Refrigerated vehicle temperature control transportation abnormity identification method

Abstract

The invention discloses a refrigeration vehicle temperature control transportation abnormity identification method, and relates to the technical field of temperature control monitoring, and the method comprises the following steps: S1, collecting and maintaining the temperature suitable for goods according to the goods category; s2, drawing a route plan and a loading and unloading place electronic fence in advance; s3, calculating and adjusting a vehicle temperature threshold interval according to a pre-collected cargo suitable temperature threshold interval in combination with the current vehicle speed and the external environment temperature; and S4, the temperature in the compartment is obtained through a sensor, the overtemperature value and the duration time are calculated, and whether temperature control of the vehicle is abnormal or not and the abnormal level are judged in combination with the current vehicle speed and the vehicle position factor, and the real-time temperature data of the compartment, the vehicle real-time speed, the vehicle position information, the loading and unloading place electronic fence and other dimensions are combined. According to the method, accurate identification and grading evaluation of refrigeration transportation temperature control abnormity can be realized, the abnormity judgment accuracy is improved, the false alarm rate can be effectively reduced, and the transportation efficiency and the cargo quality are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control monitoring, and in particular to a method for identifying abnormalities in temperature control transportation of refrigerated vehicles. Background Art

[0002] Refrigerated truck transportation refers to a mode of transportation that uses refrigerated equipment (such as refrigerated trucks, refrigerated boxes, dry ice, etc.) to keep items at a low temperature and constant temperature in order to ensure the stable quality of the products. Refrigerated truck transportation has a wide range of uses, covering food, medicine, chemical products, electronic products, flowers, high-end furniture and other fields. Among them, the food industry is the main application area of ​​refrigerated truck transportation, including fresh meat, seafood, vegetables, fruits and other perishable foods.

[0003] In cold chain logistics transportation, how to efficiently and accurately monitor the ambient temperature of the carriages carrying perishable goods such as fresh food and pharmaceutical products and feedback temperature control anomalies has become a key link in logistics transportation. At present, there are problems in the industry such as failure to monitor goods by category and packaging, single anomaly determination rules, single anomaly handling mechanism and low efficiency. Therefore, a method for identifying anomalies in temperature control transportation of refrigerated vehicles is proposed. Summary of the invention

[0004] The purpose of the present invention is to solve the problems in the prior art and to propose a method for identifying abnormalities in temperature-controlled transportation of refrigerated vehicles.

[0005] A method for identifying abnormalities in temperature-controlled transportation of refrigerated vehicles, comprising the following steps:

[0006] S1. Collect and maintain the appropriate temperature of goods according to the category of goods;

[0007] S2. Pre-draw the route plan and electronic fence of the loading and unloading area;

[0008] S3. Calculate and adjust the vehicle temperature threshold interval based on the pre-collected cargo suitable temperature threshold interval, combined with the current vehicle speed and external ambient temperature;

[0009] S4. Obtain the temperature in the vehicle compartment through the sensor, calculate the over-temperature value and duration, and determine whether the vehicle temperature control is abnormal and the abnormality level in combination with the current vehicle speed and vehicle position factors;

[0010] S5. Generate a corresponding processing plan according to the abnormal level. If there is a level increase or decrease, generate a supplementary plan;

[0011] S6. Store historical transportation routes, vehicle speeds, cargo types, cargo weights, temperature control threshold ranges, external ambient temperatures, and temperature control anomaly information into a history table. At the same time, train the model through a large model to optimize route planning and anomaly prediction.

[0012] Preferably, in step S2, the route planning comprehensively considers the historical transport route L, vehicle speed V, cargo C, temperature control threshold interval T, external environment temperature E and temperature control anomaly A, and uses the following mathematical formula to optimize the transport route:

[0013] J=α·D(L)+β·P(T,E,A)+γ·F(V,L)+δ·R(C)

[0014] Among them, J represents the comprehensive cost of the transportation route, D(L) is the historical route optimization distance function, which represents the transportation distance or time corresponding to the selection of route L, P(T,E,A) is the temperature control risk function, which represents the impact of temperature control threshold T, ambient temperature E, and temperature control anomaly A on the quality of goods, F(V,L) is the energy consumption function, which represents the energy consumption of the vehicle when traveling at a speed V on route L, R(C) is the cargo risk function, which represents the sensitivity or loss risk of cargo C to temperature control fluctuations, and α, β, γ, and δ are weight coefficients.

[0015] Preferably, in step S3, the formula for calculating the vehicle temperature threshold interval is as follows:

[0016]

[0017] Among them, T min is the lower limit of vehicle temperature threshold, T max is the upper threshold of vehicle temperature, T min base T is the lower limit of the suitable temperature of the cargo, max base T is the upper limit of the suitable temperature for the cargo. env is the external ambient temperature, v is the vehicle speed;

[0018] f env (T env ) is the external temperature adjustment factor, defined as follows;

[0019]

[0020] Among them, a is the adjustment coefficient of high temperature environment, and b is the adjustment coefficient of low temperature environment;

[0021] f V (v) is the vehicle speed adjustment factor, which is defined as follows:

[0022]

[0023] Among them, v threshold is the vehicle speed threshold, c is the temperature control adjustment coefficient under low speed conditions, and d is the temperature control adjustment coefficient under high speed conditions.

[0024] Preferably, in step S4, the over-temperature value is calculated according to the following formula:

[0025]

[0026] Where ΔT is the over-temperature value, T actual is the cabin temperature, T min and T max is the vehicle temperature threshold range;

[0027] Get the overtemperature duration T duration , set the over-temperature threshold ΔT threshold and time threshold T threshold , when ΔT>ΔT threshold And T duration >T threshold , it is determined to be abnormal temperature control;

[0028] When the vehicle speed is close to 0 km / h and is within the range of the loading and unloading point, and the door is in the open state, short-term over-temperature is allowed and the temperature control abnormality judgment is ignored.

[0029] Preferably, in step S4, the temperature control abnormality determination level rules are as follows:

[0030] S 异常 =ω 1 ·ΔT+ω 2 ·T duration -ω 3 f(v, position)

[0031] Among them, S 异常 is the abnormality score, ω 1 ,ω 2 and ω 3 is the weight coefficient, f(v, position) is the state correction function, and it is combined with the vehicle speed and position to determine whether it is a normal short-term overtemperature;

[0032] When S 异常 ≤S 轻微 , the abnormal rating was determined to be slight;

[0033] When S 轻微 异常 ≤S 中度 , the abnormal rating was determined to be moderate;

[0034] When S 异常 >S 中度 , the abnormal rating is judged to be severe.

[0035] Preferably, in step S5, generating a corresponding processing solution according to the abnormal level includes:

[0036] When the exception level is minor:

[0037] ​(1) SMS notifications from operators and APP stations;

[0038] (2) Increase the cooling power and reduce the cabin temperature;

[0039] When the abnormality level is moderate:

[0040] (1) Intelligent voice call driver processing;

[0041] (2) Abnormal information is fed back to the monitoring center to remind operators to follow up and handle the problem;

[0042] When the exception level is severe:

[0043] (1) Manual voice call to the driver for processing;

[0044] (2) Abnormal information is fed back to the monitoring center to remind operators to follow up and handle the problem;

[0045] (3) Notify the cargo owner and alert him to possible risks.

[0046] Compared with the prior art, the present invention has the following advantages:

[0047] The present invention combines multiple dimensions such as the real-time temperature data of the carriage, the real-time speed of the vehicle, the vehicle location information, and the electronic fence of the loading and unloading site to achieve accurate identification and graded evaluation of temperature control anomalies in refrigerated transportation. This method not only improves the accuracy of anomaly judgment, but also effectively reduces the false alarm rate, thereby ensuring transportation efficiency and cargo quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0049] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0050] Reference Figure 1 As shown, a method for identifying abnormalities in temperature-controlled transportation of refrigerated vehicles comprises the following steps:

[0051] S1. Collect and maintain the appropriate temperature of goods according to the category of goods;

[0052] S2. Pre-draw the route plan and electronic fence of the loading and unloading area;

[0053] S3. Calculate and adjust the vehicle temperature threshold interval based on the pre-collected cargo suitable temperature threshold interval, combined with the current vehicle speed and external ambient temperature;

[0054] S4. Obtain the temperature in the vehicle compartment through the sensor, calculate the over-temperature value and duration, and determine whether the vehicle temperature control is abnormal and the abnormality level in combination with the current vehicle speed and vehicle position factors;

[0055] S5. Generate a corresponding processing plan according to the abnormal level. If there is a level increase or decrease, generate a supplementary plan;

[0056] S6. Store historical transportation routes, vehicle speeds, cargo types, cargo weights, temperature control threshold ranges, external ambient temperatures, and temperature control anomaly information into a history table. At the same time, train the model through a large model to optimize route planning and anomaly prediction.

[0057] In this embodiment, in step S2, the route planning comprehensively considers the historical transportation route L, vehicle speed V, cargo C, temperature control threshold interval T, external environment temperature E and temperature control anomaly A, and uses the following mathematical formula to optimize the transportation route:

[0058] J=α·D(L)+β·P(T,E,A)+γ·F(V,L)+δ·R(C)

[0059] Among them, J represents the comprehensive cost of the transportation route, D(L) is the historical route optimization distance function, which represents the transportation distance or time corresponding to the selection of route L, P(T,E,A) is the temperature control risk function, which represents the impact of temperature control threshold T, ambient temperature E, and temperature control anomaly A on the quality of goods, F(V,L) is the energy consumption function, which represents the energy consumption of the vehicle when traveling at a speed V on route L, R(C) is the cargo risk function, which represents the sensitivity or loss risk of cargo C to temperature control fluctuations, and α, β, γ, and δ are weight coefficients.

[0060] In this embodiment, in step S3, the formula for calculating the vehicle temperature threshold interval is as follows:

[0061]

[0062] Among them, T min is the lower limit of vehicle temperature threshold, T max is the upper threshold of vehicle temperature, T min base T is the lower limit of the suitable temperature of the cargo, max base T is the upper limit of the suitable temperature for the cargo. env is the external ambient temperature, v is the vehicle speed;

[0063] f env (T env ) is the external temperature adjustment factor, defined as follows;

[0064]

[0065] Wherein, a is the adjustment coefficient for high temperature environment (such as 0.05), and b is the adjustment coefficient for low temperature environment (such as 0.02);

[0066] f V (v) is the vehicle speed adjustment factor, which is defined as follows:

[0067]

[0068] Among them, v threshold is the vehicle speed threshold (such as 60km / h), c is the temperature control adjustment coefficient under low speed conditions (such as 0.01), and d is the temperature control adjustment coefficient under high speed conditions (such as 0.005).

[0069] In this embodiment, in step S4, the over-temperature value is calculated using the following formula:

[0070]

[0071] Where ΔT is the over-temperature value, T actual is the cabin temperature, T min and T max is the vehicle temperature threshold range;

[0072] Get the overtemperature duration T duration , set the over-temperature threshold ΔT threshold and time threshold T threshold , when ΔT>ΔT threshold And T duration >T threshold , it is determined to be abnormal temperature control;

[0073] When the vehicle speed is close to 0 km / h and is within the range of the loading and unloading point, and the door is in the open state, short-term over-temperature is allowed, and the temperature control abnormality judgment is ignored to exclude short-term over-temperature caused by normal operation.

[0074] In this embodiment, in step S4, the temperature control abnormality determination level rules are as follows:

[0075] S 异常 =ω 1 ·ΔT+ω 2 ·T duration -ω 3 f(v, position)

[0076] Among them, S 异常 is the abnormality score, ω 1 ,ω 2 and ω 3 is the weight coefficient, f(v, position) is the state correction function, and it is combined with the vehicle speed and position to determine whether it is a normal short-term overtemperature;

[0077] When S异常 ≤S 轻微 , the abnormal rating was determined to be slight;

[0078] When S 轻微 异常 ≤S 中度 , the abnormal rating was determined to be moderate;

[0079] When S 异常 >S 中度 , the abnormal rating is judged to be severe.

[0080] In this embodiment, in step S5, generating a corresponding processing solution according to the abnormal level includes:

[0081] When the exception level is minor:

[0082] (1) SMS notifications from operators and APP stations;

[0083] (2) Increase the cooling power and reduce the cabin temperature;

[0084] When the abnormality level is moderate:

[0085] (1) Intelligent voice call driver processing;

[0086] (2) Abnormal information is fed back to the monitoring center to remind operators to follow up and handle the problem;

[0087] When the exception level is severe:

[0088] (1) Manual voice call to the driver for processing;

[0089] (2) Abnormal information is fed back to the monitoring center to remind operators to follow up and handle the problem;

[0090] (3) Notify the cargo owner and alert him to possible risks.

[0091] The present invention installs a temperature sensor, a GPS module, a door status sensor, and a vehicle speed sensor on a transport vehicle to respectively obtain the temperature in the vehicle compartment, the vehicle position, the door status, and the vehicle speed. The sensor data is collected and stored in real time through the IoT platform, and real-time vehicle temperature control abnormality analysis is performed through an algorithm based on the above-mentioned judgment rules.

[0092] The present invention can realize accurate identification and graded evaluation of temperature control anomalies in refrigerated transportation by comprehensively considering the over-temperature value, duration, vehicle speed and position, and excluding normal short-term over-temperature (such as loading and unloading). This method not only improves the accuracy of abnormality judgment, but also effectively reduces the false alarm rate, thereby ensuring transportation efficiency and cargo quality.

[0093] ​It is known from common technical knowledge that the present invention can be implemented by other embodiments that do not deviate from its spirit or essential features. Therefore, the above disclosed embodiments are only illustrative in all respects and are not exclusive. All changes within the scope of the present invention or within the scope equivalent to the present invention are included in the present invention.

Claims

1. A method for identifying abnormalities in temperature-controlled transportation of refrigerated vehicles, characterized in that: The following steps are involved: S1. Collect and maintain the appropriate temperature of goods according to the category of goods; S2. Pre-draw the route plan and electronic fence of the loading and unloading area; S3. Calculate and adjust the vehicle temperature threshold interval based on the pre-collected cargo suitable temperature threshold interval, combined with the current vehicle speed and external ambient temperature; S4. Obtain the temperature in the vehicle compartment through the sensor, calculate the over-temperature value and duration, and determine whether the vehicle temperature control is abnormal and the abnormality level in combination with the current vehicle speed and vehicle position factors; S5. Generate a corresponding processing plan according to the abnormal level. If there is a level increase or decrease, generate a supplementary plan; S6. Store historical transportation routes, vehicle speeds, cargo types, cargo weights, temperature control threshold ranges, external ambient temperatures, and temperature control anomaly information into a history table. At the same time, train the model through a large model to optimize route planning and anomaly prediction.

2. A method for identifying abnormalities in temperature-controlled transportation of refrigerated vehicles according to claim 1, characterized in that: In step S2, the route planning comprehensively considers the historical transport route L, vehicle speed V, cargo C, temperature control threshold interval T, external environment temperature E and temperature control anomaly A, and uses the following mathematical formula to optimize the transport route: J=α·D(L)+β·P(T,E,A)+γ·F(V,L)+δ·R(C) Among them, J represents the comprehensive cost of the transportation route, D(L) is the historical route optimization distance function, which represents the transportation distance or time corresponding to the selection of route L, P(T,E,A) is the temperature control risk function, which represents the impact of temperature control threshold T, ambient temperature E, and temperature control anomaly A on the quality of goods, F(V,L) is the energy consumption function, which represents the energy consumption of the vehicle when traveling at a speed V on route L, R(C) is the cargo risk function, which represents the sensitivity or loss risk of cargo C to temperature control fluctuations, and α, β, γ, and δ are weight coefficients.

3. A method for identifying abnormalities in temperature-controlled transportation of refrigerated vehicles according to claim 1, characterized in that: In step S3, the formula for calculating the vehicle temperature threshold interval is as follows: Among them, T min is the lower limit of vehicle temperature threshold, T max is the upper threshold of vehicle temperature, T min base T is the lower limit of the suitable temperature of the cargo, max base T is the upper limit of the suitable temperature for the cargo. env is the external ambient temperature, v is the vehicle speed; f env (T env ) is the external temperature adjustment factor, defined as follows; Among them, a is the adjustment coefficient of high temperature environment, and b is the adjustment coefficient of low temperature environment; f V (v) is the vehicle speed adjustment factor, which is defined as follows: Among them, v threshold is the vehicle speed threshold, c is the temperature control adjustment coefficient under low speed conditions, and d is the temperature control adjustment coefficient under high speed conditions.

4. A method for identifying abnormalities in temperature-controlled transportation of refrigerated vehicles according to claim 3, characterized in that: In step S4, the over-temperature value is calculated as follows: Where ΔT is the over-temperature value, T actual is the cabin temperature, T min and T max is the vehicle temperature threshold range; Get the overtemperature duration T duration , set the over-temperature threshold ΔT threshold and time threshold T threshold , when ΔT>ΔT threshold And T duration >T threshold , it is determined to be abnormal temperature control; When the vehicle speed is close to 0 km / h and is within the range of the loading and unloading point, and the door is open, short-term over-temperature is allowed and the temperature control abnormality judgment is ignored.

5. A method for identifying abnormalities in temperature-controlled transportation of refrigerated vehicles according to claim 4, characterized in that: In step S4, the temperature control abnormality determination level rules are as follows: S 异常 = ω1·ΔT + ω2·T duration - ω3·f(v, position) Among them, S 异常 is the abnormality score, ω1, ω2 and ω3 are weight coefficients, f(v, position) is the state correction function, and the vehicle speed and position are combined to determine whether it is a normal short-term overtemperature; When S 异常 ≤S 轻微 , the abnormal rating was determined to be slight; When S 轻微 异常 ≤S 中度 , the abnormal rating was determined to be moderate;​ When S 异常 >S 中度 , the abnormal rating is judged to be severe.

6. A method for identifying abnormalities in temperature-controlled transportation of refrigerated vehicles according to claim 5, characterized in that: In step S5, generating a corresponding processing solution according to the abnormal level includes: When the exception level is minor: (1) SMS notifications from operators and APP stations; (2) Increase the cooling power and reduce the cabin temperature; When the abnormality level is moderate: (1) Intelligent voice call driver processing; (2) Abnormal information is fed back to the monitoring center to remind operators to follow up and handle the problem; When the exception level is severe: (1) Manual voice call to the driver for processing; (2) Abnormal information is fed back to the monitoring center to remind operators to follow up and handle the problem; (3) Notify the cargo owner and alert him to possible risks.

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