CN112269885A - Method, apparatus, device and storage medium for processing data - Google Patents
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Abstract
The application discloses a method, a device, equipment and a storage medium for processing data, and relates to the fields of data processing and artificial intelligence, in particular to the fields of knowledge maps, cloud computing, big data and intelligent search. The specific implementation scheme is as follows: acquiring historical event data and received event data within preset time; constructing a knowledge graph based on historical event data; and determining target event data based on the received event data and the knowledge graph, and outputting. The realization mode constructs the knowledge graph based on the historical event data, thereby realizing that the fact that the event is frequently complained in the event data received in real time can be determined more comprehensively, quickly and accurately based on the knowledge graph, being beneficial to better monitoring urban public opinions, and timely correcting difficult and miscellaneous diseases in urban management so as to promote the fine treatment of cities.
Description
Technical Field
The present application relates to the field of data processing and artificial intelligence, and in particular, to the field of knowledge-graph, cloud computing, big data, and intelligent search, and more particularly, to a method, an apparatus, a device, and a storage medium for processing data.
Background
For city management, the system of 'street whistle blowing and department reporting' is withdrawn, and a new basic comprehensive treatment mode is started. The appeal of citizens is whistle, and all departments at all levels need to breath the wind and make a 'complaint and do the affair promptly', so that citizens can feel to have a call and should. Therefore, a robust and sophisticated "call-to-answer" sort handling mechanism is required.
In practice, the situation of 'multi-complaint' often appears, and for the manual identification of the problem of the multi-complaint, the burden of workers is greatly increased, and the accuracy is not high.
Disclosure of Invention
The present disclosure provides a method, apparatus, device, and storage medium for processing data.
According to an aspect of the present disclosure, there is provided a method for processing data, including: acquiring historical event data and received event data within preset time; constructing a knowledge graph based on historical event data; and determining target event data based on the received event data and the knowledge graph, and outputting.
According to another aspect of the present disclosure, there is provided an apparatus for processing data, including: an acquisition unit configured to acquire historical event data and reception event data within a preset time; a knowledge graph construction unit configured to construct a knowledge graph based on the historical event data; and an output unit configured to determine target event data based on the received event data and the knowledge graph, and output the target event data.
According to yet another aspect of the present disclosure, there is provided an electronic device for processing data, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method for processing data as described above.
According to yet another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to execute the method for processing data as described above.
According to the technology of the application, the problem that the burden is large and the accuracy is not high when the problem of the fact of the.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
FIG. 1 is an exemplary system architecture diagram in which one embodiment of the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a method for processing data according to the present application;
FIG. 3 is a schematic diagram of an application scenario of a method for processing data according to the present application;
FIG. 4 is a flow diagram of another embodiment of a method for processing data according to the present application;
FIG. 5 is a schematic block diagram illustrating one embodiment of an apparatus for processing data according to the present application;
fig. 6 is a block diagram of an electronic device for implementing a method for processing data according to an embodiment of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 shows an exemplary system architecture 100 to which embodiments of the present method for processing data or apparatus for processing data may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. Various communication client applications, such as data processing applications, may be installed on the terminal devices 101, 102, 103.
The terminal apparatuses 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices including, but not limited to, smart phones, tablet computers, car computers, laptop portable computers, desktop computers, and the like. When the terminal apparatuses 101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented as multiple pieces of software or software modules, or as a single piece of software or software module. And is not particularly limited herein.
The server 105 may be a server that provides various services, such as a background server that processes historical event data collected by the terminal devices 101, 102, 103 and received event data within a preset time. The background server may obtain historical event data and received event data within a preset time. The backend server may construct a knowledge graph based on historical event data. The background server can determine and output target event data based on the received event data and the knowledge graph.
The server 105 may be hardware or software. When the server 105 is hardware, it may be implemented as a distributed server cluster composed of a plurality of servers, or may be implemented as a single server. When the server 105 is software, it may be implemented as a plurality of software or software modules, or as a single software or software module. And is not particularly limited herein.
It should be noted that the method for processing data provided by the embodiment of the present application is generally performed by the server 105. Accordingly, the means for processing data is typically provided in the server 105.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to FIG. 2, a flow 200 of one embodiment of a method for processing data in accordance with the present application is shown. The method for processing data of the embodiment comprises the following steps:
step 201, obtaining historical event data and received event data within a preset time.
In this embodiment, an execution subject (for example, the server 105 in fig. 1) of the method for processing data may obtain the historical event data and the received event data within a preset time from a local storage or an external database server through a wired connection or a wireless connection. Specifically, the historical event data in this embodiment may be historical complaint case data, and specifically may include: the complainer, the case type, the related address, the name of the complainer, the address of the complainer, the telephone number of the complainer, the classification of the complainer and the subject of the complainer of the historical complainer case. The received event data within the preset time may be the complaint case data received in real time, or may be the complaint case data received within the preset time, for example, the complaint case data received within 5 minutes. Of course, it is understood that the historical event data may also be purchase order data received in a historical manner, the received event data within the preset time may be purchase order data received in real time or within several minutes, the data types of the historical event data and the received event data within the preset time are not specifically limited, and the specific duration of the preset time is not specifically limited.
Step 202, constructing a knowledge graph based on historical event data.
In this embodiment, after acquiring the historical event data, the execution subject may construct a knowledge graph based on the historical event data. Specifically, the execution subject may first perform knowledge definition on the acquired real-time updated historical event data, where the knowledge definition may refer to extraction of a knowledge category and a category attribute from the real-time updated historical event data, and specifically, the execution subject may determine the knowledge category and the category attribute corresponding to the historical event data according to the acquired historical event data and a pre-trained knowledge definition model. Knowledge categories such as "complaints," "case types," "related addresses," and the like; category attributes such as "attribute name", "attribute value", "attribute description", "data type of attribute value", and the like. For example, the attribute value of each knowledge category, which is taken as "complainer" in the knowledge category, may be defined as follows: complaint subject, complaint name, complaint address, complaint phone, complaint gender, etc. The executive may construct a knowledge graph based on the categories of knowledge and the attributes of the categories extracted for the real-time updated historical event data.
And step 203, determining target event data based on the received event data and the knowledge graph, and outputting the target event data.
After acquiring the received event data within the preset time, the execution main body may determine and output the target event data based on the knowledge graph constructed based on the historical event data and based on the received event data. Specifically, the execution main body may determine, based on the received event data and the knowledge graph, a category attribute corresponding to each received event, and then the execution main body may count the number of times each category attribute is matched, thereby determining the category attribute with the largest number of matching times, and determine, as the target event data, the received event data corresponding to the category attribute with the largest number of matching times. It can be understood that, for example, the category attribute may indicate a topic of a complaint case, the received event data corresponding to the category attribute with the highest matching frequency is determined as target event data, and it can be understood that the determined multi-complaint case with the highest complaint frequency currently received is output as a target case, and important attention and processing are performed to improve the processing efficiency of the complaint case, that is, the complaint case. Specifically, the target event data may be output on a display screen or output by voice broadcasting, and the output mode of the target event data is not specifically limited in the present application.
With continued reference to fig. 3, there is shown a schematic diagram of one application scenario of a method for processing data according to the present application. In the application scenario of fig. 3, a server 302 acquires historical event data 301 and received event data 304 within a preset time. The server 302 builds a knowledge graph 303 based on the historical event data 301. The server 302 determines target event data 305 based on the received event data 304 and the knowledge graph 303, and outputs the target event data.
According to the method, the knowledge graph is constructed based on the historical event data, so that the fact that a plurality of complaints are happened in the event data received in real time can be determined more comprehensively, quickly and accurately based on the knowledge graph, urban public opinions can be better monitored, difficult and miscellaneous diseases in urban management can be corrected in time, and fine treatment of cities can be promoted.
With continued reference to FIG. 4, a flow 400 of another embodiment of a method for processing data in accordance with the present application is shown. As shown in fig. 4, the method for processing data of the present embodiment may include the following steps:
step 401, obtaining historical event data and received event data within a preset time.
Step 402, building a knowledge graph based on historical event data.
The principle of step 401 to step 402 is similar to that of step 201 to step 202, and is not described herein again.
Specifically, step 402 can be realized through steps 4021 to 4022 or steps 4023 to 4024 as follows:
step 4021, determining entities of the historical event data, attributes corresponding to the entities, and entity-attribute correspondence between the entities and the attributes.
After the execution subject acquires the historical event data, the entity of the historical event data, the attribute corresponding to each entity and the entity-attribute corresponding relationship between the entity and the attribute can be determined. Specifically, the execution subject may determine entities in the historical event data, attributes corresponding to the entities, and entity-attribute correspondence between the entities and the attributes according to the historical event data and a pre-trained recognition model, where the pre-trained recognition model is used to represent correspondence between the historical event data and the entities, the attributes corresponding to the entities, and the entity-attribute correspondence between the entities and the attributes. In particular, the entity may be a category of knowledge in the historical event data, such as "complaint," "case type," "related address," and so forth. The attribute corresponding to each entity may be "attribute name", "attribute value", "attribute description", "data type of attribute value", or the like. The attribute corresponding to each entity, for example, "complainer", has the following values: "name of complainer", "address of complainer", "telephone of complainer", "sex of complainer", etc.; the "attribute description" corresponding to each attribute value may be: "Xiaohong", "C town D street E cell of B city, A province", "18845678910", "male"; the "data type of attribute value" corresponding to each attribute value may be: "type", "numerical type", and "type". The entity-attribute correspondence between the entities and the attributes may be correspondence between each knowledge category and each category attribute in the historical event data, for example, "complaint person" in "knowledge category" corresponds to "complaint person name" - "small red" - "category type", "complaint person address" - "C town D street E cell of B city, a" - "category type", "complaint person telephone" - "18845678910" - "numerical type", "complaint person sex" - "male" - "category type" in "category attribute".
Step 4022, building a knowledge graph based on the entity, entity-attribute correspondence and attributes.
After determining the entity, entity-attribute correspondence and attributes, the executive agent may construct a knowledge graph based on the entity, entity-attribute correspondence and attributes. Specifically, the execution subject may map each entity and each attribute according to the entity attribute correspondence, and construct an entity-attribute knowledge graph.
In the embodiment, the entity-attribute knowledge graph can be accurately constructed by determining the entity and the attribute of the historical event data and the corresponding relationship between the entity and the attribute, so that a case of a plurality of complaints in the current complaint cases can be quickly and accurately determined, and the focus can be conveniently and timely processed.
Step 4023, determining entity corresponding relations among the entities and attribute corresponding relations among the attributes.
After acquiring the historical event data, the execution subject may determine an entity correspondence between the entities and an attribute correspondence between the attributes. Specifically, the execution subject may determine an entity correspondence between entities and an attribute correspondence between attributes in the historical event data according to the historical event data and a pre-trained recognition model, where the pre-trained recognition model is used to represent a correspondence between the historical event data and its entity correspondence and attribute correspondence.
Step 4024, constructing a knowledge graph according to the entities, the entity-attribute correspondence, the entity correspondence, the attribute correspondence, and the attributes.
After determining the entity correspondence between the entities and the attribute correspondence between the attributes, the execution subject may establish a mapping between the entities based on the entity correspondence between the entities and the entities, a mapping between the attributes based on the attributes and the attribute correspondence, and a mapping between the entities and the attributes based on the entity-attribute correspondence, so as to construct a more complete knowledge graph of the mapping between the entities, the attributes, and the attributes.
In the embodiment, the knowledge graph with a more complete mapping relation can be constructed by establishing entity-entity mapping, entity-attribute mapping and attribute-attribute mapping, so that the judgment on a case with multiple complaints is more accurate.
And step 403, determining target event data based on the received event data and the knowledge graph, and outputting the target event data.
The principle of step 403 is similar to that of step 203, and is not described in detail here.
Specifically, step 403 can be implemented by steps 4031 to 4033:
step 4031, according to the received event data and the knowledge graph, determining attributes corresponding to the received event data.
In this embodiment, after acquiring the received event data and the constructed knowledge graph, the execution main body may determine the attribute corresponding to each received event data according to the received event data and the knowledge graph. For example, the attribute corresponding to each received event data may be a complaint topic corresponding to each received event data. The complaint subject can be based on attribute-attribute mapping in the knowledge graph, for example, when the complaint subject (or attribute) is determined according to the complaint person in the subject (or knowledge category), the complaint subject can be assisted by the complaint person name, the complaint person address, the complaint person telephone number, and the complaint person classification in the attribute. For example, the subject of the frequent complaints of the complaint person is determined according to the same name and attribute-attribute mapping relationship of the complaint person, the subject of the frequent complaints corresponding to the address of the complaint person is determined according to the same address of the complaint person and attribute-attribute mapping relationship of the complaint person, the subject of the frequent complaints of the complaint person is determined according to the telephone of the complaint person and attribute-attribute mapping relationship of the complaint person, and the like, and the executive body can determine the attribute (i.e. the subject of the complaint) corresponding to each received event data by selecting one or more of the attributes in the aimed knowledge graph, so as to improve the accuracy of the determination of the target event data. It is understood that the above-mentioned subject of frequent complaints may be the subject when the number of complaints exceeds a preset threshold.
Step 4032, determine the number of occurrences of each attribute.
In this embodiment, after determining the attribute corresponding to each received event data, the execution subject may continue to determine the number of occurrences of each attribute, that is, the number of occurrences of each complaint topic. For example, the subject of complaint (i.e., attribute) appears 6 times as "not being" and 4 times as "epidemic", and the subject of complaint (i.e., attribute) appears 1 time as "labor dispute".
Step 4033, target event data in the received event data is determined based on the number of times and a preset threshold.
After obtaining the number of times of occurrence of each attribute, the execution main body may determine target event data in the received event data based on the number of times and a preset threshold. The target event data may be, for example, one or more pieces of "complaints about a incident" event data. Specifically, for example, the executing entity may determine, according to the received event data and the entity-entity mapping relationship in the constructed knowledge graph, events belonging to "related addresses", events belonging to the same "case type", events belonging to the same "complainer", and events belonging to the same "appealing party" in the received events, so as to determine the number of times that each entity (i.e., the knowledge category) in each received event is matched.
The execution main body can also determine the times of matching of the 'complainer' to the 'complaint subject' in the received event according to the received event data and the entity-attribute mapping relation in the constructed knowledge graph.
The execution main body can also determine events belonging to the same 'name of a complainer', the same 'address of the complainer', the same 'telephone of the complainer', the same 'sex of the complainer' and the same 'subject of the complainer' in the received events according to the received event data and the attribute-attribute mapping relation in the constructed knowledge graph, so that the times of matching of all the attributes in all the received events are determined.
In summary, the executing entity may determine the target event data in the received event data according to the determined number of times that each entity in each received event is matched, the determined number of times that each "complainer" - "complainer subject" in the received event is matched, and the determined number of times that each attribute in each received event data is matched (occurs).
Specifically, the executing agent may determine entities whose matched times exceed a preset threshold, attributes whose matched times exceed the preset threshold, and "complainers" - "complaints" whose matched times exceed the preset threshold, then determine entities other than the "complainers" among the entities whose matched times exceed the preset threshold, such as "related addresses" and "complainers", and then determine attributes other than the "complaints" among the attributes whose matched times exceed the preset threshold, such as "complainers names", "complainers addresses", and "complainers names". Then, the execution subject can determine the complaint topics corresponding to the relevant addresses in the entities with the matched times exceeding a preset threshold value and determine the complaint topics corresponding to the complaint parties according to the entity-attribute mapping relation in the knowledge graph, so that all the complaint topics corresponding to the entities with the matched times exceeding the preset threshold value are determined; the execution main body can also determine a complaint subject corresponding to the complaint person name, a complaint subject corresponding to the complaint person address, a complaint subject corresponding to the complaint party address and a complaint subject corresponding to the complaint party name according to the attribute-attribute mapping relation in the knowledge graph, so that all the complaint subjects corresponding to the attributes with the matched times exceeding the preset threshold are determined. Finally, the executive body can obtain all the 'complaint subjects' corresponding to the entities and the attributes with the matched times exceeding the preset threshold value and the 'complainers' in the reception events determined at the beginning. Then, the execution subject may count and determine the number of times of occurrence of each complaint topic in all the "complaint topics", that is, the execution subject may determine an attribute (i.e., a complaint topic) whose matched number exceeds a preset threshold in the attributes (i.e., the complaint topics) corresponding to each received event data, and determine one or more pieces of received event data corresponding to the attributes (i.e., the complaint topics) whose matched number exceeds the preset threshold as target event data.
According to the embodiment, the attribute (namely the complaint subject) corresponding to the received event data in each preset time can be accurately determined according to the received event data and the knowledge graph constructed based on the historical event data, so that the target event data (namely the event of a complaint) is determined according to the matching times of the determined attribute and the preset threshold, the urban public opinion can be better monitored, the difficult and complicated symptoms in urban management can be corrected in time, and the fine control of the city can be promoted.
In some optional implementations of this embodiment, the method for processing data further includes the following steps not shown in fig. 4:
in response to determining that the received event data does not match the knowledge graph, labeling attributes corresponding to the received event data; the knowledge graph is updated based on the received event data and the corresponding attributes of the annotations.
Specifically, after the execution main body constructs the knowledge graph based on the historical event data, whether the received event data is matched with the constructed knowledge graph or not can be judged, and in response to the fact that the received event data is determined not to be matched with the knowledge graph, namely the received event data does not have matched historical event data, the execution main body needs to manually or automatically label the attribute corresponding to the received event data, namely the complaint subject. Specifically, the executive body may calculate similarity between the received event data and each historical event data, and label an attribute, i.e., a complaint topic, corresponding to the historical event data with the highest similarity as the attribute, i.e., the complaint topic, of the received event data. Specifically, the executing entity may convert the received event data into a received vector, convert each historical event data into each historical vector, then calculate the similarity between the received vector and each historical vector, and determine and label the attribute, i.e., the complaint subject, corresponding to the historical event data corresponding to the historical vector with the highest similarity as the attribute, i.e., the complaint subject, corresponding to the received event data. After the execution subject finishes labeling the attributes of the received event data, a mapping relation between the received event data and the corresponding attributes can be established, and the knowledge graph is updated based on the mapping relation. It can be understood that the executing entity may also store the mapping relationship in a temporary storage, update the temporary storage after completing the attribute labeling each time, and update the knowledge graph based on each new mapping relationship in the temporary storage at a preset time, where the accuracy of determining the target event data may be improved based on the updated knowledge graph.
In the embodiment, the mapping relation between the new received event data not included in the historical event data and the corresponding attribute is updated into the knowledge graph, so that the accuracy of determining the target event data can be improved.
With further reference to fig. 5, as an implementation of the methods shown in the above-mentioned figures, the present application provides an embodiment of an apparatus for processing data, which corresponds to the method embodiment shown in fig. 2, and which is particularly applicable to various electronic devices.
As shown in fig. 5, the apparatus 500 for processing data of the present embodiment includes: an acquisition unit 501, a knowledge graph construction unit 502 and an output unit 503.
An obtaining unit 501 configured to obtain historical event data and received event data within a preset time.
A knowledge graph construction unit 502 configured to construct a knowledge graph based on the historical event data.
An output unit 503 configured to determine target event data based on the received event data and the knowledge graph, and output.
In some optional implementations of this embodiment, the knowledge-graph building unit 502 is further configured to: determining entities of historical event data, attributes corresponding to the entities and entity-attribute corresponding relations between the entities and the attributes; and constructing the knowledge graph based on the entity, the entity-attribute corresponding relation and the attribute.
In some optional implementations of this embodiment, the knowledge-graph building unit 502 is further configured to: determining entity corresponding relations among the entities and attribute corresponding relations among the attributes; and constructing the knowledge graph according to the entity, the entity-attribute corresponding relation, the entity corresponding relation, the attribute corresponding relation and the attribute.
In some optional implementations of this embodiment, the output unit 503 is further configured to: determining attributes corresponding to the received event data according to the received event data and the knowledge graph; determining the occurrence frequency of each attribute; and determining target event data in the received event data based on the times and a preset threshold value.
In some optional implementations of this embodiment, the apparatus further comprises, not shown in fig. 5: an annotation unit configured to annotate an attribute corresponding to the received event data in response to determining that the received event data does not match the knowledge graph; an updating unit configured to update the knowledge-graph based on the received event data and the corresponding attributes of the annotations.
It should be understood that the units 501 to 503 recited in the apparatus 500 for processing data correspond to respective steps in the method described with reference to fig. 2. Thus, the operations and features described above for the method for processing data are equally applicable to the apparatus 500 and the units included therein and will not be described again here.
The application also provides an electronic device and a readable storage medium for processing data according to the embodiment of the application.
As shown in fig. 6, is a block diagram of an electronic device for a method of processing data according to an embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 6, the electronic apparatus includes: one or more processors 601, memory 602, and interfaces for connecting the various components, including a high-speed interface and a low-speed interface. The various components are interconnected using different buses 605 and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses 605 may be used, along with multiple memories and multiple memories, if desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). In fig. 6, one processor 601 is taken as an example.
The memory 602 is a non-transitory computer readable storage medium as provided herein. Wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the methods for processing data provided herein. The non-transitory computer readable storage medium of the present application stores computer instructions for causing a computer to perform the method for processing data provided herein.
The memory 602, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and units, such as program instructions/units corresponding to the method for processing data in the embodiment of the present application (for example, the acquisition unit 501, the knowledge graph construction unit 502, and the output unit 503 shown in fig. 5). The processor 601 executes various functional applications of the server and data processing by executing non-transitory software programs, instructions, and modules stored in the memory 602, that is, implements the method for processing data in the above method embodiments.
The memory 602 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of an electronic device for processing data, and the like. Further, the memory 602 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 602 optionally includes memory located remotely from the processor 601, which may be connected to electronic devices for processing data via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the method for processing data may further include: an input device 603 and an output device 604. The processor 601, the memory 602, the input device 603, and the output device 604 may be connected by a bus 605 or other means, and are exemplified by the bus 605 in fig. 6.
The input device 603 may receive input numeric or character information and generate key signal inputs related to user settings and function controls of the electronic apparatus for processing data, such as an input device like a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointer, one or more mouse buttons, a track ball, a joystick, etc. The output devices 604 may include a display device, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
According to the technical scheme of the embodiment of the application, the knowledge map is constructed based on the historical event data, so that the fact that a plurality of complaints are incident in the event data received in real time can be determined more comprehensively, quickly and accurately based on the knowledge map, urban public opinions can be better monitored, difficult and miscellaneous diseases in urban management can be corrected in time, and fine treatment of cities can be promoted.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (12)
1. A method for processing data, comprising:
acquiring historical event data and received event data within preset time;
constructing a knowledge graph based on the historical event data;
and determining target event data based on the received event data and the knowledge graph, and outputting the target event data.
2. The method of claim 1, wherein said building a knowledge graph based on said historical event data comprises:
determining entities of the historical event data, attributes corresponding to the entities and entity-attribute corresponding relations between the entities and the attributes;
and constructing a knowledge graph based on the entity, the entity-attribute corresponding relation and the attribute.
3. The method of claim 2, wherein said building a knowledge graph based on said historical event data further comprises:
determining entity corresponding relations among the entities and attribute corresponding relations among the attributes;
and constructing a knowledge graph according to the entity, the entity-attribute corresponding relation, the entity corresponding relation, the attribute corresponding relation and the attribute.
4. The method of claim 1, wherein the determining and outputting target event data based on the received event data and the knowledge-graph comprises:
determining attributes corresponding to the received event data according to the received event data and the knowledge graph;
determining the number of occurrences of each of the attributes;
and determining target event data in the received event data based on the times and a preset threshold value.
5. The method of any of claims 1-4, wherein the method further comprises:
in response to determining that the received event data does not match the knowledge-graph, annotating attributes corresponding to the received event data;
updating the knowledge graph based on the received event data and the labeled corresponding attributes.
6. An apparatus for processing data, comprising:
an acquisition unit configured to acquire historical event data and reception event data within a preset time;
a knowledge graph construction unit configured to construct a knowledge graph based on the historical event data;
and the output unit is configured to determine target event data based on the received event data and the knowledge graph and output the target event data.
7. The apparatus of claim 6, wherein the knowledge-graph building unit is further configured to:
determining entities of the historical event data, attributes corresponding to the entities and entity-attribute corresponding relations between the entities and the attributes;
and constructing a knowledge graph based on the entity, the entity-attribute corresponding relation and the attribute.
8. The apparatus of claim 7, wherein the knowledge-graph building unit is further configured to:
determining entity corresponding relations among the entities and attribute corresponding relations among the attributes;
and constructing a knowledge graph according to the entity, the entity-attribute corresponding relation, the entity corresponding relation, the attribute corresponding relation and the attribute.
9. The apparatus of claim 1, wherein the output unit is further configured to:
determining attributes corresponding to the received event data according to the received event data and the knowledge graph;
determining the number of occurrences of each of the attributes;
and determining target event data in the received event data based on the times and a preset threshold value.
10. The device of any one of claims 1-6, wherein the device further comprises:
an annotation unit configured to annotate an attribute corresponding to the received event data in response to determining that the received event data does not match the knowledge-graph;
an updating unit configured to update the knowledge-graph based on the received event data and the labeled corresponding attributes.
11. An electronic device for processing data, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.
12. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-5.
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| CN202011277714.9A CN112269885B (en) | 2020-11-16 | Method, apparatus, device and storage medium for processing data |
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| CN202011277714.9A CN112269885B (en) | 2020-11-16 | Method, apparatus, device and storage medium for processing data |
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