DESC:FIELD
The present disclosure relates to bay allocation systems and methods.
BACKGROUND
In today’s world, allocation of bays for articles is done manually with minimum data analysis, which leads to congestion of the articles at the location of the bay. In conventionally bay allocation system, data is fed and analyzed manually by various human operators. For example, allocation of bay terminals for incoming and outgoing busses at a bus stand, allocation of bay terminals for loading petrol/diesel in trucks at a tanker loading station, and allocation of baggage counters for delivering baggage of passengers at an airport, is predicted and approved by human operators. Congestion of articles such as busses, trucks, baggage at the above mentioned respective bays occurs due to human errors in analyzing the data and therefore the allocation of bays for articles needs to be automated and systemized. Also in conventional bay allocation systems, a lot of time is required in manually allocating bays for forthcoming requirements. Further, a lot of resources such as workers, money and time gets wasted in decongesting the congested articles from the bay location.
Hence, there is a need for an automated bay allocation system and method that: (i) reduces human involvement during allocation of bays to minimize errors (ii) reduces time taken and cost for decongesting congested articles from the location of bays and (iii) can be used in various industry with minimal customization.
OBJECTS
Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows:
It is an object of the present disclosure to ameliorate one or more problems of the prior art or to at least provide a useful alternative.
An object of the present disclosure is to provide an automated system and method that increases the efficiency of bay allotment.
Yet another object of the present disclosure is to provide a bay allocation system and method that reduces human effort.
Another object of the present disclosure is to provide an automated bay allocation system and method that increases time-efficiency and thereby enhances is cost-effectiveness.
Still another object of the present disclosure is to provide an automated bay allocation system and method that is industry agnostic.
Yet another object of the present disclosure is to provide an automated bay allocation system and method that is easy to understand and operate.
Other objects and advantages of the present disclosure will be more apparent from the following description when read in conjunction with the accompanying drawing, which are not intended to limit the scope of the present disclosure.
SUMMARY
The present disclosure envisages an automated bay allocation system and method for allocating a bay from a plurality of bays to an article. The system comprises a processor, a rules repository, a first data repository, an authentication engine, a plurality of sensors, a second data repository, and an analytics component.
The processor is configured to receive pre-determined set of rules from a rules repository and further configured to generate system processing commands sequentially. The first data repository is configured to store pre-determined information related to identity of the article. The authentication engine is configured to authenticate the article before letting it enter the area of the plurality of bays. The plurality of sensors are located at the plurality of bays and are configured to continuously sense parameters associated with the plurality of bays, capture real-time data from occupied bays, and capture data associated with the article. The sensors are further configured to generate and transmit signals corresponding to the sensed parameters and captured data. The second data repository is configured to store pre-determined information related to requirements of the article. The analytics component is in communication with the sensors and the second data repository. The analytics component receives the generated signals from the sensors and extracts the requirement information of the article from the second data repository. The analytics component is further configured to analyze the received signals and information to optimally allocate a bay for the article.
In an embodiment, the authentication engine includes a first crawler and extractor set which is configured to crawl over the first data repository and selectively extract information related to the identity of the article, and a comparator which is configured to compare information of the article provided at the entry of the plurality of bays with the pre-determined information of the article extracted from the first data repository.
In another embodiment, the analytics component includes: (i) a second crawler and extractor set which is configured to crawl over the second data repository and selectively extract information related to the requirement of the article, (ii) a third data repository which is configured to store pre-determined set of data including rules, guidelines, examples of best practices, and historical data of allocation of bays to articles, and (iii) an analytics engine configured to analyze the received signals from the sensors and requirement information from the second data repository in accordance with pre-determined data stored in the third data repository to optimally allocate a bay to the article.
In yet another embodiment, the automated bay allocation system includes a plurality of display units located strategically to cover the entire area of the plurality of bays and is configured to display the allocation information of the article.
In still another embodiment, the automated bay allocation system includes an updater/editor which is configured to continually update or edit the first data repository, the second data repository and the third data repository during the learning phase of the system.
In yet another embodiment, the authentication of the article by the authentication engine is achieved by at least one of the following: identity card, biometric identification, in person verification via Skype or video conferencing, pins and password tied to mobile phone, photos, body prints, swipe patterns, and other state of the art authentication systems as available at the time.
In still another embodiment, at least one of the sensors is selected from the group of infra-red sensors, cameras, image capturing sensors, and proximity sensors.
In another embodiment, the plurality of bays is a part of a tanker loading station or a bus stand.
BRIEF DESCRIPTION
An automated bay allocation system and method of the present disclosure will now be described with the help of an accompanying drawing, in which:
Fig. 1 illustrates a block diagram of the automated bay allocation system depicting the structure of the system, in accordance with an embodiment of the present disclosure; and
Fig. 2 illustrates a block diagram of the automated bay allocation method of the system disclosed in Fig. 1.

LIST OF REFERENCE NUMERALS
100 Automated bay allocation system
105 Rules Repository
110 Processor
115 Authentication Engine
117 Comparator
120 First Crawler and Extractor Set
125 First Data Repository
130 Plurality of Sensors
135 Analytics Component
140 Analytics Engine
142 Third Data Repository
145 Second Data Repository
150 Second Crawler and Extractor Set
155 User Data Point
160 Plurality of Display Units
180 Updater/Editor
DETAILED DESCRIPTION
An automated bay allocation system and method in accordance with an embodiment of the present disclosure will now be described with reference to the embodiments, which do not limit the scope and ambit of the disclosure. The present disclosure discloses an automated bay allocation system and method that facilitates allocation of a bay from a plurality of bays to an article(s) by sensing some parameters associated with the article(s) and each of the bays and analyzing the data captured through sensors. The term “Article” used in the present disclosure can be an animate or an inanimate object.
Fig.1 illustrates a block diagram of an automated bay allocation system 100 depicting the structure of the system 100 comprising, a rules repository 105, a processor 110, an authentication engine 115, a first crawler and extractor set 120, a first data repository 125, a plurality of sensors 130, an analytics component 135 that contains an analytics engine 140 and a second crawler and extractor set 150, a second data repository 145, at least one user data point/data points 155, a plurality of display units 160, and an updater/editor 180. The bay allocation system 100, hereinafter, will also be referred as the system 100. The dotted lines used in the figure 1 represent the flow of information or data. The solid lines used in the figure 1 represent flow of instructions.
The processor 110 of the system 100 is configured to receive pre-determined set of rules from the rules repository 105 and is further configured to generate system processing commands sequentially that guides the functioning of the system 100. Authentication of an article, under system processing commands, is facilitated by the authentication engine 115 before a bay is allocated to the article. The authentication engine 115 is configured to verify the legitimacy and legality of the article entering an area that comprises the plurality of bays. An identity of the article is provided at the entry by means of an authentication identity card, biometric identification, in person verification via Skype, pins and password tied to mobile phone number, photos, body prints, swipe pattern and other state of the art authentication systems as may be available at the time. The authentication engine 115 includes the first crawler and extractor set 120 which is configured to, under the system processing commands generated by the processor 110, crawl over the first data repository 125 and selectively extract the information related to the identity of the article. A comparator 117 is provided in the authentication engine 115, which is configured to compare information of the article provided at the entry of the plurality of bays with the pre-determined information of the article extracted from the first data repository 125. If the identity/identities of the article provided at the entry matches with the pre-determined identity/identities extracted from the first data repository 125, then under the system processing commands generated by the processor 110, the article is allowed to enter the area that comprises the plurality of bays and if they do not match then the system 100 generates and displays an alert to users monitoring the system 100 so that an appropriate action can be taken.
Take an example of a tanker loading station that comprises a plurality of bay terminals, wherein each of the plurality of bay terminals has at least one tanker filled with petrol/diesel. A truck that enters the compound of the tanker loading station is firstly authenticated by the authentication engine 115 at an entry gate before being allocated with a bay terminal. The authentication of the truck can be done by matching information associated with the truck such as the number plate, the name and phone number of the driver driving the truck, GPS tracking device, an in-built authentication chip provided on the truck and the like, with the information stored in the memory/ data repository of the system 100.
Take another example of a bus stand that comprises a plurality of bays, commonly known as bus terminals, wherein each of the bus terminals has a capacity of accommodating at least one bus. Busses that enter the compound of the bus stand are firstly authenticated by the authentication engine 115 at an entry gate before being allocated with a bus terminal. The authentication of the bus can be done by matching information associated with the bus such as the number plate, the name and phone number of the driver driving the bus, GPS tracking device, an in-built authentication chip provided on the bus and the like, with the information stored in the memory/ data repository of the system 100.
In one embodiment, new articles are permitted to register their identities before the entry and the identification data stored in the first data repository 125 can be edited and updated too after the initial registration. The updater/editor 180 of the system 100, under the system processing commands generated by the processor 110, updates/edits the data stored in the first data repository 125 in accordance with the data associated with the article, received initially in the training phase and subsequently in the dynamic learning phase of the system 100 of this disclosure.
After the authentication of the article is successful, the data generated by the plurality of sensors 130 that are located at different bays is analyzed. The plurality of sensors 130 are configured to continuously sense various parameters associated with each of the plurality of bays such as the capacity of bays, sensing the presence and absence of an authenticated article in a bay, time taken for an article to enter an allotted bay and exit the allotted bay and the like. In one embodiment, the plurality of sensors 130 may be selected from the group of infra-red sensors, cameras, image capturing sensors, proximity sensors, and the like. The above mentioned sensors are configured to capture data such as real time data available from occupied bays, article position status data available at respective time and a combination thereof. The plurality of sensors 130 is further configured to transmit the generated data to the analytics component 135 of the system 100. The analytics component 135 comprises the second crawler and extractor set 150 which is configured to, under system processing commands, crawl over the second data repository 145 and selectively extract the data related to the requirement of the article(s) which is pre-fed in the second data repository 145 of the system 100. In one embodiment, the data related to the requirement of the article(s) can be manually stored in the second data repository 145 during the authentication of the article.
The data extracted from the second data repository 145 and the data generated by the plurality of sensors 130 are transmitted to the analytics engine 140. The analytics engine 140 is configured to receive a pre-determined set of data such as rules, guidelines, examples of best practices, historical data, and the like from a third data repository 142 constituted within the analytics component 135. The analytics engine 140 analyzes the generated data received from the plurality of sensors 130 and the pre-fed data received from the second data repository 145 in accordance with the rules, guidelines, examples of best practices, and historical data that is stored in the third data repository 142 and is configured to optimally allocate a bay for the article(s). The allocation information is then transmitted to and displayed on the plurality of display units 160 that are located strategically to cover the entire bay area. The allocation information is also transmitted to and displayed on the at least one user data point/data points 155 which is operated by a manual operator to monitor the computed data. In one embodiment, the manual operator may edit the computed data via the at least one used data point 155. In another embodiment, the first data repository 125, the second data repository 145 and the third data repository 142 may be fed with pre-determined information during the training phase of the system 100 and then may be continuously updated, under system processing commands, by the updater/editor 180 configured in the system 100, during the dynamic learning phase of the system 100.
Take an example of a tanker loading station (terminal) that comprises a plurality of bays, wherein each of the plurality of bays has at least one tank filled with a fuel (petrol/diesel) to facilitate loading of the fuel (petrol/diesel) in a truck. The data related to the truck such as the storage capacity of the truck, the type of fuel to be loaded in the truck, successful authentication of the truck, the amount of fuel requested to be loaded in the truck, client/customer information, and the like, is extracted from the second data repository 145 where the data was pre-fed either by a server that connects the client/customer interfacing unit with that of the service provider or by an operator at the entry gate during the authentication of the truck. The plurality of sensors 130 provided at each of the bay is configured to simultaneously analyze the capacity of the bays, capacity of the tank provided at each of the bay, level of fuel (diesel/petrol) available inside the tanks, the presence and absence of authenticated trucks in the bays, time at which trucks entered the allotted bay and exited the allotted bay, time at which the loading of fuel in a storage unit of a truck was started and finished, and the like. All the above mentioned data related to the bays and the incoming truck is transmitted to the analytics engine 140 of the analytics component 135, where the analytics engine 140 is configured to analyze the data in accordance with the rules, guidelines, historical data associated with past practices. The analytics engine 140 is further configured to compute the parameters including, but not limited to, estimated time taken by a truck to be loaded at a particular bay, speed of loading the storage unit of the truck, speed at which the tank is being unloaded, real-time data of level of fuel present inside the tank, and after computing all this data, the analytics engine 140 predicts which bay from the plurality of bays is to be allotted to the incoming truck and the time that the truck has to wait before entering the bay, and the like.
Take another example of a bus stand that comprises a plurality of bays, commonly known as bus terminals, wherein each of the bus terminals has a capacity of accommodating at least one bus. The data related to the bus such as the time at which the bus has to leave the bus stand, storage capacity of the bus, successful authentication of the bus, the previous and future bus stops, the amount of fuel requested to be loaded in the bus while the bus is located at the bus stand, and the like, is extracted from the second data repository 145 where the data was pre-fed or was fed at the entry gate during the authentication of the bus. The plurality of sensors 130 provided at each of the bus terminals simultaneously analyze the capacity of the terminals, the presence and absence of an authenticated bus in the bays, time at which the bus enters the allotted bay and exits the allotted bay, time taken to load a bus with passengers, and the like. All the above mentioned data related to the bus terminals and the bus is transmitted to the analytics engine 140 of the analytics component 135, where the analytics engine 140 analyzes the data in accordance with the rules, guidelines, historical data associated with past practices. The analytics engine 140 is configured to compute the parameters including, but not limited to, estimated time taken by a bus to be loaded at a particular bay, real-time data of number of passengers seated inside the bus, and after computing all this data, the analytics engine 140 is configured to predict which bus terminal from the plurality of bus terminals is to be allotted to the incoming bus, the time that the bus has to wait before entering the bay, and the like. In an alternative embodiment, the automated bay allocation system 100 can be successfully implemented at an arrival terminal of the airport where baggage counters are allotted for each flight to deliver baggage of passengers who travelled in the respective flight. In yet another embodiment, the automated bay allocation system 100 can be successfully implemented at a bank where cash counters are allotted to customers/clients who visit the bank to withdraw cash.
Figure 2 illustrates the automated bay allocation method 200 for allocating a bay to an article, of the present disclosure by the means of a block diagram.
Block 202 depict a processor that receives predetermined set of rules from a rules repository and generates system processing commands sequentially that commands the actions of all the components of the system 100.
Block 204 depicts storing pre-determined information related to identity of the article.
Block 206 depicts authenticating, under system processing commands, the article before letting the article enter the area of the plurality of bays.
Block 208 depicts sensing parameters associated with the plurality of bays, capturing real-time data from occupied bays, and capturing data associated with the article.
Block 210 depicts generating and transmitting signals corresponding to the sensed parameters and captured data.
Block 212 depicts storing pre-determined information related to requirements of the article.
Block 214 depicts providing an analytics component (135) configured to receiving the generated signals and extracting requirement information of the article.
Block 216 depicts analyzing the received signals and information to optimally allocate a bay for the article.
Hence, the system 100 fulfils the need for an automated bay allocation system that: (i) reduces human involvement during allocation of bays to minimize errors (ii) reduces time taken and cost for decongesting congested articles from the location of bays and (iii) can be used in various industry with minimal customization.
TECHNICAL ADVANCES AND ECONOMICAL SIGNIFICANCE
The automated bay allotment system, in accordance with the present disclosure described herein above has several technical and/or economic advantages including but not limited to the realization of an system that:
? increases the efficiency of bay allotment;
? reduces human effort;
? increases time-efficiency and thereby enhances is cost-effectiveness;
? is industry agnostic; and
? is easy to understand and operate.
Throughout this specification the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps.
The use of the expression “at least” or “at least one” suggests the use of one or more elements or mixtures or quantities, as the use may be in the embodiment of the disclosure to achieve one or more of the desired objects or results.
Any discussion of documents, acts, materials, devices, articles or the like that has been included in this specification is solely for the purpose of providing a context for the disclosure. It is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the disclosure, as it existed anywhere before the priority date of this application. The numerical value mentioned for the various physical parameters, dimensions or quantities are only approximations and it is envisaged that the values higher/lower than the numerical values assigned to the parameters, dimensions or quantities fall within the scope of the invention, unless there is a statement in the specification specific to the contrary.
While considerable emphasis has been placed herein on the specific features of the preferred embodiment, it will be appreciated that many additional features can be added and that many changes can be made in the preferred embodiment without departing from the principles of the invention. These and other changes in the preferred embodiment of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the invention and not as a limitation.
The embodiment herein, the various features, and advantageous details thereof are explained with reference to the non-limiting embodiments in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced, and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

,CLAIMS:1. An automated bay allocation system (100) for allocating a bay from a plurality of bays to an article, said system (100) comprising:
• a processor (110) configured to receive pre-determined set of rules from a rules repository (105) and further configured to generate system processing commands sequentially;
• a first data repository (125) configured to store pre-determined information related to identity of said article;
• an authentication engine (115), under system processing commands, configured to authenticate said article before letting said article enter the area of said plurality of bays;
• a plurality of sensors (130) located at said plurality of bays, said sensors (130) configured to continuously sense parameters associated with said plurality of bays, capture real-time data from occupied bays, and capture data associated with said article, said sensors (130) are further configured to generate and transmit signals corresponding to said sensed parameters and captured data;
• a second data repository (145) configured to store pre-determined information related to requirements of said article; and
• an analytics component (135) configured to receive said generated signals from said sensors (130) and extract requirement information of said article from said second data repository (145), said analytics component (135) further configured to analyze said received signals and information to optimally allocate a bay for said article.
2. The automated bay allocation system (100) as claimed in claim 1, wherein said authentication engine (115) comprises:
• a first crawler and extractor set (120) configured to crawl over said first data repository (125) and selectively extract information related to the identity of said article; and
• a comparator (117) configured to compare information of said article provided at the entry of said plurality of bays with the pre-determined information of said article extracted from said first data repository (125).
3. The automated bay allocation system (100) as claimed in claim 1, wherein said analytics component (135) comprises:
• a second crawler and extractor set (150) configured to crawl over said second data repository (145) and selectively extract information related to the requirement of said article;
• a third data repository (142) configured to store pre-determined set of data including rules, guidelines, examples of best practices, and historical data of allocation of bays to articles;
• an analytics engine (140) configured to analyze said received signals from said sensors (130) and requirement information from said second data repository (145) in accordance with pre-determined set of data stored in said third data repository (142) to optimally allocate a bay for said article.
4. The automated bay allocation system (100) as claimed in claim 1, wherein the system (100) further comprises a plurality of display units (160) located strategically to cover the entire area of said plurality of bays and configured to display the allocation information of said article.
5. The automated bay allocation system (100) as claimed in claims 1 and 3, wherein the system (100) further comprises an updater/editor (180) configured to continually update or edit said first data repository (125), said second data repository (145) and said third data repository (142) during the learning phase of said system (100).
6. The automated bay allocation system (100) as claimed in claim 1, wherein authentication of said article by said authentication engine (115) is achieved by at least one of the following: identity card, biometric identification, in person verification via Skype or video conferencing, pins and password tied to mobile phone, photos, body prints, swipe patterns, and other state of the art authentication systems as available at the time.
7. The automated bay allocation system (100) as claimed in claim 1, wherein at least one of said sensors (130) is selected from the group of infra-red sensors, cameras, image capturing sensors, and proximity sensors.
8. The automated bay allocation system (100) as claimed in claim 1, wherein said plurality of bays is a part of a tanker loading station or a bus stand.
9. An automated bay allocation method (200) for allocating a bay from a plurality of bays to an article, said method (100) comprising:
• providing a processor (110);
• configuring said processor (110) to receive pre-determined set of rules from a rules repository (105) and further configured to generate system processing commands sequentially;
• storing pre-determined information related to identity of said article;
• authenticating, under system processing commands, said article before letting said article enter the area of said plurality of bays;
• sensing parameters associated with said plurality of bays, capturing real-time data from occupied bays, and capturing data associated with said article;
• generating and transmitting signals corresponding to said sensed parameters and captured data;
• storing pre-determined information related to requirements of said article;
• providing an analytics component (135) configured to receiving said generated signals and extracting requirement information of said article; and
• analyzing said received signals and information to optimally allocate a bay for said article.