CN112269885B - 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, relates to the fields of data processing and artificial intelligence, and particularly relates to the fields of knowledge graph, cloud computing, big data and intelligent search. The specific implementation scheme is as follows: acquiring historical event data and received event data in 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. According to the method, the knowledge graph is built based on the historical event data, so that the knowledge graph is based, the event of the event data received in real time can be comprehensively, quickly and accurately determined, urban public opinion can be better monitored, difficult and complicated symptoms in urban management can be timely corrected, and urban fine management can be promoted.
Description
Technical Field
The application relates to the fields of data processing and artificial intelligence, in particular to the fields of knowledge graph, cloud computing, big data and intelligent search, and especially relates to a method, a device, equipment and a storage medium for processing data.
Background
For city management, the 'street whistle, department report' mechanism is exited, and a new basic comprehensive treatment mode is started. The citizen's appeal is whistle, all departments at all levels should smell the wind and do the "meet complaint" and make the citizen call somehow and me answer. Therefore, a perfect 'order-to-order' classification handling mechanism is required.
In practice, the situation of "multi-complaint" often appears, and for the manual identification of multi-complaint problem, the burden of staff 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, comprising: acquiring historical event data and received event data in 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.
According to another aspect of the present disclosure, there is provided 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 an output unit configured to determine target event data based on the received event data and the knowledge-graph, and output.
According to still 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 enable the at least one processor to perform a 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 storing computer instructions for causing a computer to perform the method for processing data as described above.
According to the technology disclosed by the application, the problem that the burden is high and the accuracy is low when the problem of excessive complaints is manually resolved is solved, and the knowledge graph is constructed based on the historical event data, so that the event of excessive complaints in the event data received in real time can be more comprehensively, quickly and accurately determined based on the knowledge graph, the urban public opinion can be better monitored, and the difficult and complicated symptoms in urban management can be timely corrected, so that the urban fine treatment is promoted.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are included to provide a better understanding of the present application and are not to be construed as limiting the application. Wherein:
FIG. 1 is an exemplary system architecture diagram in which an embodiment of the present application may be applied;
FIG. 2 is a flow chart of one embodiment of a method for processing data according to the present application;
FIG. 3 is a schematic illustration of one application scenario of a method for processing data according to the present application;
FIG. 4 is a flow chart of another embodiment of a method for processing data according to the present application;
FIG. 5 is a schematic diagram of an embodiment of an apparatus for processing data in accordance with 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 application.
Detailed Description
Exemplary embodiments of the present application will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present application are included to facilitate understanding, and are to be considered merely exemplary. 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 application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
Fig. 1 shows an exemplary system architecture 100 to which an embodiment of a method for processing data or an apparatus for processing data of the present application may be applied.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as data processing applications, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 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, smartphones, tablets, car-mounted computers, laptop and desktop computers, and the like. When the terminal devices 101, 102, 103 are software, they can be installed in the above-listed electronic devices. Which may be implemented as a plurality of software or software modules, or as a single software or software module. The present invention is not particularly limited herein.
The server 105 may be a server providing various services, such as a background server processing the historical event data collected by the terminal devices 101, 102, 103 and the received event data within a preset time. The background server can acquire historical event data and received event data within a preset time. The background server may construct a knowledge graph based on historical event data. The background server may determine target event data based on the received event data and the knowledge-graph, and output.
The server 105 may be hardware or software. When the server 105 is hardware, it may be implemented as a distributed server cluster formed by a plurality of servers, or 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. The present invention 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 present embodiment includes the steps of:
step 201, acquiring historical event data and received event data within a preset time.
In this embodiment, the execution body (e.g., 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 external database server by means of a wired connection or a wireless connection. Specifically, the historical event data in this embodiment may be historical complaint case data, which may specifically include: the complaints of the historical complaints, the types of the cases, the related addresses, the names of the complaints, the addresses of the complaints, the telephones of the complaints, the gender of the complaints, the subject of the complaints, and the like. The received event data within the preset time may be complaint case data received in real time, or may be complaint case data received within the preset time, for example, complaint case data received within 5 minutes. Of course, it can be understood that the historical event data may also be historical received purchase order data, and the received event data in the preset time may be real-time received purchase order data or purchase order data received in several minutes.
Step 202, constructing a knowledge graph based on historical event data.
In this embodiment, after the execution subject acquires the historical event data, the knowledge graph may be constructed based on the historical event data. Specifically, the executing body may first perform knowledge definition on the acquired real-time updated historical event data, the knowledge definition may refer to extraction of knowledge categories and category attributes on the real-time updated historical event data, and specifically, the executing body may determine the knowledge categories and category attributes corresponding to the historical event data according to the acquired historical event data and a pre-trained knowledge definition model. Knowledge categories such as "complaint", "case type", "related address", etc.; category attributes such as "attribute name", "attribute value", "attribute description", "data type of attribute value", and the like. By way of example, the attribute of each knowledge category, exemplified by "complaints" in the knowledge category, may have an attribute value defined as follows: complaint subjects, complaint names, complaint addresses, complaint phones, complaint sexes, etc. The executive may construct a knowledge graph based on knowledge categories and category attributes extracted from the real-time updated historical event data.
And 203, determining target event data based on the received event data and the knowledge graph, and outputting the target event data.
After the execution body acquires the received event data within the preset time, the execution body can determine and output target event data based on the knowledge graph constructed by the historical event data and the received event data. Specifically, the execution body may determine category attributes corresponding to each received event based on the received event data and the knowledge graph, and then the execution body may count the number of times each category attribute is matched, thereby determining the category attribute with the largest number of times of matching, and determining the received event data corresponding to the category attribute with the largest number of times of matching as the target event data. It may be understood that, for example, the category attribute may indicate a subject of a complaint case, the received event data corresponding to the category attribute with the largest matching frequency is determined as target event data, and it may be understood that the determined event with the largest number of complaints currently received action is output as the target case, so as to pay important attention to and process the event, so as to improve the processing efficiency of the case with the largest number of complaints. Specifically, the output of the target event data may be output on a display screen or may be output by voice broadcasting, and the output mode of the target event data is not particularly limited in the present application.
With continued reference to fig. 3, a schematic diagram of one application scenario of the method for processing data according to the present application is shown. 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.
According to the embodiment, the knowledge graph is constructed based on the historical event data, so that the knowledge graph is realized, the event of the event data received in real time can be more comprehensively, quickly and accurately determined, urban public opinion can be better monitored, difficult and complicated symptoms in urban management can be timely corrected, and urban fine management can be promoted.
With continued reference to FIG. 4, a flow 400 of another embodiment of a method for processing data according to 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, acquiring historical event data and received event data within a preset time.
Step 402, constructing a knowledge graph based on historical event data.
The principle of steps 401 to 402 is similar to that of steps 201 to 202, and will not be described here again.
Specifically, step 402 can be realized by the following steps 4021 to 4022 or steps 4023 to 4024:
In step 4021, the entity of the historical event data, the attribute corresponding to each entity, and the entity-attribute correspondence between the entity and the attribute are determined.
After the execution body acquires the historical event data, the entity of the historical event data, the attribute corresponding to each entity and the entity-attribute corresponding relation between the entity and the attribute can be determined. Specifically, the execution subject may determine, according to the historical event data and a pre-trained recognition model, entities in the historical event data, attributes corresponding to each entity, and entity-attribute correspondence between the entities and the attributes, where the pre-trained recognition model is used to characterize correspondence between the historical event data and the entities, the attributes corresponding to each entity, and entity-attribute correspondence between the entities and the attributes. In particular, the entity may be a knowledge category in the historical event data, such as "complaint party", "complaint person", "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", etc. The attribute corresponding to each entity, taking "complaint" as an example, may be defined as follows: "complaint name", "complaint address", "complaint phone", "complaint gender", etc.; the "attribute description" corresponding to each attribute value may be: "reddish", "A-province B-city C-town D-street E-cell", "18845678910", "male"; the "data type of attribute value" corresponding to each attribute value may be: "category", "numerical value", "category". The entity-attribute correspondence between the entity and the attribute may be a correspondence between each knowledge category in the historical event data and each category attribute, for example, "complaint name" - "reddish" - "category", "complaint address" - "a city, B city, C town, D street E cell" - "category", "complaint phone" - "18845678910" - "numerical value" complaint gender "-" man "-" category "in the" knowledge category "corresponds to" category attribute ".
Step 4022, constructing a knowledge graph based on the entity, the entity-attribute correspondence and the attribute.
After determining the entity, entity-attribute correspondence and attribute, the execution subject may construct a knowledge graph based on the entity, entity-attribute correspondence and attribute. Specifically, the execution subject can map each entity and each attribute according to the entity attribute correspondence, and construct an entity-attribute knowledge graph.
According to 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 relation between the entity and the attribute, so that the fact that the case is complaint in the current complaint case can be rapidly and accurately determined, and the fact that the case is complaint is emphasized and timely processed is facilitated.
In step 4023, the entity correspondence between the entities and the attribute correspondence between the attributes are determined.
After the execution subject acquires the historical event data, the entity correspondence relationship between the entities and the attribute correspondence relationship between the attributes can be determined. Specifically, the execution subject may determine, according to the historical event data and the pre-trained recognition model, an entity correspondence between each entity in the historical event data and an attribute correspondence between each attribute, where the pre-trained recognition model is used to characterize the correspondence between the historical event data and the entity correspondence and the attribute correspondence thereof.
Step 4024, constructing a knowledge graph according to the entity, the entity-attribute correspondence, the entity correspondence, the attribute correspondence and the attribute.
After determining entity correspondence between entities and attribute correspondence between attributes, the execution subject may establish mapping between entities based on the entity and entity correspondence, may establish mapping between attributes based on the attribute and attribute correspondence, and may establish mapping between entities and attributes based on the entity-attribute correspondence, so as to construct a more complete knowledge graph of the mapping relationship between entity-entity, entity-attribute, and attribute-attribute.
According to the embodiment, the entity-entity mapping, the entity-attribute mapping and the attribute-attribute mapping are established, so that a knowledge graph with a more perfect mapping relation can be established, and the judgment of a case with multiple complaints can be more accurate.
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 will not be described again here.
Specifically, step 403 may be implemented by steps 4031 to 4033:
step 4031, determining the attribute corresponding to each received event data according to the received event data and the knowledge graph.
In this embodiment, after acquiring the received event data and the constructed knowledge graph, the execution body may determine, according to the received event data and the knowledge graph, an attribute corresponding to each received event data. For example, the attribute corresponding to each received event data may be a complaint theme corresponding to each received event data. The complaint topic may be based on the attribute-attribute mapping relationship in the knowledge graph, for example, when the complaint topic (i.e., attribute) is determined according to the complaint person in the subject (or knowledge category), the complaint topic may be determined assisted by the name of the complaint person, the address of the complaint person, the phone number of the complaint person, and the person-nature of the complaint person in the attribute. For example, the executing body may determine the subject of frequent complaints of the complaint according to the name of the same complaint and the attribute-attribute mapping relationship, determine the subject of frequent complaints corresponding to the address of the complaint according to the address of the same complaint and the attribute-attribute mapping relationship, determine the subject of frequent complaints of the complaint according to the phone of the complaint and the attribute-attribute mapping relationship, and the like, and determine the attribute (i.e., the subject of 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 determining the target event data. It will be appreciated that the above-mentioned subject of frequent complaints may be the subject of a number of complaints exceeding a preset threshold.
Step 4032, determining the number of times each attribute appears.
In this embodiment, after determining the attribute corresponding to each received event data, the executing body may continuously determine the number of occurrences of each attribute, that is, the number of occurrences of each complaint subject. For example, a complaint topic (i.e., attribute) appears 6 times as "not as", a complaint topic (i.e., attribute) appears 4 times as "epidemic situation", and a complaint topic (i.e., attribute) appears 1 time as "labor dispute".
Step 4033, determining target event data in the received event data based on the times and the preset threshold.
After obtaining the number of times each attribute appears, the execution body can determine target event data in the received event data based on the number of times and a preset threshold value. The target event data may be, for example, one or more "event data. Specifically, for example, the executing body may determine, according to the received event data and the entity-entity mapping relationship in the constructed knowledge graph, the number of times that each entity (i.e., knowledge category) is matched in each received event, where the event belongs to the "related address", the event belongs to the same "case type", the event belongs to the same "complaint", and the event belongs to the same "complaint".
The execution subject can also determine the number of times that a complaint person, a complaint subject, is matched in a received event according to the received event data and the entity-attribute mapping relationship in the constructed knowledge graph.
The execution main body can also determine the events belonging to the same complaint name, the same complaint address, the same complaint phone, the same complaint gender and the same complaint subject in the receiving events according to the received event data and the attribute-attribute mapping relation in the constructed knowledge graph, so as to determine the times of matching each attribute in each receiving event.
In summary, the execution body may determine the target event data in the received event data according to the determined number of times each entity is matched in each received event, the number of times each "complaint person" - "complaint subject" is matched in each received event, and the number of times each attribute is matched (appears) in each received event data.
Specifically, the execution subject may determine an entity whose number of times is matched exceeds a preset threshold, an attribute whose number of times is matched exceeds a preset threshold, and a "complaint person" - "subject of complaint" whose number of times is matched exceeds a preset threshold, then determine an entity other than "complaint person" among the entities whose number of times is matched exceeds a preset threshold, for example, may be a "related address", "subject of complaint", and then determine an attribute other than "subject of complaint" among the attributes whose number of times is matched exceeds a preset threshold, for example, may be a "name of complaint person", "address of complaint person", "name of subject of complaint person". Then the execution main body can determine the complaint theme corresponding to the related address in each entity of which the matched times exceed a preset threshold according to the entity-attribute mapping relation in the knowledge graph, and determine the complaint theme corresponding to the complaint party, so as to determine all the complaint themes corresponding to each entity of which the matched times exceed the preset threshold; the executing main body can further determine a 'complaint theme' corresponding to the 'complaint name', a 'complaint theme' corresponding to the 'complaint address' and a 'complaint theme' corresponding to the 'complaint name' according to the attribute-attribute mapping relation in the knowledge graph, so that all 'complaint themes' corresponding to the attributes of which the matched times exceed a preset threshold are determined. Finally, the execution body can obtain all 'complaint subjects' corresponding to each entity and each attribute, and 'complaints' and 'complaints subjects' in the receiving event which is determined at the beginning, wherein the matched times of the 'complaints subjects' and 'complaints subjects' are more than a preset threshold. Then, the executing body may count and determine the occurrence times of each complaint topic in all the "complaint topics", that is, the executing body may determine an attribute (i.e., complaint topic) with the matched number exceeding a preset threshold value in the attributes (i.e., complaint topics) corresponding to each received event data, and determine one or more pieces of received event data corresponding to the attribute (i.e., complaint topic) with the matched number exceeding the preset threshold value as target event data.
According to the embodiment, the attribute (namely, complaint theme) 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, a multi-complaint event, is determined according to the number of times the determined attribute is matched and the preset threshold value, urban public opinion can be better monitored, difficult and complicated symptoms in urban management can be timely corrected, and urban fine management can be promoted.
In some alternative implementations of the present 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; and updating the knowledge graph based on the received event data and the corresponding attribute of the label.
Specifically, after the executing body builds the knowledge graph based on the historical event data, the executing body can judge whether the received event data is matched with the built knowledge graph, and in response to determining that the received event data is not matched with the knowledge graph, i.e. the received event data does not have the matched historical event data, the executing body needs to label the attribute corresponding to the received event data, namely the complaint subject manually or automatically. Specifically, the executing body may calculate the similarity between the received event data and each of the historical event data, and the attribute corresponding to the historical event data with the greatest similarity, that is, the complaint subject, is marked as the attribute of the received event data, that is, the complaint subject. Specifically, the execution body 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 corresponding to the historical event data with the greatest similarity, namely, the complaint subject, as the attribute corresponding to the received event data, namely, the complaint subject. After the execution main body completes the labeling of the attribute of the received event data, a mapping relation between the received event data and the corresponding attribute can be established, and the knowledge graph is updated based on the mapping relation. It can be understood that the execution body may also store the mapping relationship in the temporary memory, update the temporary memory after each time of attribute labeling, update the knowledge graph based on each new mapping relationship in the temporary memory at a preset time, and improve accuracy of determining the target event data based on the updated knowledge graph.
According to the embodiment, the mapping relation between the new received event data which is not contained in the historical event data and the corresponding attribute is updated to enter 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 method shown in the above 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.
The acquiring unit 501 is configured to acquire historical event data and reception event data within a preset time.
The knowledge graph construction unit 502 is 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 the present embodiment, the knowledge-graph construction unit 502 is further configured to: determining an entity of the historical event data, attributes corresponding to the entities and entity-attribute correspondence between the entity and the attributes; and constructing a knowledge graph based on the entity, the entity-attribute correspondence and the attribute.
In some optional implementations of the present embodiment, the knowledge-graph construction unit 502 is further configured to: determining entity correspondence between entities and attribute correspondence between attributes; and constructing a knowledge graph according to the entity, the entity-attribute correspondence, the entity correspondence, the attribute correspondence and the attribute.
In some optional implementations of the present embodiment, the output unit 503 is further configured to: determining the attribute corresponding to each received event data according to the received event data and the knowledge graph; determining the occurrence times of each attribute; and determining target event data in the received event data based on the times and a preset threshold value.
In some alternative implementations of the present embodiment, the apparatus further includes not shown in fig. 5: the labeling unit is configured to label the attribute corresponding to the received event data in response to determining that the received event data is not matched with the knowledge graph; and an updating unit configured to update the knowledge graph based on the received event data and the corresponding attribute of the annotation.
It should be understood that the units 501 to 503 described in the apparatus 500 for processing data correspond to the respective steps in the method described with reference to fig. 2. Thus, the operations and features described above with respect to the method for processing data are equally applicable to the apparatus 500 and the units contained therein, and are not described in detail herein.
According to an embodiment of the present application, the present application also provides an electronic device for processing data and a readable storage medium.
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 telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.
As shown in fig. 6, the electronic device includes: one or more processors 601, memory 602, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. 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 executing within the electronic device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In other embodiments, multiple processors and/or multiple buses 605 may be used, if desired, along with multiple memories and multiple memories. Also, multiple electronic devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 601 is illustrated in fig. 6.
The memory 602 is a non-transitory computer readable storage medium provided by the present application. 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 provided by the present application. 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 by the present application.
The memory 602 is used as a non-transitory computer readable storage medium, and may be used to store a non-transitory software program, a non-transitory computer executable program, and units such as program instructions/units (e.g., the acquisition unit 501, the knowledge-graph construction unit 502, and the output unit 503 shown in fig. 5) corresponding to a method for processing data in an embodiment of the present application. The processor 601 executes various functional applications of the server and data processing, i.e., implements the method for processing data in the above-described method embodiments, by running non-transitory software programs, instructions, and modules stored in the memory 602.
The memory 602 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for a function; the storage data area may store data created according to the use of the electronic device for processing data, or the like. In addition, 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, memory 602 may optionally include memory located remotely from 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 for the method of processing data may further comprise: an input device 603 and an output device 604. The processor 601, memory 602, input devices 603 and output devices 604 may be connected by a bus 605 or otherwise, in fig. 6 by way of example by bus 605.
The input device 603 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic device for processing data, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointer stick, one or more mouse buttons, a track ball, a joystick, and the like. The output means 604 may include a display device, auxiliary lighting means (e.g., LEDs), tactile feedback means (e.g., vibration motors), and the like. 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 may be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASIC (application specific integrated circuit), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
These computing programs (also referred to as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. 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 pointing device (e.g., a mouse or 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 may 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 input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background 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 background, 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 a client and a server. The client and server are typically 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 provided by the embodiment of the application, the knowledge graph is constructed based on the historical event data, so that the knowledge graph is based, the event of the event data received in real time can be more comprehensively, quickly and accurately determined, urban public opinion can be better monitored, and difficult and complicated symptoms in urban management can be timely corrected, so that urban fine management is promoted.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present application may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed embodiments are achieved, and are not limited herein.
The above embodiments do not limit the scope of the present application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of the present application.
Claims (10)
1. A method for processing data, comprising:
Acquiring historical event data and received event data in preset time;
constructing a knowledge graph based on the historical event data;
determining the attribute corresponding to each piece of received event data and the occurrence frequency of each attribute according to the received event data and the knowledge graph;
And determining one or more pieces of received event data corresponding to the attribute with the times exceeding a preset threshold value as target event data.
2. The method of claim 1, wherein the constructing a knowledge-graph based on the historical event data comprises:
determining an entity of the historical event data, attributes corresponding to each entity and entity-attribute correspondence between the entity and the attributes;
And constructing a knowledge graph based on the entity, the entity-attribute correspondence and the attribute.
3. The method of claim 2, wherein the constructing a knowledge-graph based on the historical event data further comprises:
determining entity correspondence between the entities and attribute correspondence between the attributes;
And constructing a knowledge graph according to the entity, the entity-attribute correspondence, the entity correspondence, the attribute correspondence and the attribute.
4. A method according to any one of claims 1-3, wherein the method further comprises:
Labeling the attribute corresponding to the received event data in response to determining that the received event data is not matched with the knowledge graph;
and updating the knowledge graph based on the received event data and the corresponding attribute of the label.
5. 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;
An output unit configured to determine, according to the received event data and the knowledge graph, an attribute corresponding to each of the received event data and the number of occurrences of each of the attributes;
And determining one or more pieces of received event data corresponding to the attribute with the times exceeding a preset threshold value as target event data.
6. The apparatus of claim 5, wherein the knowledge-graph construction unit is further configured to:
determining an entity of the historical event data, attributes corresponding to each entity and entity-attribute correspondence between the entity and the attributes;
And constructing a knowledge graph based on the entity, the entity-attribute correspondence and the attribute.
7. The apparatus of claim 6, wherein the knowledge-graph construction unit is further configured to:
determining entity correspondence between the entities and attribute correspondence between the attributes;
And constructing a knowledge graph according to the entity, the entity-attribute correspondence, the entity correspondence, the attribute correspondence and the attribute.
8. The apparatus according to any one of claims 5-7, wherein the apparatus further comprises:
The labeling unit is configured to label the attribute corresponding to the received event data in response to determining that the received event data is not matched with the knowledge graph;
and the updating unit is configured to update the knowledge graph based on the received event data and the corresponding attribute of the label.
9. An electronic device for processing data, comprising:
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 enable the at least one processor to perform the method of any one of claims 1-4.
10. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-4.
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| CN114117004B (en) * | 2021-11-24 | 2023-06-30 | 北京百度网讯科技有限公司 | Address recognition method, address recognition device, electronic equipment and storage medium |
| CN114706992B (en) * | 2022-02-17 | 2022-09-30 | 中科雨辰科技有限公司 | Event information processing system based on knowledge graph |
| CN114969383B (en) * | 2022-08-02 | 2022-10-25 | 深圳易伙科技有限责任公司 | Application processing method and device based on zero code development |
| CN116304110B (en) * | 2023-03-30 | 2023-09-08 | 重庆工业职业技术学院 | Working method for constructing knowledge graph by using English vocabulary data |
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