Information processing systems, methods, and programs

Abstract

The present invention aims to provide an information processing system, method, and program that improve the efficiency and quality of delivery operations by utilizing the location and time information of delivery personnel to analyze compliance with work requirements and providing notifications at appropriate times. [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 the delivery terminal of a package delivery person; an analysis unit that analyzes the delivery person's compliance status based on the location information and the time information; and a notification unit that notifies the delivery person or manager based on the compliance status. Furthermore, a delivery operation monitoring method in one aspect of the present invention includes the steps of acquiring location information and time information corresponding to the location information from the delivery terminal of a package delivery person; analyzing the delivery person's compliance status based on the location information and the time information; and notifying the delivery person or manager based on the compliance status.

Description

Technical Field

[0001] The present invention relates to an information processing system, method, and program for monitoring delivery operations in physical distribution.

Background Art

[0002] In physical distribution, as part of operational efficiency improvement, methods for managing package delivery are provided.

[0003] For example, Patent Document 1 discloses a method of detecting uninput 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 Invention

Problems to be Solved by the Invention

[0005] In improving the efficiency of delivery operations, it is important to appropriately monitor the actions of delivery workers and evaluate their operational compliance status. However, conventional monitoring systems only simply grasp the position information of delivery workers, and lack sufficient functions for comprehensively monitoring compliance with operational rules and issuing notifications at appropriate timings.

[0006] Accordingly, an object of the present invention is to provide a delivery operation monitoring system, method, and program that achieve improved efficiency and quality of delivery operations by utilizing the position information and time information of delivery workers to analyze the operational compliance status of the delivery workers and issue notifications at appropriate timings.

Means for Solving the Problems

[0007] A delivery service monitoring 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 the delivery terminal of the delivery person; an analysis unit that analyzes the delivery person's compliance status based on the location information and the time information; and a notification unit that notifies the delivery person or manager based on the compliance status.

[0008] Furthermore, a delivery service monitoring method according to another aspect of the present invention includes the steps of: obtaining location information and time information corresponding to the location information from the delivery terminal of the delivery person of the package; analyzing the delivery person's compliance status based on the location information and the time information; and notifying the delivery person or manager based on the compliance status.

[0009] Furthermore, a program according to yet another aspect of the present invention causes a computer to perform the following processes: acquiring location information and time information corresponding to the location information from the delivery terminal of a package delivery person; analyzing the delivery person's compliance status based on the location information and time information; and notifying the delivery person or manager based on the compliance status. [Effects of the Invention]

[0010] According to the present invention, by utilizing the delivery person's location and time information to analyze compliance with work requirements and providing notifications at the appropriate time, it is possible to improve the efficiency and quality of delivery operations. Furthermore, multifaceted monitoring functions such as detection of locations outside the service area, detection of deviations from recommended routes, detection of parking violation risks, and working time management make it possible to prevent various problems in delivery operations. [Brief explanation of the drawing]

[0011] [Figure 1] This is a block diagram showing a delivery operations monitoring 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] Figure 1 is a functional block diagram showing the administrator terminal 300. [Figure 5] This flowchart shows an example of a basic process according to the first embodiment of the present invention. [Figure 6] This is a flowchart showing an example of area monitoring processing according to the first embodiment of the present invention. [Figure 7] This flowchart shows an example of route monitoring processing according to the first embodiment of the present invention. [Figure 8] This is a flowchart showing an example of parking surveillance processing according to the first embodiment of the present invention. [Figure 9] This is a flowchart showing an example of a break management process according to the first embodiment of the present invention. [Figure 10] A flowchart showing an example of a non-work use monitoring process according to the first embodiment of the present invention. [Modes for carrying out the invention]

[0012] 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.

[0013] <System Configuration> FIG. 1 is a block diagram showing the overall configuration of a delivery operation monitoring system according to an embodiment of the present invention. This system 1 is managed by a delivery operator or a delivery management platform operator, and is composed of a server terminal 100 that manages the position information and the like of deliverers and analyzes the operation compliance status, a plurality of deliverer terminals 200A, 200B carried by the deliverers, and terminals 300A, 300B of delivery managers, and the like. For convenience of explanation, each terminal is described as a single one or a specific number of ones, but the number of each is not limited. Hereinafter, for the convenience of explanation, unless otherwise specified, the deliverer terminals will be collectively referred to as a deliverer terminal 200, and the manager terminals will be collectively referred to as a manager terminal 300 for respective descriptions.

[0014] The server terminal 100, the deliverer terminal 200, and the manager terminal 300 are respectively connected via a network NW1. The network NW1 is configured by the Internet, an intranet, a wireless LAN (Local Area Network), a WAN (Wide Area Network), or the like.

[0015] The server terminal 100 may be, for example, a general-purpose computer such as a workstation or a personal computer, or may be logically implemented by cloud computing. As the core of the delivery operation monitoring system, the server terminal 100 executes functions such as acquisition of position information, analysis of operation compliance status, and management of notifications.

[0016] The deliverer terminal 200 is, for example, an information processing apparatus such as a smartphone, a tablet terminal, or a dedicated terminal, and is configured by a terminal having a GPS function. The deliverer terminal 200 is carried by a deliverer when the deliverer delivers a package, and has a function of acquiring the position information and time information of the deliverer and transmitting them to the server terminal 100. It also has a function of receiving and displaying alerts and notifications from the server terminal 100.

[0017] The administrator terminal 300 is, for example, an information processing apparatus such as a personal computer or a tablet terminal, and is used by an administrator of delivery operations. The administrator terminal 300 has a function of receiving and displaying the monitoring result of the work compliance status of the delivery worker. It also provides functions such as setting of delivery areas and delivery routes, and management of business rules.

[0018] Figure 2 is a block diagram showing a functional configuration of the server terminal 100. The server terminal 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

[0019] The communication unit 110 is a communication interface for communicating with the delivery worker terminal 200 and the administrator terminal 300 via the network NW1, and performs communication according to a communication protocol such as TCP / IP (Transmission Control Protocol / Internet Protocol). The communication unit 110 has a function of receiving position information and time information from the delivery worker terminal 200, and a function of transmitting notifications and alerts to the delivery worker terminal 200 and the administrator terminal 300.

[0020] 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 constituted by RAM (Random Access Memory), ROM (Read Only Memory) and the like. The storage unit 120 includes a delivery data storage unit 121, an area / route data storage unit 122, a business rule data storage unit 123, and an analysis result data storage unit 124.

[0021] The delivery data storage unit 121 stores delivery-related data such as location information, time information, delivery history, and delivery person information received from the delivery terminal 200. The area / route data storage unit 122 stores geographical information such as the delivery area and recommended routes. The business rule data storage unit 123 stores business rules that delivery persons must comply with (delivery within the assigned area, no parking in no-parking zones, maximum continuous working time, etc.) and related parameters (thresholds, etc.). The analysis result data storage unit 124 stores the results of the business compliance status analysis performed by the analysis unit 132.

[0022] The control unit 130 controls the overall operation of the server terminal 100 by executing programs stored in the memory unit 120, and is composed of a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), etc. The control unit 130 also includes an information acquisition unit 131, an analysis unit 132, a notification unit 133, and a support unit 134.

[0023] The information acquisition unit 131 has the function of acquiring location information and time information from the delivery terminal 200. The analysis unit 132 has the function of analyzing the delivery person's compliance with duties based on the acquired location information and time information. The notification unit 133 has the function of notifying the delivery person or manager based on the analysis results. The support unit 134 has the function of transmitting information on legal parking spaces around the delivery person's current location to the delivery person's terminal when the delivery person's current location is close to a no-parking zone, and the function of identifying the optimal rest points on the delivery person's current location and delivery route and recommending them to the delivery person's terminal. Further details will be described later.

[0024] Figure 3 is a block diagram showing the functional configuration of the delivery terminal 200. 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 unit 250.

[0025] The communication unit 210 is a communication interface for communicating with the server terminal 100 via the network NW1, and communication is performed using a communication protocol such as TCP / IP. The communication unit 210 has the function of sending location information and time information to the server terminal 100, and the function of receiving notifications and alerts from the server terminal 100.

[0026] The display operation unit 220 is a user interface used by delivery personnel to input instructions and display text, images, etc., in accordance with output data from the control unit 240, and consists of a touch panel display or the like. The display operation unit 220 has the function of displaying delivery routes, delivery instructions, received alerts, etc.

[0027] The memory unit 230 stores programs for executing various control processes and functions within the control unit 240, as well as input data, and is composed of RAM, ROM, etc. The memory unit 230 can also temporarily store location information and time information. Considering operation in areas with unstable network connectivity, it is configured to accumulate data even when offline.

[0028] The control unit 240 controls the overall operation of the delivery terminal 200 by executing programs stored in the memory unit 230, and is composed of a CPU, GPU, etc. The control unit 240 has functions such as processing location information and time information acquired from the GPS unit 250 and transmitting it to the server terminal 100 via the communication unit 210, and displaying delivery-related information and received alerts on the display operation unit 220.

[0029] The GPS unit 250, an abbreviation for Global Positioning System, determines the current location and time (time information) of the delivery terminal 200 using signals from artificial satellites. In addition to standard positioning functions, the GPS unit 250 is equipped with advanced positioning functions such as auxiliary positioning (A-GPS), Wi-Fi positioning, and cellular base station positioning, improving positioning accuracy inside buildings and in urban areas. It may also be equipped with a function to detect vertical movement in conjunction with a barometric pressure sensor.

[0030] Figure 4 is a block diagram showing the functional configuration of the administrator terminal 300. The administrator terminal 300 includes a communication unit 310, a display and operation unit 320, a storage unit 330, and a control unit 340.

[0031] The communication unit 310 is a communication interface for communicating with the server terminal 100 via the network NW1, and communication is performed using a communication protocol such as TCP / IP. The communication unit 310 has the function of receiving analysis results and notifications of compliance status of work transmitted from the server terminal 100, and the function of transmitting setting information of delivery areas and recommended routes to the server terminal 100.

[0032] The display operation unit 320 is a user interface used by administrators to input instructions and display text, images, etc., in accordance with output data from the control unit 340. It consists of a touch panel display and dedicated operation buttons. The display operation unit 320 has functions such as visually displaying the location and compliance status of delivery personnel on a map, displaying detailed information for each delivery person, and providing an interface for setting delivery areas and recommended routes.

[0033] The memory unit 330 stores programs for executing various control processes and functions within the control unit 340, input data, etc., and is composed of RAM, ROM, etc. The memory unit 330 stores the results of the analysis of received compliance status of work, setting information for delivery areas and recommended routes, map data, etc.

[0034] The control unit 340 controls the overall operation of the administrator terminal 300 by executing programs stored in the memory unit 330, and is composed of a CPU, GPU, etc. The control unit 340 has functions such as processing and displaying the analysis results of received work compliance status, and setting and managing delivery areas and recommended routes.

[0035] <Information acquisition section> The information acquisition unit 131 acquires the delivery person's location information and time information corresponding to the location information from the delivery person terminal 200. Specifically, the information acquisition unit 131 receives location information (e.g., latitude and longitude) and time information (e.g., date and time) acquired by the GPS function of the delivery person terminal 200, and stores them in the delivery data storage unit 121, linked to the delivery person identification information (delivery person ID).

[0036] Various implementation methods are possible for the information acquisition unit 131 to acquire location and time information. One implementation method is to automatically acquire location and time information from the delivery terminal 200 periodically (for example, at intervals of a few seconds to a few minutes). In this method, the delivery terminal 200 automatically transmits location information at a predetermined interval, and the information acquisition unit 131 receives it. The transmission interval can also be dynamically adjusted according to the delivery situation and regional characteristics. For example, it can be changed to a shorter interval (for example, every 30 seconds) in urban areas or areas with high delivery density, and a longer interval (for example, every 2 minutes) in suburban areas or areas with long travel distances.

[0037] A second implementation method involves acquiring information when a change in the delivery person's behavior (such as stopping or moving) is detected. In this method, the delivery person's behavior is monitored using an accelerometer or other sensor built into the delivery person terminal 200, and location and time information is transmitted when a change in state (for example, a change from moving to stopping, or from stopping to moving) is detected. This reduces unnecessary data communication and conserves battery power while accurately capturing changes in the delivery person's behavior.

[0038] A third implementation method involves obtaining information when the delivery person performs a specific action (e.g., starting delivery, completing delivery). In this method, when the delivery person performs a specific action on the application on the delivery terminal 200 (e.g., tapping the "start delivery" button, tapping the "complete delivery" button), the system transmits location and time information at that moment. This ensures that location information at key points in the delivery process is reliably obtained.

[0039] These methods can also be used in combination. For example, an effective combination would be to acquire location information periodically, while simultaneously acquiring it immediately when the delivery person's behavior changes or a specific action is performed. Furthermore, it is important to set the frequency and timing of location information acquisition while considering the balance between battery consumption and data accuracy.

[0040] The information acquisition unit 131 performs preprocessing on the acquired location and time information. Preprocessing includes correcting GPS errors by comparing them with past delivery data stored in the delivery data storage unit 121, removing unnatural location information, and normalizing the data so that the distances between location information become uniform. For example, for data acquired in environments where GPS accuracy is reduced, such as inside buildings or around high-rise buildings, the data is compared with past data from the same location stored in the memory unit 120, and data with deviations exceeding a predetermined threshold are detected and corrected as abnormal values.

[0041] The information acquisition unit 131 performs preprocessing on the acquired location and time information. Preprocessing includes correcting GPS errors by comparing them with past delivery data stored in the delivery data storage unit 121, removing unnatural location information, and normalizing the data so that the distances between location information become uniform. For example, for data acquired in environments where GPS accuracy is reduced, such as inside buildings or around high-rise buildings, the data is compared with past data of the same delivery person at the same location stored in the memory unit 120, and data where the deviation of the location information exceeds a predetermined threshold (e.g., 50 meters) is detected and corrected as an anomaly. Possible correction methods include excluding the anomaly, replacing it with the average value of past normal data, or linear interpolation from surrounding normal data.

[0042] The information acquisition unit 131 also has a function to evaluate the accuracy and reliability of location information. Based on information such as the reception status of GPS signals, the number of available satellites, and the horizontal accuracy degradation rate (HDOP), it determines the reliability level of the data and can either exclude unreliable data from the analysis or apply correction processing.

[0043] The information acquisition unit 131 structures the acquired data by associating it with information such as delivery ID, delivery person ID, and delivery date and time, and stores it in the delivery data storage unit 121. The stored data is used by the analysis unit 132 to analyze compliance status. In addition, the information acquisition unit 131 is equipped with data redundancy and retransmission request functions to cope with data loss and communication failures. For example, if communication is interrupted, the delivery person terminal 200 supports a function to temporarily save location information and send it all at once when communication is restored.

[0044] <Analysis Department> The analysis unit 132 analyzes the delivery person's compliance with duties based on location and time information acquired by the information acquisition unit 131. Here, compliance with duties refers to the delivery person's adherence to various work rules that must be followed when delivering packages (such as delivery within their assigned area, adherence to recommended routes, prohibition of parking in no-parking zones, and taking appropriate breaks). The analysis unit 132 has multiple analysis functions for evaluating different types of compliance with duties. Details are illustrated below.

[0045] The analysis unit 132 compares the delivery person's current location with a pre-defined service area based on location information and corresponding time information to determine whether the delivery person is located outside the service area. In this service area monitoring function, the analysis unit 132 first reads the service area information associated with the delivery person identification information (delivery person ID) from the area / route storage unit 122. The service area is defined as a geographical boundary (such as a polygon of latitude and longitude), and the analysis unit 132 determines by mathematical calculation whether the delivery person's current location is included within this boundary. Specifically, for example, it is possible to calculate whether the delivery person's position coordinates are within the boundary of the service area by using an algorithm that determines the inclusion relationship between a point and a polygon (for example, the Ray Casting method or the Winding Number method).

[0046] Furthermore, the analysis unit 132 calculates the distance between the delivery person's current location and the boundary of their assigned area, and determines that the delivery person is approaching the area boundary if this distance falls below a predetermined threshold. For example, the information indicating the boundary of the assigned area may be defined based on multiple location information (coordinate information) on the boundary. Simple Euclidean distance (straight-line distance) or the actual travel distance along the road network can be used to calculate the distance. When the distance to the area boundary falls below a certain threshold (e.g., 500 meters), it is determined that the delivery person may be leaving the area, and a notification for advance warning is generated. This threshold can be adjusted according to the characteristics of the delivery area and the means of delivery (vehicle, walking, etc.).

[0047] The analysis unit 132 calculates the degree of deviation between the delivery person's travel path and a pre-set recommended route based on location information, and determines that a route deviation has occurred if the degree of deviation exceeds a predetermined threshold. In the route monitoring function, the analysis unit 132 reconstructs the delivery person's actual travel path from continuous location information and compares it with the recommended route information stored in the area / route data storage unit 122.

[0048] There are several approaches to calculating the degree of deviation. The first method calculates the degree of deviation based on the geographical distance between the delivery person's actual route and the recommended route. Specifically, the actual route is treated as a time-series of location points, the shortest distance from each point on the actual route to the recommended route is calculated, and the average or maximum value is defined as the degree of deviation. For example, a route that is more than 100 meters on average away from the recommended route can be determined to have a high degree of deviation. The second method calculates the degree of deviation based on the overlap rate between the actual route and the recommended route. In this case, the actual route and the recommended route are each represented as a set of location points at regular intervals, and the overlap rate is calculated by dividing the distance of the overlapping section of both routes by the total distance, and "1 - overlap rate" is defined as the degree of deviation. For example, if the overlap rate is 30%, the degree of deviation will be 70%.

[0049] If the degree of deviation exceeds a predetermined threshold (e.g., 50%), the analysis unit 132 determines that a route deviation has occurred. This threshold can be adjusted according to the type of delivery and regional characteristics. For example, a stricter threshold (e.g., 30%) can be set in densely populated urban areas, while a more lenient threshold (e.g., 70%) can be set in suburban areas or areas where traffic conditions are prone to fluctuations. Several approaches can be implemented to determine this threshold. The first method involves referring to the regional characteristics table stored in the business rule data storage unit 123 and obtaining predefined basic thresholds for each type of delivery area (e.g., "city center," "residential area," "suburban area," "mountainous area," etc.). The second method involves performing automatic adjustment based on statistical analysis of past delivery data. Specifically, delivery data for the past 30 days is extracted from the delivery data storage unit 121, the frequency distribution of route deviation patterns for each region is calculated, and the threshold is dynamically set using the standard deviation of this distribution. The third method involves applying a threshold adjustment table linked to external factors such as time of day and weather. For example, rules such as "Rush hour: Basic threshold + 20%" and "Rainfall: Basic threshold + 15%" are defined in the condition-based threshold table within the business rule data storage unit 123, and the thresholds are automatically adjusted according to the current time of day and weather conditions. The analysis unit 132 also takes external factors such as traffic conditions and weather into consideration and has a function to dynamically adjust the thresholds according to these conditions.

[0050] The analysis unit 132 identifies the delivery person's stopping location and time based on location and time information, and determines whether the stopping location is within a no-parking zone. In this parking monitoring function, the analysis unit 132 first detects the stopping state from the delivery person's location and time information. Specifically, if the continuous changes in location information linked to the delivery person identification information are within a predetermined threshold (e.g., 10 meters) for a certain period of time (e.g., 3 minutes), it is determined that the vehicle is stopped at that location. At this time, the accuracy information of the location information (such as GPS accuracy) is also taken into consideration, and if the accuracy is low, processing is performed to adjust the threshold. Minor fluctuations in location information are treated as GPS errors, and if there is no actual movement, it is considered stopped.

[0051] Next, the analysis unit 132 determines whether the identified parking location is located within a no-parking zone. Information on no-parking zones is stored in the business rule storage unit 123 and is defined as a geographical area defined by multiple coordinate points (area designation by latitude and longitude). The analysis unit 132 calculates the inclusion relationship between the parking location and the no-parking zone using algorithms such as the Ray Casting method, and determines that there is a risk of parking violation if the parking location is included within a no-parking zone. In addition, the parking time is measured simultaneously based on time information, and if the parking time exceeds a predetermined threshold (e.g., 5 minutes) and the parking location is within a no-parking zone, it is determined that there is a high risk of parking violation. There are several methods for determining this parking time threshold. The first method employs a fixed threshold based on legal compliance standards. The business rule data storage unit 123 stores standard values ​​for parking time derived from laws such as the Road Traffic Act (e.g., "general roads: 5 minutes," "major arterial roads: 3 minutes"), and uses these as thresholds. The second method adjusts the threshold according to the enforcement intensity for each area. Based on past violation enforcement data and warning frequency data, an enforcement intensity index is calculated for each area, and a stricter threshold (e.g., 70% of the standard value) is applied in high-intensity areas. In the third method, self-learning threshold adjustment is performed based on past delivery data. The correlation between parking time data and violation warnings in past delivery events is analyzed from the delivery data storage unit 121, and the time when the probability of a warning occurring exceeds a certain value (e.g., 20%) is dynamically set as the threshold. The parking time threshold can also be dynamically adjusted depending on regional characteristics and time of day.

[0052] No-parking zones are defined from various sources of information. Firstly, they are based on legally mandated no-parking zones (such as near intersections, pedestrian crossings, and bus stops). Secondly, they also include parking restriction zones independently established by local governments and facilities. Thirdly, areas with a high history of parking violation enforcement are also defined as high-risk zones. This information is updated regularly and stored in the operational rules data storage unit 123.

[0053] The analysis unit 132 calculates the delivery person's continuous working time and break time based on location and time information, and determines the need for a break when the continuous working time approaches a predetermined legal limit. In this continuous working time monitoring function, the analysis unit 132 acquires information on the start time of delivery linked to the delivery person identification information, counts the time from the start time of delivery to the present as the continuous working time, and detects the rest period patterns to identify the break time.

[0054] There are several approaches to detecting breaks. The first method identifies a break based on a pattern of location changes, specifically when there is little to no change in location over a certain period of time (e.g., 15 minutes or more). In this case, a location change threshold (e.g., within 30 meters) and a minimum break time (e.g., 15 minutes) are set for the determination. The second method identifies a break when the delivery person stays at a designated rest facility (e.g., a distribution center, contract rest area) for a certain period of time. In this case, the location information of the rest facility stored in the business rule storage unit 123 is compared with the delivery person's location information, and a break is determined if the distance between the two remains within a predetermined threshold (e.g., within 50 meters) for a certain period of time. The third method records breaks through explicit break start and end operations on the delivery person terminal 200. In this case, the break time is calculated based on the break start / end operation signals and time information transmitted from the delivery person terminal 200. In actual operation, it is desirable to combine these methods to accurately identify break times.

[0055] The analysis unit 132 calculates the actual continuous working time by subtracting identified break times from the continuous working time, and determines that a break should be prompted when this time approaches the legal limit (e.g., 4 hours) (e.g., 30 minutes remaining). The following methods can be implemented to set the threshold for determining when a break is "approaching" the limit. In the first method, a fixed time threshold is obtained from the work rule data storage unit 123. This threshold is set based on legal requirements such as the Industrial Safety and Health Act, and different values ​​are defined for each delivery work category (e.g., "Truck driving: 45 minutes remaining," "Delivery work: 30 minutes remaining," etc.). In the second method, an individualized threshold is calculated based on the break preparation time history for each delivery person. Specifically, the average time from when each delivery person receives a break notification to when they actually take a break is calculated from past delivery data stored in the delivery data storage unit 121, and this time is multiplied by a safety margin (e.g., 1.5 times) and set as the threshold. In the third method, dynamic threshold adjustment is performed based on the current delivery status and the remaining delivery volume. The system retrieves the current delivery progress and the number of remaining delivery destinations from the delivery data storage unit 121, calculates the estimated time required to reach the next possible rest stop, and dynamically sets a corresponding threshold. This allows for earlier breaks in areas with high delivery density and adjustments to allow for appropriate breaks in areas with fewer delivery destinations. This statutory upper limit time is stored in the business rule data storage unit 123 and is set appropriately according to the region and regulations.

[0056] Furthermore, the analysis unit 132 also has a function to detect the movement of the delivery terminal outside of pre-set business hours based on location and time information, and to determine the possibility of off-duty use. In the off-duty use monitoring function, the analysis unit 132 grasps the delivery person's arrival and departure times, and determines that there is a possibility of off-duty use if the transmission of location information from the delivery terminal 200 continues outside of business hours (for example, after work or on holidays).

[0057] Furthermore, the analysis unit 132 determines, based on location information, whether the delivery person's movement pattern deviates from the planned delivery route by a predetermined threshold, and identifies any deviation as an abnormal behavioral pattern. Movement pattern analysis detects movements that deviate significantly from the normal delivery route, as well as stops at locations deemed unnecessary for business purposes. This allows for the early detection of potential private use of delivery vehicles or fraudulent activities.

[0058] The analysis unit 132 also has a function to analyze the relationship between the distance traveled calculated from location information and the fuel consumption obtained from the delivery vehicle, and to detect a fuel consumption anomaly if the relationship deviates from a preset normal range. This function detects an anomaly when the fuel consumption is unnaturally high compared to the actual distance traveled, suggesting the possibility of misuse of the vehicle or a mechanical problem.

[0059] As described above, the analysis unit 132 can comprehensively analyze the delivery person's location and time information to evaluate various aspects of compliance with business operations. The analysis results are stored in the analysis result data storage unit 124 as evaluation indicators (numerical scores and category classifications such as "compliance," "minor violation," and "serious violation") and used as basic information for notifications by the notification unit 133.

[0060] <Notification section> The notification unit 133 notifies the delivery person or manager based on the compliance status of the work analyzed by the analysis unit 132. The notification unit 133 selects the appropriate notification recipient, notification timing, and notification method according to the type and importance of the compliance status of the work included in the analysis result data obtained from the analysis result storage unit 124, thereby achieving effective notification. Here, the type of compliance status of the work refers to the compliance status identifier included in the analysis result data (e.g., "outside area detected," "route deviation," "parking violation risk," "break needed," "non-work use," etc.), and the importance refers to the priority indicator stored in the analysis result data as a numerical value (e.g., a number from 0 to 100) or a grade value (e.g., "low," "medium," "high," "urgent").

[0061] There are various possible implementation methods for the notification unit 133 to select notification recipients. One implementation method is to determine the notification recipients according to the type and importance of the business compliance status. Specifically, the notification unit 133 refers to the notification rule table stored in the business rule storage unit 123. This table defines the correspondence between the importance threshold and the notification recipient for each type of business compliance status. For example, in the case of "out-of-area detection," if the importance is "medium" or lower, the rule is set to notify only the delivery terminal 200, and if it is "high" or higher, the rule is set to notify both the delivery terminal 200 and the administrator terminal 300. For example, for matters that the delivery person can handle themselves, such as preventive notifications for area deviations and encouragement of breaks, the delivery terminal 200 is notified, while for matters requiring managerial action, such as the detection of serious rule violations or abnormal patterns, the administrator terminal 300 is notified. The notification unit 133 then obtains the compliance status identifier and importance value from the analysis result data and performs a process to determine the appropriate notification recipient by comparing it with the notification rule table.

[0062] A second implementation method involves determining the recipient of a notification based on the urgency of the notification content. In this method, the notification unit 133 obtains a numerical value representing the time urgency (e.g., the number of minutes remaining for response) from the analysis result data and uses this to determine the urgency. The business rule storage unit 123 defines the correspondence between urgency thresholds (e.g., "within 30 minutes remaining: urgent," "within 60 minutes remaining: semi-urgent," etc.) and the recipients of notifications. Based on this correspondence, the notification unit 133 sends low-urgency notifications (e.g., routine reports on working hours) only to the administrator terminal 300, and sends high-urgency notifications (e.g., detection of parking violation risk) to both the delivery terminal 200 and the administrator terminal 300.

[0063] A third implementation method involves setting up notification escalation rules. In this method, the notification unit 133 refers to the escalation rules stored in the business rule storage unit 123. The escalation rules define the initial notification recipient and escalation conditions (for example, "if the situation does not improve after 15 minutes from the initial notification") for each type of business compliance status. After sending the initial notification, the notification unit 133 compares the notification history recorded in the analysis result storage unit 124 with the current analysis result data and executes a process to notify additional recipients if the escalation conditions are met. This first sends a notification to the delivery terminal 200, and if the situation does not improve within a certain time, it escalates the notification to the administrator terminal 300. This realizes a two-stage notification structure that encourages the delivery person to resolve the problem themselves while also allowing administrator intervention if necessary.

[0064] There are various possible implementation methods for the notification unit 133 to select the timing of notifications. One implementation method is to use real-time notifications and batch notifications depending on the urgency of the situation. Specifically, the notification unit 133 compares the urgency index (numerical value or grade value) obtained from the analysis result data with the notification timing table stored in the business rule storage unit 123. This table defines the correspondence between urgency thresholds (for example, "80 or more: immediate notification", "50-79: notification within 30 minutes", "0-49: daily batch notification", etc.) and notification timing. Based on this correspondence, the notification unit 133 sends an immediate notification for highly urgent situations (for example, detection of intrusion outside the area or detection of parking violation risk), and performs a process to send notifications in batches (for example, daily, weekly, etc.) for less urgent situations (for example, daily compliance reports).

[0065] A second implementation method involves controlling the timing of preventative notifications. In this method, the notification unit 133 obtains a predicted value from the analysis result data indicating the remaining time or distance until a potential problem occurs, and compares it with the preventative notification criteria stored in the business rule storage unit 123. The preventative notification criteria define thresholds for remaining time or distance to be notified for each type of problem (e.g., "500 meters remaining to the area boundary," "30 minutes remaining to the maximum continuous operating time," etc.). The notification unit 133 compares this threshold and, for example, in cases of approaching the area boundary or approaching the maximum continuous operating time, it issues a notification at an appropriate time before the problem actually occurs (e.g., 500 meters to the area boundary, 30 minutes remaining to the maximum continuous operating time) to encourage preventative action.

[0066] A third implementation method involves setting a cooldown period to prevent duplicate notifications. In this method, the notification unit 133 refers to the notification history data stored in the analysis result storage unit 124 and checks whether a similar notification (a notification with the same compliance status identifier) ​​has recently been sent to a specific delivery person ID. The business rule storage unit 123 defines a cooldown period for each type of notification (for example, "Area boundary approach notification: 15 minutes," "Break recommendation notification: 30 minutes," etc.), and the notification unit 133 executes a process to suppress the notification if a similar notification has been sent within this period. This prevents the same type of notification from occurring multiple times in a short period of time, and makes it possible to set a certain cooldown period (for example, a maximum of once every 15 minutes for the same type of notification) to suppress excessive notification sending.

[0067] Various implementation methods are possible for the notification unit 133 to select a notification method. One implementation method is to select a notification method according to the importance of the notification. Specifically, the notification unit 133 compares the importance index obtained from the analysis result data with the notification method table stored in the business rule storage unit 123. This table defines the correspondence between the importance range (for example, "0-30: low importance", "31-70: medium importance", "71-100: high importance", etc.) and the notification method. Each importance level is associated with notification method parameters such as the visual display method (icon, color, size, etc.), whether or not to provide sound notification, and vibration pattern. Based on these parameters, the notification unit 133 executes a process to change the notification method according to importance: low-importance notifications are displayed only in the in-app notification center, medium-importance notifications are displayed as in-app pop-ups, and high-importance notifications are displayed as pop-ups with sound alerts or vibrations.

[0068] A second implementation method involves selecting a notification method based on the delivery status. In this method, the notification unit 133 estimates the delivery person's current status (driving, stopped, taking a break, etc.) from the analysis result data and the latest location and time information stored in the delivery data storage unit 121. Factors such as movement speed, location change pattern, and time are considered in the status estimation. The business rule storage unit 123 defines appropriate notification methods for each delivery person status (for example, "driving: voice notification only," "stopped: visual and voice notification," "taking a break: all notification methods"), and based on this, the notification unit 133 executes a process to select the optimal notification method according to the delivery person's status, such as prioritizing voice notifications when the delivery person is thought to be driving, and prioritizing text notifications or visual notifications when the delivery person is thought to be stopped or taking a break.

[0069] A third implementation method involves selecting a notification format according to the notification content. In this method, the notification unit 133 refers to the notification format table stored in the business rule storage unit 123 based on the compliance status identifier obtained from the analysis result data. This table defines appropriate notification formats (text only, with map display, with image, with operation buttons, etc.) and templates for each compliance status identifier. The notification unit 133 embeds specific values ​​from the analysis result data (location information, time information, measured values, etc.) into this template and performs the process of selecting the most suitable format according to the notification content, such as a text-only notification for simple warnings, a notification with a map display for route deviations, and a notification with alternative parking location information for parking violation risk notifications.

[0070] The notification unit 133 appropriately combines these methods to achieve the most effective notification. Specifically, the notification unit 133 sequentially executes each of the above selection processes and combines the results of each decision (notification destination, notification timing, notification method, notification format) to determine the final notification parameters. These parameters are structured as a notification object and transmitted via the communication unit 110. For example, when approaching an area boundary, a pop-up notification with sound is sent to the delivery terminal 200 in real time, visually displaying the area boundary and current location on a map. Meanwhile, the daily compliance report is sent to the administrator terminal 300 as a detailed report combining text and charts in a periodic batch process.

[0071] The notification unit 133 also manages the status of sending and receiving notifications. Specifically, it assigns a unique notification ID to each sent notification and records it in the notification history table of the analysis results storage unit 124 along with information such as the sending time, recipient, notification content, and importance level. The notification unit 133 also receives reception confirmation signals (such as read notifications) sent from the delivery terminal 200 and the administrator terminal 300 and performs processing to update the reception status of the corresponding notification ID. It tracks the arrival confirmation, opening confirmation, and response status of sent notifications, and if there are unread important notifications (notifications whose importance level is above a certain threshold and for which no reception confirmation has been made), it also performs follow-up actions such as sending reminder notifications based on the reminder conditions defined in the business rule storage unit 123 (for example, "High-importance notification: Reminder in 5 minutes"). Furthermore, if the number of notifications per unit time for the same delivery person ID exceeds a specific threshold (for example, 3 notifications in 10 minutes), notifications are prioritized and aggregated to prevent excessive frequency and volume of notifications. This includes suppressing or delaying lower-priority notifications based on the priority defined in the business rule storage unit 123.

[0072] <Support Department> The support unit 134 provides various functions to support delivery drivers in complying with their duties. Based on location and time information, the support unit 134 predicts potential problems that delivery drivers may face in their work (such as parking violation risks and insufficient rest), and provides information to the delivery driver terminal 200 to help avoid or mitigate these problems. The support unit 134 obtains analysis results data of compliance status from the analysis unit 132, and performs a process to determine whether support is needed by referring to the support conditions stored in the work rule storage unit 123.

[0073] One of the main functions of the support unit 134 is the parking assistance function. This function transmits information about legal parking spaces around the delivery terminal's current location to the delivery terminal when the delivery terminal's current location is close to a no-parking zone. Specifically, the support unit 134 compares the current location information linked to the delivery person identification information obtained from the analysis unit 132 with the no-parking zone data stored in the business rule storage unit 123 and calculates the distance between them. The distance calculation uses the Euclidean distance between the delivery person's position coordinates and the nearest point of contact on the no-parking zone boundary. By comparing the location information with the no-parking zone information stored in the business rule storage unit 123, the support unit 134 determines that parking assistance is necessary if the delivery person's current location is approaching a no-parking zone (e.g., within 100 meters) or has already entered a no-parking zone. The distance thresholds used as the criteria for this determination are defined in the business rule storage unit 123 for each regional characteristic and time of day (e.g., "city center: 100 meters", "suburbs: 200 meters"), and the support unit 134 refers to these to determine the need for assistance.

[0074] Next, the support unit 134 searches for legal parking spaces in the vicinity of the current location. In the search process, the support unit 134 refers to the parking space database stored in the business rule storage unit 123. This database stores structured information such as parking space ID, location information (latitude and longitude), space type (dedicated parking lot, public parking lot, temporary parking space, etc.), available hours, vehicle size capacity, and fee information. The sources of information for legal parking spaces include the following: The first source is a pre-registered database of parking spaces for delivery services. This includes information on parking lots with which delivery companies have usage agreements, as well as temporary parking spaces exclusively for unloading. The second source is a public parking lot database. This includes information on the location, fees, and available hours of public and private parking lots. The third source is a delivery history database. This includes historical information on places where delivery personnel were able to park safely during past deliveries.

[0075] The support unit 134 searches for legal parking spaces within a certain range from the current location (for example, a radius of 300 meters) and selects the optimal parking space considering factors such as distance to the destination, availability, and cost. Specifically, the support unit 134 calculates an evaluation score for each parking space. The evaluation score is calculated comprehensively by weighting factors such as distance from the current location (higher score for closer spaces), distance from the delivery destination (higher score for closer spaces), availability (higher score for more certain spaces), and cost (higher score for cheaper spaces). The weighting coefficients are stored in the business rule storage unit 123 and are adjusted according to regional characteristics and time of day. In particular, the system also considers maintaining delivery efficiency by prioritizing the recommendation of parking spaces closest to the delivery destination.

[0076] The selected parking space information is transmitted to the delivery terminal 200 via the notification unit 133. The transmitted information includes basic information such as the location of the parking space (address and latitude / longitude), directions from the current location (navigation information), available hours, fee information (if necessary), and vehicle size that can be accommodated, as well as real-time information such as congestion status and the number of available spaces. This information is passed to the notification unit 133 as structured data, and the notification unit 133 converts it into an appropriate notification format and transmits it to the delivery terminal 200. Notification formats include text format, map display format, and navigation-linked format, and the most suitable format is selected depending on the delivery person's status (whether they are driving or parked, etc.).

[0077] Various implementation methods are possible for the support unit 134 to provide parking space information. The first implementation method is text-based guidance. In this method, the support unit 134 structures the basic information of the selected parking space (name, address, distance, etc.) into text format and passes it to the notification unit 133. The text data is composed of a standard data format such as JSON format and is displayed in an easy-to-read format on the delivery terminal 200. This method provides the address, distance, and brief guidance of the nearest parking space in text format. The second implementation method is guidance using map display. In this method, the support unit 134 constructs the location information of the selected parking space and related information such as the current location, destination, and no-parking zones as a dataset for map display. The dataset includes parameters such as coordinate information for each element, display style (color, icon, transparency, etc.), and zoom level, and is transmitted to the delivery terminal 200 via the notification unit 133. This method visually displays the current location, destination, no-parking zones, recommended parking spaces, etc. on the map to promote intuitive understanding. The third implementation method is guidance through navigation integration. In this method, the support unit 134 encodes the location information of the selected parking space in a standard location information format (e.g., GeoJSON format) and generates a deep link URL for linking with the navigation app on the delivery terminal 200. This URL includes parameters such as destination coordinates, waypoints, and mode of transportation, allowing the delivery person to start detailed navigation with a single tap. This method automatically guides the delivery person to the recommended parking space in conjunction with the navigation app.

[0078] Furthermore, the support unit 134 is equipped with a learning function, enabling more effective parking assistance by accumulating and analyzing actual parking behavior data of delivery drivers. Specifically, the support unit 134 extracts parking patterns for each delivery driver from past location and time information stored in the delivery data storage unit 121. Pattern extraction analyzes factors such as the concentration of parking locations, the frequency of use of specific parking spaces, and the relationship between delivery destinations and parking locations. These analysis results are structured as parking behavior profiles linked to the delivery driver ID and stored in the business rule storage unit 123. For example, if a particular delivery driver frequently uses a specific parking space in a particular area, personalized assistance can be provided, such as recommending that parking space preferentially when delivering on a similar route.

[0079] Furthermore, the support unit 134 also responds to changes in parking regulations depending on the time of day and day of the week. The support unit 134 refers to the time-based parking regulation data stored in the business rule storage unit 123 and performs a process to compare it with the current date and time information. The time-based parking regulation data stores structured information such as the no-parking area ID, the time period during which the regulation applies (start time and end time), the days of the week on which the regulation applies, and exception days (holidays, etc.). It can also provide appropriate information according to the current date and time for time-specific no-parking areas (for example, no parking only in the morning and evening on weekdays) and regulations that differ depending on the day of the week (for example, parking is allowed on Sundays and holidays). This enables flexible parking support that can respond to regulations that change depending on the time of day.

[0080] Another key function of the support unit 134 is the rest support function. This function identifies the delivery person's current location and optimal rest points along the delivery route, and recommends them to the delivery person's terminal. The rest support function aims to ensure the safety and health of delivery people by supporting them in taking appropriate rests to comply with regulations and by improving the quality of their rest.

[0081] The support unit 134 first obtains continuous working time data linked to the delivery person identification information from the analysis unit 132. This data includes information such as the delivery start time, working time to date, detected break time, and actual continuous working time. The support unit 134 obtains the continuous working time information and evaluates the need for a break. The support unit 134 compares the actual continuous working time with the statutory upper limit time data stored in the business rule storage unit 123 (e.g., "truck driving: 4 hours", "delivery work: 6 hours") and calculates the remaining time. If the continuous working time reaches a predetermined threshold (e.g., 1 hour remaining against the statutory upper limit of 4 hours), it determines that break support is necessary. This threshold is defined in the support condition table stored in the business rule storage unit 123 and is adjusted based on factors such as statutory standards, company rules, the delivery person's past break patterns, and weather conditions.

[0082] Next, the support unit 134 identifies the optimal rest point, taking into account the delivery person's current location and the remaining delivery route. Specifically, the support unit 134 retrieves the day's delivery route data (delivery destination list, visit order, recommended route, etc.) linked to the delivery person ID from the area / route storage unit 122 and compares it with the current delivery progress. It also retrieves the rest facility database (rest facility ID, location information, facility type, available equipment, business hours, etc.) from the business rule storage unit 123. Potential rest points include the following types: The first type is a rest facility partnered with the delivery company (the company's own distribution center, partner convenience store, etc.). The second type is a public rest spot (roadside station, park, public toilet, etc.). The third type is a restaurant suitable for delivery people to rest (fast food restaurant, cafe, etc.).

[0083] The support unit 134 searches for potential rest points on or near the delivery route from the current location and selects the optimal rest point by considering multiple factors. In the search process, rest facilities located on the delivery route or within a specified range (e.g., within a radius of 500 meters) are extracted, and an evaluation score is calculated for each facility. Factors considered include distance from the current location, consistency with the remaining delivery route, type of facility (presence or absence of toilets, meals, rest spaces, etc.), business hours, congestion level, and past usage history. Weighting coefficients are used for these factors in calculating the evaluation score. The coefficients are stored in the business rule storage unit 123 and are adjusted according to the time of day and the delivery person's preferences. In particular, since delivery efficiency after the rest is also considered, rest points with the optimal positional relationship to the remaining delivery destinations are prioritized.

[0084] The selected rest stop information is transmitted to the delivery terminal 200 via the notification unit 133. The transmitted information includes basic information such as the location of the rest stop (address, latitude, and longitude), directions from the current location (navigation information), type of facility, available facilities (toilets, food and beverages, parking, etc.), and business hours, as well as real-time information such as congestion status and waiting time. This information is passed to the notification unit 133 as structured data (e.g., in JSON format), and the notification unit 133 converts it into an appropriate notification format and transmits it to the delivery terminal 200.

[0085] Various implementation methods are possible for the support unit 134 to provide rest point information. The first implementation method is text-based guidance. In this method, the support unit 134 converts the basic information of the selected rest points (name, address, facility type, available equipment, etc.) into a text-based data structure and passes it to the notification unit 133. The data structure also includes the priority and display order of the information, taking care to allow delivery personnel to quickly grasp important information. This method provides the name, address, distance, and equipment of recommended rest points in text format. The second implementation method is guidance using map display. In this method, the support unit 134 generates a dataset for map display that includes the location information of the selected rest points, the current location, the locations of remaining delivery destinations, and the delivery route. The dataset includes information such as coordinate information, display style, icon type, and label text for each element. This method visually displays the current location, remaining delivery destinations, and recommended rest points on a map to promote intuitive understanding. The third implementation method is the provision of time prediction information. In this method, the support unit 134 calculates the travel time to the rest point, the recommended rest time, and the estimated delivery completion time after the rest based on current traffic conditions and past travel patterns. The time prediction calculation also utilizes a prediction model learned from past delivery data stored in the delivery data storage unit 121, as well as congestion information obtained from external traffic information services. This method provides time information such as the travel time to the rest point, the recommended rest time, and the estimated delivery completion time after the rest, and supports the adjustment of the delivery plan.

[0086] Furthermore, the support unit 134 is equipped with a learning function, and by accumulating and analyzing actual rest behavior data of delivery drivers, it enables more effective rest support. Specifically, the support unit 134 extracts rest patterns for each delivery driver (preferred rest facility type, average rest time, rest frequency, etc.) from location and time information stored in the delivery data storage unit 121. This analysis result is structured as a rest behavior profile linked to the delivery driver ID and stored in the business rule storage unit 123. This makes it possible to provide personalized support, for example, by recommending that a particular rest point be given priority when a delivery driver uses a specific rest point in a particular area, when they are delivering on a similar route.

[0087] Furthermore, the support unit 134 can recommend breaks that take into account the health condition and fatigue level of the delivery person. The support unit 134 analyzes the day's delivery data (delivery start time, number of deliveries, elapsed time, etc.) stored in the delivery data storage unit 121, as well as environmental data (temperature, humidity, weather, etc.) acquired from external sources, and performs a process to evaluate fatigue risk. The fatigue risk evaluation model (relationship formula between continuous driving time and fatigue level, correspondence table between temperature and hydration needs, etc.) stored in the work rule storage unit 123 is used for the evaluation. For example, it is possible to respond flexibly by recommending earlier breaks when the continuous driving time is long or on days with sudden temperature changes. This not only meets legal standards but also realizes optimal break support that is tailored to the actual situation of the delivery person.

[0088] <Example of system operation> Next, an example of the operation of the delivery operations monitoring system 1 according to an embodiment of the present invention will be described. Figure 5 is a flowchart showing the basic operation flow of the system.

[0089] First, in step S101, location and time information is acquired by the GPS unit 250 of the delivery terminal 200 during the delivery process. The control unit 240 of the delivery terminal 200 controls the acquisition of location information automatically at a set frequency (for example, every 30 seconds). At the same time, time information (acquisition date and time) corresponding to each location is also acquired from the GPS unit 250 and temporarily stored in the storage unit 230.

[0090] In step S102, the control unit 240 of the delivery terminal 200 transmits the acquired location and time information to the server terminal 100 via the communication unit 210. Depending on the network connection status, the transmission may be performed in real time or in batch processing after a certain amount of data is stored in the storage unit 230. In areas with an unstable communication environment, the control unit 240 performs a process to transmit the data all at once when communication is restored.

[0091] In step S103, the information acquisition unit 131 of the server terminal 100 receives location information and time information transmitted from the delivery terminal 200 via the communication unit 110 and stores it in the delivery data storage unit 121. When storing the data, the information acquisition unit 131 performs a process to save it as structured data linked with related information such as delivery ID, delivery person ID, and delivery date and time.

[0092] In step S104, the analysis unit 132 of the server terminal 100 reads location and time information stored from the delivery data storage unit 121 and analyzes the delivery person's compliance with duties. The analysis is performed from multiple perspectives, including area monitoring, route monitoring, parking monitoring, rest management, and monitoring of non-work-related use. The analysis results are stored in the analysis result data storage unit 124.

[0093] In step S105, the notification unit 133 of the server terminal 100 reads the analysis results from the analysis result data storage unit 124 and generates a notification according to the compliance status. The notification content is appropriately configured according to the type, importance, and urgency of the compliance status.

[0094] In step S106, the notification unit 133 of the server terminal 100 sends the generated notification to the delivery terminal 200 or the administrator terminal 300 via the communication unit 110. The recipient of the notification is appropriately selected according to the content of the notification. For example, matters that the delivery person can handle themselves are notified to the delivery terminal 200, and matters that require administrative action are notified to the administrator terminal 300.

[0095] In step S107, the delivery terminal 200 or administrator terminal 300 displays the notification received via the communication unit 210 or 310 on the display operation unit 220 or 320. The notification is displayed in the most appropriate format depending on its content, such as text, icons, or a map display.

[0096] As step S108, the delivery person or manager takes appropriate action based on the displayed notification. For example, the delivery person may correct their route upon receiving a notification of approaching an area boundary, or move to a legal parking space upon receiving a notification of a parking violation risk. The manager may take action such as contacting the delivery person upon receiving a notification of a serious rule violation.

[0097] In addition to the basic flow described above, this system also executes various specific function flows. The processing flows for each major function are described below.

[0098] Figure 6 is a flowchart showing an example of area monitoring processing. In step S201, the analysis unit 132 obtains the current location information of the delivery person from the delivery data storage unit 121.

[0099] In step S202, area information is obtained from the area / route data storage unit 122.

[0100] In step S203, the analysis unit 132 analyzes the positional relationship between the delivery person's current location and the boundary of the assigned area, and determines whether the delivery person is inside the area, outside the area, or close to the area boundary. In step S204, conditional branching is performed based on the determination result. If the delivery person is already outside the area, the process proceeds to step S205; if they are close to the area boundary, the process proceeds to step S206; and if they are inside the area, the analysis unit 132 completes the process as a normal state and prepares for the next analysis cycle.

[0101] In step S205, the notification unit 133 generates a notification of detection of being outside the area and sends it to both the delivery terminal 200 and the administrator terminal 300. This notification includes the delivery person's current location, the distance to the area boundary, and directions for returning to the area.

[0102] In step S206, the notification unit 133 generates a notification of approaching the area boundary and sends it to the delivery terminal 200. This notification includes the distance to the area boundary, the location of the area boundary, and a warning message.

[0103] In step S203, the analysis unit 132 may calculate the distance between the delivery person's current location and the boundary of their assigned area and record this value. This record can be used for analyzing trends in approaching area boundaries and measuring the effectiveness of notifications.

[0104] Figure 7 is a flowchart showing an example of route monitoring processing. In step S301, the analysis unit 132 obtains time-series data of the delivery person's location information from the delivery data storage unit 121 and obtains the current location on the travel route.

[0105] In step S302, recommended route information is obtained from the area / route data storage unit 122.

[0106] In step S303, the analysis unit 132 compares the actual travel route of the delivery person with the recommended route and calculates the degree of deviation. As mentioned above, the method for calculating the degree of deviation uses indicators such as geographical distance, overlap rate, and difference in travel time.

[0107] In step S304, it is determined whether the calculated deviation exceeds a predetermined threshold. If the deviation exceeds the threshold, the process proceeds to step S305; otherwise, the analysis unit 132 completes the process as normal and prepares for the next analysis cycle. Even if no deviation is detected, the actual route information is recorded and used as data for future route optimization. .

[0108] In step S305, the notification unit 133 generates a route deviation notification and sends it to the delivery terminal 200. This notification includes the current location, the relationship to the recommended route, and instructions for returning to the recommended route. In step S305, if the deviation is particularly high or if the deviation continues for a certain period of time or longer, the notification unit 133 also sends a notification to the administrator terminal 300. This alerts the administrator when there is a high possibility of intentional deviation or an abnormal situation, rather than a simple difference in route selection.

[0109] Figure 8 is a flowchart showing an example of parking surveillance processing. In step S401, the analysis unit 132 obtains the delivery person's location information and time information from the delivery data storage unit 121.

[0110] In step S402, the analysis unit 132 detects the stationary state from the pattern of changes in location information and identifies the stationary position and stationary time.

[0111] In step S403, the analysis unit 132 obtains parking no-parking area information from the business rule data storage unit 123.

[0112] In step S404, the analysis unit 132 analyzes the positional relationship between the parking position and the no-parking zone and determines whether the parking position is within or close to the no-parking zone.

[0113] In step S405, a conditional branch is performed based on the judgment result. If the stopping position is within a no-parking zone, the process proceeds to step S406; if it is close to a no-parking zone, the process proceeds to step S408; and if it is unrelated to a no-parking zone, the analysis unit 132 completes the process as normal and prepares for the next analysis cycle. The stopping information is recorded and used as data for future parking pattern analysis.

[0114] In step S406, the analysis unit 132 compares the stopping time with a predetermined threshold. If the stopping time is short (for example, waiting at a traffic light or making a temporary stop), the risk of a parking violation is determined to be low. If the stopping time exceeds the threshold, the process proceeds to step S407. In step S407, the notification unit 133 generates a parking violation risk notification and sends it to the delivery terminal 200. This notification includes a warning that the current location is within a no-parking zone and a message urging the driver to move. The support unit 134 also cooperates to include information on nearby legal parking spaces in the notification.

[0115] In step S408, the support unit 134 obtains information on nearby no-parking zones and information on legal parking spaces around the current location. In step S409, the notification unit 133 generates a warning notification about approaching a no-parking zone and sends it to the delivery terminal 200. This notification includes the extent of the approaching no-parking zone and information on recommended legal parking spaces.

[0116] Figure 9 is a flowchart showing an example of the break management process. In step S501, the analysis unit 132 obtains the delivery person's location information and time information from the delivery data storage unit 121.

[0117] In step S502, the analysis unit 132 analyzes the elapsed time from the start of delivery and the pattern of location information to identify and calculate the continuous working time and rest time.

[0118] In step S503, the analysis unit 132 retrieves relevant rules, such as the legal upper limit for continuous operating time, from the business rule data storage unit 123.

[0119] In step S504, the analysis unit 132 compares the actual continuous operating time with the legal limit and calculates the remaining time.

[0120] In step S505, a conditional branch is performed based on the calculated remaining time. If the remaining time is particularly short (e.g., 15 minutes or less), the process proceeds to step S506; if it is somewhat short (e.g., 30 minutes or less), it proceeds to step S507; and if there is sufficient remaining time, the analysis unit 132 completes the process as normal and prepares for the next analysis cycle. Information on working hours and rest patterns is recorded and used as data for analyzing the work status of delivery drivers and optimizing future rest plans.

[0121] In step S506, the notification unit 133 generates an emergency rest reminder notification and sends it to both the delivery terminal 200 and the administrator terminal 300. This notification includes the remaining time until the legal limit is reached and a message emphasizing the urgency of the rest. In step S506, the support unit 134 identifies the nearest rest point from the current location and includes this information in the notification. This prompts the delivery person to take an immediate rest.

[0122] In step S507, the notification unit 133 generates a normal rest-prompt notification and sends it to the delivery terminal 200. This notification includes the remaining time until the legal limit and a message prompting the delivery person to take a rest. In step S507, the support unit 134 identifies the optimal rest point considering the current location and the remaining delivery route and includes this information in the notification.

[0123] Figure 10 is a flowchart showing an example of the process for monitoring non-work-related use. In step S601, the analysis unit 132 obtains the delivery person's location information and time information from the delivery data storage unit 121.

[0124] In step S602, the analysis unit 132 retrieves work time information (start and end times of work, break times, etc.) from the work rule data storage unit 123.

[0125] In step S603, the analysis unit 132 compares the time information with the business hours information to determine whether the current time is within or outside business hours. If it is outside business hours, the process proceeds to step S604. If it is within business hours, the analysis unit 132 completes the process as normal and prepares for the next analysis cycle.

[0126] In step S604, the analysis unit 132 detects the movement state from the pattern of change in position information and determines whether it is stopped or moving. If it is moving, the process proceeds to step S605; if it is stopped, the analysis unit 132 completes the process as a normal state and prepares for the next analysis cycle.

[0127] In step S605, the analysis unit 132 analyzes the movement pattern from the location information and evaluates the degree of agreement with the normal delivery route. If the movement pattern differs significantly from the normal delivery route, it is determined that there is a high possibility of non-work use, and the process proceeds to step S606. If the movement pattern is the same as or similar to the normal delivery route, it is determined that there is a high possibility of working outside of business hours, and the process proceeds to step S607.

[0128] In step S606, the notification unit 133 generates a notification of detection of non-work-related use and sends it to the administrator terminal 300. This notification includes information such as the fact that movement outside of business hours was detected, the current location, and the movement pattern.

[0129] In step S607, the notification unit 133 generates a notification of detection of work outside of business hours and sends it to the administrator terminal 300. This notification includes information such as the fact that movement outside of business hours was detected, the current location, and the movement pattern.

[0130] As described above, this system comprehensively analyzes the location and time information of delivery personnel and monitors various aspects of compliance with business operations. The integration of various analysis and notification functions contributes to improving the quality of delivery operations by promoting legal compliance, increasing operational efficiency, and ensuring safety.

[0131] <Variation> Although embodiments of the present invention have been described above, the present invention is not limited thereto, and various modifications and improvements are possible. The main modifications are described below.

[0132] Variation 1: Enhanced AI prediction function It is conceivable to add more advanced artificial intelligence (AI) functions to the analysis unit 132 of this embodiment. Specifically, this function utilizes machine learning algorithms to learn the behavior patterns of delivery drivers and predict future actions. In this function, the analysis unit 132 groups past location and time information stored in the delivery data storage unit 121 according to delivery driver identification information and performs a process to structure it as time-series data. A machine learning model (e.g., random forest, gradient boosting, recurrent neural network, etc.) is applied to the structured data to learn the behavior patterns of delivery drivers. The learned model is stored in the business rule storage unit 123 and updated periodically. In the prediction process, current location information, time information, delivery status, etc. are taken as input, and the model calculates future actions (e.g., predicted movement within the next 30 minutes). For example, from patterns of past location and time information, it is possible to predict in advance the possibility of a delivery driver going outside the area or committing a parking violation, and preventive notifications can be made at an earlier stage. The prediction results are assigned a probability value (for example, "Probability of leaving the area within the next 15 minutes: 85%"), and a preventative notification is generated when this probability exceeds a threshold (for example, 70%). It is also possible to learn the behavioral characteristics of each delivery person and provide notification timing and content optimized for each individual delivery person. In this case, the analysis unit 132 also learns the notification response history (reaction time to notifications, corresponding actions, etc.) for each delivery person and has the function to predict the most effective notification timing.

[0133] Variation 2: Integration with real-time traffic information It is conceivable to add a function to the system of this embodiment that allows for linkage with an external traffic information system. Specifically, this function would involve linking with an external traffic information API (such as Google Maps API or Yahoo! Maps API) via the communication unit 110 to acquire real-time traffic conditions (congestion, accidents, construction, etc.), and performing dynamic updates of recommended routes that take this into account, as well as realistic arrival time predictions. Traffic information data acquired from the external API (coordinates of congestion points, degree of congestion, location and impact area of ​​accidents / construction, etc.) is temporarily stored in the storage unit 120 as structured data. The analysis unit 132 comprehensively analyzes this traffic information data and the recommended route data stored in the area / route storage unit 122, and executes a process to dynamically generate a new recommended route that takes traffic conditions into account. The route generation algorithm uses an extended version of the shortest path algorithm, such as Dijkstra's algorithm, with weighting based on a congestion coefficient. This enables flexible route monitoring in response to changes in traffic conditions, allowing for appropriate evaluation of route changes made by delivery personnel to avoid congestion. In addition, the generated alternative route is analyzed together with the original recommended route and compared with the actual route selection of the delivery personnel. The analysis unit 132 dynamically adjusts the criteria to avoid detecting a route deviation if the delivery person's route selection is reasonable in accordance with traffic conditions. Furthermore, recommendations for rest stops and parking locations become more appropriate to the situation. This is achieved by the support unit 134 adding traffic information data as a consideration factor in its search and evaluation process for rest stops and parking spaces. For example, extended functions are provided such as prioritizing the recommendation of rest facilities located before congested areas to avoid congestion.

[0134] Variation 3: Augmented reality (AR) assistance function In this embodiment, it is conceivable to add a visual support function utilizing augmented reality (AR) technology to the delivery terminal 200. Specifically, this function utilizes an AR framework (ARKit, ARCore, etc.) implemented in the delivery terminal 200 and the camera function of the delivery terminal 200 to visually display area boundaries, no-parking zones, recommended parking spaces, optimal rest points, etc., overlaid on the actual scenery. In this function, the delivery terminal 200 recognizes the real space by combining video frames acquired from the camera with sensor data such as GPS, accelerometer, and gyroscope. The server terminal 100 calculates the information to be displayed (coordinate data of area boundaries, polygon data of no-parking zones, etc.) based on the location and orientation information of the delivery terminal 200 and sends it to the delivery terminal 200 as AR object data. The delivery terminal 200 receives this data and executes a process to display it overlaid on the camera image using the AR framework. As a result, the delivery person can intuitively obtain the necessary information without the burden of checking a map. In particular, in complex urban environments, AR display makes it easier to accurately determine location and is effective in supporting compliance with work requirements. For example, area boundaries are displayed as translucent walls along the ground, and no-parking zones are shown as red translucent areas. Recommended parking spaces and rest points are displayed as prominent icons, designed to allow for intuitive understanding of distance and direction. The level of detail in the AR display is automatically adjusted according to the delivery person's speed, and safety is prioritized by displaying only minimal information while driving.

[0135] Modification 4: Utilization of biometric information It is conceivable to add a function to the system of this embodiment that utilizes the biometric information of the delivery person. Specifically, the delivery person wears a wearable device (such as a smartwatch) via a wearable device API that is linked with the delivery person terminal 200, and biometric information such as heart rate, body temperature, and activity level is acquired. The acquired biometric data (time-series data of heart rate, body temperature value, steps taken, calorie consumption, etc.) is linked to the delivery person ID and stored in the delivery data storage unit 121. By analyzing this information in combination with location information and time information, it becomes possible to more accurately grasp the fatigue level and health status of the delivery person, promoting breaks at appropriate times and enabling early detection of health risks. The analysis unit 132 executes an algorithm that comprehensively processes biometric information and location / time information and calculates a fatigue index. This algorithm takes into account factors such as heart rate fluctuation patterns, rate of increase in body temperature, and the balance between activity level and rest time. The calculated fatigue index is expressed as a number from 0 to 100, and if this value exceeds a threshold (e.g., 70), a notification recommending a break is triggered. It is particularly effective in detecting the risk of heatstroke during the summer and in detecting overwork due to long working hours. When working in a hot environment, a heatstroke risk index is calculated by combining body temperature data with weather data (temperature, humidity, etc.) obtained from the outside. If this risk index reaches a dangerous level, a heatstroke prevention notification with a higher priority than a normal recommendation for a break is generated. In addition, when determining continuous working time, fatigue assessment based on biometric information is taken into account, making it possible to prompt breaks earlier than legal standards.

[0136] Modification 5: Integration with delivery vehicle information It is conceivable to add a function to the system of this embodiment to integrate information from delivery vehicles. Specifically, this function involves implementing an API to link a telematics device installed in a delivery vehicle with the server terminal 100, acquiring information such as the vehicle's location, speed, fuel consumption, and engine status from the telematics device installed in the delivery vehicle, and integrating and analyzing it with information from the delivery person's terminal. The acquired vehicle information data includes vehicle ID, location information (latitude and longitude), speed, rotation speed, fuel consumption, engine temperature, and various warning information (engine warning light, etc.), and this data is stored in the delivery data storage unit 121. During storage, a correspondence table between vehicle ID and delivery person ID is referenced and linked with the relevant delivery person data. The analysis unit 132 executes a location information consistency determination algorithm and calculates the difference between the delivery person's terminal's location information and the vehicle's location information. If this difference exceeds a threshold (e.g., 100 meters), it is detected as an inconsistency. This enables more comprehensive compliance monitoring by allowing for verification of the consistency between the delivery person's location and the vehicle's location, detection of speeding violations by comparing vehicle speed data with road speed limit data (stored in the business rule storage unit 123), detection of dangerous driving such as sudden acceleration and sudden braking by detecting rapid change patterns in acceleration sensor data, and monitoring of idling time by analyzing a combination of engine operating status data and vehicle movement status data. In addition, a fuel efficiency index is calculated by combining the vehicle's fuel consumption data and mileage data, and if this index drops significantly, it is possible to detect the need for vehicle maintenance or inefficient driving patterns. These analysis results are stored in the analysis results storage unit 124 and notified to the delivery person or manager at an appropriate time via the notification unit 133. Note that the above embodiment may be performed using the vehicle's location information and corresponding time information instead of the delivery person terminal 200's location information and corresponding time information.

[0137] 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. [Industrial applicability]

[0138] This invention can be widely used as a business monitoring system in various delivery operations, such as parcel delivery, food delivery, inter-company logistics, and postal delivery. In particular, in today's society where the volume of goods is increasing and both delivery efficiency and legal compliance are required, this invention is expected to be utilized as a technology with high social need. [Explanation of Symbols]

[0139] 1 System, 100 Server terminal, 110 Communication unit, 120 Storage unit, 121 Delivery data storage unit, 122 Area / route storage unit, 123 Business rule storage unit, 124 Analysis result storage unit, 130 Control unit, 131 Information acquisition unit, 132 Analysis unit, 133 Notification unit, 134 Support unit, 200 Delivery terminal, 210 Communication unit, 220 Display operation unit, 230 Storage unit, 240 Control unit, 250 GPS unit, 300 Administrator terminal, 310 Communication unit, 320 Display operation unit, 330 Storage unit, 340 Control unit, 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, An analysis unit analyzes the delivery person's compliance with duties based on the location information and time information, A notification unit that notifies the delivery person or manager based on the status of compliance with the aforementioned duties, A delivery operations monitoring system, including...

2. The delivery operation monitoring system according to claim 1, wherein the analysis unit compares the current location of the delivery person with a pre-set service area based on the location information and determines whether the delivery person is located outside the service area.

3. The delivery operation monitoring system according to claim 2, wherein the analysis unit calculates the distance between the delivery person's current location and the boundary of the assigned area, and sends an alert to the delivery person's terminal via the notification unit when the distance falls below a predetermined threshold.

4. The delivery operation monitoring system according to claim 1, wherein the analysis unit calculates the degree of deviation between the delivery person's travel path and a pre-set recommended route based on the location information, and provides notification via the notification unit when the degree of deviation exceeds a predetermined threshold.

5. The delivery operation monitoring system according to claim 1, wherein the analysis unit identifies the stopping position and stopping time of the delivery person based on the location information and the time information, and determines whether or not the stopping position is within a no-parking zone.

6. The delivery operation monitoring system according to claim 5, wherein the analysis unit determines that there is a risk of a parking violation when the stopping time exceeds a predetermined threshold and the stopping location is within a no-parking zone, and sends an alert to the delivery person terminal via the notification unit.

7. The delivery service monitoring system according to claim 5 or 6, further comprising a support unit that transmits information on legal parking spaces around the current location of the delivery terminal to the delivery terminal when the current location of the delivery terminal is close to a no-parking zone.

8. The delivery operation monitoring system according to claim 1, wherein the analysis unit calculates the continuous working time and rest time of the delivery person based on the location information and the time information, and sends an alert via the notification unit prompting the delivery person to take a break when the continuous working time approaches a predetermined legal limit.

9. The delivery operation monitoring system according to claim 8, further comprising a support unit that identifies the current location of the delivery person and the optimal rest point on the delivery route, and recommends it to the delivery person's terminal.

10. The delivery service monitoring system according to claim 1, wherein the analysis unit detects the movement of the delivery person terminal outside of pre-set business hours based on the location information and the time information, and determines the possibility of use outside of business hours.

11. The delivery operation monitoring system according to claim 10, wherein the analysis unit determines, based on the location information, whether the delivery person's movement pattern deviates from the planned delivery route by a predetermined threshold, and if such deviation is detected, notifies the administrator terminal via the notification unit.

12. The delivery operation monitoring system according to claim 10 or 11, wherein the analysis unit analyzes the relationship between the distance traveled calculated from the location information and the fuel consumption obtained from the delivery vehicle, and detects a fuel consumption anomaly if the relationship deviates from a preset normal range.

13. A step of obtaining location information and time information corresponding to the location information from the delivery terminal of the package delivery person, A step of analyzing the delivery person's compliance with duties based on the location information and time information, The steps include notifying the delivery person or manager based on the status of compliance with the aforementioned duties, Delivery operations monitoring methods, including

14. The step of analyzing the aforementioned compliance status is: Comparison of the delivery person's current location with their pre-assigned service area. Calculation of the degree of deviation between the delivery person's travel route and the pre-set recommended route. Identifying the delivery person's stopping location and duration, and determining whether or not it is within a no-parking zone. Calculation of continuous working hours and rest times for delivery drivers, Detection of delivery terminal movement outside of business hours. A delivery operation monitoring method according to claim 13, comprising at least one of the following.

15. On the computer, A process to obtain location information and time information corresponding to said location information from the delivery terminal of the package delivery person, A process to analyze the delivery person's compliance with duties based on the location information and time information, Based on the compliance status of the aforementioned duties, a process is performed to notify the delivery person or manager, A program that executes the command.

16. The process for analyzing compliance with the aforementioned business operations is: Comparing the delivery person's current location with their pre-assigned service area. Processing to calculate the degree of deviation between the delivery person's travel route and the pre-set recommended route. Identifying the delivery person's stopping location and duration, and determining whether or not it is within a no-parking zone. Calculation process for delivery drivers' continuous working hours and break times, and Processing to detect the movement of delivery personnel terminals outside of business hours. The program according to claim 15, comprising at least one of the following.

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