A method and system for detecting oxygen data

Abstract

This invention relates to the field of warehouse data detection technology, specifically disclosing an oxygen data detection method and system. The method includes setting up collection points in the warehouse and determining the data relationship between adjacent collection points based on the warehouse's building data; receiving temperature, humidity, and oxygen content data from the collection points and calibrating the obtained temperature, humidity, and oxygen content data according to the data relationship; correcting the calibrated oxygen content based on the calibrated temperature and humidity data; visually storing and displaying the calibrated oxygen content; acquiring the storage location and storage status of stored goods in real time, querying the oxygen content at the corresponding time based on the storage location, and verifying the accuracy of the oxygen content by the storage status; wherein, the accuracy rate serves as a criterion for determining whether to update the calibration and correction steps. Firstly, this invention can automatically and periodically collect various data from inside the warehouse, calibrate and process the data using algorithms to improve data accuracy, and report it to a remote server.

Description

Technical Field

[0001] This invention relates to the field of warehouse data detection technology, specifically an oxygen data detection method and system. Background Technology

[0002] In warehouse environment testing, due to the complex warehouse environment, the general data collection method is mostly single data collection, resulting in large deviations in the collected data and insufficient environmental calibration for multiple parameters. To address these issues, a testing system with higher accuracy and more precise calibration methods is needed.

[0003] As the requirements for warehouse environmental monitoring increase, the accuracy of data collection also becomes higher. Since the temperature and humidity of the environment affect the concentration of moisture and oxygen, the collected moisture and oxygen levels may deviate. Therefore, a data collection system with high accuracy and strong anti-interference ability has become an urgent problem to be solved. Summary of the Invention

[0004] The purpose of this invention is to provide an oxygen data detection method and system to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] An oxygen data detection method, the method comprising:

[0007] Set up data collection points in the warehouse and determine the data relationships between adjacent data collection points based on the warehouse's building data;

[0008] The temperature, humidity, and oxygen content data are received from the collection points, and the data are calibrated based on the data relationships.

[0009] Based on the calibrated temperature and humidity, the calibrated oxygen content is corrected, and the calibrated oxygen content is visualized, stored, and displayed.

[0010] The system can obtain the storage location and storage status of stored goods in real time, query the oxygen content at the corresponding time based on the storage location, and verify the accuracy of the oxygen content by the storage status.

[0011] Among them, the accuracy rate serves as the criterion for determining whether to update the calibration and correction processes.

[0012] As a further aspect of the present invention: the step of setting up collection points in the warehouse and determining the data relationship between adjacent collection points based on the warehouse's building data includes:

[0013] Query the warehouse's building data and create a 3D warehouse scene based on the building data;

[0014] Based on the spatial coordinates input by the staff in the 3D warehouse scene, the data collection points are set.

[0015] Query the ventilation facilities in the building data to obtain the ventilation parameters of the ventilation facilities; the ventilation parameters include ventilation direction and ventilation rate.

[0016] Input the ventilation parameters and the 3D warehouse scene into ANSYS Fluent software to determine the airflow parameters within the warehouse; the airflow parameters include flow direction and flow velocity.

[0017] The data relationship between each collection point and its adjacent collection points is determined based on the airflow parameters within the warehouse; the data relationship is used to characterize the influence of adjacent collection points on each collection point.

[0018] The method for determining the data relationship is as follows:

[0019] In the formula, Δ represents the data relationship. It is a vector determined by airflow parameters. It is a spatial vector composed of two acquisition points.

[0020] As a further aspect of the present invention: the step of calibrating the temperature, humidity, and oxygen content obtained by the receiving and collecting point according to the data relationship includes:

[0021] The receiving point acquires temperature, humidity, and oxygen content information containing time data.

[0022] Using the sampling point as the center, query the temperature, humidity and oxygen content of adjacent sampling points within a 3*3 unit;

[0023] The temperature, humidity, and oxygen content of the central collection point are calibrated based on the data relationship between each adjacent collection point;

[0024] The calibration process is as follows: In the formula, y represents the data before calibration, y′ represents the data after calibration, and Δ i f(Δ) represents the data relationship between the i-th acquisition point within a 3x3 cell and the central acquisition point; i ) is a predefined function that converts data relationships into weights. One type of function is to calculate the ratio of each data relationship to the sum of the absolute values ​​of the data relationships.

[0025] As a further aspect of the present invention: the step of visually storing and displaying the calibrated oxygen content based on the calibrated temperature and humidity correction includes:

[0026] The oxygen content was corrected based on the calibrated temperature and humidity.

[0027] The oxygen content after statistical calibration is converted into a display color value; the conversion rule is determined by the range of oxygen content and the range of display color values.

[0028] The display color value will be inserted into the spatial coordinates of the acquisition point in the 3D warehouse scene, and the 3D warehouse scene containing the display color value will be stored and displayed.

[0029] As a further aspect of the present invention: the step of correcting the calibrated oxygen content based on the calibrated temperature and humidity includes:

[0030] The collected data is sorted from smallest to largest to obtain a set of data;

[0031] Remove the two maximum and two minimum values, and then calculate the average of the remaining N-4 values;

[0032] Calculate the oxygen concentration based on the processed data;

[0033] The final oxygen concentration value is: In the formula b ij b is the compensation coefficient for temperature on oxygen sampling values. ji =α*(lgt2-lgt1) / (t2-t1), t i x is the i-th power of the average. i t1 is the i-th power of the voltage value output by the sensor; t2 and t1 are the two endpoint values ​​of the pre-acquired temperature range; α is a pre-set correction coefficient.

[0034] As a further aspect of the present invention: the step of acquiring the storage location and storage status of the stored goods in real time, querying the oxygen content at the corresponding time based on the storage location, and verifying the accuracy of the oxygen content based on the storage status includes:

[0035] Real-time acquisition of the storage location and storage status of stored goods at various times;

[0036] The stored state at each time step is input into the trained evaluation model to determine the theoretical oxygen content at each time step.

[0037] In a 3D warehouse scene, query the actual oxygen content at each time point based on the storage location;

[0038] Compare the theoretical oxygen content with the actual oxygen content, and verify the accuracy of the actual oxygen content based on the comparison results.

[0039] The present invention also provides an oxygen data detection system, the system comprising:

[0040] The data relationship determination module is used to set up collection points in the warehouse and determine the data relationship between each adjacent collection point based on the warehouse's building data.

[0041] The data acquisition and calibration module is used to receive the temperature, humidity, and oxygen content obtained from the acquisition points, and to calibrate the acquired temperature, humidity, and oxygen content according to the data relationship.

[0042] The storage and display module is used to correct the calibrated oxygen content based on the calibrated temperature and humidity, and to visually store and display the calibrated oxygen content.

[0043] The accuracy calculation module is used to obtain the storage location and storage status of the stored goods in real time, query the oxygen content at the corresponding time based on the storage location, and verify the accuracy of the oxygen content by the storage status.

[0044] Among them, the accuracy rate serves as the criterion for determining whether to update the calibration and correction processes.

[0045] As a further aspect of the present invention: the data relationship determination module includes:

[0046] The data collection point setting unit is used to set data collection points based on the spatial coordinates input by the staff in the three-dimensional warehouse scene.

[0047] The ventilation parameter query unit is used to query the ventilation facilities in the building data and obtain the ventilation parameters of the ventilation facilities; the ventilation parameters include ventilation direction and ventilation rate.

[0048] The warehouse airflow analysis unit is used to input ventilation parameters and a 3D warehouse scene into ANSYS Fluent software to determine the airflow parameters within the warehouse; the airflow parameters include flow direction and flow velocity.

[0049] A data relationship determination unit is used to determine the data relationship between each collection point and its adjacent collection points based on airflow parameters within the warehouse; the data relationship is used to characterize the influence of adjacent collection points on each collection point.

[0050] The method for determining the data relationship is as follows:

[0051] In the formula, Δ represents the data relationship. It is a vector determined by airflow parameters. It is a spatial vector composed of two acquisition points.

[0052] As a further aspect of the present invention: the data acquisition and calibration module includes:

[0053] The data receiving unit is used to receive temperature, humidity, and oxygen content data containing time information from the collection point.

[0054] The data query unit is used to query the temperature, humidity, and oxygen content of adjacent sampling points within a 3*3 unit, centered on the sampling point.

[0055] The calibration execution unit is used to calibrate the temperature, humidity, and oxygen content of the central collection point based on the data relationship between each adjacent collection point;

[0056] The calibration process is as follows: In the formula, y represents the data before calibration, y′ represents the data after calibration, and Δ i f(Δ) represents the data relationship between the i-th acquisition point within a 3x3 cell and the central acquisition point; i ) is a predefined function that converts data relationships into weights. One type of function is to calculate the ratio of each data relationship to the sum of the absolute values ​​of the data relationships.

[0057] As a further aspect of the present invention: the storage display module includes:

[0058] The correction unit is used to correct the calibrated oxygen content based on the calibrated temperature and humidity.

[0059] The color value determination unit is used to statistically analyze the oxygen content after calibration and convert the oxygen content into a color value; the conversion rule is jointly determined by the range of oxygen content and the range of color values.

[0060] The color value insertion unit is used to display the spatial coordinates of the color value insertion acquisition point in the 3D warehouse scene, store and display the 3D warehouse scene containing the color values.

[0061] Compared with existing technologies, the beneficial effects of this invention are: This invention can meet the needs of monitoring materials in multiple warehouse environments, and can detect data such as oxygen concentration and internal temperature, humidity and moisture content of materials. The collected data has high accuracy and high reliability, and the oxygen and moisture parameters are compensated by temperature and humidity, which greatly improves the testing accuracy under different environments. It provides a large amount of valuable data analysis for the safe storage of warehouse materials, and can also remind staff to deal with abnormal data in a timely manner to avoid unnecessary losses. In addition, the entire system installation process is simple and flexible, the equipment adopts a low power consumption design, and it runs stably for a long time, saving operating costs. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0063] Figure 1 This is a flowchart of the oxygen data detection method.

[0064] Figure 2 This is the first sub-flowchart of the oxygen data detection method.

[0065] Figure 3 This is the second sub-flowchart of the oxygen data detection method.

[0066] Figure 4 This is the third sub-flowchart of the oxygen data detection method.

[0067] Figure 5 This is the fourth sub-flowchart of the oxygen data detection method.

[0068] Figure 6 This is a block diagram showing the composition and structure of an oxygen data detection system. Detailed Implementation

[0069] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0070] Figure 1 This is a flowchart of an oxygen data detection method. In this embodiment of the invention, an oxygen data detection method includes:

[0071] Step S100: Set up data collection points in the warehouse and determine the data relationship between adjacent data collection points based on the warehouse's building data;

[0072] Staff set up data collection points in the warehouse and installed data collection modules at the collection points to collect data on the overall status of the warehouse. The data collected in this application includes temperature, humidity and oxygen content. Since air is fluid, there are certain relationships between adjacent collection points and they influence each other. Therefore, after the staff set up the collection points, this application also needs to obtain the data relationships between adjacent collection points.

[0073] Step S200: Receive the temperature, humidity and oxygen content obtained from the collection point, and calibrate the obtained temperature, humidity and oxygen content according to the data relationship;

[0074] When the temperature, humidity, and oxygen content are collected at the sampling points, they are uploaded to the main body of this method. The main body of this method will calibrate the data of each sampling point based on the data of the adjacent sampling points of each sampling point.

[0075] Step S300: Correct the calibrated oxygen content based on the calibrated temperature and humidity, and then visualize, store, and display the calibrated oxygen content.

[0076] Regarding temperature, humidity, and oxygen content, in real-world scenarios, oxygen content is the final data required. Temperature and humidity themselves have a certain impact on oxygen content. Therefore, oxygen content needs to be calibrated a second time based on temperature and humidity. The accuracy of the calibrated oxygen content is extremely high. To improve display and readability, this application adopts a visual storage solution, that is, converting oxygen content into color values ​​and inserting them into the 3D model corresponding to the warehouse.

[0077] Step S400: Obtain the storage location and storage status of the stored goods in real time, query the oxygen content at the corresponding time based on the storage location, and verify the accuracy of the oxygen content by the storage status.

[0078] In one example of the technical solution of this invention, a scheme for judging the correctness of the recognition result is added. By obtaining the storage status of the stored goods, a theoretical oxygen content can be determined. This relationship can be obtained through big data. The oxygen content after calibration is the actual oxygen content. By comparing the actual oxygen content with the theoretical oxygen content, the accuracy of the actual oxygen content can be roughly judged, thereby determining whether the calibration process needs to be manually updated to improve its accuracy. In layman's terms, the accuracy rate serves as the criterion for determining whether to update the calibration process and the correction process.

[0079] It is worth mentioning that the correction step is equivalent to a secondary calibration of the oxygen content.

[0080] In one embodiment of the technical solution of this invention, an environmental data monitoring system is provided, including a data acquisition module, a calibration module, a transmission module, a storage module, and a display and early warning module. The data acquisition module is used to detect the temperature, humidity, oxygen concentration, and moisture content of the environment, and provide the data to the calibration module. The calibration module is used to process and calibrate the acquired temperature, humidity, oxygen concentration, and moisture content, and forward the data to the server via the MQTT protocol. The transmission module is used to receive the data uploaded by the acquisition module and forward it to the server via the MQTT protocol. The storage module is used to store the calibrated data locally. The display and early warning module is used to perform anomaly analysis on the data, determine whether the data is abnormal, provide early warnings for abnormal data, and display the query results of the data acquired by the acquisition module.

[0081] Furthermore, the acquisition module includes a core-mounted temperature and humidity acquisition terminal, a core-mounted moisture acquisition terminal, and an oxygen concentration acquisition terminal; it can detect relevant data such as temperature, humidity, moisture, and oxygen content. The core-mounted temperature and humidity acquisition terminal, core-mounted moisture acquisition terminal, and oxygen concentration acquisition terminal are designed with rod-shaped probes to detect internal temperature, humidity, moisture, and oxygen data. The acquisition module is battery-powered, employs a low-power design, and uses LoRa wireless communication technology for data transmission, eliminating the need for wiring. The calibration module processes the acquired temperature and humidity data using algorithms to compensate for moisture and oxygen values, thereby obtaining calibrated data. The transmission module is a LoRa gateway, supporting the reception of data from multiple nodes and the transmission of data via the MQTT protocol.

[0082] Figure 2 This is a first sub-flowchart of the oxygen data detection method. The step of setting up collection points in the warehouse and determining the data relationship between adjacent collection points based on the warehouse's building data includes:

[0083] Step S101: Query the building data of the warehouse and create a three-dimensional warehouse scene based on the building data;

[0084] Step S102: Based on the spatial coordinates input by the staff in the three-dimensional warehouse scene, set the collection points;

[0085] Step S103: Query the ventilation facilities in the building data to obtain the ventilation parameters of the ventilation facilities; the ventilation parameters include ventilation direction and ventilation rate;

[0086] Step S104: Input the ventilation parameters and the 3D warehouse scene into ANSYS Fluent software to determine the airflow parameters in the warehouse; the airflow parameters include flow direction and flow velocity;

[0087] Step S105: Determine the data relationship between each collection point and its adjacent collection points based on the airflow parameters within the warehouse; the data relationship is used to characterize the influence of adjacent collection points on each collection point;

[0088] The method for determining the data relationship is as follows:

[0089] In the formula, Δ represents the data relationship. It is a vector determined by airflow parameters. It is a spatial vector composed of two acquisition points.

[0090] Each warehouse has independent building data, which is stored during warehouse construction. A 3D warehouse scene can be created based on the building data. The creation process does not require high precision and is not difficult. To display the 3D warehouse scene, an information interaction port is inserted, which receives the collection points selected by the staff.

[0091] Based on this, the ventilation facilities in the building data are queried. These facilities include exhaust fans and exhaust windows. The ventilation parameters of the ventilation facilities are obtained. The ventilation parameters and the 3D warehouse scene are input into ANSYS Fluent software to simulate the airflow parameters in the warehouse. These airflow parameters are the direction and velocity of the airflow.

[0092] Once the airflow parameters within the warehouse are determined, determining the airflow parameters at each point within the warehouse is very easy; values ​​can be directly assigned.

[0093] Figure 3 The second sub-flowchart of the oxygen data detection method includes the step of calibrating the temperature, humidity, and oxygen content obtained from the receiving point based on data relationships.

[0094] Step S201: The receiving point acquires temperature, humidity, and oxygen content containing time information;

[0095] Step S202: Using the sampling point as the center, query the temperature, humidity and oxygen content of adjacent sampling points within the 3*3 unit;

[0096] Step S203: Calibrate the temperature, humidity, and oxygen content of the central collection point based on the data relationship between each adjacent collection point;

[0097] In one embodiment of the technical solution of this invention, the first calibration process is specifically defined, and its objective is to calibrate the central sampling point based on the sampling points within a 3*3 area. The calibration process is as follows:

[0098] In one example of the technical solution of the present invention, the data calibration process is specifically defined. The basic process is that the eight collection points around each collection point jointly calibrate the collection point at the center. The calibration process can adopt a scheme similar to image blurring, that is, assign different weights to each collection point, calculate the sum of the collected values ​​based on the weights, and obtain the value at the center collection point. This method can effectively alleviate the adverse effects caused by erroneous sampling.

[0099] The calibration process is as follows: In the formula, y represents the data before calibration, y′ represents the data after calibration, and Δ i f(Δ) represents the data relationship between the i-th acquisition point within a 3x3 cell and the central acquisition point; i ) is a predefined function that converts data relationships into weights. One type of function is to calculate the ratio of each data relationship to the sum of the absolute values ​​of the data relationships.

[0100] Figure 4The third sub-flowchart of the oxygen data detection method includes the step of visually storing and displaying the calibrated oxygen content based on the calibrated temperature and humidity correction.

[0101] Step S301: Correct the calibrated oxygen content based on the calibrated temperature and humidity;

[0102] Step S302: Calculate the oxygen content after calibration and convert the oxygen content into a display color value; the conversion rule is determined by the range of oxygen content and the range of display color values.

[0103] Step S303: Insert the display color value into the spatial coordinates of the acquisition point in the 3D warehouse scene, store and display the 3D warehouse scene containing the display color value.

[0104] In one embodiment of the technical solution of the present invention, after the temperature and humidity calibration is completed, the oxygen content is calibrated a second time based on the temperature and humidity. The temperature and humidity are calibrated independently using the same method. The step of correcting the calibrated oxygen content based on the calibrated temperature and humidity includes:

[0105] The collected data is sorted from smallest to largest to obtain a set of data;

[0106] Remove the two maximum and two minimum values, and then calculate the average of the remaining N-4 values;

[0107] Calculate the oxygen concentration based on the processed data;

[0108] The final oxygen concentration value is: In the formula b ij b is the compensation coefficient for temperature on oxygen sampling values. ji =α*(lgt2-lgt1) / (t2-t1), t i x is the i-th power of the average. i t1 is the i-th power of the voltage value output by the sensor; t2 and t1 are the two endpoint values ​​of the pre-acquired temperature range; α is a pre-set correction coefficient.

[0109] The specific calculation process is explained below:

[0110] Three sets of oxygen concentration, temperature, and humidity data under the current conditions were collected. A median filtering algorithm was applied to these three sets of data to remove fluctuations and interference during the data collection process. Then, the average value of the filtered concentration values ​​was calculated to obtain the three-set average, thus controlling for interference introduced during the data collection process. The specific algorithm is as follows:

[0111] First, sort the collected data from smallest to largest to obtain a set of data:

[0112] {t1,t2,t3,…,t n};

[0113] After removing the two maximum and two minimum values, calculate the average of the remaining N-4 values:

[0114] t = (t3 + t4 + ... + t) n-2 ) / (n-4);

[0115] The data is obtained after median filtering and averaging.

[0116] Based on the processed data, the oxygen concentration is calculated. The output value of the oxygen concentration can be calculated using a polynomial:

[0117]

[0118] y represents the actual oxygen concentration, and x represents the sensor output voltage; considering the influence of temperature and humidity on the sensor, and a i It changes with temperature and humidity, and is a function of temperature and humidity t:

[0119]

[0120] Where b ji This is the compensation coefficient for oxygen sampling values ​​due to temperature. The compensation coefficient varies for different temperature values ​​and is calculated based on different temperature ranges.

[0121] b ji (t)=α*(lgt2-lgt1) / (t2-t1);

[0122] The final oxygen concentration value is:

[0123]

[0124] Figure 5 The fourth sub-flowchart of the oxygen data detection method includes the following steps: real-time acquisition of the storage location and storage status of the stored goods at various times; querying the oxygen content at the corresponding time based on the storage location; and verifying the accuracy of the oxygen content based on the storage status.

[0125] Step S401: Obtain the storage location and storage status of the stored goods in real time;

[0126] Step S402: Input the stored state at each time step into the trained evaluation model to determine the theoretical oxygen content at each time step;

[0127] Step S403: In the 3D warehouse scene, query the actual oxygen content at each time point based on the storage location;

[0128] Step S404: Compare the theoretical oxygen content with the actual oxygen content, and verify the accuracy of the actual oxygen content based on the comparison results.

[0129] In one example of the technical solution of this invention, an overall accuracy evaluation scheme is introduced. The storage location and storage status of the stored goods are acquired in real time, that is, the preservation degree of the stored goods at each location. The acquisition process can be carried out autonomously by inspection personnel or with the help of cameras. The acquired storage status is input into a trained evaluation model to determine some theoretical oxygen content. For example, assuming the stored goods are fruits, the oxygen content will affect their oxidation degree. The oxidation degree of the stored goods is acquired, and the theoretical oxygen content can be deduced from the oxidation degree. The accuracy rate can be calculated by comparing it with the actual oxygen content of the stored goods.

[0130] Figure 6 This is a block diagram of the composition of an oxygen data detection system. In this embodiment of the invention, an oxygen data detection system 10 includes:

[0131] The data relationship determination module 11 is used to set up collection points in the warehouse and determine the data relationship between each adjacent collection point based on the warehouse's building data.

[0132] The data acquisition and calibration module 12 is used to receive the temperature, humidity and oxygen content obtained from the acquisition point, and to calibrate the acquired temperature, humidity and oxygen content according to the data relationship.

[0133] The storage and display module 13 is used to correct the calibrated oxygen content based on the calibrated temperature and humidity, and to visually store and display the calibrated oxygen content.

[0134] The accuracy calculation module 14 is used to obtain the storage location and storage status of the stored goods in real time, query the oxygen content at the corresponding time according to the storage location, and verify the accuracy of the oxygen content by the storage status.

[0135] Among them, the accuracy rate serves as the criterion for determining whether to update the calibration and correction processes.

[0136] Furthermore, the data relationship determination module 11 includes:

[0137] The data collection point setting unit is used to set data collection points based on the spatial coordinates input by the staff in the three-dimensional warehouse scene.

[0138] The ventilation parameter query unit is used to query the ventilation facilities in the building data and obtain the ventilation parameters of the ventilation facilities; the ventilation parameters include ventilation direction and ventilation rate.

[0139] The warehouse airflow analysis unit is used to input ventilation parameters and a 3D warehouse scene into ANSYS Fluent software to determine the airflow parameters within the warehouse; the airflow parameters include flow direction and flow velocity.

[0140] A data relationship determination unit is used to determine the data relationship between each collection point and its adjacent collection points based on airflow parameters within the warehouse; the data relationship is used to characterize the influence of adjacent collection points on each collection point.

[0141] The method for determining the data relationship is as follows:

[0142] In the formula, Δ represents the data relationship. It is a vector determined by airflow parameters. It is a spatial vector composed of two acquisition points.

[0143] Specifically, the data acquisition and calibration module 12 includes:

[0144] The data receiving unit is used to receive temperature, humidity, and oxygen content data containing time information from the collection point.

[0145] The data query unit is used to query the temperature, humidity, and oxygen content of adjacent sampling points within a 3*3 unit, centered on the sampling point.

[0146] The calibration execution unit is used to calibrate the temperature, humidity, and oxygen content of the central collection point based on the data relationship between each adjacent collection point;

[0147] The calibration process is as follows: In the formula, y represents the data before calibration, y′ represents the data after calibration, and Δ i f(Δ) represents the data relationship between the i-th acquisition point within a 3x3 cell and the central acquisition point; i ) is a predefined function that converts data relationships into weights. One type of function is to calculate the ratio of each data relationship to the sum of the absolute values ​​of the data relationships.

[0148] In addition, the storage display module 13 includes:

[0149] The correction unit is used to correct the calibrated oxygen content based on the calibrated temperature and humidity.

[0150] The color value determination unit is used to statistically analyze the oxygen content after calibration and convert the oxygen content into a color value; the conversion rule is jointly determined by the range of oxygen content and the range of color values.

[0151] The color value insertion unit is used to display the spatial coordinates of the color value insertion acquisition point in the 3D warehouse scene, store and display the 3D warehouse scene containing the color values.

[0152] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for detecting oxygen data, characterized in that, The method includes: Data collection points are set up in the warehouse, and the data relationship between each adjacent data collection point is determined based on the warehouse's building data; the data relationship is used to characterize how each data collection point is affected by its adjacent data collection points. The system receives temperature, humidity, and oxygen content data from the collection points and calibrates these data based on the data relationships. The calibration process involves calibrating the data from each collection point based on the data from its adjacent collection points. The oxygen content is corrected based on the calibrated temperature and humidity, and then the calibrated oxygen content is visualized, stored, and displayed. The correction step is equivalent to a secondary calibration step for the oxygen content, where the oxygen content is calibrated a second time based on temperature and humidity. The system acquires the storage location and status of stored goods in real time, queries the oxygen content at the corresponding time based on the storage location, verifies the accuracy of the oxygen content based on the storage status, and compares the actual oxygen content with the theoretical oxygen content to obtain the accuracy of the actual oxygen content. Among them, the accuracy rate serves as the criterion for determining whether to update the calibration and correction processes.

2. The oxygen data detection method according to claim 1, characterized in that, The steps of setting up data collection points in the warehouse and determining the data relationships between adjacent data collection points based on the warehouse's building data include: Query the warehouse's building data and create a 3D warehouse scene based on the building data; Based on the spatial coordinates input by the staff in the 3D warehouse scene, the data collection points are set. Query the ventilation facilities in the building data to obtain the ventilation parameters of the ventilation facilities; the ventilation parameters include ventilation direction and ventilation rate. Input the ventilation parameters and the 3D warehouse scene into ANSYS Fluent software to determine the airflow parameters within the warehouse; the airflow parameters include flow direction and flow velocity. The data relationship between each collection point and its adjacent collection points is determined based on the airflow parameters within the warehouse; the data relationship is used to characterize the influence of adjacent collection points on each collection point. The method for determining the data relationship is as follows: In the formula, For data relationships, It is a vector determined by airflow parameters. It is a spatial vector composed of two acquisition points.

3. The oxygen data detection method according to claim 2, characterized in that, The steps for calibrating the temperature, humidity, and oxygen content obtained from the receiving point based on data relationships include: The receiving point acquires temperature, humidity, and oxygen content information containing time data. Using the sampling point as the center, query the temperature, humidity and oxygen content of adjacent sampling points within a 3*3 unit; The temperature, humidity, and oxygen content of the central collection point are calibrated based on the data relationship between each adjacent collection point; The calibration process is as follows: In the formula, x represents the data before calibration. For the calibrated data, This represents the data relationship between the i-th acquisition point within a 3x3 unit and the central acquisition point. It is a pre-defined function that converts data relationships into weights. One type of function is to calculate the ratio of each data relationship to the sum of the absolute values ​​of the data relationships.

4. The oxygen data detection method according to claim 1, characterized in that, The step of visually storing and displaying the calibrated oxygen content based on the corrected temperature and humidity, and then correcting the calibrated oxygen content, includes: The oxygen content was corrected based on the calibrated temperature and humidity. The oxygen content after statistical calibration is converted into a display color value; the conversion rule is determined by the range of oxygen content and the range of display color values. The display color value will be inserted into the spatial coordinates of the acquisition point in the 3D warehouse scene, and the 3D warehouse scene containing the display color value will be stored and displayed.

5. The oxygen data detection method according to claim 4, characterized in that, The step of correcting the calibrated oxygen content based on the calibrated temperature and humidity includes: The collected data is sorted from smallest to largest to obtain a set of data; Remove the two maximum and two minimum values, and then calculate the average of the remaining N-4 values; Calculate the oxygen concentration based on the processed data; The final oxygen concentration value is: In the formula This is the compensation coefficient for temperature on oxygen sampling values. , The average value raised to the power of i. The voltage value output by the sensor is raised to the power of i. and These are the two endpoint values ​​of the pre-acquired temperature range; This is a pre-set correction factor.

6. The oxygen data detection method according to claim 1, characterized in that, The steps of acquiring the storage location and storage status of stored goods in real time, querying the oxygen content at the corresponding time based on the storage location, and verifying the accuracy of the oxygen content based on the storage status include: Real-time acquisition of the storage location and storage status of stored goods at various times; The stored state at each time step is input into the trained evaluation model to determine the theoretical oxygen content at each time step. In a 3D warehouse scene, query the actual oxygen content at each time point based on the storage location; Compare the theoretical oxygen content with the actual oxygen content, and verify the accuracy of the actual oxygen content based on the comparison results.

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