CN110888877A - Event information display method and device, computing equipment and storage medium - Google Patents
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Abstract
The application is applicable to the technical field of computers, and provides an event information display method, which comprises the following steps: acquiring target event information matched with retrieval information input by a user; determining associated event information associated with the target event information; determining an association weight between each of the associated event information and the retrieval information; and generating a sorting table of the associated event information based on the associated weight, and displaying the sorting table through a preset interface. By determining the associated event information of the target event information matched with the retrieval information, generating the sorting table of the associated event information according to the associated weight between the associated event information and the retrieval information, and displaying the sorting table, the problem that the monitoring result cannot be effectively expanded in the prior art is solved, and the event information after effective expansion can be displayed.
Description
Technical Field
The present application belongs to the field of computer technologies, and in particular, to an event information display method and apparatus, a computing device, and a storage medium.
Background
With the development of the deep learning model, the deep learning model is widely applied to the field of video monitoring. At present, the recognition result output by the deep learning model is a proper noun with a special meaning, and the recognition result cannot be effectively expanded aiming at the monitoring result, so that the output result may have deviation or omit part of useful information, and the use of the deep learning model in the field of video monitoring has certain limitation.
Disclosure of Invention
In view of this, embodiments of the present application provide an event information display method, an event information display apparatus, a computing device, and a storage medium, so as to solve the problem in the prior art that a monitoring result cannot be effectively extended, and display event information after effective extension.
A first aspect of an embodiment of the present application provides an event information display method, including:
acquiring target event information matched with retrieval information input by a user;
determining associated event information associated with the target event information;
determining an association weight between each of the associated event information and the retrieval information;
and generating a sorting table of the associated event information based on the associated weight, and displaying the sorting table through a preset interface.
In an alternative implementation manner, the obtaining target event information that matches with retrieval information input by a user includes:
and traversing the incidence relation between the event information stored in advance and preset retrieval information, and acquiring the target event information matched with the retrieval information.
In an optional implementation manner, before the obtaining target event information matching with the retrieval information input by the user, the method includes:
extracting a predetermined video frame collected by monitoring equipment, wherein the video frame contains image information corresponding to a preset monitoring event;
analyzing the image information according to a pre-trained deep learning model to obtain event information corresponding to each monitoring event, wherein the event information comprises the target event information;
and storing the event information and preset retrieval information in an associated manner.
In an optional implementation manner, determining associated event information associated with the target event information includes:
and performing associated event analysis on the target event information to obtain associated event information associated with the target event information.
In an optional implementation manner, the performing associated event analysis on the target event information to obtain associated event information associated with the target event information includes:
analyzing a related event corresponding to the target event information by using a preset information vector model to obtain the related event information related to the target event information;
the input of the preset information vector model is the preset retrieval information, and the output is an information vector consisting of all the same information items of the preset retrieval information;
and a mapping relation exists between the preset retrieval information and the event information corresponding to the preset monitoring event, and each information item in the information vector is used for representing the associated event information of the target event information.
In an optional implementation manner, the determining an association weight between each piece of associated event information and the retrieved information includes:
respectively calculating the correlation discrimination between each piece of correlated event information and the retrieval information;
and determining the association weight between each piece of associated event information and the retrieval information according to the associated discrimination.
In an optional implementation manner, the separately calculating the associated differentiation between each associated event information and the retrieval information includes:
acquiring a pre-constructed target text containing an information item corresponding to the retrieval information;
respectively calculating probability values of target information items contained in each piece of associated event information and appearing in the target text;
and determining the associated discrimination according to the probability value.
A second aspect of the embodiments of the present application provides an event information display apparatus, including:
the acquisition module is used for acquiring target event information matched with retrieval information input by a user;
a first determination module, configured to determine associated event information associated with the target event information;
the second determination module is used for determining the association weight between each piece of associated event information and the retrieval information;
and the generating module is used for generating a sorting table of the associated event information based on the associated weight and displaying the sorting table through a preset interface.
In an optional implementation manner, the obtaining module is specifically configured to:
and traversing the incidence relation between the event information stored in advance and preset retrieval information, and inquiring the target event information matched with the retrieval information.
In an optional implementation manner, the method further includes:
the system comprises an extraction module, a monitoring module and a processing module, wherein the extraction module is used for extracting a predetermined video frame collected by monitoring equipment, and the video frame contains image information corresponding to a preset monitoring event;
the analysis module is used for analyzing the image information according to a depth learning model which is trained in advance to obtain event information corresponding to each monitoring event, and the event information comprises the target event information;
the storage module is used for storing the event information and preset retrieval information in an associated manner;
in an optional implementation manner, the first determining module is specifically configured to include:
and performing associated event analysis on the target event information to obtain associated event information associated with the target event information.
In an optional implementation manner, the first determining module is specifically configured to:
analyzing a related event corresponding to the target event information by using a preset information vector model to obtain the related event information related to the target event information;
the input of the preset information vector model is the preset retrieval information, and the output is an information vector consisting of all the same information items of the preset retrieval information;
and a mapping relation exists between the preset retrieval information and the event information corresponding to the preset monitoring event, and each information item in the information vector is used for representing the associated event information of the target event information.
In an optional implementation manner, the second determining module includes:
the calculation unit is used for calculating the association discrimination between each piece of associated event information and the retrieval information respectively;
and the determining unit is used for determining the association weight between each piece of associated event information and the retrieval information according to the associated discrimination.
In an optional implementation manner, the second computing unit includes:
the acquisition subunit is used for acquiring a target text which is constructed in advance and contains an information item corresponding to the retrieval information;
the calculating subunit is used for calculating probability values of target information items contained in each piece of associated event information and appearing in the target text respectively;
and the determining subunit is used for determining the associated discrimination according to the probability value.
A third aspect of the embodiments of the present application provides a computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the event information display method according to the first aspect when executing the computer program.
A fourth aspect of the embodiments of the present application provides a computer-readable storage medium, where a computer program is stored, where the computer program is executed by a processor to implement the steps of the event information display method according to the first aspect.
The event information display method provided in the first aspect of the embodiment of the present application includes: acquiring target event information matched with retrieval information input by a user; determining associated event information associated with the target event information; determining an association weight between each of the associated event information and the retrieval information; and generating a sorting table of the associated event information based on the associated weight, and displaying the sorting table through a preset interface. Compared with the prior art, the problem that the monitoring result cannot be effectively expanded in the prior art is solved, and the event information after effective expansion can be displayed by determining the associated event information of the target event information matched with the retrieval information, generating the sorting table of the associated event information according to the associated weight between the associated event information and the retrieval information, and displaying the sorting table.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a flowchart of an implementation of an event information display method according to a first embodiment of the present application;
FIG. 2 is a flowchart illustrating an implementation of S103 in FIG. 1;
FIG. 3 is a specific implementation process of S1031 in FIG. 2;
FIG. 4 is a flowchart of an implementation of an event information display method according to a second embodiment of the present application;
fig. 5 is a schematic structural diagram of an event information display device according to an embodiment of the present application;
fig. 6 is a schematic diagram of a computing device provided by an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should be noted that, at present, with the development of the deep learning technology, the deep learning technology is widely applied in the field of video monitoring, and the deep learning technology mainly performs deep learning on video image information through a deep learning model and outputs a recognition result. Generally, the recognition result output by the deep learning model is a term with a special meaning, and the problem that all key information cannot be displayed to a user exists, so that the user can accurately know the result of video monitoring only under the explanation of a professional, and certain limitation exists. According to the technical scheme provided by the embodiment of the application, the video monitoring results can be subjected to association expansion, and the association results are displayed to the user after being sorted.
In order to explain the technical solution described in the present application, the following description will be given by way of specific examples. As shown in fig. 1, which is a flowchart for implementing an event information display method according to a first embodiment of the present application, this embodiment may be implemented by hardware or software of an event information display apparatus, where the event information display apparatus may be a server or other computing device with a computing function. The details are as follows:
s101, target event information matched with retrieval information input by a user is obtained.
In this embodiment, the user may input the search information through a preset interface of the event information display device, and the search information may be a single keyword, a sentence, or a combination of multiple keywords. The event information display device may be a monitoring device, a monitoring video frame within a preset time length is stored in the monitoring device, and under a normal condition, after the monitoring device detects the retrieval information input by the user, target event information matched with the retrieval information is acquired, wherein the target event information belongs to event information stored in advance.
It can be understood that, by analyzing different monitoring video frames by using the deep learning model, event information corresponding to each monitoring video can be obtained, and in this embodiment, the event information and preset retrieval information are stored in an associated manner. By way of example and not limitation, the implementation process of S101 is:
and traversing the incidence relation between the pre-stored event information and the preset retrieval information, and acquiring the target event information matched with the retrieval information.
And S102, determining the associated event information associated with the target event information.
It should be noted that, in the embodiment of the present application, the event information is event information obtained by analyzing the monitoring video through the deep learning model, and is often a preset terminology, for example, a sentinel anomaly, for a general user, for example, a security guard, when seeing the event information of the sentinel anomaly, an accurate meaning corresponding to the sentinel anomaly cannot be quickly and accurately determined, for example, the sentinel anomaly may correspond to that the sentinel is not at a post, the sentinel is at a post but in an abnormal state, and the like, so that the security guard needs to confirm the sentinel post to determine a final state, and the use is inconvenient.
In this instance, in order to more accurately display the event information, associated event information associated with the target event information is determined.
By way of example, and not limitation, S102, comprises: and performing associated event analysis on the target event information to obtain associated event information associated with the target event information.
For example, analyzing a related event corresponding to the target event information by using a preset information vector model to obtain the related event information related to the target event information;
the input of the preset information vector model is the preset retrieval information, and the output is an information vector consisting of all the same information items of the preset retrieval information;
and a mapping relation exists between the preset retrieval information and the event information corresponding to the preset monitoring event, and each information item in the information vector is used for representing the associated event information of the target event information.
In an optional implementation manner, a key information item may be further obtained from the target event information by determining a probability that the retrieval information includes an information item appearing in the target event information, and determining associated event information associated with the key information item.
Specifically, all information items contained in the retrieval information are acquired; calculating the inverse text frequency index of each information item in the target event information by using a preset weighting algorithm; determining key information items contained in the target event information based on the inverse text frequency index; associated event information associated with the key information item is determined.
The preset weighting algorithm may be a TF-IDF (term frequency-inverse document frequency) algorithm, when an inverse text frequency Index (IDF) of an information item appearing in the target event information is greater than a preset frequency index threshold, it is determined that the information item is the key information item, and when the inverse text frequency index of an information item appearing in the target event information is less than or equal to a preset frequency index threshold, it is determined that the information item is not the key information item.
S103, determining the association weight between each piece of associated event information and the retrieval information.
It can be understood that the related event information and the retrieval information contain partially same information items, and different related event information and the retrieval information have different same information items; since the meaning of each identical information item expressed in the associated event information and the retrieval information is not necessarily identical, it is necessary to further determine the associated weight between each of the associated event information and the retrieval information. The correlation weight is used for measuring the degree of correlation between the correlation event information and the retrieval information.
Specifically, as shown in fig. 2, it is a flowchart of a specific implementation of S103 in fig. 1. As can be seen from fig. 2, S103 includes:
and S1031, respectively calculating the association discrimination between each piece of associated event information and the retrieval information.
In this embodiment, the degree of correlation distinction between the associated event information and the retrieval information is determined by calculating a co-occurrence distance between a target information item included in the associated event information and an information item included in the detection information.
Specifically, as shown in fig. 3, it is a specific implementation procedure of S1031 in fig. 2. As can be seen from fig. 3, S1031 includes:
s301, a target text which is constructed in advance and contains an information item corresponding to the retrieval information is obtained.
It is to be understood that the search information is one of a plurality of pieces of search information that are preset, and in general, in order to fully and accurately expand event information that is stored in advance, a plurality of pieces of search information are stored in advance, and each piece of search information is stored in association with an event.
S302, respectively calculating probability values of target information items contained in each piece of associated event information and appearing in the target text.
It can be understood that each of the associated event information includes at least one target information item, each of the target information items may be present in the target text, or may not be present in the target text, and in this embodiment, only the occurrence probability of the target information item present in the target text is calculated.
S303, determining the associated discrimination according to the probability value.
Specifically, the probability value and the associated partition degree are in an inverse relationship, that is, the larger the probability value is, the smaller the corresponding associated partition degree is.
S1032, determining the association weight between each piece of associated event information and the retrieval information according to the associated distinction degree.
It can be understood that the association distinction degree and the association weight are in an inverse relationship, that is, the smaller the association distinction degree, the larger the corresponding association weight.
And S104, generating a sorting table of the associated event information based on the associated weight, and displaying the sorting table through a preset interface.
In an optional implementation manner, the associated event information is sequentially sorted according to the value of the associated weight from large to small, or the associated event information with the associated weight larger than a preset associated weight threshold value is selected for sorting.
As can be seen from the foregoing embodiments, the event information display method provided in the present application includes: acquiring target event information matched with retrieval information input by a user; determining associated event information associated with the target event information; determining an association weight between each of the associated event information and the retrieval information; and generating a sorting table of the associated event information based on the associated weight, and displaying the sorting table through a preset interface. Compared with the prior art, the problem that the monitoring result cannot be effectively expanded in the prior art is solved, and the event information after effective expansion can be displayed by determining the associated event information of the target event information matched with the retrieval information, generating the sorting table of the associated event information according to the associated weight between the associated event information and the retrieval information, and displaying the sorting table.
Specifically, as shown in fig. 4, it is a flowchart of an implementation of an event information display method according to a second embodiment of the present application. As can be seen from fig. 4, in this embodiment, compared with the embodiment shown in fig. 1, the implementation processes of S404 to S407 are the same as the implementation processes of S101 to S104, except that S401 to S403 are further included before S404, where S403 and S404 are in a sequential execution relationship, and are detailed as follows:
s401, extracting a predetermined video frame collected by the monitoring equipment, wherein the video frame contains image information corresponding to a preset monitoring event.
The predetermined monitoring equipment can be an all-in-one machine, a color gun type camera, a color dome camera, an infrared camera or a monitoring camera and the like, and the preset monitoring event can be whether a worker is on duty or whether a safety hazard exists in a preset area or not.
S402, analyzing the image information according to a depth learning model which is trained in advance to obtain event information corresponding to each monitoring event, wherein the event information comprises the target event information.
The pre-trained deep learning model can be a classification recognition model such as a neural network model, a logistic regression model or a random forest model, and the classification recognition model inputs image information acquired by monitoring equipment and outputs event information corresponding to a preset monitoring event.
And S403, storing the event information and preset retrieval information in a correlation manner.
It can be understood that each event information corresponds to one monitoring event, and the preset retrieval information is generally retrieval information corresponding to each monitoring event preset according to experience, so that the event information corresponding to the same monitoring event and the preset retrieval information can be stored in an associated manner. As can be seen from the above analysis, in the embodiment of fig. 1, a predetermined video frame collected by the monitoring device is extracted, where the video frame includes image information corresponding to a preset monitoring event; analyzing the image information according to a pre-trained deep learning model to obtain event information corresponding to each monitoring event; and storing the event information and preset retrieval information in an associated manner. The method provides a basis for obtaining the target event information matched with the retrieval information input by the user and provides an information basis for realizing the effective expansion of the target event information.
Fig. 5 is a schematic structural diagram of an event information display device according to an embodiment of the present application. As can be seen from fig. 5, the event information display device 5 according to the embodiment of the present application includes: an acquisition module 501, a first determination module 502, a second determination module 503, and a generation module 504. Wherein the content of the first and second substances,
an obtaining module 501, configured to obtain target event information matched with search information input by a user;
a first determining module 502, configured to determine associated event information associated with the target event information;
a second determining module 503, configured to determine an association weight between each piece of associated event information and the retrieved information;
a generating module 504, configured to generate a sorting table of the associated event information based on the associated weight, and display the sorting table through a preset interface.
In an optional implementation manner, the obtaining module 501 is specifically configured to:
and traversing the incidence relation between the event information stored in advance and preset retrieval information, and inquiring the target event information matched with the retrieval information.
In an optional implementation manner, the method further includes:
the system comprises an extraction module, a monitoring module and a processing module, wherein the extraction module is used for extracting a predetermined video frame collected by monitoring equipment, and the video frame contains image information corresponding to a preset monitoring event;
the analysis module is used for analyzing the image information according to a depth learning model which is trained in advance to obtain event information corresponding to each monitoring event, and the event information comprises the target event information;
the storage module is used for storing the event information and preset retrieval information in an associated manner;
in an optional implementation manner, the first determining module 502 is specifically configured to include:
and performing associated event analysis on the target event information to obtain associated event information associated with the target event information.
In an optional implementation manner, the first determining module 502 is specifically configured to:
analyzing a related event corresponding to the target event information by using a preset information vector model to obtain the related event information related to the target event information;
the input of the preset information vector model is the preset retrieval information, and the output is an information vector consisting of all the same information items of the preset retrieval information;
and a mapping relation exists between the preset retrieval information and the event information corresponding to the preset monitoring event, and each information item in the information vector is used for representing the associated event information of the target event information.
In an optional implementation manner, the second determining module 503 includes:
the calculation unit is used for calculating the association discrimination between each piece of associated event information and the retrieval information respectively;
and the determining unit is used for determining the association weight between each piece of associated event information and the retrieval information according to the associated discrimination.
In an optional implementation manner, the second computing unit includes:
the acquisition subunit is used for acquiring a target text which is constructed in advance and contains an information item corresponding to the retrieval information;
the calculating subunit is used for calculating probability values of target information items contained in each piece of associated event information and appearing in the target text respectively;
and the determining subunit is used for determining the associated discrimination according to the probability value.
Fig. 6 is a schematic diagram of a computing device provided by an embodiment of the present application. As shown in fig. 6, the computing device 6 of this embodiment includes: a processor 60, a memory 61, and a computer program 62, such as an event information display program, stored in the memory 61 and operable on the processor 60. The processor 60, when executing the computer program 62, implements the steps in the various event information display method embodiments described above, such as the steps 101 to 104 shown in fig. 1.
Illustratively, the computer program 62 may be partitioned into one or more modules/units that are stored in the memory 61 and executed by the processor 60 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions that describe the execution of computer program 62 in the computing device 6. For example, the computer program 62 may be divided into an acquisition module, a first determination module, a second determination module, and a generation module (module in the virtual device), each module having the following specific functions:
the acquisition module is used for acquiring target event information matched with retrieval information input by a user;
a first determination module, configured to determine associated event information associated with the target event information;
the second determination module is used for determining the association weight between each piece of associated event information and the retrieval information;
and the generating module is used for generating a sorting table of the associated event information based on the associated weight and displaying the sorting table through a preset interface.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of communication units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. . Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. An event information display method, comprising:
acquiring target event information matched with retrieval information input by a user;
determining associated event information associated with the target event information;
determining an association weight between each of the associated event information and the retrieval information;
and generating a sorting table of the associated event information based on the associated weight, and displaying the sorting table through a preset interface.
2. The event information display method according to claim 1, wherein the acquiring target event information that matches retrieval information input by a user includes:
and traversing the incidence relation between the pre-stored event information and the preset retrieval information, and acquiring the target event information matched with the retrieval information.
3. The event information display method according to claim 1 or 2, wherein, before the acquiring target event information that matches retrieval information input by a user, comprising:
extracting a predetermined video frame collected by monitoring equipment, wherein the video frame contains image information corresponding to a preset monitoring event;
analyzing the image information according to a pre-trained deep learning model to obtain event information corresponding to each monitoring event, wherein the event information comprises the target event information;
and storing the event information and preset retrieval information in an associated manner.
4. The event information display method according to claim 3, wherein determining associated event information associated with the target event information includes:
and performing associated event analysis on the target event information to obtain associated event information associated with the target event information.
5. The method for displaying event information according to claim 4, wherein the performing the correlated event analysis on the target event information to obtain correlated event information correlated with the target event information includes:
analyzing a related event corresponding to the target event information by using a preset information vector model to obtain the related event information related to the target event information;
the input of the preset information vector model is the preset retrieval information, and the output is an information vector consisting of all the same information items of the preset retrieval information;
and a mapping relation exists between the preset retrieval information and the event information corresponding to the preset monitoring event, and each information item in the information vector is used for representing the associated event information of the target event information.
6. The event information display method according to claim 4 or 5, wherein the determining of the association weight between each of the associated event information and the retrieval information includes:
respectively calculating the correlation discrimination between each piece of correlated event information and the retrieval information;
and determining the association weight between each piece of associated event information and the retrieval information according to the associated discrimination.
7. The event information display method according to claim 6, wherein the calculating the degree of correlation distinction between each piece of correlated event information and the retrieval information, respectively, comprises:
acquiring a pre-constructed target text containing an information item corresponding to the retrieval information;
respectively calculating probability values of target information items contained in each piece of associated event information and appearing in the target text;
and determining the associated discrimination according to the probability value.
8. An event information display device characterized by comprising:
the acquisition module is used for acquiring target event information matched with retrieval information input by a user;
a first determination module, configured to determine associated event information associated with the target event information;
the second determination module is used for determining the association weight between each piece of associated event information and the retrieval information;
and the generating module is used for generating a sorting table of the associated event information based on the associated weight and displaying the sorting table through a preset interface.
9. A computing device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the event information display method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the event information display method according to any one of claims 1 to 7.
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