A refrigerated container monitoring system and monitoring method thereof

Abstract

The present invention discloses a refrigerated container monitoring system and a monitoring method thereof, and relates to the technical field of refrigerated container monitoring, including: arranging a plurality of sensors inside the refrigerated container to collect environmental data of temperature and humidity; recording the collected environmental data and comparing them with standard environmental parameters required for cargo storage, and judging whether the current environmental conditions meet the storage requirements of the cargo through the changing trends of temperature and humidity; setting safety thresholds of various environmental parameters based on historical transportation data, and establishing abnormal warning rules; continuously monitoring the data of various sensors, and comparing the current environmental parameters with the previously set warning thresholds in real time, and issuing a warning prompt when a certain parameter exceeds the threshold; optimizing the working state of the refrigeration equipment according to the energy consumption during transportation; the present invention realizes comprehensive monitoring of the temperature and humidity in the refrigerated container through the reasonable arrangement of a plurality of sensors, and ensures the accuracy and real-time performance of data collection.

Description

Technical Field

[0001] The present invention relates to the technical field of refrigerated container monitoring, in particular to a refrigerated container monitoring system and a monitoring method thereof. Background Art

[0002] With the rapid development of the global cold chain logistics industry, refrigerated containers, as an important part of cold chain transportation, have gradually become key equipment to ensure that goods maintain stable environmental conditions during long-distance transportation. The initial development of refrigerated container technology mainly focused on the stability of the refrigeration system and the optimization of energy efficiency, aiming to ensure that the temperature inside the container can be maintained within the range required for cargo storage. However, with the increasing diversity of cargo types during transportation, especially the increasing demand for the transportation of fresh food, pharmaceutical products and other goods that are sensitive to temperature and humidity, refrigerated containers need to control not only the temperature, but also the humidity. To this end, modern refrigerated container monitoring systems have gradually introduced sensor technology to collect real-time temperature and humidity data inside the container, and strive to optimize the environmental conditions inside the container through data monitoring and analysis. However, although the existing refrigerated container monitoring system has made certain progress in temperature and humidity data collection and control, there are still many deficiencies in real-time data analysis, flexibility of early warning mechanisms and energy consumption optimization.

[0003] The main shortcomings of the existing technology are that the intelligence and dynamic adjustment capabilities of the monitoring system are relatively limited. First, although many systems can collect temperature and humidity data in real time through sensors, they lack intelligent analysis functions based on historical data, resulting in the inability to effectively predict possible abnormal temperature and humidity conditions when facing complex transportation environments. In this case, the system can often only issue passive warnings when the data exceeds the preset threshold, and cannot predict potential environmental change trends in advance. In addition, the existing refrigerated container monitoring system generally relies on the operator's experience for manual adjustment in terms of equipment energy consumption optimization, and lacks an energy consumption optimization solution based on the external environment, cargo type, and historical energy consumption data. This not only easily leads to excessive operation of refrigeration equipment and increases energy consumption costs, but also may cause the environment inside the container to be unable to maintain within the standard range required by the cargo due to human operational errors, thereby adversely affecting the quality of the cargo. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a refrigerated container monitoring system and a monitoring method thereof to solve the problem that the existing refrigerated container monitoring system generally relies on the operator's experience to perform manual adjustments in terms of equipment energy consumption optimization, and lacks an energy consumption optimization solution based on comprehensive considerations of multiple dimensions such as the external environment, cargo type, and historical energy consumption data.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a refrigerated container monitoring method, comprising:

[0008] Arrange a variety of sensors inside the refrigerated container to collect environmental data on temperature and humidity;

[0009] Record the collected environmental data and compare it with the standard environmental parameters required for cargo storage, and judge whether the current environmental conditions meet the storage requirements of the cargo by the changing trends of temperature and humidity;

[0010] Based on historical transportation data, set safety thresholds for each environmental parameter and establish abnormal warning rules;

[0011] Continuously monitor the data of each sensor, compare the current environmental parameters with the previously set warning thresholds in real time, and issue a warning when a parameter exceeds the threshold;

[0012] During transportation, the working status of the refrigeration equipment is optimized according to energy consumption.

[0013] As a preferred solution of the refrigerated container monitoring method of the present invention, wherein: a plurality of sensors are arranged inside the refrigerated container to collect environmental data of temperature and humidity, specifically,

[0014] Temperature sensors are placed at the four corners and the center of the container to collect temperature;

[0015] Humidity sensors are placed on the top and bottom of the container to collect humidity;

[0016] The comprehensive environmental monitoring value is calculated based on the environmental data collected by various sensors. The expression is:

[0017]

[0018] Where E(t) represents the comprehensive environmental parameter value at time t, T(t) and H(t) represent the temperature and humidity values ​​at time t, respectively. max Indicates the maximum temperature allowed during the storage of goods, H max Indicates the maximum humidity value allowed during storage of goods.

[0019] As a preferred solution of the refrigerated container monitoring method of the present invention, the collected environmental data is recorded and compared with the standard environmental parameters required for cargo storage, and the current environmental conditions are judged according to the change trend of temperature and humidity to determine whether they meet the storage requirements of the cargo. The specific steps are:

[0020] Compare the environmental data at each time t with the known cargo storage standards and calculate the current deviation value. The calculation formula for the deviation value is:

[0021]

[0022] Among them, Δ T (t) represents the temperature deviation, Δ H (t) represents the deviation value of humidity, T req Indicates the required storage temperature of the goods, H req Indicates the required humidity for storage of materials;

[0023] Based on the collected time series data, the changing trends of temperature and humidity are analyzed. The formula for the rate of change of temperature and humidity is:

[0024]

[0025] Among them, R T (t) represents the rate of change of temperature, R H (t) represents the rate of change of humidity, T(t-1) and H(t-1) represent the temperature and humidity at the previous moment, and Δt represents the time interval.

[0026] As a preferred solution of the refrigerated container monitoring method of the present invention, wherein: the setting of safety thresholds of various environmental parameters based on historical transportation data and the establishment of abnormal warning rules refer to:

[0027] Obtain historical transportation data through historical records and cargo quality inspection reports, and set environmental data security thresholds based on historical transportation data;

[0028] Select decision tree as the machine learning algorithm, take temperature and humidity as input, identify the environmental variables that have the greatest impact on cargo damage, and directly extract key environmental variables;

[0029] According to key environmental variables, set the security threshold of each environmental data;

[0030] Get the best storage environment conditions for goods based on historical transportation data, set warning thresholds, and issue warning signals immediately when any of the environmental data exceeds the corresponding warning thresholds;

[0031] At the same time, when the rate of change of temperature and humidity is large, an early warning will be issued to prevent the parameters from quickly exceeding the safety range.

[0032] As a preferred solution of the refrigerated container monitoring method of the present invention, the data of each sensor is continuously monitored, and the current environmental parameters are compared with the previously set warning threshold in real time. When a parameter exceeds the threshold, a warning prompt is issued. The specific steps are:

[0033] Combined with the deviation value, the rate of change of temperature and humidity, a comprehensive judgment of the surrounding environment is made, and the expression is:

[0034]

[0035] Where J(t) represents the comprehensive environmental judgment value at time t, and α represents the weight coefficient;

[0036] Operators can view the changing trends of various environmental parameters in real time through the screen interface and intervene according to the content of the warning;

[0037] According to the historical transportation data, the alarm threshold and weight coefficient α are continuously adjusted, and the warning threshold of each environmental parameter is dynamically optimized through regression analysis of historical data.

[0038] As a preferred solution of the refrigerated container monitoring method of the present invention, wherein: during the transportation process, the working state of the refrigeration equipment is optimized according to the energy consumption, and the specific steps are:

[0039] Install energy consumption sensors inside the container to collect energy consumption data of refrigeration equipment in real time;

[0040] Setting upper and lower limits for internal temperature and internal humidity;

[0041] Set an upper limit for energy consumption. When energy consumption exceeds this limit, an early warning will be triggered to indicate that energy consumption is too high.

[0042] According to the external temperature and humidity, internal temperature and humidity, historical energy consumption and current energy consumption, the optimal power adjustment coefficient is calculated. The expression is:

[0043]

[0044] Among them, P opt (t) represents the optimal power adjustment coefficient at time t, T int (t) represents the current temperature inside the container, H int (t) represents the current humidity inside the container, E power (t) represents the energy consumption data at the current moment, k1 and k2 are the adjustment coefficients of temperature and humidity;

[0045] When the outside temperature is lower than the required temperature T for cargo storage req , and P opt When (t)>1, reduce the power of the refrigeration equipment;

[0046] When the outside temperature is higher than the required temperature T for cargo storage req , and P opt When (t)<1, increase the power of the refrigeration equipment;

[0047] When the outside humidity is greater than or equal to the required humidity H for cargo storage req , and P opt When (t) deviates far from 1, increase the power of the dehumidification equipment to prevent moisture from entering the container and causing the goods to get damp; conversely, when the external humidity is lower than the required humidity H for cargo storage, req When the dehumidification equipment is used, the operator can reduce the working intensity of the dehumidification equipment to prevent the cargo from drying out due to excessive dehumidification.

[0048] In a second aspect, the present invention provides a refrigerated container monitoring system, including a collection module, an analysis module, an early warning detection module, an adjustment module and an optimization module.

[0049] The acquisition module is responsible for collecting the environmental data inside and outside the container in real time through the temperature sensors, humidity sensors, and energy consumption sensors arranged inside and outside the refrigerated container;

[0050] The analysis module is responsible for processing and analyzing the real-time data obtained from the environmental data acquisition module, calculating the comprehensive environmental parameter values ​​in the container, and comparing these data with the standard environmental parameters to determine whether the current environment meets the storage requirements of the goods;

[0051] The early warning detection module is responsible for monitoring the changes in environmental data in real time based on the safety thresholds set by historical transportation data, and issuing early warning signals of different levels when the parameters exceed the thresholds;

[0052] The adjustment module is responsible for continuously optimizing the system's early warning mechanism and threshold settings of environmental parameters through analysis of historical transportation data, to ensure that the system can adapt to the storage needs of different types of goods.

[0053] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the refrigerated container monitoring method as described in the first aspect of the present invention is implemented.

[0054] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the refrigerated container monitoring method as described in the first aspect of the present invention is implemented.

[0055] The beneficial effects of the present invention are as follows: the present invention realizes comprehensive monitoring of temperature and humidity in refrigerated containers through the reasonable arrangement of multiple sensors, ensures the accuracy and real-time nature of data collection, and by comparing the collected environmental data with the cargo storage requirements and combining the temperature and humidity change trend analysis, the system can accurately evaluate the environmental conditions and issue an early warning before an abnormality occurs, effectively reducing the risk of damage to cargo due to environmental discomfort, based on historical transportation data, using a decision tree algorithm to dynamically set the safety threshold of each environmental parameter, making the early warning mechanism more flexible and able to adapt to the storage requirements of different types of cargo, by continuously monitoring the data of each sensor, combined with the deviation value and the rate of change, a multi-level early warning mechanism is established to ensure timely response to environmental changes, when environmental conditions deviate from the standard, the operator can make manual adjustments according to the early warning prompts, and flexibly adjust the operating status of the refrigeration and dehumidification equipment according to the external environmental conditions, thereby optimizing energy consumption, by real-time collection of energy consumption data and combining the optimal power adjustment coefficient, the operator can scientifically adjust the equipment operation to ensure that the refrigerated container reduces energy consumption while maintaining a suitable environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0057] Figure 1 This is a flow chart of the refrigerated container monitoring method in Example 1.

[0058] Figure 2 This is a system diagram of the refrigerated container monitoring system in Example 1. DETAILED DESCRIPTION

[0059] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0060] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0061] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0062] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a refrigerated container monitoring method, comprising the following steps:

[0063] S1. Arrange multiple sensors inside the refrigerated container to collect environmental data on temperature and humidity;

[0064] Temperature sensors are placed at the four corners and the center of the container to collect the temperature. This arrangement is chosen because the temperature inside the refrigerated container may vary significantly at different locations, especially during transportation. The temperature sensors need to have high sensitivity to ensure that they can quickly respond to subtle changes in temperature.

[0065] Humidity sensors are placed at the top and bottom of the container to collect humidity. Humidity usually differs between the top and bottom of the container, and moisture is more likely to accumulate at the bottom of the container. Therefore, the placement of sensors requires special consideration to ensure that the difference in humidity between the top and bottom can be accurately captured.

[0066] The comprehensive environmental monitoring value is calculated based on the environmental data collected by various sensors. The expression is:

[0067]

[0068] Where E(t) represents the comprehensive environmental parameter value at time t, which is used to reflect the overall environmental status in the container. T(t) and H(t) represent the temperature and humidity values ​​at time t, respectively. max Indicates the maximum temperature allowed during the storage of goods, H max Indicates the maximum humidity value allowed during storage of goods;

[0069] The value range of E(t) is [0,1];

[0070] When E(t) = 0, it means that the container environment does not meet the storage requirements of the goods at all, and there is a serious risk;

[0071] When E(t) = 1, it means that the container environment fully meets the storage requirements of the goods and is in an ideal state.

[0072] S2. Record the collected environmental data and compare it with the standard environmental parameters required for cargo storage, and determine whether the current environmental conditions meet the cargo storage requirements based on the changing trends of temperature and humidity;

[0073] Compare the environmental data at each time t with the known cargo storage standards and calculate the current deviation value. The calculation formula for the deviation value is:

[0074]

[0075] Among them, Δ T (t) represents the temperature deviation, Δ H (t) represents the deviation value of humidity, T req Indicates the required storage temperature of the goods, H req Indicates the required humidity for storage of materials;

[0076] By calculating the relative deviation of temperature and humidity, the gap between the current environment and the standard requirements can be quantified, making it easier to judge the suitability of the current environmental conditions;

[0077] Δ in the deviation value calculation formula T (t) and Δ H (t) Providing a measurement standard, this method significantly improves the adaptability of the system. Compared with the existing technology, this method can provide real-time and quantitative information to operators by accurately calculating the difference between the current temperature and humidity and the standard parameters, which is convenient for judging whether the current environment is suitable. The innovation of this step is to use relative deviation values ​​to eliminate the differences in storage conditions of different goods, so that the system can uniformly handle the monitoring needs of multiple goods. Compared with the traditional absolute value monitoring method, this relative deviation calculation method is more universal and adaptable, which effectively solves the problem that different goods have different requirements for temperature and humidity, and improves the versatility of the system;

[0078] Based on the collected time series data, the changing trends of temperature and humidity are analyzed. The formula for the rate of change of temperature and humidity is:

[0079]

[0080] Among them, R T (t) represents the rate of change of temperature, R H (t) represents the rate of change of humidity, T(t-1) and H(t-1) represent the temperature and humidity at the previous moment, and Δt represents the time interval, which can be set according to needs;

[0081] The introduction of the temperature change rate and humidity change rate is extremely creative. Existing technologies often only rely on environmental parameters at a single point in time for judgment and are unable to predict the trend of environmental changes in advance. By analyzing the temperature and humidity change rates, it is possible not only to determine whether the current environment deviates from the standard, but also to predict future environmental change trends. This trend judgment is particularly important for the dynamic environmental control of refrigerated containers. When environmental conditions begin to change, an early warning can be issued based on the change rate, and operators can take measures before problems occur to avoid further deterioration of the environment. This feature can significantly reduce the risk of cargo damage during transportation, especially when external environmental conditions fluctuate drastically.

[0082] S3. Based on historical transportation data, set safety thresholds for each environmental parameter and establish abnormal warning rules;

[0083] Obtain historical transportation data through historical records and cargo quality inspection reports, and set reasonable environmental data security thresholds based on historical transportation data;

[0084] The invention analyzes historical transportation data, extracts environmental variables related to cargo damage and sets safety thresholds, which can effectively improve the intelligence level of the system. Unlike the prior art that relies on operator experience to set fixed thresholds, this method is directly based on actual data to ensure the scientificity and adaptability of the thresholds. This data-driven setting method can solve the problem of cargo damage caused by inaccurate human settings.

[0085] Select decision tree as the machine learning algorithm, take temperature and humidity as input, identify the environmental variables that have the greatest impact on cargo damage, and directly extract key environmental variables;

[0086] In this step, the decision tree was selected as the machine learning algorithm to identify the key environmental variables that are most sensitive to cargo damage. This design is not common in the existing technology. The decision tree can automatically analyze a large amount of historical data, extract the factors that have the greatest impact on cargo, and automatically set thresholds, avoiding the subjectivity and uncertainty of manually setting thresholds. The introduction of this algorithm has greatly improved the intelligence and automation level of the system, especially in dealing with complex, multi-variable environmental monitoring problems.

[0087] According to key environmental variables, set the security threshold of each environmental data;

[0088] Get the best storage environment conditions for goods based on historical transportation data, set warning thresholds, and issue warning signals immediately when any of the environmental data exceeds the corresponding warning thresholds;

[0089] At the same time, when the rate of change of temperature and humidity is large, an early warning is issued to prevent the parameters from rapidly exceeding the safety range;

[0090] The present invention not only relies on current environmental data but also combines the change rates of temperature and humidity. When the change rates of temperature and humidity are large, the system will issue an early warning in advance. This design significantly improves the sensitivity of the early warning, enabling preventive intervention measures to be taken before the environmental conditions deteriorate rapidly, thus preventing damage to the goods. Compared with traditional early warning systems that rely on static thresholds, this innovation can actively respond to dynamic environmental changes, especially during long-distance transportation or when the external environment fluctuates violently, providing more effective protection.

[0091] S4. Continuously monitor the data of each sensor, compare the current environmental parameters with the previously set warning thresholds in real time, and issue a warning prompt when a certain parameter exceeds the threshold.

[0092] Combine the deviation value, and the change rates of temperature and humidity to make a comprehensive judgment on the surrounding environment. The expression is:

[0093]

[0094] Among them, J(t) represents the comprehensive environmental judgment value at time t, and α represents the weight coefficient used to adjust the relative importance of the deviation and the change rate.

[0095] The value range of the comprehensive environmental judgment value J(t) is from 0 to positive infinity. Set the thresholds T1 and T2.

[0096] When J(t) ≤ T1, it indicates that the environmental conditions are good and no alarm will be issued.

[0097] When T1 < J(t) ≤ T2, it belongs to the first-level early warning, indicating that the environmental conditions deviate from the standard but temporary intervention is not required, and a warning reminder will be issued.

[0098] When J(t) > T2, it belongs to the second-level early warning, indicating that the environmental conditions seriously deviate from the standard and the trend is deteriorating, and an alarm will be issued to prompt the operator to handle it immediately.

[0099] The operator can view the change trends of various environmental parameters in real time through the screen interface and intervene according to the content of the early warning.

[0100] The operator's intervention measures include manually reducing the power of the refrigeration equipment to reduce energy consumption when the external environmental temperature is low; increasing the refrigeration power when the external environmental temperature is high to maintain the environmental stability inside the container.

[0101] When the external environment is stable or at night, the operator will switch the equipment to the energy-saving mode to reduce power consumption and ensure energy consumption optimization on the premise of ensuring the safety of the goods.

[0102] According to the historical transportation data, the alarm threshold and weight coefficient α are continuously adjusted. Through the regression analysis of historical data, the warning threshold of each environmental parameter is dynamically optimized, including:

[0103] The system automatically analyzes the alarm situations that occurred during the past transportation process and, based on the quality inspection report of the goods, determines which parameter thresholds need to be adjusted;

[0104] The value of α is automatically adjusted using the decision tree in the machine learning algorithm to ensure that the impact of the environmental change rate on the alarm system is optimal. This dynamic adjustment enables the system to adapt to the storage needs of different types of goods.

[0105] S5. During transportation, optimize the working state of refrigeration equipment according to energy consumption;

[0106] Install energy consumption sensors inside the container to collect energy consumption data of refrigeration equipment in real time;

[0107] Set the upper and lower limits of the internal temperature to determine whether the current temperature exceeds the standard;

[0108] Set upper and lower limits for internal humidity to ensure humidity is maintained within the range required by the cargo;

[0109] Set an upper limit for energy consumption. When energy consumption exceeds this limit, an early warning will be triggered to indicate that energy consumption is too high.

[0110] Through the real-time monitoring system, operators can compare the current environmental data and equipment energy consumption with the set thresholds at any time. When a parameter exceeds the threshold, the system will issue an early warning prompt and the operator needs to intervene in time;

[0111] According to the external temperature and humidity, internal temperature and humidity, historical energy consumption and current energy consumption, the optimal power adjustment coefficient is calculated. The expression is:

[0112]

[0113] Among them, P opt (t) represents the optimal power adjustment coefficient at time t, T int (t) represents the current temperature inside the container, H int (t) represents the current humidity inside the container, E power (t) represents the energy consumption data at the current moment, k1 and k2 are the adjustment coefficients of temperature and humidity, which are used to weigh the relative influence of temperature and humidity in energy consumption optimization;

[0114] By calculating the optimal power adjustment coefficient, the system can dynamically adjust the equipment power according to the difference between the current internal temperature and humidity and the external environment. This formula takes into account multiple variables (such as internal temperature and humidity, external temperature and humidity, and energy consumption) to ensure that the operating power of the equipment under different environmental conditions is always close to the optimal level;

[0115] When the external environment temperature is lower than the required temperature T for cargo storage req , and P opt When (t)>1, the operator should manually reduce the power of the refrigeration equipment to reduce unnecessary energy consumption;

[0116] When the external environment temperature is higher than the required temperature T for cargo storage req , and P opt When (t)<1, the operator should increase the power of the refrigeration equipment to ensure that the temperature inside the container is maintained within the standard range, especially in hot areas or during daytime transportation;

[0117] When the external humidity is greater than or equal to the required humidity H for cargo storage req , and P opt (t) When it deviates far from 1, the operator should increase the power of the dehumidification equipment to prevent moisture from entering the container and causing the goods to get damp; conversely, when the external humidity is lower than the required humidity H for cargo storage, req When the dehumidification equipment is used, the operator can reduce the working intensity of the dehumidification equipment to prevent the cargo from drying out due to excessive dehumidification.

[0118] This embodiment also provides a refrigerated container monitoring system, including: a collection module, an analysis module, an early warning detection module, an adjustment module and an optimization module.

[0119] The collection module is responsible for collecting real-time environmental data inside and outside the refrigerated container through temperature sensors, humidity sensors, and energy consumption sensors arranged inside and outside the refrigerated container;

[0120] The analysis module is responsible for processing and analyzing the real-time data obtained from the environmental data acquisition module, calculating the comprehensive environmental parameter values ​​in the container, and comparing these data with the standard environmental parameters to determine whether the current environment meets the storage requirements of the goods;

[0121] The early warning detection module is responsible for setting safety thresholds based on historical transportation data, monitoring changes in environmental data in real time, and issuing different levels of early warning signals when parameters exceed the thresholds;

[0122] The adjustment module is responsible for continuously optimizing the system's early warning mechanism and the threshold setting of environmental parameters through analysis of historical transportation data to ensure that the system can adapt to the storage needs of different types of goods.

[0123] This embodiment also provides a computer device, which is applicable to the refrigerated container monitoring method, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to implement the refrigerated container monitoring method proposed in the above embodiment.

[0124] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0125] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the refrigerated container monitoring method proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0126] In summary, the present invention realizes comprehensive monitoring of temperature and humidity in refrigerated containers through: reasonable arrangement of multiple sensors, ensuring the accuracy and real-time nature of data collection; by comparing the collected environmental data with the cargo storage requirements, and combining the temperature and humidity change trend analysis, the system can accurately evaluate the environmental conditions and issue an early warning before an abnormality occurs, effectively reducing the risk of damage to cargo due to environmental discomfort; based on historical transportation data, the decision tree algorithm is used to dynamically set the safety threshold of each environmental parameter, making the early warning mechanism more flexible and able to adapt to the storage requirements of different types of cargo; by continuously monitoring the data of each sensor, combined with the deviation value and the rate of change, a multi-level early warning mechanism is established to ensure timely response to environmental changes; when environmental conditions deviate from the standard, the operator can make manual adjustments according to the early warning prompts, and flexibly adjust the operating status of the refrigeration and dehumidification equipment according to the external environmental conditions, thereby optimizing energy consumption; by real-time collection of energy consumption data and combining the optimal power adjustment coefficient, the operator can scientifically adjust the equipment operation to ensure that the refrigerated container reduces energy consumption while maintaining a suitable environment.

[0127] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A refrigerated container monitoring method, characterized in that: include, Arrange a variety of sensors inside the refrigerated container to collect environmental data on temperature and humidity; Record the collected environmental data and compare it with the standard environmental parameters required for cargo storage, and judge whether the current environmental conditions meet the storage requirements of the cargo by the changing trends of temperature and humidity; Based on historical transportation data, set safety thresholds for each environmental parameter and establish abnormal warning rules; Continuously monitor the data of each sensor, compare the current environmental parameters with the previously set warning thresholds in real time, and issue a warning when a parameter exceeds the threshold; During transportation, the working status of the refrigeration equipment is optimized according to energy consumption.

2. The refrigerated container monitoring method according to claim 1, characterized in that: The multiple sensors are arranged inside the refrigerated container to collect environmental data of temperature and humidity, specifically, Temperature sensors are placed at the four corners and the center of the container to collect temperature; Humidity sensors are placed on the top and bottom of the container to collect humidity; The comprehensive environmental monitoring value is calculated based on the environmental data collected by various sensors. The expression is: Where E(t) represents the comprehensive environmental parameter value at time t, T(t) and H(t) represent the temperature and humidity values ​​at time t, respectively. max Indicates the maximum temperature allowed during the storage of goods, H max Indicates the maximum humidity value allowed during storage of goods.

3. The refrigerated container monitoring method according to claim 2, characterized in that: The collected environmental data is recorded and compared with the standard environmental parameters required for cargo storage, and the current environmental conditions are judged to meet the cargo storage requirements through the change trend of temperature and humidity. The specific steps are: Compare the environmental data at each time t with the known cargo storage standards and calculate the current deviation value. The calculation formula for the deviation value is: Among them, Δ T (t) represents the temperature deviation, Δ H (t) represents the deviation value of humidity, T req Indicates the required storage temperature of the goods, H req Indicates the required humidity for storage of materials; Based on the collected time series data, the changing trends of temperature and humidity are analyzed. The formula for the rate of change of temperature and humidity is: Among them, R T (t) represents the rate of change of temperature, R H (t) represents the rate of change of humidity, T(t-1) and H(t-1) represent the temperature and humidity at the previous moment, and Δt represents the time interval.

4. The refrigerated container monitoring method according to claim 3, characterized in that: The setting of safety thresholds of various environmental parameters and establishment of abnormal warning rules based on historical transportation data means: Obtain historical transportation data through historical records and cargo quality inspection reports, and set environmental data security thresholds based on historical transportation data; Select decision tree as the machine learning algorithm, take temperature and humidity as input, identify the environmental variables that have the greatest impact on cargo damage, and directly extract key environmental variables; According to key environmental variables, set the security threshold of each environmental data; Get the best storage environment conditions for goods based on historical transportation data, set warning thresholds, and issue warning signals immediately when any of the environmental data exceeds the corresponding warning thresholds; At the same time, when the rate of change of temperature and humidity is large, an early warning will be issued to prevent the parameters from quickly exceeding the safety range.

5. The refrigerated container monitoring method according to claim 4, characterized in that: The method of continuously monitoring the data of each sensor and comparing the current environmental parameters with the previously set warning threshold in real time, and issuing a warning when a parameter exceeds the threshold, is specifically as follows: Combined with the deviation value, the rate of change of temperature and humidity, a comprehensive judgment of the surrounding environment is made, and the expression is: Where J(t) represents the comprehensive environmental judgment value at time t, and α represents the weight coefficient; Operators can view the changing trends of various environmental parameters in real time through the screen interface and intervene according to the content of the warning; According to the historical transportation data, the alarm threshold and weight coefficient α are continuously adjusted, and the warning threshold of each environmental parameter is dynamically optimized through regression analysis of historical data.

6. The refrigerated container monitoring method according to claim 5, characterized in that: During the transportation process, the working state of the refrigeration equipment is optimized according to the energy consumption. The specific steps are: Install energy consumption sensors inside the container to collect energy consumption data of refrigeration equipment in real time; Setting upper and lower limits for internal temperature and internal humidity; Set an upper limit for energy consumption. When energy consumption exceeds this limit, an early warning will be triggered to indicate that energy consumption is too high. According to the external temperature and humidity, internal temperature and humidity, historical energy consumption and current energy consumption, the optimal power adjustment coefficient is calculated. The expression is: Among them, P opt (t) represents the optimal power adjustment coefficient at time t, T int (t) represents the current temperature inside the container, H int (t) represents the current humidity inside the container, E power (t) represents the energy consumption data at the current moment, k1 and k2 are the adjustment coefficients of temperature and humidity; When the outside temperature is lower than the required temperature T for cargo storage req , and P opt When (t)>1, reduce the power of the refrigeration equipment; When the outside temperature is higher than the required temperature T for cargo storage req , and P opt When (t)<1, increase the power of the refrigeration equipment; When the outside humidity is greater than or equal to the required humidity H for cargo storage req , and P opt When (t) deviates far from 1, increase the power of the dehumidification equipment to prevent moisture from entering the container and causing the goods to get damp; conversely, when the external humidity is lower than the required humidity H for cargo storage, req When the dehumidification equipment is used, the operator can reduce the working intensity of the dehumidification equipment to prevent the cargo from drying out due to excessive dehumidification.

7. A refrigerated container monitoring system, based on the refrigerated container monitoring method according to any one of claims 1 to 6, characterized in that: Including collection module, analysis module, early warning detection module, adjustment module and optimization module, The acquisition module is responsible for collecting the environmental data inside and outside the container in real time through the temperature sensors, humidity sensors, and energy consumption sensors arranged inside and outside the refrigerated container; The analysis module is responsible for processing and analyzing the real-time data obtained from the environmental data acquisition module, calculating the comprehensive environmental parameter values ​​in the container, and comparing these data with the standard environmental parameters to determine whether the current environment meets the storage requirements of the goods; The early warning detection module is responsible for monitoring the changes in environmental data in real time based on the safety thresholds set by historical transportation data, and issuing early warning signals of different levels when the parameters exceed the thresholds; The adjustment module is responsible for continuously optimizing the system's early warning mechanism and threshold settings of environmental parameters through analysis of historical transportation data, to ensure that the system can adapt to the storage needs of different types of goods.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the refrigerated container monitoring method according to any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the refrigerated container monitoring method according to any one of claims 1 to 7 are implemented.

Images