Information processing systems, methods, and programs

Abstract

The objective is to achieve efficient delivery of goods. [Solution] An information processing system in one aspect of the present invention includes an information acquisition unit that acquires location information and time information corresponding to the location information from a delivery terminal of a package delivery person; a determination unit that determines the delivery route of the delivery person based on the location information and the time information; and a generation unit that generates a composite map by combining map information from a map application and the delivery route. Furthermore, a method in one aspect of the present invention involves causing a package delivery person to acquire location information and time information corresponding to the location information from a delivery terminal, causing the delivery route of the delivery person to be determined based on the location information and the time information, and generating a composite map by combining map information from a map application and the delivery route.

Description

TECHNICAL FIELD

[0001] The present invention mainly relates to an information processing system, method, and program for managing package delivery.

BACKGROUND ART

[0002] In the field of physical distribution, methods for managing package delivery are provided as part of efforts to improve operational efficiency.

[0003] For example, Patent Document 1 discloses a method for detecting an unentered delivery status by a delivery worker based on the position information of the delivery worker.

PRIOR ART DOCUMENTS

PATENT DOCUMENTS

[0004]

PATENT DOCUMENT 1

SUMMARY OF THE INVENTION

PROBLEMS TO BE SOLVED BY THE INVENTION

[0005] Although the technique disclosed in Patent Document 1 is premised on that the delivery position of a package is known in advance, on the other hand, there arises a problem that information on appropriate delivery positions and routes corresponding to package delivery destinations is scarce, especially in areas with little package delivery track record such as mountainous regions.

[0006] Accordingly, the present invention aims to improve delivery efficiency especially in areas with little package delivery track record.

MEANS FOR SOLVING THE PROBLEMS

[0007] An information magazine processing system according to one aspect of the present invention includes: an information acquisition unit that acquires location information and time information corresponding to the location information from a delivery terminal of a package delivery person; a determination unit that determines the delivery route of the delivery person based on the location information and the time information; and a generation unit that generates a composite map by combining map information from a map application and the delivery route. [Effects of the Invention]

[0008] According to the present invention, it is possible to improve delivery efficiency, especially in areas with a low volume of parcel deliveries. [Brief explanation of the drawing]

[0009] [Figure 1] This is a block diagram showing an information processing system according to the first embodiment of the present invention. [Figure 2] Figure 1 is a functional block diagram showing the server terminal 100. [Figure 3] Figure 1 is a functional block diagram showing the delivery terminal 200. [Figure 4] This figure shows an example of delivery data stored on the server terminal 100. [Figure 5] This figure shows an example of property data stored on the server terminal 100. [Figure 6] This figure shows an example of analysis data stored on the server terminal 100. [Figure 7] This is a flowchart showing an example of a process for generating a map of a package delivery route according to the first embodiment of the present invention. [Figure 8] This flowchart shows an example of a detailed process for generating a map according to the first embodiment of the present invention. [Figure 9] This flowchart shows another example of the process for generating a map of a package delivery route according to the first embodiment of the present invention. [Figure 10] This is a schematic diagram showing an example of an area map display on a delivery terminal according to the first embodiment of the present invention. [Modes for carrying out the invention]

[0010] Embodiments of the present invention will be described below with reference to the drawings. The embodiments described below are not intended to unduly limit the scope of the present invention as described in the claims. Furthermore, not all components shown in the embodiments are necessarily essential components of the present invention.

[0011] <Structure> Figure 1 is a block diagram showing an information processing system according to a first embodiment of the present invention. This system 1 is managed, for example, by a delivery company or a delivery information platform operator, and consists of a server terminal 100 that manages delivery data, package data, etc. of delivery personnel and analyzes information for improving delivery efficiency, multiple delivery personnel terminals 200A, 200B of delivery personnel, and multiple user terminals 300A, 300B of users at delivery destinations. For the sake of explanation, each terminal is described as a single or specific number, but there is no limit to the number of each. Hereafter, for the sake of explanation, unless otherwise explained, delivery personnel terminals will be collectively referred to as delivery personnel terminals 200, and user terminals will be collectively referred to as user terminals 300, respectively.

[0012] The server terminal 100, the delivery terminal 200, and the user terminal 300 are each connected via the network NW1. The network NW consists of the Internet, intranet, wireless LAN (Local Area Network), WAN (Wide Area Network), etc.

[0013] The server terminal 100 may be a general-purpose computer such as a workstation or personal computer, or it may be logically implemented through cloud computing.

[0014] The delivery terminal 200 and user terminal 300 are, for example, information processing devices such as personal computers or tablet terminals, but they may also be composed of smartphones, mobile phones, PDAs, etc.

[0015] Figure 2 is a functional block configuration diagram of the server terminal 100 in Figure 1. The server terminal 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

[0016] The communication unit 110 is a communication interface for communicating with the deliverer terminal 200 and the user terminal 300 via the network NW1, and communication is performed according to a communication protocol such as TCP / IP (Transmission Control Protocol / Internet Protocol).

[0017] The storage unit 120 stores programs, input data and the like for executing various control processes and each function in the control unit 130, and is composed of RAM (Random Access Memory), ROM (Read Only Memory) and the like. In addition, the storage unit 120 has a delivery data storage unit 121 that stores various data related to package delivery, a property data storage unit 122 that stores various data related to properties at delivery destinations, and an analysis data storage unit 123 that stores analysis data generated by analyzing delivery data, property data and the like. Note that a database (not shown) storing various data may be constructed outside the storage unit 120 or the server terminal 100.

[0018] The control unit 130 controls the overall operation of the server terminal 100 by executing a program stored in the storage unit 120, and is composed of a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), or the like. As functions of the control unit 130, it at least includes an information acquisition unit 131 that receives instructions and information from each terminal, a determination unit 132 that determines the delivery position of a package delivered by a deliverer and the like based on the received information, a generation unit 133 that generates information such as the delivery position and screen information for displaying the information as a result of the above determination, and a display unit 134 that causes each terminal to display the generated information and the like. The information acquisition unit 131, the determination unit 132, the generation unit 133, and the display unit 134 are activated by the program stored in the storage unit 120 and executed by the server terminal 100 which is a computer (electronic computer).

[0019] The information acquisition unit 131 receives predetermined information from the deliverer terminal 200 or the like via the communication unit 110. For example, it receives the position information of the deliverer and time information corresponding to the position from the deliverer terminal 200.

[0020] More specifically, the information acquisition unit 131 can acquire the position information and time information from the deliverer terminal 200 in various ways. For example, there are a method of periodically polling the deliverer terminal 200, a method of receiving the information as a push notification from the deliverer terminal 200, or a method of acquiring the information based on a preset condition (for example, when the moving distance of the deliverer exceeds a predetermined threshold, or when the deliverer enters a specific area, etc.).

[0021] Furthermore, the information acquisition unit 131 may include a function to evaluate the accuracy and reliability of location information and to correct or exclude low-accuracy location information. This function calculates a quality score of location data using indicators such as GPS accuracy information, number of satellites, and signal strength, and detects data that falls below a predetermined threshold. For the detected low-accuracy data, interpolation processing using statistical methods such as a Kalman filter can be applied, or the location can be replaced with a position estimated from preceding and succeeding high-accuracy data.

[0022] In addition to location and time information, the information acquisition unit 131 can also acquire sensor information such as acceleration sensor data, gyroscope data, and barometric pressure sensor data from the delivery terminal 200. By utilizing this auxiliary data, the delivery person's location can be estimated even in environments where GPS is unstable (such as high-rise building districts, tunnels, and underground parking lots). Furthermore, by combining this data with weather information, traffic information, and event information obtained from external data sources, a comprehensive analysis of the delivery situation becomes possible.

[0023] Furthermore, the information acquisition unit 131 can also be equipped with a function to dynamically adjust the frequency and accuracy of data acquisition. For example, an adaptive sampling strategy can be implemented, such as increasing the frequency of location information acquisition when the delivery person is moving at high speed and decreasing the frequency when they are stationary. This makes it possible to optimize the battery consumption of the delivery person terminal 200 while maintaining the accuracy of location tracking.

[0024] The determination unit 132 performs a predetermined determination process based on the received information. For example, it performs a process to determine the delivery location where the delivery person delivered the package, based on the location information and time information.

[0025] In determining delivery routes, high-precision determination is possible by combining multiple approaches. Specifically, the following determination methods can be implemented individually or in combination:

[0026] (1) Determination method based on the delivery person's stay time: A method that identifies a location as a potential delivery location if the delivery person stays at that location for a certain period of time or longer. This stay time threshold can be dynamically adjusted according to the characteristics of the delivery area (urban areas, suburbs, mountainous areas, etc.), time of day (daytime, evening, nighttime, etc.), and weather conditions (sunny, rainy, snowy, etc.). For example, a relatively short threshold can be set in urban areas (e.g., 3 minutes), a medium threshold in suburban areas (e.g., 5 minutes), and a longer threshold in mountainous areas (e.g., 7 minutes), and these can be further adjusted by multiplying by coefficients for weather and time of day.

[0027] (2) Determination method based on the number of times a delivery person stays at the same location: A method in which a location is determined as a delivery location when the number of times a delivery person stays at a particular location exceeds a predetermined frequency threshold. This frequency threshold can be set according to the characteristics of the delivery route and the delivery period. It is also possible to set multiple periods such as short-term, medium-term, and long-term as the aggregation period for the number of stays and apply the frequency threshold corresponding to each period. It is also possible to implement determination by integrating the number of stays by multiple delivery people.

[0028] (3) Determination method based on pattern analysis of the delivery person's movement path: A method for estimating the delivery location by detecting characteristic patterns (stopping points, turning points, dead ends, etc.) from the delivery person's movement trajectory data. For example, a point where the movement speed between consecutive location information reaches a predetermined low-speed threshold and then the direction of movement reverses can be identified as a candidate delivery location.

[0029] (4) Determination method based on the association of delivery completion notification and location information: A method for determining the delivery location by analyzing the location information at the time of the delivery completion operation of the delivery terminal 200. The location information before and after the operation is aggregated, and the average location or the most frequent location can be determined as the delivery location. It is also possible to apply weighting according to the accuracy of the location information.

[0030] (5) Determination method based on comparison with past delivery data: A method for estimating the delivery location in the current delivery by referring to past delivery location data for the same or similar delivery destinations. A confidence score based on the probability density map or clustering results of past data can be calculated and used for determination.

[0031] Furthermore, the determination unit 132 can also implement advanced predictions using machine learning models. By training various machine learning algorithms (decision trees, random forests, neural networks, etc.) using past delivery data as training data, it is possible to predict the delivery location from location and time information. It is also possible to improve prediction accuracy by ensemble learning, which combines multiple algorithms.

[0032] Furthermore, the determination unit 132 can also utilize 3D position information. By using position information including elevation data and altitude information estimated from barometric pressure sensor data, it can pinpoint delivery locations within high-rise buildings and precise locations in mountainous areas. Within buildings, it is also possible to analyze vertical movement patterns to identify the locations of elevators and stairs, as well as delivery locations on each floor.

[0033] The determination unit 132 assigns confidence scores to the determination results obtained by these methods and can determine the final delivery location by integrating multiple determination results. It is also possible to implement a function to record and analyze the history of determination results and continuously improve the determination accuracy.

[0034] The generation unit 133 performs a process to generate predetermined information based on the above determination result. For example, based on the delivery location obtained as a determination result, it performs a process to generate information indicating the delivery location and / or screen information.

[0035] In generating a composite map of delivery routes, the generation unit 133 can perform advanced map generation processing that integrates multiple map information sources and delivery data. Specifically, it can have the following functions:

[0036] (1) Support for multiple map services: By utilizing APIs from multiple map services such as Google Maps, OpenStreetMap, Apple Maps, and the Geospatial Information Authority of Japan, it is possible to generate maps that leverage the characteristics of each service. By combining the strengths of each service, more accurate and detailed map information can be provided. For example, it is possible to implement selective use, such as prioritizing the use of detailed commercial facility information from Google Maps in urban areas and precise topographic information from the Geospatial Information Authority of Japan in mountainous areas.

[0037] (2) Road Information Generation and Supplementation Function: This function extracts roads and paths that do not exist on official maps from the delivery person's movement trajectory data and supplements the map information. This function clusters location data collected from multiple delivery events and detects route patterns with a certain level of reliability to confirm the existence of unofficial roads. The detected road information is recorded along with attribute information such as passability, road surface condition, and width, and is registered in a map database that is shared with other delivery people.

[0038] (3) Route Optimization Function: This function calculates the optimal delivery order and route, taking into account conditions such as the location of the delivery destination, estimated travel time, road conditions, and delivery time slots. This optimization can apply a multi-objective optimization algorithm that considers not only simple distance but also predicted traffic conditions, delivery time constraints, and energy efficiency (including uphill and downhill slopes). Adaptive optimization, which dynamically recalculates routes based on real-time traffic and weather information, can also be implemented.

[0039] (4) Three-dimensional map generation function: A function that generates a 3D map by integrating elevation data and building floor information. In mountainous areas, it can accurately represent the topography, and in urban areas, it can display the building's floor structure (underground parking, each floor, etc.). This 3D map also visualizes gradient information and the locations of stairs and elevators, helping delivery personnel choose a more appropriate route.

[0040] (5) Augmented Reality (AR) Display Function: An AR function that overlays delivery route information onto the camera image of the delivery terminal 200. By displaying navigation information directly on the actual scenery, it enables intuitive guidance, especially in complex delivery environments. In AR mode, visual elements such as directional arrows, distance information, and highlighting of target objects are combined to help delivery personnel reach their destination without getting lost.

[0041] (6) Personalized display function: A function that customizes the map display according to the experience level of each delivery person, preferred navigation style, and characteristics of the device being used. For example, it is possible to provide adaptive information such as simplified information for experienced delivery people and more detailed guidance for new delivery people.

[0042] The generation unit 133 stores the information obtained from these generation processes in the analysis data storage unit 123 of the storage unit 120 and can also transmit it to the delivery terminal 200. The transmitted information is optimized considering the hardware performance, network status, battery level, etc. of the delivery terminal 200, and a mechanism can be implemented to display it in the most optimal format for each terminal.

[0043] The display unit 134 performs the process of displaying the generated information via a user interface such as the delivery terminal 200. For example, the display unit 134 transmits screen information to the delivery terminal 200, and the delivery terminal 200 displays the screen via its user interface based on the received screen information.

[0044] In the implementation of the display unit 134, an adaptive rendering function can be provided that corresponds to the characteristics of the delivery terminal 200 (screen size, resolution, processing power, etc.). This function automatically adjusts the map rendering quality and animation effects according to the performance of the terminal, providing an optimal user experience for various terminals. For example, it is possible to optimize the display by providing high-resolution 3D maps and smooth animation effects on high-performance terminals, and automatically switching to a simplified 2D display on low-performance terminals.

[0045] Furthermore, the display unit 134 can provide multiple display modes. For example, standard mode (normal map display), detailed mode (displays all information), simple mode (displays only important information in a larger size), night mode (color scheme that is easy to see even in dark environments), and power saving mode (display that minimizes battery consumption). Delivery personnel can manually switch between these modes depending on the situation, and it is also possible to switch them automatically based on conditions such as ambient light and battery level.

[0046] Furthermore, a voice guidance function can also be implemented. This function provides voice-guided route directions and arrival notifications even when the delivery person cannot focus on the screen (such as while driving). The voice guidance uses speech synthesis technology to generate natural-sounding instructions and is provided in the language specified by the delivery person. In addition, by linking with external audio devices such as headsets or Bluetooth earphones, clear voice guidance is possible even in noisy environments.

[0047] The display unit 134 also features a visual feedback function. Depending on the delivery person's actions (approaching the destination, deviating from the planned route, etc.), it provides visual notifications (highlighting, alert icons, etc.) and vibration feedback to appropriately attract the delivery person's attention. These feedbacks can be customized according to the delivery person's preferences and circumstances, and a frequency adjustment function is also implemented to prevent distraction due to excessive notifications.

[0048] These advanced display features allow delivery personnel to perform delivery tasks intuitively and efficiently, resulting in improved delivery efficiency and reduced burden on delivery personnel.

[0049] Figure 3 is a functional block diagram showing the delivery terminal 200 of Figure 1. The delivery terminal 200 includes a communication unit 210, a display and operation unit 220, a storage unit 230, a control unit 240, and a GPS 250.

[0050] The communication unit 210 is a communication interface for communicating with the server terminal 100 via the network NW, and communication is performed using a communication protocol such as TCP / IP.

[0051] The display operation unit 220 is a user interface used by the delivery person to input instructions and display text, images, etc., in accordance with the input data from the control unit 240. If the delivery person terminal 200 is a smartphone or tablet, the display operation unit 220 consists of a touch panel or the like. This display operation unit 220 is activated by a control program stored in the memory unit 230 and executed by the delivery person terminal 200, which is a computer (electronic calculator).

[0052] The memory unit 230 stores programs for executing various control processes and functions within the control unit 240, input data, etc., and is composed of RAM, ROM, etc. The memory unit 230 can also temporarily store the contents of communication with the server terminal 100.

[0053] The control unit 240 controls the overall operation of the delivery terminal 200 by executing a program stored in the memory unit 230, and is composed of a CPU, GPU, and the like.

[0054] GPS250 refers to the Global Positioning System, which uses artificial satellites to determine the current location and time (time information) of the delivery terminal 200.

[0055] Figure 4 shows an example of delivery data stored in the server terminal 100.

[0056] As shown in Figure 4, delivery data 1000 stores various data related to package delivery managed by the delivery company. As various data related to package delivery, first, a delivery ID (for example, a delivery event identified as "10001") is assigned as information to identify a single delivery event (delivery request, etc.), and associated with the delivery ID, package information (for example, information to identify the package (package ID, etc.), information about the sender (requester, shipper), information about the destination (recipient, recipient) (property ID, contact information, delivery location, etc.), other information such as date (reception date and time, specified delivery date and time, delivery completion date and time, etc.), package contents, package weight, fee, responsible delivery office, delivery vehicle, etc.), and delivery person information (information to identify the delivery person (delivery person ID, etc.), delivery person location information, time information corresponding to the location (time, etc.), delivery method (truck, motorcycle, UAV (drone), etc.), time spent at a specific location, delivery location where the package was delivered, parking location where the vehicle was parked, etc.) may be included, but is not limited to these.

[0057] Figure 5 shows an example of property data stored in the server terminal 100.

[0058] As shown in Figure 5, property data 2000 stores various data related to the property (house, facility, business premises, etc.) corresponding to the delivery destination of the package. As various data related to the property, a property ID (for example, a property identified as "20001") is assigned as information to identify the property, and associated with the property ID, property information (for example, location information to identify the location of the property, parking locations available for delivering packages to the property, information about the recipient (for example, name, gender, age, etc.), other information such as the type of property (detached house, apartment building, office, etc.)), and delivery information associated with that property (for example, delivery person's stay time, delivery location, drop-off location, number of deliveries, etc.) may be included, but is not limited to these.

[0059] Figure 6 shows an example of analysis data stored in the server terminal 100.

[0060] As shown in Figure 6, the analysis data 3000 stores information related to the analysis results obtained based on information received from the delivery person, and information referenced during the analysis. The analysis data 3000 may include, but is not limited to, map information obtained from external applications, etc., that the delivery person uses to identify the route and destination for delivery of the package, heat map information generated by superimposing on the map information based on the delivery person's location and time information, road information generated by superimposing on the map information to identify roads for the land transport of the package, route information that identifies the route to the delivery destination of the package, property information associated with the delivery destination of the package, and dwell time information related to the average dwell time of multiple delivery persons, etc.

[0061] <Processing flow> Referring to Figures 7 and onward, the processing method for streamlining package delivery performed by System 1 of this embodiment will be described. First, Figure 7 is a flowchart showing an example of the process for generating a map of package delivery routes according to the first embodiment of the present invention.

[0062] First, as a premise, each delivery person (deliverer) of the delivery company carries a delivery terminal 200 and delivers packages requested by the consignor to a designated destination via delivery means such as a truck. In this case, especially in mountainous areas where roads are not well-developed or accessible, it is assumed that the delivery person will park the vehicle in a parking space near the destination, carry the packages on foot to the destination, and finally deliver the packages. Alternatively, it is also assumed that the packages will be delivered by leaving them at a designated delivery location specified by the user at the destination.

[0063] Next, as part of step S101, the information acquisition unit 131 of the control unit 130 of the server terminal 100 receives the delivery person's location information from the delivery person's terminal 200. Here, the information acquisition unit 131 can receive the delivery person's current location information at predetermined time intervals or through batch processing, receiving location information corresponding to each of multiple time points. The information acquisition unit 121 stores the received location information as delivery data 1000 in the delivery data storage unit 121 of the storage unit 120.

[0064] Next, as part of step S102, the information acquisition unit 131 of the control unit 130 of the server terminal 100 receives the location information along with time information (for example, date and time) corresponding to the location information. The information acquisition unit 131 associates the received time information with the location information and stores it as delivery data 1000 in the delivery data storage unit 121 of the storage unit 120.

[0065] Next, as part of step S103, the determination unit 132 of the control unit 130 of the server terminal 100 determines the delivery route to the delivery location of the package by the delivery person based on the received location and time information of the delivery person. Specifically, the determination unit 132 determines the delivery route by tracing multiple points to the delivery location based on multiple location information at different times obtained from the delivery person terminal 200. Here, the server terminal 100 can also similarly obtain location and time information from the same or different delivery persons, associated with multiple delivery events (delivery IDs) for the same or different delivery destinations, and determine the delivery route to each delivery location. The determination unit 132 stores the information regarding the delivery route determined in this way in the delivery data storage unit 121, the property data storage unit 122, and / or the analysis data storage unit 123 of the storage unit 120, associated with the delivery location and property information.

[0066] Here, the determination unit 132 can calculate the time spent at each location based on the location information and time information, and determine the delivery location and parking location of the package based on the time spent there.

[0067] Next, as part of step S104, the generation unit 133 generates a map tracing the delivery route based on the determined delivery route. Here, when generating the map, the generation unit 133 can also generate the map so that it shows the delivery route on a map application (for example, Google Maps®) obtained by the information acquisition unit 131 from a third-party application server via an API (Application Programming Interface).

[0068] Figure 8 is a flowchart showing a detailed example of the map generation process according to the first embodiment of the present invention.

[0069] First, as part of step S201, the information acquisition unit 131 of the control unit 130 of the server terminal 100 acquires a map application (for example, Google Maps®) from a third-party map application server via an API or the like, as described above. The server terminal 100 can also store the data related to the map application acquired in this way in the analysis data storage unit 123 of the storage unit 120 beforehand.

[0070] Next, as part of step S202, the generation unit 123 of the control unit 130 of the server terminal 100 refers to the map application and extracts the portion of the delivery route to the delivery destination that can be identified as a route by major roads, within the range registered in the application, as the first delivery route.

[0071] Next, as part of step S203, the generation unit 123 of the control unit 130 of the server terminal 100 refers to the determined delivery route stored in the storage unit 120 and compares it with the first delivery route to determine the branch routes (roads not registered in the map application, unpaved roads, etc.) that are differences from the main roads included in the first delivery route. The generation unit 123 extracts such branch routes as the second delivery route and combines the first and second delivery routes, that is, by supplementing the first delivery route with the second delivery route, to generate the final delivery route. This delivery route can then be combined with the map application's map as the delivery route to the destination, thereby generating a composite map. The generation unit 123 can then transmit the generated map to the delivery terminal 200, which can then display it via the delivery terminal 200's map application. In this way, by utilizing the resources of existing third-party map applications and supplementing information such as delivery routes generated by this embodiment with information that can be displayed in map applications, it is possible to provide delivery drivers with more complete and visually appealing delivery routes.

[0072] Figure 10 is a schematic diagram showing an example of an area map display on a delivery terminal according to the first embodiment of the present invention.

[0073] As shown in Figure 10, the delivery route to the destination is clearly indicated on the map. This map shows the delivery route by combining a first delivery route consisting of major roads shown with thick lines and a second delivery route consisting of non-major roads shown with dashed lines, based on a map displayed by a third-party map application. In addition, the delivery location and parking location can be indicated on the map to correspond to the property at the delivery destination.

[0074] Figure 9 is a flowchart showing another example of the process for generating a map of a package delivery route according to the first embodiment of the present invention.

[0075] This process is equivalent to the map generation process shown in Figure 7, but as part of step S301, the determination unit 132 of the control unit 130 of the server terminal 100 sends a message to the delivery terminal 200 asking whether or not to cooperate with a third-party map application (for example, Google Maps®) before generating a map showing the delivery route.

[0076] In response to this, if the delivery person inputs a response indicating that they permit integration with a third-party map application, as the process in step S302, the generation unit 132 of the control unit 130 of the server terminal 100 references the third-party map application, either stored in the storage unit 120 in advance or via an API, as described above, generates a map showing the delivery route, and displays it to the delivery person terminal 200. On the other hand, if the delivery person inputs a response indicating that they do not permit integration with a third-party map application, as the process in step S303, the generation unit 132 of the control unit 130 of the server terminal 100 generates a map showing the delivery route by another method and displays it to the delivery person terminal 200. This makes it possible to provide a map showing the delivery route according to the delivery person's preference.

[0077] Thus, according to the present invention, it is possible to improve delivery efficiency, especially in areas with a low volume of parcel deliveries.

[0078] Furthermore, as a variation of the information processing system of this embodiment, the system may include an offline operation mode to ensure operation in environments where network connectivity is unstable or impossible. This mode is particularly designed to support delivery operations in mountainous areas and regions where communication infrastructure is inadequately developed.

[0079] In the implementation of the offline operation mode, the information acquisition unit 131 is responsible for acquiring the data necessary for the system's operation during the preparation phase before delivery. Specifically, it instructs the delivery terminal 200 to download map data of the planned delivery area in advance. This download process automatically determines the required map range by referring to the delivery plan information stored in the delivery data storage unit 121 of the memory unit 120. When downloading map data, the information acquisition unit 131 also performs a process to selectively download variable-resolution map tiles according to the importance of each region (delivery frequency, delivery difficulty, etc.), taking into account the balance between the terminal's storage capacity and the required map accuracy.

[0080] The determination unit 132 provides logic that enables the determination of the delivery location by local processing within the delivery terminal 200, even when offline. This local determination logic is a lightweight version of the full algorithm implemented on the server side, optimized to match the processing power of the terminal, and retains basic functions such as time-series analysis of GPS information, calculation of dwell time, and recognition of movement patterns. The determination unit 132 provides this local determination algorithm to the delivery terminal 200 in binary format and updates and optimizes it periodically. In addition, to improve the accuracy of offline determination, the determination unit 132 provides the terminal with past determination result statistics data for a specific area in compressed format so that it can be used as reference data.

[0081] The generation unit 133 implements a map synthesis and display function that works even in an offline environment. Specifically, it combines pre-downloaded map data with locally recorded location information and performs a process to visualize the delivery route within the terminal. This process employs an efficient rendering algorithm that takes into account the processing load on the terminal, providing sufficient visual information while minimizing battery consumption. The generation unit 133 also has a function to record route information collected offline in a compressed format and convert it into a format that can be efficiently transmitted to the server during subsequent network synchronization.

[0082] The display unit 134 provides functions to optimize information display in offline environments. For example, it implements functions such as automatically switching the display mode according to the network connection status (detailed display when connected, power-saving display when offline, etc.) and visual feedback functions to let delivery personnel know that the system is offline. It also provides an interface that clearly displays the system status when offline (available map range, data recording status, etc.).

[0083] Data collected in an offline environment is automatically synchronized with the server terminal 100 when the network connection is restored. This synchronization process is managed by the information acquisition unit 131, which adds a timestamp and a unique identifier to each data record and transmits it, while the server side applies a collision detection and resolution algorithm to ensure data integrity. Specifically, a UUID-based unique identifier is added to each location data, and if duplicate data for the same event is detected, a priority calculation is performed based on the data reliability score (GPS accuracy, sampling frequency, etc.) and temporal proximity, and the most reliable dataset is selected.

[0084] Furthermore, the information acquisition unit 131 and the control unit 130 work together to implement a power management function to extend the battery life of the delivery terminal 200 during offline operation. This function includes processes such as dynamic adjustment of the GPS acquisition frequency according to the delivery status (moving, stopped, etc.), automatic control of screen brightness based on the ambient light sensor, and temporary suspension of non-essential background processes. When the terminal's battery level falls below a certain threshold (e.g., 20%), the control unit 130 instructs the system to switch to ultra-low power mode. This transition process involves significantly extending the GPS acquisition interval (e.g., to 5-minute intervals), minimizing screen brightness, and completely stopping unnecessary processes to ensure a minimum amount of power for emergency communication.

[0085] The implementation of this offline operation mode enables continuous delivery support even in mountainous areas and regions with unstable communications, without being limited by the communication environment, contributing to the expansion of the geographical range and improvement of the reliability of delivery services.

[0086] As another variation, this system may implement advanced prediction and optimization functions using machine learning models that utilize the accumulated delivery data. These machine learning models can be used in various aspects, such as determining delivery routes, identifying delivery locations, and optimizing delivery efficiency.

[0087] The determination unit 132 implements a delivery route determination function that utilizes a machine learning model. In this implementation, past delivery data (location information, time information, delivery route, etc.) stored in the delivery data storage unit 121 and analysis data storage unit 123 of the memory unit 120 is processed as training data, and a supervised learning algorithm is applied. Specifically, the determination unit 132 implements three core modules: a preprocessing module for converting time-series data of location information into input features, a prediction module for predicting delivery routes from the features, and an evaluation module for evaluating the reliability of the prediction results.

[0088] The preprocessing module applies noise reduction filters (such as moving averages and Kalman filters), feature extraction processes (such as velocity calculation, acceleration calculation, and direction change detection), and normalization processes (such as standardization and minimum / maximum scaling) to raw GPS data, converting it into a format suitable for input to machine learning models. It also extracts temporal features such as time of day, day of the week, and season from time information, enabling the learning of the time dependence of delivery patterns.

[0089] The prediction module implements deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and especially long-term short-term memory (LSTM) and gated regression units (GRUs). These models can effectively learn spatiotemporal patterns of location data. The decision unit 132 optimizes model parameters using gradient descent during the training phase and evaluates the generalization performance of the model through cross-validation using a validation dataset. It also implements dropout layers and regularization terms to prevent overfitting, as well as a dynamic learning rate adjustment function.

[0090] The evaluation module calculates a confidence score for the model's prediction results. This confidence score is calculated by combining multiple indicators, such as the entropy of the model's output probability distribution, the similarity between the prediction result and past patterns, and the local feature matching rate. The determination unit 132 also includes a fallback mechanism that combines this confidence score with the determination result from a conventional rule-based algorithm if the confidence score falls below a threshold.

[0091] The generation unit 133 implements a delivery order optimization model using reinforcement learning. In this model, the delivery environment is modeled as a Markov decision process (MDP), and states (current location, list of remaining delivery destinations, time constraints, etc.), actions (selection of the next delivery destination), and rewards (reduction of travel time, maximization of the number of completed deliveries, improvement of customer satisfaction, etc.) are defined. The generation unit 133 implements algorithms such as Q-learning and policy gradient methods to learn the optimal delivery strategy from the delivery drivers' performance data. It is also possible to implement a multi-agent reinforcement learning model to optimize cooperative delivery by multiple delivery drivers, and it is possible to derive an overall optimal solution that considers the balance between cooperation and competition among delivery drivers.

[0092] The information acquisition unit 131 is equipped with data collection and management functions for the continuous improvement of the machine learning model. The collected data undergoes quality evaluation processing to select only the data suitable for training and stores it in the storage unit 120. It also implements a trigger function for periodic model retraining, which automatically starts retraining the model when a certain amount of new data has been accumulated or when the prediction accuracy falls below a threshold.

[0093] The control unit 130 implements transfer learning functionality to achieve highly accurate predictions even in new areas with limited data or under special delivery conditions. Specifically, it employs a fine-tuning method in which the feature extraction layer of a model pre-trained on urban delivery data is fixed, and only the output layer is fine-tuned with new data. Furthermore, domain adaptation technology to enable model knowledge sharing between multiple regions with similar characteristics, and efficient learning methods from a small number of samples using meta-learning can also be implemented.

[0094] The display unit 134 provides visualization functions to clearly communicate the prediction results of the machine learning model to delivery personnel. In addition to displaying the predicted delivery route, it implements color-coding according to the reliability of the prediction, simultaneous display and comparison of multiple candidate routes, and explanations of the basis for the prediction, supporting delivery personnel in gaining a deeper understanding of machine learning predictions and utilizing them appropriately.

[0095] The implementation of these machine learning functions enables continuous optimization of the delivery process, providing multifaceted value such as improved delivery efficiency, cost reduction, reduced environmental impact, and increased customer satisfaction.

[0096] As another variation, this system may go beyond 2D map representation and implement advanced visualization functions utilizing 3D map display and AR (augmented reality) technology. These functions are particularly useful for spatial awareness in delivery operations in complex terrain or within tall buildings.

[0097] The information acquisition unit 131 is responsible for acquiring the extended datasets necessary for implementing 3D maps and AR functions. Specifically, in addition to standard location information (latitude and longitude), it acquires elevation data (GPS altitude data, barometric pressure sensor data, etc.), terminal attitude information (accelerometer, gyroscope, magnetometer data), and camera image data. It also implements API integration functionality to acquire Digital Elevation Models (DEMs), 3D building model data, and 3D road shape data from third-party map services and open data. The information acquisition unit 131 also performs preprocessing such as format conversion, coordinate system unification, and temporal synchronization of the data acquired from these diverse data sources, generating an integrated dataset that is easy to use in subsequent processing.

[0098] The determination unit 132 analyzes the movement of the delivery person in three-dimensional space and performs a process to determine the three-dimensional delivery route and delivery location. By using three-dimensional position data including elevation, it is possible to calculate the actual travel distance and difficulty, taking into account not only the planar distance but also the elevation difference. In particular, in mountainous areas and regions with large elevation differences, this three-dimensional analysis makes it possible to evaluate the actual difficulty of delivery more accurately.

[0099] Furthermore, the determination unit 132 also implements a function to detect and analyze vertical movement patterns within the building. Specifically, it uses an algorithm that estimates rapid altitude changes in a short period of time (for example, an ascent / descent of 10m or more within 1 minute) as elevator use, and more gradual, stepwise altitude changes (for example, a continuous change of 2-5m per minute) as stair use. In this pattern recognition, features such as the speed, continuity, and periodicity of altitude changes are extracted, and machine learning-based classification models (for example, decision trees, support vector machines, etc.) are applied to determine the mode of movement. This makes it possible to automatically identify the location of vertical movement equipment (elevators, stairs, escalators, etc.) within the building and map the internal structure of the building.

[0100] The generation unit 133 is responsible for generating advanced visual representations based on the acquired 3D data and judgment results. In generating 3D maps, it integrates terrain data and building models and implements a 3D rendering engine using graphics APIs such as OpenGL and WebGL. This engine provides basic functions such as 3D mesh generation and optimization, texture mapping, lighting calculation, and viewpoint manipulation (zoom, rotate, pan). It also implements a level of detail (LOD) control function that responds to the load of 3D display, automatically adjusting the level of detail according to the distance from the viewpoint to achieve a smooth 3D operation experience.

[0101] The 3D map displays delivery routes as three-dimensional lines, allowing users to visually understand gradients and elevation changes. It also implements information highlighting features useful for delivery planning, such as color-coding slope angles (e.g., green: less than 5%, yellow: 5-10%, red: 10% or more) and displaying warning icons for steep sections. The 3D view of buildings includes features such as floor switching to represent movement between floors and building cross-section displays, supporting intuitive understanding of delivery routes within complex buildings.

[0102] The generation unit 133 also handles the generation of AR (augmented reality) content. In AR mode, it overlays delivery-related information onto the camera image of the delivery terminal 200. To implement the AR display, it utilizes AR frameworks such as ARKit (iOS) and ARCore (Android) to realize the recognition of the real space and the placement of virtual objects. Using environmental understanding functions such as feature point detection and tracking from the camera image, plane detection, and light source estimation, it generates an AR display that is appropriately integrated into the real space.

[0103] Specific AR content can be generated, such as 3D arrows indicating the direction to go, floating text showing the distance to the destination and the estimated arrival time, marker icons indicating the delivery location, and highlights indicating the optimal parking location. Because these AR objects are placed in a fixed world coordinate system in real space, they remain displayed in the correct position even as the delivery person moves. Furthermore, context-aware AR displays that recognize real-world environmental elements such as buildings and roads and are appropriately positioned relative to them can also be implemented. For example, if a building entrance is recognized, it is possible to display building information and the room number of the delivery destination on top of it.

[0104] The display unit 134 is responsible for effectively displaying the generated 3D and AR content on the delivery terminal 200. In 3D map mode, it provides an intuitive viewpoint control interface using finger gestures (pinch zoom, swipe rotation, etc.), allowing delivery personnel to view terrain and buildings from the desired angle. It also implements a function to switch between 2D and 3D maps, as well as a 2D / 3D hybrid display mode (representing height information on the 2D map using color and contour lines), allowing users to select the optimal display method according to the situation.

[0105] In AR mode, the display unit 134 provides the function of overlaying AR content on the camera preview. This AR interface implements adaptive display control that dynamically adjusts the displayed content according to the delivery person's movement and gaze point to prevent obstruction of the view due to excessive information display. For example, when the delivery person is moving quickly, only important information is displayed simply, and when stopped or moving slowly, detailed information is displayed. In addition, a function is implemented to adjust the transparency and color of the AR content based on the tilt of the terminal and information from the ambient light sensor to ensure visibility under various environmental conditions.

[0106] These 3D and AR features are integrated with standard 2D map displays, allowing delivery personnel to freely switch between display modes according to their situation and preferences. An automatic display mode selection function based on the terminal's processing power and battery level is also implemented, enabling the provision of an optimal user experience within limited resources. These advanced visualization technologies enable spatial awareness and efficient navigation in complex terrain and within buildings, contributing to improved delivery efficiency and reduced burden on delivery personnel.

[0107] Although embodiments of the invention have been described above, these can be implemented in various other forms, and can be carried out by various omissions, substitutions, and modifications. These embodiments and variations, as well as those with omissions, substitutions, and modifications, are included within the technical scope of the claims and their equivalents. [Explanation of Symbols]

[0108] 1 System: 100 Server terminal, 110 Communication unit, 120 Storage unit, 130 Control unit, 200 Delivery terminal, 300 User terminal, NW1 Network

Claims

1. An information acquisition unit that acquires location information and time information corresponding to the location information from the delivery terminal of the package delivery person, A determination unit determines the delivery route of the delivery person based on the location information and the time information, A generation unit generates a composite map by combining the map information of a map application and the delivery route, Information processing systems, including those mentioned above.

2. The information processing system according to claim 1, wherein the generation unit synthesizes the main road routes included in the map information with the delivery routes.

3. The information processing system according to claim 1, wherein the generation unit generates the composite map by supplementing the main road routes included in the map information with branch road routes based on the delivery routes.

4. The information processing system according to claim 1, wherein the information acquisition unit acquires the map application from a third-party application server.

5. The information processing system according to claim 1, wherein the generation unit checks with the delivery terminal whether to link the map application and the delivery route, and generates the composite map based on the check result.

6. The information processing system according to claim 1, wherein the generation unit confirms with the delivery terminal whether to refer to the delivery route or to cooperate with the map application, and generates the composite map based on the confirmation result.

7. The information processing system according to claim 1, wherein the determination unit calculates the time the delivery person stayed at the location corresponding to the location information based on the location information and the time information, and determines the parking position of the delivery person based on the stay time and the delivery route.

8. The information processing system according to claim 1, wherein the determination unit calculates the number of times the delivery person has stayed at a location corresponding to the location information based on the location information and the time information, and determines the parking position of the delivery person based on the number of stays and the delivery route.

9. The information processing system according to claim 7 or 8, wherein the generation unit recommends the parking location to the delivery person terminal by displaying it on the composite map.

10. The delivery person's terminal is used to obtain location information and time information corresponding to the location information. Based on the location information and time information, the delivery route of the delivery person is determined. A method for generating a composite map by combining map information from a map application with the delivery route.

11. The computer is instructed to obtain location information and time information corresponding to the location information from the delivery terminal of the package delivery person. Based on the location information and time information, the delivery route of the delivery person is determined. A program that generates a composite map by combining map information from a map application with the delivery route.

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