DESC: FIELD
The present disclosure relates to field area computation and in particular relates to estimating area and other parameters of a work zone based on geo-spatial data stream as received from a mobile machine.
DEFINITIONS:
As used in the present disclosure, the following terms are generally intended to have the meaning as set forth below, except to the extent that the context in which they are used indicate otherwise.
The expression “work zone” used hereinafter in this specification refers to, but is not limited to, an agricultural land, a real estate plot, a garden, a recreation land, a livestock farm, a camping area, area for planting windmills, area for mounting solar panels, parking spaces and storage facility.
The expression “work zone parameters” used hereinafter in this specification refers to, but is not limited to, area of the work zone, shape of the work zone, work zone boundary and time required to traverse the work zone.
The expression “mobile vehicle” used hereinafter in this specification refers to, but is not limited to, an agricultural machinery, a farm machinery, land surveying machine, cars, trucks and drones.
The expression “GPS correction technique” used hereinafter in this specification refers to techniques used to enhance the quality of location data gathered using GPS receivers/sensors based on machine learning. The conventional GPS correction techniques include Differential Global Positioning System (DGPS) correction, Wide-Area Augmentation System (WAAS) correction, L-Band frequency based correction, and Postprocessing.
The expression “density-based clustering process” used hereinafter in this specification refers to, but not limited to, an unsupervised learning method that identifies distinctive groups/clusters in data, in this case the GPS data, based on the idea that a cluster in a data space is a contiguous region of high point density, separated from other such clusters by contiguous regions of low point density.
The expression “triangulation” used hereinafter in this specification refers to, but not limited to, tracing and measuring of a series or network of triangles in order to determine the distances and relative positions of points spread over an area.
The expression “meshing” used hereinafter in this specification refers to, but not limited to, creating a mesh, a subdivision of a continuous geometric space into discrete geometric and topological cells by connecting unstructured data points identified within the geometric space.
The expression “polygons” used hereinafter in this specification refers to, but is not limited to, closed spaces formed by connecting adjacent GPS data points.
The expression “true polygons” used hereinafter in this specification refer to, but is not limited to, polygons having pre-determined shape, pre-determined geometry and area within pre-defined thresholds.
BACKGROUND:
Calculation of the area of a farmland work zone is essential for agricultural production and management and is one of the most basic parameters that directly determines the size of investment to be made for a desired output. One needs to estimate accurately as much as possible the amount of time and money that would be required to cater to an agricultural field to assess, for example, water/fertilizer usage, agricultural machinery requirement, seed production and farm labor costs – all this depends on the correct estimation of farmland area.
Governments also struggle with inaccurate topographical data available for public/private land area, which in turn hampers implementation of various government policies mainly because there is great difficult in physically accessing the area and measuring it.
Calculation of a big farmland is time consuming, error-prone task and is quite difficult in case the land has an irregular shape. US granted patent publication US6112143A talks about tracking a mobile machinery while it traverses/moves around in a particular area, sends data which is then used primarily to compute the perimeter of the defining area.
CN105718751B talks about calculating subsoiling work area based on area covered by grids using a GPS device mounted on a tractor carrying out the sub-soiling operation. GPS data is collected in real-time and coordinates of the points identified as job area are plotted on a plane to identify a rectangle which is further divided into squares, and the area is calculated based on a particular number of plotted points falling within a square.
Conventional systems do not have the ability to recognize and cluster an incoming GPS stream of data to identify which points are to be included in a work zone of work performed by a machine and which points are corresponding to transportation between two locations.
There is therefore a need for a system and/or a method for an accurate and efficient estimation of area of a work zone based on geo-spatial data streams.
OBJECTS:
It is an object of the present invention to ameliorate one or more drawbacks of the state of the art or to at least provide a useful alternative.
An object of the present invention is to provide a system for determining work zone parameters using geospatial data.
Another object of the present invention is to correctly identify the boundary of a work zone comprising an area to be used for field work.
Still another object of the present disclosure is to use a clustering process on an incoming GPS data stream to identify points lying within a work zone.
Yet another object of the present invention is to clearly distinguish between the GPS points that are part of a work field from GPS points that are part of travel.
Another object of the present invention is to be able to calculate multiple parameters associated with a work zone.
SUMMARY
The present disclosure envisages a system for determining work zone parameters using a GPS data stream generated by a GPS device associated with a mobile vehicle.
The system comprises a detection unit and a computation unit. The detection unit and the computation unit are located at a remote server and are communicatively coupled to the GPS device.
The detection unit is configured to receive the GPS data stream from the GPS device through a communication channel. The detection unit comprises a correction module and a parser. The correction module is configured to correct the received GPS data stream for location errors using a GPS correction technique based on machine learning. The parser is configured to cooperate with the correction module to receive the corrected GPS data stream, and is further configured to parse the corrected GPS data stream using a density-based clustering process to extract a plurality of probable GPS data points belonging to the work zone from the received corrected GPS data stream.
The computation unit is configured to cooperate with the detection unit to receive the extracted probable GPS data points. The computation unit comprises a triangulation module, an analyser and a work zone calculation (WZC) module. The triangulation module is configured to perform meshing on the received probable GPS data points to generate a mesh network of GPS data points defined by a plurality of polygons. The analyser is configured to analyse a plurality of features of the polygons for determining a plurality of true polygons associated with the work zone. The WZC module is configured to determine the parameters associated with the work zone based on the determined true polygons.
The present disclosure also envisages a method for determining work zone parameters using a GPS data stream generated by a GPS device associated with a mobile vehicle.
BRIEF DESCRIPTION OF DRAWINGS
Figure 1 illustrates a block diagram of a system for determining work zone parameters using geospatial data;
Figure 2 illustrates a block diagram of a server of the system of Figure 1;
Figures 3a and 3b illustrate a flow diagram for a method for determining work zone parameters using geospatial data;
Figure 4 illustrates an exemplary output of a correction module of the system of Figure 1, in accordance with the present invention;
Figure 5 illustrates an exemplary output of a detection unit of the system of Figure 1, in accordance with the present invention; and
Figure 6 illustrates an exemplary output of a computation unit of the system of Figure 1, in accordance with the present invention.
LIST OF REFERENCE NUMERALS
100 system
102 GPS device
104 detection unit
106 computation unit
108 repository
110 correction module
112 parser
114 clustering module
116 extractor
118 triangulation module
120 analyser
122 work zone calculation (WZC)
124 server
126 communication channel

DETAILED DESCRIPTION
Embodiments, of the present disclosure, will now be described with reference to the accompanying drawing.
Embodiments are provided so as to thoroughly and fully convey the scope of the present disclosure to the person skilled in the art. Numerous details, are set forth, relating to specific components, and methods, to provide a complete understanding of embodiments of the present disclosure. It will be apparent to the person skilled in the art that the details provided in the embodiments should not be construed to limit the scope of the present disclosure. In some embodiments, well-known processes, well-known apparatus structures, and well-known techniques are not described in detail.
The terminology used, in the present disclosure, is only for the purpose of explaining a particular embodiment and such terminology shall not be considered to limit the scope of the present disclosure. As used in the present disclosure, the forms "a,” "an," and "the" may be intended to include the plural forms as well, unless the context clearly suggests otherwise. The terms "comprises," "comprising," “including,” and “having,” are open ended transitional phrases and therefore specify the presence of stated features, integers, steps, operations, elements, modules, units and/or components, but do not forbid the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. The particular order of steps disclosed in the method and process of the present disclosure is not to be construed as necessarily requiring their performance as described or illustrated. It is also to be understood that additional or alternative steps may be employed.
Conventional systems do not have the ability to recognize and cluster an incoming GPS stream of data to identify which points correspond to an area of work performed by a machine and which points correspond to transportation between two locations. To overcome this problem, a system (hereinafter referred to as “system (100”)) and method (hereinafter referred to as “method (200)) for determining work zone parameters using geospatial data of the present disclosure, is described with reference to Figure 1 through Figure 6.
The system (100) is used for determining parameters associated with a work zone using geospatial data. In an embodiment, the work zone is an agricultural field area traversed by an agricultural vehicle such as a ploughing or a sowing machine. In another embodiment, the work zone is a land or a real estate plot whose area needs to be estimated. The mobile vehicle can be a manned vehicle such as car, bike, truck, or an un-manned vehicle such as a drone. The GPS device (102) is mounted on the mobile vehicle and generates a GPS data stream associated with the travel path of the mobile vehicle. In an embodiment, the GPS device (102) can be a mobile phone of the user of the mobile vehicle. In another embodiment, the GPS device (102) can be a GPS sensor mounted on or located within the mobile vehicle.
The GPS data stream includes a unique identifier associated with the GPS device and time-stamped geographical coordinates of the vehicle.
Referring to Figure 1, the system (100), comprises a detection unit (104), a computation unit (106), and a repository (108).
The detection unit (104), the computation unit (106) and the repository (108) are located on a remoted server (124). The server (124) is communicatively coupled to the GPS device (102) via a communication channel (126). The communication channel (126) can be established on a wireless connection implementing WiFi, WiMax, CDMA, GSM, 3GPP, 3GPP2, LTE, or other suitable wireless connection protocols.
The detection unit (104) is configured to receive the GPS data stream generated by the GPS device (102). The detection unit (104) comprises a correction module (110) and a parser (112). The correction module (110) is configured to correct the received GPS data stream for location errors using a GPS correction technique based on machine learning.
The parser (112) is configured to cooperate with the correction module (110) to receive the corrected GPS data stream, and is further configured to parse the corrected GPS data stream using a density-based clustering process to extract a plurality of probable GPS data points belonging to the work zone from the received corrected GPS data stream.
The detection unit (104) can be tuned for each user according to his/her work characteristics. For this, the detection unit (104) may be configured to receive user inputs relating to type of work zone being traversed, type of mobile vehicle used, and a nature of work done via a user interface.
In an embodiment, the parser (112) comprises a clustering module (114) and an extractor (116). The clustering module (114) is configured to employ the density based clustering process to compute distance between the GPS data points, density of the GPS data points, and relevance and correctness of the GPS data points with respect to the work zone. The extractor (116) is configured to extract the probable GPS data points belonging to the work zone from the received corrected GPS data stream based on the computation. Thus, GPS points corresponding to an area of work performed by the mobile vehicle are separated from the GPS points corresponding to transportation between two locations.
In an alternate embodiment, engine RPM data of the mobile vehicle is used to separate work zone points from commutation/transportation points.
The computation unit (106) is configured to cooperate with the detection unit (104) to receive the extracted probable GPS data points. The computation unit (106) comprises a triangulation module (118), an analyser (120) and a work zone calculation (WZC) module (122).
The triangulation module (118) is configured to perform meshing on the received probable GPS data points to generate a mesh network of GPS data points defined by a plurality of polygons. In an embodiment, the triangulation module (118) includes a Kalman filter configured to filter the probable GPS data points before the meshing operation by removing erroneous GPS data points. The analyser (120) is configured to analyse a plurality of features of the polygons for determining a plurality of true polygons associated with the work zone. The features of the polygons include, but are not limited to, shape, geometry, area, number of sides, length of sides, and angles between the sides of the polygons.
The WZC module (122) is configured to determine the parameters associated with the work zone based on the determined true polygons. The determined parameters include, but are not limited to, area of the work zone, shape of the work zone, work zone boundary, and time required to traverse the work zone.
The detection unit (104) and the computation unit (106) may be implemented using one or more processors.
The processors may be general-purpose processors, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), and/or the like. The processors may be configured to retrieve data from and/or write data to a memory/repository.

Figures 3a and 3b illustrate a flow diagram of the method (200) for determining work zone parameters using geospatial data. The method includes:
At Step 202: receiving, by a correction module (110) of a detection unit (104), the GPS data stream from the GPS device (102);
At Step 204: correcting, by the correction module (110), the received GPS data stream for location errors using a GPS correction technique based on machine learning;
At Step 206: receiving, by a parser (112) of the detection unit (104), the corrected GPS data stream from the correction module (110);
At Step 208: parsing, by the parser (112), the received corrected GPS data stream using a density base clustering process to extract a plurality of probable GPS data points belonging to the work zone from the received corrected GPS data stream;
At Step 210: receiving, by a computation unit (106), the extracted probable GPS data points from the detection unit (104);
At Step 212: meshing, by a triangulation module (118) of the computation unit (106), the received probable GPS data points to generate a mesh network defined by a plurality of polygons;
At Step 214: analysing, by an analyser (120) of the computation unit (106), a plurality of features of the polygons for determining true polygons associated with the work zone; and
At Step 216: determining, by a work zone calculation (WZC) module (122) of the computation unit (106), the parameters associated with the work zone based on the determined true polygons.
In an embodiment, the step of parsing (208), the received corrected GPS data stream using a density base clustering process to extract a plurality of probable GPS data points, comprises the following sub-steps:
• employing, by a clustering module (114), the density based clustering process to compute distance between the GPS data points, density of the GPS data points, and relevance and correctness of the GPS data points with respect to the work zone; and
• extracting, by an extractor (116), the probable GPS data points belonging to the work zone from the received corrected GPS data stream based on the computation.
In an exemplary embodiment, the GPS device (102) may be a mobile phone of a user of the moving vehicle. The moving vehicle may be a tractor working on an agricultural work zone. The mobile phone tracks the GPS data of the moving tractor and sends it to a remote server using a communication channel. A detection unit (104) residing on a remote server (124), receives the GPS data stream. A correction module (110) present in detection unit (104) corrects the received GPS data stream for location errors using a conventional GPS correction technique like DGPS. Following that, a parser (112) also present in detection unit (104) receives the corrected GPS data stream to parse the corrected GPS data stream using a density-based clustering process. The parser (112) extracts a plurality of probable GPS data points belonging to the work zone from the received corrected GPS data stream. A computation unit (106), residing on the remote server (124), then receives the extracted probable GPS data points. The computation unit (106) comprises a triangulation module (118) that performs meshing on the received probable GPS data points to generate a mesh network defined by a plurality of polygons using triangulation techniques. The computation unit (106) also comprises an analyser (120) that receives the mesh network to analyse a plurality of features of the polygons for determining a plurality of true polygons. Another part of the computation unit (106) is a work zone calculation (WZC) module (122) that determines the parameters like area, time consumed, work field boundary, associated with said work zone based on the determined true polygons.
The foregoing description of the embodiments has been provided for purposes of illustration and not intended to limit the scope of the present disclosure. Individual components of a particular embodiment are generally not limited to that particular embodiment, but, are interchangeable. Such variations are not to be regarded as a departure from the present disclosure, and all such modifications are considered to be within the scope of the present disclosure.
TECHNICAL ADVANCEMENTS AND ECONOMIC SIGNIFICANCE
The present disclosure described herein above has several technical advantages including, but not limited to, the system and method for determining work zone parameters using geospatial data, which:
• is adapted to be used with standard GPS sensors;
• provides an accurate estimate of an area of the work zone based on GPS data streams in an efficient manner;
• is capable of being tuned as per user requirements and/or work characteristics;
• uses a clustering process on an incoming GPS data stream to identify GPS points lying within a work zone;
• clearly distinguishes GPS points that are part of a work field from the GPS points that are part of travel; and
• calculates multiple parameters associated with a work zone.
The foregoing disclosure has been described with reference to the accompanying embodiments which do not limit the scope and ambit of the disclosure. The description provided is purely by way of example and illustration.
The embodiments herein and 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.
The foregoing description of the specific embodiments so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.
The use of the expression “at least” or “at least one” suggests the use of one or more elements or ingredients 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 a 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.
While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure 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 disclosure and not as a limitation
,CLAIMS:WE CLAIM:
1. A system (100) for determining work zone parameters using a GPS data stream generated by a GPS device (102) associated with a mobile vehicle, said system (100) comprising:
• a detection unit (104) configured to receive said GPS data stream, said detection unit (104) comprising:
? a correction module (110) configured to correct said received GPS data stream for location errors using a GPS correction technique based on machine learning; and
? a parser (112) configured to cooperate with said correction module (110) to receive said corrected GPS data stream, and further configured to parse the corrected GPS data stream using a density-based clustering process to extract a plurality of probable GPS data points belonging to said work zone from said received corrected GPS data stream, and
• a computation unit (106) configured to cooperate with said detection unit (104) to receive said extracted probable GPS data points, said computation unit (106) comprising:
? a triangulation module (118) configured to perform meshing on said received probable GPS data points to generate a mesh network of GPS data points defined by a plurality of polygons;
? an analyser (120) configured to analyse a plurality of features of said polygons for determining a plurality of true polygons associated with said work zone; and
? a work zone calculation (WZC) module (122) configured to determine said parameters associated with said work zone based on said determined true polygons.
2. The system (100) as claimed in claim 1, wherein said GPS data stream includes a unique identifier associated with the GPS device and time-stamped geographical coordinates of the vehicle.
3. The system (100) as claimed in claim 1, wherein said features include shape, geometry, area, number of sides, length of sides, and angles between the sides of said polygons.
4. The system (100) as claimed in claim 1, wherein said detection unit (104) and computation unit (106) are located on a remote server (124), said server (124) being communicatively coupled to said GPS device (102).
5. The system (100) as claimed in claim 1, wherein said triangulation module (118) includes a Kalman filter configured to filter said probable GPS data points before said meshing operation by removing erroneous GPS data points.
6. The system (100) as claimed in claim 1, wherein said parser (112) comprises:
• a clustering module (114) configured to employ said density based clustering process to compute distance between said GPS data points, density of said GPS data points, and relevance and correctness of said GPS data points with respect to said work zone; and
• an extractor (116) configured to extract said probable GPS data points belonging to said work zone from said received corrected GPS data stream based on said computation.
7. The system (100) as claimed in claim 1, which includes a repository (108) configured to store said determined parameters associated with the work zone.
8. A method (200) for determining parameters associated with a work zone using a GPS data stream generated by a GPS device (102) associated with a mobile vehicle, said method (200) comprising the following steps:
• receiving (202), by a correction module (110) of a detection unit (104), said GPS data stream from said GPS device (102);
• correcting (204), by said correction module (110), said received GPS data stream for location errors using a GPS correction technique based on machine learning;
• receiving (206), by a parser (112) of said detection unit (104), said corrected GPS data stream from said correction module (110);
• parsing (208), by said parser (112), the received corrected GPS data stream using a density base clustering process to extract a plurality of probable GPS data points belonging to said work zone from said received corrected GPS data stream;
• receiving (210), by a computation unit (106), said extracted probable GPS data points from said detection unit (104);
• meshing (212), by a triangulation module (118) of said computation unit (106), said received probable GPS data points to generate a mesh network defined by a plurality of polygons;
• analysing (214), by an analyser (120) of said computation unit (106), a plurality of features of said polygons for determining true polygons associated with said work zone; and
• determining (216), by a work zone calculation (WZC) module (122) of said computation unit (106), said parameters associated with said work zone based on said determined true polygons.
9. The method (200) as claimed in claim 8, wherein the step of parsing (208) by said parser (112), the received corrected GPS data stream using a density base clustering process to extract a plurality of probable GPS data points includes:
• employing, by a clustering module (114), said density based clustering process to compute distance between said GPS data points, density of said GPS data points, and relevance and correctness of said GPS data points with respect to said work zone; and

• extracting, by an extractor (116), said probable GPS data points belonging to said work zone from said received corrected GPS data stream based on said computation.