Description:Field of Invention
The present invention relates to artificial intelligence-driven postal service enhancement, i.e., automation and improvement of identifying delivery post offices from user-entered or ambiguous postal addresses. It uses fuzzy string matching, machine learning, and a web interface in the cloud to provide highly accurate and efficient address validation in the Indian postal system.
The Objectives of this Invention
The main goal of this invention is to create an AI-based Delivery Post Office Identification System that uses fuzzy string comparison and a dynamic weighted scoring algorithm to match wrong or unclear addresses with the best delivery post office. This will stop people from making mistakes when verifying addresses, which will cut down on the time it takes to deliver mail because of wrong-addressed or misdirected posts. It also supports safe user authentication and a simple web interface that makes it easy for both postal and end users to interact. Using well-maintained and regularly updated postal databases, the solution should be able to handle changes in the postal network, such as the creation of nodal centers and changes in PIN codes. The whole system is built as an extensible real-time web app using Flask and SQLite. It can do calculations quickly, is reliable, and can be easily added to existing postal systems.
Background of the Invention
India has the biggest postal network in the world, with over 165,000 post offices and almost 19,000 PIN codes. This huge network is very important for getting letters and packages to both cities and rural areas. The Indian postal system still uses manual address validation methods, but they face many problems. These include different ways that users enter addresses (like misspellings, abbreviations, and non-standard address formats), a lot of changes in delivery jurisdictions because of the opening of nodal delivery centers, and a lot of changes in PIN code assignments. These usually cause a difference between the delivery post office that was supposed to be used and the one that was actually used based on the address the user gave. This can cause delays, mail that is returned, or even complete delivery failure.
Also, the current manual or rule-based systems used by postal workers are not only slow, but they also can't handle the size and complexity of India's different address formats, which vary a lot from region to region, language to language, and city to country.These systems also have trouble adjusting to changes in the postal network that happen all the time.
Recent developments in artificial intelligence, especially natural language processing (NLP), fuzzy string matching, and machine learning, offer the potential for a solution. In other countries and industries, tests and deployments of AI-based postal logistics have shown that fuzzy logic (to find near string matches even with typos), weighted scoring algorithms (to rank important items like post office name or PIN code), and geospatial inference can all make addresses more accurate and cut down on delivery failures.
Most of these current AI systems, on the other hand, are limited in scope and are usually designed to work with Western address formats or city-only logistics. They don't have the linguistic, geographical, and infrastructural variety needed to handle Indian postal addresses. Also, they don't work with secure, scalable user platforms that could be used across the country.
This invention fills these important gaps by providing a specialized AI-based system for the Indian postal network. It combines fuzzy matching algorithms with a weighted similarity algorithm that is made to work with Indian address structures. It also adds a secure web-based interface made with Flask and SQLite, which lets postal workers and customers interact with the system in real time. Also, the database is set up so that it can be easily changed with the most recent PIN code updates and nodal center information. This makes it very flexible and requires little human input. The system makes postal operations in India more reliable, efficient, and scalable by automating address validation and making it easier to identify post offices.
Summary of the Invention
The purpose of this invention is to make a smart system that can take addresses from users and figure out which delivery post office is best for that address. The goal of these technologies is to give their users intelligent systems. You can use a scoring system based on weighted fuzzy logic to reach this goal. This is why it is possible. A normalized postal dataset is used to compare a lot of address fields, such as Office Name, District, State, and PIN Code, to name a few. This dataset is used to compare the addresses. This makes it possible to compare these address fields, which would not have been possible otherwise. Lastly, the results of this comparison are used to make similarity scores. After the last step is done, this is done.
Detailed Description of the Invention
The technology being described allows for real-time checking and matching of postal addresses. Artificial intelligence makes this possible. This information is relevant to the situation because it has to do with using the post office to send identification. The invention that was talked about before makes this situation possible. A user enters an address that may not be complete, spelled correctly, or clear through a secure online or mobile interface. There may be some information missing from this address. Also, this address may not be completely clear. There may be some information missing about this address. On top of that, this address might be missing some important identifying information. If the user decides to use this option, they will be able to give the address of the person who will be getting the delivery. A smart matching process that uses fuzzy logic is done on a database of Indian postal records that has been cleaned up and made standard in the past. The Indian postal service's records are kept in this database. This process takes place in the database. The Indian Postal Service has kept these records in this database since it started. Every time this operation is run, it runs against the whole database. This process starts right after the user's input in the address has been read. Then, it is sent to the back end, where it is finished from the point where it was first sent. An application has been created to help users find and suggest the best and most suitable delivery post office for their shipping needs. The idea behind making this app was to use it. The application is built on the information that the user gives, which is what makes it possible for this to happen. A new method that uses a weighted scoring system and a FuzzyWuzzy-based string similarity to figure out and rank possible delivery post office matches is used. This method was created to help reach the goals listed above. This method was created specifically to help reach this goal. This method is used to make sure that the tasks that were set out at the start are finished. This method was created while the procedure was going on so that the necessary calculations could be done. The goal of creating this strategy was to figure out how to calculate and sort potential matches so that the work that needed to be done to finish the project could be done. To get a score, a number of different parts of the address are looked at and taken into account. This is done to make sure that the results are correct. The most important parts of these features are the Office Name (50%), the District (20%), the State (15%), and the Pincode (15%). These features are made up of many different parts.
Real postal records are used as part of the system to carry out operations. To cut down on noise and duplication, these records have been cleaned up by removing duplicate and incorrect entries. Then, they have been made to fit with standard practices. After getting the records from official sources, this process has been done. The organization's premises are where this process takes place. The documents in question have gone through a thorough cleaning and standardization process. After SQLAlchemy organizes and manages the data, it is saved in a SQLite database after the cleaning process is done. This happens after the data has been cleaned up. This happens after the data has been cleaned. Following the conclusion of the process of cleaning the data, this phase is carried out. This phase happens right after the data cleaning process is over. It is planned that this action will happen soon after the cleaning is done. After getting an input address, the system will analyze it to find out which fields are most likely to contain important information. We will find out which fields have the most information during this study so that we can better understand the situation. After that, it uses fuzzy matching algorithms to compare those fields with the dataset. Then, it gives each field a similarity score and ranks the top five delivery post offices that are the best fits based on the overall score that was obtained from the comparison. This is done to find out which delivery post offices are the best fits. This is done to find out which delivery post offices are the best places to send the items. In the next step, the user sees the result for the first time since the process started. This goal is successfully achieved by using interface technology, which is the medium through which the outcome is shown.
The system design has a number of modules, including: a user authentication module that uses Flask-Login and bcrypt hashing to make signing up and logging in safe; a postal data management module that makes it easy to find and update postal data; an address parsing and normalization module that works with noisy and inconsistent input formats; and a fuzzy matching and scoring module that creates match rankings based on how similar the input is. To build the interface module, which can handle user-friendly interactions, templates made in Flask and Jinja are used. This lets the module support nice interactions. This is done to make sure that the module can handle the interactions that are going on. One good thing about these interactions is that they can give real-time feedback in the form of Flask Flash messages while they are happening. This information also includes situations where mistakes were made, as well as feedback on actions that were done correctly.
The flow of the system use case starts with the user account registration on the website at the beginning of the scenario. This is where the scenario begins. So, this could be the beginning of the scenario. This is the result that has happened because registering a user account is the first step in getting into the system. After the user has successfully registered, they will need to enter their postal address in order to use the free texting service. This activity is done to protect the use of the service so that it can be used in the future. The input is sent to the backend after the analysis is done. There, it is processed by using the fuzzy match method on the postal database that was saved locally. This happens after the analysis is done. This happens right after the analysis is over. The event in question occurs shortly after the completion of the research study. The user gets suggestions that come with metadata like the district, state, and similarity score that go along with the suggestions. The user gets these suggestions. It also gives the user suggestions. It also gives the user suggestions that are relevant to the topic at hand. As soon as the system has finished getting and rating the top five best matches, it will show the user these suggestions. The system is in charge of doing this. Another good thing about the system is that it lets users get suggestions from it. Adding this part to the system makes it work better. The method not only makes it easier to choose the right post office for delivery, but it also cuts down on the amount of manual work, lost packages, and inefficiencies that happen in the postal service while it is working. This is because the method does away with the need for the postal service to choose the right post office by hand. In other words, the method makes it easier to choose the right post office for delivery by making the process clearer. This is because the method gets rid of the need for the postal service to choose the right post office by hand, which was a requirement before. This is because the system makes it impossible for the postal service to be needed to make the decision. This is why this is the case. This is why things are the way they are right now.
One of the most important goals of the idea is to automate the process of checking names and addresses. The goal of this is to modernize and improve India's large-scale postal system. This is the reason that the idea was first thought of. The system can be used in many different types of infrastructure and still be available to a lot of people because it uses technologies that are both lightweight and scalable. The technologies it uses make this possible. This is because it uses technologies that are not only light but also can be used in more than one way. This can be done by using the methods that are available because it will still be available. This category includes a number of examples of technology. There are a few examples in this group. Flask, Python, and SQLite are some of these. The system has a number of features, such as the ability to handle mistakes, the fact that it was made to be flexible, and the fact that it is ready to be used all over the country. These are just a few of the things that make up the system. There are more. It also helps modernize dynamic postal infrastructure, which includes adding nodal centers and changing PIN numbers, among other things. It also helps with the improvement of dynamic postal infrastructure, which is a big plus. The program is how participants can get this help that is being given. Adding features like the ability to geolocate, the ability to parse input in different languages, and the ability to connect to India Post's backend systems for free mail routing and tracking could make it even better. These are all examples of features that could be added. These are all things that could be added to the product. All of these are examples of parts that could be added to the product. All of these examples show parts that could be added to the product.
Brief description of Drawing
Figure 1, System Architecture of Proposed method , Claims:The scope of the invention is defined by the following claims:
Claim:
1. A method/system for intelligent post office delivery identification with artificial intelligence and fuzzy logic address validation, the method/system involving the following steps:
a) The system is opened, and the user inputs a partial or complete address via a web-based application interface (1).
b) The address input is normalized and preprocessed by undergoing processes of normalization and then routed to the backend system where it is compared against a structured postal database in a database (2).
c) A weighted similarity score fuzzy string matching algorithm is used on the parsed input fields—Office Name, District, State, and Pincode—with weights assigned to determine top-ranked matches (3, 4).
d) The system presents the top five delivery post office matches with similarity scores via the user interface and recommends the most suitable post office based on highest scoring (5, 6).
e) The system provides secure login and session management, and the entire backend is hosted on a cloud-based platform to enable real-time address validation and recommendation (7, 8).
2. As stated in claim 1, the address input is made through a secure Flask-based web application and is captured and normalized to lowercase, stripping unwanted spaces and characters for proper parsing and matching.
3. According to claim 1, the backend utilizes a structured database of Indian postal records stored and handled using SQLAlchemy and SQLite, and the parsed input is matched by FuzzyWuzzy's Token Set Ratio scoring algorithm.
4. As per claim 1, the weighted similarity score is calculated with the following weighting: 50% weight to Office Name, 20% to District, 15% to State, and 15% to Pincode, and top five results are ranked and returned on the basis of total score.
5. According to claim 1, the system comprises user authentication modules, postal address parsing modules, fuzzy matching modules, ranking modules for results, and secure data handling modules, and is able to cope with dynamic shifts in postal facilities like nodal center realignment or PIN code changes.