Patent Application
20220237411
The present invention relates to a data conversion application system and a method thereof, and more particularly to an application system for open geospatial consortium (OGC) data conversion standardization and a method thereof.
In recent years, with the popularization and rapid development of artificial intelligence (AI), various applications that combine artificial intelligence have sprung up. However, how to train the most effective artificial intelligence model has always been one of the problems that manufacturers want to solve.
Generally speaking, the conventional model training method is to continuously input training data into the model, and determine performance of the model based on an evaluation index. However, because of the different formats of the training data, the compatibility and usability of the training data are insufficient, and it causes that the trained model is only suitable for specific situations. In addition, because the conventional training data is not structured and standardized and also not time-space sequence data, the prediction performance of the trained model is affected. Therefore, the conventional model training method may have problems of insufficient warning accuracy and insufficient training data compatibility and availability.
Therefore, what is needed is to develop an improved technical solution to solve the conventional technical problems of insufficient alarm accuracy and insufficient training data compatibility and availability.
In order to solve the conventional problem, the present invention discloses an application system for open geospatial consortium data conversion standardization and a method thereof.
According to an embodiment, the present invention discloses an application system for open geospatial consortium data conversion standardization, and the application system includes an identifying module, a conversion module, a training module and an alarm module. The identifying module is configured to receive at least one streaming image data and provide a completely-trained image identifying model for identification, and output at least one object message contained in the streaming image data, based on an identification result. The conversion module is connected to the identifying module and configured to detect a coordinate position, an image sampling time and a receiving time of the streaming image data, and embed the coordinate position, the image sampling time and the receiving time in the object message based on an open geospatial consortium data standard, so as to convert the streaming image data into a time-space sequence metadata. The training module is connected to the conversion module and configured to continuously input the time-space sequence metadata meeting the open geospatial consortium data standard to the alarm model as training data, and calculate at least one evaluation index of the alarm model until the at least one evaluation index of the alarm model meets a preset value. The alarm module is connected to the training module and configured to receive at least one streaming data meeting the open geospatial consortium data standard, and input the streaming data to the completely-trained alarm model to analyze and predict whether an abnormal condition is occurred, and generate and output an alarm message if the abnormal condition is occurred.
According to an embodiment, the present invention discloses an application method for open geospatial consortium data conversion standardization. The application method includes steps of: providing a completely-trained image identifying model to receive at least one streaming image data for identification, and outputting at least one object message contained in the streaming image data based on an identification result; detecting a coordinate position, an image sampling time and a receiving time of the streaming image data, and embedding the detected coordinate position, the image sampling time and the receiving time in the at least one object message based on an open geospatial consortium data standard, to convert the streaming image data into a time-space sequence metadata; continuously inputting the time-space sequence metadata, which meets the open geospatial consortium data standard, to an alarm model as training data, and calculating at least one evaluation index of the alarm model until the at least one evaluation index meets a preset value; receiving at least one streaming data which meets the open geospatial consortium data standard, and inputting the received streaming data to the completely-trained alarm model to analyze and predict whether an abnormal condition is occurred, and generating and outputting an alarm message if the abnormal condition is occurred.
According to above-mentioned contents, the difference between the system and method of the present invention and conventional technology is that in the present invention, the trained image identifying model is used to identify the object message contained in the streaming image data, and the coordinate position, the image sampling time and the receiving time of the streaming image data are detected and embedded into the object message based on the open geospatial consortium data standard, so that the streaming image data can be converted into a time-space sequence metadata as training data for training the alarm model; the streaming data meeting the open geospatial consortium data standard is received and inputted to the completely-trained alarm model for performing prediction; when the prediction result indicates an abnormal condition, the alarm message is outputted.
By the aforementioned technical solution, the present invention can achieve the technical effect of improving alarm accuracy and compatibility and availability of the training data.
The structure, operating principle and effects of the present invention will be described in detail by way of various embodiments which are illustrated in the accompanying drawings.
The following embodiments of the present invention are herein described in detail with reference to the accompanying drawings. These drawings show specific examples of the embodiments of the present invention. These embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. It is to be acknowledged that these embodiments are exemplary implementations and are not to be construed as limiting the scope of the present invention in any way. Further modifications to the disclosed embodiments, as well as other embodiments, are also included within the scope of the appended claims.
These embodiments are provided so that this disclosure is thorough and complete, and fully conveys the inventive concept to those skilled in the art. Regarding the drawings, the relative proportions and ratios of elements in the drawings may be exaggerated or diminished in size for the sake of clarity and convenience. Such arbitrary proportions are only illustrative and not limiting in any way. The same reference numbers are used in the drawings and description to refer to the same or like parts. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. As used herein, the term “or” includes any and all combinations of one or more of the associated listed items.
It will be acknowledged that when an element or layer is referred to as being “on,” “connected to” or “coupled to” another element or layer, it can be directly on, connected or coupled to the other element or layer, or intervening elements or layers may be present. In contrast, when an element is referred to as being “directly on,” “directly connected to” or “directly coupled to” another element or layer, there are no intervening elements or layers present.
In addition, unless explicitly described to the contrary, the words “comprise” and “include”, and variations such as “comprises”, “comprising”, “includes”, or “including”, will be acknowledged to imply the inclusion of stated elements but not the exclusion of any other elements.
First of all, the terms defined in the present invention will be described in following paragraphs before illustration of the application system for open geospatial consortium data conversion standardization and a method thereof according to the present invention. The term “metadata” defined in the present invention means the time-space sequence data converted based on open geospatial consortium data standard, and the metadata is used as training data for an artificial intelligence model. Compared with the general training data for artificial intelligence, the metadata is structured and standardized, and integrated with other open data. In actual implementation, a camera of an in-vehicle system or a smart road lamp can be installed with the model trained based on the metadata, to detect and predict an abnormal condition with artificial intelligence.
The application system for open geospatial consortium data conversion standardization and a method thereof according to the present invention will hereinafter be described in more detail with reference to the accompanying drawings. Please refer to
The conversion module 120 is connected to the identifying module 110 and configured to detect a coordinate position, an image sampling time, and a receiving time of the streaming image data, and embed the coordinate position, the image sampling time and the receiving time into the corresponding object message based on an open geospatial consortium data standard, so as to convert the streaming image data into a time-space sequence metadata. In actual implementation, besides the coordinate position, the image sampling time and the receiving time, the metadata can also include a distance to the front vehicle, an overtaking direction, a travel speed, a travel direction, a visibility, a temperature, a humidity or a rainfall. Furthermore, the coordinate position includes a coordinate (such as a longitude and a latitude) positioned by a global position system or other similar coordinate positioning system, and the coordinate position can also include X-axis and Y-axis coordinates of the identified object in the image, so that the position of the object can be determined based on the coordinate, or the change in the velocity and the direction of the object can be calculated based on an change in the coordinate position.
The training module 130 is connected to the conversion module 120 and configured to continuously input the metadata, meeting the open geospatial consortium data standard, to the alarm model as training data, and calculate an evaluation index of the alarm model until the evaluation index meets a preset value. The alarm model and the image identifying model are artificial intelligence models, and the difference between these two models is that the alarm model is a model to be trained with the training data and the image identifying model is a well-trained model. In actual implementation, the evaluation index is used to evaluate performance of the artificial intelligence model, and the general evaluation indexes can be divided into two categories including recursion and classification, for example, the evaluation index for recursion can include mean square error (MSE), root mean square error (RMSE), or mean absolute error (MAE), and the evaluation index for classification includes a confusion matrix, a receiver operating characteristic (ROC) curve, or an area under curve (AUC).
The alarm module 140 is connected to the training module 130 and configured to receive streaming data meeting the open geospatial consortium data standard, and input the streaming data to the completely-trained alarm model, so as to analyze and predict whether an abnormal condition is occurred, and if the prediction result indicates that an abnormal condition is occurred, the alarm module 140 generates and outputs an alarm message. In actual implementation, the alarm module 140 can pre-store a link parameter value, and after the alarm message is generated, the alarm module 140 loads the link parameter value to establish link with an application programming interface (API) of an instant messaging (IM) program and transmit the alarm message to the IM program, so that an IM message can be generated. In an embodiment, the link parameter value can include a uniform resource identifier and an authorization token of the application programming interface.
It is to further explain that the training module 130 and the alarm module 140 can be connected to a meteorological database to load the temperature, the humidity and the rainfall corresponding to the coordinate position and the receiving time, and the loaded temperature, the humidity and the rainfall are then embedded into the corresponding metadata and streaming data with the open geospatial consortium data standard. For example, in the training module 130 the loaded data is embedded into the metadata, and in the alarm module 140 the loaded data is embedded with the streaming data meeting the open geospatial consortium data standard.
It is to be particularly noted that, in actual implementation, the modules of the present invention can be implemented by various manners, including software, hardware or any combination thereof, for example, in an embodiment, the module can be implemented by software and hardware, or one of software and hardware. Furthermore, the present invention can be implemented fully or partly based on hardware, for example, one or more module of the system can be implemented by integrated circuit chip, system on chip (SOC), a complex programmable logic device (CPLD), or a field programmable gate array (FPGA). The concept of the present invention can be implemented by a system, a method and/or a computer program. The computer program can include computer-readable storage medium which records computer readable program instructions, and the processor can execute the computer readable program instructions to implement concepts of the present invention. The computer-readable storage medium can be a tangible apparatus for holding and storing the instructions executable of an instruction executing apparatus Computer-readable storage medium can be, but not limited to electronic storage apparatus, magnetic storage apparatus, optical storage apparatus, electromagnetic storage apparatus, semiconductor storage apparatus, or any appropriate combination thereof. More particularly, the computer-readable storage medium can include a hard disk, a RAM memory, a read-only-memory, a flash memory, an optical disk, a floppy disc or any appropriate combination thereof, but this exemplary list is not an exhaustive list. The computer-readable storage medium is not interpreted as the instantaneous signal such a radio wave or other freely propagating electromagnetic wave, or electromagnetic wave propagated through waveguide, or other transmission medium (such as electric signal transmitted through electric wire), or optical signal transmitted through fiber cable). Furthermore, the computer readable program instruction can be downloaded from the computer-readable storage medium to each calculating/processing apparatus, or downloaded through network, such as internet network, local area network, wide area network and/or wireless network, to external computer equipment or external storage apparatus. The network includes copper transmission cable, fiber transmission, wireless transmission, router, firewall, switch, hub and/or gateway. The network card or network interface of each calculating/processing apparatus can receive the computer readable program instructions from network, and forward the computer readable program instruction to store in computer-readable storage medium of each calculating/processing apparatus. The computer program instructions for executing the operation of the present invention can include source code or object code programmed by assembly language instructions, instruction-set-structure instructions, machine instructions, machine-related instructions, micro instructions, firmware instructions or any combination of one or more programming language. The programming language include object oriented programming language, such as Common Lisp, Python, C++, Objective-C, Smalltalk, Delphi, Java, Swift, C#, Perl, Ruby, and PHP, or regular procedural programming language such as C language or similar programming language. The computer readable program instruction can be fully or partially executed in a computer, or executed as independent software, or partially executed in the client-end computer and partially executed in a remote computer, or fully executed in a remote computer or a server.
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In an embodiment, a step 221 can be performed after the step 220. As shown in
The operations of the system and method of the present invention are described in following embodiment with reference to
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According to above-mentioned contents, the difference between the system and method of the present invention and conventional technology is that in the present invention, the trained image identifying model is used to identify the object message contained in the streaming image data, and the coordinate position, the image sampling time and the receiving time of the streaming image data are detected and embedded into the object message based on the open geospatial consortium data standard, so that the streaming image data can be converted into a time-space sequence metadata as training data for training the alarm model; the streaming data meeting the open geospatial consortium data standard is received and inputted to the completely-trained alarm model for performing prediction; when the prediction result indicates an abnormal condition, the alarm message is outputted. Therefore, the technical solution of the present invention is able to solve the conventional technical problems, to achieve the technical effect of improving alarm accuracy and compatibility and availability of the training data.
The present invention disclosed herein has been described by means of specific embodiments. However, numerous modifications, variations and enhancements can be made thereto by those skilled in the art without departing from the spirit and scope of the disclosure set forth in the claims.
