Parcel thermodynamic diagram generation and risk prompting system and method

Abstract

The invention provides a parcel thermodynamic diagram generation and risk prompting system and method, and the system comprises an order information library which is used for recording order information, marking a high-risk address, and forming a high-risk label; the order information at least comprises a user address; the coordinate library is used for converting the user address into latitude and longitude coordinates through geocoding; the thermodynamic diagram module is used for combining the order information with latitude and longitude coordinates and carrying out visual grid thermodynamic diagram presentation on a map; and the risk prompting module is used for screening out coordinates with high-risk labels, counting grids of the coordinates, combining adjacent grids to form a high-risk fence, and dynamically displaying the high-risk fence on the grid thermodynamic diagram.

Description

Technical Field

[0001] The present invention relates to the field of logistics technology, and in particular to a package heat map generation and risk warning system and method. Background Art

[0002] Logistics and supply chain management are the cornerstones of modern economic operations, providing essential support for production and consumption activities across countries. With the rapid development of e-commerce, cross-border and cross-regional logistics demand has exploded, posing unprecedented challenges to the responsiveness and resource allocation flexibility of traditional logistics networks.

[0003] To meet the ever-expanding global cargo flow, logistics companies must continuously optimize internal decision-making, strengthen internal data analysis, achieve optimal resource allocation, and improve warehouse site selection, personnel transfers and other links to effectively improve cross-regional distribution efficiency and reduce operating costs.

[0004] In South Africa's logistics and distribution system, regional division mostly relies on manual methods. Dispatchers or product managers usually manually identify and create "delivery fences" based on past experience and intuitive judgment to determine the delivery area and resource allocation plan.

[0005] This approach is different from the existing logistics system in China, which is often set up with methods such as postal codes, cities or administrative area lists. South Africa does not have a provincial and municipal system, and it is necessary to collect and analyze data on the local three-level geographical units of City, Suburb, and Postcode. The existing system can parse the three-level geographical unit information in South Africa. However, it lacks data-driven dynamic adjustment capabilities, and adjustments based solely on manual experience often deviate from the actual situation. It is difficult to cope with real-time changes in order distribution, and it is easy to have an over- or under-allocation of drivers. At the same time, different carriers (such as self-operated and third-party) often perform tasks according to pre-set static delivery areas, and cannot flexibly optimize quickly according to order density or business type - this not only leads to a waste of resources, but also makes it difficult to ensure consistency in service levels, affecting delivery timeliness and customer satisfaction.

[0006] Furthermore, due to the unique environment in South Africa, delivery drivers face significant safety risks during their operations. For example, when delivering packages to remote, poorly managed communities or in the evening when light is low, relying solely on manual experience to identify high-risk areas is often inaccurate and often lacks information, placing drivers in potentially dangerous situations. This can put drivers at risk of robbery or physical injury. If a driver encounters a robbery or dispute, in addition to the workers' compensation for personal injury, they can also face significant customer complaints and claims costs, increasing legal risks and severely impacting the company's reputation and operational stability.

[0007] In the delivery execution process, many companies, due to cost or scale considerations, outsource some areas they could operate in-house to third-party logistics providers (3PLs). While this approach can alleviate the short-term problem of insufficient in-house network coverage, it poses two major challenges in the long term: first, outsourcing costs are generally higher than in-house dispatch; second, the quality of third-party services varies widely, making unified management and control difficult, leading to unpredictable risks such as delivery delays and increased lost items. Research shows that the selection and management strategies of 3PL providers are directly related to overall supply chain performance, and traditional quality control methods based primarily on contracts and manual inspections are no longer able to meet the growing demand for data transparency.

[0008] In summary, existing technologies cannot meet the urgent needs of logistics companies in South Africa for delivery security, cost controllability, and timeliness. Solving these problems is a key concern for those skilled in the art. Summary of the Invention

[0009] The purpose of the present invention is to propose a package heat map generation and risk warning system and method, which parses the data in the order information database and reorganizes it into a visual heat grid map, improves the efficiency of cargo distribution, and provides intelligent management.

[0010] To achieve the above objectives, the present invention provides a package heat map generation and risk warning system, comprising:

[0011] An order information database, used to record order information and mark high-risk addresses to form high-risk tags; the order information at least includes the user address;

[0012] Coordinate library, used to convert user addresses into latitude and longitude coordinates through geocoding;

[0013] A heat map module is used to combine the order information with the latitude and longitude coordinates to present a visual grid heat map on the map;

[0014] The risk warning module is used to filter out coordinates with high-risk labels, count their grids and merge adjacent grids to form a high-risk fence, which is dynamically displayed on the grid heat map.

[0015] In an optional solution, the heat map module supports multi-level and multi-business type switching views.

[0016] In an optional solution, the system further includes an early warning module for determining whether the coordinates fall within the high-risk fence after a new order is entered into the order information database. If so, a high-risk early warning mark is automatically added to the new order.

[0017] In an optional solution, the system further includes an electronic fence module for displaying a self-operated or third-party delivered electronic fence layer on a map, and supports overlaying the electronic fence layer on the grid heat map.

[0018] The present invention also provides a package heat map generation and risk warning method, comprising:

[0019] Call the geocoding interface to convert the user address into longitude and latitude coordinates and present the coordinates on the map;

[0020] At any map zoom level, the current viewport is divided into rectangular grid cells of equal size;

[0021] For each grid cell, count the number of coordinate points contained in it or the sum of the weights of each point, and present a visual grid heat map on the map;

[0022] Based on the existing order information, high-risk addresses are marked to form high-risk labels; the coordinates with high-risk labels are screened out, their grids are counted and adjacent grids are merged to form a high-risk fence, which is dynamically displayed on the grid heat map.

[0023] In an optional solution, for a new order, it is determined whether the coordinate point falls within the high-risk fence. If it does, a high-risk warning mark is automatically added to the new order, and a high-risk reminder is sent to the corresponding assigned driver.

[0024] Among the optional options, the visual grid heat map presentation includes: users obtain the required package distribution information based on business scenarios, supporting three search dimensions: "time range", "business type" and "geographic level".

[0025] Among the options, dynamic scheduling of the distribution network is performed based on real-time heat maps.

[0026] In the optional solution, based on the generated high-risk heat map, in multi-site pickup and delivery tasks, the path planning engine automatically marks high-risk areas as avoidance zones and gives higher cost weights when calculating the optimal path, balancing the shortest path and the lowest risk.

[0027] In the optional solution, K-Means clustering or heuristic center point calculation is performed on several densely clustered high-heat grid clusters to determine candidate new warehouse locations;

[0028] Combining the existing warehouse locations and coverage radius model, the warehouse network optimization benefit is calculated for each candidate point, and the optimal location plan for new delivery warehouses is automatically output.

[0029] The beneficial effects of the present invention are:

[0030] The present invention accurately analyzes the data in the order information database and reorganizes it into a visual thermal grid map, thereby improving the efficiency of cargo distribution and providing intelligent management. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings, in which like reference numerals generally represent like components.

[0032] Figure 1 This is a flow chart of a method for generating a heat map and providing risk warnings in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The advantages and features of the present invention will become more apparent from the following description and drawings. However, it should be noted that the technical solutions of the present invention can be implemented in a variety of different forms and are not limited to the specific embodiments described herein. The drawings are highly simplified and not to exact scale, and are intended solely to facilitate and clearly illustrate the embodiments of the present invention.

[0034] It should be understood that when an element or layer is referred to as being "on," "adjacent to," "connected to," or "coupled to" another element or layer, it can be directly on, adjacent to, connected to, or coupled to the other element or layer, or there can be intervening elements or layers. Conversely, when an element is referred to as being "directly on," "directly adjacent to," "directly connected to," or "directly coupled to" another element or layer, there are no intervening elements or layers. It should be understood that although the terms first, second, third, etc. may be used to describe various elements, components, regions, layers, and / or parts, these elements, components, regions, layers, and / or parts should not be limited by these terms. These terms are merely used to distinguish one element, component, region, layer, or part from another element, component, region, layer, or part. Thus, a first element, component, region, layer, or part discussed below may be represented as a second element, component, region, layer, or part without departing from the teachings of the present invention.

[0035] Spatially relative terms such as "under," "beneath," "below," "under," "above," "above," etc., may be used herein for convenience of description to describe the relationship of one element or feature shown in the figures to other elements or features. It should be understood that the spatially relative terms are intended to include different orientations of the device in use and operation in addition to the orientations shown in the figures. For example, if the device in the drawings is flipped, then the elements or features described as "under" or "beneath" or "beneath" the other elements will be oriented as "over" the other elements or features. Thus, the exemplary terms "under" and "under" may include both the upper and lower orientations. The device may be oriented otherwise (rotated 90 degrees or in other orientations) and the spatial descriptors used herein are interpreted accordingly.

[0036] The purpose of the terms used herein is only to describe specific embodiments and is not intended to limit the present invention. When used herein, the singular forms "a", "an", and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "comprising" and / or "comprising", when used in this specification, determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0037] Data management and visualization are crucial in cross-border logistics. Logistics processes are complex and span multiple regions and countries. Accurate and real-time data capture at every stage is crucial for optimizing logistics operations. Effective data management can integrate and cleanse the massive amounts of data generated by each process, eliminating information silos and ensuring data flow and collaboration across business modules. Furthermore, data visualization can intuitively present key information such as logistics trajectory, customs clearance status, and warehouse utilization, helping business personnel monitor logistics operations in real time, identify potential issues, and make quick decisions.

[0038] Furthermore, data management is crucial for improving the customer experience. By analyzing and mining logistics data, companies can optimize delivery routes, shorten delivery times, and provide customers with accurate package status updates, thereby enhancing customer satisfaction. In the face of global competition, data-driven decision-making has become a key means for cross-border logistics companies to build core competitiveness.

[0039] Example 1

[0040] This embodiment provides a package heat map generation and risk warning system, including:

[0041] An order information database, used to record order information and mark high-risk addresses to form high-risk tags; the order information at least includes the user address;

[0042] Coordinate library, used to convert user addresses into latitude and longitude coordinates through geocoding;

[0043] A heat map module is used to combine the order information with the latitude and longitude coordinates to present a visual grid heat map on the map;

[0044] The risk warning module is used to filter out coordinates with high-risk labels, count their grids and merge adjacent grids to form a high-risk fence, which is dynamically displayed on the grid heat map.

[0045] Specifically, the order information database records standardized and parsed order information (including user address, package quantity, after-sales service, order time, order source, and other historical order data). It also labels addresses with a history of security incidents or high complaints as high-risk. The coordinate database converts user order addresses into longitude and latitude coordinates through geocoding (using the Google Maps API), continuously updating them incrementally to ensure timely receipt of new orders.

[0046] The heat map module maps package delivery data into an N×N grid heat map, supporting multi-level (City, Suburb, Postcode) and multi-business type (collection, delivery, high-risk / remote) switching views, greatly enriching the dimensions of data insight. On-demand retrieval is designed to provide the heat map system with flexible, multi-dimensional data filtering capabilities, allowing users to accurately obtain the required package distribution information based on business scenarios. This module combines interface controls with back-end interfaces to support the three core retrieval dimensions of "time range", "business type" and "geographic level", meeting a variety of needs from macro trend analysis to micro regional tracking.

[0047] Query by time range: The system uses a date selector component, allowing users to freely select an analysis period between any start and end dates within the past three years. The interface responds quickly and supports switching between daily, weekly, and monthly granularity levels. After the search is completed, the heat map and statistics panel are dynamically refreshed, allowing users to intuitively compare the changing trends of package density in different time periods.

[0048] Filter by business type: A multiple-selection box or drop-down list is provided in the sidebar of the interface, containing predefined layer labels such as "Pickup", "Delivery", and "High-risk Area". Users can flexibly overlay or exclude different business scenarios based on operational needs; the backend filters the corresponding data in the order information database based on the label field, and realizes real-time filtering and aggregation of massive data through conditional statements and parallel computing technology.

[0049] Query by geographic level: The system supports switching queries among three levels of geographic units: City (equivalent to cities in China), Suburb (equivalent to districts in China), and Postcode (equivalent to zip codes in China). Cascading drop-down menus or map pick controls allow users to quickly locate the target area. The backend uses spatial indexes and multi-level administrative division tables to implement spatial filtering and statistics of geographic units of different granularities, ensuring retrieval efficiency and accuracy. The query results not only update the heat map, but also automatically adjust the map field of view and zoom level to highlight the detailed distribution characteristics of the selected area, and support drill-down and rollback operations.

[0050] This embodiment also includes an early warning module, which is used to determine whether the coordinates fall into the high-risk fence after a new order is entered into the order information database. If it does, a high-risk early warning mark is automatically added to the new order, and a high-risk reminder is sent to the corresponding assigned driver.

[0051] This embodiment also includes an electronic fence module for displaying self-operated or third-party delivery electronic fence layers on the map, and supports overlaying the electronic fence layers on the grid heat map. Interactive comparative analysis can be achieved through transparency adjustment.

[0052] This embodiment provides a comprehensive solution from data collection and grid-based statistics to dynamic thermal visualization, integrating safety alerts. By comprehensively analyzing historical complaints, robberies, and delays, the system dynamically identifies high-risk grid areas in the heat map. Drivers can also see safety alerts when viewing orders, reducing personal and property risks during delivery.

[0053] By overlaying self-operated or third-party delivery electronic fence layers on the heat map and adjusting the transparency to compare the package density distribution in outsourced and self-operated areas, managers are provided with a more intuitive means of outsourcing quality assessment.

[0054] It supports dynamic filtering of heat maps by time range, order source (international, local) and business type; it also has an intelligent recommendation function that can flexibly adjust warehouse and driver resource allocation according to real-time popularity changes.

[0055] Example 2

[0056] Reference Figure 1 This embodiment provides a method for generating a package heat map and providing a risk warning, including:

[0057] Call the geocoding interface to convert the user address into longitude and latitude coordinates and present the coordinates on the map;

[0058] At any map zoom level, the current viewport is divided into rectangular grid cells of equal size;

[0059] For each grid cell, count the number of coordinate points contained in it or the sum of the weights of each point, and present a visual grid heat map on the map;

[0060] Based on the existing order information, high-risk addresses are marked to form high-risk labels; the coordinates with high-risk labels are screened out, their grids are counted and adjacent grids are merged to form a high-risk fence, which is dynamically displayed on the grid heat map.

[0061] Specifically, the method of this embodiment can be based on the system of embodiment 1.

[0062] Call the geocoding interface to accurately project the longitude and latitude coordinates stored in the coordinate library onto the map. Display each coordinate point as a discrete marker on the front-end map component to ensure accurate location visualization.

[0063] At any map zoom level, the current viewport is divided into rectangular cells of equal size according to the preset N×N grid parameters. For each grid cell, the number of coordinate points contained within it or the sum of the weights of each point is counted to quantify the density of parcels within the area. Each row of grid statistics (Row) is normalized to eliminate visual deviations caused by differences in the total amount of different rows and ensure balanced heat map rendering.

[0064] Based on the normalized values, each grid cell is mapped to a corresponding color gradient (e.g., from cool to warm), visually displaying the density of packages. Multiple layers are supported, including geo-fences (both self-operated and outsourced), and transparency can be adjusted to enable interactive comparative analysis.

[0065] Based on the existing order information, high-risk addresses are marked to form high-risk labels; the coordinates with high-risk labels are screened out, their grids are counted and adjacent grids are merged to form a high-risk fence, which is dynamically displayed on the grid heat map.

[0066] For new orders, the system checks whether the coordinates fall within the high-risk fence. If so, a high-risk warning mark is automatically added to the new order, and a high-risk alert is sent to the assigned driver. Safety alerts are sent to the driver's app in real time via the Firebase CloudMessaging push service.

[0067] Based on the generated high-risk heat map, in multi-site pickup and delivery tasks, the path planning engine automatically marks high-risk areas as avoidance zones and gives higher cost weights when calculating the optimal path, balancing the shortest path with the lowest risk to minimize drivers from passing through dangerous sections of the road and avoiding dangerous areas as much as possible.

[0068] Based on the real-time generated heat map, K-Means clustering or heuristic center point calculation is performed on several densely clustered high-heat grid clusters to determine candidate new warehouse locations; combined with the existing warehouse locations and coverage radius model, the warehouse network optimization benefit is calculated for each candidate point, and the optimal new delivery warehouse site selection plan is automatically output to minimize distribution costs and improve overall timeliness.

[0069] The above two embodiments achieve full-chain optimization from driver safety to operational decision-making through technical means such as real-time risk mapping and early warning, intelligent warehouse site selection, dynamic resource scheduling, multi-dimensional visualization and open interfaces. Compared with traditional purely manual methods, the system has shown significant results in reducing accident rates, reducing costs, improving delivery timeliness and customer experience, and supporting cross-regional expansion, providing logistics companies with a powerful data-driven tool. It has the following beneficial effects:

[0070] 1. Improve delivery safety

[0071] Risk mapping and alerting functions effectively prevent drivers from entering high-risk areas unprepared, reducing operational risks for drivers and helping companies shift from "after-the-fact remediation" to "preemptive prevention," effectively reducing the incidence of safety incidents. Through continuously updated database maps, management can continuously monitor safety conditions within the distribution network and quickly intervene when necessary, significantly reducing customer complaints and claims costs during driver operations.

[0072] 2. Reduce operating costs

[0073] The intelligent warehouse location selection function, based on heat map hotspot clustering and coverage radius models, generates precise recommendations for new warehouse layouts, maximizing the location within the calculated address perimeter. Combined with digitally driven route planning and optimization, the system helps drivers select optimal delivery and pickup routes, reducing empty loads and fuel consumption, further reducing delivery costs.

[0074] Re-classify remote areas previously reserved for third-party logistics (3PL) delivery and expand self-operated networks. While reducing reliance on third-party logistics (3PLs), companies can integrate more orders into their own networks, reducing outsourcing costs while improving control over delivery quality.

[0075] 3. Optimize delivery time

[0076] Dynamic scheduling of the distribution network based on real-time heat maps can more rationally allocate drivers and warehouse resources, avoiding delays caused by driver allocation imbalances during peak order periods. According to industry practices, dynamic scheduling can shorten average delivery time by 10%–20%.

[0077] In addition, the system supports data linkage with multiple systems such as inventory and CRM to achieve automatic data synchronization, eliminating the need for manual secondary data updates, thereby further improving overall delivery efficiency and response speed.

[0078] 4. Improve customer satisfaction

[0079] If the delivery of a package is affected by a security issue, the driver will report it on the interface and a communication channel will be established. Customer service can proactively contact the customer in a timely manner to discuss subsequent compensation matters, enhancing information transparency and trust, thereby effectively reducing customer anxiety and complaints caused by missing packages and improving user experience and satisfaction.

[0080] 5. Support decision-making and expansion

[0081] The system can have built-in visual reports and open API interfaces, allowing management to quickly identify distribution bottlenecks in different regions, formulate precise network expansion and improvement strategies based on data, and achieve unified cross-regional management and control.

[0082] The above description is only a description of the preferred embodiments of the present invention and does not limit the scope of the present invention. Any changes and modifications made by ordinary technicians in the field of the present invention based on the above disclosure shall fall within the scope of protection of the claims.

Claims

1. A package heat map generation and risk warning system, characterized by: include: The order information database is used to record order information and mark high-risk addresses to form high-risk labels; The order information at least includes the user address; Coordinate library, used to convert user addresses into latitude and longitude coordinates through geocoding; A heat map module is used to combine the order information with the latitude and longitude coordinates to present a visual grid heat map on the map; The risk warning module is used to filter out coordinates with high-risk labels, count their grids and merge adjacent grids to form a high-risk fence, which is dynamically displayed on the grid heat map.

2. The package heat map generation and risk warning system according to claim 1, characterized in that: The heat map module supports multi-level and multi-business type switching views.

3. The package heat map generation and risk warning system according to claim 1, characterized in that: The system also includes an early warning module for determining whether the coordinates of a new order fall within the high-risk fence after a new order is entered into the order information database. If so, a high-risk early warning mark is automatically added to the new order.

4. The package heat map generation and risk warning system according to claim 1, characterized in that: The system also includes an electronic fence module for displaying self-operated or third-party delivery electronic fence layers on the map, and supports overlaying the electronic fence layers on the grid heat map.

5. A package heat map generation and risk warning method, characterized in that: include: Call the geocoding interface to convert the user address into longitude and latitude coordinates and present the coordinates on the map; At any map zoom level, the current viewport is divided into rectangular grid cells of equal size; For each grid cell, count the number of coordinate points contained in it or the sum of the weights of each point, and present a visual grid heat map on the map; Based on the existing order information, high-risk addresses are marked to form high-risk labels; The coordinates with high-risk labels are filtered out, their grids are counted and adjacent grids are merged to form a high-risk fence, which is dynamically displayed on the grid heat map.

6. The package heat map generation and risk warning method according to claim 5, characterized in that: For new orders, it is determined whether the coordinate point falls within the high-risk fence. If it does, a high-risk warning mark is automatically added to the new order, and a high-risk reminder is sent to the corresponding assigned driver.

7. The package heat map generation and risk warning method according to claim 5, characterized in that: The grid heat map visualization includes: Users can obtain the required package distribution information based on their business scenarios, supporting three search dimensions: "time range", "business type" and "geographic level".

8. The package heat map generation and risk warning method according to claim 5, characterized in that: Dynamic scheduling of the distribution network is carried out based on real-time heat maps.

9. The package heat map generation and risk warning method according to claim 5, characterized in that: Based on the generated high-risk heat map, in multi-site pickup and delivery tasks, the path planning engine automatically marks high-risk areas as avoidance zones and gives higher cost weights when calculating the optimal path, balancing the shortest path and the lowest risk.

10. The package heat map generation and risk warning method according to claim 5, characterized in that: Perform K-Means clustering or heuristic center point calculation on several densely clustered high-heat grid clusters to determine candidate new warehouse locations; Combining the existing warehouse locations and coverage radius model, the warehouse network optimization benefit is calculated for each candidate point, and the optimal location plan for new delivery warehouses is automatically output.

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