CN117308501A - Real-time monitoring system and method for refrigerator based on Internet of things - Google Patents

Abstract

The invention discloses a cold storage real-time monitoring system and method based on the Internet of Things, and relates to the technical field of cold storage monitoring. The system includes an environmental data collection module, a central control module, a cloud server processing module and a background management module. The environmental data collection module can be installed in the cold storage. Environmental monitoring equipment is installed at multiple key points to obtain environmental data values; the central control module receives the environmental data values collected from the environmental monitoring equipment, preprocesses the environmental data values to obtain environmental parameters; the technical points are: using the built-in Corresponding model, multi-level processing of each parameter, combined with environmental factors and fruit's own factors, generates and based on the cold storage storage early warning evaluation value Ypgs, can switch to different working modes, on the one hand, through regular data collection, it can reduce the overall system performance On the other hand, by changing the frequency of inspections and data collection, it helps to detect problems in time and take corresponding measures to protect the quality of the fruit.

Description

Cold storage real-time monitoring system and method based on Internet of Things

Technical field

The present invention relates to the technical field of cold storage monitoring, specifically a cold storage real-time monitoring system and method based on the Internet of Things.

Background technique

Cold storage monitoring refers to the real-time monitoring and recording of the environmental parameters of the cold storage to ensure that the temperature, humidity and other related parameters inside the cold storage are in good condition. The cold storage is used to store and preserve fresh refrigerated food, medicine and other perishable items. facilities, so monitoring the cold storage environment is very important. The content of cold storage monitoring usually includes: temperature, humidity and switch conditions in the cold storage. The purpose of cold storage monitoring is to ensure the stability and controllability of the internal environmental parameters of the cold storage to ensure that the stored items For quality and safety, through real-time monitoring, recording and alarm systems, problems can be discovered and dealt with in time to prevent items from being damaged or deteriorating.

The existing publication number is CN116538740A, and the Chinese invention patent titled a cold storage monitoring method and system based on the Internet of Things points out that: the temperature monitoring result of the cold storage is determined based on the first monitoring data of the first sensor; the first monitoring data is Temperature monitoring data of the cold storage within the first preset time period; if it is determined that the temperature abnormality of the cold storage is based on the temperature monitoring result, determine whether the temperature abnormality of the cold storage is artificially controlled based on the second monitoring data of the second sensor; if it is determined The temperature anomaly of the cold storage is manually controlled, and the safety monitoring result of the cold storage is determined based on the third monitoring data of the third sensor.

From the above documents and related existing technologies, it can be known that when monitoring freshly picked fruits in cold storage, it is necessary to ensure the stability of the environmental data. However, comprehensive monitoring of the environment cannot be achieved only by collecting temperature information. On the one hand, only considering environmental factors cannot ensure the good preservation and quality of freshly picked fruits in the cold storage. On the other hand, for the sensors used to collect relevant information, when setting up real-time data collection, it will increase the energy consumption of the system. , when setting up regular data collection, it may not be possible to detect fruit preservation problems in time, so data collection in a single mode has extremely poor practicality.

Contents of the invention

(1) Technical problems solved

In view of the shortcomings of the existing technology, the present invention provides a real-time monitoring system and method for cold storage based on the Internet of Things. It uses the corresponding model to perform multi-level processing on each parameter, combines environmental factors and the factors of the fruit itself, and generates and based on cold storage early warning The evaluation value Ypgs can switch between different working modes. On the one hand, regular data collection can reduce the energy consumption of the overall system. On the other hand, by changing the inspection frequency and data collection frequency, it can help to detect problems in time and take corresponding measures. To protect the quality of fruits and solve the problems raised in the background technology.

(2) Technical solutions

In order to achieve the above objectives, the present invention is achieved through the following technical solutions:

A real-time monitoring system for cold storage based on the Internet of Things, which includes:

The environmental data acquisition module obtains environmental data values by installing environmental monitoring equipment at multiple key points in the cold storage;

The central control module receives environmental data values collected from environmental monitoring equipment, preprocesses the environmental data values to obtain environmental parameters, and controls the environmental parameters within the parameter range required by the fruit;

The cloud server processing module uploads environmental parameters and environmental data values to the cloud server. The cloud server includes a sample construction unit, an information collection unit and an evaluation analysis unit;

In the sample construction unit, three fruits of the same type are manually selected and combined into parallel and spaced distributed samples. The fruit indicator information parameters of different types of fruits are obtained through the information collection unit, and the environment is obtained separately in the evaluation and analysis unit. parameters and fruit index information parameters, build two sets of data analysis models running in parallel, generate the comprehensive environmental assessment coefficient Pxs and fruit quality assessment coefficient Spx, and obtain the comprehensive environmental assessment coefficient Pxs by setting the judgment control sub-unit in the assessment analysis unit and fruit quality evaluation coefficient Spx, build a data processing center model, and generate cold storage storage early warning evaluation value Ypgs;

The background management module builds a preservation model to set the early warning thresholds Ypgz _ρ and Ypgz _ω , and Ypgz _ρ < Ypgz _ω . It compares the cold storage storage early warning evaluation value Ypgs with the early warning threshold Ypgz _ρ , and judges the degree of early warning based on the comparison results. to implement corresponding strategies based on the level of early warning.

Further, the locations of multiple key points are located at the centers of the four side walls of the cold storage, and the environmental monitoring equipment is embedded in the center of the corresponding side walls, and the environmental monitoring equipment at least includes: a temperature sensor, a humidity sensor and an oxygen concentration sensor. The environmental data values obtained through the environmental monitoring equipment are: at the same time node, the actual temperature Cr, the actual humidity Hr and the actual oxygen concentration Or in the corresponding areas of each side wall; the central control module and the environmental monitoring equipment adopt IoT technology communicates, and the IoT technology used includes Lora, WiFi or 4G/5G.

Furthermore, the central control module uses the built-in microcontroller when preprocessing the environmental data values, and the preprocessing steps are:

S101. Number the side walls of the cold storage in clockwise order, and establish data sets {Cr ₁ , Cr ₂ ,..., Cr _n }, {Hr ₁ , Hr ₂ ,..., Hr _n for the same type of data }, {Or ₁ , Or ₂ ,..., Or _n }, n is a positive integer, and n≤4;

S102. Use the built-in calculation program of the unit computer to obtain the average value in each data set of the same type, and convert the obtained average temperature Average humidity/> and average oxygen concentration/> as environmental parameters;

S103. Use the visual dynamic adjustment unit built into the central control module to display environmental parameters in real time through visual tools, and at the same time control related equipment in the cold storage. Related equipment at least includes temperature and humidity regulators and oxygen generators, as formulated in the rule engine. Specifications are controlled, and continuous insulation, humidification, and oxygen supply and ventilation operations are performed to ensure that the average temperature in the cold storage Keep at 0℃～4℃, average humidity/> Maintain at 70%RH~90%RH, average oxygen concentration/> Keep it at 1% to 5% to match the storage environment required for freshly picked fruits.

Further, an alarm subunit is configured in the visual dynamic adjustment unit. If a corresponding environmental parameter is detected to exceed the storage environment required for the fruit just after picking, the following operations will be performed:

S201. Adjust the power of the relevant equipment and operate according to the needs of the storage environment. If the range specified by the storage environment cannot be reached within the response time T, proceed to the next step;

S202. After the operation performed in S201 exceeds the response time T, an alarm operation is performed. The representation is displayed through the visual tool in the visual dynamic adjustment unit. The alarm lights of different colors flash on the visual tool to prompt the staff to respond. Perform maintenance on different related equipment.

Furthermore, the cloud server processing module adopts a cloud server. The data processed by the central control module is uploaded to the cloud server through the mqtt protocol, and the cloud server implements data processing and storage.

Further, the fruit index information obtained in the information collection unit at least includes: sugar content Td, hardness Yr and water content Hs, and the fruit index information parameters of different types of fruits are calculated, and the fruit index information parameters include: average sugar content. Average hardness/> and average moisture content/>

Furthermore, during the process of running the two sets of data analysis models, the formula based on generating the comprehensive environmental assessment coefficient Pxs is:

In the formula, the parameter meanings are: average temperature influence factor α, 0.38≤α≤0.97, average humidity influence factor β, 0.12≤β≤0.88, average oxygen concentration influence factor γ, 0.69≤γ≤1.21, H ₁ =1, and H ₁ is the correction coefficient;

The formula based on the fruit quality evaluation coefficient Spx is:

In the formula, the meaning of the parameters is: o ₁ , o ₂ , o ₃ are the average sugar content respectively. Average hardness/> and average moisture content/> The preset proportional coefficient of , and o ₁ , o ₂ , o ₃ are all greater than 0, H ₂ is the correction coefficient;

In the process of running the data processing center model, the formula based on generating the cold storage early warning evaluation value Ypgs is:

In the formula, the parameter meaning is: H ₃ is the correction coefficient.

Further, compare the cold storage storage early warning evaluation value Ypgs with the early warning threshold Ypgz _ρ ;

If the cold storage storage early warning evaluation value Ypgs < the early warning threshold Ypgz _ρ , it represents the level of early warning. The implementation strategy is: the environmental monitoring equipment wakes up regularly, works within a predetermined time period, and then enters a dormant state to perform periodic collection. data, and regularly check the quality of fruits outside the sample;

If the early warning threshold Ypgz _ρ ≤ cold storage storage early warning evaluation value Ypgs < the early warning threshold Ypgz _ω , it represents the level of secondary warning. The implementation strategy is: the environmental monitoring equipment wakes up regularly, reduces the predetermined time period, and increases the frequency of data collection. And simultaneously increase the frequency of random inspections of fruit quality outside the sample;

If the early warning threshold Ypgz _ω ≤ the cold storage storage early warning evaluation value Ypgs, it indicates the level of three-level early warning. The implementation strategy is: the environmental monitoring equipment wakes up regularly, records the predetermined time period as 0, performs real-time data collection operations, and doubles Increase the frequency of spot checks on the quality of fruits outside the sample.

The real-time monitoring method for cold storage based on the Internet of Things includes the following steps:

Step 1. Install environmental monitoring equipment at multiple key points in the cold storage to obtain environmental data values;

Step 2: Receive the environmental data values collected from the environmental monitoring equipment, preprocess the environmental data values to obtain the environmental parameters, and control the environmental parameters within the parameter range required by the fruit;

Step 3: Before performing the monitoring operation, select any three fruits of the same type and combine them into parallel and spaced distributed samples. Upload the environmental parameters and environmental data values to the cloud server, and obtain different types of fruits in the cloud server. The fruit index information parameters of fruits are obtained, and the environmental parameters and fruit index information parameters are obtained through model calculation and based on the comprehensive environmental assessment coefficient Pxs and fruit quality assessment coefficient Spx, the cold storage storage early warning assessment value Ypgs is obtained;

Step 4: Build a preservation model to set the early warning thresholds Ypgz _ρ and Ypgz _ω , and Ypgz _ρ < Ypgz _ω . Compare the cold storage storage early warning evaluation value Ypgs with the early warning threshold Ypgz _ρ . Based on the comparison results, determine the degree of early warning, and based on the early warning to implement corresponding strategies.

(3) Beneficial effects

The present invention provides a cold storage real-time monitoring system and method based on the Internet of Things, which has the following beneficial effects:

By designing the environmental data collection module and the central control module in the system, the environment in the cold storage can be obtained regularly, and preliminary regulation and alarm can be carried out to control and alarm the storage environment that does not meet the requirements of freshly picked fruits to ensure the stability of the environment in the cold storage. Then a cloud server processing module and a backend management module are added to monitor the fruit indicator information parameters of the samples, and the corresponding model is used to perform multi-level processing of each parameter. Combined with environmental factors and the fruit's own factors, the cold storage storage early warning evaluation value Ypgs is generated and based on , determine the degree of early warning and implement corresponding inspection strategies;

According to the size of the cold storage early warning evaluation value Ypgs, different working modes can be switched. On the one hand, regular data collection can reduce the energy consumption of the overall system to extend the life of related sensors. On the other hand, by changing the inspection frequency and data collection frequency , helps to detect problems in time and take corresponding measures to protect the quality of fruits. Specific strategies need to be determined according to different levels of early warning and actual conditions to ensure good preservation and quality of freshly picked fruits in cold storage.

Description of the drawings

Figure 1 is a modular block diagram of the cold storage real-time monitoring system based on the Internet of Things according to the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

Embodiment 1: Please refer to Figure 1. The present invention provides a cold storage real-time monitoring system based on the Internet of Things. The system includes:

The environmental data acquisition module obtains environmental data values by installing environmental monitoring equipment at multiple key points in the cold storage;

Multiple key points are located at the centers of the four side walls of the cold storage. Environmental monitoring equipment is embedded in the center of the corresponding side walls. The environmental monitoring equipment at least includes: temperature sensor, humidity sensor and oxygen concentration sensor. Through environmental monitoring The environmental data values regularly obtained by the equipment are: the actual temperature Cr, the actual humidity Hr and the actual oxygen concentration Or in the corresponding areas of each side wall at the same time node;

The central control module uses Internet of Things technology to communicate with the environmental monitoring equipment, and receives environmental data values collected from the environmental monitoring equipment. The Internet of Things technology used includes Lora, WiFi or 4G/5G. The central control module has a built-in microcontroller. Used to preprocess environmental data values. The preprocessing steps are:

S101. Number the side walls of the cold storage in clockwise order, and establish data sets {Cr ₁ , Cr ₂ ,..., Cr _n }, {Hr ₁ , Hr ₂ ,..., Hr _n for the same type of data }, {Or ₁ , Or ₂ ,..., Or _n }, n is a positive integer, and n≤4;

S102. Use the built-in calculation program of the unit computer to obtain the average value in each data set of the same type, and convert the obtained average temperature Average humidity/> and average oxygen concentration/> As an environmental parameter, the built-in calculation program is a simple average calculation program, which will not be described in detail here;

For example: average temperature

S103. Use the built-in visual dynamic adjustment unit of the central control module to display the environmental parameters in real time through the visual tool. At the same time, the temperature and humidity regulator and the oxygen generating fan in the cold storage are controlled according to the specifications set in the rule engine for continuous control. Thermal insulation, humidification and oxygen supply ventilation operations make the average temperature in the cold storage Keep between 0℃～4℃, average humidity/> Maintain at 70%RH~90%RH, average oxygen concentration/> Keep it between 1% and 5% to match the storage environment required for freshly picked fruits. The numerical range mentioned here is the parameter range actually required by the fruit, and the average temperature/> Keep between 0℃～4℃, average humidity/> Maintain at 70%RH~90%RH, average oxygen concentration/> Maintaining it between 1% and 5% is within the prescribed range of the storage environment;

The visual dynamic adjustment unit is also equipped with an alarm sub-unit. If the corresponding average temperature is monitored, After exceeding the specified range of 0℃~4℃, perform the following operations:

S201. Adjust the power of the temperature and humidity regulator, and perform rapid cooling or heating operations as needed (that is, in the direction of the specified range of 0°C to 4°C). If it still cannot reach 0°C to 0°C within the response time T. If the specified range is 4℃, proceed to the next step;

S202. After the operation performed in S201 exceeds the response time T, an alarm operation is performed, and the representation is displayed through the visualization tool in the visual dynamic adjustment unit;

For example: If the room temperature in the cold storage still does not reach the specified range of 0°C to 4°C after the response time, the red alarm light will flash on the visualization tool, indicating that the components used to control the temperature in the temperature and humidity regulator are damaged. Prompt the staff to carry out subsequent maintenance work; the visual tool strobes the orange alarm light, indicating that the components used to control humidity in the temperature and humidity regulator are damaged, prompting the staff to carry out subsequent maintenance work; the visual tool strobes the purple alarm light , it means that the oxygen-generating ventilator is damaged, prompting the staff to perform subsequent maintenance work; the oxygen-generating ventilator is a combination structure of adding an oxygen-generating pump to a traditional ventilator, thereby achieving the function of oxygen production + ventilation.

Specifically, when performing cold storage storage operations, real-time specific data of environmental parameters can be displayed on the visualization tool, and the visualization tool used uses an LCD display. The above-mentioned response time T can also be set as needed.

The cloud server processing module uploads the data processed by the central control module to the cloud server through the mqtt protocol. The cloud server includes a sample construction unit, an information collection unit and an evaluation analysis unit, and the cloud server implements data processing and storage;

Manually select three fruits of the same type in the sample assembly unit and combine them into parallel and spaced samples. Obtain the sugar content Td, hardness Yr and water content Hs of the three fruits of the same type through the information collection unit, and Calculate the fruit index information parameters of different types of fruits. The calculation process is the same as using the built-in calculation program of the unit computer to obtain the average value in each data set of the same type. Therefore, we will not go into details here. The fruit index information Parameters include: Average Brix Average hardness/> and average moisture content/> The environmental parameters and fruit indicator information parameters are obtained in the assessment and analysis unit respectively, and two sets of data analysis models are built in parallel operation to generate the comprehensive environmental assessment coefficient Pxs and the fruit quality assessment coefficient Spx, and by setting the decision control sub-unit in the assessment and analysis unit Unit is used to obtain the comprehensive environmental assessment coefficient Pxs and fruit quality assessment coefficient Spx, build a data processing center model, and generate the cold storage early warning assessment value Ypgs;

It should be noted that an infrared non-destructive sugar meter can be used in the information collection unit. Top Yunnong TPF-750 non-destructive sugar meter can be used to directly detect the fruit surface. Sugar content, color, total acidity, etc. can be measured within 4-6 seconds. Ripeness, moisture content, hardness and other related indicators, built-in GPS positioning system, can conduct non-contact measurement of freshly picked fruits;

There are many two sets of data analysis models running in parallel. The model used in this application is a parallel distributed data processing model. MapReduce is a parallel computing model proposed by Google. It divides the data into multiple small blocks for parallel processing. Process, and then merge the results to achieve large-scale data processing and analysis. It is suitable for processing large-scale data sets and has high scalability and fault tolerance; Apache Spark is a fast and versatile distributed computing system that supports multiple A variety of data processing tasks, including batch processing, interactive query, streaming processing and machine learning, which greatly speeds up data processing through memory computing and task scheduling optimization.

Among them, the formula based on generating the comprehensive environmental assessment coefficient Pxs is:

In the formula, the parameter meanings are: average temperature influence factor α, 0.38≤α≤0.97, average humidity influence factor β, 0.12≤β≤0.88, average oxygen concentration influence factor γ, 0.69≤γ≤1.21, H ₁ =1, and H ₁ is the correction coefficient;

The formula based on the fruit quality evaluation coefficient Spx is:

In the formula, the meaning of the parameters is: o ₁ , o ₂ , o ₃ are the average sugar content respectively. Average hardness/> and average moisture content/> The preset proportional coefficient of , and o ₁ , o ₂ , o ₃ are all greater than 0, H ₂ is the correction coefficient;

The formula based on which the cold storage storage early warning evaluation value Ypgs is generated in the judgment control subunit is:

In the formula, the parameter meaning is: H ₃ is the correction coefficient.

It should be noted that those skilled in the art collect multiple sets of sample data and set a corresponding preset proportion coefficient for each set of sample data; the set preset proportion coefficient can be an influence factor and the collected sample data into the formula , any three formulas constitute a system of linear equations of two variables. The calculated coefficients are filtered and averaged to obtain the values of α, β, and γ; the size of the coefficient is a specific value obtained by quantifying each parameter. To facilitate subsequent comparison, the size of the coefficient depends on the amount of sample data and the preliminary setting of the corresponding preset proportion coefficient for each set of sample data by those skilled in the art. It can also be said to be preset according to actual conditions. As long as it is not The proportional relationship between the influencing parameters and the quantized values suffices. The above explanations are also adopted for the influencing factors, preset proportional coefficients and constant correction coefficients described in other formulas.

The background management module builds a preservation model to set the early warning thresholds Ypgz _ρ and Ypgz _ω , and Ypgz _ρ < Ypgz _ω . Compare the cold storage storage early warning evaluation value Ypgs with the early warning threshold Ypgz _ρ . If the cold storage storage early warning evaluation value Ypgs < early warning threshold Ypgz When _ρ , it represents the first-level warning level, indicating that the cold storage environment and fruit quality are within the acceptable range. The strategy implemented in this case is: the environmental monitoring equipment wakes up regularly, works within a predetermined time period, and then enters a dormant state. , collect data periodically, and regularly check the quality of fruits outside the samples;

If the early warning threshold Ypgz _ρ ≤ cold storage storage early warning evaluation value Ypgs < early warning threshold Ypgz _ω , it indicates the level of secondary warning, indicating that there are minor problems with the cold storage environment or fruit quality. The strategy implemented in this case is: Environmental monitoring equipment Wake up regularly, reduce the predetermined time period, increase the frequency of data collection, and simultaneously increase the frequency of random inspections of fruit quality outside the sample;

If the early warning threshold Ypgz _ω ≤ the cold storage storage early warning evaluation value Ypgs, it indicates the level three warning level, indicating that there are serious problems with the cold storage environment or fruit quality. The strategy implemented in this case is: the environmental monitoring equipment wakes up regularly and the scheduled The time period is recorded as 0, real-time data collection operations are performed, and the frequency of random inspections of fruit quality outside the sample is doubled.

It should be noted that the specific strategy needs to be determined according to the different levels of warning and the actual situation. The purpose is to ensure the good preservation and quality of fruits in the cold storage. Frequent monitoring and maintenance can help to detect problems in time and take corresponding measures. to protect the quality of the fruit.

By adopting the above technical solutions:

The environmental data acquisition module and the central control module are designed in the system to regularly obtain the environment in the cold storage, and initially regulate and alarm the environment that does not meet the required storage environment of freshly picked fruits to ensure the stability of the environment in the cold storage. Add a cloud server processing module and a backend management module to monitor the fruit indicator information parameters of the samples. Use the corresponding model to perform multi-level processing of each parameter. Combined with environmental factors and the fruit's own factors, generate and based on the cold storage early warning evaluation value Ypgs, Determine the degree of warning and implement corresponding inspection strategies.

Embodiment 2: The present invention provides a cold storage real-time monitoring method based on the Internet of Things, including the following steps:

Step 1. Install environmental monitoring equipment at multiple key points in the cold storage to obtain environmental data values;

Step 2: Receive the environmental data values collected from the environmental monitoring equipment, preprocess the environmental data values to obtain the environmental parameters, and control the environmental parameters within the parameter range required by the fruit;

Step 3: Before performing the monitoring operation, select any three fruits of the same type and combine them into parallel and spaced distributed samples. Upload the environmental parameters and environmental data values to the cloud server, and obtain different types of fruits in the cloud server. The fruit index information parameters of fruits are obtained, and the environmental parameters and fruit index information parameters are obtained through model calculation and based on the comprehensive environmental assessment coefficient Pxs and fruit quality assessment coefficient Spx, the cold storage storage early warning assessment value Ypgs is obtained;

Step 4: Build a preservation model to set the early warning thresholds Ypgz _ρ and Ypgz _ω , and Ypgz _ρ < Ypgz _ω . Compare the cold storage storage early warning evaluation value Ypgs with the early warning threshold Ypgz _ρ . Based on the comparison results, determine the degree of early warning, and based on the early warning to implement corresponding strategies;

The above-mentioned switching of different working modes is based on the size of the cold storage early warning evaluation value Ypgs. On the one hand, regular data collection can reduce the energy consumption of the overall system to extend the life of related sensors. On the other hand, by changing the inspection frequency and data collection Frequency helps to detect problems in time and take corresponding measures to protect the quality of fruits. Specific strategies need to be determined according to the different levels of warning and actual conditions to ensure good preservation and quality of freshly picked fruits in cold storage.

The above formulas are all numerical calculations without dimensions. The formula is a formula obtained by collecting a large amount of data and conducting software simulation to obtain the latest real situation. The preset parameters in the formula are set by those skilled in the field according to the actual situation.

The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

The above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application. should be covered by the protection scope of this application.

Claims (10)

1. Freezer real-time monitoring system based on thing networking, its characterized in that: the system comprises:

the environment data acquisition module is used for acquiring environment data values by installing environment monitoring equipment at a plurality of key points in the refrigeration house;

the central control module is used for receiving the environmental data value acquired by the environmental monitoring equipment, preprocessing the environmental data value to obtain environmental parameters, and controlling the environmental parameters within a parameter interval range required by fruits;

the cloud server processing module is used for uploading the environment parameters and the environment data values to a cloud server, wherein the cloud server comprises a sample building unit, an information collecting unit and an evaluation analysis unit;

manually selecting three fruits of any type in a sample building unit, combining the three fruits into samples distributed at intervals in parallel, acquiring fruit index information parameters of the fruits of different types through an information collecting unit, respectively acquiring environment parameters and fruit index information parameters in an evaluation analysis unit, building two groups of data analysis models running in parallel, generating a comprehensive environment evaluation coefficient Pxs and a fruit quality evaluation coefficient Spx, setting a judgment control subunit in the evaluation analysis unit, acquiring the comprehensive environment evaluation coefficient Pxs and the fruit quality evaluation coefficient Spx, building a data processing center model, and generating a cold storage early warning evaluation value Ypgs;

background management module for setting up fresh-keeping model to set early warning threshold value Ypgz _ρ And Ypgz _ω And Ypgz _ρ ＜Ypgz _ω The cold storage is used for storing the early warning evaluation value Ypgs and the early warning threshold value Ypgz _ρ And comparing, judging the early warning degree according to the comparison result, and executing a corresponding strategy according to the early warning degree.

2. The internet of things-based refrigerator real-time monitoring system of claim 1, wherein: the positions of the key points are located at the centers of four side walls of the refrigerator, the environment monitoring equipment is assembled at the center of the corresponding side wall in an embedded mode, and the environment monitoring equipment at least comprises: temperature sensor, humidity transducer and oxygen concentration sensor, the environmental data value that obtains through environmental monitoring equipment is: at the same time node, the actual temperature Cr, the actual humidity Hr, and the actual oxygen concentration Or of the respective sidewall corresponding regions.

3. The internet of things-based refrigerator real-time monitoring system of claim 2, wherein: the central control module and the environment monitoring equipment are communicated by adopting the Internet of things technology, and the adopted Internet of things technology comprises Lora, wiFi or 4G/5G.

4. The internet of things-based refrigerator real-time monitoring system of claim 2, wherein: the central control module uses a built-in singlechip when preprocessing the environmental data value, and the preprocessing steps are as follows:

s101, numbering the side walls of the refrigeration house according to a clockwise sequence, and establishing a data set { Cr ] for the same type of data ₁ 、Cr ₂ 、...、Cr _n }、{Hr ₁ 、Hr ₂ 、...、Hr _n }、{Or ₁ 、Or ₂ 、...、Or _n N is a positive integer, and n is less than or equal to 4;

s102, acquiring average values in all data sets of the same type by using a built-in calculation program of the unit machine, and obtaining average temperaturesAverage humidity->Average oxygen concentration->As an environmental parameter;

s103, using a built-in visual dynamic regulation unit of the central control module to display environmental parameters in real time through visual tools, and regulating and controlling related equipment in the refrigeration house at the same time, wherein the related equipment at least comprises a temperature and humidity regulator and an oxygen-making ventilator, and performs regulation and control according to the specification formulated in a rule engine to perform continuous heat preservation, humidification and oxygen supply ventilation operation, so that the average temperature in the refrigeration house is enabledMaintaining at 0-4 deg.c and average humidity->Maintaining at 70% RH-90% RH, average oxygen concentration +.>Is kept at 1 to 5 percent to matchMatching with the preservation environment required by the fruits just picked.

5. The internet of things-based refrigerator real-time monitoring system of claim 4, wherein: an alarm subunit is configured in the visual dynamic adjusting unit, and if the corresponding certain environmental parameter is monitored to exceed the storage environment required by the fruits just picked, the following operation is performed:

s201, adjusting the power of related equipment, and operating according to the requirement of a storage environment, if the power of the related equipment cannot reach the range specified by the storage environment within the response time T, performing the next operation;

and S202, after the operation performed in S201 exceeds the response time T, performing alarm operation, and displaying the representation form through the visualization tools in the visualization dynamic adjusting unit, wherein the visualization tools are strobed with alarm lights with different colors so as to prompt staff to repair different related equipment.

6. The internet of things-based refrigerator real-time monitoring system of claim 1, wherein: the cloud server processing module adopts a cloud server, the data processed by the central control module is uploaded to the cloud server through an mqtt protocol, and the cloud server is used for processing and storing the data.

7. The internet of things-based refrigerator real-time monitoring system of claim 1, wherein: the fruit index information acquired in the information collecting unit at least comprises: sugar degree Td, hardness Yr and water content Hs to fruit index information parameter of fruit under the different grade type is obtained in the calculation, and fruit index information parameter includes: average sugar degreeAverage hardness->Average moisture content->

8. The internet of things-based refrigerator real-time monitoring system of claim 7, wherein: in the process of running the two groups of data analysis models, the formula according to which the comprehensive environment evaluation coefficient Pxs is generated is as follows:

in the formula, the meaning of parameters is as follows: an average temperature influence factor alpha, alpha is more than or equal to 0.38 and less than or equal to 0.97, an average humidity influence factor beta, beta is more than or equal to 0.12 and less than or equal to 0.88, an average oxygen concentration influence factor gamma, gamma is more than or equal to 0.69 and less than or equal to 1.21, H ₁ =1, and H ₁ Is a correction coefficient;

the fruit quality assessment coefficient Spx is based on the following formula:

in the formula, the meaning of parameters is as follows: o (o) ₁ 、o ₂ 、o ₃ Average sugar degreeAverage hardness->Average moisture content->And o is equal to the preset proportionality coefficient of ₁ 、o ₂ 、o ₃ Are all greater than 0, H ₂ Is a correction coefficient;

in the process of operating the data processing center model, a formula according to which the early warning evaluation value Ypgs is generated in the refrigeration house is as follows:

in the formula, the meaning of parameters is as follows: h ₃ Is a correction coefficient.

9. The internet of things-based refrigerator real-time monitoring system of claim 8, wherein: the cold storage is stored with the early warning evaluation value Ypgs and the early warning threshold value Ypgz _ρ Comparing;

if the pre-warning evaluation value Ypgs is less than the pre-warning threshold value Ypgz in the refrigeration house _ρ And when the method is used, the first-level early warning degree is indicated, and the executed strategy is as follows: the environment monitoring equipment wakes up regularly, works in a preset time period, then performs a dormant state, periodically collects data, and periodically checks the quality of fruits outside the spot check sample;

if yes, the early warning threshold value Ypgz _ρ The storage early warning evaluation value Ypgs is less than or equal to the early warning threshold value Ypgz of the refrigeration house _ω And when the method is used, the second-level early warning degree is indicated, and the executed strategy is as follows: the environment monitoring equipment wakes up regularly, reduces a preset time period, increases the frequency of data acquisition, and synchronously increases the frequency of fruit quality outside the spot check sample;

if yes, the early warning threshold value Ypgz _ω When the pre-warning evaluation value Ypgs is less than or equal to the storage threshold value, the pre-warning degree of three levels is represented, and the implemented strategy is as follows: the environment monitoring equipment wakes up regularly, marks the preset time period as 0, performs real-time data acquisition operation, and increases the frequency of fruit quality outside the spot check sample by times.

10. The method for monitoring the refrigerator in real time based on the Internet of things, which uses the system according to any one of claims 1 to 9, is characterized in that: the method comprises the following steps:

installing environment monitoring equipment at a plurality of key points in a refrigeration house to obtain an environment data value;

step two, receiving an environmental data value acquired from environmental monitoring equipment, preprocessing the environmental data value to obtain an environmental parameter, and controlling the environmental parameter in a parameter interval range required by fruits;

step three, selecting three fruits of any type before monitoring operation, combining the three fruits into samples distributed at intervals in parallel, uploading environmental parameters and environmental data values to a cloud server, acquiring fruit index information parameters of the fruits of different types in the cloud server, acquiring the environmental parameters and the fruit index information parameters, generating by model calculation and obtaining a cold storage early warning evaluation value Ypgs according to a comprehensive environment evaluation coefficient Pxs and a fruit quality evaluation coefficient Spx;

fourth, a fresh-keeping model is built to set an early warning threshold value Ypgz _ρ And Ypgz _ω And Ypgz _ρ ＜Ypgz _ω The cold storage is used for storing the early warning evaluation value Ypgs and the early warning threshold value Ypgz _ρ And comparing, judging the early warning degree according to the comparison result, and executing a corresponding strategy according to the early warning degree.