Lung cancer monitoring reagent and use method thereof

Abstract

The invention discloses a lung cancer monitoring reagent and a use method thereof, and relates to the technical field of biomedical treatment, sample collection: blood, sputum and tissue samples of a human body simulation incubator are collected, then the collected samples are treated, detected and analyzed, and the treated samples are detected and analyzed; the method has the advantages that the time when the human body simulation incubator needs to be monitored again is determined through the neural network model according to the health type data of the human body simulation incubator, and the neural network model facilitates notification detection on the treated and dangerous human body simulation incubators; therefore, the secondary monitoring time of the human body simulation incubator can be reflected according to the health states of the lungs of different human body simulation incubators, meanwhile, the lung cancer human body simulation incubator is treated in the monitoring process, and the monitoring reagent system adjusts the monitoring frequency according to different human body simulation incubators.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to a lung cancer monitoring reagent and a use method thereof. Background Art

[0002] Lung cancer is one of the malignant tumors with the highest morbidity and mortality rates worldwide, posing a serious threat to human health. Early detection and accurate diagnosis of lung cancer are crucial to improving the survival rate and quality of life of patients. Although traditional lung cancer diagnosis methods such as chest X-rays and CT scans can detect lung lesions to a certain extent, they have some limitations, such as low sensitivity to early lung cancer and possible false positive results. At the same time, tissue biopsy, as the gold standard for diagnosing lung cancer, is invasive and may bring pain and risks to patients, and is not suitable for large-scale screening. With the continuous development of biotechnology, the efficiency of lung cancer monitoring has been improved, and lung cancer monitoring reagents have emerged to predict different patients who may have lung cancer, providing a non-invasive, rapid and accurate method for early diagnosis, disease monitoring and treatment evaluation of lung cancer.

[0003] Although the existing lung cancer monitoring reagents can monitor the human body simulation incubator very well, it is difficult to adjust the frequency of lung monitoring of the human body simulation incubator according to the lung health status of different human body simulation incubators and the surrounding environment of the human body simulation incubator, and adjust the overall lung cancer monitoring reagent monitoring accuracy. To this end, we propose a lung cancer monitoring reagent and its use method. Summary of the invention

[0004] The object of the present invention is to provide a lung cancer monitoring reagent and a method of using the same.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for using a lung cancer monitoring reagent, the specific steps are as follows:

[0006] S100, sample collection, collecting blood, sputum and tissue samples from the human body simulation incubator, and then processing the collected samples;

[0007] S200, detection and analysis, performing detection and analysis on the processed samples, and then transmitting the processed samples to the monitoring reagent system, adjusting the ratio between the total amount of reagents and the total amount of samples through the monitoring reagent system, and then adjusting the total amount of samples according to different equipment;

[0008] S300, monitoring adjustment, the monitoring reagent system generates a human body simulation incubator data table, transmits the detection and analysis data to the human body simulation incubator data table, generates a monitoring report for the data of different lung cancer human body simulation incubators through the monitoring reagent system, sets a neural network model in the monitoring reagent system, uses the neural network model to judge the lung cancer risk data of the human body simulation incubator, and then the neural network model adjusts the monitoring frequency according to the lung cancer risk data of the human body simulation incubator, inserts the reagent probe into the lungs related to lung cancer during the monitoring process, and uses the monitoring reagent system to record the lung cancer reagent data, after reacting with the lung cancer when inserted into the lungs, the monitoring reagent system uses the electric field formed between the two electrodes when the probe enters the lungs to perform electric shock treatment on the lung cancer, thereby facilitating the neural network model to adjust the monitoring time of the lungs of the human body simulation incubator.

[0009] As a further solution of the present invention: the neural network model identifies the human body simulation incubator as three types: normal, dangerous and therapeutic. The neural network model determines the type of the human body simulation incubator according to the surrounding environment of the human body simulation incubator and the lung cancer status data of the human body simulation incubator. When the neural network model determines that the human body simulation incubator is of the normal type, the neural network model analyzes the detection time of the human body simulation incubator according to the health status of the human body simulation incubator, the longest detection time is once every two years, and the shortest detection time is once every year. When the neural network model determines that the human body simulation incubator is of the dangerous type, the neural network model analyzes the detection time of the human body simulation incubator according to the smoking history, disease and working environment of the human body simulation incubator, the longest detection time is once every six months. When the neural network model determines that the human body simulation incubator is of the therapeutic type, the neural network model analyzes the detection time according to the surgical data of the human body simulation incubator.

[0010] As a further solution of the present invention: the neural network model creates timing instructions for the detection time of normal and dangerous types of human body simulation incubator analysis, and transmits the timing instructions to the human body simulation incubator list. When the timing instructions are the same as the time of the current year and month, the monitoring reagent system retrieves the contact information on the human body simulation incubator data table, and the monitoring reagent system notifies the human body simulation incubator to perform detection according to the contact information.

[0011] As a further solution of the present invention: the neural network model obtains the detection time of a normal type of human body simulation incubator according to a formula, and the specific formula is as follows:

[0012]

[0013] Among them, H 正 Indicates the next test time data of a normal type human body simulation incubator, M i represents the lung health data of the human body simulation incubator, Mmax Indicates the best health data of the lungs under normal conditions in the normal type, H 正max Indicates the maximum detection time data of normal type in the monitoring reagent system;

[0014]

[0015] Among them, H 危 Indicates the next detection time data of the dangerous type human simulation incubator, f i represents the lung health data of the human body simulation incubator, Y represents the data of the impact of smoking history on the lungs of the human body simulation incubator, J represents the data of the impact of disease on the lungs of the human body simulation incubator, G represents the data of the impact of working environment on the lungs of the human body simulation incubator, and f max Indicates the maximum value of the lungs in the best condition in the risk type, H 危max Indicates the maximum detection time data of the hazardous type in the monitoring reagent system;

[0016] The neural network model adjusts the lung detection time of the treatment type human simulation incubator by postoperative time.

[0017] As a further solution of the present invention: the neural network model monitors the lung cancer monitoring reagent and the human body simulation incubator at different times according to the treatment detection time of the human body simulation incubator. When the monitoring reagent system detects that the reagent is inserted into the body of the human body simulation incubator, the monitoring reagent system captures the combination of the reagent and the target substance to generate a signal. After capturing the combined signal, positive and negative poles are formed between the two reagent probes, so that an electric field is formed between the probes, and then when monitoring the dangerous type of human body simulation incubator, the electric field is used for electric shock treatment.

[0018] As a further solution of the present invention: the specific steps of S100 are as follows:

[0019] S110, when collecting lung cancer samples in a human body simulation incubator, identifying the QR code on the sample pipeline through the monitoring reagent system, and matching the human body simulation incubator data with the pipeline sample data;

[0020] S120, then separate, insulate and mix the collected samples, and update the human body simulation incubator data in the monitoring reagent system.

[0021] As a further solution of the present invention: the monitoring reagent system monitors the sample data in real time during the sample collection stage and analyzes the sample data in real time.

[0022] As a further solution of the present invention: the specific steps of S200 are as follows:

[0023] S210, dividing the reagent samples, evenly distributing the samples according to the processing equipment in the monitoring reagent system, and when the number of divided samples is lower than the normal value of the test, reducing the number of processing equipment to test the samples;

[0024] S220, the monitoring reagent system adjusts the added reagent according to the total amount of the divided sample, mixes the reagent with the sample, obtains the mixed lung cancer data, and enables the monitoring reagent system to obtain the human body simulation incubator data.

[0025] As a further solution of the present invention: when the monitoring reagent system detects that a sample is abnormal, the monitoring reagent system creates an abnormal instruction, transmits the abnormal instruction to a corresponding alarm module, and then retrieves the sample data in S100 again.

[0026] A lung cancer monitoring reagent is applicable to a method for using the lung cancer monitoring reagent.

[0027] By adopting the above technical solution, compared with the prior art, the beneficial effects of the present invention are:

[0028] 1. The present invention performs reagent analysis on samples of human body simulation incubators, and then the neural network model determines the type of health status of the human body simulation incubator according to the analysis results and the human body simulation incubator data, so that the neural network model determines the time when it needs to be monitored again according to the health type data of the human body simulation incubator. The neural network model is convenient for notifying and detecting treatment and dangerous types of human body simulation incubators, and then can reflect the secondary monitoring time of the human body simulation incubator according to the health status of the lungs of different human body simulation incubators, and at the same time, the lung cancer human body simulation incubator is treated during the monitoring process, so that the monitoring reagent system adjusts the monitoring frequency according to different human body simulation incubators;

[0029] 2. The present invention uses a neural network model to obtain the type of human body simulation incubator, and obtains its data information through a monitoring reagent system, analyzes different types of detection time according to the human body simulation incubator information, and then enables the neural network model to analyze the next detection time according to the lung health of the human body simulation incubator, and adjusts the detection time according to the treatment progress, so that the monitoring reagent system can adjust the monitoring time more accurately according to different human body simulation incubators;

[0030] 3. When the probe is inserted into the human body simulation incubator through the lung monitoring reagent, the monitoring reagent system is used to collect the response signal of the probe, so that an electric field is formed between the probes, and the electric field is used to perform electric shock treatment on the lungs;

[0031] 4. The present invention divides samples according to the number of different devices through the monitoring reagent system, divides liquid samples into the same milliliter and solid samples into the same weight, and then different devices adjust the reagents in proportion to the samples, so that the monitoring reagent system can obtain more accurate reagent data.

[0032] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 1 is a step diagram of a lung cancer monitoring reagent and a method for using the same in an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0035] In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0036] Please see attached Figure 1 The present invention provides a method for using a lung cancer monitoring reagent, and the specific steps are as follows:

[0037] S100, sample collection, collecting blood, sputum and tissue samples from the human body simulation incubator, and then processing the collected samples;

[0038] S200, detection and analysis, performing detection and analysis on the processed samples, and then transmitting the processed samples to the monitoring reagent system, adjusting the ratio between the total amount of reagents and the total amount of samples through the monitoring reagent system, and then adjusting the total amount of samples according to different equipment;

[0039] S300, monitoring adjustment, the monitoring reagent system generates a human body simulation incubator data table, transmits the detection and analysis data to the human body simulation incubator data table, generates a monitoring report for the data of different lung cancer human body simulation incubators through the monitoring reagent system, sets a neural network model in the monitoring reagent system, uses the neural network model to judge the lung cancer risk data of the human body simulation incubator, and then the neural network model adjusts the monitoring frequency according to the lung cancer risk data of the human body simulation incubator, inserts the reagent probe into the lungs related to lung cancer during the monitoring process, and uses the monitoring reagent system to record the lung cancer reagent data, after reacting with the lung cancer when inserted into the lungs, the monitoring reagent system uses the electric field formed between the two electrodes when the probe enters the lungs to perform electric shock treatment on the lung cancer, thereby facilitating the neural network model to adjust the monitoring time of the lungs of the human body simulation incubator.

[0040] In the first embodiment, the neural network model identifies the human body simulation incubator as three types: normal, dangerous, and therapeutic. The neural network model determines the type of the human body simulation incubator according to the surrounding environment of the human body simulation incubator and the lung cancer status data of the human body simulation incubator. When the neural network model determines that the human body simulation incubator is of the normal type, the neural network model analyzes the detection time of the human body simulation incubator according to the health status of the human body simulation incubator. The longest detection time is once every two years, and the shortest detection time is once every year (and the notification method can be SMS notification or no notification to prevent overly healthy people from getting bored). When the neural network model determines that the human body simulation incubator is of the dangerous type, the neural network model analyzes the detection time of the human body simulation incubator according to the health status of the human body simulation incubator. The longest detection time is once every two years, and the shortest detection time is once every year. The model analyzes the detection time of the human body simulation incubator according to the smoking history, diseases and working environment of the human body simulation incubator. The longest detection time is once a year and the shortest detection time is once every six months. When the neural network model determines that the human body simulation incubator is a treatment type, the neural network model analyzes the detection time according to the surgical data of the human body simulation incubator. The neural network model creates a timing instruction for the detection time of normal and dangerous types of human body simulation incubators, and transmits the timing instruction to the human body simulation incubator list. When the timing instruction is the same as the time of the current year and month, the monitoring reagent system retrieves the contact information on the human body simulation incubator data table, and the monitoring reagent system notifies the human body simulation incubator to perform the detection according to the contact information;

[0041] Specifically, reagent analysis is performed on samples of the human body simulation incubator, and then the neural network model determines the type of health status of the human body simulation incubator based on the analysis results and the human body simulation incubator data, so that the neural network model determines the time when it needs to be monitored again based on the health type data of the human body simulation incubator. The neural network model is convenient for notifying and detecting treatment and dangerous types of human body simulation incubators, and then can reflect the secondary monitoring time of the human body simulation incubator according to the health status of the lungs of different human body simulation incubators. At the same time, treatment is carried out during the monitoring of the lung cancer human body simulation incubator, so that the monitoring reagent system adjusts the monitoring frequency according to different human body simulation incubators.

[0042] In the second embodiment, the neural network model obtains the detection time of a normal type of human body simulation incubator according to a formula, and the specific formula is as follows:

[0043]

[0044] Among them, H 正 Indicates the next test time data of a normal type human body simulation incubator, M i represents the lung health data of the human body simulation incubator, M max Indicates the best health data of the lungs under normal conditions in the normal type, H 正max Indicates the maximum detection time data of normal type in the monitoring reagent system;

[0045]

[0046] Among them, H 危 Indicates the next detection time data of the dangerous type human simulation incubator, f i represents the lung health data of the human body simulation incubator, Y represents the data of the impact of smoking history on the lungs of the human body simulation incubator, J represents the data of the impact of disease on the lungs of the human body simulation incubator, G represents the data of the impact of working environment on the lungs of the human body simulation incubator, and f max Indicates the maximum value of the lungs in the best condition in the risk type, H 危max Indicates the maximum detection time data of the hazardous type in the monitoring reagent system;

[0047] The neural network model adjusts the lung detection time of the treatment type human simulation incubator by the postoperative time. After the doctor performs lung cancer surgery on the human simulation incubator, the postoperative monitoring time period and postoperative recovery plan data are transmitted to the neural network model. The neural network model uses historical data to adjust the time of each postoperative detection, and can also use the formula Obtain the human simulated incubator lung monitoring time T after treatment i , S lrepresents the historical human incubator lung health data, T l Indicates historical human simulation incubator monitoring reagent, S i It represents the lung health data of the human body simulation incubator, so that the monitoring system can perform regular monitoring and analysis on the postoperative human body simulation incubator. The neural network model monitors the lung cancer monitoring reagent and the human body simulation incubator at different times according to the treatment detection time of the human body simulation incubator. When the monitoring reagent system detects that the reagent is inserted into the human body simulation incubator, the monitoring reagent system captures the reagent and the target substance to generate a signal. After capturing the combined signal, the positive and negative poles are formed between the two reagent probes, so that an electric field is formed between the probes. When monitoring the dangerous type of human body simulation incubator, the electric field is used for electric shock treatment. The lungs of the monitored human body simulation incubator are treated by fluorescence, chemiluminescence, enzymatic reaction products, etc., and then the lungs of lung cancer and human body simulation incubators with abnormal lungs are treated by electric shock. The lung cell membrane is irreversibly electroporated by electric shock, so that the diseased cells are apoptotic, and then the body is restored through metabolism.

[0048] Specifically, the type data of the human body simulation incubator is obtained through the neural network model, and then the monitoring reagent system is used to obtain the data information of the human body simulation incubator. The detection times of different types are analyzed according to the human body simulation incubator information. Then, the neural network model analyzes the next detection time according to the lung health of the human body simulation incubator, and adjusts the detection time according to the treatment progress. The monitoring reagent system achieves the effect of adjusting the monitoring time more accurately according to different human body simulation incubators.

[0049] In the third embodiment, the specific steps of S100 are as follows:

[0050] S110, when collecting lung cancer samples in a human body simulation incubator, identifying the QR code on the sample pipeline through the monitoring reagent system, and matching the human body simulation incubator data with the pipeline sample data;

[0051] S120, then the collected samples are separated, kept warm and mixed, and the human body simulation incubator data in the monitoring reagent system is updated at the same time. The monitoring reagent system monitors the sample data in real time during the sample collection stage and analyzes the sample data in real time. The specific steps of S200 are as follows:

[0052] S210, dividing the reagent samples, evenly distributing the samples according to the processing equipment in the monitoring reagent system, and when the number of divided samples is lower than the normal value of the test, reducing the number of processing equipment to test the samples;

[0053] S220, the monitoring reagent system adjusts the added reagent according to the total amount of the divided sample, mixes the reagent with the sample, obtains the mixed lung cancer data, and enables the monitoring reagent system to obtain the human body simulation incubator data. When the monitoring reagent system detects that the sample is abnormal, the monitoring reagent system creates an abnormal instruction, transmits the abnormal instruction to the corresponding alarm module, and then re-calls the sample data in S100;

[0054] Specifically, the monitoring reagent system divides the samples according to the number of different devices, with liquid samples divided into the same milliliter and solid samples divided into the same weight. Different devices then adjust reagents in proportion to the samples, allowing the monitoring reagent system to obtain more accurate reagent data.

[0055] A lung cancer monitoring reagent is applicable to a method for using the lung cancer monitoring reagent.

[0056] Working principle:

[0057] First, obtain samples from the human body simulation incubator through step S100, and then use the monitoring reagent system to monitor the samples in real time to prevent sample abnormalities. At the same time, perform centrifugation, sputumization and other treatments on different samples, and transfer the treated samples to step S200. Then use the monitoring reagent system to divide the treated samples into equal amounts, and transfer the divided samples to different detection equipment to obtain more accurate data of the lungs in the human body simulation incubator. Then use the monitoring reagent system to adjust the time when the human body simulation incubator needs to be tested next time according to the data of different human body simulation incubators to ensure the health of the lungs in the human body simulation incubator. At this point, the entire workflow is completed.

[0058] The relevant organ nouns mentioned in the application document all refer to the organs simulated in the human body simulation incubator.

[0059] The above-mentioned front, back, left, right, top and bottom are all based on the figures in the specification. Figure 1 As a benchmark, according to the person's observation perspective, the side of the device facing the observer is defined as the front, the left side of the observer is defined as the left, and so on.

[0060] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the scope of protection of the present invention.

[0061] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments.

[0062] It is apparent to those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the invention, and the changes still fall within the protection scope of the invention.

Claims

1. A method for using a lung cancer monitoring reagent, characterized in that: The specific steps are as follows: S100, sample collection, collecting blood, sputum and tissue samples from the human body simulation incubator, and then processing the collected samples; S200, detection and analysis, performing detection and analysis on the processed samples, and then transmitting the processed samples to the monitoring reagent system, adjusting the ratio between the total amount of reagents and the total amount of samples through the monitoring reagent system, and then adjusting the total amount of samples according to different equipment; S300, monitoring adjustment, the monitoring reagent system generates a human body simulation incubator data table, transmits the detection and analysis data to the human body simulation incubator data table, generates a monitoring report for the data of different lung cancer human body simulation incubators through the monitoring reagent system, sets a neural network model in the monitoring reagent system, uses the neural network model to judge the lung cancer risk data of the human body simulation incubator, and then the neural network model adjusts the monitoring frequency according to the lung cancer risk data of the human body simulation incubator, inserts the reagent probe into the lungs related to lung cancer during the monitoring process, and uses the monitoring reagent system to record the lung cancer reagent data, after reacting with the lung cancer when inserted into the lungs, the monitoring reagent system uses the electric field formed between the two probe electrodes when the probe enters the lungs to perform electric shock treatment on the lung cancer, thereby facilitating the neural network model to adjust the monitoring time of the lungs of the human body simulation incubator.

2. The method for using a lung cancer monitoring reagent according to claim 1, characterized in that: The neural network model identifies the human body simulation incubator as three types: normal, dangerous, and therapeutic. The neural network model determines the type of the human body simulation incubator based on the surrounding environment of the human body simulation incubator and the lung cancer status data of the human body simulation incubator. When the neural network model determines that the human body simulation incubator is of the normal type, the neural network model analyzes the detection time of the human body simulation incubator based on the health status of the human body simulation incubator, with the longest detection time being once every two years and the shortest detection time being once every year. When the neural network model determines that the human body simulation incubator is of the dangerous type, the neural network model analyzes the detection time of the human body simulation incubator based on the smoking history, disease, and working environment of the human body simulation incubator, with the longest detection time being once every six months. When the neural network model determines that the human body simulation incubator is of the therapeutic type, the neural network model analyzes the detection time based on the surgical data of the human body simulation incubator.

3. The method for using a lung cancer monitoring reagent according to claim 2, characterized in that: The neural network model creates timing instructions for the detection time of normal and dangerous types of human body simulation incubator analysis, and transmits the timing instructions to the human body simulation incubator list. When the timing instructions are the same as the time of the current year and month, the monitoring reagent system retrieves the contact information on the human body simulation incubator data table, and the monitoring reagent system notifies the human body simulation incubator to perform detection according to the contact information.

4. The method for using a lung cancer monitoring reagent according to claim 2, characterized in that: The neural network model obtains the detection time of a normal type of human body simulation incubator according to a formula, and the specific formula is as follows: Among them, H 正 Indicates the next test time data of a normal type human body simulation incubator, M i represents the lung health data of the human body simulation incubator, M max Indicates the best health data of the lungs under normal conditions in the normal type, H 正max Indicates the maximum detection time data of normal type in the monitoring reagent system; Among them, H 危 Indicates the next detection time data of the dangerous type human simulation incubator, f i represents the lung health data of the human body simulation incubator, Y represents the data of the impact of smoking history on the lungs of the human body simulation incubator, J represents the data of the impact of disease on the lungs of the human body simulation incubator, G represents the data of the impact of working environment on the lungs of the human body simulation incubator, and f max Indicates the maximum value of the lungs in the best condition in the risk type, H 危max Indicates the maximum detection time data of the hazardous type in the monitoring reagent system; The neural network model adjusts the lung detection time of the treatment type human simulation incubator by postoperative time.

5. The method for using a lung cancer monitoring reagent according to claim 4, characterized in that: The neural network model monitors the lung cancer monitoring reagent and the human body simulation incubator at different times according to the treatment detection time of the human body simulation incubator. When the monitoring reagent system detects that the reagent is inserted into the body of the human body simulation incubator, the monitoring reagent system captures the combination of the reagent and the target substance to generate a signal. After capturing the combined signal, positive and negative poles are formed between the two reagent probes, so that an electric field is formed between the probes, and then when monitoring the dangerous type of human body simulation incubator, electric shock treatment is performed using the electric field.

6. The method for using a lung cancer monitoring reagent according to claim 1, characterized in that: The specific steps of S100 are as follows: S110, when collecting lung cancer samples in a human body simulation incubator, identifying the QR code on the sample pipeline through the monitoring reagent system, and matching the human body simulation incubator data with the pipeline sample data; S120, then separate, insulate and mix the collected samples, and update the human body simulation incubator data in the monitoring reagent system.

7. The method for using a lung cancer monitoring reagent according to claim 6, characterized in that: The monitoring reagent system monitors the sample data in real time during the sample collection phase and analyzes the sample data in real time.

8. The method for using a lung cancer monitoring reagent according to claim 7, characterized in that: The specific steps of S200 are as follows: S210, dividing the reagent samples, evenly distributing the samples according to the processing equipment in the monitoring reagent system, and when the number of divided samples is lower than the normal value of the test, reducing the number of processing equipment to test the samples; S220, the monitoring reagent system adjusts the added reagent according to the total amount of the divided sample, mixes the reagent with the sample, obtains the mixed lung cancer data, and enables the monitoring reagent system to obtain the human body simulation incubator data.

9. The method for using a lung cancer monitoring reagent according to claim 8, characterized in that: When the monitoring reagent system detects that the sample is abnormal, the monitoring reagent system creates an abnormal instruction, transmits the abnormal instruction to the corresponding alarm module, and then re-calls the sample data in S100.

10. A lung cancer monitoring reagent, which is suitable for the use method described in any one of claims 1 to 9.

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