A method for managing vegetable transportation data

Abstract

The present invention relates to the technical field of data management, and in particular to a vegetable transportation data management method, which comprises setting a plurality of sensors corresponding to environmental parameters for real-time monitoring and uploading the data to a management terminal; the management terminal determines abnormal data and normal data, uploads the normal data to a database for storage and backup, and determines whether to issue an abnormal warning for the abnormal data based on a maximum delay duration and a fluctuation frequency of the abnormal data within a preset duration; determines an early warning method for the abnormal data based on a change rate of the abnormal data for which the abnormal warning is issued and whether the abnormal data has associated abnormal data; determines whether to adjust parameters of a vegetable transportation data management process based on a loss rate of vegetables within a preset period and an early warning efficiency; and the present invention improves the authenticity of the actual environmental state of vegetable transportation by analyzing the authenticity of the vegetable transportation data monitored by the sensor.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a vegetable transportation data management method. Background Art

[0002] Vegetables are an important food that is indispensable in people's daily lives, and their transportation is crucial. However, due to the phenomenon that vegetables are damaged during transportation due to improper transportation data management, vegetable transportation data management has become an urgent problem that needs to be solved. Traditional vegetable transportation data management methods usually use sensors to monitor the vegetable transportation environment and check in real time based on sensor data whether the vegetable transportation environment meets the transportation standards. Not only are the accuracy and efficiency low, but the vegetables still cannot be accurately protected.

[0003] Chinese patent application publication number: CN110648069A discloses a transport vehicle-based vegetable quality analysis system, comprising a temperature detection module, a humidity detection module, a bacteria detection module, an image acquisition module, a screening and preprocessing module, a data modeling module, a data processing module, an analysis and evaluation module, and a display terminal. The invention provides a transport vehicle-based vegetable quality analysis system that detects the temperature, humidity, bacterial species, and bacterial count in the vegetable storage environment and, in conjunction with the screening and preprocessing module, extracts the red, green, and blue components from the vegetable image to calculate the vegetable quality coefficient and display the quality changes during storage. The system has high quality detection accuracy and good real-time performance, allowing managers to intuitively understand the quality changes of vegetables during storage, providing reliable reference data for vegetable storage, and providing an optimal storage environment based on the quality of the vegetables, thereby optimizing the quality of the vegetables and reducing losses.

[0004] It can be seen from this that the existing technology has the problem that when using sensors to monitor environmental parameters of vegetables during transportation, the sensors may suffer from data distortion due to interference from the transportation environment. The above technical solution does not perform authenticity analysis on the sensor monitoring data, resulting in the monitoring data being unable to reflect the actual environmental status, leading to inaccurate abnormal monitoring, and thus causing a high damage rate of transported vegetables. Summary of the Invention

[0005] To this end, the present invention provides a vegetable transportation data management method to overcome the problem in the prior art that when using sensors to monitor environmental parameters of vegetables during transportation, the sensors may suffer from data distortion due to interference from the transportation environment. The above technical solution does not perform authenticity analysis on the sensor monitoring data, resulting in the monitoring data being unable to reflect the actual environmental status, resulting in inaccurate abnormal monitoring, and thus leading to a high damage rate of transported vegetables.

[0006] To achieve the above object, the present invention provides a vegetable transportation data management method, comprising:

[0007] Based on the characteristic information of the vegetables to be transported and the transportation route information, the types and ranges of the transportation environment parameters of the vegetables to be transported are determined, and a plurality of sensors corresponding to the environmental parameters are set to monitor the transportation environment parameters in real time and upload the monitored data to the management terminal;

[0008] The management terminal determines abnormal data and normal data of the sensor based on the transport environment parameter range of the vegetables to be transported, and uploads the normal data to a database for storage and backup. The management terminal determines whether to issue an abnormal warning for the abnormal data based on the maximum delay time and fluctuation frequency of the abnormal data within a preset time period;

[0009] Determining whether to issue a vibration abnormality warning, an electromagnetic interference warning, or a transportation environment abnormality warning for the abnormal data based on a change rate of the abnormal data for which the abnormality warning is issued and whether the abnormal data for which the abnormality warning is issued has associated abnormal data;

[0010] Based on the loss rate of vegetables after transportation within the preset period and the early warning efficiency, it is determined whether to adjust the parameters of the vegetable transportation data management process.

[0011] Furthermore, the determining of the type and range of the transportation environment parameters of the vegetables to be transported includes:

[0012] Collect characteristic information of different types of vegetables, including the influence of temperature, humidity, oxygen content and carbon dioxide content, to determine the types of transportation environment parameters for the vegetables to be transported;

[0013] Collect transport route information, including route length, altitude changes, temperature and humidity changes, and expected transport time;

[0014] Simulate temperature and humidity changes under different transportation durations and routes;

[0015] According to the simulated vegetable status, the range of transportation environment parameters under the preset transportation time and transportation route is determined.

[0016] Furthermore, the management terminal determines abnormal data and normal data of the sensor including:

[0017] Determining that the real-time data is abnormal data according to a comparison result that the real-time data is not within the transportation environment parameter range;

[0018] The real-time data is determined to be normal data based on a comparison result that the real-time data is within a range of transportation environment parameters.

[0019] Furthermore, the management terminal determines whether to issue an abnormality warning for the abnormal data, including:

[0020] According to the comparison result that the maximum delay time of the abnormal data within the preset time period is greater than or equal to the preset delay time period, or the fluctuation frequency of the abnormal data is greater than or equal to the preset fluctuation frequency, it is determined to issue an abnormal warning for the abnormal data.

[0021] Furthermore, the fluctuation frequency of the abnormal data is determined according to the ratio of the duration of occurrence of abnormal data of the same type within a preset time period to the preset time period.

[0022] Furthermore, the management terminal determines to issue a vibration abnormality warning, an electromagnetic interference warning, or a transportation environment abnormality warning for the abnormal data, including:

[0023] Determining to issue a vibration abnormality warning for the abnormal data according to a comparison result that a change rate of the abnormal data for which the abnormality warning is issued is greater than or equal to a preset change rate;

[0024] Determining to issue an electromagnetic interference warning for the abnormal data according to a comparison result that a change rate of the abnormal data for which the abnormal warning is issued is less than a preset change rate and that the abnormal data for which the abnormal warning is issued does not have abnormal associated data;

[0025] According to the comparison result that the change rate of the abnormal data for issuing the abnormal warning is less than the preset change rate and the abnormal data for issuing the abnormal warning has abnormal associated data, a maximum difference analysis of the timestamps of the associated abnormal data is determined.

[0026] Furthermore, the determination of the abnormal associated data includes:

[0027] Collect environmental parameter data monitored by sensors, including temperature, humidity, oxygen concentration, and abnormal warning data issued by sensors;

[0028] Identify abnormal data points in the sensor data based on the above data, which indicate that environmental parameters are outside of a preset range or other abnormal conditions;

[0029] Analyze the correlation between abnormal data points and determine the correlation patterns between abnormal data;

[0030] Extracting features associated with the abnormal data, the features including timestamps, sensor locations, and environmental parameter values;

[0031] Based on the characteristics of the abnormal data and the correlation analysis results, the abnormal correlation data of the abnormal data is determined.

[0032] Furthermore, the determining of the maximum difference analysis of the timestamps of the associated abnormal data includes:

[0033] Determining to issue a transportation environment abnormality warning for the abnormal data based on a comparison result that the maximum difference between the timestamps of the associated abnormal data is greater than or equal to a preset difference;

[0034] According to the comparison result that the maximum difference of the timestamps of the associated abnormal data is less than the preset difference, it is determined that an electromagnetic interference warning is issued for the abnormal data.

[0035] Furthermore, when determining whether to adjust the parameters of the vegetable transportation data management process, it is determined that the parameters of the vegetable transportation data management process need to be adjusted based on the comparison result that the loss rate of vegetables after transportation is completed within a preset period is greater than or equal to the preset loss rate or the early warning efficiency is less than the preset early warning efficiency, wherein the vegetable transportation data management process parameters include a preset delay time, a preset fluctuation frequency, a preset change rate and a preset difference.

[0036] Furthermore, the adjustment amount of the preset delay time is negatively correlated with the loss rate of vegetables after the transportation is completed within the preset period, the adjustment amount of the preset fluctuation frequency is negatively correlated with the loss rate of vegetables after the transportation is completed within the preset period, the adjustment amount of the preset change rate is positively correlated with the early warning efficiency, and the adjustment amount of the preset difference is negatively correlated with the early warning efficiency.

[0037] Compared with the existing technology, the beneficial effect of the present invention is that the present invention can monitor the changes of abnormal data in real time through the management terminal, and respond to abnormal situations in a timely manner according to the set conditions, which helps to reduce delays and deal with problems in a timely manner. By considering the fluctuation frequency, it can avoid false alarms caused by instantaneous data fluctuations or short-term abnormal situations, and improve the accuracy and reliability of abnormal warnings. Determining whether to issue an abnormal warning requires comprehensive consideration of the delay time and fluctuation frequency of the abnormal data, which helps to improve the efficiency and accuracy of the warning, ensure that only abnormal situations that really need attention will trigger warnings, thereby improving the authenticity of the data monitored by the sensor.

[0038] Furthermore, by analyzing the change rate and correlation of abnormal data, the present invention enables the management terminal to realize refined monitoring of different types of abnormal conditions such as vibration, electromagnetic interference and transportation environment, thereby improving the accuracy and sensitivity of monitoring. According to the change rate of abnormal data and the associated abnormal data, the management terminal can respond to different types of abnormal conditions more quickly. By comprehensively analyzing the change rate and correlation of abnormal data, false alarms caused by instantaneous fluctuations or isolated abnormal data can be reduced, and the accuracy and reliability of early warnings can be improved. Different types of early warnings are issued according to the type of abnormal data, which helps managers to better allocate resources and take countermeasures, thereby improving the efficiency and cost control of problem handling during transportation. By issuing vibration abnormality early warnings, electromagnetic interference early warnings or transportation environment abnormality early warnings, different types of potential risks and problems can be effectively identified and responded to, thereby improving the safety and stability of the transportation process. Different types of early warnings are issued based on the change rate of abnormal data and the correlation between abnormal data, thereby avoiding the problem that when using sensors to monitor environmental parameters of vegetables during transportation, the sensor monitoring data cannot reflect the actual environmental status due to interference from the transportation environment, resulting in inaccurate abnormal monitoring, thereby leading to a high damage rate of transported vegetables.

[0039] Furthermore, the present invention can optimize the vegetable transportation process, reduce the vegetable loss rate, and improve transportation efficiency and quality by adjusting management parameters according to the loss rate and early warning efficiency. Based on the vegetable loss rate and early warning efficiency within a preset period, the management terminal can monitor the transportation situation in real time and adjust parameters in time to reduce vegetable losses. By optimizing data management parameters, the vegetable loss rate can be reduced, the cost caused by losses can be reduced, and transportation efficiency can be improved. Adjusting management parameters can improve the accuracy of the early warning system, ensuring that only abnormal situations that really need attention will trigger early warnings, reduce false alarm rates, improve the efficiency of monitoring and responding to abnormal situations, and ensure that vegetables remain in good condition during transportation. By adjusting parameters, the quality and freshness of vegetables during transportation can be improved, increasing product competitiveness.

[0040] Furthermore, according to the comparison results that the loss rate of vegetables after the transportation is completed within the preset period is greater than or equal to the preset loss rate and the early warning efficiency is greater than or equal to the preset early warning efficiency, the present invention indicates that the vegetable loss rate is large due to the failure to issue an early warning in time during the transportation process. At this time, the preset delay time and the preset fluctuation frequency are adjusted with the first adjustment coefficient so as to issue an early warning in time. According to the comparison results that the loss rate of vegetables after the transportation is completed within the preset period is less than the preset loss rate and the early warning efficiency is less than the preset early warning efficiency, it indicates that the early warning method selection during the vegetable transportation process is inaccurate, resulting in low early warning efficiency. At this time, the corresponding preset threshold is adjusted with the corresponding adjustment coefficient according to the early warning efficiency of the corresponding early warning method. The above method improves the authenticity of the sensor monitoring data and reduces the damage rate of the transported vegetables. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a workflow diagram of the vegetable transportation data management method according to an embodiment of the present invention;

[0042] Figure 2 A flowchart of a method for managing vegetable transportation data according to an embodiment of the present invention for determining whether data from a plurality of sensors corresponding to the environmental parameters being monitored are normal data or abnormal data;

[0043] Figure 3 This is a workflow diagram for determining whether to issue an abnormal warning for abnormal data in the vegetable transportation data management method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0045] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0046] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0047] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0048] See also Figure 1-Figure 3 As shown, Figure 1 This is a workflow diagram of the vegetable transportation data management method according to an embodiment of the present invention; Figure 2 A flowchart of a method for managing vegetable transportation data according to an embodiment of the present invention for determining whether data from a plurality of sensors corresponding to the environmental parameters being monitored are normal data or abnormal data; Figure 3This is a workflow diagram for determining whether to issue an abnormal warning for abnormal data in the vegetable transportation data management method according to an embodiment of the present invention.

[0049] The vegetable transportation data management method according to the embodiment of the present invention includes:

[0050] Step S1, determining the type and range of the transportation environment parameters of the vegetables to be transported based on the characteristic information of the vegetables to be transported and the transportation route information, setting a plurality of sensors corresponding to the environmental parameters to monitor the transportation environment parameters in real time and uploading the monitored data to the management terminal;

[0051] Step S2, the management terminal determines abnormal data and normal data of the sensor based on the range of the transportation environment parameters of the vegetables to be transported, and the management terminal uploads the normal data to a database for storage and backup. The management terminal determines whether to issue an abnormal warning for the abnormal data based on the maximum delay duration and fluctuation frequency of the abnormal data within a preset time period;

[0052] Step S3, determining to issue a vibration abnormality warning, an electromagnetic interference warning, or a transportation environment abnormality warning for the abnormal data based on a change rate of the abnormal data for which the abnormality warning is issued and whether the abnormal data for which the abnormality warning is issued has associated abnormal data;

[0053] Step S4, determining whether to adjust the parameters of the vegetable transportation data management process based on the loss rate of vegetables after transportation is completed within a preset period and the early warning efficiency.

[0054] The transportation environment parameters in the embodiment of the present invention include but are not limited to "temperature, humidity and gas concentration", the gases include but are not limited to ethylene, oxygen and carbon dioxide, the sensors set up to monitor the environmental parameters include but are not limited to "temperature corresponding to temperature sensor, humidity corresponding to humidity sensor and gas concentration corresponding to gas sensor", the management terminal includes but is not limited to "cloud server, mobile APP and Web platform", and the preset transportation time is the transportation time calculated according to the transportation route.

[0055] Specifically, in step S1, determining the type and range of the transportation environment parameters of the vegetables to be transported includes:

[0056] Collect characteristic information of different types of vegetables, including the influence of temperature, humidity, oxygen content and carbon dioxide content, to determine the types of transportation environment parameters for the vegetables to be transported;

[0057] Collect transport route information, including route length, altitude changes, temperature and humidity changes, and expected transport time;

[0058] Simulate temperature and humidity changes under different transportation durations and routes;

[0059] According to the simulated vegetable status, the range of transportation environment parameters under the preset transportation time and transportation route is determined.

[0060] In the embodiment of the present invention, it is assumed that the vegetables to be transported are broccoli. By consulting agricultural scientific research materials and consulting professional growers, it is learned that broccoli is relatively sensitive to temperature, and the suitable transportation temperature is between 0°C and 4°C; the humidity requirement is also relatively high, and the suitable humidity is about 90% to 95%. At the same time, there are certain requirements for oxygen content and carbon dioxide content. Excessive carbon dioxide content will affect the quality of broccoli. Therefore, the types of transportation environment parameters of broccoli are determined to be temperature, humidity, oxygen content and carbon dioxide content; taking the transportation from vegetable production area A to sales area B as an example, the transportation route length is 500 kilometers. By querying map software and weather forecast, it is learned that the route will pass through some mountainous areas, and the altitude varies between 500 meters and 1500 meters. According to historical meteorological data, the temperature and humidity along the way vary greatly, and high temperature and high humidity or low temperature and low humidity may occur. The preset transportation time is two days; it is simulated that during the transportation process, the temperature may fluctuate between -5℃ and 30℃, and the humidity varies between 60% and 95%. An environmental test chamber can also be used to simulate similar transportation conditions to observe the state changes of broccoli under different temperature and humidity conditions; through simulation and observation, it is found that broccoli is in good condition in an environment with a temperature of 0℃ to 4℃, a humidity of 90% to 95%, a moderate oxygen content and a low carbon dioxide content. Combined with the preset transportation time and transportation route, the transportation environment parameter range of broccoli on this transportation route is determined to be: temperature 0℃ to 4℃, humidity 90% to 95%, oxygen content maintained at normal air levels, and carbon dioxide content is controlled as much as possible within the range of 0.03%-1%.

[0061] The vegetable status in the embodiment of the present invention includes good and damaged. The good vegetable status includes but is not limited to "the color difference between the vegetables after transportation and the vegetables before transportation is less than 2 and the vegetables after transportation do not emit any peculiar smell." The damaged vegetable status includes but is not limited to "the color difference between the vegetables after transportation and the vegetables before transportation is greater than or equal to 2 or the vegetables after transportation emit any peculiar smell." The vegetable status can be determined by comparing the vegetable image after transportation with the vegetable image before transportation.

[0062] Specifically, in step S2, when determining abnormal data and normal data of the plurality of sensors corresponding to the environmental parameters, the management terminal determines the abnormal data and normal data of the plurality of sensors corresponding to the environmental parameters based on a comparison result of the real-time data of the plurality of sensors corresponding to the environmental parameters with the range of the transportation environmental parameters;

[0063] When the real-time data of a plurality of sensors corresponding to the environmental parameters are not within the range of the transportation environmental parameters, the management terminal determines that the real-time data is abnormal data;

[0064] When the real-time data of a plurality of sensors corresponding to the environmental parameters are within the range of the transportation environmental parameters, the management terminal determines that the real-time data is normal data.

[0065] Specifically, in step S2, when determining whether to issue an abnormal warning for the abnormal data, the management terminal determines whether to issue an abnormal warning for the abnormal data according to the maximum delay duration and fluctuation frequency of the abnormal data within a preset duration;

[0066] When the maximum delay time of the abnormal data within the preset time period is greater than or equal to the preset delay time period or the fluctuation frequency of the abnormal data is greater than or equal to the preset fluctuation frequency, the management terminal determines to issue an abnormal warning for the abnormal data;

[0067] When the maximum delay duration of the abnormal data within the preset duration is less than the preset delay duration and the fluctuation frequency of the abnormal data is less than the preset fluctuation frequency, the management terminal determines not to issue an abnormal warning for the abnormal data.

[0068] In the embodiment of the present invention, the value range of the preset time length is set to 60 seconds-240 seconds, and the value of the preset time length is preferably 100 seconds. The value range of the preset delay time length is set to 30 seconds-100 seconds, and the value of the preset delay time length is preferably 40 seconds. The fluctuation frequency of the abnormal data is determined according to the ratio of the duration of occurrence of abnormal data of the same type within the preset time length to the preset time length. The value range of the preset fluctuation frequency is set to 0.4-1, and the value range of the preset fluctuation frequency is preferably 0.58, but the above values ​​are not limited thereto, and those skilled in the art can also adjust the value according to actual needs.

[0069] The present invention can monitor the changes of abnormal data in real time through the management terminal, and respond to abnormal situations in a timely manner according to the set conditions, which helps to reduce delays and deal with problems in a timely manner. By considering the fluctuation frequency, it can avoid false alarms caused by instantaneous data fluctuations or short-term abnormal situations, and improve the accuracy and reliability of abnormal warnings. Determining whether to issue an abnormal warning requires comprehensive consideration of the delay time and fluctuation frequency of the abnormal data, which helps to improve the efficiency and accuracy of the warning, ensure that only abnormal situations that really need attention will trigger warnings, thereby improving the authenticity of the data monitored by the sensor.

[0070] Specifically, in step S3, when it is determined that an abnormal warning is issued for the abnormal data, the management terminal determines whether to issue a vibration abnormality warning, an electromagnetic interference warning, or a transportation environment abnormality warning for the abnormal data based on the rate of change of the abnormal data for which the abnormal warning is issued and whether there is associated abnormal data with the abnormal data for which the abnormal warning is issued;

[0071] When the change rate of the abnormal data for issuing the abnormal warning is greater than or equal to a preset change rate, determining to issue a vibration abnormality warning for the abnormal data;

[0072] When the change rate of the abnormal data for which the abnormal warning is issued is less than a preset change rate and the abnormal data for which the abnormal warning is issued has abnormal associated data, determining to issue an electromagnetic interference warning for the abnormal data;

[0073] When the change rate of the abnormal data for which the abnormal warning is issued is less than a preset change rate and there is no abnormal associated data for the abnormal data for which the abnormal warning is issued, a maximum difference analysis of the timestamps of the associated abnormal data is determined.

[0074] Specifically, in step S3, when analyzing the maximum difference of the timestamps of the associated abnormal data, it is determined whether to issue a transportation environment abnormality warning or an electromagnetic interference warning for the abnormal data based on the comparison result of the maximum difference of the timestamps of the associated abnormal data with the preset difference;

[0075] When the maximum difference between the timestamps of the associated abnormal data is greater than or equal to a preset difference, determining to issue a transportation environment abnormality warning for the abnormal data;

[0076] When the maximum difference of the timestamps of the associated abnormal data is less than a preset difference, it is determined to issue an electromagnetic interference warning for the abnormal data.

[0077] In the embodiment of the present invention, the rate of change of the abnormal data for issuing the abnormal warning is determined by the ratio of the maximum change of the abnormal data to the change time. The preset change rate is based on the historical average value of the change rate of the abnormal data monitored by the corresponding sensor during the vegetable transportation process. The maximum difference in the timestamps of the associated abnormal data is the difference obtained by subtracting the timestamp of the earliest abnormal data from the timestamp of the last abnormal data. The value range of the preset difference is set to 10-200 seconds. The value of the preset difference is preferably 100 seconds, but the above value is not limited to this. Those skilled in the art can also adjust the value according to actual needs.

[0078] In the embodiment of the present invention, the abnormality-related data of the abnormal data for issuing the abnormality warning can be determined according to the following method, including:

[0079] Collect environmental parameter data monitored by sensors, including temperature, humidity, oxygen concentration, and abnormal warning data issued by sensors;

[0080] Identify abnormal data points in the sensor data based on the above data, which indicate that environmental parameters are outside of a preset range or other abnormal conditions;

[0081] Analyze the correlation between abnormal data points and determine the correlation patterns between abnormal data;

[0082] Extracting features associated with the abnormal data, the features including timestamps, sensor locations, and environmental parameter values;

[0083] Based on the characteristics of the abnormal data and the correlation analysis results, the abnormal correlation data of the abnormal data is determined.

[0084] In the embodiment of the present invention, during transportation, the sensor continuously monitors environmental parameters, and the collected temperature data is: 1°C, 2°C, 3°C, 5°C, 4°C, 6°C, 7°C, and 8°C; the humidity data is: 90%, 91%, 92%, 88%, 90%, 85%, 80%, and 75%; the oxygen concentration data is stable at around 21%; at the same time, the sensor issues an abnormal warning when the temperature reaches 7°C and the humidity drops to 75%; the preset temperature range is 2°C-5°C, and the humidity range is 85%-95%. By comparison, it was found that 6℃, 7℃, and 8℃ in the temperature data and 80% and 75% in the humidity data were abnormal data points, because these data points indicated that the environmental parameters exceeded the preset range; it was observed that with the passage of time, the temperature gradually increased while the humidity gradually decreased; further analysis found that the humidity began to drop significantly after the temperature rose to a certain level, indicating that the increase in temperature may be a factor leading to the decrease in humidity, and it was determined that there was a certain correlation between the abnormal data; for these abnormal data, the extracted features are as follows: Timestamp: When the temperature is 6℃, the corresponding timestamp is 5 hours and 30 minutes after the start of transportation; when the humidity is 80%, the corresponding timestamp is 5 hours and 45 minutes after the start of transportation; Sensor location: distributed in different locations of the truck compartment, but mainly concentrated in the position near the air outlet of the refrigeration equipment; Environmental parameter values: The abnormal temperature data are 6℃, 7℃, and 8℃, and the abnormal humidity data are 80% and 75%; Based on the characteristics of the abnormal data and the correlation analysis results, it can be determined that temperature and humidity are abnormally correlated data.

[0085] In an embodiment of the present invention, when it is determined that a vibration abnormality warning is issued for the abnormal data, the sensor installation position and circuit connection are checked to determine whether the sensor data is abnormal data due to vibration during transportation causing the sensor to fall from the installation position or the circuit connection to loosen. When it is determined that an electromagnetic interference warning is issued for the abnormal data, the machine that can emit electromagnetic interference is shut down. When it is determined that a transportation environment abnormality warning is issued for the abnormal data, it is checked whether the vegetable transportation environment has changed, making it unsuitable for transporting vegetables.

[0086] By analyzing the change rate and correlation of abnormal data, the present invention enables the management terminal to realize refined monitoring of different types of abnormal situations such as vibration, electromagnetic interference and transportation environment, thereby improving the accuracy and sensitivity of monitoring. According to the change rate of abnormal data and the correlation of abnormal data, the management terminal can respond to different types of abnormal situations more quickly. By comprehensively analyzing the change rate and correlation of abnormal data, false alarms caused by instantaneous fluctuations or isolated abnormal data can be reduced, and the accuracy and reliability of early warnings can be improved. Different types of early warnings are issued according to the type of abnormal data, which helps managers to better allocate resources and take countermeasures, thereby improving the efficiency and cost control of problem handling during transportation. By issuing vibration abnormality early warnings, electromagnetic interference early warnings or transportation environment abnormality early warnings, different types of potential risks and problems can be effectively identified and responded to, thereby improving the safety and stability of the transportation process. Different types of early warnings are issued based on the change rate of abnormal data and the correlation between abnormal data, thereby avoiding the problem that when using sensors to monitor environmental parameters of vegetables during transportation, the sensor monitoring data cannot reflect the actual environmental status due to interference from the transportation environment, resulting in inaccurate abnormal monitoring, thereby leading to a high damage rate of transported vegetables.

[0087] Specifically, in step S4, when determining whether to adjust the parameters of the vegetable transportation data management process, it is determined whether to adjust the parameters of the vegetable transportation data management process according to the loss rate of vegetables after transportation is completed within a preset period and the early warning efficiency;

[0088] When the loss rate of vegetables after transportation is completed within a preset period is greater than or equal to the preset loss rate and the early warning efficiency is greater than or equal to the preset early warning efficiency, determining to adjust the parameters of the vegetable transportation data management process in a first adjustment manner;

[0089] When the loss rate of vegetables after transportation is completed within a preset period is less than a preset loss rate and the early warning efficiency is less than a preset early warning efficiency, determining to adjust the parameters of the vegetable transportation data management process in a second adjustment manner;

[0090] When the loss rate of vegetables after transportation is completed within a preset period is greater than or equal to the preset loss rate and the early warning efficiency is less than the preset early warning efficiency, determining to adjust the parameters of the vegetable transportation data management process in a first adjustment manner and a second adjustment manner respectively;

[0091] When the loss rate of vegetables after transportation within the preset period is less than the preset loss rate and the early warning efficiency is greater than or equal to the preset early warning efficiency, it is determined that there is no need to adjust the parameters of the vegetable transportation data management process.

[0092] In the embodiment of the present invention, the vegetable transportation data management process parameters include a preset delay time, a preset fluctuation frequency, a preset change rate and a preset difference. The preset period is set to transport vegetables 10 times. The loss rate of vegetables after the transportation is completed within the preset period is the ratio of the number of unqualified vegetables after the transportation is completed within the preset period to the total amount of vegetables. The early warning effectiveness rate is the ratio of the number of correct early warnings for vegetables after the transportation is completed within the preset period to the total number of early warnings. The unqualified vegetables include but are not limited to "vegetables that change color, vegetables that rot and vegetables that become moldy". The correct early warning includes that the same abnormal data does not continue to appear after the corresponding early warning measures are taken after the early warning.

[0093] In the embodiment of the present invention, the value range of the preset loss rate is set to 0-0.3, and the value of the preset loss rate is preferably 0.15. The value range of the preset warning efficiency is set to 0.7-1, and the value range of the preset warning efficiency is preferably 0.8, but the above values ​​are not limited to this, and those skilled in the art can also adjust the values ​​according to actual needs.

[0094] The present invention can optimize the vegetable transportation process, reduce the vegetable loss rate, and improve transportation efficiency and quality by adjusting management parameters according to the loss rate and early warning efficiency. Based on the vegetable loss rate and early warning efficiency within a preset period, the management terminal can monitor the transportation situation in real time and adjust parameters in time to reduce vegetable losses. By optimizing data management parameters, the vegetable loss rate can be reduced, the cost caused by the loss can be reduced, and the transportation efficiency can be improved. Adjusting the management parameters can improve the accuracy of the early warning system, ensuring that only abnormal situations that really need attention will trigger early warnings, reduce the false alarm rate, improve the efficiency of monitoring and responding to abnormal situations, and ensure that vegetables remain in good condition during transportation. By adjusting the parameters, the quality and freshness of vegetables during transportation can be improved, and the competitiveness of products can be increased.

[0095] Specifically, when adjusting the vegetable transportation data management process parameters in a first adjustment manner, it is determined that the preset delay time and the preset fluctuation frequency are adjusted by a first adjustment coefficient;

[0096] When the vegetable transportation data management process parameters are adjusted in the second adjustment manner, the corresponding preset threshold is adjusted with the corresponding adjustment coefficient according to the warning efficiency of the corresponding warning manner.

[0097] In the embodiment of the present invention, the adjustment of the corresponding preset threshold value with the corresponding adjustment coefficient according to the warning efficiency of the corresponding warning method includes adjusting the preset change speed with the second adjustment coefficient when the warning efficiency of the vibration abnormality warning is lower than the preset warning efficiency, adjusting the preset difference with the second adjustment coefficient when the warning efficiency of the electromagnetic interference warning is lower than the preset warning efficiency, and adjusting the preset difference with the third adjustment coefficient when the warning efficiency of the transportation environment abnormality warning is lower than the preset warning efficiency.

[0098] In the embodiment of the present invention, the value range of the first adjustment coefficient is set to 0.8-0.98, the value range of the second adjustment coefficient is set to 0.82-0.99, and the value range of the third adjustment coefficient is set to 1.01-1.22. The adjustment amount of the preset delay time is negatively correlated with the loss rate of vegetables after the transportation is completed within the preset period, the adjustment amount of the preset fluctuation frequency is negatively correlated with the loss rate of vegetables after the transportation is completed within the preset period, the adjustment amount of the preset change rate is positively correlated with the early warning efficiency, and the adjustment amount of the preset difference is negatively correlated with the early warning efficiency, but the above values ​​are not limited to this, and those skilled in the art can also adjust the values ​​according to actual needs.

[0099] According to the comparison result that the loss rate of vegetables after the transportation is completed within a preset period is greater than or equal to the preset loss rate and the early warning efficiency is greater than or equal to the preset early warning efficiency, the present invention indicates that the vegetable loss rate is large due to the failure to issue an early warning in time during the transportation process. At this time, the preset delay time and the preset fluctuation frequency are adjusted with the first adjustment coefficient so as to issue an early warning in time. According to the comparison result that the loss rate of vegetables after the transportation is completed within the preset period is less than the preset loss rate and the early warning efficiency is less than the preset early warning efficiency, it indicates that the early warning method selection during the vegetable transportation process is inaccurate, resulting in low early warning efficiency. At this time, the corresponding preset threshold is adjusted with the corresponding adjustment coefficient according to the early warning efficiency of the corresponding early warning method. The above method improves the authenticity of the sensor monitoring data and reduces the damage rate of the transported vegetables.

[0100] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0101] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A vegetable transportation data management method, characterized in that: include: Based on the characteristic information of the vegetables to be transported and the transportation route information, the types and ranges of the transportation environment parameters of the vegetables to be transported are determined, and a plurality of sensors corresponding to the environmental parameters are set to monitor the transportation environment parameters in real time and upload the monitored data to the management terminal; The management terminal determines abnormal data and normal data of the sensor based on the transport environment parameter range of the vegetables to be transported, and uploads the normal data to a database for storage and backup. The management terminal determines whether to issue an abnormal warning for the abnormal data based on the maximum delay time and fluctuation frequency of the abnormal data within a preset time period; Determining whether to issue a vibration abnormality warning, an electromagnetic interference warning, or a transportation environment abnormality warning for the abnormal data based on a change rate of the abnormal data for which the abnormality warning is issued and whether the abnormal data for which the abnormality warning is issued has associated abnormal data; Determine whether to adjust the parameters of the vegetable transportation data management process based on the loss rate of vegetables after transportation within a preset period and the effectiveness of early warning; The management terminal determines to issue a vibration abnormality warning, an electromagnetic interference warning, or a transportation environment abnormality warning for the abnormal data, including: Determining to issue a vibration abnormality warning for the abnormal data according to a comparison result that a change rate of the abnormal data for which the abnormality warning is issued is greater than or equal to a preset change rate; Determining to issue an electromagnetic interference warning for the abnormal data according to a comparison result that a change rate of the abnormal data for which the abnormal warning is issued is less than a preset change rate and that the abnormal data for which the abnormal warning is issued does not have abnormal associated data; According to the comparison result that the change rate of the abnormal data for which the abnormal warning is issued is less than the preset change rate and the abnormal data for which the abnormal warning is issued has abnormal associated data, a maximum difference analysis of the timestamps of the associated abnormal data is determined; The determination of the abnormal associated data includes: Collect environmental parameter data monitored by sensors, including temperature, humidity, oxygen concentration, and abnormal warning data issued by sensors; Identify abnormal data points in the sensor data based on the above data, which indicate that environmental parameters are outside of a preset range or other abnormal conditions; Analyze the correlation between abnormal data points and determine the correlation patterns between abnormal data; Extracting features associated with the abnormal data, the features including timestamps, sensor locations, and environmental parameter values; Determine abnormal related data of the abnormal data based on the characteristics and correlation analysis results of the abnormal data; The determining of the maximum difference analysis of the timestamps of the associated abnormal data includes: Determining to issue a transportation environment abnormality warning for the abnormal data based on a comparison result that the maximum difference between the timestamps of the associated abnormal data is greater than or equal to a preset difference; Determining to issue an electromagnetic interference warning for the abnormal data based on a comparison result that the maximum difference between the timestamps of the associated abnormal data is less than a preset difference; The fluctuation frequency of the abnormal data is determined according to the ratio of the duration of occurrence of abnormal data of the same type within a preset time period to the preset time period.

2. The vegetable transportation data management method according to claim 1, characterized in that: Determining the type and range of the transportation environment parameters of the vegetables to be transported includes: Collect characteristic information of different types of vegetables, including the influence of temperature, humidity, oxygen content and carbon dioxide content, to determine the types of transportation environment parameters for the vegetables to be transported; Collect transport route information, including route length, altitude changes, temperature and humidity changes, and expected transport time; Simulate temperature and humidity changes under different transportation durations and routes; According to the simulated vegetable status, the range of transportation environment parameters under the preset transportation time and transportation route is determined.

3. The vegetable transportation data management method according to claim 2, characterized in that: The management terminal determines abnormal data and normal data of the sensor including: Determining that the real-time data is abnormal data according to a comparison result that the real-time data is not within the transportation environment parameter range; The real-time data is determined to be normal data based on a comparison result that the real-time data is within a range of transportation environment parameters.

4. The vegetable transportation data management method according to claim 3, characterized in that: The management terminal determines whether to issue an abnormality warning for the abnormal data, including: According to the comparison result that the maximum delay time of the abnormal data within the preset time period is greater than or equal to the preset delay time period, or the fluctuation frequency of the abnormal data is greater than or equal to the preset fluctuation frequency, it is determined to issue an abnormal warning for the abnormal data.

5. The vegetable transportation data management method according to claim 1, characterized in that: When determining whether to adjust the parameters of the vegetable transportation data management process, it is determined that the parameters of the vegetable transportation data management process need to be adjusted based on the comparison result that the loss rate of vegetables after transportation is completed within a preset period is greater than or equal to the preset loss rate or the early warning efficiency is less than the preset early warning efficiency, wherein the vegetable transportation data management process parameters include a preset delay time, a preset fluctuation frequency, a preset change rate and a preset difference.

6. The vegetable transportation data management method according to claim 5, characterized in that: The adjustment amount of the preset delay time is negatively correlated with the loss rate of vegetables after the transportation is completed within the preset period, the adjustment amount of the preset fluctuation frequency is negatively correlated with the loss rate of vegetables after the transportation is completed within the preset period, the adjustment amount of the preset change rate is positively correlated with the early warning efficiency, and the adjustment amount of the preset difference is negatively correlated with the early warning efficiency.

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