Lycopene detection method in lycopene production process

Abstract

The invention relates to the technical field of lycopene detection, and provides a lycopene detection method in a lycopene production process, which effectively solves the problem of lycopene moisture detection in a humid environment through the technologies of lycopene moisture data acquisition, natural environment prediction model, data comparison, detection and the like. According to the invention, environmental data is collected in real time, a prediction model is constructed, and the accuracy of moisture data is verified by comparing actual and predicted data. Meanwhile, the content of the lycopene in the lycopene can be detected, the quality of the lycopene is improved, when the moisture of the lycopene and the content of the lycopene reach the standard, it is judged that the lycopene detection result is qualified, the detection precision is improved, and an efficient and reliable solution is provided for lycopene quality detection.

Description

Technical Field

[0001] The present invention relates to the technical field of lycopene detection, and in particular to a lycopene detection method in a lycopene production process. Background Art

[0002] With the rapid development of lycopene production technology, the quality detection of lycopene has become important. As a functional natural pigment and nutritional enhancer, lycopene can improve the utilization rate of lycopene, promote the growth of livestock and poultry, and may prevent certain diseases. In the quality detection of lycopene, moisture detection is a key link. At present, lycopene moisture detection technology has made significant progress, and a variety of detection methods have emerged, such as weighing method, quick drying method and drying method. Lycopene moisture detection methods also include infrared spectroscopy technology, near-infrared spectroscopy technology, etc., which are gradually being applied to lycopene moisture detection. Lycopene moisture detection methods can provide multiple nutrient content information in a short time.

[0003] In practical applications, existing lycopene moisture detection technologies mainly use specific instruments and methods to heat, dry, weigh and other operations on lycopene samples, so as to accurately determine the moisture content in lycopene. For example, the weighing method heats the sample in an oven until the weight no longer changes, and calculates the moisture content based on the ratio of the sample weight loss to the sample mass, while the quick-drying method uses microwave radiation technology to heat and dry the lycopene sample, and calculates the moisture content based on the weight change. These methods require rigorous operating procedures and precise equipment support to ensure the accuracy of the test results.

[0004] The existing lycopene moisture detection technology has the following pain points: in order to detect lycopene more accurately, it is necessary to detect it on site where the lycopene is stored. The humid environment in which lycopene is stored is an important reason for the error in lycopene moisture detection. In a humid environment, lycopene samples and sensors absorb moisture in the air. If there are fluctuations in temperature or humidity in the operating environment, it will affect the test results, resulting in inaccurate measurement results. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a lycopene detection method in a lycopene production process, which solves the problem that in a humid environment, lycopene samples and sensors absorb moisture in the air. If there are temperature or humidity fluctuations in the operating environment, the detection results will be affected, resulting in inaccurate measurement results.

[0006] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows:

[0007] The present invention provides a method for detecting lycopene in a lycopene production process, comprising:

[0008] Step S101, obtaining the location information of lycopene, and determining the sensor information of the location of lycopene and the natural environment data of the location of lycopene according to the location information of lycopene;

[0009] Step S102, receiving data information collected by the sensor at the location of lycopene, and constructing a natural environment prediction model corresponding to the location of lycopene according to the data information collected by the sensor at the location of lycopene and the natural environment data of the location of lycopene;

[0010] Step S103, substituting the location information of lycopene and the real-time natural environment data of the location of lycopene into the natural environment prediction model corresponding to the location of lycopene to obtain the estimated natural environment data corresponding to the location of lycopene;

[0011] Step S104, collecting lycopene moisture data corresponding to the position where the lycopene is located, comparing the natural environment data of the position where the lycopene is located when collecting the lycopene moisture data corresponding to the position where the lycopene is located with the natural environment data corresponding to the estimated position where the lycopene is located, if the comparison results are consistent, the collected lycopene moisture data corresponding to the position where the lycopene is located is accurate, if the comparison results are inconsistent, the lycopene moisture data corresponding to the position where the lycopene is located is corrected;

[0012] Step S105, after correcting the lycopene moisture data corresponding to the position of lycopene, if the corrected lycopene moisture data corresponding to the position of lycopene meets the preset value, then detecting whether the lycopene content in the lycopene meets the preset standard, if the lycopene content in the lycopene meets the preset standard, the lycopene detection result is qualified.

[0013] Furthermore, the lycopene detection method in the lycopene production process provided by the present invention, step S101, comprises:

[0014] The server receives sensor data, including GPS locator, temperature and humidity sensor, and obtains the location information of lycopene through the GPS locator;

[0015] After obtaining the location information of lycopene, the sensor information at the location corresponding to the location information of lycopene is determined. The sensor information includes a sensor for detecting the moisture content of lycopene, a temperature sensor, and a humidity sensor. The server obtains various data of the environment where the lycopene is located, such as temperature and humidity, through communication with the sensors.

[0016] The server collects natural environment data of the location of Lycopene, and the natural environment data includes weather conditions and air humidity data;

[0017] The server optimizes detection parameters based on the use of lycopene's location information, sensor information, and natural environment data. If the server detects that the humidity in a certain area is high, it automatically adjusts the humidity control equipment in the production environment.

[0018] Furthermore, the lycopene detection method in the lycopene production process provided by the present invention, step S102, comprises:

[0019] The server receives and stores real-time data information from the sensor where the lycopene is located, and the real-time data information from the sensor where the lycopene is located includes temperature, humidity, and air pressure;

[0020] Collect and organize the natural environment data of Lycopene's location, including weather conditions, wind speed, wind direction and solar radiation intensity;

[0021] Feature selection and extraction are performed on the collected sensor data and natural environment data, and a linear regression algorithm is selected to build a prediction model. Historical data is used as a training set, and model parameters are optimized through cross-validation.

[0022] Furthermore, the lycopene detection method in the lycopene production process provided by the present invention, step S103, comprises:

[0023] Preprocessing the sensor data and natural environment data of the location of lycopene received in real time by the server, and substituting the preprocessed real-time data into the natural environment prediction model corresponding to the location of lycopene constructed in step S102;

[0024] The lycopene position corresponding to the natural environment prediction model calculates the expected lycopene position corresponding to the natural environment data according to the input data, and the lycopene position corresponding to the natural environment data includes the expected temperature and humidity;

[0025] The server outputs the natural environment data predicted by the natural environment prediction model corresponding to the lycopene position.

[0026] Furthermore, the lycopene detection method in the lycopene production process provided by the present invention, step S103, comprises:

[0027] While collecting the lycopene moisture data, the actual natural environment data of the location of the lycopene is recorded, and the actual natural environment data is compared with the natural environment data corresponding to the estimated lycopene location obtained by the prediction model;

[0028] If the actual natural environment data is consistent with the expected natural environment data within the allowable error range, the collected lycopene moisture data is accurate;

[0029] If the actual natural environment data is inconsistent with the expected natural environment data, the collected lycopene moisture data needs to be corrected.

[0030] Furthermore, the lycopene detection method in the lycopene production process provided by the present invention, step S104, comprises:

[0031] The server compares the pre-processed lycopene moisture data with the estimated data obtained through the natural environment prediction model.

[0032] If there is a deviation in the data, the server further analyzes the cause of the deviation, which may include sensor failure, environmental changes, or model prediction errors;

[0033] If the deviation is caused by a sensor failure, the server sends a command to calibrate or replace the sensor and recollect the data;

[0034] If the deviation is caused by a sudden change in the natural environment, the server re-acquires the current natural environment data and updates the natural environment prediction model corresponding to the lycopene position;

[0035] If the deviation is caused by inaccurate prediction of the natural environment prediction model corresponding to the lycopene position, the server optimizes the natural environment prediction model corresponding to the lycopene position to improve the prediction accuracy of the natural environment prediction model corresponding to the lycopene position.

[0036] Furthermore, the lycopene detection method in the lycopene production process provided by the present invention, step S105, comprises:

[0037] comparing the corrected lycopene moisture data with a preset moisture range;

[0038] If the corrected data is within the preset range, it is considered that the moisture data meets the preset value. If it is not within the preset range, the moisture test is judged to be unqualified and the process ends;

[0039] Collecting lycopene samples, determining the lycopene content by high performance liquid chromatography, comparing the measured lycopene content with a preset standard, and if the lycopene content is within the preset standard range, the lycopene content in the lycopene is considered to be qualified;

[0040] If it is not within the preset standard range, the lycopene content in the lycopene is judged to be unqualified. If both the lycopene moisture data and the lycopene content meet the preset standards, the lycopene test result is judged to be qualified. If any one does not meet the standard, the lycopene moisture test is judged to be unqualified.

[0041] Beneficial effects of the present invention: The present invention significantly improves the accuracy of lycopene moisture detection in the lycopene production process. By real-time collection of environmental data and combining with a natural environment prediction model, the present invention can accurately predict the natural environment state of the location of lycopene under variable environmental conditions, thereby reducing the impact of environmental factors on the detection results. At the same time, by comprehensively considering the moisture content and lycopene content of lycopene, the detection results are more comprehensive and reliable.

[0042] The present invention optimizes the detection process, realizes the automation and intelligence of the whole process from data acquisition, model prediction, data comparison to result determination, reduces manual intervention, and improves the efficiency and consistency of the detection process. When data deviation is detected, the cause can be automatically analyzed and corresponding measures can be taken to correct it, making the detection process more flexible and efficient.

[0043] The present invention also improves the detection efficiency. By instantly acquiring the location of lycopene and its environmental data, the instant detection of lycopene moisture is achieved, shortening the detection cycle. At the same time, it supports parallel processing of data collection, model prediction and data comparison, further improving the overall detection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0045] Figure 1 A schematic diagram of a method flow chart provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The technical solutions provided by the embodiments of the present invention are described in detail below in conjunction with the drawings.

[0047] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

[0048] See also Figure 1 The present invention provides a method for detecting lycopene in a lycopene production process, comprising:

[0049] Step S101, obtaining the location information of lycopene, and determining the sensor information of the location of lycopene and the natural environment data of the location of lycopene according to the location information of lycopene;

[0050] Step S101 obtains the location information of lycopene. Through GPS positioning, step S101 can determine the specific location of lycopene, and then determine the sensor information corresponding to the location. The sensor includes a sensor for detecting the moisture content of lycopene, a temperature sensor, and a humidity sensor.

[0051] Step S102, receiving data information collected by the sensor at the location of lycopene, and constructing a natural environment prediction model corresponding to the location of lycopene according to the data information collected by the sensor at the location of lycopene and the natural environment data of the location of lycopene;

[0052] The server receives and stores real-time data information from the sensor at the location of the lycopene, including temperature, humidity, etc. At the same time, it also collects and organizes the natural environment data at the location of the lycopene, such as weather conditions.

[0053] Preprocess the collected data to ensure data consistency and availability. After preprocessing, the system will perform feature selection and extraction to screen out features that have a significant impact on the prediction results. Select a suitable algorithm to build a prediction model and use historical data as a training set to train the model. Optimize model parameters through cross-validation to improve the generalization ability of the model. Evaluate the prediction performance of the model and make necessary adjustments and optimizations to the model based on the evaluation results.

[0054] Deploy the trained model to the server or edge device. During the real-time detection process, input the real-time sensor data and natural environment data into the model to obtain the estimated natural environment data of the lycopene location.

[0055] Substituting the location information of lycopene and the real-time natural environment data of the location of lycopene into the natural environment prediction model corresponding to the location of lycopene to obtain the natural environment data corresponding to the location of lycopene;

[0056] Step S104, collecting lycopene moisture data corresponding to the position where the lycopene is located, comparing the natural environment data of the position where the lycopene is located when collecting the lycopene moisture data corresponding to the position where the lycopene is located with the natural environment data corresponding to the estimated position where the lycopene is located, if the comparison results are consistent, the collected lycopene moisture data corresponding to the position where the lycopene is located is accurate, if the comparison results are inconsistent, the lycopene moisture data corresponding to the position where the lycopene is located is corrected;

[0057] Step S104 is responsible for collecting the lycopene moisture data at the location of the lycopene, and comparing the data with the estimated natural environment data obtained through the natural environment prediction model to verify the accuracy of the data.

[0058] Step S104 collects the lycopene moisture data at the location of the lycopene and records the actual natural environment data at that time. Next, step S104 compares the natural environment data at the location of the lycopene when the collected lycopene moisture data with the estimated data. If the comparison result is consistent, the collected data is considered accurate; if not, it needs to be corrected.

[0059] When data deviation is found, step S104 analyzes the cause to be sensor failure, environmental change or model prediction error, etc. According to the analysis result, step S104 takes corresponding correction measures, such as calibrating or replacing sensors, reacquiring natural environment data or optimizing prediction models, etc.

[0060] Finally, the corrected lycopene moisture data is passed to the subsequent step S105 to improve the accuracy of lycopene quality detection.

[0061] Step S105, after correcting the lycopene moisture data corresponding to the position of lycopene, if the corrected lycopene moisture data corresponding to the position of lycopene meets the preset value, then detecting whether the lycopene content in the lycopene meets the preset standard, if the lycopene content in the lycopene meets the preset standard, the lycopene detection result is qualified.

[0062] Step S105 plays a key role in the lycopene detection method in the lycopene production process. Its main task is to verify the corrected lycopene moisture data, detect the lycopene content in the lycopene, and comprehensively determine whether the lycopene moisture is qualified based on these data.

[0063] Step S105 will compare the corrected lycopene moisture data with the preset moisture range. If the corrected data falls within the preset range, the moisture data is considered qualified. Step S105 collects lycopene samples and uses high performance liquid chromatography to accurately determine the lycopene content. This content will be compared with the preset standard to determine whether the lycopene content meets the standard.

[0064] Finally, if the water content data and lycopene content of lycopene meet the preset standards, step S105 determines that the lycopene test result is qualified. On the contrary, if any of the indicators does not meet the standards, then the lycopene water content test is determined to be unqualified. Step S105 can accurately evaluate the quality of lycopene, thereby ensuring the safety and effectiveness of lycopene.

[0065] Specifically, the lycopene detection method in the lycopene production process provided by the present invention, step S101, comprises:

[0066] The server receives sensor data, including GPS locator, temperature and humidity sensor, and obtains the location information of lycopene through the GPS locator;

[0067] After obtaining the location information of lycopene, the sensor information at the location corresponding to the location information of lycopene is determined. The sensor information includes a sensor for detecting the moisture content of lycopene, a temperature sensor, and a humidity sensor. The server obtains various data of the environment where the lycopene is located, such as temperature and humidity, through communication with the sensors.

[0068] The server collects natural environment data of the location of Lycopene, and the natural environment data includes weather conditions and air humidity data;

[0069] The server optimizes detection parameters based on the use of lycopene's location information, sensor information, and natural environment data. If the server detects that the humidity in a certain area is high, it automatically adjusts the humidity control equipment in the production environment.

[0070] Specifically, the lycopene detection method in the lycopene production process provided by the present invention, step S102, comprises:

[0071] The server receives and stores real-time data information from the sensor where the lycopene is located, and the real-time data information from the sensor where the lycopene is located includes temperature, humidity, and air pressure;

[0072] Collect and organize the natural environment data of Lycopene's location, including weather conditions, wind speed, wind direction and solar radiation intensity;

[0073] Feature selection and extraction are performed on the collected sensor data and natural environment data, and a linear regression algorithm is selected to build a prediction model. Historical data is used as a training set, and model parameters are optimized through cross-validation.

[0074] Specifically, the lycopene detection method in the lycopene production process provided by the present invention, step S103, comprises:

[0075] Preprocessing the sensor data and natural environment data of the location of lycopene received in real time by the server, and substituting the preprocessed real-time data into the natural environment prediction model corresponding to the location of lycopene constructed in step S102;

[0076] The lycopene position corresponding to the natural environment prediction model calculates the expected lycopene position corresponding to the natural environment data according to the input data, and the lycopene position corresponding to the natural environment data includes the expected temperature and humidity;

[0077] The server outputs the natural environment data predicted by the natural environment prediction model corresponding to the lycopene position.

[0078] Specifically, the lycopene detection method in the lycopene production process provided by the present invention, step S103, comprises:

[0079] While collecting the lycopene moisture data, the actual natural environment data of the location of the lycopene is recorded, and the actual natural environment data is compared with the natural environment data corresponding to the estimated lycopene location obtained by the prediction model;

[0080] If the actual natural environment data is consistent with the expected natural environment data within the allowable error range, the collected lycopene moisture data is accurate;

[0081] If the actual natural environment data is inconsistent with the expected natural environment data, the collected lycopene moisture data needs to be corrected.

[0082] Specifically, the lycopene detection method in the lycopene production process provided by the present invention, step S104, comprises:

[0083] The server compares the pre-processed lycopene moisture data with the estimated data obtained through the natural environment prediction model.

[0084] If there is a deviation in the data, the server further analyzes the cause of the deviation, which may include sensor failure, environmental changes, or model prediction errors;

[0085] If the deviation is caused by a sensor failure, the server sends a command to calibrate or replace the sensor and recollect the data;

[0086] If the deviation is caused by a sudden change in the natural environment, the server re-acquires the current natural environment data and updates the natural environment prediction model corresponding to the lycopene position;

[0087] If the deviation is caused by inaccurate prediction of the natural environment prediction model corresponding to the lycopene position, the server optimizes the natural environment prediction model corresponding to the lycopene position to improve the prediction accuracy of the natural environment prediction model corresponding to the lycopene position.

[0088] Step S104 plays a crucial role in the lycopene detection method in the lycopene production process, and compares the actually collected lycopene moisture data with the estimated data obtained through the natural environment prediction model to improve the accuracy of the data.

[0089] The server preprocesses the collected lycopene moisture data, which may include data cleaning, data formatting, and possible normalization or standardization operations. The purpose of preprocessing is to ensure that the actual collected data is consistent with the data format output by the prediction model, so that an accurate comparison can be made.

[0090] After the preprocessing is completed, the server compares the actual collected lycopene moisture data with the predicted data output by the natural environment prediction model, evaluates the difference between the actual data and the predicted data, and thus determines the accuracy of the data.

[0091] If a deviation is found between the actual data and the expected data, the server will further analyze the causes of the deviation. These causes may include sensor failure, environmental changes, or model prediction errors. By deeply analyzing these causes, a strong basis can be provided for subsequent data corrections.

[0092] If the deviation is caused by a sensor failure, the server will send instructions to calibrate or replace the sensor and recollect the data.

[0093] If the deviation is caused by a sudden change in the natural environment, the server will re-acquire the current natural environment data and update the natural environment prediction model corresponding to the lycopene position to ensure that the model can more accurately reflect the actual environmental conditions.

[0094] If the deviation is caused by inaccurate prediction of the lycopene position corresponding to the natural environment prediction model, the server will optimize the lycopene position corresponding to the natural environment prediction model to improve its prediction accuracy.

[0095] Specifically, the lycopene detection method in the lycopene production process provided by the present invention, step S105, comprises:

[0096] comparing the corrected lycopene moisture data with a preset moisture range;

[0097] If the corrected data is within the preset range, it is considered that the moisture data meets the preset value. If it is not within the preset range, the moisture test is judged to be unqualified and the process ends;

[0098] Collecting lycopene samples, determining the lycopene content by high performance liquid chromatography, comparing the measured lycopene content with a preset standard, and if the lycopene content is within the preset standard range, the lycopene content in the lycopene is considered to be qualified;

[0099] If it is not within the preset standard range, the lycopene content in the lycopene is judged to be unqualified. If both the lycopene moisture data and the lycopene content meet the preset standards, the lycopene test result is judged to be qualified. If any one does not meet the standard, the lycopene moisture test is judged to be unqualified.

[0100] Step S105 plays a vital role in the lycopene detection method in the lycopene production process. It is mainly responsible for comparing the corrected lycopene moisture data with the preset moisture range, detecting the lycopene content in the lycopene, and combining these data to determine whether the lycopene moisture content is qualified.

[0101] Step S105 compares the corrected lycopene moisture data with a preset moisture range, which is pre-set according to the quality requirements and storage conditions of lycopene, to evaluate whether the moisture content of lycopene is appropriate.

[0102] If the corrected data falls within this preset range, then the moisture data can be considered to meet the preset value.

[0103] If the data is not within the preset range, the moisture test is directly judged as unqualified and the entire test process ends.

[0104] The lycopene content of these samples was precisely determined using high performance liquid chromatography, a commonly used analytical method that can accurately determine the amount of specific compounds in a sample. Once the determination is complete, the measured lycopene content is compared with a preset standard.

[0105] If the measured lycopene content is within the preset standard range, then the lycopene content in the lycopene can be considered to be qualified. If the lycopene content is not within the preset standard range, then it is judged that the content is unqualified.

[0106] Finally, if the water content data of lycopene and the lycopene content both meet the preset standards, then step S105 will determine that the lycopene test result is qualified. However, if any of the data does not meet the standards, then the lycopene water content test will be determined to be unqualified.

[0107] The method of the present invention for solving the problem of inaccurate lycopene humidity detection results in a humid environment mainly comprises the following steps: In step S101, the present invention obtains environmental data of the location of lycopene, such as temperature, humidity, etc. in real time. These data are provided by devices such as temperature and humidity sensors, so as to monitor the environmental conditions in real time.

[0108] The present invention uses the real-time collected environmental data and lycopene location information to construct a natural environment prediction model corresponding to the lycopene location. This can predict the environmental conditions at the location of lycopene based on historical data and current real-time data, providing a basis for subsequent data comparison.

[0109] The present invention substitutes real-time environmental data into the prediction model to obtain predicted environmental data, and compares the predicted environmental data with the actual environmental data when the lycopene moisture data is collected. If the two are consistent, the collected lycopene moisture data is considered accurate; otherwise, the lycopene moisture data will be corrected.

[0110] When it is found that the actual environmental data is inconsistent with the expected data, the system analyzes the cause of the deviation and takes appropriate measures to correct it, such as calibrating or replacing sensors, re-acquiring current environmental data, optimizing prediction models, etc. This ensures the accuracy of the data.

[0111] The server dynamically adjusts the detection parameters based on real-time environmental data. For example, when high humidity is detected, the humidity control equipment in the production environment is automatically adjusted. At the same time, the accuracy of detection is improved by continuously optimizing the prediction model and detection parameters.

[0112] After confirming that the lycopene water content data is accurate, the present invention further tests the lycopene content in the lycopene. Only when the lycopene water content and lycopene content both meet the preset standards, the lycopene test result is determined to be qualified.

[0113] In summary, the present invention effectively solves the problem of inaccurate detection results in humid environments through strategies such as real-time collection of environmental data, construction of prediction models, data comparison and correction, dynamic adjustment of detection parameters, and multi-parameter comprehensive judgment, thereby improving the accuracy and reliability of detection.

[0114] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations, and the above-described embodiments of the present invention do not constitute a limitation on the scope of protection of the present invention.

Claims

1. A method for detecting lycopene in a lycopene production process, characterized in that: include: Step S101, obtaining the location information of lycopene, and determining the sensor information of the location of lycopene and the natural environment data of the location of lycopene according to the location information of lycopene; Step S102, receiving data information collected by the sensor at the location of lycopene, and constructing a natural environment prediction model corresponding to the location of lycopene according to the data information collected by the sensor at the location of lycopene and the natural environment data of the location of lycopene; Step S103, substituting the location information of lycopene and the real-time natural environment data of the location of lycopene into the natural environment prediction model corresponding to the location of lycopene to obtain the estimated natural environment data corresponding to the location of lycopene; Step S104, collecting lycopene moisture data corresponding to the position where the lycopene is located, comparing the natural environment data of the position where the lycopene is located when collecting the lycopene moisture data corresponding to the position where the lycopene is located with the natural environment data corresponding to the estimated position where the lycopene is located, if the comparison results are consistent, the collected lycopene moisture data corresponding to the position where the lycopene is located is accurate, if the comparison results are inconsistent, the lycopene moisture data corresponding to the position where the lycopene is located is corrected; Step S105, after correcting the lycopene moisture data corresponding to the position of lycopene, if the corrected lycopene moisture data corresponding to the position of lycopene meets the preset value, then detecting whether the lycopene content in the lycopene meets the preset standard, if the lycopene content in the lycopene meets the preset standard, the lycopene detection result is qualified.

2. The method for detecting lycopene in the lycopene production process according to claim 1, characterized in that: Step S101 includes: The server receives sensor data, including GPS locator, temperature and humidity sensor, and obtains the location information of lycopene through the GPS locator; After obtaining the location information of lycopene, the sensor information at the location corresponding to the location information of lycopene is determined. The sensor information includes a sensor for detecting the moisture content of lycopene, a temperature sensor, and a humidity sensor. The server obtains various data of the environment where the lycopene is located, such as temperature and humidity, through communication with the sensors. The server collects natural environment data of the location of Lycopene, and the natural environment data includes weather conditions and air humidity data; The server optimizes detection parameters based on the use of lycopene's location information, sensor information, and natural environment data. If the server detects that the humidity in a certain area is high, it automatically adjusts the humidity control equipment in the production environment.

3. The method for detecting lycopene in the lycopene production process according to claim 1, characterized in that: Step S102 includes: The server receives and stores real-time data information from the sensor where the lycopene is located, and the real-time data information from the sensor where the lycopene is located includes temperature, humidity, and air pressure; Collect and organize the natural environment data of Lycopene's location, including weather conditions, wind speed, wind direction and solar radiation intensity; Feature selection and extraction are performed on the collected sensor data and natural environment data, and a linear regression algorithm is selected to build a prediction model. Historical data is used as a training set, and model parameters are optimized through cross-validation.

4. The method for detecting lycopene in the lycopene production process according to claim 1, characterized in that: Step S103 includes: Preprocessing the sensor data and natural environment data of the location of lycopene received in real time by the server, and substituting the preprocessed real-time data into the natural environment prediction model corresponding to the location of lycopene constructed in step S102; The lycopene position corresponding to the natural environment prediction model calculates the expected lycopene position corresponding to the natural environment data according to the input data, and the lycopene position corresponding to the natural environment data includes the expected temperature and humidity; The server outputs the natural environment data predicted by the natural environment prediction model corresponding to the lycopene position.

5. The method for detecting lycopene in the lycopene production process according to claim 1, characterized in that: Step S103 includes: While collecting the lycopene moisture data, the actual natural environment data of the location of the lycopene is recorded, and the actual natural environment data is compared with the natural environment data corresponding to the estimated lycopene location obtained by the prediction model; If the actual natural environment data is consistent with the expected natural environment data within the allowable error range, the collected lycopene moisture data is accurate; If the actual natural environment data is inconsistent with the expected natural environment data, the collected lycopene moisture data needs to be corrected.

6. The method for detecting lycopene in the lycopene production process according to claim 1, characterized in that: Step S104 includes: The server compares the pre-processed lycopene moisture data with the estimated data obtained through the natural environment prediction model. If there is a deviation in the data, the server further analyzes the cause of the deviation, which may include sensor failure, environmental changes, or model prediction errors; If the deviation is caused by a sensor failure, the server sends a command to calibrate or replace the sensor and recollect the data; If the deviation is caused by a sudden change in the natural environment, the server re-acquires the current natural environment data and updates the natural environment prediction model corresponding to the lycopene position; If the deviation is caused by inaccurate prediction of the natural environment prediction model corresponding to the lycopene position, the server optimizes the natural environment prediction model corresponding to the lycopene position to improve the prediction accuracy of the natural environment prediction model corresponding to the lycopene position.

7. The method for detecting lycopene in the lycopene production process according to claim 1, characterized in that: Step S105 includes: comparing the corrected lycopene moisture data with a preset moisture range; If the corrected data is within the preset range, it is considered that the moisture data meets the preset value. If it is not within the preset range, the moisture test is judged to be unqualified and the process ends; Collecting lycopene samples, determining the lycopene content by high performance liquid chromatography, comparing the measured lycopene content with a preset standard, and if the lycopene content is within the preset standard range, the lycopene content in the lycopene is considered to be qualified; If it is not within the preset standard range, the lycopene content in the lycopene is judged to be unqualified. If the lycopene moisture data and the lycopene content both meet the preset standards, the lycopene test result is judged to be qualified. If it does not meet the standards, the lycopene moisture test is judged to be unqualified.

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