CN111427915B - Information processing method and device, storage medium and electronic equipment - Google Patents
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Abstract
The disclosure provides an information processing method, an information processing device, a storage medium and electronic equipment, and relates to the technical field of computers. The information processing method comprises the following steps: acquiring semantic information associated with a non-deterministic event; acquiring a matching rule of the non-deterministic event according to the semantic information; if an event is received, identifying the event through a matching rule of the non-deterministic event so as to determine whether the event is a non-deterministic event. According to the technical scheme, the non-deterministic event can be timely identified, the delay is avoided, and the identification efficiency is improved.
Description
Technical Field
The present disclosure relates to the field of computer technology, and in particular, to an information processing method, an information processing apparatus, a computer-readable storage medium, and an electronic device.
Background
In a practical application scenario, there are a large number of behavior detection requirements, and the behaviors can be monitored in real time.
In the related art, complex event processing is generally used to calculate a given event matching rule. But with complex event processing, it is necessary to rely on the occurrence of events and to trigger pattern matching rules calculation results based on the occurrence of events. When processing non-deterministic events, real-time computation cannot be adopted, but the difference set can be computed on the user data of different events only in an offline mode.
In the process of realizing the method, the inventor finds that in the mode, the non-deterministic event is calculated with certain delay, the timeliness is poor, the calculation efficiency is low, the accuracy is low, and the service requirement is difficult to meet.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
Embodiments of the present disclosure provide an information processing method, an information processing apparatus, a computer-readable storage medium, and an electronic device, so as to overcome, at least to some extent, the problem of delay in calculating a non-deterministic event in the related art.
Other features and advantages of embodiments of the present disclosure will be apparent from the following detailed description, or may be learned by practice of embodiments of the disclosure in part.
According to an aspect of the embodiments of the present disclosure, there is provided an information processing method including: acquiring semantic information associated with a non-deterministic event; acquiring a matching rule of the non-deterministic event according to the semantic information; if an event is received, identifying the event through a matching rule of the non-deterministic event so as to determine whether the event is a non-deterministic event.
In an exemplary embodiment of the disclosure, the obtaining, according to the semantic information, a matching rule of the non-deterministic event includes: and analyzing the semantic information according to the analysis rules corresponding to the non-deterministic event to obtain grammar information, and determining the matching rules of the non-deterministic event according to the grammar information.
In an exemplary embodiment of the disclosure, the identifying the event by the matching rule of the non-deterministic event to determine whether the event is a non-deterministic event includes: and calculating and identifying the event through a plurality of rules obtained by analyzing the matching rules of the non-deterministic event so as to determine whether the event is the non-deterministic event.
In an exemplary embodiment of the disclosure, the computing the event to determine whether the event is the non-deterministic event by analyzing the plurality of rules obtained by parsing the matching rule of the non-deterministic event includes: and according to the arrangement sequence among the rules, calculating and identifying the event, and determining whether the event is the non-deterministic event according to a calculation result.
In an exemplary embodiment of the disclosure, the computing the identifying the event according to the arrangement sequence among the rules includes: filtering the events according to the filtering rules, and combining the filtered events according to the pattern matching rules; and calculating the combined event by adopting a calculation rule to obtain a calculation result, and determining whether the combined event is the non-deterministic event according to the calculation result.
In an exemplary embodiment of the disclosure, the determining whether the combined event is the non-deterministic event according to a calculation result includes: if the calculation result is that the combined event meets the calculation rule, determining that the event belongs to the non-deterministic event; and if the calculation result is that the combined event does not meet the calculation rule, determining that the event is not the non-deterministic event.
In an exemplary embodiment of the present disclosure, the method further comprises: and if the event is determined to be the non-deterministic event, executing auxiliary operation on the object corresponding to the non-deterministic event.
According to an aspect of the embodiments of the present disclosure, there is provided an information processing apparatus including: the information acquisition module is used for acquiring semantic information associated with the nondeterministic event; the rule acquisition module is used for acquiring the matching rule of the non-deterministic event according to the semantic information; and the event identification module is used for identifying the event through the matching rule of the non-deterministic event if the event is received, so as to determine whether the event is the non-deterministic event.
According to an aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the information processing method of any one of the above.
According to an aspect of the embodiments of the present disclosure, there is provided an electronic device including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the information processing method of any one of the above via execution of the executable instructions.
According to the information processing method, the information processing device, the computer readable storage medium and the electronic equipment, whether the received event is a non-deterministic event is determined by acquiring the newly added semantic information associated with the non-deterministic event, acquiring the matching rule of the non-deterministic event according to the semantic information and further identifying the received event according to the matching rule of the non-deterministic event. On one hand, the matching rule of the nondeterminacy event can be determined through semantic information, and then the received event can be identified on line in real time through the matching rule of the nondeterminacy event, so that the delay problem caused by calculating the user data only in an off-line mode is avoided, the identification efficiency of the nondeterminacy event can be improved through real-time processing, the limitation is avoided, the timeliness and the identification efficiency are improved, and the accuracy and the comprehensiveness of the identification are also improved. On the other hand, the event can be identified in time, so that the problem of missing identification caused by delay is avoided, the comprehensiveness is improved, the conversion efficiency of the event can be improved, and the service requirement is met.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely some embodiments of the present disclosure and that other drawings may be derived from these drawings without undue effort.
Fig. 1 schematically illustrates a system architecture diagram for implementing an information processing method according to an embodiment of the present disclosure.
Fig. 2 schematically illustrates a schematic diagram of an information processing method in an embodiment of the present disclosure.
Fig. 3 schematically illustrates a flow diagram of complex event processing in an embodiment of the present disclosure.
Fig. 4 schematically illustrates a specific flow diagram for identifying events in an embodiment of the present disclosure.
Fig. 5 schematically illustrates a specific flowchart of an information processing method in an embodiment of the disclosure.
Fig. 6 schematically shows a block diagram of an information processing apparatus in an embodiment of the present disclosure.
Fig. 7 schematically illustrates a block diagram of an electronic device in an embodiment of the disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the present disclosure. One skilled in the relevant art will recognize, however, that the aspects of the disclosure may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.
Fig. 1 shows a schematic diagram of an exemplary system architecture to which the technical solution of the embodiments of the present disclosure may be applied.
As shown in fig. 1, the system architecture 100 may include a first end 101, a network 102, and a second end 103. The first end 101 may be a client, for example, may be various handheld devices (smart phones) with display functions, desktop computers, vehicle-mounted devices, and the like. Network 102 is a medium used to provide a communication link between first end 101 and second end 103. Network 102 may include various connection types, such as a wired communication link, a wireless communication link, etc., and in embodiments of the present disclosure, network 102 between first end 101 and second end 103 may be a wired communication link, such as a communication link may be provided over a serial connection, or a wireless communication link may be provided over a wireless network. The second end 103 may be a client, such as a terminal device with data processing capabilities, e.g. a portable computer, a desktop computer, a smart phone, etc., for identifying an event. When the first end and the second end are both clients, they may be the same client. The second end may also be a server, which is not limited herein.
It should be understood that the number of first ends, networks, and second ends in fig. 1 are merely illustrative. There may be any number of clients, networks, and servers, as desired for implementation.
It should be noted that, the information processing method provided by the embodiment of the present disclosure may be completely executed by the second terminal, and accordingly, the information processing apparatus may be disposed in the second terminal 103.
Based on the system architecture, an information processing method is provided in an embodiment of the disclosure. Referring to fig. 2, the information processing method includes steps S210 to S230, and is described in detail as follows:
in step S210, semantic information associated with a non-deterministic event is acquired;
In step S220, according to the semantic information, a matching rule of the non-deterministic event is obtained;
in step S230, if an event is received, the event is identified by the matching rule of the non-deterministic event to determine whether the event is a non-deterministic event.
In the information processing method provided by the embodiment of the disclosure, on one hand, the matching rule of the nondeterminacy event can be determined through semantic information, and then the received event can be identified online in real time through the matching rule of the nondeterminacy event, so that the delay problem caused by calculating the user data only in an offline mode is avoided, the identification efficiency of the nondeterminacy event can be improved through real-time processing, the limitation is avoided, the timeliness and the identification efficiency are improved, and the accuracy and the comprehensiveness of the identification are also improved. On the other hand, the event can be identified in time, so that the problem of missing identification caused by delay is avoided, the comprehensiveness is improved, the conversion efficiency of the event can be improved, and the service requirement is met.
Next, an information processing method in an embodiment of the present disclosure will be further explained with reference to the drawings.
Referring to fig. 2, in step S210, semantic information associated with a non-deterministic event is acquired.
In the disclosed embodiments, an event is some action performed by the user or the browser itself. The types of events may include both deterministic events and non-deterministic events. Wherein each deterministic event and non-deterministic event may consist of at least two events with a fixed order of execution between the at least two events. Deterministic events refer to all events of the at least two events occurring in a fixed order of execution, non-deterministic events refer to one or more events of the at least two events occurring in no fixed order of execution, e.g. only the preceding events occur in a fixed order of execution and no following events occur. For example, for events a to D, if the execution order of deterministic events is ABCD, but the execution order of events during execution is ABDC, two events may be considered to belong to non-deterministic events because two events are in order and two events are out of order.
The semantic information is used for describing rules of the event to be identified by using a text information mode, and can be specifically determined according to the type of the object to be identified, the object to be identified can be determined according to actual service requirements, and the objects to be identified corresponding to different application scenes can be the same or different. The event to be identified may be a deterministic event or a non-deterministic event. For example, if the object to be identified is a non-deterministic event, the semantic information is used to represent rules associated with the non-deterministic event; if the object to be identified is a deterministic event, the semantic information is used to represent rules associated with the deterministic event. The semantic information may be represented by a program, code, or the like. In the embodiment of the disclosure, for the initial CEP (complex event processing ) process, the related content of the non-deterministic event is not contained therein, so that the semantic information associated with the non-deterministic event can be added to the CEP grammar rule first, so that the semantic information of the deterministic event and the semantic information of the non-deterministic event are contained in the grammar rule at the same time. Further, semantic information of the added matching rule for describing the non-deterministic event may be acquired from the complex event processing grammar rule, so that the WITH TIME OUT semantic added WITH respect to the matching rule definition in the CEP grammar rule can be acquired. The semantic information may be specifically represented as ONE ROW PER MATCH WITH TIME OUT.
In this semantic information, description information of a plurality of rules that can be used to identify non-deterministic events can be included. For example, the plurality of rules may include, but are not limited to, filtering rules, pattern matching rules, and computing rules, among others. The descriptive information may be, for example, text information, and may be represented by a code. By means of semantic information, it is possible to describe exactly how to identify non-deterministic events.
Next, in step S220, a matching rule of the non-deterministic event is obtained according to the semantic information.
In the embodiment of the disclosure, after the newly added semantic information is obtained, the matching rule of the non-deterministic event can be obtained according to the semantic information. Specifically, according to the parsing rule corresponding to the non-deterministic event, the semantic information is parsed to obtain the grammar information, and the matching rule of the non-deterministic event is determined according to the grammar information. The parsing rules may be determined from a parser, which here may be, for example, CALCITE parser. In order to realize real-TIME calculation and identification, the parsing rule corresponding to the non-deterministic event can be added in the parser, and the added parsing rule can be a TIME limit rule TIME OUT. Based on this, the parsing rules may include rules of multiple dimensions, such as filtering dimensions, partitioning dimensions, and ordering dimensions, among others.
And CALCITE, when the abstract syntax tree is constructed by parsing, the semantic information is parsed by the parser to obtain syntax information, so as to construct a syntax NODE NODE corresponding to the semantic information. The specific process by which the parser parses SQL (Structured Query Language ) may include: and analyzing the semantic information into a plurality of rules with different dimensions based on the grammar information so as to convert the semantic information into the grammar information, processing the semantic information according to the grammar information, and acquiring the matching rule of the non-deterministic event according to the obtained combination of the plurality of rules. The rules of the multiple dimensions may be the same as the description information contained in the semantic information, and specifically may be filtering rules, pattern matching rules, calculation rules, and the like. Further, matching rules for non-deterministic events may be derived from a combination of these multiple rules.
For example, the semantic information is text information represented by a piece of code. The syntax parser processes the semantic information to get filtering rules for filtering the type of event, e.g. t1.type= 'a'. The semantic information may also be parsed into pattern matching rules that are used to represent a pattern temporal order, i.e., an ordering rule, which may be (t 1, t 2), where t1 is represented to occur first. The semantic information may also be parsed into a calculation rule, such as INTERVAL '15' minute, to represent a 15 minute timeout, so the calculation rule herein may be a timeout calculation rule.
With continued reference to fig. 2, in step S230, if an event is received, the event is identified by a matching rule of the non-deterministic event to determine whether the event is a non-deterministic event.
In the embodiment of the present disclosure, an event refers to an event included in an event stream, and may specifically be an event at a current time. Multiple events in the event stream may be matched to identify non-deterministic events. The event stream describes the order in which events are received from a page. The type of the event may or may not be a type in the matching rule of the non-deterministic event, and is not limited herein.
In the embodiment of the disclosure, the non-deterministic event is identified based on complex event processing CEP (complex event processing), which is one or more event streams composed of simple events, and the complex events satisfying the rule are output after matching by a certain rule. The specific procedure of complex event processing may be as shown in fig. 3. Referring to fig. 3, the goal is to find some higher-order features from an ordered simple event stream, whose input is one or more event streams composed of simple events, then the inherent links between simple events can be identified by rule, multiple simple events conforming to a certain rule compose a complex event, whose output is a complex event meeting the rule.
On the basis of complex event processing, since the matching rule of the non-deterministic event may be composed of a plurality of rules, the received event may be identified by a combination of the plurality of rules. A specific flow chart of identifying events is schematically shown in fig. 4, and with reference to fig. 4, mainly comprises the following steps:
In step S410, the event is filtered according to a filtering rule;
in step S420, the filtered events are combined according to a pattern matching rule;
in step S430, a calculation rule is used to calculate the combined event, and according to the calculation result, it is determined whether the combined event is the non-deterministic event.
In the embodiment of the disclosure, the filtering rule is used for filtering the event which does not accord with the non-deterministic event from a plurality of events so as to screen out the event matched with the non-deterministic event. Upon receipt of an event, the type of event may first be determined, and further filtering may be performed based on the type of event received. For example, the type of the received event is matched with the type of the event contained in the nondeterministic event, if the matching is successful, the event can be reserved, and if the matching is failed, the event can be filtered out. For example, the non-deterministic events may include event a and event B, and if event C is included in the received events, the events may be filtered out. The accuracy can be improved by filtering the received event through the filtering rule.
The filtered events may then be combined according to pattern matching rules. The types of the filtered events are consistent with the types of the events included in the nondeterministic events, in order to accurately identify the nondeterministic events, the filtered events can be processed according to the time sequence in the pattern matching rule, for example, the event A and the event B are combined together according to the time sequence, so that the event A occurs first and the event B occurs later, and the composition of the events and the nondeterministic events is kept consistent.
And further, calculating the combined event by adopting a calculation rule, and determining whether the combined event is the non-deterministic event according to a calculation result. The calculation rule may be used to determine whether the combined event is completed within a preset time, and the calculation rule may be determined according to the preset time, so the calculation time may be understood as a timeout calculation time. The preset time may be determined by specific semantic information, etc., and may be, for example, 15 minutes or 20 minutes, etc., which is not particularly limited herein. The calculation rule may be used to determine whether at least two events occur and whether the occurrence time of an event meets a preset time specified in the rule of non-deterministic events, i.e. to detect whether a subsequent event is received within the preset time. When the combined events are matched by adopting the calculation rules, whether the event type and the event occurrence time meet the calculation rules or not can be judged, so that a calculation result is obtained. Specifically, if the event type and the event occurrence time meet the conditions, determining that the calculation result meets the calculation rule; and if the event type and the event occurrence time do not meet the conditions, determining that the calculation result does not meet the calculation rule. The event occurrence time meeting the condition means that the occurrence time of the subsequent event does not meet the preset time, that is, the subsequent event is not detected within the preset time.
The result of the calculation is different, and the recognition result of the corresponding event is also different. Specifically, if the calculation result is that the combined event meets the calculation rule, it may be determined that the combined event is a non-deterministic event; if the calculation result is that the combined event does not meet the calculation rule, it may be determined that the combined event does not belong to a non-deterministic event.
For example, if an event is received and the type of the event is determined to be event a by type detection, it may be recorded and continuously detected whether event B is received within a preset time. If event B is not received within the preset time (event B is detected after exceeding the preset time), the event B can be considered to belong to a non-deterministic event; if event B is received within a preset time, it may be considered to be a deterministic event.
According to the technical scheme in FIG. 4, when an event is received, the received event is matched through the matching rule of the non-deterministic event formed by a plurality of rules with a plurality of dimensions, so that whether the received event is a function of the non-deterministic event can be identified on line in real time, delay caused by off-line event identification is avoided, and instantaneity, identification efficiency and identification comprehensiveness are improved.
A specific flowchart of the information processing is schematically shown in fig. 5, and referring to fig. 5, the method mainly includes the steps of:
In step S510, semantic rules associated WITH the non-deterministic event added in the complex event processing, such as the wit TIME OUT, are acquired.
In step S520, an parsing rule added in the parser, for example, an added TIME OUT timeout rule, is acquired such that the sorting rule, the pattern matching rule, and the timeout rule (calculation rule) are included in the parsing rule.
In step S530, the matching rule of the non-deterministic event added in the pattern matching rule is acquired such that the filtering rule, the partition rule, the sorting rule, the deterministic event matching rule, and the matching rule of the non-deterministic event are included therein. The ordering rule is a pattern matching rule.
In step S540, an event stream is received, which includes a plurality of events a and events B.
In step S550, the received event is processed using the filtering rule, the partitioning rule, and the ordering rule, and stored in the queue as a result of the matching. The PARTITION rule may be a parameter BY pin, where pin refers to a certain value in an event, and may be, for example, a user. The user data may be partitioned according to the user identifier, and each user corresponds to one partition. For events, t1, t2 belong to the same partition. It should be noted that the partition may be a queue. The received events are processed by using the filtering rule, the partitioning rule and the ordering rule and stored in the queue, so that mutual interference among a large amount of data can be avoided, the data of each user are mutually independent, and the accuracy of information processing is improved.
In step S560, the events in the queue are matched using a pattern matching rule to obtain a calculation result, and a deterministic event and a non-deterministic event are identified according to the calculation result. If the event A is detected and the event B is detected within the preset time, the event A is a deterministic event; if event A is detected but event B is not detected within a preset time, then it is a non-deterministic event.
Through the technical scheme in fig. 5, the received event can be accurately identified, the accuracy and timeliness of identification are improved, and the comprehensiveness of event identification is improved.
In the embodiment of the disclosure, WITH TIME OUT semantics are added by defining a matching rule in a complex event processing CEP grammar rule; processing the semantic information when parsing tool CALCITE parses and constructs AST (Abstract SyntaxTree ), and constructing corresponding syntax NODE NODE; when the abstract syntax tree converts the pattern matching rule, a matching rule for processing a non-deterministic event is newly added in the pattern matching rule; the output result of the matching rule for the non-deterministic event is added in the NFA (non-DETERMINTISTIC FINITE automaton, non-deterministic finite state automaton) implementation.
This approach may be considered feasible if it is determined that the expected computational requirements are met for the computational results that require non-deterministic events via simulation of the production environment real data stream. By adding semantic information into SQL, new calculation rules can be expanded, and then matching rules of non-deterministic events are obtained, so that event identification is facilitated. Only semantic information is added in SQL, so that operation steps are simplified, convenience is improved, event processing can be timely and real-timely performed, and accuracy is improved.
After the non-deterministic event is identified, an auxiliary operation may be performed with respect to the object to which the non-deterministic event corresponds. The object herein may be any suitable application object, such as an article or a commodity, etc. In the field of e-commerce, a non-deterministic event may consist of both joining a shopping cart and paying, such as an event that joins an item to a shopping cart but does not pay within a preset time limit (e.g., 15 minutes). The auxiliary operations herein may be operations for assisting in the transaction of an item. For example, a virtual object may be allocated, where the virtual object may be a coupon, a red envelope, a gold coin, a discount, or the like, which may be a plurality of types of objects that can be used for the transaction of an item. By using the virtual object, the payment amount of the user for the item can be reduced. The item herein may be an item corresponding to a non-deterministic event. In the embodiment of the disclosure, the non-deterministic event can be timely identified, the problem of identification omission is avoided, the comprehensiveness is improved, the conversion efficiency of the article transaction can be improved in the electronic commerce scene, the success rate of the article transaction is improved, the business requirement can be met, and the marketing effect is improved. For example, such as detecting that user a browses something details pages on an application, but does not purchase; or something has been ordered but not paid. Abnormal behaviors of the non-deterministic events are monitored in real time, and then active marketing is carried out to promote conversion and improve marketing effect.
According to the technical scheme, the non-deterministic event (timeout event) can be calculated on the basis of the software flink for real-time calculation, and the rule of task calculation is expanded by expanding SQL grammar rules so as to support the application scene of timeout output. And by expanding SQL simple grammar, the learning cost is lower for service users, and the service requirements can be more conveniently met.
In an embodiment of the present disclosure, there is also provided an information processing apparatus, referring to fig. 6, the information processing apparatus 600 mainly includes the following modules:
An information acquisition module 601, configured to acquire semantic information associated with a non-deterministic event;
a rule obtaining module 602, configured to obtain a matching rule of the non-deterministic event according to the semantic information;
The event recognition module 603 is configured to, if an event is received, recognize the event according to a matching rule of the non-deterministic event, so as to determine whether the event is a non-deterministic event.
In one exemplary embodiment of the present disclosure, the rule acquisition module includes: and the rule analysis module is used for analyzing the semantic information according to the analysis rule corresponding to the non-deterministic event to obtain grammar information, and determining the matching rule of the non-deterministic event according to the grammar information.
In one exemplary embodiment of the present disclosure, the event recognition module includes: and the rule matching module is used for calculating and identifying the event through a plurality of rules obtained by analyzing the matching rules of the non-deterministic event so as to determine whether the event is the non-deterministic event.
In one exemplary embodiment of the present disclosure, the rule matching module includes: and the matching control module is used for carrying out calculation and identification on the event according to the arrangement sequence among the rules and determining whether the event is the non-deterministic event according to a calculation result.
In one exemplary embodiment of the present disclosure, the matching control module includes: the event combination module is used for filtering the events according to the filtering rules and combining the filtered events according to the pattern matching rules; and the calculation module is used for calculating the combined event by adopting a calculation rule to obtain a calculation result, and determining whether the combined event is the non-deterministic event according to the calculation result.
In one exemplary embodiment of the present disclosure, the computing module includes: the first recognition module is used for determining that the event belongs to the non-deterministic event if the calculation result is that the combined event meets the calculation rule; and the second recognition module is used for determining that the event does not belong to the deterministic event if the calculation result is that the combined event does not meet the calculation rule.
In an exemplary embodiment of the present disclosure, the apparatus further comprises: and the auxiliary operation module is used for executing auxiliary operation on the object corresponding to the non-deterministic event if the event is determined to be the non-deterministic event.
In addition, the specific details of each part in the above information processing apparatus are already described in detail in the embodiment of the information processing method part, and the details not disclosed can be referred to the embodiment of the method part, so that the details are not described again.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
Furthermore, although the steps of the methods in the present disclosure are depicted in a particular order in the drawings, this does not require or imply that the steps must be performed in that particular order, or that all illustrated steps be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.
In an embodiment of the disclosure, an electronic device capable of implementing the above method is also provided.
Those skilled in the art will appreciate that the various aspects of the present disclosure may be implemented as a system, method, or program product. Accordingly, various aspects of the disclosure may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
An electronic device 700 according to such an embodiment of the present disclosure is described below with reference to fig. 7. The electronic device 700 shown in fig. 7 is merely an example and should not be construed to limit the functionality and scope of use of embodiments of the present disclosure in any way.
As shown in fig. 7, the electronic device 700 is embodied in the form of a general purpose computing device. Components of electronic device 700 may include, but are not limited to: the at least one processing unit 710, the at least one memory unit 720, a bus 730 connecting the different system components (including the memory unit 720 and the processing unit 710), and a display unit 740.
Wherein the storage unit stores program code that is executable by the processing unit 710 such that the processing unit 710 performs steps according to various exemplary embodiments of the present disclosure described in the above-described "exemplary methods" section of the present specification. For example, the processing unit 710 may perform the steps as shown in fig. 2.
The memory unit 720 may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) 7201 and/or cache memory 7202, and may further include Read Only Memory (ROM) 7203.
The storage unit 720 may also include a program/utility 7204 having a set (at least one) of program modules 7205, such program modules 7205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 730 may be a bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, a graphics acceleration interface, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 700 may also communicate with one or more external devices 800 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 700, and/or any device (e.g., router, modem, etc.) that enables the electronic device 700 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 750. Also, electronic device 700 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through network adapter 760. As shown, network adapter 760 communicates with other modules of electronic device 700 over bus 730. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 700, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
In an embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the method described above in the present specification. In some possible implementations, various aspects of the disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the disclosure as described in the "exemplary methods" section of this specification, when the program product is run on the terminal device.
A program product for implementing the above-described method according to an embodiment of the present disclosure may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
Furthermore, the above-described figures are merely schematic illustrations of processes involved in methods according to embodiments of the present disclosure, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (8)
1. An information processing method, characterized by comprising:
Acquiring semantic information associated with a non-deterministic event, wherein the semantic information comprises a filtering rule, a pattern matching rule and a calculating rule;
Analyzing the semantic information according to the analysis rules corresponding to the non-deterministic event to obtain grammar information, analyzing the semantic information into a plurality of rules with different dimensions according to the grammar information, and obtaining a matching rule of the non-deterministic event according to the obtained combination of the rules, wherein the rules with the dimensions are the same as description information contained in the semantic information; the parsing rule comprises a filtering dimension, a partitioning dimension and a plurality of dimensions of a sequencing dimension;
if an event is received, identifying the event through a matching rule of the non-deterministic event so as to determine whether the event is a non-deterministic event;
if the event is determined to be the non-deterministic event, executing auxiliary operation on an object corresponding to the non-deterministic event;
Wherein said determining said non-deterministic event comprises:
filtering the events according to a filtering rule, and combining the filtered events according to a time sequence in a pattern matching rule;
Matching the combined events by adopting a calculation rule, and judging whether the event type and the event occurrence time meet the calculation rule or not to obtain a calculation result, wherein if the event type and the event occurrence time meet the conditions, the calculation result is determined to meet the calculation rule; if the event type and the event occurrence time do not meet the conditions, determining that the calculation result does not meet the calculation rule, wherein the event occurrence time meeting the conditions means that the occurrence time of the event which occurs later does not meet the preset time;
and determining whether the event is the non-deterministic event according to a calculation result.
2. The information processing method according to claim 1, wherein the identifying the event by the matching rule of the non-deterministic event to determine whether the event is a non-deterministic event comprises:
and calculating and identifying the event through a plurality of rules obtained by analyzing the matching rules of the non-deterministic event so as to determine whether the event is the non-deterministic event.
3. The information processing method according to claim 2, wherein the computing and identifying the event by analyzing a plurality of rules obtained by parsing matching rules of the non-deterministic event to determine whether the event is the non-deterministic event, includes:
and according to the arrangement sequence among the rules, calculating and identifying the event, and determining whether the event is the non-deterministic event according to a calculation result.
4. The information processing method according to claim 3, wherein said performing calculation and identification of said event in an arrangement order among said plurality of rules includes:
Filtering the events according to the filtering rules, and combining the filtered events according to the pattern matching rules;
and calculating the combined event by adopting a calculation rule to obtain a calculation result, and determining whether the combined event is the non-deterministic event according to the calculation result.
5. The information processing method according to claim 4, wherein the determining whether the combined event is the non-deterministic event according to a calculation result includes:
if the calculation result is that the combined event meets the calculation rule, determining that the event belongs to the non-deterministic event;
and if the calculation result is that the combined event does not meet the calculation rule, determining that the event is not the non-deterministic event.
6. An information processing apparatus, characterized by comprising:
The information acquisition module is used for acquiring semantic information associated with a non-deterministic event, wherein the semantic information comprises a filtering rule, a pattern matching rule and a calculation rule;
rule acquisition module for
Analyzing the semantic information according to the analysis rules corresponding to the non-deterministic event to obtain grammar information, analyzing the semantic information into a plurality of rules with different dimensions according to the grammar information, and obtaining a matching rule of the non-deterministic event according to the obtained combination of the rules, wherein the rules with the dimensions are the same as description information contained in the semantic information; the parsing rule comprises a filtering dimension, a partitioning dimension and a plurality of dimensions of a sequencing dimension;
the event identification module is used for identifying the event through the matching rule of the non-deterministic event if the event is received, so as to determine whether the event is a non-deterministic event;
the auxiliary operation module is used for executing auxiliary operation on the object corresponding to the non-deterministic event if the event is determined to be the non-deterministic event;
Wherein said determining said non-deterministic event comprises:
filtering the events according to a filtering rule, and combining the filtered events according to a time sequence in a pattern matching rule;
Matching the combined events by adopting a calculation rule, and judging whether the event type and the event occurrence time meet the calculation rule or not to obtain a calculation result, wherein if the event type and the event occurrence time meet the conditions, the calculation result is determined to meet the calculation rule; if the event type and the event occurrence time do not meet the conditions, determining that the calculation result does not meet the calculation rule, wherein the event occurrence time meeting the conditions means that the occurrence time of the event which occurs later does not meet the preset time;
and determining whether the event is the non-deterministic event according to a calculation result.
7. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the information processing method of any one of claims 1-5.
8. An electronic device, comprising:
A processor; and
A memory for storing executable instructions of the processor;
wherein the processor is configured to perform the information processing method of any one of claims 1-5 via execution of the executable instructions.
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