Description:FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
(SEE SECTION 10, RULE 13)

A SYSTEM AND METHOD FOR IDENTIFICATION OF FUEL CONSUMPTION WITH REFILING PATTERN
BY
TOR.AI LIMITED
303-303A, 403-403A, B Junction, Survey No. 1/2, Next to Kothrud Post office, near Karve Statue, Kothrud, Pune, Maharashtra 411038
[An Indian Company]

THE FOLLOWING SPECIFICATION PARTICULARLY DESCRIBES THE INVENTION AND THE MANNER IN WHICH IT IS TO BE PERFORMED

TITLE
A System And Method For Identifications of Fuel Consumption and Refilling Pattern
BACKGROUND
A. Technical Field:
[001] The present disclosure relates to a system and method for identification of fuel consumption and fuel refilling pattern for a construction equipment, and more particularly, the present invention relates to the system and method for monitoring fuel level of construction equipment and compute the total fuel consumed along with fuel refilling to the equipment in a day.
B. Background Art:
[002] Determination of fuel level in a fuel tank of a construction equipment or heavy vehicles is a need of hour. Various types of sensors such as capacitive sensos are fitted within the vehicle fuel tank to measure the fuel levels at different intervals which provides consumption of fuel by the vehicle.
[003] In prior art, one method is disclosed for fuel level identification that comprises using at least one hardware processor to receive data comprising discrete fuel levels in a fuel tank supplying an internal combustion engine, and fuel injection rates for the internal combustion engine; integrate the fuel injection rates over a traversed distance to produce a fuel consumption series; cluster the discrete fuel levels into clusters over the traversed distance to produce a fuel level series; synchronize the fuel consumption series and the fuel level series into a model by mapping sub-ranges of the fuel consumption series to corresponding bins in the fuel level series; and generate sub-resolution measurements of fuel levels, between the discrete fuel levels, based on the model. The method further comprises using the at least one hardware processor to calculate dispersion characteristics in the fuel level series according to one or more clustering parameters; and detect one or more refill events based on the dispersion characteristics.
[004] Another method of identifying fuel level consumption is an IoT-based system for safeguarding fuel in motor vehicles. The system consists of a control device installed on the fuel tank lid that allows remote control of the fuel tank lid opening and shutting. The control device consists of a microcontroller unit linked to a number of IoT sensors, as well as a GSM module for data transfer between the authorized user and the control device. The control device additionally includes a microcontroller unit that can perform a variety of tasks, as well as a motor that is integrated with the microcontroller unit and can lock or unlock the fuel tank lid in response to a signal from an authorized user. The user's mobile device is loaded with an application that monitors and tracks the actions in the fuel tank. The measured fuel data is stored and analysed on a cloud-based database server.
[005] However, the methods of the prior art for fuel level identification and fuel refilling pattern are not accurate as the data received from the sensor or other similar hardware is not consistent. Further, there are chances of manipulation of the data received from the fuel tank.
[006] Hence, there is a need to provide a system and method for fuel level identification and refilling pattern in a construction equipment which overcomes the abovementioned drawbacks.
[007] For the reasons stated above, which will become apparent to those skilled in the art upon reading and understanding the specification, there is a need in the art for a system and method for the systema and method for identification of fuel consumption and refilling pattern which is useable, scalable and independent of new technology platforms, uses minimum resources that is easy and cost effectively maintained and can be deployed anywhere in very little time.
[008] Proposed invention overcomes these lacunae by proposing a unique system and methodology implemented thereof for identification of the fuel consumption and refilling pattern of the construction equipment as detailed hereinafter.
SUMMARY
[009] In an implementation a method for detecting fuel consumption and fuel refill for construction equipment. The method comprises receiving 101 raw data associated with fuel in a fuel tank of the construction equipment. The raw data is received at a receiving module from an IoT sensor mounted within the fuel tank, or is communicably connected to a fuel sensor. Analysing 102 the raw data to detect if the raw data is clean or has error, or any missing data. The method further comprises removing 103 the missing data from the raw data by the processor, wherein the removing of the missing data enables obtaining a clean data.
[0010] Further removing 104, noisy data from the clean data using SDM to obtain a smoothen fuel level time series data. In accordance with the implementation a noise removing module communicatively coupled to the receiver module and the processor is configured to employ the SDM to obtain the smoothen fuel level time series data. Receiving 105, the smoothen fuel level time series data from the noise removing module, or the IoT sensor. Further checking 106, fuel data pattern by extracting relevant data from the smoothen fuel level time series data. Generating 107, fuel refill time series data from the relevant data extracted from the smoothen fuel level time series data.
[0011] The method further comprises generating 108, fuel merge series data, by merging fuel refill time series data. Obtaining 109, fuel refill in liters from the fuel merge series data. Further generating 110 fuel consumption data.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to reference like features and modules.
[0013] Figure 1 shows a flowchart of a method for identification of fuel consumption and refilling pattern, in accordance with the present invention;
[0014] Figure 2 shows flowchart of noise removal method from the fuel level data, in accordance with the present invention;
[0015] Figure 3 and 4 shows graphs showing the difference between raw fuel data and the smoothened fuel data for a User Data;
[0016] Figure 5 and 6 shows graphs showing the difference between raw fuel data and the smoothened fuel data for another User Data;
[0017] Figure 7 and 8 shows graphs showing the difference between raw fuel data and the smoothened fuel data for another User Data;
[0018] Figure 9, shows a flow chart illustrating generating a fuel refill time series data in accordance with the present invention;
[0019] Figure 10, shows a flow chart illustrating generating a fuel merge refill series data in accordance with the present invention; and
[0020] Figure 11, shows a flow chart illustrating generating fuel consumption data in accordance with the present invention.
[0021] It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present invention. Similarly, it will be appreciated that any flowcharts, flow diagrams, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.

DETAILED DESCRIPTION OF THE INVENTION

[0022] The embodiments herein provide a method and system for identification of fuel consumption and refilling pattern in a construction equipment.
[0023] The systems and methods described herein are explained using examples with specific details for better understanding. However, the disclosed embodiments can be worked on by a person skilled in the art without the use of these specific details.
[0024] Throughout this application, with respect to all reasonable derivatives of such terms, and unless otherwise specified (and/or unless the particular context clearly dictates otherwise), each usage of:
“a” or “an” is meant to read as “at least one.”
“the” is meant to be read as “the at least one.”
References in the specification to “one embodiment” or “an embodiment” mean that a particular feature, structure, characteristic, or function described in connection with the embodiment is included in at least one embodiment. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.
[0025] Hereinafter, embodiments will be described in detail. For clarity of the description, known constructions and functions will be omitted.
[0026] According to an embodiment, the present invention provides a system for identification of fuel consumption and fuel refilling pattern for the construction equipment.
[0027] In an implementation according to one of the embodiments of the present invention, the system for identification of fuel consumption and refilling pattern from remote sensing fuel data of the construction equipment comprises a processor and IoT Sensor communicatively coupled to the processor. In preferred implementation, the IoT sensor(s) is configured within the fuel tank. The system further comprises a receiver module for receiving frequency-based fuel level data along with datetime from the construction equipment which is configured to communicate with the processor. Specifically, the system provides the comparison of fuel level data received from the construction device with the time duration. The system furthermore comprises a computation module configured for computing fuel consumed in a day with fuel refill for any construction equipment.
[0028] The system also comprises noise removing module communicatively coupled to the receiver module and the processor. Also, the processor of the system for identification of fuel consumption and refilling pattern from remote sensing fuel data of the construction equipment is configured to store and maintain records of the fuel level data.
[0029] Specifically, the noise removing module remove the noise of the received fuel level data by smoothening of the fuel level data. The difference between two consecutives smoothen fuel level values are compared with the predefined threshold limits and refill values are computed.
Specifically, the system computes fuel consumption based on the refilling of the fuel quantity in a day. In an embodiment, smoothing of the fuel level helps to identify pattern of fuel withing the time interval data using a smoothening data method (SDM) performed by the noise removing module using a processor.
[0030] Specifically, smoothening data method (SDM) excludes fuel level fluctuations from the received fuel data and showcase the fuel level pattern more visible. In an implementation of the present invention, at the first step, the SDM method reads raw fuel level values and place them in order first. The SDM method uses an optimum threshold bucket size. Further, the SDM method at second step, divides ordered data into buckets of the fuel data series. Furthermore, the SDM method comprises adjusting the bucket by "relocating forward"; that is, ignoring the first number of the fuel data series and entering the next value in the bucket. By these method steps the fuel data segregated into buckets. Specifically, for each bucket smoothen value is calculated.
[0031] The advantage of the SDM method is all unwanted outliers are eliminated from the fuel data and clean data is available. This outlier free fuel data is more accurate to identify the drop which compares the difference between two consecutive fuel level values with time difference. Once predetermined filter conditions are satisfied, the drop is generated.
[0032] The system further includes an analytical module configured to perform comparison of two consecutive fuel levels i.e. the current fuel level is compared with prior fuel level and the satisfied fuel level stored in the refilling process array. This computes fuel consumption based on the refilling of the fuel quantity in a day.
[0033] In an implementation according to one of the embodiments of the present invention, the present invention provides a method for identification of fuel consumption and refilling pattern for a construction equipment.
[0034] At first step, the method comprises receiving by a processor a frequency-based fuel level data along with datetime from a construction equipment. In an embodiment, the frequency-based fuel level data is provided by an IoT sensor configured within the fuel tank.
[0035] At second step, the method comprises removing noise from the received fuel data, by a noise removing module configured within the processor. Specifically, the removal of noise smoothens fuel level data which helps to identify the pattern of fuel consumption withing the time interval data.
[0036] At third step, the method comprises comparing current fuel level with prior fuel level and the satisfied fuel level is stored in the refilling process array, by analytical module configured within the processor.
[0037] The method of present invention monitors the fuel level of construction equipment and compute the total fuel consumed along with fuel refilling of the device in a day. The method also highlights the removal of noise in the data. The smoothen data is used to compute the fuel refill and fuel consumption process.
[0038] Figure 1 shows a flowchart of a method for identification of fuel consumption and refilling pattern in accordance with the present invention.
[0039] Further, the figure 2 shows flowchart of noise removal method from the fuel level data, in accordance with the present invention. In an implementation, the noise removal is carried out using smoothening data method.
[0040] Figure 3 and 4 shows graphs showing the difference between raw fuel data and the smoothened fuel data. The smoothening of the fuel level data helps to identify the pattern of fuel within the time interval data. Generally, it is difficult to recognize the fuel refill events in fluctuated data. Smaller uptrends may be captured in refill event if abnormality is available in the fuel refill data. Hence, Smoothened data is useful to avoid abnormal data and assists to find the refill patterns easily.
[0041] Part List
- a processor
- IoT Sensor mounted on a construction equipment communicatively coupled to the processor
- a receiver module for receiving time series fuel level data from the construction equipment which is configured to communicate with the processor
- a communication means
- noise removing module communicatively coupled to the receiver module and the processor.
[0042] In accordance with the exemplary embodiment Figure 1 to Figure 2, and Figure 9 to Figure 11, a method for detecting a fuel theft is disclosed. The method as disclosed comprises, at step 101, capturing or receiving raw data associated with fuel in a fuel tank of the construction equipment. The raw data is received at a receiving module from an IoT sensor mounted on or within the fuel tank, or in proximity to fuel tank. In another aspect the IoT sensor, may be communicably connected to a fuel sensor.
[0043] Further the IoT sensor may be configured to send the raw data to the processor via the communication means. The processor may be placed at a remote location. Further the processor may be coupled to a memory module. The memory module may be volatile memory or non-volatile memory.
[0044] At the processor, analysing the raw data at step 102 to detect if the raw data is clean or has error, or any missing data. Further at step 103, removing the missing data from the raw data by the processor. The removing of the missing data enables obtaining a clean data. At step 104, removing noisy data from the clean data using SDM to obtain a smoothen fuel level time series data. Further a noise removing module communicatively coupled to the receiver module and the processor is configured to employ the SDM to obtain the smoothen fuel level time series data.
[0045] Further in accordance with the exemplary embodiment, removing noisy data from the clean data comprises, reading the time series data at step 201. At step 202, arranging the raw data received by ascending data time. Removing the noise in the raw data and find the stability of fuel level parameter by using smoothening data method (SDM) at step 203.
[0046] At step 204, defining a bucket or set. The bucket or the set may be configured to comprise No. of Observations/ Data Points in Raw Data (n) that are deducted from a Threshold Bucket Size with Minimum Data Points (s), and further incremented with 1. In an equation form:
Bucket or Set = n – s + 1
[0047] Further 206, dividing the data set from the received raw data, and the smoothed data. At 207, checking the length of the backet or the set. In case the bucket size is not odd, at step 208, the smoothen value is obtained via the following equation:
Sk = [ D [s/2]th Value + D [s/2 + 1]th Value ] /2
[0048] Further in case the bucket size is odd, at step 209, the smoothen value is obtained via the following equation:
Sk = D [s/2]th Value
[0049] Further at step 210, computing by the processor the smoothen fuel level time series data after SDM.
[0050] At step 105, receiving the smoothen fuel level time series data from the noise removing module, or the IoT sensor. Further at step 106, checking fuel data pattern by extracting relevant data from the smoothen fuel level time series data.
[0051] In accordance with the exemplary embodiment, at step 107, generating fuel refill time series data from the relevant data extracted from the smoothen fuel level time series data. The generating fuel refill time series data further comprise at step 901, maintaining the fuel refill series, refill start flag, refill sum, and down trend counter. At step 902, reading fuel level from smoothen fuel level series data from ith to nth location. Further in an aspect of the present invention, at step 903, computing the difference of two consecutive fuel level values at ith and (i+1)th location from the smoothen fuel level series.
[0052] Further the method as disclosed comprises detecting fuel difference to be more than Zero (0), at step 904. At step 906, upon detecting fuel difference is more than zero, setting refill start flag as False. Further at step 907, checking the fuel refill start session in case the fuel difference is less than zero. At step 905, checking if Refill Start Flag is set. Further at step 908, setting refill start flag as true. In case the at step 905, if refill start flag is set, then at step 908, rising up Down Trend Counter by one and Refill Sum by Fuel Level Difference.
[0053] At step 910, appending refill tuple in fuel refill series with down trend counter, refill sum and time difference. Further at step 911, reading the next consecutive tuple from the series. At step 912, fuel refill series is generated.
[0054] At step 108, generating fuel merge series data, by merging fuel refill time series data. Generating fuel merge series data, further comprises reading fuel refill series with current and previous tuple 1001. Further the current level is determined to be less than previous fuel level and time difference and less than predefined difference. At step 1002, updating refill record with of current record in case the previous step Yes, or in case of a No, at step 1003, maintaining merge refill series.
[0055] At step 1004, discarding the record in case Time Difference in Refills >=Fuel Filling Threshold Time and Total Refill >= Fuel Filling Threshold Refill is false. In case the condition is true, total fuel refills is observed.
[0056] Further at step 109, obtaining fuel refill in Liters from the fuel merge series data. In accordance with the exemplary embodiment, at step 110, generating fuel consumption data. Further the fuel consumption data generation comprises at step 1101, reading pre-processed fuel series data. Further at step 1102, Start Median Fuel Level as Median of Start Predefined Fuel Level Values, End Median Fuel Level as Median of End Predefined Fuel Level Values, and Refill Total as Total Refill. Further at step 1103 computing Fuel Consumption as End Median Fuel Level in addition with Refill Total is deducted from Start Median Fuel Level.
[0057] In some embodiments, the disclosed techniques can be implemented, at least in part, by computer program instructions encoded on a non-transitory computer-readable storage media in a machine-readable format, or on other non-transitory media or articles of manufacture. Such computing systems (and non-transitory computer-readable program instructions) can be configured according to at least some embodiments presented herein, including the processes shown and described in connection with figures.
[0058] The programming instructions can be, for example, computer executable and/or logic implemented instructions. In some examples, a computing device is configured to provide various operations, functions, or actions in response to the programming instructions conveyed to the computing device by one or more of the computers readable medium, the computer recordable medium, and/or the communications medium. The non-transitory computer readable medium can also be distributed among multiple data storage elements, which could be remotely located from each other. The computing device that executes some or all of the stored instructions can be a microfabrication controller, or another computing platform. Alternatively, the computing device that executes some or all of the stored instructions could be remotely located computer system, such as a server.
[0059] Further, while one or more operations have been described as being performed by or otherwise related to certain modules, devices or entities, the operations may be performed by or otherwise related to any module, device or entity.
[0060] Further, the operations need not be performed in the disclosed order, although in some examples, an order may be preferred. Also, not all functions need to be performed to achieve the desired advantages of the disclosed system and method, and therefore not all functions are required.

Claims:We Claim:

1. A method for detecting fuel consumption and fuel refill for construction equipment, the method comprises:
receiving 101 raw data associated with fuel in a fuel tank of the construction equipment, wherein the raw data is received at a receiving module from an IoT sensor mounted within the fuel tank, or is communicably connected to a fuel sensor;
analysing 102 the raw data to detect if the raw data is clean or has error, or any missing data;
removing 103 the missing data from the raw data by the processor, wherein the removing of the missing data enables obtaining a clean data;
removing 104, noisy data from the clean data using SDM to obtain a smoothen fuel level time series data, wherein a noise removing module communicatively coupled to the receiver module and the processor is configured to employ the SDM to obtain the smoothen fuel level time series data;
receiving 105, the smoothen fuel level time series data from the noise removing module, or the IoT sensor;
checking 106, fuel data pattern by extracting relevant data from the smoothen fuel level time series data;
generating 107, fuel refill time series data from the relevant data extracted from the smoothen fuel level time series data.
generating 108, fuel merge series data, by merging fuel refill time series data;
obtaining 109, fuel refill in liters from the fuel merge series data; and
generating 110 fuel consumption data, wherein the fuel consumption data generation comprises;
reading 1101 pre-processed fuel series data;
defining 1102, Start Median Fuel Level as Median of Start Predefined Fuel Level Values, End Median Fuel Level as Median of End Predefined Fuel Level Values, and Refill Total as Total Refill;
computing, 1103 Fuel Consumption as End Median Fuel Level in addition with Refill Total is deducted from Start Median Fuel Level.

2. The method as claimed in claim 1, wherein removing noisy data from the clean data comprises, reading the time series data 201.

3. The method as claimed in claim 2, comprises arranging the raw data received by ascending data time.

4. The method as claimed in claim 2, comprises removing the noise in the raw data and find the stability of fuel level parameter by using smoothening data method (SDM) 203.

5. The method as claimed in claim 2, comprises defining 204, a bucket or set.

6. Further 206, dividing the data set from the received raw data, and the smoothed data.

7. The method as claimed in claim 2, comprises checking 207, the length of the backet or the set.

8. The method as claimed in claim 2, comprises computing 210, by the processor the smoothen fuel level time series data after SDM.

9. The method as claimed in claim 1, wherein the generating fuel refill time series data further comprise maintaining 901, the fuel refill series, refill start flag, refill sum, and down trend counter.

10. The method as claimed in claim 9, comprises reading 902, fuel level from smoothen fuel level series data from ith to nth location.

11. The method as claimed in claim 9, comprises computing 903, the difference of two consecutive fuel level values at ith and (i+1)th location from the smoothen fuel level series.

12. The method as claimed in claim 9, comprises detecting 904, fuel difference to be more than Zero (0).

13. The method as claimed in claim 9, comprises upon detecting fuel difference is more than zero, setting refill start flag as False 906.

14. The method as claimed in claim 9, comprises checking 907, the fuel refill start session in case the fuel difference is less than zero.

15. The method as claimed in claim 9, comprises rising 908, up Down Trend Counter by one and Refill Sum by Fuel Level Difference.

16. The method as claimed in claim 9, comprises appending 910, refill tuple in fuel refill series with down trend counter, refill sum and time difference.

17. The method as claimed in claim 9, comprises reading 911, the next consecutive tuple from the series.

18. The method as claimed in claim 1, wherein generating fuel merge series data, further comprises reading fuel refill series with current and previous tuple 1001.

19. The method as claimed in claim 18, comprises updating 1002 refill record with of current record in case the previous step Yes, or in case of a No, maintaining 1003, merge refill series.

Dated this on 24 July 2024

Prafulla Wange
(Agent for Applicant)
IN/PA-2058