Data transmission supervision system based on Internet of Things

Abstract

The invention discloses a data transmission supervision system based on the Internet of Things, and relates to the technical field of supervision systems, and the supervision method comprises the following steps: carrying out the preprocessing of the received related data through a central server, building a climate model after the comprehensive analysis of the related data, monitoring the internal environment climate of a greenhouse in real time through the climate model, and carrying out the real-time monitoring of the internal environment climate. And when the climate of the internal environment of the greenhouse is abnormal, the central server carries out climate regulation and control on the greenhouse based on the Internet of Things, if the climate in the greenhouse is still abnormal after regulation and control, an early warning signal is sent to greenhouse management personnel, all the greenhouses are analyzed based on a climate model, and a greenhouse management scheme is generated according to an analysis result. According to the invention, a plurality of related data of strawberry growth are comprehensively analyzed, and the greenhouse climate monitoring precision is effectively improved, so that the stability of the climate in the greenhouse is guaranteed, and the strawberry quality is improved.

Description

Technical Field

[0001] This invention relates to the field of regulatory system technology, and more specifically to a data transmission regulatory system based on the Internet of Things. Background Technology

[0002] Strawberries are a high-value fruit crop widely grown around the world. Due to their high environmental requirements, greenhouse cultivation has become a common practice to provide a more controlled and suitable growing environment. A greenhouse is a closed agricultural production environment, usually constructed of metal or plastic structures and covered with transparent materials such as glass or plastic film to form a sealed space. The purpose of greenhouses is to provide controlled climatic conditions to optimize the growing environment of strawberries and increase yield and quality.

[0003] In traditional agricultural production, greenhouses typically rely on human experience and regular inspections to judge climate conditions and environmental changes. However, this method has many limitations, such as high subjectivity, slow response, and inability to conduct comprehensive monitoring. To solve these problems, greenhouse climate monitoring and early warning systems have been developed.

[0004] The existing technology has the following shortcomings:

[0005] Existing early warning systems typically monitor individual parameters inside greenhouses by setting thresholds (e.g., monitoring the temperature inside the greenhouse using temperature sensors and issuing an early warning when the temperature is outside the set range). However, because strawberries are sensitive to the climate inside the greenhouse and many parameters affect their growth, when multiple parameters change simultaneously and the trend is unfavorable to strawberry growth, even if a single parameter does not exceed the set threshold, it will still affect the growth of strawberries and thus reduce their quality. Summary of the Invention

[0006] The purpose of this invention is to provide an Internet of Things-based data transmission monitoring system to address the shortcomings of the prior art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a data transmission monitoring system based on the Internet of Things, wherein the monitoring method includes the following steps:

[0008] S1: Based on the needs of strawberry growth monitoring, a monitoring terminal is installed inside the planting greenhouse;

[0009] S2: The monitoring terminal monitors relevant data about the growth of strawberries inside the greenhouse in real time. After the monitoring terminal collects the relevant data, the data is sent to the central server via wireless network.

[0010] S3: The central server preprocesses the received data and constructs a climate model after comprehensively analyzing the data.

[0011] S4: The climate inside the greenhouse is monitored in real time through a climate model. When the climate inside the greenhouse becomes abnormal, the central server uses the Internet of Things to regulate the climate of the greenhouse.

[0012] S5: If the climate inside the greenhouse is still abnormal after regulation, a warning signal will be sent to the greenhouse management personnel.

[0013] S6: Analyze all greenhouses based on climate models and generate greenhouse management plans based on the analysis results.

[0014] In a preferred embodiment, the monitoring terminal collects relevant data, including greenhouse data and environmental data. The greenhouse data includes the air pressure deviation value inside the greenhouse, and the environmental data includes the air condition abnormality fluctuation index and the soil pH value fluctuation amplitude.

[0015] In a preferred embodiment, the central server calculates a climate coefficient by removing dimensions from the internal air pressure deviation, air condition anomaly fluctuation index, and soil pH fluctuation amplitude. In the formula, kq h The air condition anomaly fluctuation index, tr h qy represents the amplitude of soil pH fluctuation. p The value represents the air pressure deviation inside the greenhouse. α, β, and γ are the proportional coefficients of the air condition abnormality fluctuation index, the soil pH fluctuation amplitude, and the air pressure deviation inside the greenhouse, respectively, and α, β, and γ are all greater than 0.

[0016] In a preferred embodiment, the logic for obtaining the internal air pressure deviation value of the greenhouse is as follows: the greenhouse is divided into several areas based on its coverage area, and a separate air pressure sensor is installed in each area. The air pressure sensor is used to collect the internal air pressure of the greenhouse. The formula for calculating the internal air pressure deviation value is: qy p =qy max -qy min In the formula, qy max The maximum air pressure value in all regions, qy min This represents the minimum air pressure value across all regions.

[0017] In a preferred embodiment, the formula for calculating the air state anomaly fluctuation index is: In the formula, i is the abnormal air condition number library, and i = {1, 2, 3, ..., n}; n is a positive integer greater than 0, kq i It represents the sum of the normalized values ​​for the i-th abnormal air state.

[0018] In a preferred embodiment, the logic for obtaining the soil pH fluctuation amplitude is as follows: the stable range of soil pH is labeled as pH.min ~PH max The real-time monitored soil pH value is labeled as PH. s When PH s >PH max At that time, the fluctuation amplitude of soil pH value tr h =|PH s -PH max |, when PH s <PH min At that time, the fluctuation amplitude of soil pH value tr h =|PH s -PH min |

[0019] In a preferred embodiment, step S3, constructing the climate model includes the following steps:

[0020] Obtain the climate coefficient qh x Set the warning threshold yz a The climate coefficient qh x With warning threshold yz a By making comparisons, the climate model can be established.

[0021] In a preferred embodiment, step S4, which involves real-time monitoring of the internal climate of the greenhouse using a climate model, includes the following steps:

[0022] S4.1: Analyze the climate inside the greenhouse using a climate model, when the climate coefficient qh x ≤Warning threshold yz a At that time, it was determined that the internal environment and climate of the greenhouse were normal and no management was required;

[0023] S4.2: Analyze the climate inside the greenhouse using a climate model, when the climate coefficient qh x >Early warning threshold yz a When an abnormal climate is detected inside the greenhouse, the central server uses the Internet of Things to regulate the greenhouse's climate.

[0024] In a preferred embodiment, step S6, generating a greenhouse management plan based on the analysis results, includes the following steps:

[0025] Of all the climate coefficients collected from the greenhouse, climate coefficient qh x ≤Warning threshold yz a The climate coefficient is labeled as X1, and the climate coefficient qh x >Early warning threshold yz aThe climate coefficient is labeled as X2. The management value GL is calculated using the formula: GL=X1 / (X1+X2). If the management value GL< the management threshold GY, the greenhouse needs to be maintained and managed in a timely manner. If the management value GL≥ the management threshold GY, the greenhouse is managed by regular maintenance.

[0026] In a preferred embodiment, the air condition anomaly fluctuation index includes temperature, humidity, light intensity, and carbon dioxide concentration. Substituting n=4 into the formula... In the middle, after calculation, kq h = kq1 + kq2 + kq3 + kq4, where kq1 is the temperature index, kq2 is the humidity index, kq3 is the light intensity index, and kq4 is the carbon dioxide concentration index.

[0027] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0028] 1. This invention preprocesses the received data through a central server, and constructs a climate model after comprehensive analysis of the data. The climate model monitors the internal climate of the greenhouse in real time. When the internal climate of the greenhouse becomes abnormal, the central server uses the Internet of Things to regulate the climate of the greenhouse. If the climate is still abnormal after regulation, an early warning signal is sent to the greenhouse manager. The climate model is used to analyze all greenhouses, and a greenhouse management plan is generated based on the analysis results. The climate model is combined with several relevant data on strawberry growth for comprehensive analysis, which effectively improves the monitoring accuracy of the greenhouse climate, thereby ensuring the stability of the internal climate of the greenhouse and improving the quality of strawberries.

[0029] 2. This invention collects relevant data through a monitoring terminal, including greenhouse data and environmental data. The greenhouse data includes the internal air pressure deviation value, and the environmental data includes the air condition anomaly fluctuation index and the soil pH fluctuation amplitude. After removing the dimensions of the internal air pressure deviation value, air condition anomaly fluctuation index, and soil pH fluctuation amplitude, a climate coefficient is calculated. By comprehensively analyzing multi-source data, the accuracy of greenhouse climate early warning is improved, thus ensuring the quality of agricultural product growth.

[0030] 3. After monitoring and issuing early warnings to the climate inside the greenhouse and generating a prediction model, this invention analyzes the usage status of the greenhouse based on the climate coefficient in the prediction model, thereby generating a maintenance and management method for the greenhouse, which is conducive to improving the efficiency of greenhouse maintenance and management. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0032] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] Example 1: Please refer to Figure 1 As shown in this embodiment, the data transmission monitoring system based on the Internet of Things includes the following monitoring methods:

[0035] Based on the needs of strawberry growth monitoring, monitoring terminals are set up inside the greenhouse. The monitoring terminals monitor relevant data on strawberry growth inside the greenhouse in real time. After the monitoring terminals collect relevant data, the data is sent to the central server via a wireless network, including Wi-Fi, LoRaWAN, and NB-IoT. The central server preprocesses the received data.

[0036] in:

[0037] 1) Wi-Fi (Wireless Local Area Network):

[0038] Application scenarios: Wi-Fi is widely used for local area network (LAN) connections in homes, businesses, and public places, typically for connecting computers, smartphones, tablets, and other devices;

[0039] Coverage: Typically provides high-speed data transmission within a limited area, such as within a home or business premises;

[0040] Data transfer speed: Wi-Fi networks provide high-speed data transfer, typically measured in megabits per second (Mbps), and can reach hundreds of Mbps or higher;

[0041] Power consumption: Wi-Fi typically requires relatively high power consumption and is suitable for devices with sufficient power supply;

[0042] Features: Suitable for scenarios requiring high-speed data transmission and connection of a large number of devices, but usually requires a stable power supply, so it is not suitable for IoT devices powered by low-power, long-life batteries.

[0043] 2) LoRaWAN (Local Low Energy Wide Area Network):

[0044] Application scenarios: LoRaWAN is suitable for remote monitoring and control, such as agriculture, smart cities, industrial automation, and environmental monitoring;

[0045] Coverage: LoRaWAN offers extensive coverage, enabling long-distance communication, with a maximum range of tens of kilometers;

[0046] Data transmission speed: LoRaWAN typically provides low-speed data transmission, with a maximum of several hundred bits per second (bps);

[0047] Power consumption: LoRaWAN is designed as a low-power network, suitable for battery-powered IoT devices, and can achieve a battery life of many years;

[0048] Features: Suitable for remote sensor monitoring, with high requirements for battery life and long-distance communication.

[0049] 3) NB-IoT (Narrowband Internet of Things):

[0050] Application scenarios: NB-IoT is a cellular Internet of Things (IoT) technology suitable for a wide range of IoT applications, including smart cities, smart homes, and industrial automation.

[0051] Coverage: NB-IoT is built on existing cellular networks, with wide coverage, covering both urban and rural areas;

[0052] Data transmission speed: NB-IoT offers relatively low data transmission speeds, ranging from hundreds of bits per second (bps) to hundreds of kilobits per second (kbps);

[0053] Power consumption: NB-IoT typically has relatively low power consumption, making it suitable for battery-powered IoT devices and enabling battery life of many years;

[0054] Features: NB-IoT is built on the existing cellular network, so it has good security and reliability and is suitable for a wide range of IoT applications.

[0055] In summary, this application monitors strawberry greenhouses and uses LoRaWAN (Low Power Wide Area Network) for remote monitoring and control.

[0056] Receive data packets from the monitoring end, perform basic data verification, such as checking the integrity of the data packets, the rationality of the timestamps, and the correctness of the data format, exclude any unqualified data, and if the data is encrypted or compressed, decrypt and decompress the data to restore the original data;

[0057] Detecting and removing noise or outliers that may be introduced by sensors or communication processes can be achieved using filtering techniques or statistical methods.

[0058] Unify the data from different sensors into the same data format and units for subsequent processing, such as converting temperature from Fahrenheit to Celsius;

[0059] The preprocessed data is stored in a database or data warehouse for future querying and analysis. Typically, timestamps are used to sort and index the data.

[0060] When necessary, aggregate the data to reduce the amount of data and generate summary information. For example, aggregate one hour's data into daily data. Ensure data backup to prevent data loss or corruption. Redundancy strategies can be implemented to ensure data availability.

[0061] Ensure data security during transmission and storage by employing appropriate encryption and access control measures, and provide API interfaces so that other applications can easily access and utilize this data.

[0062] After comprehensively analyzing relevant data, a climate model is constructed. The climate model is used to monitor the internal climate of the greenhouse in real time. When the internal climate of the greenhouse becomes abnormal, the central server uses the Internet of Things to regulate the climate of the greenhouse.

[0063] Once an anomaly is detected, the server will trigger an alert to notify relevant personnel, such as farmers or operators, which can be done via SMS, email, or app notification.

[0064] Based on the analysis results, the server can automatically control the equipment in the greenhouse to make adjustments. In some cases, the server can also provide remote operation options to allow operators to manually intervene in the system and make more complex adjustments.

[0065] The server records all control operations and changes and generates reports for subsequent analysis and review. It can also provide operators with feedback information on control operations.

[0066] Once environmental parameters return to normal range, the server can automatically stop the control measures to avoid unnecessary intervention.

[0067] If the climate inside the greenhouse is still abnormal after regulation, an early warning signal will be issued to the greenhouse management personnel. All greenhouses will be analyzed based on the climate model, and a greenhouse management plan will be generated based on the analysis results.

[0068] First, the central server checks whether the abnormal climate conditions inside the greenhouse still exist. If the abnormality persists, the server will trigger further alarms to notify the greenhouse management personnel.

[0069] The management plan includes:

[0070] 1) Adjust the operating parameters of the equipment inside the greenhouse, such as temperature, humidity, and light.

[0071] 2) Specific irrigation strategies are recommended to ensure adequate soil moisture.

[0072] 3) Recommend appropriate plant protection measures to combat pests and diseases.

[0073] 4) Provide recommendations for resource management, such as optimizing water and electricity usage.

[0074] 5) It is recommended to take emergency measures, such as sunshade and ventilation.

[0075] This application preprocesses the received data through a central server, and constructs a climate model after comprehensive analysis of the data. The climate model monitors the internal climate of the greenhouse in real time. When the internal climate of the greenhouse becomes abnormal, the central server uses the Internet of Things to regulate the climate of the greenhouse. If the climate is still abnormal after regulation, an early warning signal is sent to the greenhouse management personnel. Based on the climate model, all greenhouses are analyzed, and a greenhouse management plan is generated based on the analysis results. The climate model is combined with several relevant data on strawberry growth for comprehensive analysis, which effectively improves the monitoring accuracy of the greenhouse climate, thereby ensuring the stability of the internal climate of the greenhouse and improving the quality of strawberries.

[0076] In this embodiment, based on the needs of strawberry growth monitoring, the following steps are included in setting up a monitoring terminal inside the greenhouse:

[0077] A. Determine monitoring parameters: First, it is necessary to determine the parameters that need to be monitored. These parameters can be selected based on the growth characteristics and environmental requirements of strawberries.

[0078] B. Selecting a suitable sensor: Based on the determination of the monitoring parameters, select a suitable sensor for monitoring to ensure that the selected sensor has the characteristics of high accuracy, stability and adaptability to the greenhouse environment;

[0079] C. Sensor Placement: Place the selected sensors in appropriate locations within the greenhouse to obtain accurate monitoring data. The sensor locations should be able to fully reflect the environmental conditions in different areas of the greenhouse, such as placing sensors at different heights and distances from the plants.

[0080] D. Connecting sensors and data acquisition systems: Connecting sensors to a central server to acquire data collected by sensors in real time, via wireless connection, such as using wireless communication technology.

[0081] Example 2: After the monitoring terminal collects relevant data, the data is sent to the central server via a wireless network, including Wi-Fi, LoRaWAN, and NB-IoT. The central server preprocesses the received data and constructs a climate model after comprehensive analysis. The climate model is used to monitor the internal climate of the greenhouse in real time. When the internal climate of the greenhouse is abnormal, the central server uses the Internet of Things to regulate the climate of the greenhouse. If the climate is still abnormal after regulation, an early warning signal is sent to the greenhouse management personnel.

[0082] The monitoring terminal collected relevant data, including greenhouse data and environmental data. The greenhouse data included the air pressure deviation value inside the greenhouse, and the environmental data included the air condition abnormality fluctuation index and the soil pH value fluctuation amplitude.

[0083] After removing the dimensions from the internal air pressure deviation, air condition anomaly fluctuation index, and soil pH fluctuation amplitude, the climate coefficient is calculated by combining these factors. In the formula, kq h The air condition anomaly fluctuation index, tr h qy represents the amplitude of soil pH fluctuation. p The value represents the air pressure deviation inside the greenhouse. α, β, and γ are the proportional coefficients of the air condition abnormality fluctuation index, the soil pH fluctuation amplitude, and the air pressure deviation inside the greenhouse, respectively, and α, β, and γ are all greater than 0.

[0084] The logic for obtaining the internal air pressure deviation value of the greenhouse is as follows: Divide the greenhouse into several areas based on its coverage area, and set up an air pressure sensor for each area. The air pressure sensor is used to collect the internal air pressure of the greenhouse. The formula for calculating the internal air pressure deviation value is: qy p =qy max -qy min In the formula, qy max The maximum air pressure value in all regions, qy min The minimum air pressure value in all areas. When the air pressure deviation value inside the greenhouse continues to increase, it indicates that the airtightness of the greenhouse is gradually decreasing, and there is even a possibility of large-scale damage, affecting the stability of the climate inside the greenhouse.

[0085] The formula for calculating the air condition anomaly fluctuation index is: In the formula, i is the abnormal air condition number library, and i = {1, 2, 3, ..., n}; n is a positive integer greater than 0, kq i This represents the sum of the normalized values ​​for the i-th abnormal air state.

[0086] During the growth process, strawberries have high requirements for the stability of temperature, humidity, light intensity, and carbon dioxide concentration inside the greenhouse. There is a suitable range for the temperature, humidity, light intensity, and carbon dioxide concentration of strawberries. In order to achieve the effect of early warning in this application, this application prefers to reduce the suitable range of temperature, humidity, light intensity, and carbon dioxide concentration by 60% as the warning range.

[0087] Therefore, in this embodiment, After calculation kq h = kq1 + kq2 + kq3 + kq4, where kq1 is the temperature index, kq2 is the humidity index, kq3 is the light intensity index, and kq4 is the carbon dioxide concentration index.

[0088] in:

[0089] The optimal temperature range for strawberry growth is 15-25 degrees Celsius. When the real-time temperature in the greenhouse is below 15 degrees Celsius, the growth rate of strawberries slows down, delaying plant development and flower bud formation. Strawberry plants grow more slowly under low-temperature conditions and may take longer to reach the flowering and fruiting stages. When the real-time temperature in the greenhouse is above 25 degrees Celsius, flower buds wither, thus reducing yield and potentially causing the fruit to taste worse, the flesh to soften, or the fruit to become uneven, affecting the quality of the strawberries. Therefore, the warning temperature range for strawberry growth is reduced by 60% to 18-22 degrees Celsius. When the real-time monitored temperature inside the greenhouse is within the range of 18-22 degrees Celsius, the temperature index kq1 = 0; when the real-time monitored temperature inside the greenhouse is outside the range of 18-22 degrees Celsius, the temperature index kq1 = 1.

[0090] The optimal humidity range for strawberry growth is 60-80%. When the real-time humidity in the greenhouse is below 60%, the low humidity weakens the transpiration of strawberry leaves, reduces the rate of water evaporation, and affects the plant's water absorption and transport capacity. This leads to dry and damaged strawberry flower buds, causing them to fall off and reducing yield. Strawberry leaves may also become dry and brittle, with leaf tips drying out and edges curling. In severe cases, this can lead to wilting and death of the plant. When the real-time humidity in the greenhouse is above 80%, the high humidity environment easily promotes the growth and spread of pathogens, increasing the risk of strawberry diseases such as gray mold and anthracnose. This can also cause strawberry leaves to become too soft, leading to plant lodging and damage. Therefore, the warning humidity range for strawberry growth is reduced to 62-78% after narrowing the optimal humidity range to 60%. When the real-time monitored humidity inside the greenhouse is within the range of 62-78%, the humidity index kq2 = 0. When the real-time monitored humidity inside the greenhouse is outside the range of 62-78%, the temperature index kq2 = 1.

[0091] The optimal light intensity for strawberry growth is 10,000-20,000 lux. When the real-time light intensity in the greenhouse is below 10,000 lux, it affects strawberry photosynthesis, leading to slower plant growth, stunted plant height, reduced yield and fruit quality, poor flower bud differentiation, fewer flower buds, and impact on subsequent flowering and fruiting. It also affects the photosynthesis and sugar accumulation in the strawberry fruit, resulting in smaller and lower-quality fruit. When the real-time light intensity in the greenhouse exceeds 20,000 lux, it can cause scorching symptoms on the strawberry leaves, such as yellowing and scorched leaf tips. This is because excessive light can impair photosynthesis. Excessive light energy during the process, exceeding the plant's tolerance range, damages leaf tissue, inhibits the formation and development of strawberry flower buds, leading to a reduction in the number of flower buds and thus affecting yield. Therefore, the warning light intensity range, after reducing the suitable light intensity for strawberry growth from 10,000-20,000 lux by 60%, is 11,000-19,000 lux. When the real-time monitored light intensity inside the greenhouse is within 11,000-19,000 lux, the light intensity index kq3 = 0; when the real-time monitored light intensity inside the greenhouse is outside 11,000-19,000 lux, the light intensity index kq3 = 1.

[0092] The optimal carbon dioxide concentration range for strawberry growth is 400-800 ppm. When the real-time light intensity in the greenhouse is below 400 ppm, it limits the photosynthetic rate of the strawberry plants, affecting plant growth and nutrient accumulation, thus impacting growth rate and yield. Plants may grow slowly and have sparse foliage, leading to a decline in fruit quality, including color, taste, and sugar content. Conversely, when the real-time light intensity in the greenhouse exceeds 800 ppm, it inhibits the photosynthetic efficiency of the strawberry plants. Although photosynthesis requires carbon dioxide, excessively high concentrations can cause fruit stomata to close, limiting the entry of carbon dioxide into the plant. Carbon dioxide levels affect the rate of photosynthesis, leading to restricted growth of strawberry plants, shorter plant height, poor branch growth, and impacting yield and fruit quality. It also makes the plants more susceptible to diseases such as gray mold. Therefore, the suitable carbon dioxide concentration range for strawberry growth is reduced by 60% from 400-800 ppm to a warning carbon dioxide concentration range of 480-720 ppm. When the real-time monitored carbon dioxide concentration inside the greenhouse is within 480-720 ppm, the carbon dioxide concentration index kq4 = 0; when the real-time monitored carbon dioxide concentration inside the greenhouse is outside 480-720 ppm, the carbon dioxide concentration index kq4 = 1.

[0093] The logic for obtaining the soil pH fluctuation range is as follows: the stable range of soil pH is marked as pH. min ~PH max The real-time monitored soil pH value is labeled as PH. s When PH s >PH max At that time, the fluctuation amplitude of soil pH value tr h =|PH s -PH max |, when PH s <PH min At that time, the fluctuation amplitude of soil pH value tr h =|PH s -PH min The soil pH value is monitored online using a pH sensor, which is an electrochemical sensor that determines the soil pH value by measuring the concentration of hydrogen ions in the soil. The sensor typically consists of a glass electrode and a reference electrode. During measurement, the electrode is inserted into the soil, and the soil pH value is determined by the chemical reaction between the electrode and the soil. When the fluctuation range of the soil pH value is too large, it indicates that the real-time monitored soil pH value deviates too much from the stable range of soil pH value, thus affecting the normal growth of strawberries.

[0094] This application collects relevant data through a monitoring terminal, including greenhouse data and environmental data. The greenhouse data includes the internal air pressure deviation value, and the environmental data includes the air condition anomaly fluctuation index and the soil pH fluctuation amplitude. After removing the dimensions of the internal air pressure deviation value, air condition anomaly fluctuation index, and soil pH fluctuation amplitude, a climate coefficient is calculated. By comprehensively analyzing multi-source data, the accuracy of greenhouse climate early warning is improved, thereby ensuring the quality of agricultural product growth.

[0095] Example 3: The central server preprocesses the received data and constructs a climate model after comprehensive analysis. The climate model is used to monitor the internal climate of the greenhouse in real time. When the internal climate of the greenhouse is abnormal, the central server uses the Internet of Things to regulate the climate of the greenhouse. If the climate inside the greenhouse is still abnormal after regulation, an early warning signal is sent to the greenhouse management personnel.

[0096] Obtain the climate coefficient qh x Set the warning threshold yz a The climate coefficient qh x With warning threshold yz a By making comparisons, the climate model can be established.

[0097] The climate inside the greenhouse was analyzed using a climate model, and the climate coefficient qh was... x ≤Warning threshold yz a At that time, it was determined that the internal environment and climate of the greenhouse were normal and no management was required;

[0098] The climate inside the greenhouse was analyzed using a climate model, and the climate coefficient qh was... x >Early warning threshold yz a When an abnormal climate is detected inside the greenhouse, the central server uses the Internet of Things to regulate the greenhouse climate. The regulation methods include temperature control, humidity control, light intensity control, and carbon dioxide concentration control.

[0099] Temperature is regulated by temperature control equipment, which includes heating equipment such as electric heaters, gas heaters, or underfloor heating systems to increase the temperature inside the greenhouse; and cooling equipment such as fans, cooling towers, or cooling systems to decrease the temperature inside the greenhouse.

[0100] Humidity is regulated by humidity control equipment, which includes humidification equipment, such as humidifiers or spray systems, to increase the humidity inside the greenhouse; and dehumidification equipment, such as dehumidifiers or ventilation systems, to reduce the humidity inside the greenhouse.

[0101] Light intensity is controlled by light control equipment, which includes artificial lighting systems such as fluorescent lamps, LED lamps, or sodium lamps to provide sufficient artificial light.

[0102] Carbon dioxide concentration is regulated by carbon dioxide control equipment, which includes a CO2 supply system, such as CO2 cylinders and spraying devices, to increase the carbon dioxide concentration inside the greenhouse; and an exhaust system, such as exhaust fans, to circulate air outside the greenhouse to reduce the carbon dioxide concentration when it is too high.

[0103] If the climate inside the greenhouse remains abnormal after climate control measures are implemented, an early warning signal will be issued to the greenhouse management personnel. If the climate inside the greenhouse remains abnormal after climate control measures are implemented, it indicates that the climate regulation inside the greenhouse is out of control. At this time, the greenhouse may have excessive damage or other factors, and an early warning signal needs to be issued to the management personnel. After receiving the early warning signal, the management personnel will conduct an abnormality investigation of the greenhouse.

[0104] Example 4: Based on the climate model, all greenhouses are analyzed, and a greenhouse management plan is generated based on the analysis results. In this application, the climate inside the greenhouse is monitored every 30 minutes, which can not only ensure the monitoring quality, but also effectively avoid the problem of over-monitoring and increasing the system's computing burden.

[0105] In existing technologies, greenhouses typically require regular maintenance and management. However, this regular management approach can easily lead to problems not being addressed in a timely manner. Therefore, we propose the following solution:

[0106] At 18:00 every afternoon, among all the climate coefficients collected in the greenhouse, climate coefficient qh x ≤Warning threshold yz a The climate coefficient is labeled as X1, and the climate coefficient qh x >Early warning threshold yz a The climate coefficient is labeled as X2. The management value GL is calculated using the formula: GL=X 1 / (X1+X 2). If the management value GL< management threshold GY, the greenhouse needs to be maintained and managed in a timely manner. If the management value GL≥ management threshold GY, the greenhouse is managed by regular maintenance.

[0107] This application, after monitoring and issuing early warnings for the climate inside the greenhouse to generate a prediction model, analyzes the usage status of the greenhouse based on the climate coefficient in the prediction model, thereby generating a maintenance and management method for the greenhouse, which is conducive to improving the efficiency of greenhouse maintenance and management.

[0108] Example 5: The IoT-based climate monitoring and early warning system for greenhouses described in this example includes a data acquisition module, a preprocessing module, a model building module, a monitoring module, a control module, a judgment module, and a management module.

[0109] Data Acquisition Module: Collects real-time data related to strawberry growth inside the greenhouse. The acquisition module then transmits the data to the preprocessing module via a wireless network, which may include Wi-Fi, LoRaWAN, or NB-IoT.

[0110] Preprocessing module: preprocesses the received data and sends the preprocessed data to the model building module;

[0111] Model building module: After comprehensively analyzing relevant data, a climate model is built, and the climate model is sent to the monitoring module and the management module;

[0112] Monitoring module: It monitors the internal climate of the greenhouse in real time through a climate model. When the internal climate of the greenhouse becomes abnormal, it sends a control signal to the control module.

[0113] Control module: Uses IoT to regulate the climate of the greenhouse;

[0114] Judgment module: If the climate inside the greenhouse is still abnormal after adjustment, a warning signal will be sent to the greenhouse management personnel;

[0115] Management module: Analyzes all greenhouses based on climate models and generates greenhouse management plans based on the analysis results.

[0116] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0117] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0118] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A data transmission monitoring system based on the Internet of Things, characterized in that: The regulatory approach includes the following steps: S1: Based on the needs of strawberry growth monitoring, a monitoring terminal is installed inside the planting greenhouse; S2: The monitoring terminal monitors relevant data about the growth of strawberries inside the greenhouse in real time. After the monitoring terminal collects the relevant data, the data is sent to the central server via wireless network. S3: The central server preprocesses the received data and constructs a climate model after comprehensively analyzing the data. S4: The climate inside the greenhouse is monitored in real time through a climate model. When the climate inside the greenhouse becomes abnormal, the central server uses the Internet of Things to regulate the climate of the greenhouse. S5: If the climate inside the greenhouse is still abnormal after regulation, a warning signal will be sent to the greenhouse management personnel. S6: Analyze all greenhouses based on climate models and generate greenhouse management plans based on the analysis results.

2. The data transmission monitoring system based on the Internet of Things according to claim 1, characterized in that: The monitoring terminal collects relevant data, including greenhouse data and environmental data. The greenhouse data includes the air pressure deviation value inside the greenhouse, and the environmental data includes the air condition abnormality fluctuation index and the soil pH value fluctuation amplitude.

3. The data transmission monitoring system based on the Internet of Things according to claim 2, characterized in that: The central server removes the dimensions from the internal air pressure deviation, air condition anomaly fluctuation index, and soil pH fluctuation amplitude of the greenhouse, and then calculates the climate coefficient accordingly. In the formula, kq h The air condition anomaly fluctuation index, tr h qy represents the amplitude of soil pH fluctuation. p The value represents the air pressure deviation inside the greenhouse. α, β, and γ are the proportional coefficients of the air condition abnormality fluctuation index, the soil pH fluctuation amplitude, and the air pressure deviation inside the greenhouse, respectively, and α, β, and γ are all greater than 0.

4. The data transmission monitoring system based on the Internet of Things according to claim 3, characterized in that: The logic for obtaining the internal air pressure deviation value of the greenhouse is as follows: the greenhouse is divided into several areas based on its coverage area, and a separate air pressure sensor is installed in each area. The air pressure sensor is used to collect the internal air pressure of the greenhouse. The formula for calculating the internal air pressure deviation value is: qy p =qy max -qy min In the formula, qy max The maximum air pressure value in all regions, qy min This represents the minimum air pressure value across all regions.

5. The data transmission monitoring system based on the Internet of Things according to claim 4, characterized in that: The formula for calculating the abnormal air condition fluctuation index is as follows: In the formula, i is the abnormal air condition number library, and i = {1, 2, 3, ..., n}; n is a positive integer greater than 0, kq i It represents the sum of the normalized values ​​for the i-th abnormal air state.

6. The data transmission monitoring system based on the Internet of Things according to claim 5, characterized in that: The logic for obtaining the soil pH fluctuation amplitude is as follows: the stable range of soil pH is marked as pH. min ~PH max The real-time monitored soil pH value is labeled as PH. s When PH s >PH max At that time, the fluctuation amplitude of soil pH value tr h =|PH s -PH max |, when PH s <PH min At that time, the fluctuation amplitude of soil pH value tr h =|PH s -PH min | 7. The data transmission monitoring system based on the Internet of Things according to claim 3, characterized in that: Step S3, constructing the climate model includes the following steps: Obtain the climate coefficient qh x Set the warning threshold yz a The climate coefficient qh x With warning threshold yz a By making comparisons, the climate model can be established.

8. The data transmission monitoring system based on the Internet of Things according to claim 7, characterized in that: In step S4, real-time monitoring of the internal climate of the greenhouse using a climate model includes the following steps: S4.1: Analyze the climate inside the greenhouse using a climate model, when the climate coefficient qh x ≤Warning threshold yz a At that time, it was determined that the internal environment and climate of the greenhouse were normal and no management was required; S4.2: Analyze the climate inside the greenhouse using a climate model, when the climate coefficient qh x >Early warning threshold yz a When an abnormal climate is detected inside the greenhouse, the central server uses the Internet of Things to regulate the greenhouse's climate.

9. The data transmission monitoring system based on the Internet of Things according to claim 3, characterized in that: Step S6, generating a greenhouse management plan based on the analysis results, includes the following steps: Of all the climate coefficients collected from the greenhouse, climate coefficient qh x ≤Warning threshold yz a The climate coefficient is labeled as X1, and the climate coefficient qh x >Early warning threshold yz a The climate coefficient is labeled as X2. The management value GL is calculated using the formula: GL=X1 / (X1+X2). If the management value GL< the management threshold GY, the greenhouse needs to be maintained and managed in a timely manner. If the management value GL≥ the management threshold GY, the greenhouse is managed by regular maintenance.

10. The data transmission monitoring system based on the Internet of Things according to claim 5, characterized in that: The air condition anomaly fluctuation index includes temperature, humidity, light intensity, and carbon dioxide concentration. Substituting n=4 into the formula... In the middle, after calculation, kq h = kq1 + kq2 + kq3 + kq4, where kq1 is the temperature index, kq2 is the humidity index, kq3 is the light intensity index, and kq4 is the carbon dioxide concentration index.

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