6, characterized in that A time series database, which is used to store sensor real-time data, generate a water quality parameter trend chart, and assist in anomaly detection, and supports high-frequency data writing and fast query; A relational database, which is used to manage sample metadata including source, collection time, and responsible person, and store experimental records including detection items, instrument parameters, and results; A data lake, which is used to archive the original data including mass spectrometry diagrams, microscopic images, and original detection reports, and realize hierarchical storage of hot and cold data and support big data analysis. The pollutant identification module is based on a microscopic camera to take images of water samples to identify the microbial morphology in the water samples and extract corresponding detection data, and combines the YOLOv5 model to locate the positions of pollutant particles.

10. The integrated system for automated processing and intelligent management of laboratory water sample counting according to claim 6, characterized in that, ​

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