A cold chain cargo transportation monitoring method, device, equipment and storage medium

Abstract

The present invention discloses a cold chain cargo transportation monitoring method, device, equipment and storage medium. The present invention can periodically predict the impact damage degree of the cargo in the package in the current cycle based on the driving data of the vehicle and the picture of the package box transported by the vehicle, and when the impact damage degree is greater than the preset damage degree, determine whether the driver is driving dangerously based on the driving data of the vehicle, so as to determine whether the impact of the cargo is related to the driver's dangerous driving. If the driver is driving dangerously, it is determined that the impact is related to the driver, and a dangerous driving alarm is sent to the vehicle's on-board terminal. The present invention can predict the impact damage degree of the cargo during the cold chain transportation process, and determine whether the impact of the cargo is related to the driver's dangerous driving. If there is a correlation, an alarm is issued in time to remind the driver not to drive dangerously, which solves the technical problem that there is no detection of whether the impact of the cargo is related to the driver's dangerous driving in the prior art.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of logistics, and in particular to a cold chain cargo transportation monitoring method, device, equipment and storage medium. Background Art

[0002] At present, with the rapid development of e-commerce, the types of goods that users can buy on shopping platforms are increasing, making the logistics and transportation business grow day by day. In order to maintain the freshness of fresh food during transportation, cold chain transportation came into being. In the process of cold chain transportation, in order to ensure transportation efficiency and service quality, cold chain transportation also needs to adopt efficient logistics management systems and advanced logistics technologies to achieve real-time tracking and status monitoring of goods. In the process of transportation of goods, the goods in the cargo compartment may move due to the dangerous driving of the driver, resulting in a collision. However, when the existing technology monitors the collision of goods, it generally does not associate the collision of goods with the dangerous driving of the driver.

[0003] In summary, there is a technical problem in the prior art of failing to detect whether the collision of the cargo is associated with the driver's dangerous driving. Summary of the invention

[0004] The embodiments of the present invention provide a cold chain cargo transportation monitoring method, device, equipment and storage medium, which solve the technical problem in the prior art that there is no detection of whether the impact of the cargo is related to the driver's dangerous driving.

[0005] In a first aspect, an embodiment of the present invention provides a cold chain cargo transportation monitoring method, the method comprising:

[0006] receiving driving data and pictures uploaded by the vehicle terminal at preset intervals, wherein the driving data includes driving information of the vehicle in the period, and the pictures are obtained by photographing a packaging box in the cargo compartment of the vehicle through a camera, wherein the packaging box contains goods;

[0007] Extracting external features of the packaging box according to the image;

[0008] Predicting, based on the driving data, the impact condition of the packaging box during transportation in the current cycle;

[0009] Determining the degree of impact damage of the goods in the packaging box in the current cycle according to the impact situation and the external characteristics;

[0010] Determining whether the impact damage degree is greater than a preset damage degree;

[0011] If it is greater than, obtaining the road condition information of the road on which the vehicle is traveling in the current cycle, and determining whether the driver is driving dangerously according to the driving data and the road condition information;

[0012] If it is dangerous driving, determining that the damage to the cargo is related to the dangerous driving, and sending a dangerous driving alert to the vehicle terminal;

[0013] If there is no dangerous driving, it is determined that the damage to the cargo is not related to the dangerous driving, and a road condition warning message is sent to the vehicle-mounted terminal.

[0014] Preferably, predicting the impact of the packaging box during transportation in the current cycle based on the driving data includes:

[0015] Determining acceleration data of the vehicle in a current cycle according to the driving data, the acceleration data including straight-line acceleration data and turning acceleration data;

[0016] Determining an acceleration threshold corresponding to the packaging box according to the type of the goods, the acceleration threshold being a critical acceleration at which the packaging box moves;

[0017] The number of collisions of the packaging box in the current cycle is predicted based on the acceleration data, the acceleration threshold and the pictures received in the previous cycle.

[0018] Preferably, determining the impact damage degree of the goods in the packaging box in the current cycle according to the impact condition and the external features includes:

[0019] Inputting the external features into a surface damage prediction model so that the surface damage prediction model outputs the degree of surface damage of the packaging box;

[0020] The impact damage degree of the goods in the packaging box in the current cycle is determined according to the surface damage degree and the number of impacts.

[0021] Preferably, determining the impact damage degree of the goods in the packaging box in the current cycle according to the surface damage degree and the number of impacts includes:

[0022] Obtaining the degree of surface damage of the packaging box in the previous cycle, comparing the degree of surface damage in the current cycle with the degree of surface damage in the previous cycle, and determining the degree of change of the impact damage of the packaging box;

[0023] The impact damage degree of the goods in the packaging box in the current cycle is determined according to the number of impacts and the degree of change of the impact damage.

[0024] Preferably, the driving path of the vehicle includes at least one transfer station between the starting point and the end point, and after determining that the driver is driving dangerously, it also includes:

[0025] Acquire the location information of the vehicle in real time, and determine whether the transfer station exists in the direction of travel of the vehicle according to the location information;

[0026] If so, determining a candidate driver who is idle at the nearest target transfer station in the direction of travel;

[0027] Determining a driving score of the candidate driver and the number of times the candidate driver has passed the current driving path of the vehicle, wherein the driving score is generated based on the candidate driver's historical driving data;

[0028] Determining a target driver according to the driving score and the number of passes;

[0029] A transfer rest instruction is sent to the vehicle terminal, and a target driver allocation instruction is sent to the transfer site, wherein the transfer rest instruction is used to instruct the driver of the vehicle to enter the transfer site for rest, and the target driver allocation instruction is used to instruct the transfer site to call the target driver to complete the remaining journey of the vehicle.

[0030] Preferably, determining the target driver according to the driving score and the number of passes includes:

[0031] determining a first weight corresponding to the driving score and a second weight corresponding to the number of passes;

[0032] Performing a weighted summation of the driving score and the number of passes according to the first weight and the second weight to obtain a target score corresponding to each of the candidate drivers;

[0033] A target driver is determined according to the target score and the driving score.

[0034] Preferably, determining the target driver according to the target score and the driving score includes:

[0035] Sorting the candidate drivers from high to low according to the target scores to obtain a ranking order;

[0036] Traversing each of the candidate drivers in turn according to the ranking order, and determining whether the driving score of the currently traversed candidate driver is higher than the driving score of the driver, wherein the driving score of the driver is generated according to the historical driving data of the driver;

[0037] If higher, the candidate driver is taken as the target driver;

[0038] If it is less than or equal to, continue to traverse the next candidate driver until the target driver is determined or all the candidate drivers are traversed.

[0039] In a second aspect, an embodiment of the present invention provides a cold chain cargo transportation monitoring device, the device comprising:

[0040] A data receiving module, used for receiving driving data and pictures uploaded by the vehicle terminal at preset intervals, wherein the driving data includes driving information of the vehicle in the period, and the pictures are obtained by photographing a packaging box in the cargo compartment of the vehicle through a camera, wherein the packaging box contains goods;

[0041] A feature extraction module, used to extract the external features of the packaging box according to the image;

[0042] An impact prediction module, used to predict the impact of the packaging box during the transportation process of the current cycle according to the driving data;

[0043] A damage determination module, used to determine the degree of impact damage of the goods in the packaging box in the current cycle according to the impact situation and the external characteristics;

[0044] A judgment module, used to determine whether the impact damage degree is greater than a preset damage degree;

[0045] A dangerous driving determination module, configured to obtain road condition information of the road on which the vehicle is traveling in a current cycle if the impact damage degree is greater than a preset damage degree, and determine whether the driver is driving dangerously based on the driving data and the road condition information;

[0046] An alarm sending module is used for, if dangerous driving occurs, determining that the damage to the cargo is related to the dangerous driving, and sending a dangerous driving alarm to the vehicle-mounted terminal;

[0047] The message sending module is used to determine that the damage to the goods is not related to the dangerous driving if there is no dangerous driving, and send a road condition warning message to the vehicle terminal.

[0048] In a third aspect, an embodiment of the present invention provides a cold chain cargo transportation monitoring device, wherein the cold chain cargo transportation monitoring device includes a processor and a memory;

[0049] The memory is used to store a computer program and transmit the computer program to the processor;

[0050] The processor is used to execute a cold chain cargo transportation monitoring method as described in the first aspect according to the instructions in the computer program.

[0051] In a fourth aspect, an embodiment of the present invention provides a storage medium storing computer executable instructions, which, when executed by a computer processor, are used to execute a cold chain cargo transportation monitoring method as described in the first aspect.

[0052] As described above, the embodiments of the present invention provide a cold chain cargo transportation monitoring method, device, equipment and storage medium. The embodiments of the present invention can periodically predict the impact damage degree of the cargo in the package in the current cycle based on the driving data of the vehicle and the pictures of the packaging boxes transported by the vehicle, and when the impact damage degree is greater than the preset damage degree, determine whether the driver is driving dangerously based on the driving data of the vehicle, thereby determining whether the impact of the cargo is related to the driver's dangerous driving. If the driver is driving dangerously, it is determined that the impact is related to the driver, and a dangerous driving alarm is sent to the vehicle's on-board terminal. The embodiments of the present invention can predict the impact damage degree of cargo during cold chain transportation, and determine whether the impact of the cargo is related to the driver's dangerous driving. If there is a correlation, an alarm is promptly issued to remind the driver not to drive dangerously, which solves the technical problem in the prior art that there is no detection of whether the impact of the cargo is related to the driver's dangerous driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A schematic flow chart of a cold chain cargo transportation monitoring method provided in an embodiment of the present invention.

[0054] Figure 2 A schematic diagram of the structure of a cold chain cargo transportation monitoring device provided in an embodiment of the present invention.

[0055] Figure 3 A schematic diagram of the structure of a cold chain cargo transportation monitoring device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The following description and accompanying drawings fully illustrate the specific embodiments of the present application so that those skilled in the art can practice them. The examples represent possible variations only. Unless explicitly required, separate components and functions are optional, and the order of operation can vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments. The scope of the embodiments of the present application includes the entire scope of the claims, and all available equivalents of the claims. In this article, each embodiment may be represented individually or generally by the term "invention", which is only for convenience, and if more than one invention is disclosed in fact, it is not intended to automatically limit the scope of the application to any single invention or inventive concept. In this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, without requiring or implying any actual relationship or order between these entities or operations. Moreover, the term "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method or device including a series of elements includes not only those elements, but also other elements that are not explicitly listed. The various embodiments are described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other. As for the structures, products, etc. disclosed in the embodiments, since they correspond to the parts disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0057] like Figure 1 As shown, Figure 1 A flowchart of a cold chain cargo transportation monitoring method provided in an embodiment of the present invention. The cold chain cargo transportation monitoring method provided in an embodiment of the present invention can be executed by a cold chain cargo transportation monitoring device, and the cold chain cargo transportation monitoring device can be implemented by software and / or hardware. The cold chain cargo transportation monitoring device can be composed of two or more physical entities, or it can be composed of one physical entity. For example, the cold chain cargo transportation monitoring device can be a computer, a host computer, a tablet and other devices. A cold chain cargo transportation monitoring method provided in an embodiment of the present invention includes the following steps:

[0058] Step 101: Receive driving data and pictures uploaded by the vehicle terminal at preset intervals, wherein the driving data includes driving information of the vehicle during the period, and the pictures are obtained by photographing a packaging box in a cargo compartment of the vehicle through a camera, wherein the packaging box contains goods.

[0059] In this embodiment, it is necessary to receive driving data and pictures uploaded by the vehicle terminal at each preset period, wherein the vehicle terminal is installed on a vehicle used for cold chain transportation, and the vehicle terminal is wirelessly connected to the cold chain cargo transportation monitoring device. The vehicle terminal will collect the driving information of the vehicle during the driving process of the vehicle, such as driving information including vehicle speed, steering wheel rotation angle, and throttle opening. A camera is also provided in the cargo compartment of the vehicle for loading goods, and the vehicle terminal is connected to the camera, and the vehicle terminal can take pictures of the packaging boxes in the cargo compartment through the camera. It should be noted that in order to be able to take pictures of each packaging box, each packaging box cannot be stacked with other packaging boxes. In order to improve space utilization, users can use larger packaging boxes to place goods. In addition, when the brightness in the cargo compartment is too low, a flash can also be set on the camera, and the flash can be used for fill light when taking pictures. The vehicle terminal will generate driving data according to the driving information of the vehicle in this period at each preset period, and after controlling the camera to take pictures of the packaging boxes, the driving data and pictures will be uploaded to the cold chain cargo transportation monitoring device. In addition, the time interval between adjacent cycles can be set according to actual needs. For example, the cycle can be set to 15 minutes or 30 minutes, etc., which is not specifically limited in this embodiment.

[0060] Step 102: extract the external features of the packaging box according to the image.

[0061] After receiving the picture uploaded by the vehicle terminal, the cold chain cargo transportation monitoring device can extract the external features of the packaging box from the picture. Specifically, the cold chain cargo transportation monitoring device can extract the external features of the packaging box in the picture through an edge detection algorithm or a deep learning neural network. The specific method can refer to the existing technology and will not be repeated in this embodiment.

[0062] Step 103: predict the impact of the packaging box during transportation in the current cycle based on the driving data.

[0063] While extracting external features from the image, the cold chain cargo transportation monitoring device needs to predict the impact of the packaging box during the transportation process of the current cycle based on the received driving data. For example, the cold chain cargo transportation monitoring device can analyze the acceleration of the vehicle during driving based on the driving data, and analyze the movement of the packaging box containing the goods during transportation based on the acceleration of the vehicle, thereby predicting the impact of the packaging box.

[0064] Based on the above embodiment, in step 103, the impact of the packaging box during the transportation of the current cycle is predicted according to the driving data, including:

[0065] Step 1031: Determine the acceleration data of the vehicle in the current cycle according to the driving data, where the acceleration data includes linear acceleration data and turning acceleration data.

[0066] In this embodiment, it is first necessary to determine the acceleration data of the vehicle in the current cycle based on the driving data. Specifically, in this embodiment, the driving speed includes speed data and steering wheel rotation angle data, etc. The cold chain cargo transportation monitoring device can determine the speed data of the vehicle when it is traveling in a straight line and the speed data when it is turning based on the speed data and the steering wheel rotation angle data, and then determine the linear acceleration data and turning acceleration data of the vehicle based on the speed data of the vehicle when it is traveling in a straight line and the speed data of the vehicle when it is turning.

[0067] Step 1032: Determine an acceleration threshold corresponding to the packaging box according to the type of goods, where the acceleration threshold is a critical acceleration for the packaging box to move.

[0068] When determining the acceleration data, the cold chain cargo transportation monitoring equipment also needs to determine the type of cargo, where the type of cargo is used to characterize the type of cargo. For example, the type of cargo can be apples, strawberries, or blueberries, etc. The type of cargo can be obtained by querying business information in the background system. Afterwards, it is necessary to further obtain the acceleration threshold corresponding to the cargo according to the type of cargo, where the acceleration threshold is the critical acceleration for the cargo to move. Different types of cargo have different weights, so that the acceleration thresholds of different types of cargo are different. In this embodiment, the acceleration thresholds of different types of cargo can be calibrated in advance. Specifically, when calibrating the acceleration threshold, the weight of each packaging box can be determined first according to the weight of the cargo and the volume of the packaging box, and then the friction between the bottom of the packaging box and the contact surface can be determined according to the weight of each packaging box. Finally, the acceleration of each packaging box when it moves is determined according to the friction, and this acceleration is the critical acceleration for the movement of the packaging box.

[0069] Step 1033: predict the number of collisions of the packaging box in the current cycle based on the acceleration data, the acceleration threshold, and the pictures received in the previous cycle.

[0070] After obtaining the acceleration threshold, the number of collisions of the packaging box in the current cycle can be determined based on the acceleration data and the acceleration threshold. Specifically, the time when the acceleration data is greater than the acceleration threshold and the duration of the acceleration data being greater than the acceleration threshold can be determined, and the number of times the packaging box has moved, the direction, and the theoretical distance moved can be calculated based on the duration.

[0071] Afterwards, the placement of the packaging box in the cargo compartment at the beginning of this cycle can be determined based on the pictures received in the previous cycle, and the number of collisions of the packaging box in the current cycle can be predicted based on the placement of the packaging box and the number of times, directions, and distances that the packaging box moves in this cycle. Specifically, the number of times each packaging box collides with other packaging boxes or the wall of the cargo compartment in the direction of movement can be predicted based on the placement of the packaging box, the direction in which the packaging box first moved, and the theoretical distance of movement, and then the position of each packaging box is updated, and the number of times each packaging box collides with other packaging boxes or the wall of the cargo compartment in the direction of movement is continued to be predicted based on the direction in which the packaging box moves next time and the theoretical distance of movement, and the position of the packaging box is re-updated, and the past is repeated to obtain the number of collisions of the packaging box in the current cycle.

[0072] Step 104: Determine the impact damage degree of the goods in the packaging box in the current cycle according to the impact situation and external characteristics.

[0073] After determining the number of impacts of the packaging box and extracting the external features of the packaging box, the impact damage degree of the goods in the packaging box in the current cycle can be determined based on the number of impacts and the external features of the packaging box. For example, the surface damage degree of the packaging box can be determined first based on the external features of the packaging box, and then the impact damage degree of the goods in the packaging box in the current cycle can be determined based on the number of impacts and the surface damage degree.

[0074] On the basis of the above embodiment, in step 104, determining the impact damage degree of the goods in the packaging box in the current cycle according to the impact situation and external features includes:

[0075] Step 1041: Input the external features into the surface damage prediction model so that the surface damage prediction model outputs the surface damage degree of the packaging box.

[0076] When determining the degree of impact damage in the current cycle, the external features of the packaging box can first be input into the surface damage prediction model, and the surface damage prediction model outputs the degree of surface damage of the packaging box based on the external features of the packaging box, where the surface damage degree refers to the degree of damage on the outside of the packaging box. Exemplarily, the surface damage prediction model can identify the wrinkles and deformed areas of the packaging box based on the external features, thereby determining the degree of surface damage of the packaging box. It can be understood that the more wrinkles and the greater the deformation, the more surface damage and the higher the degree of surface damage.

[0077] Step 1042: Determine the impact damage degree of the goods in the packaging box in the current cycle according to the surface damage degree and the number of impacts.

[0078] After determining the degree of surface damage of the packaging box, the degree of impact damage of the goods in the packaging box in the current cycle can be determined based on the degree of surface damage of the packaging box and the number of impacts. Specifically, the degree of surface damage of the packaging box reflects the impact strength of the packaging box to a certain extent, and the number of impacts of the packaging box reflects the impact frequency of the packaging box to a certain extent. Based on the impact strength and impact frequency, the degree of impact damage of the goods in the packaging box in the current cycle can be predicted.

[0079] Based on the above embodiment, in step 1042, the impact damage degree of the goods in the packaging box in the current cycle is determined according to the surface damage degree and the number of impacts, including:

[0080] Step 10421: Obtain the surface damage degree of the packaging box in the previous cycle, compare the surface damage degree of the current cycle with the surface damage degree of the previous cycle, and determine the change degree of the impact damage of the packaging box.

[0081] In this embodiment, when determining the degree of impact damage of the goods in the current cycle, it is first necessary to determine the degree of surface damage of the packaging box of the goods in the previous cycle, and compare the surface damage degree of the packaging box in the current cycle with the surface damage degree of the previous cycle, so as to determine the degree of change of the impact damage of the packaging box in the current cycle, that is, the change of the surface damage degree in the current cycle compared with the surface damage degree in the previous cycle. The degree of change of the impact damage reflects, to a certain extent, the severity of the impact of the packaging box in the current cycle.

[0082] Step 10422: Determine the degree of impact damage to the goods in the packaging box during the current cycle based on the number of impacts and the degree of change in impact damage.

[0083] After determining the degree of change in the impact damage, the degree of impact damage of the goods in the packaging box in the current cycle can be determined based on the number of impacts and the degree of change in the impact damage. It can be understood that when the packaging box collides, the goods loaded inside the packaging box will also collide with each other and between the goods and the packaging box to cause damage. In one embodiment, the corresponding damage prediction model can be called according to the type of goods in the packaging box, and the number of impacts and the degree of change in the impact damage are input into the damage prediction model, so that the damage prediction model outputs the degree of impact damage of the goods in the current cycle. The damage prediction model corresponding to different types of goods can be obtained by users through pre-training. For example, after placing a type of goods in the packaging box, the user determines the degree of impact damage of the goods inside the packaging box after each impact. After multiple tests to obtain training data, the training data can be used to train the neural network to obtain a damage prediction model corresponding to the type of goods. In another embodiment, corresponding weights can also be assigned to the number of impacts and the degree of change in impact damage, and after the number of impacts and the degree of change in impact damage are normalized, the normalized values ​​are multiplied by the corresponding weights and added together to determine the degree of impact damage of the goods in the packaging box in the current cycle, where the weights can be set in advance.

[0084] Step 105: Determine whether the impact damage degree is greater than a preset damage degree.

[0085] After determining the impact damage degree of the goods in the packaging box in the current cycle, it is necessary to further determine whether the impact damage degree is greater than a preset damage degree, where the preset damage degree is a pre-set damage degree.

[0086] Step 106: If it is greater than, obtain the road condition information of the road on which the vehicle is traveling in the current cycle, and determine whether the driver is driving dangerously based on the driving data and the road condition information.

[0087] If the impact damage of the goods in the packaging box in the current cycle is greater than the preset damage, it is necessary to further determine whether the driver has engaged in dangerous driving behavior. Specifically, the road condition information of the road on which the vehicle is traveling in the current cycle can be obtained, and the degree of dangerous driving of the driver can be determined based on the driving data and road condition information in the current cycle. For example, the number of lane changes and speed of the vehicle when driving in a straight line and the turning speed of the vehicle when turning can be determined based on the road condition information and driving data, so as to determine whether the driver is driving dangerously.

[0088] Step 107: If it is dangerous driving, it is determined that the damage to the cargo is related to the dangerous driving, and a dangerous driving alarm is sent to the vehicle terminal.

[0089] If the driver drives dangerously, it can be determined that the impact on the cargo in the current cycle is greatly correlated with the driver's dangerous driving, and the impact on the cargo is largely caused by the driver's dangerous driving. At this time, a dangerous driving alarm can be sent to the vehicle terminal. After receiving the dangerous driving alarm, the vehicle terminal issues a warning to alert the driver and warns him not to drive dangerously.

[0090] Step 108: If there is no dangerous driving, it is determined that the damage to the cargo is not related to the dangerous driving, and a road condition warning message is sent to the vehicle terminal.

[0091] If the driver did not drive dangerously, it can be determined that the impact on the cargo in the current cycle has nothing to do with the driver's dangerous driving, and the impact on the cargo is caused by road conditions (such as uneven road surface or too many sharp turns). A road condition attention message is sent to the vehicle terminal so that the vehicle terminal reminds the driver to pay attention to the road conditions.

[0092] The embodiment of the present invention can periodically predict the degree of impact damage of the goods in the package in the current cycle based on the driving data of the vehicle and the pictures of the packaging boxes transported by the vehicle, and when the impact damage degree is greater than the preset damage degree, determine whether the driver is driving dangerously based on the driving data of the vehicle, thereby determining whether the impact of the goods is related to the driver's dangerous driving. If the driver is driving dangerously, it is determined that the impact is related to the driver, and a dangerous driving alarm is sent to the vehicle's on-board terminal to remind the driver to pay attention. The embodiment of the present invention can predict the degree of impact damage of goods during cold chain transportation, and determine whether the impact of the goods is related to the driver's dangerous driving. If there is a correlation, an alarm is issued in time to remind the driver not to drive dangerously, which solves the technical problem in the prior art that there is no detection of whether the impact of the goods is related to the driver's dangerous driving.

[0093] Based on the above embodiment, the vehicle's driving path includes at least one transfer station between the starting point and the end point, wherein the transfer station is a place set up in the driving path for temporary parking or other processing of goods in the transportation process, and then continuing to send them to the destination. The main functions of the transfer station include: distribution transportation, pre-processing or reprocessing of goods or information, docking and separation of multiple transportation tools, driver rest or other services, etc. In this embodiment, after determining that the driver is driving dangerously, it also includes:

[0094] Step 109: Acquire the location information of the vehicle in real time, and determine whether there is a transfer station in the direction of travel of the vehicle based on the location information.

[0095] After determining that the driver is driving dangerously, it is necessary to obtain the vehicle's location information in real time through the on-board terminal, and based on the vehicle's location information, determine whether there are any transfer stations in the direction of the vehicle's travel.

[0096] Step 110: If so, determine the candidate driver who is idle in the target transfer station closest to the direction of travel.

[0097] If there is a transfer station, it is necessary to determine the candidate driver who is currently idle in the target transfer station closest to the direction of travel, where the candidate driver is a driver who is currently idle and waiting for the assignment of a transportation task. Exemplarily, after each driver arrives at the transfer station, the transfer station can update the status of each driver according to the transportation schedule, and upload the status of each driver to the cold chain cargo transportation monitoring device, so that the cold chain cargo transportation monitoring device can query the idle drivers in the target transfer station.

[0098] Step 111: Determine the driving score of the candidate driver and the number of times the candidate driver passes the driving path of the current vehicle, wherein the driving score is generated based on the candidate driver's historical driving data.

[0099] After the candidate driver is determined, the driving score of the candidate driver and the number of times the candidate driver passes the current vehicle's driving path are determined, where the driving score can be automatically generated by the background system based on the candidate driver's historical driving data. For example, the number of dangerous driving in the candidate driver's history is analyzed based on the driving data, and the candidate driver is scored based on the number of dangerous driving. The more dangerous driving times, the lower the candidate driver's driving score. In addition, the background system can also count the number of times the driver passes on different driving paths based on the driver's historical transportation tasks. The cold chain cargo transportation monitoring equipment can obtain the candidate driver's driving score and number of passes through the background system.

[0100] Step 112: Determine the target driver based on the driving score and the number of passes.

[0101] After determining the driving scores and the number of passes of the candidate drivers, the target driver can be determined based on the driving scores and the number of passes. In one embodiment, determining the target driver based on the driving scores and the number of passes includes:

[0102] Step 1121: Determine a first weight corresponding to the driving score and a second weight corresponding to the number of passes.

[0103] Step 1122: Perform a weighted sum of the driving score and the number of passes according to the first weight and the second weight to obtain a target score corresponding to each candidate driver.

[0104] Step 1123: Determine the target driver according to the target score and the driving score.

[0105] When determining the target driver from the candidate drivers, first determine the first weight corresponding to the driving score and the second weight corresponding to the number of passes, and the first weight and the second weight can be set in advance by the user. Then, the driving score and the number of passes can be weighted and summed according to the first weight and the second weight, that is, the product of the first weight multiplied by the driver score and the product of the second weight multiplied by the number of passes are summed to obtain the target score corresponding to each candidate driver. After determining the target score corresponding to each candidate driver, the target driver can be determined according to the target score and the driving score.

[0106] Based on the above embodiment, determining the target driver according to the target score and the driving score in step 1123 includes:

[0107] Step 11231: Sort the candidate drivers from high to low according to the target scores to obtain a ranking order.

[0108] Specifically, in this embodiment, the candidate drivers may be sorted from high to low according to the target scores to obtain a ranking order.

[0109] Step 11232: traverse each candidate driver in order of ranking, and determine whether the driving score of the currently traversed candidate driver is higher than the driver's driving score, where the driver's driving score is generated based on the driver's historical driving data.

[0110] After obtaining the ranking order, each candidate driver can be traversed in turn to determine whether the driving score of the currently traversed candidate driver is higher than the driving score of the driver currently driving the vehicle. It can be understood that the driving score of the driver currently driving the vehicle can also be generated based on the driver's historical driving data.

[0111] Step 11233: If higher than, the candidate driver is taken as the target driver.

[0112] If the driving score of the currently traversed candidate driver is higher than the driving score of the driver currently driving the vehicle, the currently traversed candidate driver may be determined as the target driver.

[0113] Step 11234: If it is less than or equal to, continue to traverse the next candidate driver until the target driver is determined or all candidate drivers are traversed.

[0114] If the driving score of the candidate driver currently traversed is less than or equal to the driving score of the driver currently driving the vehicle, then continue to traverse the next candidate driver until the target driver is determined or all candidate drivers are traversed. If the driving scores of all candidate drivers are less than the driving score of the driver currently driving the vehicle, the current driver of the vehicle may not be replaced. In addition, when there is only one candidate driver, if the driving score of the candidate driver is higher than the driving score of the driver of the current vehicle, the candidate driver may be directly used as the target driver. It is understandable that only when the driving score of the candidate driver is higher than the driving score of the driver of the current vehicle, it indicates that the driving habit of the candidate driver is good, the driving is relatively stable, and the number of collisions of the goods in the transportation process can be reduced as much as possible. In the present embodiment, the method of determining the target driver by comprehensively considering the driving score and the number of passes can select a driver who drives relatively smoothly and is relatively familiar with the road conditions to reduce the number of collisions of the goods in the subsequent transportation process as much as possible.

[0115] Step 113, sending a transfer rest instruction to the vehicle terminal, and sending a target driver allocation instruction to the transfer station, the transfer rest instruction is used to instruct the driver of the vehicle to enter the transfer station for rest, and the target driver allocation instruction is used to instruct the transfer station to call the target driver to complete the remaining journey of the vehicle.

[0116] After the target driver is determined, it is necessary to send a transfer rest instruction to the vehicle's on-board terminal. After receiving the transfer rest instruction, the on-board terminal will issue a prompt to the current driver, prompting the driver of the vehicle to enter the transfer station for a rest after arriving at the transfer station. At the same time, it is necessary to send a target driver allocation instruction to the transfer station, instructing the transfer station to call the target driver to replace the current driver of the vehicle to complete the remaining journey after the vehicle enters. The embodiment of the present invention determines the candidate drivers in the nearest transfer station after detecting dangerous driving of the driver, and selects the target driver with relatively stable driving habits and familiarity with road conditions from the candidate drivers, so that after the vehicle reaches the transfer station, the target driver is instructed to replace the driver of the current vehicle to complete the remaining journey, so as to minimize the number of collisions of goods during subsequent transportation.

[0117] As described above, an embodiment of the present invention provides a cold chain cargo transportation monitoring method. The embodiment of the present invention can periodically predict the impact damage degree of the cargo in the package in the current cycle based on the driving data of the vehicle and the pictures of the packaging boxes transported by the vehicle, and when the impact damage degree is greater than the preset damage degree, determine whether the driver is driving dangerously based on the driving data of the vehicle, thereby determining whether the impact of the cargo is related to the driver's dangerous driving. If the driver is driving dangerously, a dangerous driving alarm is sent to the vehicle's on-board terminal to remind the driver to pay attention. The embodiment of the present invention can promptly detect the situation where the driver's dangerous driving causes the cargo to collide, and issue an alarm in time, which solves the technical problem in the prior art that there is no detection of whether the impact of the cargo is related to the driver's dangerous driving.

[0118] The embodiment of the present invention also provides a cold chain cargo transportation monitoring device, such as Figure 2 , Figure 2 A schematic diagram of the structure of a cold chain cargo transportation monitoring device provided by an embodiment of the present invention. A cold chain cargo transportation monitoring device provided by an embodiment of the present invention includes:

[0119] The data receiving module 201 is used to receive driving data and pictures uploaded by the vehicle terminal at preset intervals. The driving data includes the driving information of the vehicle during the period. The pictures are obtained by photographing the packaging boxes in the cargo compartment of the vehicle through a camera, and the packaging boxes contain goods.

[0120] The feature extraction module 202 is used to extract the external features of the packaging box according to the image.

[0121] The collision prediction module 203 is used to predict the collision of the packaging box during the transportation process of the current cycle according to the driving data.

[0122] The damage determination module 204 is used to determine the impact damage degree of the goods in the packaging box in the current cycle according to the impact situation and external characteristics.

[0123] The judgment module 205 is used to determine whether the impact damage degree is greater than a preset damage degree.

[0124] The dangerous driving determination module 206 is used to obtain the road condition information of the road on which the vehicle is traveling in the current cycle if the impact damage degree is greater than the preset damage degree, and determine whether the driver is driving dangerously based on the driving data and the road condition information.

[0125] The alarm sending module 207 is used to send a dangerous driving alarm to the vehicle terminal if dangerous driving is involved and it is determined that the damage to the cargo is related to the dangerous driving.

[0126] The message sending module 208 is used to send a road condition warning message to the vehicle terminal if there is no dangerous driving and it is determined that the damage to the goods is not related to the dangerous driving.

[0127] Based on the above embodiment, the collision prediction module 203 includes:

[0128] The acceleration acquisition submodule is used to determine the acceleration data of the vehicle in the current cycle according to the driving data, and the acceleration data includes straight-line acceleration data and turning acceleration data;

[0129] A threshold determination submodule is used to determine an acceleration threshold corresponding to the packaging box according to the type of goods, and the acceleration threshold is a critical acceleration at which the packaging box moves;

[0130] The collision number prediction submodule is used to predict the number of collisions of the packaging box in the current cycle based on the acceleration data, the acceleration threshold and the pictures received in the previous cycle.

[0131] Based on the above embodiment, the damage determination module 204 includes:

[0132] A surface damage determination submodule is used to input the external features into the surface damage prediction model so that the surface damage prediction model outputs the surface damage degree of the packaging box;

[0133] The impact damage determination submodule is used to determine the impact damage degree of the goods in the packaging box in the current cycle according to the surface damage degree and the number of impacts.

[0134] Based on the above embodiments, the impact damage determination submodule is specifically used to obtain the degree of surface damage of the packaging box in the previous cycle, compare the degree of surface damage in the current cycle with the degree of surface damage in the previous cycle, and determine the degree of change of the impact damage of the packaging box; according to the number of impacts and the degree of change of impact damage, determine the degree of impact damage of the goods in the packaging box in the current cycle.

[0135] Based on the above embodiment, the driving path of the vehicle includes at least one transfer station between the starting point and the end point, and the cold chain cargo transportation monitoring device further includes:

[0136] The transfer station determination module is used to obtain the vehicle's location information in real time after determining that the driver is driving dangerously, and determine whether there is a transfer station in the vehicle's direction of travel based on the location information;

[0137] A candidate driver determination module, for determining a candidate driver who is idle in the nearest target transfer station in the direction of travel, if any;

[0138] A score acquisition module, used to determine the driving score of the candidate driver and the number of times the candidate driver has passed the driving path of the current vehicle, the driving score being generated based on the candidate driver's historical driving data;

[0139] A target driver determination module is used to determine the target driver based on the driving score and the number of passes;

[0140] The instruction sending module is used to send a transfer rest instruction to the vehicle terminal and a target driver allocation instruction to the transfer station. The transfer rest instruction is used to instruct the driver of the vehicle to enter the transfer station for rest, and the target driver allocation instruction is used to instruct the transfer station to call the target driver to complete the remaining journey of the vehicle.

[0141] Based on the above embodiment, the target driver determination module includes:

[0142] A weight determination submodule, used to determine a first weight corresponding to the driving score and a second weight corresponding to the number of passes;

[0143] A scoring submodule, configured to perform a weighted summation of the driving score and the number of passes according to the first weight and the second weight to obtain a target score corresponding to each candidate driver;

[0144] The target driver determination submodule is used to determine the target driver according to the target score and the driving score.

[0145] On the basis of the above embodiment, the target driver determination submodule is specifically used to sort the candidate drivers from high to low according to the target score to obtain a ranking order; traverse each candidate driver in turn according to the ranking order to determine whether the driving score of the currently traversed candidate driver is higher than the driver's driving score, and the driver's driving score is generated according to the driver's historical driving data; if higher, the candidate driver is used as the target driver; if less than or equal to, continue to traverse the next candidate driver until the target driver is determined or all candidate drivers are traversed.

[0146] The cold chain cargo transportation monitoring device provided in the embodiment of the present invention is included in the cold chain cargo transportation monitoring equipment, and can be used to execute the cold chain cargo transportation monitoring method provided in the above embodiment, and has corresponding functions and beneficial effects.

[0147] It is worth noting that in the above-mentioned embodiment of the cold chain cargo transportation monitoring device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific name of each functional unit is only for the convenience of distinguishing each other, and is not used to limit the scope of protection of the present invention.

[0148] This embodiment also provides a cold chain cargo transportation monitoring device, such as Figure 3 As shown, Figure 3 A schematic diagram of the structure of a cold chain cargo transportation monitoring device provided in an embodiment of the present invention. The cold chain cargo transportation monitoring device 30 provided in an embodiment of the present invention includes a processor 300 and a memory 301;

[0149] The memory 301 is used to store the computer program 302 and transmit the computer program 302 to the processor 300;

[0150] The processor 300 is used to execute the steps in the above-mentioned cold chain cargo transportation monitoring method embodiment according to the instructions in the computer program 302.

[0151] Exemplarily, the computer program 302 may be divided into one or more modules / units, one or more modules / units are stored in the memory 301, and are executed by the processor 300 to complete the present application. One or more modules / units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program 302 in the cold chain cargo transportation monitoring device 30.

[0152] The cold chain cargo transportation monitoring device 30 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The cold chain cargo transportation monitoring device 30 may include, but is not limited to, a processor 300 and a memory 301. Those skilled in the art will appreciate that Figure 3 It is merely an example of the cold chain cargo transport monitoring device 30 and does not constitute a limitation of the cold chain cargo transport monitoring device 30. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the cold chain cargo transport monitoring device 30 may also include input and output devices, network access devices, buses, etc.

[0153] The processor 300 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 gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0154] The memory 301 may be an internal storage unit of the cold chain cargo transportation monitoring device 30, such as a hard disk or memory of the cold chain cargo transportation monitoring device 30. The memory 301 may also be an external storage device of the cold chain cargo transportation monitoring device 30, such as 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), etc. equipped on the cold chain cargo transportation monitoring device 30. Further, the memory 301 may also include both an internal storage unit and an external storage device of the cold chain cargo transportation monitoring device 30. The memory 301 is used to store computer programs and other programs and data required by the cold chain cargo transportation monitoring device 30. The memory 301 may also be used to temporarily store data that has been output or is to be output.

[0155] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0156] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0157] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0158] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0159] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store computer programs.

[0160] An embodiment of the present invention further provides a storage medium containing computer executable instructions, which are used to execute a cold chain cargo transportation monitoring method when executed by a computer processor. The method includes the following steps:

[0161] The driving data and pictures uploaded by the vehicle terminal are received at each preset period, wherein the driving data includes the driving information of the vehicle in the period, and the pictures are obtained by photographing the packaging box in the cargo compartment of the vehicle through a camera, wherein the packaging box contains goods;

[0162] Extract the external features of the packaging box based on the picture;

[0163] Based on driving data, predict the impact of packaging boxes during transportation in the current cycle;

[0164] Determine the degree of impact damage to the goods in the packaging box during the current cycle based on the impact situation and external features;

[0165] Determine whether the impact damage level is greater than the preset damage level;

[0166] If it is greater, obtain the road condition information of the road the vehicle is traveling on in the current cycle, and determine whether the driver is driving dangerously based on the driving data and the road condition information;

[0167] If the vehicle is driving dangerously, it is determined that the damage to the cargo is related to the dangerous driving, and a dangerous driving alert is sent to the vehicle terminal;

[0168] If there is no dangerous driving, it is determined that the damage to the cargo is not related to the dangerous driving, and a road condition warning message is sent to the vehicle terminal.

[0169] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the embodiments of the present invention are not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the protection scope of the embodiments of the present invention. Therefore, although the embodiments of the present invention are described in more detail through the above embodiments, the embodiments of the present invention are not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the embodiments of the present invention, and the scope of the embodiments of the present invention is determined by the scope of the appended claims.

Claims

1. A cold chain cargo transportation monitoring method, characterized in that: The method comprises: receiving driving data and pictures uploaded by the vehicle terminal at preset intervals, wherein the driving data includes driving information of the vehicle in the period, and the pictures are obtained by photographing a packaging box in the cargo compartment of the vehicle through a camera, wherein the packaging box contains goods; Extracting external features of the packaging box according to the image; Predicting, based on the driving data, the impact condition of the packaging box during transportation in the current cycle; Determining the degree of impact damage of the goods in the packaging box in the current cycle according to the impact situation and the external characteristics; Determining whether the impact damage degree is greater than a preset damage degree; If it is greater than, obtaining the road condition information of the road on which the vehicle is traveling in the current cycle, and determining whether the driver is driving dangerously according to the driving data and the road condition information; If it is dangerous driving, determining that the damage to the cargo is related to the dangerous driving, and sending a dangerous driving alert to the vehicle terminal; If there is no dangerous driving, it is determined that the damage to the cargo is not related to the dangerous driving, and a road condition warning message is sent to the vehicle-mounted terminal.

2. A cold chain cargo transportation monitoring method according to claim 1, characterized in that: The predicting, based on the driving data, the collision condition of the packaging box during the transportation process of the current cycle includes: Determining acceleration data of the vehicle in a current cycle according to the driving data, the acceleration data including straight-line acceleration data and turning acceleration data; Determining an acceleration threshold corresponding to the packaging box according to the type of the goods, the acceleration threshold being a critical acceleration at which the packaging box moves; The number of collisions of the packaging box in the current cycle is predicted based on the acceleration data, the acceleration threshold and the pictures received in the previous cycle.

3. A cold chain cargo transportation monitoring method according to claim 2, characterized in that: Determining the impact damage degree of the goods in the packaging box in the current cycle according to the impact condition and the external features includes: Inputting the external features into a surface damage prediction model so that the surface damage prediction model outputs the degree of surface damage of the packaging box; The impact damage degree of the goods in the packaging box in the current cycle is determined according to the surface damage degree and the number of impacts.

4. A cold chain cargo transportation monitoring method according to claim 3, characterized in that: The determining, according to the surface damage degree and the number of impacts, the impact damage degree of the goods in the packaging box in the current cycle includes: Obtaining the degree of surface damage of the packaging box in the previous cycle, comparing the degree of surface damage in the current cycle with the degree of surface damage in the previous cycle, and determining the degree of change of the impact damage of the packaging box; The impact damage degree of the goods in the packaging box in the current cycle is determined according to the number of impacts and the degree of change of the impact damage.

5. A cold chain cargo transportation monitoring method according to claim 1, characterized in that: The driving path of the vehicle includes at least one transfer station between the starting point and the end point, and after determining that the driver is driving dangerously, further includes: Acquire the location information of the vehicle in real time, and determine whether the transfer station exists in the direction of travel of the vehicle according to the location information; If so, determining a candidate driver who is idle at the nearest target transfer station in the direction of travel; Determining a driving score of the candidate driver and the number of times the candidate driver has passed the current driving path of the vehicle, wherein the driving score is generated based on the candidate driver's historical driving data; Determining a target driver according to the driving score and the number of passes; A transfer rest instruction is sent to the vehicle terminal, and a target driver allocation instruction is sent to the transfer site, wherein the transfer rest instruction is used to instruct the driver of the vehicle to enter the transfer site for rest, and the target driver allocation instruction is used to instruct the transfer site to call the target driver to complete the remaining journey of the vehicle.

6. A cold chain cargo transportation monitoring method according to claim 5, characterized in that: The determining of the target driver according to the driving score and the number of passes includes: determining a first weight corresponding to the driving score and a second weight corresponding to the number of passes; Performing a weighted summation of the driving score and the number of passes according to the first weight and the second weight to obtain a target score corresponding to each of the candidate drivers; A target driver is determined according to the target score and the driving score.

7. A cold chain cargo transportation monitoring method according to claim 6, characterized in that: The determining a target driver according to the target score and the driving score comprises: Sorting the candidate drivers from high to low according to the target scores to obtain a ranking order; Traversing each of the candidate drivers in turn according to the ranking order, and determining whether the driving score of the currently traversed candidate driver is higher than the driving score of the driver, wherein the driving score of the driver is generated according to the historical driving data of the driver; If higher, the candidate driver is taken as the target driver; If it is less than or equal to, continue to traverse the next candidate driver until the target driver is determined or all the candidate drivers are traversed.

8. A cold chain cargo transportation monitoring device, characterized in that: The device comprises: A data receiving module, used for receiving driving data and pictures uploaded by the vehicle terminal at preset intervals, wherein the driving data includes driving information of the vehicle in the period, and the pictures are obtained by photographing a packaging box in the cargo compartment of the vehicle through a camera, wherein the packaging box contains goods; A feature extraction module, used to extract the external features of the packaging box according to the image; An impact prediction module, used to predict the impact of the packaging box during the transportation process of the current cycle according to the driving data; A damage determination module, used to determine the degree of impact damage of the goods in the packaging box in the current cycle according to the impact situation and the external characteristics; A judgment module, used to determine whether the impact damage degree is greater than a preset damage degree; A dangerous driving determination module, configured to obtain road condition information of the road on which the vehicle is traveling in a current cycle if the impact damage degree is greater than a preset damage degree, and determine whether the driver is driving dangerously based on the driving data and the road condition information; An alarm sending module is used for, if dangerous driving occurs, determining that the damage to the cargo is related to the dangerous driving, and sending a dangerous driving alarm to the vehicle-mounted terminal; The message sending module is used to send a road condition warning message to the vehicle terminal if there is no dangerous driving and determine that the damage to the goods is not related to the dangerous driving.

9. A cold chain cargo transportation monitoring device, characterized in that: The cold chain cargo transportation monitoring device includes a processor and a memory; The memory is used to store a computer program and transmit the computer program to the processor; The processor is used to execute a cold chain cargo transportation monitoring method as described in any one of claims 1-7 according to the instructions in the computer program.

10. A storage medium storing computer executable instructions, characterized in that: The computer executable instructions, when executed by a computer processor, are used to execute a cold chain cargo transportation monitoring method as described in any one of claims 1-7.

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