Sample detection system and method

Abstract

The invention discloses a sample detection system and method. The system comprises a sample detection module and a report generation module, the sample detection module is used for determining the sample type of a to-be-detected sample; determining a detection item matched with the sample type according to the detection standard knowledge base; the detection standard knowledge base comprises a matching relationship between sample types and detection items; detecting the to-be-detected sample according to the detection item to obtain at least one item of detection data; and the report generation module is used for generating a sample detection report according to the detection data of the sample detection module. According to the technical scheme, the sample detection module and the report generation module are integrated, data intercommunication is achieved, the automatic detection process of the to-be-detected sample and the automatic generation process of the detection report of the to-be-detected sample are achieved, closed-loop management of the detection process is achieved, the data island problem is solved, and the detection efficiency is improved. The operation efficiency of a sample detection laboratory is improved.

Description

Technical Field

[0001] The present invention relates to the field of power technology, and in particular to a sample detection system and method. Background Art

[0002] In the power industry, lubricants are used on large-scale power generation equipment to prevent metal contact and reduce friction and wear. Especially in the wind power industry, lubricants for wind turbines are widely used in gearboxes, yaw and pitch systems, main bearings and generator bearings. To carry out new energy oil product detection services, by using modern analytical instruments to detect key indicators of wind turbine gear oil and hydraulic oil, evaluate the equipment lubrication and wear status, and conduct equipment fault diagnosis and early warning, the current detection demand for wind turbine oil will show a blowout.

[0003] At present, there is a shortage of detection personnel in the detection of wind turbine oil. The collection and entry of detection data are still based on paper original records, which seriously affects the detection efficiency. At the same time, the separate reports are scattered and unable to track and statistically analyze the historical detection data of wind turbine oil samples, which is not conducive to providing more in-depth technical services for wind farms. Summary of the Invention

[0004] The present invention provides a sample detection system and method to achieve closed-loop processing of the detection process.

[0005] In a first aspect, an embodiment of the present invention provides a sample detection system, which includes a sample detection module and a report generation module;

[0006] The sample detection module is used to determine the sample type of the sample to be detected;

[0007] According to the detection standard knowledge base, determine the detection items matching the sample type; the detection standard knowledge base includes the matching relationship between the sample type and the detection items;

[0008] Detect the sample to be detected according to the detection items to obtain at least one piece of detection data;

[0009] The report generation module is used to generate a sample detection report according to the detection data of the sample detection module.

[0010] In a second aspect, an embodiment of the present invention further provides a sample detection method, which includes:

[0011] Determine the sample type of the sample to be detected;

[0012] According to the detection standard knowledge base, determine the detection items matching the sample type; the detection standard knowledge base includes the matching relationship between the sample type and the detection items;

[0013] Detect the sample to be detected according to the detection items to obtain at least one piece of detection data;

[0014] Generate a sample test report based on the test data.

[0015] The technical solution of the embodiment of the present invention integrates a sample detection module and a report generation module to achieve data interconnection, realize the automated detection process of samples to be detected and the automated generation process of test reports for samples to be detected, realize the closed-loop management of the detection process, solve the data island problem, and is beneficial to improving the operation efficiency of the sample detection laboratory.

[0016] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0018] Figure 1 It is a schematic structural diagram of a sample detection system provided by Embodiment 1 of the present invention;

[0019] Figure 2 It is a schematic structural diagram of another sample detection system provided by Embodiment 1 of the present invention;

[0020] Figure 3 It is a schematic diagram of the sample detection process provided by Embodiment 1 of the present invention;

[0021] Figure 4 It is a flowchart of a sample detection method provided by Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] In order to enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0023] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0024] Embodiment 1

[0025] Figure 1 FIG. is a schematic structural diagram of a sample detection system provided in Embodiment 1 of the present invention, and this embodiment is applicable to the situation of sample detection. As Figure 1 shown, the sample detection system 100 of the present invention includes a sample detection module 110 and a report generation module 120.

[0026] The sample detection module 110 is used to determine the sample type of the sample to be detected;

[0027] According to the detection standard knowledge base, determine the detection items matching the sample type; the detection standard knowledge base includes the matching relationship between the sample type and the detection items;

[0028] Detect the sample to be detected according to the detection items, and obtain at least one piece of detection data.

[0029] It should be noted that this embodiment is to solve the problem that the detection data collection and detection data entry in the detection process of fan oil in the power industry are still recorded manually, resulting in low detection efficiency. Therefore, this embodiment can be used in the detection scenario of fan oil samples.

[0030] In the embodiment of the present application, the sample to be detected can be a fan oil sample to be detected. In this embodiment, the sample types of the fan oil sample include new oil samples, in-use oil samples, old oil samples, scrapped oil samples, filter residue samples, sediment / sludge samples, and mixed oil samples, etc. The detection items of the fan oil sample include viscosity detection, acid value detection, wear metal detection, moisture content detection, and particle contamination detection, etc.

[0031] Among them, according to the key factors affecting the fan oil sample, a detection standard knowledge base can be established in advance in this system. The detection standard knowledge base includes the matching relationship between the sample type and the detection items, so that different typical detection items correspond to different sample types.

[0032] For example, the main detection items for the new oil sample type are viscosity detection and acid value detection. The main detection items for the in-use oil sample type are viscosity detection, acid value detection, wear metal analysis detection, moisture content detection, and particle contamination detection. The main detection item for the old oil sample type is mainly moisture content detection.

[0033] By reasonably planning the detection items for different sample types, it helps to accurately diagnose the status of the fan lubrication system, prevent fan mechanical failures, and optimize the maintenance cycle.

[0034] Among them, the detection data obtained after detecting the sample to be detected includes viscosity value, acid value, moisture content value, and particle contamination value, etc.

[0035] It can be understood that the sample detection module in this embodiment is specifically used to determine the detection items matching the sample type according to the detection standard knowledge base, and detect the sample to be detected according to the detection items to obtain the detection data.

[0036] In addition, the sample detection module can also be used to connect the detection equipment and detection instruments, and then collect the detection data of the sample to be detected in real time through the preset interfaces on the detection equipment and detection instruments, and perform data storage and outlier verification on the detection data.

[0037] Furthermore, the data storage process of the detection data of the sample to be detected includes determining the data type of the detection data, and storing the detection data in different databases according to the data type of the detection data. Among them, the data types of the detection data include structured data and unstructured data. Structured data has a fixed format and fields. Structured data can be data represented in tabular form, and unstructured data can be text or images, etc.

[0038] Specifically, if the data type of the detection data is structured data, the detection data can be stored in the PostgreSQL database to facilitate transaction processing and complex queries of sample information; if the data type of the detection data is unstructured data, the detection data can be stored in the MinIO database to archive the original records, report documents, and images of the sample to be detected.

[0039] In addition, the outlier verification process of the detection data includes verification and comparison according to the detection data and the data standard range. Among them, the outlier types of the detection data include outliers, drift values, mutation values, and logical contradiction values.

[0040] Specifically, according to different types of outliers, the reasons for the occurrence of anomalies can be judged, and then the detection data can be verified or recollected. For example, if the moisture content in the detection data is 5000 ppm, which is much greater than the standard range of the moisture content in the fan oil (the standard range of the moisture content in the fan oil < 500 ppm), it is judged that it may be due to contamination during the sample sampling process or an error in the detection instrument, resulting in an outlier in the detection data. Then, the sample can be recollected or the detection equipment can be inspected and calibrated, and the abnormal value can be replaced with the calibrated data. If the acid value in the detection data shows a linear increase in three consecutive test results, it is judged that it may be caused by the slow deterioration of the fan oil sample, and the outlier can be retained.

[0041] It should be noted that when processing outliers in the detection data, the verification traces of the outliers can be retained to meet the compliance requirements of sample detection.

[0042] In this embodiment, the sample detection module can be used to automatically detect the sample to be detected, obtain the corresponding detection data, and realize the automatic detection process of the sample to be detected.

[0043] The report generation module 120 is used to generate a sample detection report according to the detection data of the sample detection module.

[0044] In this embodiment, the detection report is a report generated according to the detection data, which is used to record the results or conclusions of the detection of the sample to be detected.

[0045] In this embodiment, the report generation module can generate a detection report for the corresponding sample according to the detection data of the sample detection module.

[0046] As an optional but non-limiting implementation manner, the report generation module is specifically used for:

[0047] Determine a report template that matches the sample type of the sample to be detected;

[0048] Fill the detection data into the report template that matches the sample type of the sample to be detected to generate a detection report for the sample to be detected.

[0049] It should be noted that different sample types may correspond to different detection items. Therefore, it can be understood that the report templates for different sample types may also be different.

[0050] In this embodiment, the report template at least includes contents such as sample type, detection items, and sample detection time. Further, in practical applications, the report template can also include constraints on standardized description terms, constraints on the standard expression methods of detection results, constraints on data unit symbols, constraints on data presentation methods (tables, curves, etc.), records of detection environmental conditions (temperature, humidity, etc.), setting of marks for determining review personnel, and setting of report copyright constraint statements, etc.

[0051] It is understandable that the report template is a document for recording sample information, test data, test equipment information, test environment information, etc. of the sample to be tested.

[0052] Specifically, in the process of determining the report template that matches the sample type of the sample to be tested, a historical test report that matches the sample type of the sample to be tested can be obtained through a database, and the report template of the historical test report that matches the sample type of the sample to be tested can be used as the report template that matches the sample type of the sample to be tested. Or, a report template is automatically generated based on the sample information, test equipment information, environmental information, and test items of the sample to be tested, and used as the report template that matches the sample type of the sample to be tested. By accurately matching the sample type with the report template, the readability and guiding value of the test results can be significantly improved.

[0053] Furthermore, after determining the report template that matches the sample type of the sample to be tested, the test data, sample information, test equipment information, environmental information, and test items, etc. can be automatically matched and filled into the report template that matches the sample type of the sample to be tested to generate a test report for the sample to be tested.

[0054] In this embodiment, a test report for the sample to be tested can be generated through a report generation module, realizing the automatic generation process of the test report for the sample to be tested.

[0055] A sample detection system disclosed in an embodiment of the present invention includes a sample detection module and a report generation module; the sample detection module is used to determine the sample type of the sample to be tested; according to a detection standard knowledge base, determine the detection items that match the sample type; the detection standard knowledge base includes the matching relationship between the sample type and the detection items; perform a test on the sample to be tested according to the detection items to obtain at least one piece of test data; the report generation module is used to generate a sample test report according to the test data of the sample detection module. The technical solution of the present invention integrates the sample detection module and the report generation module, enables data intercommunication in the sample detection system to realize the automatic detection process of the sample to be tested and the automatic generation process of the test report of the sample to be tested, realizes the closed-loop management of the detection process, solves the data island problem, and is beneficial to improving the operation efficiency of the sample detection laboratory.

[0056] See Figure 2 The structure diagram of another sample detection system according to this embodiment is shown. The sample detection system 100 includes a sample detection module 110, a report generation module 120, an equipment management module 130, a data analysis module 140, and a sample status tracking module 150.

[0057] As an optional but non-limiting implementation, the system further includes an equipment management module 130;

[0058] The device management module 130 is used to determine the quantity of management objects;

[0059] When the inventory quantity of the management object is less than a preset threshold, an inventory shortage warning message is issued.

[0060] In this embodiment, the management objects include at least one of the following: reagents, reference materials, and consumables.

[0061] Among them, the reagent is a chemical reagent used in the sample detection process, such as the Karl Fischer reagent used for detecting the water content of the fan oil sample, and the sulfuric acid reagent used for detecting the acid value of the fan oil sample. The reference material is used to establish the correlation between the detection result and the international standard, eliminate the instrument system error, and ensure the measurement accuracy. It can be understood that the reference material plays the role of a "measurement scale" and a "quality cornerstone" in the fan oil sample detection laboratory. Among them, the consumables include the detection equipment and instruments used in the detection process, such as sampling bottles, test tubes, etc.

[0062] Among them, the inventory quantity of the management object is the inventory quantity of the laboratory to which the sample detection system belongs, including the reagent quantity, the reference material quantity, and the consumable quantity, etc. Among them, the preset threshold can be flexibly set according to maintaining the normal detection process and detection scale of the sample.

[0063] It should be noted that in traditional sample detection, the management objects rely on manual inventory, which is likely to cause the experiment to be interrupted. In this embodiment, through the device management module, the inventory quantity information of the management objects is recorded. When the inventory quantity of the management object is less than the preset threshold, an inventory shortage warning message is issued, which can prompt the laboratory management personnel to replenish the inventory of the management objects in time. Or the device management module is linked with the management object supplier system. When the inventory quantity of the management object is less than the preset threshold, the replenishment process is triggered to achieve automatic replenishment.

[0064] Furthermore, this embodiment can also predict according to the historical consumption of reagents, reference materials, and consumables, determine the inventory exhaustion time based on the historical consumption and the current inventory quantity, and set a shortage warning in advance to avoid emergency replenishment caused by warning when the reagents, reference materials, and consumables are too few.

[0065] In this embodiment, the quantity of the management objects can be managed and warned through the device management module, and the automatic replenishment process of the management objects of the sample detection system can be realized.

[0066] As an optional but non-limiting implementation manner, the system further includes a data analysis module 140:

[0067] The data analysis module is used to determine the historical detection data matching the sample type of the sample to be detected;

[0068] Through a machine learning algorithm, sample trend analysis is performed based on historical detection data and the detection data of the sample to be detected, and the deterioration law of the sample to be detected is determined.

[0069] In this embodiment, the historical detection data is the detection data of the detected samples, and the historical detection data matching the sample type of the sample to be detected can be the detection data of samples with the same sample type as the sample to be detected.

[0070] Furthermore, if the sample to be detected is a fan oil sample, by analyzing the key indicators of the fan oil sample (such as viscosity, acid value, moisture content, etc.), the deterioration trend of the fan oil sample can be predicted, and a maintenance plan can be formulated in advance, thereby avoiding fan equipment failures and extending the life of the fan equipment.

[0071] Specifically, through a machine learning algorithm, sample trend analysis is performed based on historical detection data and the detection data of the sample to be detected, and the deterioration law of the sample to be detected is determined. The machine learning algorithm can automatically learn the sample data change law from the historical detection data. Among them, the machine learning algorithms include random forest, time series model, and neural network model, etc. In this embodiment, the time series model can be used to analyze the deterioration law according to the key indicators in the sample detection data.

[0072] For example, according to the viscosity in the historical detection data and the viscosity in the detection data of the sample to be detected, the growth trend of the viscosity over time is predicted. And, according to the acid value in the historical detection data and the acid value in the detection data of the sample to be detected, the relationship between the acid value and the equipment temperature is analyzed.

[0073] Specifically, the time series model predicts the deterioration degree of the fan oil sample, can display the predicted value and the deterioration trend of the fan oil sample in a visual way, and compare the predicted value with the actual value. The actual value is input into the time series model to automatically update the time series model parameters. Furthermore, the reasons for sample deterioration can also be correlated with the values of the detection data. For example, high water content and high iron element content may be related to the failure of the sample seal.

[0074] Furthermore, in practical applications, a maintenance plan can be formulated in advance according to the prediction results of the time series model. For example, according to industry standards or historical detection data, thresholds for key indicators in the detection data are set. If it is predicted that the viscosity will exceed the viscosity threshold within the next 5 days, it is recommended to immediately check the fan equipment where the fan oil sample is located. When the acid value exceeds the acidity threshold, it is recommended to replace the oil of the fan equipment.

[0075] In this embodiment, the data analysis module can analyze the detection data of the sample to be detected and the matched historical detection data to determine the change trend of samples of the same sample type, so as to evaluate the state of the equipment where the sample is located or give a fault alarm, realizing the automated data analysis process of the sample.

[0076] As an optional but non-limiting implementation manner, the system further includes a sample status tracking module 150;

[0077] The sample status tracking module is used to generate sample identification information according to the sample information of the sample to be detected;

[0078] Determine the sample detection status of the sample to be detected according to the sample identification information.

[0079] In this embodiment, the sample information includes the sample number, sample type, and detection item, and the sample detection status includes not detected, detecting, and detected. The sample identification is used to represent the identification information of the sample to be detected, where the sample identification can be two-dimensional code information, bar code information, etc.

[0080] In this embodiment, the sample status tracking module can identify and track the sample to be detected through radio waves or two-dimensional code technology.

[0081] In practical applications, the two-dimensional code information identifying the sample to be detected can be processed through two-dimensional code technology, and the sample detection status of the sample to be detected can be tracked according to the two-dimensional code information of the sample to be detected. In addition, the sample status tracking module can also be used to track the sample commission registration status, sample detection report generation status, and sample detection report distribution status.

[0082] Specifically, the sample commission registration status includes pre-registration (the customer submits the basic commission information but does not send the sample, not sent), received sample (the sample has been received but not registered), registering (information entry and sample pre-treatment), registered (information has been recorded), etc.; the sample detection report generation status includes sample detecting (the sample is being detected), report preparation (detection report document generation), report confirmation (detection report review process), report approval (detection report review passed), etc.; the sample detection report distribution status includes to be distributed (the detection report has been approved but not sent), sent (the detection report has been sent), signed for (the third party has confirmed receipt), etc.

[0083] Specifically, the information on the sample commission registration status, sample detection status, sample detection report generation status, and sample detection report distribution status is recorded in the sample detection system at different processing stages, and the status of each stage can be queried by scanning the sample identification information of the sample to be detected, which is beneficial to the efficient management of the sample during the detection process.

[0084] In this embodiment, the sample status tracking module can perform full-process tracking management on samples from commission registration to report generation and then to report release according to the sample identification information, ensuring the transparency and traceability of sample testing management and realizing the intelligent tracking management process of samples.

[0085] See Figure 3 The figure shows the schematic diagram of the sample testing process. After confirming the receipt of the samples to be tested, the sample information of the samples to be tested is determined, including the sample number, sample type, etc. Then, the sample identification information (QR code) of the samples to be tested is determined according to the sample information, and the sample commission registration status is recorded in the sample testing system according to the sample identification information. The sample testing module determines the testing items of the samples to be tested according to the sample type in the sample information, and tests the samples to be tested according to the testing items, and collects the testing data. Then, the report generation module generates a sample testing report according to the testing data of the sample testing module and the matching report module. After the sample testing report is manually confirmed to be correct, the sample report generation status is changed to report approval, and the sample testing report is automatically issued by the sample testing system according to the commission information. Further, the sample status tracking module tracks the sample testing report generation status and the sample testing report release status.

[0086] Further, in the actual architecture process of the system, the sample testing system of this embodiment can be built using a five-layer cloud-native architecture, including:

[0087] The front-end application layer can realize dynamic data rendering and user interaction based on the Vue framework, and provide users with a specific function for querying the sample testing status;

[0088] The interface access layer provides RESTful interfaces and integrates zero-trust security authentication;

[0089] The business application layer supports the management of the sample testing system, the business of the sample testing laboratory, and the resource management function of the sample testing laboratory through the modular design of the system;

[0090] The basic service layer can be based on a microservice cluster of Spring Boot to provide services for testing data collection, testing data storage, and testing data query;

[0091] The infrastructure layer supports public cloud, private cloud, and hybrid cloud deployments, provides underlying computing, storage, and network resources, and supports the operation of the cloud-native sample testing system.

[0092] In addition, the data storage of the sample testing system of this embodiment can adopt a hybrid database solution. Structured data is stored in the PostgreSQL database, which supports transaction processing and complex queries; unstructured data is stored in the MinIO database for original records, report documents, and image archiving.

[0093] The network architecture of the sample detection system in this embodiment may include an experimental network area and an office network area. Among them, a zero-trust gateway is deployed in the experimental network area to achieve secure access and encrypted data transmission for detection devices and detection instruments; the office network area adopts dual-machine hot standby and a wireless mesh network to ensure high availability of the sample detection system business service.

[0094] A sample detection system disclosed in an embodiment of the present invention includes a sample detection module, a report generation module, a device management module, a data analysis module, and a sample status tracking module; the sample detection module is used to determine the sample type of the sample to be detected; according to the detection standard knowledge base, determine the detection items matching the sample type; the detection standard knowledge base includes the matching relationship between the sample type and the detection items; detect the sample to be detected according to the detection items to obtain at least one piece of detection data; the report generation module is used to generate a sample detection report according to the detection data of the sample detection module; the device management module is used to determine the quantity of management objects; when the quantity of the management objects is less than a preset threshold, send out a warning message of insufficient inventory; wherein, the management objects include at least one of the following: reagents, reference substances, and consumables; the data analysis module is used to determine the historical detection data matching the sample type of the sample to be detected; through a machine learning algorithm, perform sample trend analysis according to the historical detection data and the detection data of the sample to be detected to determine the deterioration law of the sample to be detected; the sample status tracking module is used to generate sample identification information according to the sample information of the sample to be detected; determine the sample detection status of the sample to be detected according to the sample identification information; wherein, the sample information includes a sample number, a sample type, and detection items, and the sample detection status includes undetected, detecting, and detected.

[0095] The technical solution of the present invention integrates the sample detection module, the report generation module, the device management module, the data analysis module, and the sample status tracking module, enables data intercommunication of the sample detection system, realizes the automated detection process of the sample to be detected, the automated generation process of the detection report of the sample to be detected, the automatic replenishment process of management objects, the automated data analysis process of samples, and the intelligent tracking and management process of samples, realizes closed-loop management of the detection process, solves the data island problem, and is beneficial to improving the operation efficiency of the sample detection laboratory.

[0096] Embodiment 2

[0097] Figure 4 It is a flowchart of a sample detection method provided in Embodiment 2 of the present invention. Based on the above embodiment, the present invention provides a sample detection method. This embodiment is applicable to sample detection situations, such as Figure 4 As shown, the method includes:

[0098] S210. Determine the sample type of the sample to be detected.

[0099] S220. According to the detection standard knowledge base, determine the detection items that match the sample type; the detection standard knowledge base includes the matching relationship between the sample type and the detection items.

[0100] S230. Detect the sample to be detected according to the detection items to obtain at least one piece of detection data.

[0101] S240. Generate a sample detection report according to the detection data.

[0102] As an optional but non-limiting implementation manner, generating a sample detection report according to the detection data includes:

[0103] Determine the report template that matches the sample type of the sample to be detected;

[0104] Fill the detection data into the report template that matches the sample type of the sample to be detected to generate a detection report for the sample to be detected. The report template includes at least the sample type, detection items, and sample detection time.

[0105] As an optional but non-limiting implementation manner, the method further includes:

[0106] Determine the number of management objects;

[0107] When the number of the management objects is less than a preset threshold, send out an inventory shortage warning message;

[0108] Wherein, the management objects include at least one of the following: reagents, reference materials, and consumables.

[0109] As an optional but non-limiting implementation manner, after obtaining at least one piece of detection data, it further includes:

[0110] Determine the historical detection data that matches the sample type of the sample to be detected;

[0111] Through a machine learning algorithm, perform sample trend analysis according to the historical detection data and the detection data of the sample to be detected to determine the deterioration law of the sample to be detected.

[0112] As an optional but non-limiting implementation manner, the method further includes:

[0113] Generate sample identification information according to the sample information of the sample to be detected;

[0114] Determine the sample detection status of the sample to be detected according to the sample identification information;

[0115] Among them, the sample information includes a sample number, a sample type, and a detection item, and the sample detection status includes undetected, detecting, and detected.

[0116] An embodiment of the present invention discloses a sample detection method, which includes: determining the sample type of a sample to be detected; determining a detection item matching the sample type according to a detection standard knowledge base, where the detection standard knowledge base includes a matching relationship between a sample type and a detection item; detecting the sample to be detected according to the detection item to obtain at least one piece of detection data; and generating a sample detection report according to the detection data. The technical solution of the present invention realizes closed-loop management of the detection process and solves the data island problem through the automatic detection process of the sample to be detected and the automatic generation process of the detection report, which is beneficial to improving the operation efficiency of the sample detection laboratory.

[0117] Note that the above is only a preferred embodiment of the present invention and the applied technical principle. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described here. Various obvious changes, re-adjustments, and substitutions can be made by those skilled in the art without departing from the protection scope of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments. Without departing from the concept of the present invention, it can also include more other equivalent embodiments, and the scope of the present invention is determined by the scope of the appended claims.

[0118] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. No limitation is imposed herein.

[0119] The above specific implementation manners do not constitute a limitation to the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A sample detection system, characterized in that, The system includes a sample detection module and a report generation module; The sample detection module is used to determine the sample type of the sample to be detected; According to the detection standard knowledge base, determine the detection items matching the sample type; the detection standard knowledge base includes the matching relationship between the sample type and the detection items; Detect the sample to be detected according to the detection items to obtain at least one piece of detection data; The report generation module is used to generate a sample detection report according to the detection data of the sample detection module.

2. The system according to claim 1, wherein The report generation module is specifically used for: Determine the report template matching the sample type of the sample to be detected; Fill the detection data into the report template matching the sample type of the sample to be detected to generate the detection report of the sample to be detected, and the report template at least includes the sample type, the detection items, and the sample detection time.

3. The system according to claim 1, wherein The system further includes an equipment management module; The equipment management module is used to determine the quantity of the management objects; When the quantity of the management objects is less than the preset threshold, send out a warning message of insufficient inventory; Wherein, the management objects include at least one of the following: reagents, reference materials, and consumables.

4. The system according to claim 1, characterized in that The system further includes a data analysis module; The data analysis module is used to determine the historical detection data matching the sample type of the sample to be detected; Through a machine learning algorithm, conduct sample trend analysis based on the historical detection data and the detection data of the sample to be detected to determine the deterioration law of the sample to be detected.

5. The system according to claim 1, characterized in that The system further includes a sample status tracking module; The sample status tracking module is used to generate sample identification information according to the sample information of the sample to be detected; Determine the sample detection status of the sample to be detected according to the sample identification information; Wherein, the sample information includes the sample number, the sample type, and the detection items, and the sample detection status includes undetected, detecting, and detected.

6. A sample detection method, characterized in that, The method includes: Determine the sample type of the sample to be detected; According to the detection standard knowledge base, determine the detection items matching the sample type; the detection standard knowledge base includes the matching relationship between the sample type and the detection items; Detect the sample to be detected according to the detection items to obtain at least one piece of detection data; Generate a sample detection report according to the detection data.

7. The method according to claim 6, wherein Generating a sample detection report according to the detection data includes: Determine the report template matching the sample type of the sample to be detected; Fill the detection data into the report template matching the sample type of the sample to be detected to generate the detection report of the sample to be detected, and the report template at least includes the sample type, the detection items, and the sample detection time.

8. The method according to claim 6, characterized in that, The method further includes: Determine the quantity of the management objects; When the quantity of the management objects is less than the preset threshold, send out a warning message of insufficient inventory; Wherein, the management objects include at least one of the following: reagents, reference materials, and consumables.

9. The method according to claim 6, characterized in that, After obtaining at least one piece of detection data, it further includes: Determine the historical detection data matching the sample type of the sample to be detected; Through a machine learning algorithm, conduct sample trend analysis based on the historical detection data and the detection data of the sample to be detected to determine the deterioration law of the sample to be detected.

10. The method according to claim 6, characterized in that, The method further includes: Generate sample identification information according to the sample information of the sample to be detected; Determine the sample detection status of the sample to be detected according to the sample identification information; Among them, the sample information includes the sample number, sample type, and detection items, and the sample detection status includes not detected, detecting, and detection completed.

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