CN114996661B - Refrigerator car temperature monitoring method and system - Google Patents

Refrigerator car temperature monitoring method and system Download PDF

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CN114996661B
CN114996661B CN202210929645.8A CN202210929645A CN114996661B CN 114996661 B CN114996661 B CN 114996661B CN 202210929645 A CN202210929645 A CN 202210929645A CN 114996661 B CN114996661 B CN 114996661B
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阮辉
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Abstract

The invention relates to the technical field of refrigeration control, in particular to a method and a system for monitoring the temperature of a refrigerator car, wherein the method comprises the following steps: collecting the internal temperature, the distance to transported articles and the ambient temperature of each set position in the refrigerator car at each detection time in a historical period; obtaining a plurality of distance grades according to the distance; calculating the internal and external temperature difference according to the internal temperature and the environmental temperature to obtain a plurality of temperature difference grades; calculating a second surface temperature of the transported item according to the internal temperature, and calculating temperature influence values of all temperature difference levels based on the first surface temperature and the second surface temperature; calculating distance accurate values of all distance grades and the correlation and credibility of all set positions according to the internal temperature and the first surface temperature; and calculating the actual surface temperature of the transported goods according to the correlation, the credibility, the accurate distance value and the temperature influence value, thereby realizing the monitoring of the temperature of the refrigerated truck. The invention can accurately acquire the actual surface temperature and realize the control of the temperature of the refrigerated truck.

Description

Refrigerator car temperature monitoring method and system
Technical Field
The invention relates to the technical field of refrigeration control, in particular to a method and a system for monitoring the temperature of a refrigerator car.
Background
In recent years, cold chain transportation is gradually popularized and rapidly developed in China, and a refrigerator car serving as key equipment of cold chain logistics has heat preservation and refrigeration functions and can meet the requirement of short-distance refrigeration transportation of refrigerated goods. The internal temperature of maintenance refrigerator carriage is invariable, keeps the inside less difference in temperature everywhere of box, plays important role to the quality of guaranteeing article in the transportation. Therefore, the temperature in the refrigerator compartment is required to be guaranteed to be within the refrigeration temperature range of transported goods at all times in the transportation process, and the temperature in the refrigerator compartment is required to be monitored and regulated in real time.
The traditional refrigerated vehicle monitoring system directly utilizes the temperature sensor to obtain the temperature inside the refrigerated vehicle, and does not consider that the distribution of the temperature inside the refrigerated vehicle is not uniform, namely, the distance from the temperature sensor to the transported goods is different, so that the influence on the surface temperature of the transported goods is different, and the traditional refrigerated vehicle monitoring system has a plurality of defects, such as simple and crude control, incapability of monitoring the actual state inside the vehicle in real time, scientific and representative control strategy and low monitoring precision; therefore, how to reasonably and accurately acquire the actual state of the interior of the carriage so as to control the temperature distribution in the refrigerated vehicle within a reasonable range is a technical problem which needs to be solved urgently by the technical personnel in the field.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a refrigerator car temperature monitoring method, which adopts the following technical scheme:
collecting the internal temperature and the distance from each set position in the refrigerator van to the transported object at each detection time in the historical period, and collecting the environmental temperature corresponding to each detection time in the historical period; and dividing the distance into a plurality of distance levels; setting a first surface temperature corresponding to each detection time of the transported object in a historical time period;
calculating the internal and external temperature difference corresponding to each detection time in the historical time period according to the internal temperature and the environmental temperature; dividing the internal and external temperature difference into a plurality of temperature difference grades; the temperature difference grade at least comprises a detection moment;
calculating a second surface temperature corresponding to each detection time of the transported object in a historical period according to the internal temperature, and calculating a temperature influence value corresponding to each temperature difference grade based on a first surface temperature and a second surface temperature corresponding to each detection time of the transported object in each temperature difference grade;
calculating distance accurate values corresponding to the distance grades according to the internal temperature corresponding to the distance grades and the first surface temperature;
calculating the absolute value of the difference between each internal temperature and the corresponding first surface temperature, and calculating the correlation and credibility corresponding to each set position according to the absolute value of the difference;
and calculating the actual surface temperature of the transported object at the current detection moment according to the weight, the internal temperature corresponding to the current detection moment of each set position and the temperature influence value, so as to realize the monitoring of the temperature of the refrigerated truck.
Further, the calculation method of the internal and external temperature difference comprises the following steps: and calculating the average value corresponding to each internal temperature, and recording the difference value between the environmental temperature and the average value as the internal and external temperature difference.
Further, the method for acquiring the second surface temperature comprises the following steps: and according to the distance corresponding to each internal temperature, obtaining the temperature weight corresponding to each internal temperature, and carrying out weighted summation on the internal temperature and the temperature weight corresponding to the internal temperature to obtain the second surface temperature.
Further, the temperature influence value is:
Figure 723427DEST_PATH_IMAGE001
wherein
Figure 23959DEST_PATH_IMAGE002
Is the temperature influence value corresponding to the temperature difference grade o,
Figure 207570DEST_PATH_IMAGE003
the number of detection moments in the temperature difference grade o;
Figure 630461DEST_PATH_IMAGE004
for the first surface temperature of the transported item at the jth detection moment in the temperature difference level o,
Figure 830629DEST_PATH_IMAGE005
and the corresponding second surface temperature of the transported object at the jth detection time in the temperature difference grade o.
Further, the method for obtaining the accurate distance value comprises the following steps: all internal temperatures corresponding to the distance grade; calculating the difference value of each internal temperature and the first surface temperature corresponding to the internal temperature; obtaining an accurate distance value according to the difference value, wherein the calculation formula is as follows:
Figure 997168DEST_PATH_IMAGE006
in the formula,
Figure 261665DEST_PATH_IMAGE007
for the distance accuracy value corresponding to the distance level y,
Figure 121037DEST_PATH_IMAGE008
for the distance class y to correspond to the number of internal temperatures,
Figure 542922DEST_PATH_IMAGE009
for the ith internal temperature in the distance class y,
Figure 513152DEST_PATH_IMAGE010
a first surface temperature corresponding to the ith internal temperature in the distance class y;
Figure 835418DEST_PATH_IMAGE011
to adjust the coefficient;
the method for acquiring the first surface temperature corresponding to the internal temperature comprises the following steps: and acquiring the detection time corresponding to the internal temperature in the historical time period, and recording the first surface temperature corresponding to the transported article at the detection time as the first surface temperature corresponding to the internal temperature.
Further, the correlation corresponding to each setting position is as follows:
Figure 537795DEST_PATH_IMAGE012
wherein,
Figure 696244DEST_PATH_IMAGE013
for the correlation corresponding to the kth setting position,
Figure 689739DEST_PATH_IMAGE014
the absolute value of the difference value between the internal temperature corresponding to the kth set position at the e-th detection moment and the first surface temperature corresponding to the transported object at the e-th detection moment is obtained;
Figure 882822DEST_PATH_IMAGE015
the number of detected time instants within the history period,
Figure 333264DEST_PATH_IMAGE016
is the number of the set positions;
the credibility corresponding to each set position is as follows:
Figure 775747DEST_PATH_IMAGE017
wherein,
Figure 41774DEST_PATH_IMAGE018
for the correlation corresponding to the kth setting position,
Figure 886102DEST_PATH_IMAGE014
the absolute value of the difference value between the internal temperature corresponding to the kth set position at the e-th detection moment and the first surface temperature corresponding to the transported object at the e-th detection moment is obtained;
Figure 507445DEST_PATH_IMAGE015
the number of detected time instants within the history period,
Figure 374907DEST_PATH_IMAGE019
is an exponential function with e as the base.
Further, the method for obtaining the weight comprises the following steps: forming all distance levels in the historical time period into a distance level set, obtaining the distance from each set position to the transported object at the current detection time, searching the distance level corresponding to each distance in the distance level set and obtaining a corresponding distance accurate value, calculating the product of the relevance, the credibility and the distance accurate value corresponding to each set position, and taking the value after the normalization of the product as the weight corresponding to each set position.
Further, the method for calculating the actual surface temperature of the transportation object corresponding to the current detection time comprises the following steps:
forming all temperature difference levels in a historical period into a temperature difference level set, calculating the internal and external temperature difference corresponding to the current detection moment, searching the temperature difference level corresponding to the internal and external temperature difference in the temperature difference level set and obtaining a corresponding temperature influence value; and weighting and summing the internal temperature corresponding to each set position at the current detection moment and the weight corresponding to each set position, and adding the obtained temperature influence value to obtain the actual surface temperature of the transported goods.
Further, the method further comprises the steps of obtaining the actual surface temperature of the transported object corresponding to the current detection time through an FC neural network; the specific process is as follows:
calculating actual surface temperature corresponding to each detection time of the transported object in a historical period, obtaining volume and heat conductivity corresponding to each detection time of the transported object in the historical period, obtaining a vector corresponding to each detection time in the historical period by using the volume, the heat conductivity, the internal temperature, the environmental temperature and the distance as data sets, training an FC neural network by using the vector corresponding to each detection time in the historical period and the actual surface temperature corresponding to each detection time of the transported object in the historical period as the data sets, obtaining the trained FC neural network, obtaining the vector corresponding to the current detection time, inputting the vector into the trained FC neural network, and outputting the actual surface temperature corresponding to the current detection time of the transported object;
the corresponding loss function of the FC neural network during training is as follows:
Figure 162735DEST_PATH_IMAGE020
in the formula,
Figure 143460DEST_PATH_IMAGE021
as a corresponding loss function of the FC neural network during training,
Figure 358541DEST_PATH_IMAGE022
in order to be a function of the cross-entropy loss,
Figure 962566DEST_PATH_IMAGE023
is a difference lossA loss function;
the difference loss function is:
Figure 881981DEST_PATH_IMAGE024
in the formula,
Figure 123738DEST_PATH_IMAGE023
in order to be a function of the difference loss,
Figure 634353DEST_PATH_IMAGE025
for the corresponding actual surface temperature in the data set,
Figure 991254DEST_PATH_IMAGE026
actual surface temperature output for the FC neural network;
Figure 448780DEST_PATH_IMAGE027
in order to take the function of the minimum value,
Figure 810623DEST_PATH_IMAGE028
is an exponential function with e as the base.
The invention also provides a refrigerator car temperature monitoring system which comprises a processor and a memory, wherein the processor executes a program of the refrigerator car temperature monitoring method stored in the memory.
The embodiment of the invention at least has the following beneficial effects:
according to the method, the temperature influence value of each temperature difference grade is calculated through the first surface temperature and the second surface temperature of the transported object at each detection time in the temperature difference grade, the different influences of the different temperature difference grades inside and outside the refrigerator car on the actual surface temperature of the transported object are considered, the temperature influence value of each temperature difference grade on the actual surface temperature of the transported object is calculated, and the calculation result is more accurate; according to the method, the distance is divided into a plurality of distance grades according to different distances from each set position to the transported articles, and the distance accurate value corresponding to each distance grade is calculated; considering that the distances from the set positions to the transported objects are different, the contribution of the internal temperature corresponding to the set positions to the actual surface temperature of the transported objects is different; calculating the correlation and credibility corresponding to each set position based on the absolute value of the difference value between the internal temperature of each set position and the corresponding first surface temperature in the historical time period; and calculating the actual surface temperature of the transported goods at the current detection time according to the weight, the internal temperature of each set position in the refrigerator car at the current detection time and the temperature influence value, so as to realize the monitoring of the refrigerator car temperature. According to the method, the influence of the distance and the temperature difference grade of the internal environment and the external environment of the refrigerator compartment on the actual surface temperature of the transported goods is considered, the relevance and the credibility corresponding to each set position are also considered, the weight corresponding to each set position is calculated according to the relevance and the credibility, and the actual surface temperature of the transported goods is further obtained. The invention analyzes the influence of different reference factors on the actual surface temperature from multiple angles, so that the obtained actual surface temperature is more accurate, the precision of monitoring the temperature of the refrigerator car is improved, the quality of transported goods is ensured to the greatest extent, and the damage rate of the transported goods is reduced.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart illustrating the steps of a method for monitoring the temperature of a refrigerated vehicle according to an embodiment of the present invention.
Detailed Description
To further explain the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description of the proposed solution, its specific implementation, structure, features and effects will be made with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The specific scenes aimed by the invention are as follows: in the cold chain logistics industry, a refrigerator car is used as important equipment of cold chain logistics, and whether the temperature in the refrigerator car can be in a fresh-keeping temperature range corresponding to transported goods all the time in the transportation process is a key factor for guaranteeing the quality of the transported goods. Therefore, the actual surface temperature of the transported goods should be monitored in real time and adjusted in the transportation process, so that the transported goods are kept fresh to the maximum extent, and the damage rate of the transported goods is reduced.
Referring to fig. 1, a flow chart illustrating steps of a method for monitoring a temperature of a refrigerator car according to an embodiment of the present invention is shown, the method including the steps of:
step 1, collecting the internal temperature and the distance from each set position in the refrigerator van to a transported object at each detection time in a historical period, and collecting the environmental temperature corresponding to each detection time in the historical period; dividing the distance into a plurality of distance grades; and setting a first surface temperature corresponding to each detection time of the transported object in the historical time.
Specifically, because the distribution of the inside temperature of the refrigerator car is not uniform, corresponding set positions are uniformly arranged on two sides of the inside car body of the refrigerator car, namely, the distance between two adjacent set positions in the set positions corresponding to the same side of the car body is equal, a temperature sensor is respectively arranged at each set position and used for collecting the inside temperature corresponding to each set position at each detection moment in the historical time period, and a temperature sensor is arranged outside the refrigerator car and used for collecting the environment temperature corresponding to each detection moment in the historical time period.
The distance from each set position to the transported object is obtained according to the neural network model, and the specific process is as follows: the method comprises the following steps of installing a camera right above the interior of a refrigerator van, shooting the interior of the refrigerator van by using a wide-angle camera to obtain a corresponding image, inputting the image into a neural network model trained in advance to obtain the distance from each set position to a transported object, further dividing the distance into a plurality of distance grades, and setting the number of the distance grades by an implementer according to actual conditions; wherein the distance is the closest distance of each set position to the transported item, i.e. the vertical distance of each set position to the transported item. The selection of the neural network model is determined by an implementer, and the training process of the neural network model is a known technology and is not described in detail.
It should be noted that the refrigerator car in this embodiment is specially used for transporting single articles, and the articles are placed in the storage box or the storage frame, so that the placement is not neat, and the articles are placed from inside to outside when placed, that is, when the articles are placed well at the innermost position of the carriage and cannot be placed, the articles are placed outwards in sequence; the distance from each set position to the transported article is the distance from each set position to the placed article.
The first surface temperature corresponding to the transported object is set by relevant workers according to human experience based on the collected internal temperature and the collected environmental temperature corresponding to each set position and the distance from each set position to the transported object.
The embodiment is directed to multiple transportation of a plurality of refrigerated vehicles in a historical time period, namely, one refrigerated vehicle completes one transportation from a starting place to a destination, and in the route from the starting place to the destination, the transported goods in the refrigerated vehicle are not increased or reduced; the quantity of transported goods which can be contained by different refrigerated vehicles is different, and the quantity of the corresponding transported goods is also different during each transportation, although the capacities of the different refrigerated vehicles are different, the quantity of the set positions in each refrigerated vehicle is consistent; the distance from each set position to the transported item varies over the history of time. Because the transported goods in the refrigerated truck can not be increased or reduced in the route from the starting place to the destination, when the distance from each set position to the transported goods is obtained, only one time of obtaining is needed in each transportation process, the repeated obtaining of data is avoided, the complexity of the subsequent analysis process is increased, and the accuracy of the analysis result is reduced.
The time interval between two adjacent detection moments is 10 minutes, the implementer can adjust the time interval according to specific conditions, and the specific time length of the historical time period is set by the implementer according to actual conditions.
Step 2, calculating the internal and external temperature difference corresponding to each detection moment according to the internal temperature and the environmental temperature; dividing the internal and external temperature difference into a plurality of temperature difference grades; at least one detection moment is included in one temperature difference grade.
The method for acquiring the internal and external temperature difference comprises the following steps: calculating the average value corresponding to each internal temperature, and recording the difference value between the environment temperature and the average value as the internal and external temperature difference, wherein the calculation formula is as follows:
Figure 164244DEST_PATH_IMAGE029
wherein,
Figure 742861DEST_PATH_IMAGE030
the temperature difference between the inside and the outside corresponding to the e-th detection moment,
Figure 738499DEST_PATH_IMAGE031
is the ambient temperature corresponding to the e-th detection moment,
Figure 220427DEST_PATH_IMAGE032
for the internal temperature corresponding to the kth set position at the e-th detection time,
Figure 541687DEST_PATH_IMAGE033
to set the number of positions.
The difference between the temperature in the refrigerator compartment and the external environment temperature is reflected by the internal and external temperature difference, namely, the internal and external temperature difference of the refrigerator car, the temperature difference can influence the temperature in the refrigerator compartment, the influence degree generated by different temperature differences is different, the greater the internal and external temperature difference is, the greater the influence degree on the temperature in the refrigerator compartment is, and therefore the internal and external temperature difference corresponding to each detection moment is calculated according to the environment temperature and the internal temperature, and the influence degree on the temperature in the refrigerator compartment is reflected. The smaller the difference between the average value of the internal temperatures at each set position at a certain detection time and the ambient temperature at that detection time, the smaller the internal-external temperature difference, and the smaller the influence on the temperature in the refrigerator compartment.
Further, dividing all internal and external differences in the historical period into a plurality of temperature difference levels, wherein one temperature difference level at least comprises one internal and external difference, namely one temperature difference level at least comprises one detection moment; the number of temperature difference levels is set by an implementer according to actual conditions.
And 3, calculating second surface temperatures corresponding to the transported articles at all detection moments according to the internal temperatures, and calculating temperature influence values corresponding to all temperature difference grades based on the first surface temperatures and the second surface temperatures corresponding to all detection moments of the transported articles in all temperature difference grades.
The method for calculating the second surface temperature of the transported article at each detection time comprises the following steps: and according to the distance from each set position to the transported object at any detection moment, obtaining the temperature weight corresponding to each internal temperature at the detection moment, and performing weighted summation on the internal temperature and the corresponding temperature weight to obtain a second surface temperature.
The specific method for acquiring the temperature weight comprises the following steps: calculating the distance accumulation sum of the distances corresponding to the detection moment, normalizing the distances according to the distance accumulation sum to ensure that the value of each normalized distance is between 0 and 1, ensuring that the sum of all normalized distances is 1, then calculating the difference value between 1 and each normalized distance, and normalizing the difference value to obtain the temperature weight corresponding to each internal temperature. The calculation formula of the temperature weight is as follows:
Figure 342022DEST_PATH_IMAGE034
in the formula (I), the reaction is carried out,
Figure 406930DEST_PATH_IMAGE035
the temperature weight of the internal temperature corresponding to the kth set position at the detection moment;
Figure 743364DEST_PATH_IMAGE036
is the difference between 1 and the corresponding normalized distance from the k-th set position to the transported item at the detection moment,
Figure 235525DEST_PATH_IMAGE016
is the number of the set positions;
Figure 788735DEST_PATH_IMAGE037
in the formula (I), wherein,
Figure 391755DEST_PATH_IMAGE038
for the distance of the kth set position to the transported item at the moment of detection,
Figure 582696DEST_PATH_IMAGE039
is the number of set positions.
The distribution of the internal temperature of the refrigerator compartment is not uniform, the distance from each set position to the transported item at the detection time determines the degree of contribution of the corresponding internal temperature to the calculation of the second surface temperature, and the closer the distance from the set position to the transported item, the closer the internal temperature corresponding to the set position is to the surface temperature of the transported item, the greater the contribution of the internal temperature corresponding to the set position to the calculation of the second surface temperature, the greater the corresponding temperature weight, and conversely, the smaller the contribution of the internal temperature corresponding to the set position to the calculation of the second surface temperature, the smaller the corresponding temperature weight.
Specifically, the temperature influence value is:
Figure 822922DEST_PATH_IMAGE040
wherein
Figure 348582DEST_PATH_IMAGE041
Is the temperature influence value corresponding to the temperature difference grade o,
Figure 506025DEST_PATH_IMAGE042
the number of detection moments in the temperature difference grade o;
Figure 66319DEST_PATH_IMAGE043
for the first surface temperature of the transported item at the jth detection moment in the temperature difference level o,
Figure 883971DEST_PATH_IMAGE044
and the corresponding second surface temperature of the transported object at the jth detection time in the temperature difference grade o.
Figure 428085DEST_PATH_IMAGE045
The difference between the first surface temperature and the second surface temperature corresponding to the jth detection time of the transported object in the temperature difference grade o represents the setting accuracy of the first surface temperature corresponding to the transported object, and the larger the difference is, the lower the setting accuracy of the first surface temperature is; the setting accuracy of the first surface temperature corresponding to different detection moments in the same temperature difference grade is approximately the same, and the influence degrees of the different temperature difference grades on the setting accuracy of the first surface temperature are different, so that the average value of the corresponding difference of all the detection moments in the same temperature difference grade is calculated and used as the temperature influence value corresponding to the temperature difference grade, and the acquired data are more accurate when the actual surface temperature of the transported object is subsequently calculated.
And 4, calculating distance accurate values corresponding to the distance grades according to the internal temperature corresponding to each distance grade and the first surface temperature, wherein one distance grade at least comprises one internal temperature.
The method for acquiring the accurate distance value comprises the following steps: all internal temperatures corresponding to the distance grade; calculating the difference value of each internal temperature and the first surface temperature corresponding to the internal temperature; obtaining an accurate distance value according to the difference value, wherein the calculation formula is as follows:
Figure 326902DEST_PATH_IMAGE046
in the formula,
Figure 741703DEST_PATH_IMAGE047
for the distance accuracy value corresponding to the distance level y,
Figure 730256DEST_PATH_IMAGE048
for the distance class y to correspond to the number of internal temperatures,
Figure 230508DEST_PATH_IMAGE049
for the ith internal temperature in the distance class y,
Figure 995332DEST_PATH_IMAGE050
a first surface temperature corresponding to the ith internal temperature in the distance class y;
Figure 841803DEST_PATH_IMAGE011
is an adjustment factor; the value of the adjustment coefficient in this embodiment is 100, which ensures that
Figure 689674DEST_PATH_IMAGE051
Is sufficiently large that
Figure 755850DEST_PATH_IMAGE051
The value of (2) is less than 1, and an implementer can set the value of the adjustment coefficient according to specific conditions.
The method for acquiring the first surface temperature corresponding to the internal temperature comprises the following steps: and acquiring the detection time corresponding to the internal temperature in the historical time period, and recording the first surface temperature corresponding to the transported article at the detection time as the first surface temperature corresponding to the internal temperature.
It should be noted that the distance accuracy value represents the accuracy of the corresponding internal temperature representing the surface temperature of the transported object at the distance grade,
Figure 714579DEST_PATH_IMAGE052
the difference between the ith internal temperature and the corresponding first surface temperature in the distance grade y is represented, and the smaller the difference is, the higher the accuracy is; conversely, the lower the accuracy; generally, the accuracy of representing the surface temperature of the corresponding transported object by a plurality of internal temperatures in the same distance grade is approximately the same, so the embodiment calculates the average value of the differences in the distance grade to reflect the accurate distance value corresponding to the distance grade.
And 5, calculating the absolute value of the difference between each internal temperature and the corresponding first surface temperature, and calculating the correlation and credibility corresponding to each set position according to the absolute value of the difference.
The correlation corresponding to each set position is as follows:
Figure 681135DEST_PATH_IMAGE053
wherein,
Figure 27803DEST_PATH_IMAGE054
for the correlation corresponding to the kth setting position,
Figure 987800DEST_PATH_IMAGE055
the absolute value of the difference value between the internal temperature corresponding to the kth set position at the e-th detection moment and the first surface temperature corresponding to the transported object at the e-th detection moment is obtained;
Figure 343695DEST_PATH_IMAGE056
the number of detected time instants within the history period,
Figure 836862DEST_PATH_IMAGE057
to set the number of positions, the denominator is increased by 1 in order to avoid the case where the denominator is 0 in the formula.
Figure 354431DEST_PATH_IMAGE058
Is a firstThe difference absolute value between the internal temperature corresponding to the k set positions at the e-th detection time and the first surface temperature corresponding to the transported object at the e-th detection time represents the difference between the internal temperature corresponding to the k-th set positions and the first surface temperature corresponding to the transported object, and the larger the difference is, the smaller the correlation between the internal temperature corresponding to the set positions is, namely, the larger the difference between the internal temperature corresponding to the set positions and the first surface temperature of the transported object is; if the difference between the internal temperature corresponding to any one set position and the first surface temperature of the transported object is smaller than the difference between the internal temperature corresponding to other set positions and the first surface temperature of the transported object, the smaller the difference between the internal temperature corresponding to the set position and the first surface temperature of the transported object is, the greater the correlation between the internal temperature corresponding to the set position and the first surface temperature of the transported object is.
The credibility corresponding to each set position is as follows:
Figure 67303DEST_PATH_IMAGE059
wherein,
Figure 898993DEST_PATH_IMAGE060
for the reliability of the correspondence of the kth setting position,
Figure 270104DEST_PATH_IMAGE058
the absolute value of the difference value between the internal temperature corresponding to the kth set position at the e-th detection moment and the first surface temperature corresponding to the transported object at the e-th detection moment is obtained;
Figure 958574DEST_PATH_IMAGE015
the number of detected time instants within the history period,
Figure 689901DEST_PATH_IMAGE061
is an exponential function with e as the base.
In the same way as above, the first and second,
Figure 325282DEST_PATH_IMAGE058
for the absolute value of the difference between the internal temperature corresponding to the kth set position at the e-th detection time and the first surface temperature corresponding to the transported object at the e-th detection time, obtaining the change condition of the difference between the internal temperature corresponding to the set position and the first surface temperature of the transported object through the absolute value of the difference between all the internal temperatures corresponding to one set position in the historical period and the corresponding first surface temperatures, wherein the smaller the change is, the smaller the difference between the internal temperature corresponding to the set position and the first surface temperature of the transported object is represented, the more accurate the data of the internal temperature corresponding to the set position is obtained, and the greater the credibility corresponding to the set position is; since the absolute value of the difference between the internal temperature corresponding to the set position and the first surface temperature corresponding to the transported object is in a negative correlation with the reliability corresponding to the set position, the present embodiment adopts
Figure 589779DEST_PATH_IMAGE062
The function determines the reliability corresponding to each set position.
And 6, obtaining weights corresponding to all set positions according to the correlation, the credibility and the accurate distance values, and calculating the actual surface temperature of the transported object corresponding to the current detection time according to the weights, the internal temperature of all the set positions in the refrigerator car corresponding to the current detection time and the temperature influence value to realize the monitoring of the refrigerator car temperature.
Specifically, the method for obtaining the weight includes: forming all distance levels in the historical time period into a distance level set, obtaining the distance from each set position to the transported object at the current detection time, searching the distance level corresponding to each distance in the distance level set and obtaining a corresponding distance accurate value, calculating the product of the relevance, the credibility and the distance accurate value corresponding to each set position, and taking the value after the normalization of the product as the weight corresponding to each set position. For example, the weight corresponding to the kth setting position is:
Figure 652413DEST_PATH_IMAGE063
in the formula,
Figure 74298DEST_PATH_IMAGE064
the weight corresponding to the kth setting position,
Figure 44528DEST_PATH_IMAGE065
the product of the correlation, the credibility and the accurate value of the distance corresponding to the kth set position;
Figure 632373DEST_PATH_IMAGE016
is the number of set positions.
Figure 662646DEST_PATH_IMAGE066
Wherein
Figure 899723DEST_PATH_IMAGE067
for the correlation corresponding to the kth setting position,
Figure 922912DEST_PATH_IMAGE060
for the reliability of the correspondence of the kth setting position,
Figure 115996DEST_PATH_IMAGE068
and when the distance from the kth set position to the transported article is the distance grade x, obtaining the accurate distance value corresponding to the distance grade x.
It should be noted that, in the following description,
Figure 67902DEST_PATH_IMAGE068
when the distance from the kth set position to the transported object is the distance grade x, calculating the accurate distance value corresponding to the distance grade x according to the accurate distance value, the correlation and the credibility, and considering the distance from each set position to the transported object to obtain the accuracy rate of the internal temperature of the set position corresponding to the distance, namely the accurate distance value corresponding to the distance, wherein if the accurate distance value is larger, the weight corresponding to the set position is larger; if the correlation corresponding to one set position is larger, calculatingThe weighting of the internal temperature corresponding to the set position is larger when the actual surface temperature of the transport object is measured, and the weighting of the internal temperature corresponding to the set position when the actual surface temperature of the transport object is calculated is larger when the reliability of the corresponding set position is larger. The weight setting is considered from multiple angles, so that the weight is more convincing to obtain, and meanwhile, the actual surface temperature of the transported goods calculated subsequently is more accurate.
The method for calculating the actual surface temperature of the transported goods comprises the following steps: forming all temperature difference levels in a historical period into a temperature difference level set, calculating the internal and external temperature difference corresponding to the current detection moment, searching the temperature difference level corresponding to the internal and external temperature difference in the temperature difference level set and obtaining a corresponding temperature influence value; weighting and summing the internal temperature corresponding to each set position at the current detection time and the weight corresponding to each set position, and adding the obtained temperature influence value to obtain the actual surface temperature of the transported object, wherein the calculation formula is as follows:
Figure 448068DEST_PATH_IMAGE069
wherein,
Figure 274948DEST_PATH_IMAGE070
the actual surface temperature of the transported goods at the current detection moment when the corresponding temperature difference grade is the temperature difference grade o,
Figure 322538DEST_PATH_IMAGE064
the weight corresponding to the kth setting position,
Figure 445346DEST_PATH_IMAGE071
for the internal temperature corresponding to the current detection time at the kth set position,
Figure 578387DEST_PATH_IMAGE041
is the temperature influence value corresponding to the temperature difference grade o.
The actual surface temperature of the transported object at the current detection moment is obtained by calculating the weighted sum of the internal temperature corresponding to each set position and the weighted sum of the temperature influence values corresponding to the temperature difference grade of the current detection moment and the weighted sum of the weighted sum and the temperature influence value corresponding to the temperature difference grade of the current detection moment, the influence of different temperature differences of the internal environment and the external environment of the refrigerator compartment on the surface temperature of the transported object is also considered, the obtained actual surface temperature is more accurate, and the quality of the transported object can be guaranteed to the greatest extent.
And further, acquiring the actual surface temperature of the transported objects in the refrigerator van in real time, judging whether the actual surface temperature is in the temperature range for keeping the transported objects fresh, and if not, giving an alarm by the system to remind related workers of adjusting the temperature in the refrigerator van.
As other embodiments, the actual surface temperature of the transported object can also be obtained through the FC neural network; the specific process is as follows:
calculating the actual surface temperature of the transported object corresponding to each detection time in the historical period, obtaining the volume and the heat conductivity of the transported object corresponding to each detection time in the historical period, obtaining the vector corresponding to each detection time in the historical period by using the volume, the heat conductivity, the internal temperature, the environmental temperature and the distance as the multi-dimensional vector, training the FC neural network by using the vector corresponding to each detection time in the historical period and the actual surface temperature of the transported object corresponding to each detection time in the historical period as a data set to obtain the trained FC neural network, obtaining the vector corresponding to the current detection time, inputting the vector into the trained FC neural network, and outputting the actual surface temperature of the transported object corresponding to the current detection time.
Specifically, the volume and the thermal conductivity corresponding to the transported goods are obtained through a neural network model, namely, the image obtained in the step 1 is input into the neural network model trained in advance, and the corresponding volume and the corresponding thermal conductivity are output; wherein the thermal conductivity is not described in detail in the prior art; the loss function of the neural network model is a cross entropy loss function, and the training process of the neural network model is a known technology and is not described in detail. The neural network model is the neural network model in the step 1, namely, the images are input into the neural network model trained in advance, the distance from each set position to the transported object is output, the volume and the heat conductivity corresponding to the transported object are obtained, and only the distance needs to be selected in the step 1 for subsequent analysis, namely, only one neural network model needs to be trained in the embodiment, so that the complexity of operation is reduced.
The corresponding loss function of the FC neural network is:
Figure 943378DEST_PATH_IMAGE020
in the formula,
Figure 579896DEST_PATH_IMAGE072
as a function of the loss of the FC neural network,
Figure 139184DEST_PATH_IMAGE073
in order to be a function of the cross-entropy loss,
Figure 759522DEST_PATH_IMAGE074
as a function of the difference loss.
The difference loss function is:
Figure 662624DEST_PATH_IMAGE024
in the formula,
Figure 419228DEST_PATH_IMAGE074
in order to be a function of the difference loss,
Figure 805210DEST_PATH_IMAGE025
for the corresponding actual surface temperature in the data set,
Figure 132417DEST_PATH_IMAGE075
actual surface temperature output for the FC neural network;
Figure 589943DEST_PATH_IMAGE076
in order to take the function of the minimum value,
Figure 715900DEST_PATH_IMAGE062
is an exponential function with e as the base.
Figure 397417DEST_PATH_IMAGE077
The difference between the corresponding actual surface temperature in the data set and the actual surface temperature output by the FC neural network is smaller, which indicates that the better the training result of the FC neural network is, and the difference and the loss of the FC neural network present a positive correlation relationship, namely, the smaller the difference is, the smaller the loss of the FC neural network is, the better the training result is; otherwise the worse the training result. Thus, calculated in this embodiment
Figure 477500DEST_PATH_IMAGE078
In the step (1), the first step,
Figure 738717DEST_PATH_IMAGE079
and
Figure 719180DEST_PATH_IMAGE078
the result of the calculation of (a) exhibits a negative correlation,
Figure 774861DEST_PATH_IMAGE078
is in the range of 0 to 1, and therefore is useful in this embodiment
Figure 76660DEST_PATH_IMAGE080
As a function of the differential loss of the FC neural network, such that the difference
Figure 141568DEST_PATH_IMAGE077
The loss of the FC neural network is positively correlated.
It should be noted that the loss function corresponding to the FC neural network is obtained through the difference loss function and the cross entropy loss function, so that the training time of the FC neural network is shorter, the training speed is faster, the efficiency of the FC neural network in the training process is improved, and the convergence of the FC neural network is accelerated; the training process of the FC neural network is well known in the art and will not be described in detail.
The invention also provides a refrigerator car temperature monitoring system, which comprises a processor and a memory, wherein the processor executes a program of a refrigerator car temperature monitoring method stored in the memory, and the specific implementation mode of the refrigerator car temperature monitoring method is given in detail in the steps 1 to 6 and is not described in detail.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; the modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present application, and are included in the protection scope of the present application.

Claims (7)

1. A method of monitoring the temperature of a refrigerated vehicle, the method comprising the steps of:
collecting the internal temperature and the distance from each set position in the refrigerator van to the transported object at each detection time in the historical period, and collecting the environmental temperature corresponding to each detection time in the historical period; and dividing the distance into a plurality of distance levels; artificially setting a first surface temperature corresponding to each detection time of the transported object in a historical period;
calculating the internal and external temperature difference corresponding to each detection time in the historical time period according to the internal temperature and the environmental temperature; dividing the internal and external temperature difference into a plurality of temperature difference grades; the temperature difference grade at least comprises a detection moment;
calculating a second surface temperature corresponding to each detection time of the transported object in a historical period according to the internal temperature, and calculating a temperature influence value corresponding to each temperature difference grade based on a first surface temperature and a second surface temperature corresponding to each detection time of the transported object in each temperature difference grade;
the second surface temperature obtaining method comprises the following steps: according to the distance corresponding to each internal temperature, obtaining a temperature weight corresponding to each internal temperature, and carrying out weighted summation on the internal temperature and the temperature weight corresponding to the internal temperature to obtain a second surface temperature;
the temperature influence values are:
Figure 764846DEST_PATH_IMAGE002
wherein
Figure DEST_PATH_IMAGE003
Is the temperature influence value corresponding to the temperature difference grade o,
Figure 362181DEST_PATH_IMAGE004
the number of detection moments in the temperature difference grade o;
Figure DEST_PATH_IMAGE005
for the first surface temperature of the transported item at the jth detection moment in the temperature difference level o,
Figure 789751DEST_PATH_IMAGE006
a second surface temperature corresponding to the j detection time of the transported object in the temperature difference grade o;
calculating distance accurate values corresponding to the distance grades according to the internal temperature corresponding to the distance grades and the first surface temperature;
the method for acquiring the accurate distance value comprises the following steps: all internal temperatures corresponding to the distance grade; calculating the difference value of each internal temperature and the first surface temperature corresponding to the internal temperature; obtaining an accurate distance value according to the difference value, wherein the calculation formula is as follows:
Figure 818625DEST_PATH_IMAGE008
in the formula,
Figure DEST_PATH_IMAGE009
for the distance accuracy value corresponding to the distance level y,
Figure 40659DEST_PATH_IMAGE010
the distance level y corresponds to the number of internal temperatures,
Figure DEST_PATH_IMAGE011
for the ith internal temperature in the distance class y,
Figure 390868DEST_PATH_IMAGE012
a first surface temperature corresponding to the ith internal temperature in the distance grade y;
Figure DEST_PATH_IMAGE013
is an adjustment factor;
the method for acquiring the first surface temperature corresponding to the internal temperature comprises the following steps: acquiring the detection time corresponding to the internal temperature in a historical period, and recording the first surface temperature of the transported article corresponding to the detection time as the first surface temperature corresponding to the internal temperature;
calculating the absolute value of the difference between each internal temperature and the corresponding first surface temperature, and calculating the correlation and credibility corresponding to each set position according to the absolute value of the difference;
and according to the correlation, the credibility and the accurate distance value, obtaining the weight corresponding to each set position, and according to the weight, the internal temperature corresponding to each set position at the current detection time and the temperature influence value, calculating the actual surface temperature corresponding to the transported object at the current detection time to realize the monitoring of the temperature of the refrigerated truck.
2. A refrigerator car temperature monitoring method as claimed in claim 1, wherein the calculation method of the difference between the internal and external temperatures is: and calculating the average value corresponding to each internal temperature, and recording the difference value between the environmental temperature and the average value as the internal and external temperature difference.
3. A refrigerator car temperature monitoring method according to claim 1,
the corresponding correlation of each set position is as follows:
Figure DEST_PATH_IMAGE015
wherein,
Figure 825392DEST_PATH_IMAGE016
for the correlation corresponding to the kth setting position,
Figure DEST_PATH_IMAGE017
the absolute value of the difference value between the internal temperature corresponding to the kth set position at the e-th detection moment and the first surface temperature corresponding to the transported article at the e-th detection moment;
Figure 177614DEST_PATH_IMAGE018
the number of detected time instants within the history period,
Figure DEST_PATH_IMAGE019
is the number of the set positions;
the credibility corresponding to each set position is as follows:
Figure DEST_PATH_IMAGE021
wherein,
Figure 773811DEST_PATH_IMAGE022
for the correlation corresponding to the kth set position,
Figure 939213DEST_PATH_IMAGE017
set the position for the kth at the e-th testThe absolute value of the difference value between the internal temperature corresponding to the measuring moment and the first surface temperature corresponding to the transported object at the e-th detecting moment;
Figure 974165DEST_PATH_IMAGE018
the number of detected time instants within the history period,
Figure DEST_PATH_IMAGE023
is an exponential function with e as the base.
4. A refrigerator car temperature monitoring method as claimed in claim 1, wherein the weight is obtained by: forming all distance levels in the historical time period into a distance level set, obtaining the distance from each set position to the transported object at the current detection time, searching the distance level corresponding to each distance in the distance level set and obtaining a corresponding distance accurate value, calculating the product of the relevance, the credibility and the distance accurate value corresponding to each set position, and taking the value after the normalization of the product as the weight corresponding to each set position.
5. A refrigerator car temperature monitoring method as claimed in claim 1, wherein the method of calculating the actual surface temperature of the transported object at the current detection time is:
forming all temperature difference levels in a historical period into a temperature difference level set, calculating the internal and external temperature difference corresponding to the current detection moment, searching the temperature difference level corresponding to the internal and external temperature difference in the temperature difference level set and obtaining a corresponding temperature influence value; and weighting and summing the internal temperature corresponding to each set position at the current detection time and the weight corresponding to each set position, and adding the obtained temperature influence value to obtain the actual surface temperature corresponding to the transported article at the current detection time.
6. A refrigerator car temperature monitoring method as claimed in claim 1, further comprising obtaining an actual surface temperature of the transported object at a current detection time through an FC neural network; the specific process is as follows:
calculating actual surface temperature corresponding to each detection time of the transported object in a historical period, obtaining volume and heat conductivity corresponding to each detection time of the transported object in the historical period, obtaining a vector corresponding to each detection time in the historical period by using the volume, the heat conductivity, the internal temperature, the environmental temperature and the distance as data sets, training an FC neural network by using the vector corresponding to each detection time in the historical period and the actual surface temperature corresponding to each detection time of the transported object in the historical period as the data sets, obtaining the trained FC neural network, obtaining the vector corresponding to the current detection time, inputting the vector into the trained FC neural network, and outputting the actual surface temperature corresponding to the current detection time of the transported object;
the corresponding loss function of the FC neural network during training is as follows:
Figure DEST_PATH_IMAGE025
in the formula,
Figure 180894DEST_PATH_IMAGE026
for the corresponding loss function of the FC neural network during training,
Figure DEST_PATH_IMAGE027
in order to be a function of the cross-entropy loss,
Figure 213572DEST_PATH_IMAGE028
is a difference loss function;
the difference loss function is:
Figure 69532DEST_PATH_IMAGE030
in the formula,
Figure 908175DEST_PATH_IMAGE028
in order to be a function of the difference loss,
Figure DEST_PATH_IMAGE031
for the corresponding actual surface temperature in the data set,
Figure 533192DEST_PATH_IMAGE032
actual surface temperature output for the FC neural network;
Figure DEST_PATH_IMAGE033
in order to take the function of the minimum value,
Figure 267929DEST_PATH_IMAGE023
is an exponential function with e as the base.
7. A refrigerator car temperature monitoring system comprising a processor and a memory, wherein the processor executes a program of a refrigerator car temperature monitoring method as claimed in any one of claims 1 to 6 stored in the memory.
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