Apparatus and method for sensing and processing a vehicle's ground transportation environment

Abstract

The present disclosure relates to an apparatus and method for sensing and processing a ground transportation environment of a vehicle. The apparatus according to one embodiment of the present disclosure includes an inertial sensor that senses an impact on cargo contained in a cargo compartment of the vehicle due to sudden acceleration or deceleration of the vehicle, a processor that calculates a second frequency to offset the first frequency based on a first frequency corresponding to the impact sensed by the inertial sensor and outputs a control signal including the second frequency, and a vibration generator that vibrates at the second frequency in response to the control signal.

Description

[Technical Field]

[0001] The present disclosure relates to electronic devices and methods, and more particularly to devices and methods for sensing and processing the ground transportation environment of a vehicle using a data logger, and for sensing various physical quantities in the ground transportation environment and detecting various factors in ground distribution that affect the transportation environment.

[0002] The present disclosure relates to electronic devices and methods, and more particularly to systems and methods for analyzing errors between a predicted and actual vehicle route, calculating the causes and impacts of the errors, and optimizing the vehicle's ground transportation route. [Background technology]

[0003] With the development of technology and industry, distribution networks are gradually expanding, and as this distribution network expands, the storage and transportation conditions of goods during the distribution process are becoming increasingly important factors. In particular, when expensive products such as large-capacity batteries are transported, if the product is damaged due to high heat, vibration, humidity, etc., it is not just the damage to one product that is occurring, but there is a high possibility that other products loaded in the same space or environment will also be damaged in a chain reaction.

[0004] As such, high-value-added products require a different logistics and transportation environment than low-value-added products. Managing transportation quality to prevent damage or loss of transported goods is of utmost importance in logistics management, and technological advances have led to the adoption of a variety of devices and methods for efficient logistics management. However, even in the case of high-value-added products, there is a problem of not monitoring the deterioration or damage of products due to high heat, vibration, humidity, etc. during transportation, which calls for the introduction of a more efficient and systematic transportation quality control system. Summary of the Invention [Problem to be solved by the invention]

[0005] The embodiments disclosed in this disclosure aim to build a system that prevents sudden acceleration or deceleration of a vehicle, safely prevents accidents by detecting impacts on cargo in advance, protects cargo contained in the cargo bay, stably provides safely protected cargo to consumers, clarifies the responsibility of shippers and logistics companies in situations where the condition of cargo in the cargo bay changes due to changes in internal temperature, and provides a stable land transportation environment.

[0006] The disclosed embodiments of the present disclosure are intended to build a system for analyzing the error between the predicted route and the actual route of a vehicle and optimizing the vehicle's land transportation route.

[0007] The problems to be solved by the present disclosure are not limited to those mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the following description. [Means for solving the problem]

[0008] To achieve the above-mentioned technical objectives, one aspect of the present disclosure provides an apparatus for sensing and processing the land transportation environment of a vehicle, including an inertial sensor that senses an impact on cargo contained in a cargo compartment of the vehicle due to sudden acceleration or deceleration of the vehicle, a processor that calculates a second frequency to offset the first frequency based on a first frequency corresponding to the impact sensed by the inertial sensor and outputs a control signal including the second frequency, and a vibration generator that vibrates at the second frequency in response to the control signal.

[0009] According to another aspect of the present disclosure, a method for sensing and processing a vehicle's land transportation environment includes a sensing step of sensing an impact on cargo contained in a cargo compartment of the vehicle due to sudden acceleration or deceleration of the vehicle, a calculation step of calculating a second frequency to offset a first frequency corresponding to the sensed impact based on the first frequency, a vibration step of vibrating at the second frequency, and a determination step of determining a cause of a temperature change contained in temperature data based on temperature data received from the outside.

[0010] In addition, a computer program stored on a computer-readable recording medium for execution to embody the present disclosure may also be provided.

[0011] In addition, a computer program stored on a recording medium that executes the method for embodying the present disclosure in combination with the hardware may also be provided.

[0012] To achieve the above-mentioned technical objectives, one aspect of the present disclosure provides a system for optimizing land transportation routes of transportation means, including a tracker that is disposed on the transportation means and senses transportation environment data including the environment of cargo contained in the transportation means and transmits the transportation environment data; a server that analyzes an error between a predicted transportation route and an actual transportation route for transporting cargo between a departure point of the transportation means and a destination point of the transportation means based on the transportation environment data, calculates an impact degree indicating the extent to which the error affects the cargo, and optimizes the transportation route based on the error and the impact degree; and a transportation database constructed to optimize the transportation route.

[0013] A method for optimizing a land transportation route of a transportation means according to another aspect of the present disclosure includes a sensing step of sensing transportation environment data including the environment of cargo contained in the transportation means, an analysis and calculation step of analyzing an error between a predicted transportation route and an actual transportation route for transporting cargo between a departure point of the transportation means and a destination point of the transportation means based on the transportation environment data, and calculating an impact degree indicating the extent to which the error affects the cargo, and a route optimization step of optimizing the transportation route based on the error and the impact degree.

[0014] In addition, a computer program stored on a computer-readable recording medium for execution to embody the present disclosure may also be provided.

[0015] In addition, a computer program stored on a recording medium that executes the method for embodying the present disclosure in combination with the hardware may also be provided. [Effects of the Invention]

[0016] The above-mentioned problem-solving means of the present disclosure have the following effects: preventing sudden acceleration or deceleration of a vehicle; safely preventing accidents by detecting impacts on cargo in advance; protecting cargo contained in the cargo hold; providing convenience and satisfaction to consumers by steadily providing safely protected cargo to consumers; clarifying the responsibility of the shipper and logistics group in situations where the condition of cargo in the cargo hold changes due to changes in internal temperature; and providing a stable land transportation environment.

[0017] According to the above-mentioned solution to the problem of the present disclosure, through an advanced route optimization solution, route planners can manage vehicles, check driving routes, add multiple stops, improve productivity, and increase compliance with service level agreements (SLAs); route optimization can provide fuel cost savings, additional pickup / delivery stops, reduced labor and overtime costs, and minimize human dependency.

[0018] The effects of the present disclosure are not limited to those mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description. [Brief explanation of the drawings]

[0019] [Figure 1a] 1 is a diagram illustrating an exemplary system of the present disclosure. [Figure 1b] 1 is a diagram illustrating an exemplary system of the present disclosure. [Figure 2] 1 is a diagram illustrating an example of a tracker of the present disclosure. [Figure 3] 1 is a diagram illustrating an example of a tracker of the present disclosure. [Figure 4a] 1 is a diagram illustrating an example of a tracker of the present disclosure. [Figure 4b] 1 is a diagram illustrating an example of a tracker of the present disclosure. [Figure 5] 1 is a flowchart for illustratively explaining an embodiment of the present disclosure for sensing and attenuating vibrations due to shock. [Figure 6] 1 is a diagram for illustrating an example of an embodiment of the present disclosure for canceling vibrations generated in a cargo hold. [Figure 7] 1 is a graph showing temperature over time according to the present disclosure, illustrating a pattern of temperature change as an example. [Figure 8] 1 is a diagram for exemplarily explaining pattern data of the present disclosure; [Figure 9] 1 is a diagram for exemplifying flag data of the present disclosure. [Figure 10] 1 is a diagram for illustrating an example of setting a sensing area in a cargo hold according to frequent door opening and closing in the present disclosure; [Figure 11] 1 is a diagram for illustrating an example of a region where temperature changes frequently and a region where temperature changes little in the present disclosure. [Figure 12] 1 is a flowchart illustrating a method according to the present disclosure. [Figure 13a] 1 is a diagram illustrating an exemplary system of the present disclosure. [Figure 13b] 1 is a diagram illustrating an exemplary system of the present disclosure. [Figure 13c] 1 is a diagram illustrating an exemplary system of the present disclosure. [Figure 14] 1 is a diagram illustrating an example of a tracker of the present disclosure. [Figure 15] 1 is a diagram illustrating an example of a tracker of the present disclosure. [Figure 16] 1 is a flowchart illustrating an embodiment of optimizing a transportation route according to the present disclosure. [Figure 17] 10 is a flowchart illustrating an example of generating an alert due to changes in temperature and humidity according to the present disclosure. [Figure 18] 10 is a flowchart illustrating an example of generating an alert due to an impact according to the present disclosure. [Figure 19] 10 is a flowchart illustrating another embodiment of optimizing a transportation route according to the present disclosure. [Figure 20]10 is a flowchart illustrating yet another embodiment of optimizing transportation routes according to the present disclosure. [Figure 21] 10 is a flowchart illustrating an embodiment of analyzing the cause of an error for each alternative transportation route according to the present disclosure. [Figure 22] 10 is a flowchart illustrating an example of calculating correlations between microscopic factors and weights according to the present disclosure. [Figure 23] 1 is a flowchart illustrating a method according to the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0020] The same reference numerals refer to the same elements throughout this disclosure. This disclosure does not describe all elements of the embodiments, and content that is common in the technical field to which the disclosure belongs or that is duplicated between embodiments will be omitted. The terms "unit, module, component, block" used in the specification may be embodied in software or hardware, and depending on the embodiment, multiple "units, modules, components, blocks" may be embodied in one component, or one "unit, module, component, block" may include multiple components.

[0021] Throughout this specification, when a part is said to be "coupled" to another part, this includes not only direct coupling but also indirect coupling, including coupling via a wireless communication network.

[0022] Furthermore, when a part "comprises" a certain element, this does not mean that it excludes other elements, but that it may further include other elements, unless otherwise specified.

[0023] Throughout this specification, when an element is said to be "on" another element, this includes not only when the element is in contact with the other element, but also when there is another element between the two elements.

[0024] The terms "first," "second," etc. are used to distinguish one component from another, and are not intended to limit the components to the terms previously used.

[0025] The singular expression includes the plural expression unless the context clearly dictates otherwise.

[0026] The identification numbers in each step are used for convenience of explanation, and do not describe the order of each step. Each step may be performed in a different order unless the context clearly dictates a specific order.

[0027] Hereinafter, the working principle and embodiments of the present disclosure will be described with reference to the accompanying drawings.

[0028] 1. Example 1 The following embodiments describe an apparatus and method for sensing and processing the ground transportation environment of a leading vehicle.

[0029] In this specification, the term "device according to the present disclosure" includes various devices capable of performing computations and providing results to a user. For example, the device according to the present disclosure may include all or any one of a computer, a server device, and a portable terminal.

[0030] Here, the computer may include, for example, a notebook computer, desktop computer, laptop computer, tablet PC, slate PC, etc. equipped with a web browser.

[0031] The server device is a server that communicates with external devices and processes information, and may include an application server, a computing server, a database server, a file server, a game server, a mail server, a proxy server, and a web server.

[0032] The portable terminal is, for example, a wireless communication device that ensures portability and mobility, and may include all kinds of handheld-based wireless communication devices such as PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminals, smartphones, etc., as well as wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted devices (HMDs).

[0033] The AI-related functions of the present disclosure operate through a processor and memory. The processor may be composed of one or more processors. In this case, the one or more processors may be general-purpose processors such as a CPU, AP, or DSP (Digital Signal Processor), dedicated graphics processors such as a GPU or VPU (Vision Processing Unit), or dedicated AI processors such as an NPU. The one or more processors control the processing of input data according to predefined operating rules or AI models stored in memory. Alternatively, if the one or more processors are dedicated AI processors, the dedicated AI processors may be designed with a hardware structure specialized for processing a specific AI model. For example, the processor may include an MCU (microcontroller unit), a fan control actuator, an APU (Accelerated Processing Unit), etc.

[0034] The predefined behavioral rules or AI models are characterized by being created through learning. Here, "created through learning" means that a basic AI model is trained by a learning algorithm using a large amount of learning data to create predefined behavioral rules or AI models configured to achieve desired characteristics (or goals). Such learning may be performed within the device itself on which the AI ​​according to the present disclosure is performed, or may be performed through a separate server and / or system. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning, but are not limited to the above examples.

[0035] An AI model may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values, and performs neural network operations by performing operations between the multiple weight values ​​and the operation results of previous layers. The multiple weight values ​​of the multiple neural network layers may be optimized based on the learning results of the AI ​​model. For example, the multiple weight values ​​may be updated to reduce or minimize the loss or cost values ​​acquired by the AI ​​model during the learning process. The artificial neural network may include a deep neural network (DNN), such as a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), or a deep Q-network, but is not limited to these examples.

[0036] According to an exemplary embodiment of the present disclosure, a processor may embody artificial intelligence. Artificial intelligence refers to a machine learning method based on an artificial neural network, which allows a machine to learn by imitating human biological neurons. Artificial intelligence methodologies can be classified into supervised learning, in which an answer (output data) to a problem (input data) is determined by providing both input data and output data as training data in a learning method; unsupervised learning, in which only input data is provided without output data, so the answer (output data) to the problem (input data) is not determined; and reinforcement learning, in which a reward is provided from an external environment each time an action is taken in the current state, and learning progresses in a direction to maximize this reward. Furthermore, artificial intelligence methodologies may be classified by their architecture, which is the structure of the learning model. The architectures of widely used deep learning technologies can be classified into convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, generative adversarial networks (GANs), etc.

[0037] The present device and system may include an artificial intelligence model. The artificial intelligence model may be a single artificial intelligence model or may be embodied as multiple artificial intelligence models. The artificial intelligence model may be composed of a neural network (or artificial neural network) and may include statistical learning algorithms that mimic biological neurons in machine learning and cognitive science. A neural network may refer to a general model that has problem-solving capabilities, in which artificial neurons (nodes) formed through synaptic connections change the strength of synaptic connections through learning. Neurons in a neural network may include a combination of weights or biases. A neural network may include one or more layers composed of one or more neurons or nodes. For example, a device may include an input layer, a hidden layer, and an output layer. The neural network constituting the device can infer the desired output from any input by changing the neuron weights through learning.

[0038] The processor may generate a neural network, train or learn the neural network, perform calculations based on received input data, generate an information signal based on the results of the calculations, or retrain the neural network. Neural network models may include, but are not limited to, various types of models, such as convolution neural networks (CNNs) like GoogleNet, AlexNet, and VGG Networks, region with convolution neural networks (R-CNNs), region proposal networks (RPNs), recurrent neural networks (RNNs), stacking-based deep neural networks (S-DNNs), state-space dynamic neural networks (S-SDNNs), deconvolution networks, deep belief networks (DBNs), restricted Boltzmann machines (RBMs), fully convolutional networks, long short-term memory (LSTM) networks, and classification networks. The processor may include one or more processors for performing calculations according to the neural network models. For example, the neural network can include a deep neural network.

[0039] Neural networks include CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), perceptron, multilayer perceptron, FF (Feed Forward), RBF (Radial Basis Network), DFF (Deep Feed Forward), LSTM (Long Short Term Memory), GRU (Gated Recurrent Unit), AE (Auto Encoder), VAE (Variational Auto Encoder), DAE (Denoising Auto Encoder), SAE (Sparse Auto Encoder), MC (Markov Chain), HN (Hopfield Network), BM (Boltzmann Machine), RBM (Restricted Boltzmann Machine), DBN (Deep Belief Network), DCN (Deep Convolutional Network), DN (Deconvolutional Network), DCIGN (Deep Convolutional Inverse Graphics Network), GAN (Generative Adversarial Network), and LSM (Liquid State Machine). It will be understood by those of ordinary skill in the art that the neural network may include, but is not limited to, any neural network, including, but not limited to, an ELM (Extreme Learning Machine), an ESN (Echo State Network), a DRN (Deep Residual Network), a DNC (Differentiable Neural Computer), an NTM (Neural Turning Machine), a CN (Capsule Network), a KN (Kohonen Network), and an AN (Attention Network).

[0040] According to an example embodiment of the present disclosure, the processor may support a variety of neural networks, including Convolution Neural Networks (CNNs) such as GoogleNet, AlexNet, and VGG Network, Region with Convolution Neural Network (R-CNN), Region Proposal Network (RPN), Recurrent Neural Network (RNN), Stacking-based deep Neural Network (S-DNN), State-Space Dynamic Neural Network (S-SDNN), Deconvolution Network, Deep Belief Network (DBN), Restricted Boltzmann Machine (RBM), Fully Convolutional Network, Long Short-Term Memory (LSTM) Network, Classification Network, Generative Modeling, eXplainable AI, Continual AI, Representation Learning, AI for Material Design, BERT, SP-BERT, MRC / QA, Text Analysis, Dialog System, GPT-3, GPT-4 for Natural Language Processing, Visual Analytics, Visual Understanding, Video Synthesis for Vision Processing, Anomaly Detection, Prediction, and Time-Series for ResNet Data Intelligence. Various artificial intelligence structures and algorithms may be used, including but not limited to, forecasting, optimization, recommendation, data creation, etc. Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

[0041] Figures 1a and 1b are diagrams showing an example of a system of the present disclosure. Figures 2 and 3 are diagrams showing an example of a tracker of the present disclosure. Figure 2 is a front view of the tracker. Figure 3 is a rear view of the tracker.

[0042] Referring to FIG. 1a, a cargo space tracker 10A, a user terminal 20A, a temperature measurement sensor 30A, and a distance measurement sensor 40A may be provided to perform the operations of the present disclosure.

[0043] The tracker 10A includes a variety of devices that can perform computations and provide results to the user.

[0044] The user terminal 20A may be either a computer or a portable user terminal, or may be any one of these. Here, the computer may be, for example, a notebook PC, desktop PC, laptop, tablet PC, slate PC, etc. equipped with a web browser.

[0045] The device (server) is a server that communicates with external devices and processes information, and may include an application server, computing server, database server, file server, game server, mail server, proxy server, web server, etc.

[0046] The portable user terminal is, for example, a wireless communication device that ensures portability and mobility, and may include all kinds of handheld-based wireless communication devices such as PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminals, smartphones, etc., as well as wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted devices (HMDs).

[0047] The temperature measurement sensor 30A may be attached within the cargo transport space (e.g., inside the cargo). The temperature measurement sensor 30A may be attached at a position predetermined by the user. The temperature measurement sensor 30A may sense the temperature and humidity inside the cargo and output sensing information regarding the temperature and humidity to the tracker 10A.

[0048] The distance measuring sensor 40A may be attached within the cargo carrying space and may generate distance data by measuring distances between a plurality of first positions predetermined by a user and a plurality of second positions, which are the remaining vertex positions to which the temperature measuring sensor 30A is not attached. The distance measuring sensor 40A may include any one of a lidar sensor, an ultrasonic sensor, a short / medium-range radar sensor, a long-range radar sensor, and a camera.

[0049] Referring to Figure 1b, system 100B may include tracker 10B, first user terminal 20B, second user terminal 50B, and communication network 60B. Although Figure 1 shows two user terminals, the number of user terminals is not limited to two and may be one, three, or more.

[0050] The tracker 10B may be a device for sensing and correcting the vehicle's land transportation environment. Examples of vehicles include automobiles, motorcycles, trucks, and trains. In one embodiment, the vehicle may be a train equipped with at least one cargo compartment or connected to at least one cargo compartment, but is not limited thereto. The tracker 10B can communicate with first and second user terminals 20B and 50B via a communication network 60B. The tracker 10B may include various devices capable of performing computations and providing results to users. For example, the tracker 10B may include all or any one of a computer, a device (server), and a mobile terminal. Here, the computer may include, for example, a notebook computer, desktop computer, laptop computer, tablet PC, or slate PC equipped with a web browser. The device (server) is a server that communicates with external devices and processes information, and may include an application server, computing server, database server, file server, game server, mail server, proxy server, and web server.

[0051] Referring to FIG. 2, the front of tracker 10B may include sensing unit 131, switch 132, input units 133 and 134, fingerprint recognition button 121, and a display. A user can input start and end dates on the display through input unit 133. Referring to FIG. 3, the rear of tracker 10B may include various buttons 122 and 123 and a power indicator 135. The input unit is for receiving information from a user, and information may be input through the user input unit. Such user input units may include hardware physical keys (e.g., buttons, dome switches, jog wheels, jog switches, etc. located on at least one of the front, rear, and side of the device) and software touch keys. For example, the touch keys may be virtual keys, soft keys, or visual keys displayed on a touchscreen-type display through software processing, or they may be touch keys located on a portion other than the touchscreen. Meanwhile, virtual keys or visual keys can be displayed on the touch screen in various forms, for example, graphics, text, icons, videos, or a combination thereof.

[0052] 1b, the first user terminal 20B and the second user terminal 50B may be either one or all of the above-mentioned computers and portable user terminals. The portable user terminal may be, for example, a wireless communication device that ensures portability and mobility, and may include all kinds of handheld-based wireless communication devices such as a Personal Communication System (PCS), Global System for Mobile communications (GSM), Personal Digital Cellular (PDC), Personal Handyphone System (PHS), Personal Digital Assistant (PDA), International Mobile Telecommunication (IMT)-2000, Code Division Multiple Access (CDMA)-2000, W-Code Division Multiple Access (W-CDMA), Wireless Broadband Internet (WiBro) terminal, and a smartphone, as well as wearable devices such as a watch, a ring, a bracelet, an anklet, a necklace, glasses, contact lenses, or a head-mounted device (HMD).

[0053] 4a and 4b are diagrams illustrating exemplary trackers of the present disclosure.

[0054] 4a, tracker 200a may be a device for sensing and processing the ground transportation environment of a vehicle. For example, the vehicle may be a train with a cargo compartment, and tracker 200a may be provided inside the train and / or inside the cargo compartment to sense and process the ground transportation environment, including the interior temperature and / or humidity. Tracker 200a may include processor 210, inertial sensor 220, vibration generator 230, memory 240, and communication module 250.

[0055] The processor 210 may calculate a second frequency for canceling the first frequency based on the first frequency corresponding to the impact sensed by the inertial sensor 220. The processor 210 may then output a control signal including the second frequency. The control signal may be input to the vibration generator 230. This is to prevent sudden acceleration or deceleration of the vehicle, to detect impacts on cargo caused by sudden acceleration or deceleration of the vehicle, to indirectly enforce safe operation in the distribution network, and to reduce vibrations caused by friction between the vehicle and the track. This has the effect of preventing sudden acceleration or deceleration of the vehicle, to safely prevent accidents by detecting impacts on cargo in advance, to protect cargo contained in the cargo compartment, and to provide consumers with convenience and satisfaction by steadily providing safely protected cargo to them.

[0056] The processor 210 can also receive temperature data from the outside. For example, the processor 210 can receive temperature data from the temperature measuring sensor 30A of FIG. 1a. In this case, the temperature measuring sensor 30A can be installed inside the cargo hold, measure the internal temperature of the cargo hold, and transmit the temperature data to the processor 210. The processor 210 can check the temperature over time or at regular intervals. The processor 210 can determine the cause of the change in the internal temperature based on the received temperature data. For example, the cause of the change in the internal temperature can be the inflow of outside air into the cargo hold and the outflow of the internal air of the cargo hold to the outside when a door installed in the cargo hold is opened or closed by a user (e.g., a shipper, a consignor, etc.), i.e., air circulation, causing the internal temperature of the cargo hold to fluctuate. Alternatively, the cause of the change in the internal temperature can be the inflow of outside air into the cargo hold and the outflow of the internal air of the cargo hold to the outside when the door installed in the cargo hold is closed, causing the internal temperature of the cargo hold to fluctuate solely due to the external environment of the cargo hold (e.g., weather, travel route, etc.). By having the processor 210 identify the cause of the change in the internal temperature of the cargo hold, it is possible to clarify the responsibility of the shipper and the logistics group in the situation where the condition of the cargo in the cargo hold changes due to the change in the internal temperature.

[0057] In one embodiment, the processor 210 may include a first calculation unit 211 and a correction unit 212 .

[0058] The first arithmetic unit 211 can perform various operations within the processor 210. In one embodiment, the first arithmetic unit 211 can be implemented as, but is not limited to, an arithmetic and logic unit (ALU). The first arithmetic unit 211 can calculate the phase of each first signal that induces an impact sensed by the inertial sensor 220. The impact sensed by the inertial sensor 220 can have a first frequency, which can be a combination of the frequencies of at least one first signal.

[0059] The correction unit 212 can generate second signals each having an opposite phase to the phase of the respective first signal. The first and second signals are described below with reference to Figure 6. The correction unit 212 can output a control signal to the vibration generator that includes the second signal.

[0060] In one embodiment, the processor 210 may further include a second arithmetic unit 213. The second arithmetic unit 213 may determine a pattern of temperature changes contained in the temperature data based on temperature data received from an external source. For example, the second arithmetic unit 213 may receive temperature data through the communication module 250 and determine a pattern using the temperature changes over time contained in the temperature data. The second arithmetic unit 213 may diagnose the cause of the temperature change by determining whether the temperature change is due to the external environment or to the carrier's intervention based on the pattern. The second arithmetic unit 213 may generate cause data including the cause of the temperature change.

[0061] According to an embodiment, the second calculation unit 213 can estimate a trend line (approximation line) showing the trend of temperature change over time based on the temperature data and the cause data. The trend line will be described later with reference to FIG.

[0062] According to an embodiment, the second arithmetic unit 213 may control the memory 240 to store the cause data. The second arithmetic unit 213 may be implemented as an ALU, like the first arithmetic unit 211.

[0063] Depending on the embodiment, the first arithmetic unit 211 and the second arithmetic unit 213 may be implemented as separate hardware, or may be implemented as an integrated arithmetic unit capable of performing the operations and functions of the first arithmetic unit 211 and the second arithmetic unit 213, respectively.

[0064] In one embodiment, the processor 210 may further include an artificial neural network processing unit 214. The artificial neural network processing unit 214 may generate an artificial intelligence model. The artificial neural network processing unit 214 may train the artificial intelligence model. The artificial intelligence model may learn a training data set including first data including points consisting of sequential temperature distributions over time and second data including pattern identifiers representing graphs in which the points are interconnected. The artificial neural network processing unit 214 may evaluate the performance of the artificial intelligence model based on the training results. The artificial neural network processing unit 214 may then tune or fit the artificial intelligence model based on the performance of the artificial intelligence model.

[0065] In the present disclosure, the artificial neural network processing unit 214 may be a separate unit provided on a printed circuit board that physically configures the tracker 200a, or may be a logically operating module within a processor chipset. For example, the artificial neural network processing unit 214 may be stored in the memory 240 as program code, and may represent a functional unit that embodies a machine learning model trained to achieve a specific purpose by being fetched and analyzed in sequence by the processor 210.

[0066] According to the embodiment, the second arithmetic unit 213 may input input data including points to the artificial intelligence model. Then, the second arithmetic unit 213 may predict a pattern identifier as output data of the artificial intelligence model. Then, the second arithmetic unit 213 may receive pattern data from the memory 240. Then, the second arithmetic unit 213 may identify a pattern by searching for a pattern identifier that matches the predicted pattern identifier among a plurality of pattern identifiers in the pattern data.

[0067] The inertial sensor 220 can sense impacts on cargo contained in the cargo compartment of the vehicle due to sudden acceleration or deceleration of the vehicle. The inertial sensor 220 can measure vehicle roll, vehicle vibration, and / or impacts applied to the vehicle. The inertial sensor 220 can sense the movement of the vehicle on a 6DoF (Degree of Freedom) or 9DoF basis. The object sensed by the inertial sensor 220 can be cargo that is sensitive to impacts and vibrations, such as industrial equipment, to prevent sudden acceleration or deceleration. The inertial sensor 220 can be embodied as an IMU (Inertial Measurement Unit).

[0068] In one embodiment, the inertial sensor 220 can sense the natural frequency of the cargo, which vibrates up and down within a unit time depending on the acceleration and inertial direction of the vehicle. The first calculation unit 211 can calculate the phase of each first signal based on the physical characteristics of the cargo, which are determined based on the acceleration of the vehicle and the mass of the cargo. The correction unit 212 can generate a second signal having the same natural frequency and amplitude but an opposite phase.

[0069] The vibration generator 230 can vibrate at the second frequency in response to the control signal. In one embodiment, the vibration generator 230 can include a haptic module that generates vibrations, a sonic module that generates sound waves, or the like.

[0070] Memory 240 can store data supporting various functions of tracker 200a and programs for the operation of processor 210, can store input / output data (e.g., music files, still images, videos, etc.), can store a number of application programs (or applications) run by tracker 200a, and data and commands for the operation of tracker 200a. At least some of these application programs can be downloaded from an external server via wireless communication. Such memory 240 may include at least one type of storage medium selected from the group consisting of a flash memory type, a hard disk type, a solid state disk type (SSD type), a silicon disk drive type (SDD type), a multimedia card micro type, a card-type memory (e.g., SD or XD memory), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, and an optical disk.

[0071] In one embodiment, the memory 240 may store pattern data and flag data. The pattern data may include features for each of a plurality of predefined patterns and a plurality of pattern identifiers that indicate each of the plurality of patterns. The features for the patterns may include, for example, visually indicating the shape of the pattern or indicating the shape of the pattern with text. The pattern identifier may be expressed as numbers, letters, or a combination of numbers and letters. The flag data may include a flag for each pattern. If there are a plurality of patterns, there may be a plurality of flags. For example, the flag data may include a flag for a first pattern, a flag for a second pattern, and a flag for a third pattern. The flag may indicate whether or not there is an external environment or carrier intervention for each pattern identifier. For example, if the cause of the temperature change is the external environment (e.g., travel route, force majeure due to weather, etc.), the flag may be a first value (e.g., “1”). If the cause of the temperature change is carrier intervention (e.g., opening or closing of a cargo hold door, etc.), the flag may be a second value (e.g., “0”). However, the present invention is not limited thereto, and specific flag values ​​may be set differently depending on the embodiment. Meanwhile, the second arithmetic unit 213 can load pattern data and flag data from the memory 240. Then, the second arithmetic unit 213 can extract features of a pattern to be recognized based on sequential temperatures over time in the temperature data. For example, the second arithmetic unit 213 can calculate the shape of a pattern using points including temperatures over time. Then, the second arithmetic unit 213 can acquire a pattern identifier of the pattern to be recognized based on the features of the pattern to be recognized and the pattern data. For example, the second arithmetic unit 213 can acquire a pattern identifier of the pattern to be recognized by extracting features (e.g., shape) of the pattern that match the shape of the pattern calculated from the pattern data. Then, the second arithmetic unit 213 can diagnose the cause based on the acquired pattern identifier and flag data.For example, the cause can be diagnosed by reading the specific value of the flag corresponding to the pattern identifier obtained in the flag data.

[0072] The communication module 250 may implement a communication interface. The communication interface may include one or more components that enable communication with an external device. For example, the communication interface may include at least one of a wired communication module, a wireless communication module, and a short-range communication module. The wired communication module may include various wired communication modules such as a local area network (LAN) module, a wide area network (WAN) module, or a value-added network (VAN) module, as well as various cable communication modules such as Universal Serial Bus (USB), High Definition Multimedia Interface (HDMI), Digital Visual Interface (DVI), recommended standard 232 (RS-232), power line communication, or plain old telephone service (POTS). The wireless communication module may include a Wi-Fi module, a wireless broadband module, and other wireless communication modules that support various wireless communication methods such as GSM (global system for mobile communication), CDMA (code division multiple access), WCDMA (wideband code division multiple access), UMTS (universal mobile telecommunications system), TDMA (time division multiple access), LTE (long term evolution), 4G, 5G, 6G, etc. The wireless communication module may include a wireless communication interface including an antenna for transmitting signals and a transmitter.The wireless communication module may further include a signal conversion module that modulates a digital control signal output from the processor 210 through the wireless communication interface into an analog wireless signal under the control of the processor 210. The short-range communication module is for short-range communication and is compatible with Bluetooth (registered trademark). TM The device may support short-range communication using at least one of the following technologies: RFID (Radio Frequency Identification), Infrared Data Association (IrDA), UWB (Ultra-Wideband), ZigBee (registered trademark), NFC (Near Field Communication), Wi-Fi (Wireless-Fidelity), Wi-Fi Direct, and Wireless Universal Serial Bus (Wireless USB).

[0073] In one embodiment, the communication module 250 can receive temperature data from the external temperature measurement sensor 30A and communicate the received temperature data to the processor 210.

[0074] In one embodiment, the communication module 250 can transmit the cause data to an external device via a communication network, where the external device can include, for example, the user terminal 20A, the first user terminal 20B, and / or the second user terminal 50B.

[0075] 4b, tracker 200b may include processor 210b, inertial sensor 220, vibration generator 230, memory 240, and communication module 250, and processor 210b may include first calculation unit 211 and correction unit 212. Server 300b may include second calculation unit 213b and artificial neural network processing unit 214b. Server 300b may directly generate an artificial neural network or train the artificial neural network and provide the trained results and hyperparameters to tracker 200b, or tracker 200b may load already determined values ​​as firmware on memory 240 or processor 210b.

[0076] In one embodiment, the processor 210b may store program code corresponding to an artificial neural network processing unit in the memory 240, or may be installed in the processor 210b as firmware to perform the corresponding functions. In the present disclosure, the artificial neural network processing unit may be an operating module that logically operates within the processor 210b chipset. For example, the artificial neural network processing unit may be stored in the memory 240 as program code, and may refer to a functional unit that embodies a machine learning model trained to achieve a specific purpose by being fetched and analyzed by the processor 210b.

[0077] The server 300b may be installed outside the transportation environment. For example, the server 300b is a server that communicates with external devices and processes information, and may include an application server, computing server, database server, file server, game server, mail server, proxy server, cloud and web server, etc. In this case, the artificial neural network processing unit 213b may train a machine learning model on a large scale in the server environment or calculate hyper-parameter values ​​that minimize a loss function for the trained model.

[0078] The tracker 200b can store the machine learning model calculated by the server 300b and the hyperparameter values ​​that drive the machine learning model in the memory 130b, and the processor 210b can analyze the program code that drives the machine learning model to quickly and lightweightly derive computing results that are essentially the same as the inference values ​​of an artificial neural network.

[0079] FIG. 5 is a flow chart for illustratively explaining an embodiment of the present disclosure for sensing and attenuating shock-induced vibrations.

[0080] 1, 2, and 5, in step S100, the tracker 10A detects the acceleration and inertial direction of the moving object using the IMU. Referring to FIG. 2, for example, the inertial sensor 220 can sense the acceleration and inertial direction of the vehicle.

[0081] In step S200, the tracker 10A detects a natural frequency caused by the vehicle vibrating up and down within a unit time using the IMU. Referring to FIG. 2, for example, when the vehicle or cargo compartment moves up and down, cargo contained in the cargo compartment may move up and down, and the inertial sensor 220 may sense the frequency generated by the cargo moving up and down. More specifically, for example, the inertial sensor 220 may sense the natural frequency caused by the cargo vibrating up and down within a unit time depending on the acceleration and inertial direction of the vehicle.

[0082] In step S300, the tracker 10A determines the physical characteristics of the cargo taking into account the vehicle acceleration and the cargo mass. In step S400, the tracker 10A generates vibrations having the same natural frequency and amplitude but opposite directions. For example, the first calculation unit 211 can calculate the phase of each first signal based on the physical characteristics of the cargo, which are based on the vehicle acceleration and the cargo mass. Then, the correction unit 212 can generate second signals having the same natural frequency and amplitude but opposite phases. The vibration generator 230 can generate vibrations corresponding to the second signals.

[0083] FIG. 6 is a diagram for explaining an example of an embodiment of the present disclosure for canceling vibrations occurring in a cargo hold.

[0084] Referring to FIG. 6 , external impacts, such as vibrations from roads or railroad tracks, may be transmitted to the cargo hold 610, causing the cargo hold 610 to vibrate up and down at a natural frequency. When vibrations in the cargo hold 610 are detected, a first signal 620 may be measured. The first signal 620 may have a first natural frequency and a first amplitude. Meanwhile, the device of the present disclosure may generate a second signal 630 to cancel out the first signal 620. The second signal 630 may have a second natural frequency and a second amplitude. The first and second natural frequencies may be identical. The first and second amplitudes may have the same magnitude but opposite signs (or signal phases). The first and second signals 620 and 630 may be combined to cancel out the vibrations in the cargo hold 610. This has the effect of providing a stable ground transportation environment.

[0085] FIG. 7 is a graph showing temperature over time according to the present disclosure, illustrating an example of a pattern according to temperature change.

[0086] Referring to Figure 7, the horizontal axis of the graph in Figure 7 is time (T) and the vertical axis is temperature (°C). Here, the unit of temperature may be Celsius, but is not limited thereto, and may be other units such as Fahrenheit. Assume that the internal temperature at time T1 is 26°C, the internal temperature at time T2 is 27°C, the internal temperature at time T3 is 25°C, the internal temperature at time T4 is 26°C, the internal temperature at time T5 is 25°C, the internal temperature at time T6 is 26°C, and the internal temperature at time Tk is 25°C, where k may be a natural number greater than or equal to 6.

[0087] A graph outline can be formed by connecting the point consisting of time T2 and the internal temperature (27°C), the point consisting of time T3 and the internal temperature (25°C), and the point consisting of time T4 and the internal temperature (26°C). The graph outline connecting the points at time T2, time T3, and time T4 can be represented by pattern 710. While the number of points required to represent pattern 710 is shown as three in FIG. 7, this is not limitative. Pattern 710 can be a pattern for temperature changes that can occur due to the carrier's intervention. That is, pattern 710 can be generated by the carrier performing an action such as opening or closing the cargo hold door.

[0088] In a manner similar to pattern 710, a graph outline connecting points of the internal temperature at times after Tk and at times after Tk may be formed, and the outline of the graph may be represented by pattern 720. Pattern 720 may be a pattern for temperature that fluctuates depending on the external environment. That is, pattern 720 may be generated solely by the external environment without intervention of a transporter or the like.

[0089] A trend line 730 can be estimated and fitted depending on whether or not there is driver intervention, such as door opening and closing. In this case, the trend line 730 can be a graph estimated when there is no door opening and closing.

[0090] FIG. 8 is a diagram for exemplifying the pattern data of the present disclosure.

[0091] 8, in one embodiment, the pattern data may include a pattern type, a pattern identifier, and a pattern shape. The pattern type may include, for example, a first pattern to an Nth pattern. N may be a natural number equal to or greater than 2. The pattern identifier may include, for example, P1 to PN. The pattern shape may be various, such as a straight line or a broken line, as shown in FIG.

[0092] FIG. 9 is a diagram for exemplifying flag data of the present disclosure.

[0093] 8 and 9, in one embodiment, the flag data may include a pattern identifier and a flag. The flag may indicate a flag value that is matched for each pattern identifier. For example, if the pattern identifier is P1, the flag value may be "0." If the pattern identifier is P2, P3, or PN, the flag value may be "1." In this case, a flag value of "0" may mean that the cause of the temperature change is the carrier's intervention, and a flag value of "1" may mean that the cause of the temperature change is the external environment. However, the present invention is not limited thereto, and the meaning of the flag value may be designed opposite to the above example.

[0094] FIG. 10 is a diagram for illustratively explaining an embodiment of the present disclosure in which a sensing area is set in a cargo hold based on frequent door opening and closing.

[0095] Referring to FIG. 10 , the door 1011 of the cargo hold 1010 may be opened and closed. When the door 1011 of the cargo hold 1010 is opened, the external temperature may be propagated from the door 1011 of the cargo hold 1010 to the interior of the cargo hold 1010 in the direction exemplarily illustrated in FIG. 10 . Depending on how often the door 1011 of the cargo hold 1010 is opened, areas for precise sensing of the internal temperature, vibration, etc. may be set. For example, a first area 1020 and a second area 1030 may be set in the cargo hold 1010. In addition, the locations of the plurality of trackers 1021, 1022, 1023, 1031, 1032, and 1033 in each of the first area 1020 and the second area 1030 may change depending on how often the door 1011 of the cargo hold 1010 is opened. For example, trackers 1021, 1022, and 1023 may be exemplarily arranged in a first area 1020 as shown in FIG. 10 , and trackers 1031, 1032, and 1033 may be exemplarily arranged in a second area 1030 as shown in FIG. 10 . For point a where tracker 1021 is arranged, the internal temperature may change to Ta1, Ta2, etc. over time. For example, Ta1 may be 10°C, and Ta2 may be 5°C. However, this is not limiting. Tracker 1023 may be arranged at point x, and the distance between some trackers 1021 and 1023 may be p, and the distance between some trackers 1023 and 1031 may be q. For point b where tracker 1033 is arranged, the internal temperature may change to Tb1, Tb2, etc. over time. For example, Tb1 may be 10°C, and Tb2 may be 9°C. However, this is not limiting.

[0096] FIG. 11 is a diagram for explaining an example of a region where temperature changes frequently and a region where temperature changes little in the present disclosure.

[0097] Referring to FIG. 11, a first zone 1120 and a second zone 1130 may be established to precisely sense internal temperature, vibrations, and the like depending on how frequently the door 1111 of the cargo hold 1110 is opened. The first zone 1120 may be an area where temperature changes frequently, while the second zone 1130 may be an area where temperature changes little. In each of the first zone 1120 and the second zone 1130, the positions of the trackers 1121, 1122, 1123, 1131, 1132, and 1133 may vary. Meanwhile, at least one tracker included in a specific zone may additionally sense portions of other zones where no trackers are attached. For example, the trackers 1131 and 1132 included in the second zone 1130 may each sense portions of the first zone 1120 where no trackers are attached.

[0098] FIG. 12 is a flowchart illustrating a method according to the present disclosure.

[0099] Referring to FIG. 12, a method for sensing and processing a vehicle's ground transportation environment may include a sensing step (S1000), a calculation step (S2000), a vibration step (S3000), and a determination step (S4000).

[0100] The sensing step S1000 is a step of sensing an impact on cargo contained in the cargo compartment of the vehicle due to sudden acceleration or deceleration of the vehicle. The sensing step S1000 is performed by the inertial sensor 220.

[0101] The calculation step (S2000) is a step of calculating a second frequency for canceling the first frequency based on the first frequency corresponding to the sensed impact. The calculation step (S2000) is performed by the processor 210, for example, the first arithmetic unit 211 and the correction unit 212.

[0102] The vibration step (S3000) is a step of vibrating at a second frequency. The vibration step (S3000) is performed by the vibration generator 230.

[0103] The determination step (S4000) is a step of determining the cause of the temperature change contained in the temperature data based on the temperature data received from the outside. The determination step (S4000) is performed by the processor 210, for example, the second arithmetic unit 213. The determination step (S4000) is performed by the processor 210, for example, the second arithmetic unit 213 and the correction unit 212.

[0104] Meanwhile, the disclosed embodiments may be embodied in the form of a recording medium storing computer-executable instructions. The instructions may be stored in the form of program code, which, when executed by a processor, generates program modules to perform the operations of the disclosed embodiments. The recording medium may be embodied as a computer-readable recording medium.

[0105] Computer-readable recording media include all types of recording media that store instructions that can be read by a computer, such as ROM (Read Only Memory), RAM (Random Access Memory), magnetic tape, magnetic disk, flash memory, and optical data storage devices.

[0106] As mentioned above, the disclosed embodiments have been described with reference to the attached drawings. Those skilled in the art will understand that the present disclosure may be implemented in forms different from the disclosed embodiments without changing the technical idea or essential features of the present disclosure. The disclosed embodiments are illustrative and should not be interpreted as limiting.

[0107] 2. Example 2 Other embodiments are illustrated below. The following embodiments illustrate systems and methods for optimizing land transportation routes of vehicles. In this disclosure, reference numerals in the second embodiment may be given the same numbers or letters as the reference numerals in the first embodiment, but they may be understood to indicate different configurations.

[0108] In this specification, the term "device according to the present disclosure" includes various devices capable of performing computations and providing results to a user. For example, the device according to the present disclosure may include all or any one of a computer, a server device, and a portable terminal.

[0109] Here, the computer may include, for example, a notebook computer, desktop computer, laptop computer, tablet PC, slate PC, etc. equipped with a web browser.

[0110] The server device is a server that communicates with external devices and processes information, and may include an application server, a computing server, a database server, a file server, a game server, a mail server, a proxy server, and a web server.

[0111] The portable terminal is, for example, a wireless communication device that ensures portability and mobility, and may include all kinds of handheld-based wireless communication devices such as PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminals, smartphones, etc., as well as wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted devices (HMDs).

[0112] Figures 13a, 13b, and 13c are diagrams showing exemplary systems of the present disclosure. Figures 14 and 15 are diagrams showing exemplary trackers of the present disclosure. Figure 14 is a front view of the tracker. Figure 15 is a rear view of the tracker.

[0113] Referring to FIG. 13a, a cargo space tracker 10A-1, a user terminal 20A-1, a temperature measurement sensor 30A-1, and a distance measurement sensor 40A-1 may be provided to perform the operations of the present disclosure.

[0114] The tracker 10A-1 includes a variety of devices that can perform calculations and provide results to the user.

[0115] The user terminal 20A-1 may be either a computer or a portable user terminal, or may be in the form of one of the following: a notebook PC, a desktop PC, a laptop PC, a tablet PC, a slate PC, etc., equipped with a web browser.

[0116] The device (server) is a server that communicates with external devices and processes information, and may include an application server, computing server, database server, file server, game server, mail server, proxy server, web server, etc.

[0117] The portable user terminal is, for example, a wireless communication device that ensures portability and mobility, and may include all kinds of handheld-based wireless communication devices such as PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminals, smartphones, etc., as well as wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted devices (HMDs).

[0118] The temperature / humidity measurement sensor 30A-1 may be attached within the cargo transport space (e.g., inside the cargo). The temperature / humidity measurement sensor 30A-1 may be attached at a position predetermined by a user. The temperature / humidity measurement sensor 30A-1 may sense the temperature and humidity inside the cargo and output sensing information regarding the temperature and humidity to the tracker 10A-1. In one embodiment, the temperature / humidity measurement sensor 30A-1 may include a temperature measurement sensor and a humidity measurement sensor. The unit of temperature may be °C and the unit of humidity may be %, but is not limited thereto.

[0119] The distance measuring sensor 40A-1 may be attached within the cargo transport space and may generate distance data by measuring distances between a plurality of first positions predetermined by a user and a plurality of second positions, which are the remaining vertex positions to which the temperature measuring sensor 30A-1 is not attached. The distance measuring sensor 40A-1 may include any one of a lidar sensor, an ultrasonic sensor, a short / medium-range radar sensor, a long-range radar sensor, and a camera.

[0120] 13b, system 100B-1 may include tracker 10B-1, first user terminal 20B-1, second user terminal 50B-1, and communication network 60B-1. While the number of user terminals is two in FIG. 1, the number may be one, three, or more.

[0121] The tracker 10B-1 may be a device for sensing and correcting the land transportation environment of a vehicle. The vehicle may be a vehicle. Examples of vehicles include automobiles, motorcycles, trucks, and trains. In one embodiment, the vehicle may have at least one cargo compartment, but is not limited to this. The tracker 10B-1 may communicate with first and second user terminals 20B-1 and 50B-1 via a communication network 60B-1. The tracker 10B-1 may include any of a variety of devices capable of performing computations and providing results to a user. For example, the tracker 10B-1 may include all or any of a computer, a device (server), and a portable terminal. Here, the computer may include, for example, a notebook computer, desktop computer, laptop computer, tablet PC, or slate PC equipped with a web browser. The device (server) is a server that communicates with external devices and processes information, and may include an application server, computing server, database server, file server, game server, mail server, proxy server, web server, etc.

[0122] Referring to FIG. 14, the front of tracker 10B-1 may include sensing unit 131-1, switch 132-1, input units 133 and 134-1, fingerprint recognition button 121-1, and a display. A user can input start and end dates on the display through input unit 133-1. Referring to FIG. 15, the rear of tracker 10B-1 may include various buttons 122-1 and 123-1 and a power indicator 135-1. The input unit is for receiving information from a user, and information may be input through the user input unit. Such user input units may include hardware physical keys (e.g., buttons, dome switches, jog wheels, jog switches, etc. located on at least one of the front, rear, and side of the device) and software touch keys. For example, the touch keys may be virtual keys, soft keys, or visual keys that are displayed on a touchscreen display through software processing, or may be touch keys located outside the touchscreen. Meanwhile, the virtual keys or visual keys may have various forms and be displayed on the touchscreen, and may be, for example, graphics, text, icons, videos, or combinations thereof.

[0123] 13b, the first user terminal 20B-1 and the second user terminal 50B-1 may include all or any one of the above-mentioned computers and portable user terminals. The portable user terminal may be, for example, a wireless communication device that ensures portability and mobility, and may include all kinds of handheld-based wireless communication devices such as a Personal Communication System (PCS), Global System for Mobile communications (GSM), Personal Digital Cellular (PDC), Personal Handyphone System (PHS), Personal Digital Assistant (PDA), International Mobile Telecommunication (IMT)-2000, Code Division Multiple Access (CDMA)-2000, W-Code Division Multiple Access (W-CDMA), Wireless Broadband Internet (WiBro) terminal, and a smartphone, as well as wearable devices such as a watch, a ring, a bracelet, an anklet, a necklace, glasses, contact lenses, or a head-mounted device (HMD).

[0124] 13c, a system 100C-1 may include a tracker 10C-1, a server 70C-1, and a transportation database 80C-1. The system 100C-1 may be for optimizing ground transportation routes of vehicles.

[0125] The tracker 10C-1 may be installed in a vehicle and may sense transportation environment data, including the environment of the cargo contained in the vehicle, and may transmit the transportation environment data to the server 70C-1.

[0126] The server 70C-1 can analyze the error between the predicted route and the actual route and calculate the impact of the goods. For example, the server 70C-1 can generate a predicted route for transportation between a departure point and a destination, record the temperature and humidity along the actual route based on GPS, and calculate the impact of the error on the goods by analyzing the cause of the error and taking into account i) the average travel speed by transportation means and ii) temperature / humidity / impact changes by region. The server 70C-1 can build a logistics database. It can collect local-scale micro factors through information on the actual land route, derive correlations between the collected micro factors, and calculate weights to minimize the error rate. Micro factors can include, for example, road impact, speed sections, curves, stopping locations, stopping times, and loading / unloading times.

[0127] The server 70C-1 can analyze the error between the predicted transportation route and the actual transportation route for transporting cargo between the departure point of the transportation means and the destination point of the transportation means based on the transportation environment data, and calculate the impact degree indicating the degree to which the error affects the cargo.The server 70C-1 can then optimize the transportation route based on the error and the impact degree.

[0128] The server 70C-1 can take real-time traffic conditions into account. Taking real-time traffic into account not only reduces logistics costs, but also ensures on-time delivery, better adherence to SLAs, and increases customer satisfaction. The server 70C-1 can run software that plans routes and calculates ETAs based on dynamic real-time data. The server 70C-1 can set order vehicle constraints. Electronic products and perishable products can be delivered together, and certain types of products, such as pharmaceuticals, can only be delivered by specialized vehicles. Software that takes order vehicle constraints into account can be useful for businesses in real-world scenarios. The server 70C-1 can perform accurate geocoding. The server 70C-1 can convert addresses to precise latitude and longitude coordinates for specific points on a map, understand ambiguous addresses, understand local context, and maintain an advanced database of local addresses and apartment locations. The server 70C-1 can examine historical data. The server 70C-1 runs route optimization software and examines historical data at three levels: passengers, customers, and time. A rider's past evidence can inform their skill set, expertise, preferred delivery times, and preferred work areas; customer history data can inform their preferred times of day, availability, and special delivery guidelines; and records for specific times of day can provide insight into general traffic conditions in the area and opening / closing times for specific buildings. The route optimization software running on server 70C-1 can learn from past experience and plan routes accordingly. Server 70C-1 can also consider passenger preferences. One of the biggest challenges in implementing route planning can be resistance from on-site operations teams. These teams are accustomed to working in a particular way, and changing the entire operations structure can be a major change. To facilitate this process, it can be important for the software to listen to on-site team preferences in order to phase out existing systems rather than changing the entire procedure all at once. Server 70C-1 can also perform change management.If field teams still adhere to existing methods, it may be reasonable for the software provider to assign a group of field experts who can persuade field resources and facilitate the conversion. Educational modules, incentives, and success stories from other organizations can help motivate field personnel to become familiar with the route optimization software. The server 70C-1 can perform analysis and report management. The route optimization software can provide the ability to track and manage the entire operation in real time on a single platform. This should enable tracking of actual and planned routes, which can be useful for comparing the performance of various business hubs. The server 70C-1 software can also provide an integrated dashboard that tracks operations in real time. The server 70C-1 can set dynamic route plans. The server 70C-1 software can provide the ability to optimally process on-demand orders as well as reserved orders. On-the-go route changes may be another feature increasingly popular among businesses. If a customer's order / preferences change while the rider is out making a delivery, the route optimization software can modify and generate a new route for the rider. Server 70C-1 allows a company to select Route Optimization software as needed as the company's routing requirements become more complex.

[0129] The transportation database 80C-1 can be configured to optimize transportation routes. The transportation database 80C-1 can store a variety of data.

[0130] FIG. 16 is a flowchart illustrating an embodiment of optimizing a transportation route according to the present disclosure.

[0131] 16, in step S110-1, the server 70C-1 may receive transportation environment data including a Global Positioning System (GPS), temperature and humidity for the cargo, and impacts that have occurred on the cargo. For example, the tracker 10C-1 may transmit transportation environment data including a Global Positioning System (GPS) indicating the position of the transportation means, temperature and humidity for the cargo, and impacts that have occurred on the cargo to the server 70C-1.

[0132] In step S120-1, the server 70C-1 may generate a first transportation route by a departure point and a destination point based on the departure point data and the destination point data of the transportation database 80C-1. For example, the server 70C-1 may generate a first transportation route by a departure point of the departure point data and a destination point of the destination point data based on the departure point data and the destination point data stored in the transportation database 80C-1.

[0133] In step S130-1, the server 70C-1 may acquire temperature and humidity changes and shocks that occur while the vehicle is traveling along the first transportation route, and the geographical location of the vehicle using GPS.

[0134] In step S140-1, server 70C-1 may generate a second transportation route corresponding to the optimized transportation route based on temperature and humidity changes, shock, and geographic location.

[0135] FIG. 17 is a flowchart illustrating an example of generating an alert due to changes in temperature and humidity according to the present disclosure.

[0136] Referring to FIG. 17, in step S210-1, server 70C-1 may map temperature and humidity changes to geographic locations.

[0137] In step S220-1, the server 70C-1 may update the geographical location where temperature and humidity changes exceeding the first threshold value occur and the type of transportation means to the transportation database 80C-1.

[0138] In step S230-1, the server 70C-1 may output an alert signal indicating an alert (alert-1) when the change in temperature and humidity is equal to or greater than a first critical value.

[0139] FIG. 18 is a flowchart illustrating an embodiment of generating an alert due to an impact according to the present disclosure.

[0140] Referring to FIG. 18, in step S310-1, server 70C-1 may map impacts to geographic locations.

[0141] In step S320-1, the server 70C-1 may update the geographic location where the impact equal to or greater than the second threshold occurs and the type of transportation means to the transportation database 80C-1.

[0142] In step S330-1, the server 70C-1 may output an alert signal if the impact is equal to or greater than a second critical value.

[0143] FIG. 19 is a flowchart illustrating another embodiment of optimizing transportation routes according to the present disclosure.

[0144] Referring to FIG. 19 , in step S410-1, the server 70C-1 may receive micro-factors. For example, the tracker 10C-1 may transmit the micro-factors to the server 70C-1. For example, the micro-factors may include road impacts occurring on the road surface on which the vehicle is traveling. For example, the micro-factors may include speed sections on the actual transportation route. For example, the micro-factors may include curves on the actual transportation route. For example, the micro-factors may include a dwell time indicating the time the vehicle will dwell at a dwelling point located around the actual transportation route. For example, the dwelling point may include a rest area, a rest stop, a roadside, a road with little vehicle traffic, a secluded pedestrian path, or other official and temporary dwelling points where the vehicle can stop for a predetermined period of time. For example, the micro-factors may include the loading and unloading time of the vehicle. For example, the micro-factors may include a transportation time indicating the time required for the vehicle to transport cargo. For example, the microfactors may include a total distance traveled, which indicates the distance a vehicle is required to travel to transport the cargo. For example, the microfactors may include a temperature / humidity change rate, which indicates the rate of change of temperature and humidity. For example, the microfactors may include an impact frequency, which indicates the frequency at which an impact occurs. For example, the microfactors may include a type of vehicle.

[0145] In step S420-1, the server 70C-1 may collect microfactors and assign different weights to each of the microfactors.

[0146] In step S430-1, the server 70C-1 may generate a plurality of alternative transportation routes based on micro-factors, origin data, and destination data, to which different weightings are assigned.

[0147] In step S440-1, server 70C-1 can analyze the cause of the error based on the updated alerts in transportation database 80C-1, multiple alternative transportation routes, average travel speed by transportation means, changes in temperature and humidity by transportation means and region, and changes in impact by transportation means.

[0148] In step S450-1, the server 70C-1 may generate a second transportation route by generating an optimal alternative transportation route based on the analysis result of the error cause.

[0149] FIG. 20 is a flowchart illustrating yet another embodiment of optimizing transportation routes according to the present disclosure.

[0150] Referring to FIG. 20, in step S510-1, the server 70C-1 may generate a primary transportation route based on departure and destination information based on a transportation database 80C-1 constructed for route optimization.

[0151] In step S520-1, the server 70C-1 can map temperature / humidity changes and shocks occurring during travel along the primary transportation route to geographic locations corresponding to GPS sensing values.

[0152] In step S530-1, server 70C-1 may update the transportation database with an alert for a geographic location where a temperature / humidity change above a first threshold or an impact above a second threshold occurs, along with the type of transportation means.

[0153] In step S540-1, the server 70C-1 can pre-generate multiple alternative transportation routes by combining transportation time, total transportation distance, temperature / humidity change rate, impact frequency, and transportation means type, each of which is assigned a different weighting value.

[0154] In step S550-1, the server 70C-1 may generate a second transportation route that minimizes the occurrence of alerts by setting required parameters according to transportation requirements with a prior probability.

[0155] FIG. 21 is a flowchart illustrating an embodiment of analyzing the cause of an error for each alternative transportation route according to the present disclosure.

[0156] 21, in step S610-1, the server 70C-1 may start a simulation of an alternative transportation route. For example, the server 70C-1 may start, for each of a plurality of alternative transportation routes, a simulation in which a vehicle virtually moves along each alternative transportation route.

[0157] In step S620-1, the server 70C-1 determines whether the alert signal is concentrated in the first type. The first type may be a type corresponding to an area where the possibility of driver-related changes is relatively high. That is, the first type may be an area where the possibility of driver-related changes is relatively high, such as a rest area or a merging area. For example, the server 70C-1 may determine whether the alert signal corresponds to the first type.

[0158] If the alert signal corresponds to the first type (S620, Y), the server 70C-1 can determine the cause of the error as human error in step S621.

[0159] If the alert signal does not correspond to the first type (S620, N), the server 70C-1 may determine in step S630-1 whether the alert signal is detected as a second type. The second type may correspond to the location of a vehicle that deviates from an alternative transportation route. That is, the second type may correspond to a case where the vehicle is found far from the route or outside the expected transportation radius. For example, the server 70C-1 may determine whether the alert signal corresponds to the second type.

[0160] If the alert signal corresponds to the second type (S630, Y), the server 70C-1 may determine the cause of the error as a route error in step S631.

[0161] If the alert signal does not correspond to the second type (S630, N), the server 70C-1 may determine whether the alert signal is continuously and repeatedly generated in step S640-1. In this case, the type in which the alert signal is continuously and repeatedly generated may be referred to as the third type, which may correspond to an error that occurred in the actual cargo or the tracker. That is, the third type may correspond to a problem with the actual cargo or an error in the device itself. For example, the server 70C-1 may determine whether the alert signal corresponds to the third type.

[0162] If the alert signal does not correspond to the third type (S640, N), the server 70C-1 may generate an alternative transportation route in step S641.

[0163] If the alert signal corresponds to the third type (S640, Y), the server 70C-1 may determine that the cause of the error is another error. Specifically, in step S650-1, the server 70C-1 may determine whether the alert signal occurs continuously over time. If the alert signal corresponds to the third type and occurs discontinuously over time (S650, N), the server 70C-1 may process noise in step S651.

[0164] If the alert signal corresponds to the third type and occurs continuously over time (S650, Y), the server 70C-1 may determine whether the measurement value exceeds the confidence range in step S660-1. Here, the measurement value may be the value of a collected microscopic factor. If the alert signal corresponds to the third type and the collected value that occurs continuously over time exceeds the confidence range (S660, Y), the server 70C-1 may determine the cause of the error as an equipment error or a tracker error in step S661. If the alert signal corresponds to the third type and the collected value that occurs continuously over time is within the confidence range (S660, N), the server 70C-1 may determine the cause of the error as a cargo transportation environmental problem (or cargo transportation environmental error) in step S662.

[0165] FIG. 22 is a flowchart illustrating an example of calculating the correlations between microscopic factors and the weights according to the present disclosure.

[0166] Referring to FIG. 19, in step S710-1, the server 70C-1 can collect micro-factors on the actual transportation route.

[0167] At step S720-1, server 70C-1 may derive correlations between micro-factors.

[0168] In step S730-1, the server 70C-1 may calculate weights for minimizing errors based on the correlations.

[0169] FIG. 23 is a flowchart illustrating a method according to the present disclosure.

[0170] Referring to FIG. 23, a method for optimizing a land transportation route of a vehicle may include a sensing step (S1000-1), an analysis and calculation step (S2000-1), and a route optimization step (S3000-1).

[0171] The sensing step (S1000-1) is a step of sensing transportation environment data including the environment of the cargo contained in the transportation means. The sensing step (S1000-1) is performed by the tracker 10C-1.

[0172] The analysis and calculation step (S2000-1) is a step of analyzing the error between the predicted transportation route and the actual transportation route for transporting cargo between the departure point of the transportation means and the destination point of the transportation means based on the transportation environment data, and calculating the impact degree indicating the extent to which the error affects the cargo. The analysis and calculation step (S2000-1) is performed by the server 70C-1.

[0173] The route optimization step (S3000-1) is a step of optimizing a transportation route based on the error and the influence degree. The route optimization step (S3000-1) is performed by the server 70C-1.

[0174] Meanwhile, the disclosed embodiments may be embodied in the form of a recording medium storing computer-executable instructions. The instructions may be stored in the form of program code, which, when executed by a processor, generates program modules to perform the operations of the disclosed embodiments. The recording medium may be embodied as a computer-readable recording medium.

[0175] Computer-readable recording media include all types of recording media that store instructions that can be read by a computer, such as ROM (Read Only Memory), RAM (Random Access Memory), magnetic tape, magnetic disk, flash memory, and optical data storage devices.

[0176] As mentioned above, the disclosed embodiments have been described with reference to the attached drawings. Those skilled in the art will understand that the present disclosure may be implemented in forms different from the disclosed embodiments without changing the technical idea or essential features of the present disclosure. The disclosed embodiments are illustrative and should not be interpreted as limiting.

[0177] 3. Components of Example 2 Claim 1: A system for optimizing land transportation routes of transportation means, comprising: a tracker that is placed on the transportation means and senses transportation environment data including the environment of cargo contained in the transportation means and transmits the transportation environment data; a server that analyzes the error between the predicted transportation route and the actual transportation route for transporting the cargo between the departure point of the transportation means and the arrival point of the transportation means based on the transportation environment data, calculates an impact degree indicating the extent to which the error affects the cargo, and optimizes the transportation route based on the error and the impact degree; and a transportation database constructed to optimize the transportation route.

[0178] Claim 2: The system of claim 1, wherein the tracker transmits the transportation environment data, including a Global Positioning System (GPS) indicating the position of the transportation means, the temperature and humidity of the cargo, and any impacts that have occurred to the cargo, to the server; and the server generates a first transportation route based on the departure point data and the arrival point data stored in the transportation database, acquires the changes in temperature and humidity and the impacts that occur while the transportation means is moving on the first transportation route, and the geographical position of the transportation means based on the GPS, and generates a second transportation route corresponding to the optimized transportation route, based on the changes in temperature and humidity, the impacts, and the geographical position.

[0179] Claim 3: The system of claim 2, wherein the server maps the temperature and humidity changes to the geographical locations, updates the geographical locations where temperature and humidity changes equal to or greater than a first critical value occur and the type of transportation means to the transportation database, and outputs an alert signal indicating an alert when the temperature and humidity changes equal to or greater than the first critical value.

[0180] Claim 4: The system of claim 3, wherein the server maps the impact to the geographic location, updates the geographic location where the impact equals or exceeds a second critical value and the type of vehicle to the transportation database, and outputs the alert signal if the impact equals or exceeds the second critical value.

[0181] Claim 5: In claim 4, the tracker is configured to calculate micro factors (micro metric) including road impacts occurring on the road surface on which the transportation means travels, speed sections on the actual transportation route, curves existing on the actual transportation route, dwell times indicating the time the transportation means stays at dwelling points located around the actual transportation route, loading and unloading times of the transportation means, transportation times indicating the time required for the transportation means to transport the cargo, total transportation distance indicating the distance traveled by the transportation means to transport the cargo, temperature / humidity change rates indicating the rates of change of the temperature and humidity, impact occurrence frequency indicating the frequency at which the impacts occur, and the type of the transportation means. and transmitting the micro-factors to the server, the server collecting the micro-factors, assigning different weights to each of the micro-factors, generating a plurality of alternative transportation routes based on the micro-factors assigned different weights, the departure point data, and the arrival point data, analyzing the cause of the error based on the alerts updated in the transportation database, the plurality of alternative transportation routes, the average travel speed by transportation means, changes in temperature and humidity by transportation means and by region, and changes in impact by transportation means, and generating an optimal alternative transportation route based on the analysis result of the cause of the error, thereby generating the second transportation route.

[0182] Claim 6: In claim 5, the server starts a simulation in which the transportation means virtually moves along each of the multiple alternative transportation routes, and if the alert signal corresponds to a first type corresponding to an area where there is a relatively high possibility of fluctuations caused by the driver, determines the cause of the error to be a human error, if the alert signal corresponds to a second type corresponding to a position of the transportation means that has deviated from the alternative transportation route, determines the cause of the error to be a route error, and if the alert signal corresponds to a third type corresponding to an error that has actually occurred in the cargo or the tracker, determines the cause of the error to be another error.

[0183] Claim 7: The system of claim 6, wherein the server processes noise when the alert signal corresponds to the third type and occurs discontinuously in time, determines the cause of the error to be a tracker error when the alert signal corresponds to the third type and occurs continuously in time and the collected value exceeds a confidence range, and determines the cause of the error to be a cargo transportation environment error when the alert signal corresponds to the third type and occurs continuously in time and the collected value is within a confidence range.

[0184] Claim 8: The system according to claim 7, characterized in that the server collects the microscopic factors on the actual transportation route, derives correlations between the microscopic factors, and calculates weights to minimize the error based on the correlations.

[0185] Claim 9: A method for optimizing a land transportation route of a transportation means, comprising: a sensing step of sensing transportation environment data including the environment of cargo contained in the transportation means; an analysis and calculation step of analyzing an error between a predicted transportation route and an actual transportation route for transporting the cargo between a departure point of the transportation means and a destination point of the transportation means based on the transportation environment data, and calculating an impact degree indicating the degree to which the error affects the cargo; and a route optimization step of optimizing the transportation route based on the error and the impact degree.

[0186] Claim 10: A computer program stored on a recording medium that, in combination with hardware, executes the method of claim 9.

Claims

1. 1. An apparatus for sensing and processing a ground transportation environment of a vehicle, comprising: an inertial sensor for sensing an impact on cargo contained in a cargo compartment of the vehicle due to sudden acceleration or deceleration of the vehicle; a processor that calculates a second frequency for canceling the first frequency based on a first frequency corresponding to an impact sensed by the inertial sensor, and outputs a control signal including the second frequency; and an oscillator responsive to said control signal to vibrate at said second frequency;

2. The processor: a first computing unit for calculating a phase of each of the first signals inducing the impact; and 2. The apparatus of claim 1, further comprising a correction unit that generates second signals each having a phase opposite to that of the first signals, and outputs the control signal including the second signals to the vibration generator.

3. The inertial sensor sensing a natural frequency caused by the cargo vibrating up and down within a unit time due to the acceleration and inertial direction of the vehicle; The first arithmetic unit calculating a phase of each of the first signals based on a physical characteristic of the cargo based on the acceleration of the vehicle and the mass of the cargo; The correction unit 3. The apparatus of claim 2, wherein said second signal is generated with the same natural frequency and amplitude but opposite phase.

4. The processor:

4. The device according to claim 3, further comprising a second calculation unit that, based on temperature data received from an external source, identifies a pattern of temperature change contained in the temperature data, determines whether the temperature change is due to an external environment or an intervention by the carrier, and diagnoses the cause of the temperature change, thereby generating cause data including the cause of the temperature change.

5. The method further includes a memory for storing pattern data including a feature for each of a plurality of preset patterns and a plurality of pattern identifiers indicating each of the plurality of patterns, and flag data including a flag indicating whether or not the external environment or the carrier is involved for each pattern identifier, The second arithmetic unit loading the pattern data and the flag data from the memory; Extracting a pattern feature to be recognized based on sequential temperature changes over time from the temperature data; obtaining a pattern identifier for the recognized pattern based on the characteristics of the recognized pattern and the pattern data; 5. The apparatus of claim 4, wherein the cause is diagnosed based on the acquired pattern identifier and the flag data.

6. The processor: further comprising an artificial neural network processing unit that generates an artificial intelligence model trained on a training data set including first data including points of the sequential temperatures according to the time and second data including a pattern identifier indicating a graph in which the points are connected to each other; The second arithmetic unit inputting input data including the points into the artificial intelligence model and predicting the pattern identifier as output data of the artificial intelligence model; The apparatus of claim 5, wherein the pattern is identified by searching the plurality of pattern identifiers for a pattern identifier that matches the predicted pattern identifier.

7. The second arithmetic unit The apparatus of claim 6, further comprising: estimating a trend line indicating a trend of temperature change over time based on the temperature data and the cause data.

8. a communication module for transmitting the cause data to an external device through a communication network; 8. The apparatus of claim 7, wherein the second computing unit controls the memory to store the cause data.

9. 1. A method for sensing and processing a ground transportation environment of a vehicle, comprising: a sensing step of sensing an impact on cargo contained in a cargo compartment of the vehicle due to sudden acceleration or deceleration of the vehicle; a calculating step of calculating a second frequency for canceling the first frequency based on the first frequency corresponding to the sensed impact; a vibration stage vibrating at the second frequency; and A method comprising a determining step based on externally received temperature data, determining a cause for a temperature change contained in said temperature data.

10. A computer program stored on a recording medium which, in combination with hardware, performs the method of claim 9.

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