A fruit and vegetable transportation loss reduction supervision method and system based on the Internet of Things

Abstract

The invention belongs to the technical fields of fruit and vegetable transportation and the Internet of Things, and proposes a fruit and vegetable transportation loss reduction supervision method and system based on the Internet of Things, which specifically comprises the following steps: firstly, initializing an Internet of Things fruit and vegetable transportation supervision scene, wherein the scene includes a laser rangefinder; secondly, collecting point cloud data from the laser rangefinder and obtaining a front-end measurement group; then, performing a front-end damage risk analysis according to the front-end measurement group, and obtaining a road vibration level; finally, using the road vibration level to give an early warning or adjust a suspension platform; the road vibration level is a quantitative calculation performed according to a height value and a slope value of a transportation road surface, and effectively quantifies the correlation between the complexity of point cloud distribution in a three-dimensional image and the road surface height difference and the road inclination gradient, reduces the accuracy risk caused by the difference in the continuously obtained front-end measurement groups due to tiny foreign matter on the road surface, provides a mathematical basis for a control terminal to adjust and control the suspension platform, and has an improvement effect on reducing the damage to fruits and vegetables in a transportation fruit and vegetable scene and ensuring the quality of fruits and vegetables during transportation.

Description

Technical Field

[0001] The invention belongs to the technical field of fruit and vegetable transportation and the Internet of Things, and particularly relates to a fruit and vegetable transportation loss reduction supervision method and system based on the Internet of Things. Background Art

[0002] In the modern logistics and transportation industry, the transportation of fruits and vegetables is more challenging than other transportation. Fruits and vegetables are perishable and easily damaged, so how to reduce damage during transportation is an important and common problem. Traditional fruit and vegetable transportation methods usually utilize the optimization of fruit and vegetable loading methods and the use of buffer materials inside the transport compartment to protect the smooth transportation of fruits and vegetables. Although this method can reduce damage to fruits and vegetables, in actual transportation, the road environment encountered by fruit and vegetable transport vehicles is highly random, especially during transportation. It is highly common to encounter uneven or slope-changing roads, and fruit and vegetable transport vehicles produce vertical Vibration will cause strong squeezing between fruits and vegetables. Even if the vehicle suspension system of the fruit and vegetable transport vehicle itself can reduce the vibration, for fruit and vegetable varieties with weak skin that are easily damaged by mechanical damage or squeezing, the vehicle suspension system often cannot effectively resolve this type of squeezing risk, so it will still cause great damage to such fruits and vegetables with weak skin characteristics. Therefore, a fruit and vegetable transportation damage reduction supervision method and system are urgently needed to provide real-time feedback on road conditions and reduce damage to fruits and vegetables. This method can not only more effectively reduce the damage risk of transporting fruits and vegetables with weak skin characteristics, but also has considerable advantages in processing costs. Summary of the invention

[0003] The purpose of the present invention is to propose a method and system for fruit and vegetable transportation loss reduction supervision based on the Internet of Things to solve one or more technical problems existing in the prior art and at least provide a beneficial option or create conditions.

[0004] In order to achieve the above object, according to one aspect of the present invention, a method for monitoring loss reduction in fruit and vegetable transportation based on the Internet of Things is provided, and the method comprises the following steps:

[0005] S100, initializing the IoT fruit and vegetable transportation supervision scenario, which includes a laser rangefinder;

[0006] S200, collecting point cloud data from the laser rangefinder and obtaining a front position measurement group;

[0007] S300, conduct anterior damage risk analysis based on the anterior measurement group and obtain the road shock level;

[0008] S400, using road vibration level to warn or adjust the suspension platform;

[0009] Further, in step S100, the IoT fruit and vegetable transportation supervision scene is initialized, and the method of including a laser rangefinder in the scene is: the IoT fruit and vegetable transportation supervision scene includes a fruit and vegetable transport vehicle, a hanging platform and a laser rangefinder, wherein the hanging platform is mounted on the bottom of the fruit and vegetable transport vehicle, and adopts an active suspension system; the laser rangefinder is one of a mobile laser radar, a short-range laser radar or a phase difference laser radar.

[0010] Further, in step S200, the method for collecting point cloud data from the laser rangefinder and obtaining the front-end measurement group is: setting a time period WDS, WDS∈[5,20] seconds, and every WDS time period, the control terminal obtains a three-dimensional image of the ground in the driving direction of the fruit and vegetable transport vehicle through the laser rangefinder, and the three-dimensional image is composed of a point cloud; the standard deviation of the height values ​​of all point clouds in the three-dimensional image is recorded as the first front-end value; the top view of the three-dimensional image is evenly divided into a number of square areas, and the point cloud at the center position of each area is recorded as an equidistant point cloud, and the vectors between each equidistant point cloud are obtained by vector analysis and calculation. For any obtained vector, the gradient of the vector is obtained by gradient calculation, and the average value of all vector gradients is recorded as the second front-end value; the tuple consisting of the first front-end value and the second front-end value is recorded as the front-end measurement group of the fruit and vegetable transport vehicle at that moment.

[0011] Further, in step S300, the method of performing the front position damage risk analysis according to the front position measurement group and obtaining the road shock level is: setting a time period BhTg, BhTg∈[15,30] minutes, and normalizing all the first front position values ​​and all the second front position values ​​in the BhTg time period respectively;

[0012] Calculate the sum of squares of the first and second preceding values ​​in the preceding measurement group at the same time as the preceding indicator; obtain the preceding indicators at different times in the BhTg time period to form a sequence recorded as the preceding indicator sequence; record the times of the maximum and minimum elements in the preceding indicator sequence as the strong earthquake time points and weak earthquake time points respectively; obtain the number of elements between the strong earthquake time point and the first strong earthquake time point searched in reverse time order as its strong earthquake delay coefficient; take the current strong earthquake time point as the starting point of the earthquake domain, and traverse each strong earthquake time point in reverse time order from the starting point of the earthquake domain to form a single earthquake-related area, specifically:

[0013] Compare the preceding index of the traversed strong earthquake time point with the preceding index of the starting point of the earthquake domain. If the preceding index of the traversed strong earthquake time point is not greater than the preceding index of the starting point of the earthquake domain, and the strong earthquake delay coefficient of the traversed strong earthquake time point is not greater than the strong earthquake delay coefficient of the starting point of the earthquake domain, then the strong earthquake time point is taken as the end point of the earthquake domain, and each element between the starting point of the earthquake domain and the end point of the earthquake domain is formed into an interval and recorded as a single-involved earthquake zone. The end point of the earthquake domain is taken as the new starting point of the earthquake domain and a new single-involved earthquake zone is formed repeatedly.

[0014] Calculate the difference between the average value and the minimum value of the preceding index at each moment in the single earthquake-related area as the lower deviated earthquake distance; construct each lower deviated earthquake distance into a sequence and record it as the lower deviated sequence. If any element in the lower deviated sequence is greater than the median value of the lower deviated sequence, the single earthquake-related area where the element is located is recorded as the sensitive earthquake area, otherwise the single earthquake-related area where the element is located is recorded as the low earthquake area; record the extreme difference of the preceding index at each moment in any low earthquake area as its low earthquake amplitude; record the number of moments between the corresponding moment when the preceding index in the sensitive earthquake area is the minimum value and the first strong earthquake time point obtained by reverse time search as the attenuation coefficient;

[0015] The ratio of the extreme difference of each front index in the sensitive seismic area to its lower eccentric distance is taken as its magnitude gradient; the road earthquake level RdScL is calculated by the low earthquake amplitude and magnitude gradient, and the calculation method is:

[0016] ;

[0017] Where j1 is the cumulative variable, NSQM is the number of seismically sensitive areas, hs{} is the harmonic mean function, Rt_Qtx j1 is the magnitude gradient of the j1th seismically sensitive area, Ns_Erg j1 represents the low amplitude value corresponding to the first low earthquake zone obtained by searching the j1th sensitive earthquake zone in reverse time direction, TL_Psr j1 Auop is the sum of all the previous indicators between the maximum and minimum values ​​of the previous indicators in the j1th sensitive seismic area. j1 is the attenuation coefficient of the j1th sensitive seismic zone, and Qd_Bep is the average value of the front-end indicators at the weak earthquake time point of each low seismic zone.

[0018] Since the road vibration level is calculated by the derivative value of the previous measurement group, the correlation between the complexity of the point cloud distribution in the three-dimensional image and the road inclination gradient is effectively quantified. However, in the case of frequent changes in road inclination, such as driving on mountain roads, the road vibration level calculated by the above method may be insufficiently quantified. This is because each input quantity of this method has the same sensitivity and screening and discrimination ability, and it is impossible to achieve a relatively uniform quantification of the characteristic level of the abnormal input quantity, resulting in the problem of under-fitting of the processed road vibration level. At present, there is no feasible technology to compensate for the under-quantification phenomenon caused by this method. In order to eliminate the influence of abnormal input on the under-fitting of the road vibration level, the present invention proposes a more preferred solution:

[0019] Further, in step S300, the method for performing the front damage risk analysis based on the front measurement group and obtaining the road shock level is as follows: setting a time period TdTg, TdTg∈[20,30] minutes, obtaining the front measurement groups at different times in the current TdTg time period to form a sequence recorded as the front measurement group sequence; if a moment is greater than the first front value of the previous moment, then the moment is a first-class scale, otherwise it is a second-class scale; taking the current moment as the starting point of the slope measurement, dividing the slope measurement interval in the reverse time direction from the starting point of the slope measurement, the specific method is as follows:

[0020] The initialization traversal moment is the first moment in the reverse time direction of the slope measurement starting point, and each moment is traversed in the reverse time direction starting from the initialization traversal moment; the traversal termination condition is: when a traversal moment satisfies that its second previous value is greater than the second previous value of the slope measurement starting point, and the number of the second type of scales in the traversed traversal moments is greater than the number of the first type of scales, then the traversal moment is taken as the slope measurement end point and the traversal is stopped; the slope measurement starting point, the slope measurement end point and each moment between them form an interval and are recorded as the slope measurement interval, the average value of the second previous value at each moment in the slope measurement interval is taken as the slope pre-shock index, the slope measurement end point is used to search for the moment that meets the slope measurement starting point continuation condition in the reverse time direction as the new slope measurement starting point, and the slope measurement interval is continued to be divided in combination with the traversal termination condition, wherein the slope measurement starting point continuation condition is: the second previous value at a moment is not greater than the slope pre-shock index of the current slope measurement interval; the current slope measurement interval is the slope measurement interval closest to the current moment;

[0021] The maximum value of the second preceding value in the slope measurement interval is recorded as the slope peak level; the time at which the slope peak level is located in the slope measurement interval is obtained, and the median value of the second preceding value at each time between it and the slope measurement starting point of the slope measurement interval is used as the first boundary marker of the slope measurement interval, and the numerical interval formed by the first boundary marker and the slope peak level is used as the first boundary marker interval, and the proportion of the number of elements whose values ​​are within the first boundary marker interval among all the second preceding values ​​in the slope measurement interval is defined as the boundary marker ratio;

[0022] The product of the proportion of a type of scale in the slope measurement interval and the proportion of the boundary marker is recorded as the peak-to-average ratio; the road shock level RdScL is calculated by the peak-to-average ratio, and the calculation method is:

[0023] ;

[0024] Where i1 is the cumulative variable, NTEP is the number of slope measurement intervals, RVFY i1 is the landmark ratio of the i1th slope measurement interval, PVRT i1 is the peak-to-average ratio of the i1th slope measurement interval, ePiHL represents the average value of all the second previous values ​​in the previous measurement group sequence, and ePlOE i1is the average value of the second front values ​​in the i1th slope measurement interval, and exp() is an exponential function with the natural number e as the base;

[0025] sc() is a backtracking function, and the set of slope measurement intervals from the 1st to the i1th is obtained through sc(i1), e.PVRT sc(i1) Represents the average value of the peak-to-average ratio corresponding to each slope measurement interval returned by the backtracking function.

[0026] Beneficial effects: As can be seen from the above, the road vibration level is quantitatively calculated based on the height and slope values ​​of the transport road surface. By lateral comparison of the front measurement group at different times, the correlation between the complexity of the point cloud distribution in the three-dimensional image and the road inclination gradient is effectively quantified, and the weights of the sites with large point cloud height differences and high inclination gradients are increased, reducing the risk of large differences in the front measurement groups between consecutive moments due to tiny foreign objects on the road surface, providing a reliable mathematical basis for the control terminal to adjust the control suspension platform.

[0027] Further, in step S400, the method of using the road vibration level to warn or adjust the suspension platform is: obtain the average value of the road vibration level of the fruit and vegetable transport vehicle in the current road section through the cloud computing platform in the big data technology and record it as the first road vibration level; preset the moving average degree KLN to [3,10], the second road vibration level at any moment is the average value of the road vibration level at KLN moments in the reverse time direction of the moment, and perform linear fitting on the second road vibration level by the least squares method to obtain the fitting value at the current moment;

[0028] If the current road vibration level is greater than the first road vibration level, the damping force intervention is performed:

[0029] When the road vibration level is greater than 5% and less than 10% of the fitting value, the current road section is recorded as the first steep vibration section, and the control terminal increases the damping force of the suspension platform by 5%-10%;

[0030] When the road vibration level is greater than 10% of the fitting value, the current road section is recorded as the second steep vibration section, and the control terminal increases the damping force of the suspension platform by 10%-20%;

[0031] If the current road vibration level is less than the first road vibration level, the damping force intervention is stopped. Preferably, all undefined variables in the present invention, if not clearly defined, may be manually set thresholds.

[0032] The present invention also provides a method and system for monitoring loss reduction in transportation of fruits and vegetables based on the Internet of Things. The method and system for monitoring loss reduction in transportation of fruits and vegetables based on the Internet of Things include: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the method for monitoring loss reduction in transportation of fruits and vegetables based on the Internet of Things are implemented. The method and system for monitoring loss reduction in transportation of fruits and vegetables based on the Internet of Things can be run in computing devices such as desktop computers, laptop computers, PDAs, and cloud data centers. The executable system may include, but is not limited to, a processor, a memory, and a server cluster. The processor executes the computer program to run in the following system units:

[0033] A scene layout unit is used to initialize the IoT fruit and vegetable transportation supervision scene, which includes a laser rangefinder;

[0034] A front position measurement group acquisition unit, used for collecting point cloud data from the laser rangefinder and obtaining a front position measurement group;

[0035] A road shock level calculation unit, used for performing a front-end damage risk analysis based on the front-end measurement group and obtaining a road shock level;

[0036] The early warning and adjustment unit is used to use the road vibration level to give early warning or adjust the suspension platform.

[0037] The beneficial effects of the present invention are as follows: the present invention provides a method and system for fruit and vegetable transportation damage reduction supervision based on the Internet of Things, the method and system quantify the road vibration level, which is a quantitative calculation based on the height value and slope value of the transportation road surface, and effectively quantifies the correlation between the point cloud distribution complexity and the road surface height difference and the road inclination gradient in the three-dimensional image through the lateral comparison of the front measurement group at different times, increases the weight of the sites with large point cloud height difference and high inclination gradient, reduces the risk of large differences in the front measurement group between consecutive moments due to tiny foreign matter on the road surface, provides a reliable mathematical basis for the control terminal to adjust the control suspension platform, and has a practical effect on reducing fruit and vegetable damage in actual fruit and vegetable transportation scenarios, ensuring the quality of fruits and vegetables during transportation, and improving the efficiency and reliability of the fruit and vegetable transportation logistics process. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and other features of the present invention will become more obvious by describing in detail the embodiments shown in the accompanying drawings. The same reference numerals in the accompanying drawings of the present invention represent the same or similar elements. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other accompanying drawings can be obtained based on these accompanying drawings without creative work. In the accompanying drawings:

[0039] Figure 1 Shown is a flow chart of a fruit and vegetable transportation loss reduction supervision method based on the Internet of Things;

[0040] Figure 2 Shown is a fruit and vegetable transportation loss reduction supervision method and system structure diagram based on the Internet of Things. DETAILED DESCRIPTION

[0041] The following will be combined with the embodiments and drawings to clearly and completely describe the concept, specific structure and technical effects of the present invention, so as to fully understand the purpose, scheme and effect of the present invention. It should be noted that the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0042] like Figure 1 The figure shows a flow chart of a fruit and vegetable transportation loss reduction supervision method based on the Internet of Things. Figure 1 A method for monitoring loss reduction in fruit and vegetable transportation based on the Internet of Things according to an embodiment of the present invention is described below. The method comprises the following steps:

[0043] S100, initializing the IoT fruit and vegetable transportation supervision scenario, which includes a laser rangefinder;

[0044] S200, collecting point cloud data from the laser rangefinder and obtaining a front position measurement group;

[0045] S300, conduct anterior damage risk analysis based on the anterior measurement group and obtain the road shock level;

[0046] S400, using road vibration level to warn or adjust the suspension platform;

[0047] Further, in step S100, the IoT fruit and vegetable transportation supervision scene is initialized, and the method of including a laser rangefinder in the scene is: the IoT fruit and vegetable transportation supervision scene includes a fruit and vegetable transport vehicle, a hanging platform and a laser rangefinder, wherein the hanging platform is mounted on the bottom of the fruit and vegetable transport vehicle, and adopts an active suspension system; the laser rangefinder is one of a mobile laser radar, a short-range laser radar or a phase difference laser radar.

[0048] In the default state, the laser rangefinder is arranged on the roof; the fruit and vegetable transport vehicle also includes a control terminal, which is used for calculation, analysis and adjustment of the suspension platform.

[0049] Further, in step S200, the method for collecting point cloud data from the laser rangefinder and obtaining the front-end measurement group is: setting a time period WDS, WDS∈[5,20] seconds, and every WDS time period, the control terminal obtains a three-dimensional image of the ground in the driving direction of the fruit and vegetable transport vehicle through the laser rangefinder, and the three-dimensional image is composed of a point cloud; the standard deviation of the height values ​​of all point clouds in the three-dimensional image is recorded as the first front-end value; the top view of the three-dimensional image is evenly divided into a number of square areas, and the point cloud at the center position of each area is recorded as an equidistant point cloud, and the vectors between each equidistant point cloud are obtained by vector analysis and calculation. For any obtained vector, the gradient of the vector is obtained by gradient calculation, and the average value of all vector gradients is recorded as the second front-end value; the tuple consisting of the first front-end value and the second front-end value is recorded as the front-end measurement group of the fruit and vegetable transport vehicle at that moment.

[0050] Further, in step S300, the method of performing the front position damage risk analysis according to the front position measurement group and obtaining the road shock level is: setting a time period BhTg, BhTg∈[15,30] minutes, and normalizing all the first front position values ​​and all the second front position values ​​in the BhTg time period respectively;

[0051] The value range of normalization processing is between 0 and 1;

[0052] Calculate the sum of squares of the first and second preceding values ​​in the preceding measurement group at the same time as the preceding indicator; obtain the preceding indicators at different times in the BhTg time period to form a sequence recorded as the preceding indicator sequence; record the times of the maximum and minimum elements in the preceding indicator sequence as the strong earthquake time points and weak earthquake time points respectively; obtain the number of elements between the strong earthquake time point and the first strong earthquake time point searched in reverse time order as its strong earthquake delay coefficient; take the current strong earthquake time point as the starting point of the earthquake domain, and traverse each strong earthquake time point in reverse time order from the starting point of the earthquake domain to form a single earthquake-related area, specifically:

[0053] Compare the preceding index of the traversed strong earthquake time point with the preceding index of the starting point of the earthquake domain. If the preceding index of the traversed strong earthquake time point is not greater than the preceding index of the starting point of the earthquake domain, and the strong earthquake delay coefficient of the traversed strong earthquake time point is not greater than the strong earthquake delay coefficient of the starting point of the earthquake domain, then the strong earthquake time point is taken as the end point of the earthquake domain, and each element between the starting point of the earthquake domain and the end point of the earthquake domain is formed into an interval and recorded as a single-involved earthquake zone. The end point of the earthquake domain is taken as the new starting point of the earthquake domain and a new single-involved earthquake zone is formed repeatedly.

[0054] The current strong earthquake time point refers to the strong earthquake time point closest to the current time; the traversed strong earthquake time point refers to the strong earthquake time point being traversed, and the starting point of the earthquake domain is not used as the traversed strong earthquake time point;

[0055] Calculate the difference between the average value and the minimum value of the preceding index at each moment in the single earthquake-related area as the lower deviated earthquake distance; construct each lower deviated earthquake distance into a sequence and record it as the lower deviated sequence. If any element in the lower deviated sequence is greater than the median value of the lower deviated sequence, the single earthquake-related area where the element is located is recorded as the sensitive earthquake area, otherwise the single earthquake-related area where the element is located is recorded as the low earthquake area; record the extreme difference of the preceding index at each moment in any low earthquake area as its low earthquake amplitude; record the number of moments between the corresponding moment when the preceding index in the sensitive earthquake area is the minimum value and the first strong earthquake time point obtained by reverse time search as the attenuation coefficient;

[0056] The ratio of the extreme difference of each front index in the sensitive seismic area to its lower eccentric distance is taken as its magnitude gradient; the road earthquake level RdScL is calculated by the low earthquake amplitude and magnitude gradient, and the calculation method is:

[0057] ;

[0058] Where j1 is the cumulative variable, NSQM is the number of seismically sensitive areas, hs{} is the harmonic mean function, Rt_Qtx j1 is the magnitude gradient of the j1th seismically sensitive area, Ns_Erg j1 represents the low amplitude value corresponding to the first low earthquake zone obtained by searching the j1th sensitive earthquake zone in reverse time direction, TL_Psr j1 Auop is the sum of all the previous indicators between the maximum and minimum values ​​of the previous indicators in the j1th sensitive seismic area. j1 is the attenuation coefficient of the j1th sensitive seismic zone, and Qd_Bep is the average value of the front-end indicators at the weak earthquake time point of each low seismic zone.

[0059] Where exp() is an exponential function with the natural constant e as the base; the number of sensitive seismic areas refers to the number of sensitive seismic areas identified in the most recent period BhTg; when the first low-seismic area obtained by searching the j1th sensitive seismic area in the reverse time direction does not exist, Ns_Erg is traversed in turn j1-1， Ns_Erg j1-2 ...Ns_Erg j1-k Until the j1-kth sensitive seismic area appears to be a sensitive seismic area with low amplitude, then Ns_Erg j1 The value is the same as Ns_Erg j1-k Same value.

[0060] Preferably, in step S300, the method for performing the front-end damage risk analysis according to the front-end measurement group and obtaining the road shock level is as follows: setting a time period TdTg, TdTg∈[20, 30] minutes, obtaining the front-end measurement groups at different moments in the current TdTg time period to form a sequence recorded as the front-end measurement group sequence; if a moment is greater than the first front-end value of the previous moment, the moment is a first-class scale, otherwise it is a second-class scale;

[0061] There is a one-to-one correspondence between the previous measurement group sequence and its first previous value and the second previous value; the current TdTg time period refers to the time period with a length of TdTg in the reverse time direction at the current moment;

[0062] Take the current time as the starting point of the slope measurement, and divide the slope measurement interval in reverse time direction from the starting point of the slope measurement. The specific method is:

[0063] The initialization traversal moment is the first moment in the reverse time direction of the slope measurement starting point, and each moment is traversed in the reverse time direction starting from the initialization traversal moment; the traversal termination condition is: when a traversal moment satisfies that its second previous value is greater than the second previous value of the slope measurement starting point, and the number of the second type of scales in the traversed traversal moments is greater than the number of the first type of scales, then the traversal moment is taken as the slope measurement end point and the traversal is stopped; the slope measurement starting point, the slope measurement end point and each moment between them form an interval and are recorded as the slope measurement interval, the average value of the second previous value at each moment in the slope measurement interval is taken as the slope pre-shock index, the slope measurement end point is used to search for the moment that meets the slope measurement starting point continuation condition in the reverse time direction as the new slope measurement starting point, and the slope measurement interval is continued to be divided in combination with the traversal termination condition, wherein the slope measurement starting point continuation condition is: the second previous value at a moment is not greater than the slope pre-shock index of the current slope measurement interval; the current slope measurement interval is the slope measurement interval closest to the current moment;

[0064] The traversed traversal time refers to the traversal time that has been traversed after the slope measurement starting point is set, and all the times between the initialization traversal time and the current traversal time are traversed;

[0065] The maximum value of the second preceding value in the slope measurement interval is recorded as the slope peak level;

[0066] The time at which the slope peak level is located in the slope measurement interval is obtained, and the median value of the second previous value at each time between the median value and the slope measurement starting point of the slope measurement interval is used as the first boundary marker of the slope measurement interval, and the numerical interval formed by the first boundary marker and the slope peak level is used as the first boundary marker interval, and the proportion of the number of elements whose values ​​are within the first boundary marker interval among all the second previous values ​​in the slope measurement interval is defined as the boundary marker ratio;

[0067] The product of the proportion of one type of scale in the slope measurement interval and the proportion of the boundary marker is recorded as the peak-to-average ratio;

[0068] The road shock level RdScL is calculated by the peak-to-average ratio, and the calculation method is:

[0069] ;

[0070] Where i1 is the cumulative variable, NTEP is the number of slope measurement intervals, RVFY i1 is the landmark ratio of the i1th slope measurement interval, PVRT i1is the peak-to-average ratio of the i1th slope measurement interval, ePiHL represents the average value of all the second previous values ​​in the previous measurement group sequence, and ePlOE i1 is the average value of the second front values ​​in the i1th slope measurement interval, and exp() is an exponential function with the natural number e as the base;

[0071] sc() is a backtracking function, and the set of slope measurement intervals from the 1st to the i1th is obtained through sc(i1), e.PVRT sc(i1) Represents the average value of the peak-to-average ratio corresponding to each slope measurement interval returned by the backtracking function.

[0072] Further, in step S400, the method of using the road vibration level to warn or adjust the suspension platform is: obtain the average value of the road vibration level of the fruit and vegetable transport vehicle in the current road section through the cloud computing platform in the big data technology and record it as the first road vibration level; preset the moving average degree KLN to [3,10], the second road vibration level at any moment is the average value of the road vibration level at KLN moments in the reverse time direction of the moment, and perform linear fitting on the second road vibration level by the least squares method to obtain the fitting value at the current moment;

[0073] If the current road vibration level is greater than the first road vibration level, the damping force intervention is performed:

[0074] When the road vibration level is greater than 5% and less than 10% of the fitting value, the current road section is recorded as the first steep vibration section, and the control terminal increases the damping force of the suspension platform by 5%-10%;

[0075] When the road vibration level is greater than 10% of the fitting value, the current road section is recorded as the second steep vibration section, and the control terminal increases the damping force of the suspension platform by 10%-20%;

[0076] The data used in the process of obtaining the road vibration level of the fruit and vegetable transport vehicle in the current road section through the cloud computing platform in big data technology to obtain the first road vibration level should be not limited to the data of the current fruit and vegetable transport vehicle; the current road section refers to the road section that the vehicle has traveled in the WDS time period in the reverse time direction at the current moment; where WDS∈[5,20] seconds.

[0077] When the first steep earthquake segment or the second steep earthquake segment appears, the corresponding road segment position is sent to the server; each marked steep earthquake segment obtained in the server can be used to evaluate the road condition risk.

[0078] The embodiments of the present invention provide a method and system for monitoring loss reduction in fruit and vegetable transportation based on the Internet of Things, such as Figure 2The figure shows a structure diagram of a method and system for reducing loss in transport of fruits and vegetables based on the Internet of Things of the present invention. The method and system for reducing loss in transport of fruits and vegetables based on the Internet of Things of this embodiment include: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the embodiment of the method for reducing loss in transport of fruits and vegetables based on the Internet of Things are implemented.

[0079] The system comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to run in the following units of the system:

[0080] A scene layout unit is used to initialize the IoT fruit and vegetable transportation supervision scene, which includes a laser rangefinder;

[0081] A front position measurement group acquisition unit, used for collecting point cloud data from the laser rangefinder and obtaining a front position measurement group;

[0082] A road shock level calculation unit, used for performing a front-end damage risk analysis based on the front-end measurement group and obtaining a road shock level;

[0083] The early warning and adjustment unit is used to use the road vibration level to give early warning or adjust the suspension platform.

[0084] The method and system for reducing loss in transport of fruits and vegetables based on the Internet of Things can be run on computing devices such as desktop computers, laptop computers, PDAs, and cloud servers. The system that can run the method and system for reducing loss in transport of fruits and vegetables based on the Internet of Things can include, but is not limited to, processors and memories. Those skilled in the art can understand that the example is only an example of a method and system for reducing loss in transport of fruits and vegetables based on the Internet of Things, and does not constitute a limitation on a method and system for reducing loss in transport of fruits and vegetables based on the Internet of Things. It can include more or fewer components than the example, or a combination of certain components, or different components. For example, the method and system for reducing loss in transport of fruits and vegetables based on the Internet of Things can also include input and output devices, network access devices, buses, etc.

[0085] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the fruit and vegetable transportation loss reduction supervision method and system operation system based on the Internet of Things, and uses various interfaces and lines to connect the various parts of the entire fruit and vegetable transportation loss reduction supervision method and system operation system based on the Internet of Things.

[0086] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the method and system for reducing the loss of fruit and vegetable transportation based on the Internet of Things by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0087] Although the description of the present invention has been quite detailed and has been described in particular with respect to several described embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, so as to effectively cover the intended scope of the present invention. In addition, the present invention is described above with the embodiments foreseeable by the inventors, and its purpose is to provide a useful description, and those non-substantial changes to the present invention that are not currently foreseen may still represent equivalent changes of the present invention.

Claims

1. A fruit and vegetable transportation loss reduction supervision method based on the Internet of Things, characterized in that: The method comprises the following steps: S100, initializing the IoT fruit and vegetable transportation supervision scenario, which includes a laser rangefinder; S200, collecting point cloud data from the laser rangefinder and obtaining a front position measurement group; S300, conduct anterior damage risk analysis based on the anterior measurement group and obtain the road shock level; S400, using road vibration level to warn or adjust the suspension platform; Wherein, in step S200, the method for collecting point cloud data from the laser rangefinder and obtaining the front position measurement group is as follows: the standard deviation of the height values ​​of all point clouds in the three-dimensional image is recorded as the first front position value; the top view of the three-dimensional image is evenly divided into a plurality of square areas, the point clouds at the center positions of each area are recorded as equidistant point clouds, the vectors between each equidistant point cloud are obtained by vector analysis calculation, for any obtained vector, the gradient of the vector is obtained by gradient calculation, and the average value of all vector gradients is recorded as the second front position value; the binary group consisting of the first front position value and the second front position value is recorded as the front position measurement group at the corresponding moment of obtaining the three-dimensional image; Wherein, in step S300, the method for performing the front-position damage risk analysis according to the front-position measurement group and obtaining the road earthquake level is as follows: calculating the square sum of the first front-position value and the second front-position value in the front-position measurement group at the same time as the front-position index, dividing each time into a strong earthquake time point or a weak earthquake time point according to the horizontal comparison of the front-position index, dividing the single earthquake-related area according to the strong earthquake time point, calculating the lower eccentric distance according to each front-position index in the single earthquake-related area, classifying the single earthquake-related area into sensitive earthquake area and low earthquake area by using the lower eccentric distance, recording the extreme difference of the front-position index at each time in any low earthquake area as its low earthquake amplitude, calculating the attenuation coefficient according to the distance between the sensitive earthquake area and the strong earthquake time point, taking the ratio of the extreme difference of each front-position index in the sensitive earthquake area to its lower eccentric distance as its earthquake magnitude gradient, and calculating the road earthquake level by the low earthquake amplitude and the earthquake magnitude gradient.

2. The method for reducing loss of fruits and vegetables in transportation based on the Internet of Things according to claim 1 is characterized in that: In step S100, the IoT fruit and vegetable transportation supervision scene is initialized. The method of including a laser rangefinder in the scene is: the IoT fruit and vegetable transportation supervision scene includes a fruit and vegetable transport vehicle, a hanging platform and a laser rangefinder, wherein the hanging platform is mounted on the bottom of the fruit and vegetable transport vehicle and adopts an active suspension system; the laser rangefinder is one of a mobile laser radar, a short-range laser radar or a phase difference laser radar.

3. The method for reducing loss of fruits and vegetables in transportation based on the Internet of Things according to claim 1 is characterized in that: In step S200, the method for collecting point cloud data from the laser rangefinder and obtaining the front-end measurement group is: setting a time period WDS, WDS∈[5,20] seconds, and every WDS time period, the control terminal obtains a three-dimensional image of the ground in the driving direction of the fruit and vegetable transport vehicle through the laser rangefinder, and the three-dimensional image is composed of a point cloud.

4. The method for reducing loss of fruits and vegetables in transportation based on the Internet of Things according to claim 1 is characterized in that: In step S300, the method of performing the front position damage risk analysis according to the front position measurement group and obtaining the road shock level is as follows: setting a time period BhTg, BhTg∈[15,30] minutes, and normalizing all the first front position values ​​and all the second front position values ​​in the BhTg time period respectively; Obtain the leading indicators at different times in the BhTg time period to form a sequence recorded as the leading indicator sequence; record the times of the maximum and minimum elements in the leading indicator sequence as the strong earthquake time point and the weak earthquake time point respectively; obtain the number of elements between the strong earthquake time point and the first strong earthquake time point searched in reverse time order as its strong earthquake delay coefficient; take the current strong earthquake time point as the starting point of the earthquake domain, and traverse each strong earthquake time point in reverse time order from the starting point of the earthquake domain to form a single earthquake-related area, specifically: Compare the preceding index of the traversed strong earthquake time point with the preceding index of the starting point of the earthquake domain. If the preceding index of the traversed strong earthquake time point is not greater than the preceding index of the starting point of the earthquake domain, and the strong earthquake delay coefficient of the traversed strong earthquake time point is not greater than the strong earthquake delay coefficient of the starting point of the earthquake domain, then the strong earthquake time point is taken as the end point of the earthquake domain, and each element between the starting point of the earthquake domain and the end point of the earthquake domain is formed into an interval and recorded as a single-involved earthquake zone. The end point of the earthquake domain is taken as the new starting point of the earthquake domain and a new single-involved earthquake zone is formed repeatedly. The difference between the average value and the minimum value of the previous index at each moment in the single earthquake-related area is calculated as the lower deviated earthquake distance; each lower deviated earthquake distance is constructed into a sequence and recorded as a lower deviated sequence. If any element in the lower deviated sequence is greater than the median value of the lower deviated sequence, the single earthquake-related area where the element is located is recorded as a sensitive earthquake area, otherwise the single earthquake-related area where the element is located is recorded as a low earthquake area; the number of moments between the corresponding moment when the previous index in the sensitive earthquake area is the minimum value and the first strong earthquake time point obtained by reverse time search is recorded as the attenuation coefficient; The road earthquake level RdScL is calculated by using the low earthquake amplitude and earthquake gradient. The calculation method is: ； Where j1 is the cumulative variable, NSQM is the number of sensitive seismic areas, Rt_Qtx j1 is the magnitude gradient of the j1th seismically sensitive area, Ns_Erg j1 represents the low amplitude value corresponding to the first low earthquake zone obtained by searching the j1th sensitive earthquake zone in reverse time direction, TL_Psr j1 Auop is the sum of all the previous indicators between the maximum and minimum values ​​of the previous indicators in the j1th sensitive seismic area. j1 is the attenuation coefficient of the j1th sensitive seismic zone, and Qd_Bep is the average value of the front-end indicators at the weak earthquake time point of each low seismic zone.

5. The method for reducing loss of fruits and vegetables in transportation based on the Internet of Things according to claim 1 is characterized in that: In step S400, the method of using the road vibration level to warn or adjust the suspension platform is: obtain the average value of the road vibration level of the fruit and vegetable transport vehicle in the current road section through the cloud computing platform in the big data technology and record it as the first road vibration level; preset the moving average degree KLN to [3,10], the second road vibration level at any moment is the average value of the road vibration level at KLN moments in the reverse time direction at the moment, and perform linear fitting on the second road vibration level by the least squares method to obtain the fitting value at the current moment; If the current road vibration level is greater than the first road vibration level, the damping force intervention is performed: When the road vibration level is greater than 5% and less than 10% of the fitting value, the current road section is recorded as the first steep vibration section, and the control terminal increases the damping force of the suspension platform by 5%-10%; When the road vibration level is greater than 10% of the fitting value, the current road section is recorded as the second steep vibration section, and the control terminal increases the damping force of the suspension platform by 10%-20%; If the current road vibration level is less than the first road vibration level, the damping force intervention is stopped.

6. A fruit and vegetable transportation loss reduction supervision system based on the Internet of Things, characterized in that: The fruit and vegetable transportation loss reduction and supervision system based on the Internet of Things includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the fruit and vegetable transportation loss reduction and supervision method based on the Internet of Things described in any one of claims 1 to 5 are implemented. The fruit and vegetable transportation loss reduction and supervision system based on the Internet of Things runs on desktop computers, laptop computers, PDAs, and computing devices in cloud data centers.

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