Event detection device and method

Abstract

An event detection device and a method thereof are provided. The event detection device according to some embodiments of the present disclosure may include a rule registration unit that registers a rule for detecting an event and a tag value assigned to the rule, and an event detection unit that acquires monitoring data and the tag value assigned to the monitoring data, determines a rule associated with the monitoring data from among the pre-registered rules using the acquired tag value, and detects an event from the monitoring data by applying the determined rule.

Description

[Technical field]

[0001] The present disclosure relates to an event detection device and method, and more particularly to a device that automatically detects events from a large amount of data and tags the detected event information, and a method performed by the device. [Background technology]

[0002] As sensor technology develops, there is a rapid demand for methods to automatically detect and tag points of interest from the huge amount of data that is constantly being generated. For example, vehicle systems equipped with numerous sensors generate large amounts of sensing data regarding vehicle operation, and there is a demand for methods to efficiently analyze such large amounts of sensing data and detect specific operating conditions (e.g., a situation in which a vehicle suddenly brakes while turning right). Summary of the Invention [Problem to be solved by the invention]

[0003] The technical problem to be solved through some embodiments of the present disclosure is to provide an apparatus for automatically detecting events from a massive amount of data and tagging the detected event information, and a method performed by the apparatus.

[0004] Another technical problem to be solved through some embodiments of the present disclosure is to provide rules capable of detecting various events.

[0005] The technical problems of the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by a person of ordinary skill in the art to which the present disclosure pertains from the following description. [Means for solving the problem]

[0006] In order to solve the above technical problems, an event detection device according to some embodiments of the present disclosure may include a rule registration unit that registers rules for detecting an event and tag values ​​assigned to the rules, and an event detection unit that acquires monitoring data and tag values ​​assigned to the monitoring data, determines a rule associated with the monitoring data from among pre-registered rules using the acquired tag value, and applies the determined rule to detect an event from the monitoring data.

[0007] In some embodiments, the event detector is capable of tagging the monitoring data with information about the detected event.

[0008] In some embodiments, the rule registration unit stores the rule in a rule storage and registers an identifier of the rule and a tag value assigned to the rule in a mapping table, and the event detection unit can query the identifier of the associated rule from the mapping table using the tag value assigned to the monitoring data, and retrieve the associated rule from the rule storage using the identifier of the query rule.

[0009] In some embodiments, the monitoring system further includes a metadata registration unit that registers metadata for analyzing the monitoring data and a tag value assigned to the metadata, and the event detection unit can use the acquired tag value to determine metadata associated with the monitoring data from the pre-registered metadata, analyze the monitoring data using the determined metadata, and detect the event from the analyzed monitoring data.

[0010] In some embodiments, in response to determining that a new rule has been registered, the event detection unit may use a tag value assigned to the new rule to determine data associated with the new rule from among pre-stored monitoring data, and apply the new rule to detect an event from the determined data.

[0011] In some embodiments, the monitoring data includes values ​​related to one or more monitoring elements, the rule includes a function item and a condition item, the function item being set with a function that performs a predetermined calculation based on the values ​​of the one or more monitoring elements, and the condition item being set with a condition for an output value of the set function.

[0012] In order to solve the above technical problems, an event detection method according to some embodiments of the present disclosure is performed by a computing device and may include the steps of registering rules for detecting an event and tag values ​​assigned to the rules, acquiring monitoring data and tag values ​​assigned to the monitoring data, determining a rule associated with the monitoring data from among pre-registered rules using the acquired tag value, and applying the determined rule to detect an event from the monitoring data.

[0013] In order to solve the above technical problem, a computer program according to some embodiments of the present disclosure may be stored in a computer-readable recording medium to be combined with a computing device to execute the steps of registering rules for detecting events and tag values ​​assigned to the rules, acquiring monitoring data and tag values ​​assigned to the monitoring data, determining a rule associated with the monitoring data from among the pre-registered rules using the acquired tag value, and applying the determined rule to detect an event from the monitoring data. Effect of the Invention

[0014] According to some embodiments of the present disclosure described above, a common tag value may be assigned between monitoring data and an associated rule. As a result, when specific monitoring data is acquired, a rule associated with the specific monitoring data may be automatically determined using the tag value, and an event may be automatically detected from the specific monitoring data by applying the determined rule. That is, the process of determining the associated rule and detecting an event may be fully automated via the tag value assigned in advance, thereby greatly improving the efficiency of monitoring data analysis.

[0015] In addition, a common tag value may be assigned between the monitoring data and the associated metadata. Thus, when specific monitoring data is acquired, metadata associated with the specific monitoring data may be automatically determined using the tag value, and the specific monitoring data may be automatically analyzed (e.g., parsed, decoded) using the determined metadata, and the event detection process may also be automatically performed. That is, the process of determining associated metadata, data decoding, and event detection may be fully automated through the pre-assigned tag value, thereby greatly improving the efficiency of monitoring data analysis.

[0016] In addition, various types of rules may be provided, through which the number of detectable events may be increased and various forms of events may be defined.

[0017] The effects of the technical ideas of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description. [Brief description of the drawings]

[0018] [Figure 1] 1 is an exemplary block diagram illustrating an event detection device according to some embodiments of the present disclosure. [Diagram 2]FIG. 2 is an exemplary diagram for explaining the structure of monitoring data that can be referenced in some embodiments of the present disclosure. [Diagram 3] 10 is an exemplary flowchart illustrating detailed operations of a metadata registration unit according to some embodiments of the present disclosure. [Figure 4] 10A and 10B are exemplary diagrams for explaining tagging operations of an event detection unit according to some embodiments of the present disclosure. [Diagram 5] 11 is an exemplary flowchart illustrating a first detailed operation of an event detection unit according to some embodiments of the present disclosure. [Figure 6] 11 is an exemplary flowchart illustrating a second detailed operation of the event detection unit according to some embodiments of the present disclosure. [Figure 7] 1 is an exemplary diagram for explaining an example of use of an event detection device according to some embodiments of the present disclosure. FIG. [Figure 8] 1 is an exemplary flow chart illustrating an event detection method according to some embodiments of the present disclosure. [Figure 9] FIG. 2 is an exemplary diagram illustrating examples of application of various types of event detection rules that may be referenced in some embodiments of the present disclosure. [Figure 10] FIG. 2 is an exemplary diagram illustrating examples of application of various types of event detection rules that may be referenced in some embodiments of the present disclosure. [Figure 11] FIG. 2 is an exemplary diagram illustrating examples of application of various types of event detection rules that may be referenced in some embodiments of the present disclosure. [Figure 12] FIG. 2 is an exemplary diagram illustrating examples of application of various types of event detection rules that may be referenced in some embodiments of the present disclosure. [Figure 13] FIG. 2 is an exemplary diagram illustrating examples of application of various types of event detection rules that may be referenced in some embodiments of the present disclosure. [Figure 14] FIG. 2 is an exemplary diagram illustrating examples of application of various types of event detection rules that may be referenced in some embodiments of the present disclosure. [Figure 15] FIG. 2 is an exemplary diagram illustrating examples of application of various types of event detection rules that may be referenced in some embodiments of the present disclosure. [Figure 16] FIG. 2 is an exemplary diagram illustrating examples of application of various types of event detection rules that may be referenced in some embodiments of the present disclosure. [Figure 17] FIG. 2 is an exemplary diagram illustrating examples of application of various types of event detection rules that may be referenced in some embodiments of the present disclosure. [Figure 18] FIG. 2 is an exemplary diagram illustrating examples of application of various types of event detection rules that may be referenced in some embodiments of the present disclosure. [Figure 19] FIG. 2 illustrates an example computing device capable of implementing an event detection apparatus according to some embodiments of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0019] Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. The advantages and features of the present disclosure, and the methods for achieving them, will be apparent from the following detailed embodiments with reference to the accompanying drawings. However, the technical idea of ​​the present disclosure is not limited to the following embodiments, and can be realized in various different forms. The following embodiments are merely provided to complete the technical idea of ​​the present disclosure and to fully inform those skilled in the art of the present disclosure of the scope of the present disclosure, and the technical idea of ​​the present disclosure is only defined by the scope of the claims.

[0020] In adding reference symbols to components in each drawing, it should be noted that the same components are assigned the same symbols as much as possible even if they are displayed in different drawings. In addition, in describing this disclosure, if it is determined that a detailed description of such known configurations or functions may obscure the gist of this disclosure, the detailed description will be omitted.

[0021] Unless otherwise defined, all terms (including technical and scientific terms) used herein may be used as meanings commonly understood by those having ordinary skill in the art to which this disclosure belongs. In addition, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless clearly and specifically defined. The terms used herein are intended to describe the embodiments and are not intended to limit the present disclosure. In this specification, the singular form includes the plural form unless otherwise specified in the phrase.

[0022] In addition, in describing components of the present disclosure, terms such as first, second, A, B, (a), (b), etc. may be used. Such terms are merely used to distinguish the components from other components, and do not limit the nature, order, or sequence of the components. When a component is described as being "coupled," "coupled," or "connected" to another component, it should be understood that the component may be directly coupled or connected to the other component, but other components may also be "coupled," "coupled," or "connected" between each component.

[0023] As used in the specification, "comprises" and / or "comprising" does not exclude the presence or addition of one or more other components, steps, operations and / or elements other than the stated components, steps, operations and / or elements.

[0024] Various embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings.

[0025] FIG. 1 is an exemplary block diagram illustrating an event detection device 100 according to some embodiments of the present disclosure.

[0026] As shown in FIG. 1, the event detection device 100 may include a storage 140, a metadata registration unit 110, a rule registration unit 120, and an event detection unit 130. However, this is merely a preferred embodiment for achieving the object of the present disclosure, and it is of course possible to add or delete some components as necessary. Also, it should be noted that each component (e.g., 110) shown in FIG. 1 is a functional component that is functionally divided, and a plurality of components may be embodied in a form in which they are integrated with each other in an actual physical environment. Also, in an actual physical environment, each component (e.g., 110) may be separated into a plurality of detailed functional components or embodied in a plurality of physical computing devices. Also, not all of the components (e.g., 110) illustrated in FIG. 1 may be essential components for implementing the event detection device 100. For example, the event detection device 100 may be embodied in a form in which some of the components (e.g., 110) illustrated in FIG. 1 are omitted.

[0027] The computing device may be, for example, a notebook computer, a desktop, a laptop, etc., but is not limited thereto, and may include any type of device equipped with a computing function. For an example of a computing device, see FIG. 19. Hereinafter, each component of the event detection device 100 will be described, and for convenience of description, the event detection device 100 will be abbreviated to "detection device 100".

[0028] Storage 140 may store various information and / or data related to the operation of detection apparatus 100. In some embodiments, storage 140 may be configured to include data storage 141, metadata storage 142, rule storage 143, and a mapping table 144.

[0029] Data related to a monitoring target (hereinafter, "monitoring data") may be stored in the data storage 141. For example, the monitoring data may be stored together with a corresponding data identifier in the data storage 141. The monitoring data may be, for example, sensing data measured via various sensors provided in the monitoring target, but is not limited thereto. In addition, the monitoring target may be set in various ways, such as a vehicle system, process equipment, etc., and may be anything.

[0030] The monitoring data may be designed in various structures and / or formats. For example, as shown in Fig. 2, the monitoring data may include a timestamp and data (e.g., measurement values) related to one or more monitoring elements. Specifically, the first monitoring data D1 may include measurement values ​​related to a plurality of monitoring elements (elements 1 to 3) and a timestamp TS1 indicating a measurement time point, and the second monitoring data D2 may include measurement values ​​related to a plurality of monitoring elements (elements 1 to 3) and a timestamp TS2.

[0031] In this case, the monitoring element means an element measured to monitor the monitoring target, and for example, if the monitoring target is a vehicle system, the monitoring element may include speed, position, heading angle, turn signal status (e.g., blinking status), front and rear images, etc., but is not limited thereto.

[0032] For reference, the term "element" may be used interchangeably with "attribute," "feature," "field," and the like in the art.

[0033] Next, metadata associated with the monitoring data may be stored in the metadata storage 142. For example, the metadata may be stored together with a metadata identifier corresponding to the metadata. The metadata may include various information for analyzing (e.g., decoding, parsing, etc.) the monitoring data. For example, the metadata may include a format of the monitoring data (e.g., a format of sensing data, a message format, etc.), a communication method (e.g., an encoding / decoding method of communication data, a format of a communication data frame, etc.), etc., but is not limited thereto.

[0034] Next, rules for detecting events from monitoring data may be stored in the rule storage 143. For example, rules and corresponding rule identifiers may be stored together in the rule storage 143. Various examples of events and rules will be described later with reference to Tables 1 to 10.

[0035] Next, the association (mapping) relationship between the monitoring data, the metadata, and the rules may be stored in the mapping table 144. For example, each entry in the mapping table 144 may have a key-value format, and identifiers of the monitoring data, the metadata, and the rules associated with each other may be stored in the value field.

[0036] In some embodiments, a common tag value may be assigned to the monitoring data, metadata, and rules that are related to each other. Also, the assigned tag value may be used as a key value of the mapping table 144. In such a case, the event detection unit 130 may easily obtain the related metadata and rules by querying the mapping table 144 with the tag value of the monitoring data.

[0037] Next, the metadata registration unit 110 can perform a registration process for the metadata, an example of such a registration process is shown in FIG.

[0038] 3, the registration process may start with step S10 of receiving a metadata registration request. In this step, the metadata registration unit 110 may receive the registration request including metadata M, a metadata identifier M-ID, and a tag value TagVal from a user. In some embodiments, the metadata registration unit 110 may provide a predetermined user interface, and the user may input information for the metadata registration request (e.g., metadata, metadata identifier, tag value, etc.) through the provided user interface.

[0039] In step S20, the metadata registration unit 110 may store the requested metadata M and the metadata identifier M-ID in the metadata storage 142. In some cases, the metadata registration unit 110 may also store in the metadata storage 142 a tag value TagVal assigned to the metadata M.

[0040] In step S30, the metadata registration unit 110 can register the identifier M-ID of the requested metadata M and the assigned tag value TagVal in the mapping table 144. For example, the metadata registration unit 110 can register the identifier M-ID of the requested metadata in an entry of the mapping table 144 having the tag value TagVal as a key value. If the entry does not exist, the metadata registration unit 110 can generate an entry having the tag value TagVal as a key value and register the identifier M-ID of the metadata in the generated entry.

[0041] For reference, FIG. 3 shows an example in which steps S20 and S30 are performed sequentially, but steps S20 and S30 may be performed in an order different from that shown in FIG. 3 or may be performed simultaneously.

[0042] Next, the rule registration unit 120 can perform a registration process for the rule. The registration process can be performed in a manner similar to that shown in Fig. 3. For example, the rule registration unit 120 can receive a registration request including a rule, a rule identifier, and a tag value from a user, and in response thereto, store the requested rule and rule identifier in the rule storage 143. In some cases, the tag value assigned to the rule can also be stored in the rule storage 143. In addition, the rule registration unit 120 can register the rule identifier in an entry of the mapping table 144 having the tag value as a key value.

[0043] Next, the event detection unit 130 may acquire the monitoring data and perform an event detection process on the acquired monitoring data. For example, the event detection unit 130 may receive data from the monitoring target in real time (continuously). Alternatively, the event detection unit 130 may acquire the monitoring data from a predetermined storage.

[0044] More specifically, in response to receiving new monitoring data, the event detection unit 130 may perform an event detection process on the new monitoring data. Alternatively, in response to registering a new rule, the event detection unit 130 may perform an event detection process using the registered new rule. Such an event detection process will be described in detail later with reference to FIGS. 5 and 6.

[0045] Furthermore, the event detection unit 130 may tag the monitoring data with information about the detected event. The information about the event may be, for example, but is not limited to, the name of the event, an identifier of the event, the name of the rule, an identifier of the rule, etc. For example, as shown in FIG 4, the event detection unit 130 may tag the monitoring data in which the event is detected (e.g., D1) with the rule identifier (e.g., R1) of the event.

[0046] The monitoring data on which the event detection process has been performed may be stored in the data storage 141. For example, the monitoring data in which an event has been detected and tagged event information may be stored together in the data storage 141. Of course, monitoring data in which no event has been detected may also be stored in the data storage 141. Such an operation may be performed by the event detection unit 130 or by another module.

[0047] Meanwhile, at least a part of each component (e.g., 110) shown in FIG. 1 may represent software or hardware such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC). However, the components are not limited to software or hardware, and may be configured to be in an addressable storage medium or to be executed by one or more processors. The functions provided in the components may be implemented by further subdivided components, or may be implemented by a single component that performs a specific function by combining multiple components.

[0048] Hereinafter, detailed operations of the event detector 130 according to some embodiments of the present disclosure will be further described with reference to FIGS.

[0049] 5 is an exemplary flowchart illustrating a first detailed operation of the event detector 130 according to some embodiments of the present disclosure. The first detailed operation relates to an event detection process for new monitoring data.

[0050] 5, the first detailed operation may start from step S110 in which the event detection unit 130 receives new monitoring data D. In this step, the event detection unit 130 may receive the new monitoring data D, a data identifier D-ID, and an assigned tag value TagVal.

[0051] In step S120, the event detection unit 130 can use the tag value TagVal to query the metadata and rules associated with the new monitoring data from the mapping table 144. As a result, the event detection unit 130 can obtain an identifier M-ID of the metadata and an identifier R-ID of the rule (rule set) associated with the new monitoring data.

[0052] In step S130, the event detection unit 130 may obtain metadata associated with the new monitoring data from the metadata storage 142 using the obtained metadata identifier M-ID.

[0053] In step S140, the event detection unit 130 can obtain a rule associated with the new monitoring data from the rule storage 143 using the obtained rule identifier R-ID.

[0054] For reference, FIG. 4 shows an example in which steps S130 and S140 are performed sequentially, but steps S130 and S140 may be performed in a different order than that shown in FIG. 5 or may be performed simultaneously.

[0055] In step S150, the event detection unit 130 may detect an event from the new monitoring data. For example, the event detection unit 130 may analyze (e.g., parse, decode) the new monitoring data using associated metadata and apply associated rules to the analyzed monitoring data to detect an event. The event detection unit 130 may determine that an event has occurred if a condition specified in a rule is satisfied.

[0056] Examples of rule types, forms, and events will be described in detail later with reference to Tables 1 to 10.

[0057] 5, if an event is detected, the new monitoring data may be tagged with information about the detected event (e.g., the name of the event, an identifier of the event, an identifier of the rule, the name of the rule, etc.), and the new monitoring data may be stored in data storage 141 along with the tagged event information.

[0058] 6 is an exemplary flowchart illustrating a second detailed operation of the event detector 130 according to some embodiments of the present disclosure. The second detailed operation relates to an event detection process based on a new rule.

[0059] 6, the second detailed operation may start from step S210 in which the event detection unit 130 receives a new rule R. In this step, the event detection unit 130 may obtain the new rule R, a rule identifier R-ID, and an assigned tag value TagVal from the rule registration unit 120. Alternatively, the event detection unit 130 may obtain the identifier R-ID of the new rule from the rule registration unit 120, and obtain the new rule R and the tag value TagVal from the rule storage 142 using the obtained identifier R-ID. As described above, there are various methods for the event detection unit 130 to receive information about the new rule, and any method may be used.

[0060] In step S220, the event detection unit 130 can use the tag value TagVal to query the monitoring data and metadata associated with the new rule from the mapping table 144. As a result, the event detection unit 130 can obtain the identifier D-ID of the monitoring data and the identifier M-ID of the metadata associated with the new rule.

[0061] In step S230, the event detection unit 130 can obtain monitoring data associated with the new rule (ie, to which the new rule is applied) from the data storage 141 using the obtained data identifier D-ID.

[0062] In step S240, the event detection unit 130 may retrieve metadata associated with the new rule from the metadata storage 142 using the retrieved metadata identifier M-ID.

[0063] For reference, FIG. 6 shows an example in which steps S230 and S240 are performed sequentially, but steps S230 and S240 may be performed in an order different from that shown in FIG. 6 or may be performed simultaneously.

[0064] In step S250, the event detection unit 130 may detect an event from the acquired monitoring data. For example, the event detection unit 130 may analyze (e.g., parse, decode) the acquired monitoring data using related metadata and apply a new rule to the analyzed monitoring data to detect an event. The event detection unit 130 may determine that an event has occurred if a condition specified in the new rule is satisfied.

[0065] 6, when an event is detected, the acquired monitoring data may be tagged with information about the detected event (e.g., the name of the event, an identifier of the event, an identifier of the rule, the name of the rule, etc.), and the tagged event information may be stored in the data storage 141.

[0066] Detailed operations of the event detector 130 according to some embodiments of the present disclosure have been described above with reference to Figures 5 and 6. Hereinafter, an example of use of the detection device 100 will be described with reference to Figure 7.

[0067] 7 is an exemplary diagram for explaining an example of use of the detection device 100 according to some embodiments of the present disclosure. In particular, FIG. 7 illustrates an example in which a monitoring target is a vehicle system 10.

[0068] 7, the vehicle system 10 includes various sensors for sensing (measuring) various monitoring elements (e.g., speed, position, heading angle, blinker status, front and rear images, etc.). The vehicle system 10 can transmit monitoring data (e.g., D1, D2, D3) including measurement values ​​and timestamps for the monitoring elements to the detection device 100 in real time or non-real time. At this time, a predetermined tag value can be assigned in advance to each monitoring data (e.g., D1, D2, D3).

[0069] As described above, the detection device 100 can use tag values ​​assigned to the monitoring data (e.g., D1, D2, D3) to obtain associated metadata and rules (e.g., R1) and apply the associated metadata and rules (e.g., R1) to detect an event from the monitoring data (e.g., D1). The detection device 100 can also tag the monitoring data (e.g., D1) with information about the detected event (e.g., rule identifier R1) and store the monitoring data (e.g., D1) together with the tagged event information (e.g., R1) in the data storage 141.

[0070] For example, assume that a first rule for detecting a right turn event and a second rule for detecting a hard braking event are registered, and a common tag value is assigned to the monitoring data of the vehicle system 10 and the first and second rules. In this case, the detection device 100 can detect a hard braking event (situation) and a right turn event (situation) from the monitoring data received from the vehicle system 10, and tag the event information. In addition, when a hard braking event and a right turn event are detected simultaneously, the detection device 100 can recognize a situation in which the brakes are applied hard while turning right. By repeating the process of detecting and tagging events from the monitoring data of the vehicle system 10, the operation record of the vehicle system 10 can be efficiently analyzed.

[0071] The configuration and operation of the detection device 100 according to some embodiments of the present disclosure have been described above with reference to FIGS. 1 to 7. According to the above, a common tag value may be assigned between monitoring data and an associated rule. As a result, when specific monitoring data is acquired, a rule associated with the specific monitoring data may be automatically determined using the tag value, and an event may be automatically detected from the specific monitoring data by applying the determined rule. That is, the process of determining the associated rule and detecting an event may be fully automated via the tag value assigned in advance, thereby greatly improving the efficiency of monitoring data analysis.

[0072] A common tag value may also be assigned between the monitoring data and the associated metadata. Thus, when specific monitoring data is acquired, metadata associated with the specific monitoring data may be automatically determined using the tag value, the specific monitoring data may be automatically decoded using the determined metadata, and the event detection process may also be automatically performed. That is, the process of determining associated metadata, data decoding, and event detection may be fully automated through the pre-assigned tag value, thereby greatly improving the efficiency of monitoring data analysis.

[0073] In the following, an event detection method according to some embodiments of the present disclosure will be described with reference to FIG.

[0074] Each step of the method described below may be realized by one or more instructions executed by a processor of a computing device, and for ease of understanding, the description will be continued on the assumption that the steps are performed by the detection device 100 having the configuration illustrated in Fig. 1. Therefore, when the subject of an operation of a particular step is omitted, it can be understood that the operation is performed by a component (e.g., 110) of the detection device 100 illustrated in Fig. 1.

[0075] 8 is an exemplary flowchart showing an event detection method according to some embodiments of the present disclosure. However, this is merely a preferred embodiment for achieving the object of the present disclosure, and it is obvious that some steps may be added or deleted as necessary.

[0076] 8, the event detection method may start with step S310 of registering rules and metadata. For the registration process of rules and metadata, please refer to the description of the metadata registration unit 110 and the rule registration unit 120, and further description will be omitted to avoid duplication.

[0077] In step S320, it may be determined whether new monitoring data has been received. If new monitoring data has been received, step S330 may be performed, in which the new monitoring data may also be received along with a data identifier and a tag value assigned to the data.

[0078] In step S330, the tag value of the monitoring data may be used to obtain associated metadata and rules. For example, the detection device 100 may use the tag value of the monitoring data to query the mapping table 144 for associated metadata identifiers and rule identifiers, and use the query results to obtain associated metadata and rules from the metadata storage 142 and the rule storage 143.

[0079] In step S340, an event may be detected using the associated metadata and the rules. For example, the detection device 100 may analyze new monitoring data using the associated metadata and apply the associated rules to the analyzed monitoring data to detect an event.

[0080] In step S350, if an event is detected, the new monitoring data may be tagged with information about the event, and both the new monitoring data and the tagged event information may be stored in data storage 141.

[0081] For reference, step S310 may be performed by the metadata registration unit 110 and the rule registration unit 120, and steps S320 to S350 may be performed by the event detection unit .

[0082] Meanwhile, according to some embodiments of the present disclosure, an event detection process may also be performed when a new rule is registered. For example, in response to the new rule being registered, the detection device 100 may obtain monitoring data associated with the new rule from the data storage 141, and apply the new rule to the obtained monitoring data to detect an event. For a more detailed description of this embodiment, please refer to the description of FIG. 6.

[0083] Above, an event detection method according to some embodiments of the present disclosure has been described with reference to Fig. 8. In the following, various types of event detection rules that can be referenced in some embodiments of the present disclosure will be introduced.

[0084] The first type rule is the most basic rule, and may be defined to include, for example, the items listed in Table 1 below. Of course, some items may be omitted or added depending on the case. For example, the first type rule may further include an action item to be performed when a condition is met, a tagging condition item (e.g., an item used when an event occurrence condition and a tagging condition are set separately), an item in which tagged information is set, etc.

[0085] [Table 1]

[0086] As shown in Table 1, a function that performs a predetermined calculation based on the values ​​of one or more monitoring elements may be set in the function item. For reference, the name of the rule may be used as the rule identifier, or a separate identifier may be assigned to each rule. In addition, when the rule condition is satisfied, the monitoring data (e.g., the timestamp of the monitoring data) may be tagged with information about the event. For example, the timestamp of the corresponding data may be tagged with the name of the first rule. Events detected through the first type of rule may be various. For example, a speeding event (e.g., a situation in which the driving speed exceeds a specified value) may be detected through the first type of rule. As a more specific example, as shown in FIG. 9, an event in which the sum of two types of signals (A, B) is equal to or less than a certain value ("71") may be detected through the first type of rule. FIG. 9 illustrates an example in which a function that calculates the sum of two types of signals (A, B) is set in the function item, and a condition that the output value of the function is equal to or less than "71" is set in the condition item, resulting in the event being detected at time "t". For reference, in FIG. 9 and subsequent figures, "abs" means a function that calculates an absolute value, and a "V" shaped symbol indicates that an event has been detected.

[0087] Next, the second type of rule is a rule defined based on history, and may be defined to include, for example, the items listed in Table 2 below. Of course, some items may be omitted or added depending on the case. For an explanation of each item, please refer to the contents of Table 2 below.

[0088] [Table 2]

[0089] Events detected through the second type rule may vary. For example, as shown in FIG. 10, an event in which the difference between the sum values ​​of two types of signals (A, B) at different times (t-1, t) exceeds a certain value ("10") may be detected through the second type rule. FIG. 10 illustrates an example in which a "function 1" that obtains the sum value of two types of signals (A, B) and a "function 2" that obtains the difference between the function output values ​​at two times are set in the function item, and a condition that the output value of function 2 exceeds "10" is set in the condition item, resulting in the event being detected at time "t". Next, the third type rule is a rule defined based on multiple functions and multiple conditions, and may be defined to include, for example, the items listed in Table 3 below. Of course, some items may be omitted or added depending on the case.

[0090] [Table 3]

[0091] Referring to Table 3, when the sequential item is set to "T (True)", if the output value of the kth function (where k is a natural number equal to or greater than 1) satisfies the kth condition, and the output value of the k+1th function satisfies the k+1th condition. Conversely, when the sequential item is set to "F (False)", if the output value of the xth function satisfies the xth condition, regardless of order, and the output value of the kth function satisfies the kth condition, then information about the event may be tagged. Events detected through the third type rule may vary. For example, as shown in FIG. 11, an event in which the sum of two types of signals (A, B) satisfies multiple conditions (condition 1 to condition 3) in sequence may be detected through the third type rule (when the sequential item is "T"). FIG. 11 illustrates an example in which a function for calculating the sum of two types of signals (A, B) is set in the function item, and three conditions (condition 1 to condition 3) are set in the condition item, resulting in detection of an event at time "t" and time "t+1" (i.e., the time when the set conditions are satisfied in sequence). For reference, FIG. 11 assumes that a plurality of the same functions are set in the multiple function items. Next, the fourth type of rule is a rule that combines the second type and third type of rules, and may be defined to include, for example, the items listed in Table 4 below. Of course, some items may be omitted or added depending on the case. For an explanation of each item, please refer to the contents of Table 4 below.

[0092] [Table 4]

[0093] Events detected through the fourth type rule may vary. For example, as shown in FIG. 12, an event in which the difference between the sum of two types of signals (A, B) at different times (t-1, t, t+1) non-sequentially satisfies multiple conditions (condition 1 and condition 2) may be detected through the fourth type rule (when the sequential item is "F"). FIG. 12 illustrates an example in which "function 1" for calculating the sum of two types of signals (A, B) and "function 2" for calculating the difference between function output values ​​at two times are set in the function item, and two conditions (condition 1 and condition 2) are set in the condition item, resulting in the detection of an event at time "t+1" (i.e., the time when condition 1 is satisfied after condition 2). For reference, FIG. 12 also assumes that multiple identical functions are set in the multiple function items. Next, the fifth type rule is a rule related to trends, and may be defined to include, for example, the items listed in Table 5 below. Of course, some items may be omitted or added depending on the circumstances.

[0094] [Table 5]

[0095] Referring to Table 5, when at least one of the three conditions (e.g., upper limit condition, lower limit condition, and coefficient of determination condition) specified in the fifth type rule is satisfied, information about the event may be tagged. For an explanation of each item, refer to the contents of Table 5. The events detected through the fifth type rule may be various. For example, a sudden braking event (e.g., a situation in which the slope of the driving speed is equal to or lower than a lower limit) or a sudden acceleration event (e.g., a situation in which the slope of the driving speed is equal to or higher than an upper limit) may be detected through the fifth type rule. For a more specific example, refer to FIG. 13. FIG. 13 illustrates that an event is detected at time “t+20” as a result of a “function” for obtaining a negative value of the signal (A) being set in the function item and a condition that the slope is “3” or more and the coefficient of determination is “0.7” or more being set in the condition item. For reference, FIG. 13 illustrates that 10 signal values ​​are line-fitted with a linear function because the buffer size item is set to “10”. Next, the sixth type of rule is a rule related to the heading angle of a moving body such as a vehicle system, and may be defined to include, for example, the items listed in the following Table 6. Of course, some items may be omitted or added depending on the case.

[0096] [Table 6]

[0097] Referring to Table 6, when at least one of two conditions (e.g., upper limit condition, lower limit condition) specified in the sixth type rule is satisfied, information on the event may be tagged. For an explanation of each item, refer to the contents of Table 6. For reference, the lateral heading angle of the moving object may be calculated from the GPS coordinate value. Events detected through the sixth type rule may be various. For example, through the sixth type rule, events such as a left turn, a right turn, a U-turn, etc. (e.g., when the lateral heading angle is equal to or greater than the upper limit value) may be detected. As a more specific example, when the rule is set as shown in FIG. 14, an event may be detected at a point (P8) when the lateral heading angle falls within the range of "240°" to "300°". Next, the seventh type rule is also a rule related to the heading angle of a moving object such as a vehicle system, and may be defined to include, for example, the items listed in Table 7 below. Of course, some items may be omitted or added depending on the case.

[0098] [Table 7]

[0099] Referring to Table 7, information about an event may be tagged if at least one of the two conditions (e.g., upper limit condition, lower limit condition) specified in the seventh type rule is satisfied. For an explanation of each item, refer to the contents of Table 7. For reference, the vertical heading angle of the moving body may be calculated from the altitude value. Next, the eighth type rule is a rule based on the number of occurrences of an event of interest, and may be defined to include, for example, the items listed in Table 8 below. Of course, some items may be omitted or added depending on the case.

[0100] [Table 8]

[0101] Referring to Table 8, when the counting condition specified in the rule of type 8 is satisfied, information on the event may be tagged. For an explanation of each item, refer to the contents of Table 8. The event detected through the rule of type 8 may be various. For example, through the rule of type 8, an event in which sudden braking is repeated a certain number of times or more may be detected. As a more specific example, when the rule is set as shown in FIG. 15, an event (e.g., a situation in which an error occurs three or more times within 30 ms) may be detected at a time point ("110 ms") when the event of interest "A" occurs three or more times within "30 ms". Or, as shown in FIG. 16, an event (e.g., a situation in which a left turn occurs two or more times within 10 seconds) may be detected at a time point ("9000 ms") when the event of interest "A" occurs two or more times within "10,000 ms". Next, the rule of type 9 is a rule related to the duration of an event, and may be defined to include, for example, the items listed in Table 9 below. Of course, some items may be omitted or added depending on the case.

[0102] [Table 9]

[0103] Events detected through the ninth type rule may vary. For example, through the ninth type rule, an event in which a speed within a specified range is sustained for a certain period of time may be detected. As a more specific example, when the rule is set as shown in FIG. 17, an event (e.g., a situation in which a vehicle is traveling at a speed of over 50 for 50 ms or more) may be detected at a point in time ("180 ms") when the occurrence of an event of interest "A" is sustained for "150 ms" or more. Next, the tenth type rule is a rule for detecting a compound event, and may be defined to include, for example, the items listed in Table 10 below. Of course, some items may be omitted or added depending on the case.

[0104] [Table 10]

[0105] Referring to Table 10, when the conditions specified in the tenth type rule are satisfied (i.e., when multiple events of interest set within the maximum allowable delay occur), information about the event may be tagged. For an explanation of each item, refer to the contents of Table 10. Events detected through the tenth type rule may be various. For example, an event of braking suddenly while turning right may be detected through the tenth type rule. As a more specific example, when the rule is set as shown in FIG. 18, an event (e.g., a situation in which a left turn is made within 10 ms while driving at a speed of more than 50) may be detected at a time point ("110 ms") when the events of interest "A" and "B" occur within "10 ms". As described above, various types of rules that can be referred to in some embodiments of the present disclosure have been described with reference to Tables 1 to 10 and FIGS. 9 to 18. Through the various types of rules described above, the number of detectable events may be increased, and various types of events may be defined.

[0106] In the following, with reference to FIG. 19, an exemplary computing device 200 capable of implementing the detection device 100 according to some embodiments of the present disclosure will be described.

[0107] FIG. 19 is an exemplary hardware configuration diagram of a computing device 200.

[0108] As shown in Fig. 19, a computing device 200 may include one or more processors 210, a bus 250, a communication interface 270, a memory 230 for loading a computer program 291 executed by the processor 210, and a storage 290 for storing the computer program 291. However, Fig. 19 shows only components related to the embodiments of the present disclosure. Therefore, a person skilled in the art to which the present disclosure pertains will understand that other general-purpose components may be included in addition to the components shown in Fig. 19.

[0109] The processor 210 can control the overall operation of each component of the computing device 200. The processor 210 can be configured to include a central processing unit (CPU), a micro processor unit (MPU), a micro controller unit (MCU), a graphic processing unit (GPU), or any other type of processor well known in the technical field of the present disclosure. The processor 210 can also perform calculations for at least one application or program for performing operations / methods according to the embodiments of the present disclosure. The computing device 200 can include one or more processors.

[0110] In turn, the memory 230 can store various data, instructions, and / or information. The memory 230 can load one or more computer programs 291 from the storage 290 to execute operations / methods according to embodiments of the present disclosure. When the computer programs 291 are loaded, modules (e.g., 110) as illustrated in FIG. 1 can be implemented on the memory 230. The memory 230 can be implemented as a volatile memory such as a RAM, although the scope of the present disclosure is not limited thereto.

[0111] The bus 250 may then provide a communication function between the components of the computing device 200. The bus 250 may be implemented as various types of buses, such as an address bus, a data bus, and a control bus.

[0112] Next, the communication interface 270 can support wired and wireless Internet communication of the computing device 200. The communication interface 270 can also support various communication methods other than Internet communication. To this end, the communication interface 270 can be configured to include a communication module well known in the technical field of the present disclosure.

[0113] Next, the storage 290 may non-temporarily store the one or more computer programs 291. The storage 290 may include a non-volatile memory such as a read only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a flash memory, a hard disk, a removable disk, or any other form of computer-readable recording medium well known in the art to which the present disclosure pertains.

[0114] In turn, the computer program 291 may include one or more instructions that, when loaded into the memory 230, cause the processor 210 to perform an operation / method according to an embodiment of the present disclosure. That is, the processor 210 executes the one or more instructions to perform the operation / method according to an embodiment of the present disclosure.

[0115] For example, the computer program 291 may include instructions for performing an operation of registering a rule for detecting an event and a tag value assigned to the rule, an operation of acquiring monitoring data and a tag value assigned to the monitoring data, an operation of determining a rule associated with the monitoring data from among the pre-registered rules using the acquired tag value, and an operation of detecting an event from the monitoring data by applying the determined rule. In this case, the detection device 100 according to some embodiments of the present disclosure may be embodied through the computing device 200.

[0116] Above, with reference to FIG. 19, an exemplary computing device 200 capable of implementing the detection device 100 according to some embodiments of the present disclosure has been described.

[0117] The technical ideas of the present disclosure described above with reference to Figures 1 to 19 may be embodied as computer readable codes on a computer readable medium. The computer readable recording medium may be, for example, a portable recording medium (CD, DVD, Blu-ray disc, USB storage device, portable hard disk) or a fixed recording medium (ROM, RAM, computer-installed hard disk). The computer program recorded on the computer readable recording medium may be transmitted to another computing device via a network such as the Internet and installed in the other computing device, and thus may be used in the other computing device.

[0118] Although all components constituting the embodiments of the present disclosure have been described as being combined together or operating in combination, the technical idea of ​​the present disclosure is not necessarily limited to such embodiments. In other words, within the scope of the purpose of the present disclosure, all components may be selectively combined to operate in one or more combinations.

[0119] Although the figures show operations in a particular order, it should not be understood that the operations must be performed in the particular order or sequentially shown, or that all of the operations shown must be performed to achieve desired results. In certain situations, multitasking and parallel processing may be advantageous. Furthermore, the separation of various components in the above-described embodiments should not be understood as necessarily requiring such separation, and it should be understood that the program components and systems described may generally be integrated together in a single software product or packaged in multiple software products.

[0120] Although the embodiments of the present disclosure have been described above with reference to the accompanying drawings, those skilled in the art to which the present disclosure pertains can understand that the present disclosure can be embodied in other specific forms without changing its technical ideas or essential features. Therefore, it should be understood that the embodiments described above are illustrative in all respects and are not limiting. The scope of protection of the present disclosure should be interpreted by the scope of the appended claims, and all technical ideas within the scope equivalent thereto should be interpreted as being included in the scope of rights of the technical ideas defined by the present disclosure.

Claims

1. a rule registration unit that registers rules for detecting an event and tag values ​​assigned to the rules; an event detection unit that acquires monitoring data and a tag value assigned to the monitoring data, determines a rule associated with the monitoring data from among pre-registered rules using the acquired tag value, and detects an event from the monitoring data by applying the determined rule; a metadata registration unit that registers metadata for analyzing the monitoring data and a tag value assigned to the metadata, The event detection unit determining metadata associated with the monitoring data from among the pre-registered metadata using the acquired tag value; Analyzing the monitoring data using the determined metadata; An event detection device that applies the determined rules to detect the events from the analyzed monitoring data.

2. The event detection unit The event detection apparatus of claim 1 , further comprising: a step of tagging the monitoring data with information about the detected event.

3. The rule registration unit storing the rule in a rule storage, and registering an identifier of the rule and a tag value assigned to the rule in a mapping table; The event detection unit The event detection device according to claim 1 , further comprising: a tag value assigned to the monitoring data is used to query an identifier of the associated rule from the mapping table; and the identifier of the query rule is used to retrieve the associated rule from the rule storage.

4. A rule registration unit that registers rules for detecting an event and tag values ​​assigned to the rules; an event detection unit that acquires monitoring data and a tag value assigned to the monitoring data, determines a rule associated with the monitoring data from among pre-registered rules using the acquired tag value, and detects an event from the monitoring data by applying the determined rule; The event detection unit In response to determining that a new rule has been registered, determining which of the pre-stored monitoring data is associated with the new rule using a tag value assigned to the new rule; An event detector that applies the new rules to detect events from the determined data.

5. the monitoring data includes values ​​for one or more monitoring factors; The rule includes a function item and a condition item, A function for performing a predetermined calculation based on the value of the one or more monitoring elements is set in the function item, The event detection device according to claim 1 , wherein the condition item is a condition for an output value of the set function.

6. the monitoring data further includes a timestamp for the value of the monitoring element; The rule further includes a maximum allowable delay item, The event detection device according to claim 5 , wherein a maximum timestamp difference value of values ​​of a plurality of monitoring elements input to the set function is set in the maximum allowable delay item.

7. The rule includes multiple function terms, multiple condition terms, and sequential terms; A first function and a second function are set in the multiple function item, In the multiple condition item, a first condition for an output value of the first function and a second condition for an output value of the second function are set, The event detection device according to claim 1 , wherein a value indicating whether or not the first condition and the second condition are determined sequentially is set in the sequential item.

8. The rule includes a function item and a condition item, A function for performing a predetermined calculation based on the monitoring data is set in the function item, The event detection device according to claim 1 , wherein the condition item is a condition for a gradient of a linear approximation function obtained from a plurality of values ​​output by the set function.

9. The rule includes a function item and a condition item, A function for performing a predetermined calculation based on the monitoring data is set in the function item, The event detection device according to claim 1 , wherein the condition item is set with respect to a coefficient of determination of a linear approximation function obtained from a plurality of values ​​output by the set function.

10. The monitoring data is data relating to a moving object, The rule includes a condition item, The event detection device according to claim 1 , wherein the condition item is a condition for a lateral heading angle or a vertical heading angle of the moving object.

11. The rule includes a condition item, The event detection device according to claim 1 , wherein the condition item is a condition for a number of occurrences of an event of interest.

12. The rule includes a condition item, The event detection device according to claim 1 , wherein the condition item is a condition for a duration of the event of interest.

13. 1. A method of event detection performed by a computing device, comprising: registering a rule for detecting an event and a tag value assigned to the rule; acquiring monitoring data and a tag value assigned to the monitoring data; determining a rule associated with the monitoring data from among pre-registered rules using the acquired tag value; registering metadata for analyzing the monitoring data and tag values ​​assigned to the metadata; determining metadata associated with the monitoring data from among the pre-registered metadata using the acquired tag value; analyzing the monitoring data using the determined metadata; and applying the determined rules to detect events from the analyzed monitoring data.

14. In combination with a computing device, registering a rule for detecting an event and a tag value assigned to the rule; acquiring monitoring data and a tag value assigned to the monitoring data; registering metadata for analyzing the monitoring data and tag values ​​assigned to the metadata; determining metadata associated with the monitoring data from among the pre-registered metadata using the acquired tag value; analyzing the monitoring data using the determined metadata; and applying the determined rules to detect the event from the analyzed monitoring data.

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