A method for collecting and processing large data of intensive baking rooms based on the Internet of Things

Abstract

The present application discloses a method for collecting and processing big data of intensive flue-curing barns based on the Internet of Things, aiming to solve the technical problem that the application scope of the Internet of Things for baking is narrow and it cannot play a role in baking work management, process execution analysis, baking process defects and suggestions, etc. On the basis of eliminating invalid data from the flue-curing barns, the present application conducts big data analysis on the execution of the baking process, and displays the baking cycle, baking habits, key temperature and humidity point control, baking time control and other information of each flue-curing barn or each area flue-curing barn, so that tobacco farmers and tobacco technicians can find problems in each flue-curing barn in real time, thereby changing bad baking habits, improving the baking effect of tobacco leaves, reducing the occurrence of bad tobacco leaves, and increasing the economic benefits of tobacco farmers.

Description

Technical Field

[0001] The present invention relates to the technical field of tobacco baking data processing, and specifically to a method for collecting and processing big data of intensive baking rooms based on the Internet of Things. Background Art

[0002] With the continuous development of Internet of Things (IoT) technology, the development of IoT tobacco curing barns is also accelerating. IoT tobacco curing barns use sensors, intelligent control systems, cloud computing, and other technologies to achieve real-time monitoring and control of environmental parameters within the tobacco curing barn, thereby improving tobacco curing quality and efficiency while reducing energy consumption and labor costs.

[0003] Currently, major tobacco companies at home and abroad, such as China National Tobacco Corporation, Japan Tobacco, and Philip Morris USA, have begun promoting the use of IoT in tobacco curing barns. These companies have installed a variety of sensors, including temperature and humidity sensors, oxygen sensors, carbon dioxide sensors, and flue gas sensors, in their tobacco curing barns. These companies use IoT platforms to collect and process data in real time, enabling automated control and optimization of the barns.

[0004] Currently, IoT applications in tobacco curing are primarily focused on warning of abnormal temperature and humidity conditions, but their role in curing management, process execution analysis, and the development of recommendations for curing process defects has yet to be fully realized. Furthermore, the development of IoT-enabled tobacco curing barns still faces challenges, such as the scientific and rational nature of data collection, the adequacy of data mining and utilization, and the reliability of data processing and analysis, which require further refinement and improvement.

[0005] The information disclosed in this background technology section is only used to deepen the understanding of the background technology of the present disclosure and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this application is to provide a method for collecting and processing intensive baking room baking big data based on the Internet of Things, aiming to solve the technical problems that the baking Internet of Things has a narrow application scope and cannot play a role in baking work management, process execution analysis, baking process defects and suggestions, etc.

[0007] According to one aspect of the present disclosure, a method for collecting and processing large data of intensive baking rooms based on the Internet of Things is provided, comprising the following steps:

[0008] (1) Collect basic information of each drying room:

[0009] Based on the corresponding barn settings, collect relevant information including barn location, barn number, user name, user contact number, and barn heating method (coal, biomass, heat pump, etc.);

[0010] (2) Collecting big data information:

[0011] After each baking room is in operation, based on the corresponding sensors or settings, relevant information including collection time, baking stage code, set temperature, set humidity, upper shed actual temperature, upper shed actual humidity, lower shed actual temperature, lower shed actual humidity, baking mode, and fire gear is recorded every 3 to 6 minutes;

[0012] (3) Extracting baking big data parameters:

[0013] ① Obtain a valid baking curve for a single baking cycle: When the dry-bulb temperature in the baking room rises to 29.8-30.2 degrees Celsius and the temperature is higher than 30 degrees Celsius for three consecutive times, the first temperature point collected is used as the starting point of the baking curve; when the dry-bulb temperature drops to 63.8-64.2 degrees Celsius and the temperature is lower than 64 degrees Celsius for three consecutive times, the first temperature point collected is used as the end point of the baking curve; obtain the temperature curve from the starting point to the end point, and eliminate invalid baking curves;

[0014] ② Baking habit extraction: The total time from the start to the end of a single baking session is recorded as the baking time of a single baking session; the number of valid baking curves in the same year is recorded as the baking times of that year; the total number of days from the start of the first baking session to the end of the last baking session in the same year is recorded as the baking cycle of that year;

[0015] ③ Time extraction for each baking stage: when the dry bulb temperature in the baking room rises to the temperature ranges of 35.8-36.2 degrees, 37.8-38.2 degrees, 41.8-42.2 degrees, 46.8-47.2 / 47.8-48.2 degrees, 52.8-53.2 / 53.8-54.2 degrees in sequence, extract the 36-degree stabilization time, the pre-38-degree stabilization time, the 38-degree stabilization time, the 42-degree stabilization time, the yellowing stage time, the 47 / 48-degree stabilization time, the 53 / 54-degree stabilization time, the color fixing stage time, and the reinforcement drying stage time respectively;

[0016] ④ Extraction of wet-bulb temperature at key baking temperature points: record the corresponding wet-bulb temperatures when the dry-bulb temperature of the lower shed is set to 38 degrees, 42 degrees, 47 / 48 degrees, 53 / 54 degrees, and 65 / 68 degrees. If there are 2 to 3 temperature points before and after the wet-bulb temperature is set, record all of them.

[0017] ⑤ Extraction of temperature and humidity differences at key baking temperature points: record the temperature and humidity differences at the 38°C steady temperature stage, 42°C steady temperature stage, and 44-48°C steady temperature stage respectively;

[0018] (4) Data processing: Collect relevant data information collected from various baking rooms through the Internet of Things protocol, and perform data analysis and / or data modeling based on the standard process algorithm of Internet of Things baking big data analysis;

[0019] Data flow:

[0020] ① The data of the previous day is obtained every day through the IoT interface of the baking device and stored locally.

[0021] ②After successfully storing the data, analyze whether the data meets the extraction conditions:

[0022] a. Since three consecutive records need to be compared, the analyzed data is the complete data of the previous day + the last two data points of the previous two days.

[0023] b. The current system requires extensive data analysis and judgment, resulting in repeated judgments of the same conditions across different baked curves. To improve system flexibility, it is necessary to decouple business rules from system code. Using a rule engine can separate complex, repetitive business rules from individual business systems, improving business logic reuse and development change efficiency. By accumulating and backtracking decision results, it can drive iterative optimization of rules, helping organizations build a continuously evolving knowledge base for business intelligence analysis. To ensure rapid development, Aviator was selected as the rule engine. Its underlying architecture reads expressions and dynamically compiles them into JVM bytecode in real time, achieving high performance, good decoupling, lightweight architecture, and rich function library support.

[0024] c. First, analyze the cycle of each complete curve by turning the lighter on and off. Next, extract the temperature stabilization points during heating and cooling. Determine whether the curve is heating or cooling by saving the highest temperature point. For heating, extract the curve start, 36°C stabilization time, pre-38°C stabilization time, 38°C stabilization time, 42°C stabilization time, yellowing phase, 47 / 48°C stabilization time, 53 / 54°C stabilization time, and the temperature and humidity differences between each phase. For cooling, extract the curve ending at 64°C. Finally, extract the humidity settings for each temperature control point at least five times. Record the corresponding wet-bulb temperatures for shed dry-bulb temperatures of 38°C, 42°C, 47 / 48°C, 53 / 54°C, and 65 / 68°C, respectively.

[0025] (5) Output and display the big data analysis results of tobacco leaf baking.

[0026] In some embodiments of the present disclosure, in step ①, a baking curve in which the time from the start point to the end point of a single baking is less than 100 hours or more than 240 hours is determined to be an invalid baking curve.

[0027] In some embodiments of the present disclosure, in step ③, when the dry-bulb temperature rises to 35.8-36.2 degrees, and the temperature collected three times in a row is higher than 36 degrees, the first collected temperature is taken as the starting point of the 36-degree temperature stabilization; when the dry-bulb temperature rises to above 36.5 degrees, and the temperature collected three times in a row is higher than 36.5 degrees, the first collected temperature is taken as the ending point of the 36-degree temperature stabilization; the period between the 36-degree temperature stabilization starting point and the 36-degree temperature stabilization ending point is recorded as the 36-degree temperature stabilization time; the 38-degree temperature stabilization time, 42-degree temperature stabilization time, 47 / 48-degree temperature stabilization time, and 53 / 54-degree temperature stabilization time are determined respectively by the same method; at the same time, the time between the starting temperature and the first time exceeding 38 degrees is determined as the temperature stabilization time before 38 degrees.

[0028] In some embodiments of the present disclosure, in step ③, the time difference between the starting point of the baking curve and the end point of the constant temperature at 42 degrees is determined as the yellowing stage time; the period between the end point of the constant temperature at 42 degrees and the end point of the constant temperature at 53 / 54 degrees is determined as the color fixing stage time; and the period between the end point of the constant temperature at 53 / 54 degrees and the end point of the curve is determined as the dry tendon stage time.

[0029] In some embodiments of the present disclosure, in step ⑤, the temperature difference is the difference between the actual temperature of the upper shed and the actual temperature of the lower shed; the humidity difference is the difference between the actual humidity of the upper shed and the actual humidity of the lower shed.

[0030] In some embodiments of the present disclosure, in step (4), based on the Java EE enterprise-level software development standard, using the Spring Boot microservice development architecture, using the MariaDB database to store the tobacco leaf baking Internet of Things database, an Internet of Things big data analysis software is established.

[0031] In some embodiments of the present disclosure, in step (5), the tobacco leaf baking big data analysis results are output and displayed to the tobacco farmers, tobacco technicians, and tobacco leaf production management end respectively: at the tobacco farmers' end, their baking habits, baking process execution status, and differences between the tobacco farmers' baking process and the standard baking process are displayed to the tobacco farmers; at the tobacco technicians' end, the baking habits, baking process execution status, and differences between the baking process implemented and the standard baking process of all the baking rooms in the area under their jurisdiction are displayed to the tobacco technicians; at the tobacco leaf production management end, the overall baking status of the baking rooms in the cities, counties, and towns under their jurisdiction can be displayed to the management, specifically including baking habits, baking process execution status, and differences between the baking process and the standard baking process.

[0032] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0033] 1. The data and information related to this application are collected in a comprehensive, scientific and reasonable manner, and can fully and effectively reflect and provide feedback on the quality control of flue-cured tobacco in the flue-curing barn.

[0034] 2. Through this application method, the results of intensive baking big data analysis can be displayed from the tobacco farmer side, tobacco technician side, and tobacco leaf production management side respectively: on the tobacco farmer side, the tobacco farmer's baking habits, baking process execution status, and the differences between the tobacco farmer's baking process and the standard baking process can be displayed to the tobacco farmer, guiding the tobacco farmer to continuously optimize the baking process and correct bad baking habits, thereby improving the economic benefits of baking. On the tobacco technician side, the baking habits, baking process execution status, and the differences between the baking process and the standard baking process of all the baking rooms in the area under their jurisdiction can be displayed to the tobacco technician, so that the tobacco technician can find problems in the baking process execution of the baking rooms in the jurisdiction, thereby facilitating the tobacco technician to carry out technical guidance and technical improvements, improve the baking effect of the baking rooms in the tobacco technician area, and reduce the occurrence of bad tobacco. On the tobacco production management side, the overall baking situation of the tobacco barns in the cities, counties, and towns under its jurisdiction can be displayed to the management, including baking habits, baking process execution, and differences between baking processes and standard baking processes. This allows the management to discover problems with tobacco baking in the production areas under its jurisdiction, thereby providing big data support for baking scheduling and baking decisions, helping the entire production area correct bad baking habits, reduce baking losses, and improve the intrinsic quality of the tobacco leaves after baking, thereby improving the economic benefits of tobacco cultivation. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is the 2022 baking data table for baking room equipment No. 009194221420 in one embodiment of this application.

[0036] Figure 2 This is a chart for determining the starting data of a 30-degree curve in one embodiment of the present application.

[0037] Figure 3 This is a chart for determining the starting data of a 36-degree stable temperature in one embodiment of the present application.

[0038] Figure 4 This is a data chart for determining the end of 36-degree temperature stabilization in one embodiment of the present application.

[0039] Figure 5 This is a chart showing the starting data determination for a 38-degree temperature stabilization start in one embodiment of the present application.

[0040] Figure 6 This is a data chart for determining the end of 38-degree temperature stabilization in one embodiment of the present application.

[0041] Figure 7 This is a chart showing the start and end data determination of a 42-degree temperature stabilization in one embodiment of the present application.

[0042] Figure 8 This is a chart showing the start and end data for determining a 47-degree temperature stabilization in one embodiment of the present application.

[0043] Figure 9This is a data chart for determining the start of a 53-degree temperature stabilization in one embodiment of the present application.

[0044] Figure 10 This is a data chart for determining the end of 53-degree temperature stabilization in one embodiment of the present application.

[0045] Figure 11 This is a chart for determining the end data of a 64-degree curve in one embodiment of the present application.

[0046] Figure 12 This is a second curve analysis chart in one embodiment of the present application.

[0047] Figure 13 This is a time chart of the temperature stabilization time, the yellowing stage, the color fixing stage, and the dry stage in one embodiment of the present application.

[0048] Figure 14 This is a chart showing the extraction results of wet-bulb temperatures at key baking points in an embodiment of the present application.

[0049] Figure 15 This is a baking habit and effectiveness determination chart of a baking data summary curve in one embodiment of the present application. DETAILED DESCRIPTION

[0050] Unless otherwise specified, the instruments and equipment involved in the following embodiments are all conventional instruments and equipment; the detection and testing methods involved are all conventional methods unless otherwise specified.

[0051] In order to better understand the technical solution of the present application, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0052] Example 1

[0053] In this case, based on the elimination of invalid data from the flue-curing rooms, a big data analysis of the execution of the baking process is conducted, and information such as the baking cycle, baking habits, key temperature and humidity point control, and baking time control of each flue-curing room or each area flue-curing room is displayed, so that tobacco farmers and technicians can discover problems in each flue-curing room in real time, thereby changing bad baking habits, improving the tobacco leaf baking effect, reducing the occurrence of bad tobacco leaves, and increasing the economic benefits of tobacco farmers.

[0054] The present invention mainly includes four parts: the IoT intensive baking house big data collection method, the big data analysis standard process algorithm, the big data analysis software implementation, and the big data analysis platform display:

[0055] (1) IoT baking room baking big data collection method

[0056] 1. Collection content

[0057] The IoT baking room data collection indicators include two aspects:

[0058] ①Basic information of the baking room: including the location of the baking room, the baking room number, the user's name, the user's contact number, and the baking room heating method (coal, biomass, heat pump, etc.).

[0059] ② Big data information collection content of the baking room: After the baking room automatic control instrument is put into operation, data is automatically recorded every 5 minutes, mainly recording the collection time, baking stage code, set temperature, set humidity, actual temperature of the upper shed, actual humidity of the upper shed, actual temperature of the lower shed, actual humidity of the lower shed, baking mode, fire power level, etc.

[0060] 2. Collection Methods

[0061] Through the Internet of Things protocol, the baking data of the Internet of Things baking room is collected to the industry intranet, and unified standards are implemented for the data collection of the winning manufacturers of the Internet of Things baking room, including the use of interfaces, interface addresses, data formats, call permission control, device data push interfaces, etc., to adapt and collect the baking big data of different manufacturers to facilitate subsequent data processing.

[0062] (2) IoT baking big data parameter extraction algorithm

[0063] 1. Extraction of effective baking curve for a single baking cycle

[0064] ① Single-bake start point extraction: When the dry-bulb temperature rises to around 30°C (with a fluctuation of 0.2°C), and the temperature is above 30°C for three consecutive times, the first time it exceeds 30°C is set as the curve starting point. If the burner gear is continuously at gear 0 during this stage, re-identification should be performed, as the air temperature may have exceeded 30°C in summer.

[0065] ② Curve endpoint extraction: When the dry-bulb temperature drops to around 64 degrees (with a fluctuation of 0.2 degrees), and the collected temperature is below 64 degrees for three consecutive times, the first time it is below 64 degrees is set as the curve endpoint.

[0066] ③ Invalid curve rejection: If the time from the start to the end of a single baking is less than 100 hours (or more than 240 hours), it is considered an invalid curve by default. (It may be in a drying room, drying rice, etc.)

[0067] 2. Baking Habit Extraction

[0068] ① Extraction of total baking time for a single baking: The total time from the start to the end of a single baking is used as the baking time for a single baking.

[0069] ② Annual baking times extraction: The number of valid baking curves in the same year is recorded as the baking times of that year.

[0070] ③ Extraction of baking cycle of the year: The total number of days from the starting time of the first baking to the end time of the last baking in the same year is recorded as the baking cycle of the year (days).

[0071] Here, the first, third and fifth baking sections of a single baking barn are defined as the lower leaf, middle leaf and upper leaf respectively.

[0072] The following data is based on the fact that each drying barn only extracted the lower leaves, middle leaves and upper leaves for three drying times each year.

[0073] 3. Time extraction for each baking stage

[0074] ① 36°C Stabilization Time Extraction: When the dry-bulb temperature rises to approximately 36°C (with a fluctuation of 0.2°C) and the sampled temperature exceeds 36°C for three consecutive times, the first time it exceeds 36°C is designated as the 36°C Stabilization Start Point. When the dry-bulb temperature rises to above 36.5°C and the sampled temperature exceeds 36.5°C for three consecutive times, the first time it exceeds 36.5°C is designated as the 36°C Stabilization End Point. The time difference between the 36°C Stabilization Start Point and the 36°C Stabilization End Point is recorded as the 36°C Stabilization Time (h).

[0075] ② Extraction of stable temperature time before 38 degrees: When the dry-bulb temperature rises to around 38 degrees (fluctuating by 0.2 degrees), and the temperature is higher than 38 degrees for three consecutive times, the time from the starting temperature to the first time exceeding 38 degrees is recorded as the stable temperature time before 38 degrees (h).

[0076] ③ 38°C Temperature Stabilization Time Extraction: When the dry-bulb temperature rises to approximately 38°C (with a fluctuation of 0.2°C) and the temperature is measured above 38°C for three consecutive times, the first temperature exceeding 38°C is designated as the 38°C Temperature Stabilization Start Point. When the dry-bulb temperature rises to above 38.5°C and the temperature is measured above 38.5°C for three consecutive times, the first temperature exceeding 38.5°C is designated as the 38°C Temperature Stabilization End Point. The time difference between the 38°C Temperature Stabilization Start Point and the 38°C Temperature Stabilization End Point is recorded as the 38°C Temperature Stabilization Time (h).

[0077] ④ 42°C Stabilization Time: When the dry-bulb temperature rises to approximately 42°C (with a fluctuation of 0.2°C) and the sampled temperature exceeds 42°C for three consecutive times, the first time exceeding 42°C is designated as the 42°C Stabilization Start Point. When the dry-bulb temperature rises to above 42.5°C and the sampled temperature exceeds 42.5°C for three consecutive times, the first time exceeding 42.5°C is designated as the 42°C Stabilization End Point. The time difference between the 42°C Stabilization Start Point and the 42°C Stabilization End Point is recorded as the 42°C Stabilization Time (h).

[0078] ⑤ Yellowing stage time extraction: The time difference between the starting point of the curve and the end point of the 42-degree steady temperature is recorded as the yellowing stage time (h).

[0079] ⑥ Extraction of 47 (48) degree steady temperature time: When the dry bulb temperature rises to about 47 (48) degrees (fluctuation of 0.2 degrees), and the temperature is higher than 47 (48) degrees for three consecutive times, the first time it exceeds 47 (48) degrees is set as the starting point of 47 (48) degree steady temperature. When the dry bulb temperature rises to above 47 (48) .5 degrees, and the temperature is higher than 47 (48) .5 degrees for three consecutive times, the first time it exceeds 47 (48) .5 degrees is set as the ending point of 47 (48) degree steady temperature. The time difference between the starting point of 47 (48) degree steady temperature and the ending point of 47 (48) degree steady temperature is recorded as 47 (48) degree steady temperature time (h).

[0080] ⑦ Extraction of 53 (54) degree steady temperature time: When the dry bulb temperature rises to about 53 (54) degrees (fluctuation of 0.2 degrees), and the temperature is higher than 53 (54) degrees for three consecutive times, the first time it exceeds 53 (54) degrees is set as the starting point of 53 (54) degree steady temperature. When the dry bulb temperature rises to above 53 (54) .5 degrees, and the temperature is higher than 53 (54) .5 degrees for three consecutive times, the first time it exceeds 53 (54) .5 degrees is set as the ending point of 53 (54) degree steady temperature. The time difference between the starting point of 53 (54) degree steady temperature and the ending point of 53 (54) degree steady temperature is recorded as 53 (54) degree steady temperature time (h).

[0081] ⑧ Color fixing stage time extraction: The time difference between the end point of the stable temperature at 42 degrees and the end point of the stable temperature at 53 (54) degrees is recorded as the color fixing stage time (h).

[0082] ⑨ Extraction of drying stage time: The time difference between the end point of 53 (54) degrees of stable temperature and the end point of the curve is recorded as the drying stage time (h).

[0083] 4. Extraction of wet-bulb temperature at key baking temperature points

[0084] ① 38 degrees wet-bulb temperature extraction: record the set wet-bulb temperature when the dry-bulb temperature of the shed is set to 38 degrees. If there are 2 to 3 temperature points before and after the set wet-bulb temperature, record all of them.

[0085] ② 42 degrees wet-bulb temperature extraction: record the set wet-bulb temperature when the dry-bulb temperature of the shed is set to 42 degrees. If there are 2-3 temperature points before and after the set wet-bulb temperature, record all of them.

[0086] ③ Extraction of 47 (48) degree wet-bulb temperature: record the set wet-bulb temperature when the dry-bulb temperature of the shed is set to 47 (48) degrees. If there are 2 to 3 temperature points before and after the set wet-bulb temperature, record all of them.

[0087] ④ Extraction of 53 (54) degrees wet-bulb temperature: record the set wet-bulb temperature when the dry-bulb temperature of the shed is set to 53 (54) degrees. If there are 2 to 3 temperature points before and after the set wet-bulb temperature, record all of them.

[0088] ⑤ 65 (68) degrees wet-bulb temperature extraction: record the set wet-bulb temperature when the dry-bulb temperature of the shed is set to 65 (68) degrees. If there are 2 to 3 temperature points before and after the set wet-bulb temperature, record all of them.

[0089] 5. Extraction of temperature and humidity difference at key baking temperature points

[0090] ① 38-degree temperature and humidity difference extraction: Record the temperature difference (the difference between the actual temperature of the upper shed and the actual temperature of the lower shed, and record the maximum difference) and humidity difference (the difference between the actual humidity of the upper shed and the actual humidity of the lower shed, and record the maximum difference) during the 38-degree stable temperature stage.

[0091] ② 42-degree temperature and humidity difference extraction: Record the temperature difference (the difference between the actual temperature of the upper shed and the actual temperature of the lower shed, and record the maximum difference) and humidity difference (the difference between the actual humidity of the upper shed and the actual humidity of the lower shed, and record the maximum difference) during the 42-degree stable temperature stage.

[0092] ③ Extraction of temperature and humidity difference between 46 and 48 degrees: Record the temperature difference (the difference between the actual temperature of the upper shed and the actual temperature of the lower shed, record the maximum difference) and humidity difference (the difference between the actual humidity of the upper shed and the actual humidity of the lower shed, record the maximum difference) during the stable temperature stage of 44 to 48 degrees.

[0093] (3) Implementation of IoT Baking Big Data Analysis Software

[0094] We developed IoT big data analytics software based on standard process algorithms for IoT tobacco baking big data analysis. This software is based on Java EE enterprise-level software development standards, utilizes a Spring Boot microservices architecture, and uses a MariaDB database to store the tobacco leaf baking IoT database. The software primarily includes data collection, analysis, modeling, and presentation capabilities.

[0095] (IV) Big Data Analysis Platform Display

[0096] Through the implementation of the above three aspects, relying on WeChat mini-programs or apps, the big data analysis results of tobacco leaf baking will be displayed from the tobacco farmer side, tobacco technician side, and tobacco leaf production management side respectively.

[0097] Example 2

[0098] For example, we'll analyze data from 2022 for a baking room with device number 009194221420. Create a baking room record by adding a new baking room and entering the device number. Then, use the interface to collect records for that baking room's device number. If data is returned, change the baking room number to active.

[0099] The data collected through the interface will be stored in the intranet database for analysis of the start of the curve, the temperature stabilization time of each stage, the temperature and humidity, and the difference (see Figures 1-12 ), and the subsequent baking times are processed in the same way. Then the extraction temperature stabilization time will affect the time of the yellowing stage, color setting stage and dry rib stage (see Figure 13 ), and extract the wet bulb temperature at the key baking point (see Figure 14 ), finally, after all the data are formed, the baking habits of the summary curve are summarized and the effectiveness is determined (see Figure 15 ).

[0100] Although some preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0101] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications of the present invention fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for collecting and processing large data of intensive baking rooms based on the Internet of Things, characterized in that: The steps include: (1) Collect basic information of each drying room: Based on the corresponding baking room settings, collect relevant information including baking room location, baking room number, user name, user contact number, and baking room heating method; (2) Collecting big data information: After each baking room is in operation, based on the corresponding sensors or settings, relevant information including collection time, baking stage code, set temperature, set humidity, upper shed actual temperature, upper shed actual humidity, lower shed actual temperature, lower shed actual humidity, baking mode, and fire gear is recorded every 3 to 6 minutes; (3) Extracting baking big data parameters: ① Obtain a valid baking curve for a single baking cycle: When the dry-bulb temperature in the baking room rises to 29.8-30.2°C and the temperature is >30°C for three consecutive times, the first temperature point collected is used as the starting point of the baking curve; when the dry-bulb temperature drops to 63.8-64.2°C and the temperature is below 64°C for three consecutive times, the first temperature point collected is used as the end point of the baking curve. Obtain the temperature curve from the starting point to the end point, and eliminate invalid baking curves. ② Baking habit extraction: The total time from the start to the end of a single baking session is recorded as the baking time of a single baking session; the number of valid baking curves in the same year is recorded as the baking session of that year; the total number of days from the start of the first baking session to the end of the last baking session in the same year is recorded as the baking cycle of that year; ③ Time extraction for each baking stage: When the dry bulb temperature in the baking room rises to 35.8-36.2 degrees, 37.8-38.2 degrees, 41.8-42.2 degrees, 46.8-47.2 / 47.8-48.2 degrees, 52.8-53.2 / 53.8-54.2 degrees, extract the 36-degree stabilization time, the pre-38-degree stabilization time, the 38-degree stabilization time, the 42-degree stabilization time, the yellowing stage time, the 47 / 48-degree stabilization time, the 53 / 54-degree stabilization time, the color fixing stage time, and the rib drying stage time respectively; ④ Extraction of wet-bulb temperature at key baking temperature points: Record the corresponding wet-bulb temperatures when the dry-bulb temperature of the lower shed is set to 38 degrees, 42 degrees, 47 / 48 degrees, 53 / 54 degrees, and 65 / 68 degrees. If there are 2 to 3 temperature points before and after the wet-bulb temperature is set, record all of them. ⑤ Extract the temperature and humidity differences at key baking temperature points: record the temperature and humidity differences at the 38°C steady temperature stage, 42°C steady temperature stage, and 44-48°C steady temperature stage respectively; (4) Data processing: The collected data from various tobacco curing rooms are collected through the Internet of Things protocol, and data analysis and / or data modeling are performed based on the standard process algorithm of Internet of Things tobacco curing big data analysis. Specifically, based on the Java EE enterprise-level software development standard, the Spring Boot microservice development architecture is used, and the MariaDB database is used to store the tobacco curing Internet of Things database, and Internet of Things big data analysis software is established. (5) Output and display the big data analysis results of tobacco leaf baking.

2. The method for collecting and processing large data of intensive baking rooms according to claim 1, characterized in that: In step ①, a baking curve in which the duration from the start point to the end point of a single baking is less than 100 hours or more than 240 hours is determined to be an invalid baking curve.

3. The method for collecting and processing large data of intensive baking rooms according to claim 1, characterized in that: In step ③, when the dry-bulb temperature rises to 35.8-36.2 degrees, and the temperature collected three times is higher than 36 degrees, the first collected temperature is used as the starting point of the 36-degree temperature stabilization; when the dry-bulb temperature rises to above 36.5 degrees, and the temperature collected three times is higher than 36.5 degrees, the first collected temperature is used as the ending point of the 36-degree temperature stabilization; The period between the 36-degree temperature stabilization start point and the 36-degree temperature stabilization end point is recorded as the 36-degree temperature stabilization time; Use the same method to determine the 38-degree stabilization time, 42-degree stabilization time, 47 / 48-degree stabilization time, and 53 / 54-degree stabilization time respectively; at the same time, the time between the starting temperature and the first time exceeding 38 degrees is determined as the stabilization time before 38 degrees.

4. The method for collecting and processing large data of intensive baking rooms according to claim 1, characterized in that: In step ③, the time difference between the starting point of the baking curve and the end point of the constant temperature of 42 degrees is determined as the yellowing stage time; the period between the end point of the constant temperature of 42 degrees and the end point of the constant temperature of 53 / 54 degrees is determined as the color fixing stage time; and the period between the end point of the constant temperature of 53 / 54 degrees and the end point of the curve is determined as the dry rib stage time.

5. The method for collecting and processing large data of intensive baking rooms according to claim 1, characterized in that: In step ⑤, the temperature difference is the difference between the actual temperature of the upper shed and the actual temperature of the lower shed; the humidity difference is the difference between the actual humidity of the upper shed and the actual humidity of the lower shed.

6. The method for collecting and processing large data of intensive baking rooms according to claim 1, characterized in that: In the step (5), the tobacco leaf baking big data analysis results are output and displayed to the tobacco farmers, tobacco technicians, and tobacco leaf production management end respectively: at the tobacco farmers' end, their baking habits, baking process execution status, and differences between the tobacco farmers' baking process and the standard baking process are displayed to the tobacco technicians; at the tobacco technicians' end, the baking habits, baking process execution status, and differences between the baking process and the standard baking process of all the baking houses in the area under their jurisdiction are displayed to the tobacco technicians; at the tobacco leaf production management end, the overall baking status of the baking houses in the cities, counties, and towns under their jurisdiction is displayed to the management, specifically including baking habits, baking process execution status, and differences between the baking process and the standard baking process.

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