CN114936780B - Activity resource prediction method and device, electronic equipment and readable storage medium - Google Patents
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Abstract
The invention relates to an artificial intelligence technology, and discloses an activity resource prediction method, which comprises the following steps: performing first classification on the historical topic cases according to the historical topic tags of the historical topic cases, and extracting historical activity topic texts of the historical activity topic texts under the first classified historical topic tags; classifying the historical activity topic text according to the historical topic feature dimension labels; constructing a multi-path resource estimation link according to historical activity topic text of each historical topic feature dimension label under each historical topic label obtained by the historical topic label, the historical topic feature dimension label and the second classification; and predicting the resources required by the activities of the currently applied topics by utilizing the multipath resource predicting links to obtain the predicted resources of the activities of the currently applied topics. The invention further provides an activity resource estimating device, electronic equipment and a storage medium. The method and the device can improve the reasonability of the activity resource estimation.
Description
Technical Field
The present invention relates to the field of artificial intelligence technologies, and in particular, to a method and apparatus for predicting an activity resource, an electronic device, and a computer readable storage medium.
Background
In the existing event, related experience of historical event is generally utilized to artificially estimate resources required by the event. In practical implementation, the problem of insufficient or excessive resource prediction often occurs due to the manually predicted resource demand, and the resource prediction cannot reach a more reasonable state.
Disclosure of Invention
The invention provides an activity resource estimation method, an activity resource estimation device and a computer readable storage medium, which mainly aim to solve the problem of poor resource estimation rationality generated when activity resource estimation is carried out.
In order to achieve the above object, the present invention provides a method for predicting activity resources, including:
acquiring historical activity topic cases, and performing first classification on the historical activity topic cases according to preset historical topic labels to obtain historical activity topic cases under each type of the historical topic labels;
extracting text information of the historical activity topic cases to obtain historical activity topic texts under each historical activity topic label;
performing second classification on the historical activity topic text under each historical topic label according to a preset historical topic feature dimension label to obtain the historical activity topic text of each historical topic feature dimension under each historical topic label;
Constructing a multipath resource estimation link by taking the historical topic labels, the historical topic feature dimension labels and the historical activity topic text of each historical topic feature dimension label under each historical topic label as link nodes;
extracting a current topic label and a current topic feature dimension label from a current application topic activity;
Calculating a first matching degree of the current theme label and each main link node in the multipath resource estimation link, and calculating a second matching degree of the current theme feature dimension label and each subordinate link node in the multipath resource estimation link;
And extracting historical activity topic texts meeting the matching conditions in the multipath resource estimation link according to the first matching degree and the second matching degree, and obtaining the estimated resources of the current application topic activities according to the historical activity topic texts meeting the matching conditions.
Optionally, the first classifying the historical activity topic cases according to a preset historical topic label to obtain each historical activity topic case under the historical topic label includes:
Acquiring activity information of the historical activity topic case;
extracting an activity theme name from the activity information;
acquiring a historical topic label to which each historical activity topic case belongs according to a preset corresponding relation between the activity topic name and the historical topic label;
the historical activity topic cases with the same historical topic label are classified into one type, and the historical activity topic case under each historical topic label is obtained.
Optionally, the extracting text information of the historical activity topic case to obtain the historical activity topic text under each historical topic label includes:
Recognizing characters in the historical activity theme cases through an OCR character recognition technology;
And converting the recognized characters into standardized characters by using the set character formats such as character types, character sizes and the like, and storing the standardized characters to obtain the historical activity theme text.
Optionally, the constructing a multi-path resource estimation link by using the historical topic label, the historical topic feature dimension label and the historical activity topic text of each historical topic feature dimension label under each historical topic label as a link node includes:
converting the historical topic label into a corresponding historical topic label vector, and constructing a main link node by using the historical topic label and the corresponding historical topic label vector;
converting the historical theme feature dimension labels into corresponding historical theme feature dimension label vectors, and constructing subordinate link nodes by utilizing the historical theme feature dimension labels and the corresponding historical theme feature dimension label vectors;
Constructing a subordinate link node of the subordinate link node by utilizing the historical activity subject text affiliated to the subordinate link node;
And connecting the main link node, the subordinate link node and the subordinate link node by using a link connecting line according to a link node-to-link correspondence rule to obtain the multipath resource estimated link.
Optionally, the connecting the main link node, the subordinate link node and the subordinate link node by using a link connection according to a link node-to-link correspondence rule to obtain the multipath resource estimated link includes:
constructing the co-occurrence relation of all the link nodes in the multipath resource pre-estimated link according to the corresponding rule among the link nodes;
and according to the co-occurrence relation, utilizing a link connection line to link all the link nodes to obtain the multipath resource estimated link.
Optionally, the calculating a first matching degree between the current topic feature tag and each main link node in the multipath resource estimation link, and calculating a second matching degree between the current topic feature tag and each subordinate link node in the multipath resource estimation link includes:
Calculating a first matching degree of the current theme label and each main link node in the multipath resource pre-estimated link;
Extracting multipath resource estimated links corresponding to the main link nodes with the matching degree larger than or equal to a preset first matching degree threshold value as candidate estimated links;
and calculating a second matching degree of the current theme feature dimension label and each subordinate link node in the candidate estimated link.
Optionally, the calculating a first matching degree between the current topic label and each main link node in the multipath resource estimation link includes:
Calculating a first matching degree of the current theme label and each main link node in the multipath resource estimation link by adopting the following similarity formula:
Wherein Item (r 1) is a historical topic label vector in the multipath resource pre-estimated link, item (r 2) is the current topic label vector, and Similarity is the Similarity of the historical topic label vector and the current topic label vector.
In order to solve the above problems, the present invention further provides an activity resource predicting apparatus, which includes:
The historical activity topic case processing module is used for acquiring historical activity topic cases, and carrying out first classification on the historical activity topic cases according to preset historical topic labels to obtain the historical activity topic cases under each historical topic label; extracting text information of the historical activity topic cases to obtain historical activity topic texts under each historical activity topic label; performing second classification on the historical activity topic text under each historical topic label according to a preset historical topic feature dimension label to obtain the historical activity topic text of each historical topic feature dimension under each historical topic label;
The multi-path resource estimation link establishment module is used for establishing multi-path resource estimation links by taking the historical topic labels, the historical topic feature dimension labels and the historical activity topic text of each historical topic feature dimension label under each historical topic label as link nodes;
The resource estimation module is used for extracting a current theme label and a current theme feature dimension label from the current application theme activity; calculating a first matching degree of the current theme label and each main link node in the multipath resource estimation link, and calculating a second matching degree of the current theme feature dimension label and each subordinate link node in the multipath resource estimation link; and extracting historical activity topic texts meeting the matching conditions in the multipath resource estimation link according to the first matching degree and the second matching degree, and obtaining the estimated resources of the current application topic activities according to the historical activity topic texts meeting the matching conditions.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the activity resource estimation method described above.
In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium having stored therein at least one computer program that is executed by a processor in an electronic device to implement the above-mentioned activity resource estimation method.
According to the embodiment of the invention, the key information of the historical activity topic case is extracted by performing operations such as text extraction, text classification and the like on the historical activity topic case, the link nodes of the multi-channel resource prediction link are constructed by utilizing the key information, the construction of the whole multi-channel resource prediction link is finished by utilizing the link nodes according to the preset corresponding relation among the link nodes, the matching degree of the key information of the current application topic activity and the link nodes in the multi-channel resource prediction link is calculated, and the resource information, which is related to the key information of the current application topic activity, in the multi-channel resource prediction link with the highest matching degree is used as the prediction resource of the current application activity. Therefore, the method, the device, the electronic equipment and the computer readable storage medium for predicting the active resources can solve the problem of poor resource prediction rationality generated when the active resources are predicted.
Drawings
FIG. 1 is a flowchart illustrating a method for predicting an active resource according to an embodiment of the present invention;
FIG. 2 is a functional block diagram of an activity resource estimation device according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device for implementing the activity resource estimation method according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides an activity resource prediction method. The execution subject of the activity resource estimation method includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the active resource estimation method may be performed by software or hardware installed in a terminal device or a server device, where the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flowchart of an activity resource estimation method according to an embodiment of the invention is shown. In this embodiment, the activity resource estimation method includes:
S1, acquiring historical activity topic cases, and performing first classification on the historical activity topic cases according to preset historical topic labels to obtain historical activity topic cases under each type of the historical topic labels;
In the embodiment of the invention, the historical activity theme cases can be all activity cases held in the history. The activity cases include, but are not limited to, activity topic names, activity overall flows, activity participation numbers, activity resource consumption and other activity information. For example, the historical activity topic case may be a promotional topic that is built when an enterprise makes an enterprise product campaign.
In the embodiment of the invention, the historical theme label can be obtained by analyzing and inducing the historical theme label in advance according to the activity theme name of the historical activity theme case. For example, if the active subject name of a certain historical active subject case is "XX product release meeting", the corresponding historical subject label may be a release meeting subject label, and if the active subject name of a certain historical active subject case is "XX exhibition", the corresponding historical subject label may be an exhibition meeting subject label, etc.
In detail, the first classification of the historical activity topic cases according to the preset historical topic label, to obtain each historical activity topic case under the historical topic label, includes:
Acquiring activity information of the historical activity topic case;
extracting an activity theme name from the activity information;
acquiring a historical topic label to which each historical activity topic case belongs according to a preset corresponding relation between the activity topic name and the historical topic label;
And classifying the historical activity topic cases with the same historical topic label into one type to obtain the historical activity topic case under each historical topic label.
S2, extracting text information of the historical activity topic cases to obtain historical activity topic texts under each historical topic label;
according to the embodiment of the invention, text information in the historical activity theme case is identified through an OCR text recognition technology; and converting the identified text information into a standardized text by using the set text formats such as text types, text sizes and the like, and storing the standardized text to obtain the historical activity topic text under each historical topic label.
S3, performing second classification on the historical activity topic text under each historical topic label according to a preset historical topic feature dimension label to obtain the historical activity topic text of each historical topic feature dimension under each historical topic label;
In the embodiment of the invention, the history theme feature dimension label can be other activity information except the activity theme name, such as an activity overall flow, the number of the participants in the activity, activity resource consumption and the like.
The second classification in the embodiment of the present invention is similar to the first classification method described above, and will not be described herein.
S4, constructing a multipath resource estimation link by taking the historical topic labels, the historical topic feature dimension labels and the historical activity topic text of each historical topic feature dimension label under each historical topic label as link nodes;
in the embodiment of the invention, the multipath resource estimation link comprises a resource estimation link structure composed of a link node, a directional link connection and a vector of characters contained in the link node, wherein the directional link connection is determined by the vector in the link node.
Specifically, the constructing a multi-path resource estimation link by using the historical topic label, the historical topic feature dimension label and the historical activity topic text of each historical topic feature dimension label under each historical topic label as a link node includes:
Converting the historical theme label, the historical theme feature dimension label and the historical activity theme text into vectors to obtain a historical theme label vector, a historical theme feature dimension label vector and a historical activity theme text vector;
Constructing a main link node by using the historical topic tag vector;
Constructing subordinate link nodes by using the historical subject feature dimension label vector;
Constructing a subordinate link node of the subordinate link node by using the historical active subject text vector attached to the subordinate link node;
And connecting the main link node, the subordinate link node and the subordinate link node by utilizing a link connection according to a link node-to-link correspondence rule to obtain the multipath resource estimated link.
And one or more link connecting lines are connected among the link nodes.
Further, the connecting the main link node, the subordinate link node and the subordinate link node by using a link connection according to a link node-to-link correspondence rule to obtain the multipath resource estimated link, including:
constructing the co-occurrence relation of all the link nodes in the multipath resource pre-estimated link according to the corresponding rule among the link nodes;
and according to the co-occurrence relation, utilizing a link connection line to link all the link nodes to obtain the multipath resource estimated link.
In the embodiment of the present invention, the co-occurrence relationship may be understood as a correspondence relationship between two link nodes.
Further, the embodiment of the invention can calculate the weight O of the directional link connection between the main link node and the subordinate link node in the multipath resource estimation link through the following formula:
V j is the historical subject feature dimension label vector included by the subordinate link node in the multipath resource estimation link, and V i is the historical subject label vector included by the main link node in the multipath resource estimation link.
In the embodiment of the present invention, the method for calculating the weight of the directional link connection between the slave link node and the subordinate link node in the multipath link node is consistent with the method for calculating the weight of the directional link connection between the main link node and the slave link node in the multipath resource estimation link, and will not be described herein.
According to the embodiment of the invention, the link node text can be converted into the vector through a Word2vec Word vector conversion model.
S5, extracting a current theme label and a current theme feature dimension label from the current application theme activity;
The embodiment of the invention obtains the current theme label by extracting the activity theme names of the current application theme activities, and extracts all activity information except the activity theme names of the current application theme activities as the current theme feature dimension label.
S6, calculating a first matching degree of the current theme label and each main link node in the multipath resource estimation link, and calculating a second matching degree of the current theme feature dimension label and each subordinate link node in the multipath resource estimation link;
In the embodiment of the present invention, the matching degree may be a similarity between two tags. For example, in the embodiment of the present invention, the matching degree may be a similarity between the current theme label and a main link node in the multipath resource estimation link.
In detail, the step S6 includes:
Calculating a first matching degree of the current theme label and each main link node in the multipath resource pre-estimated link;
extracting a resource estimated link corresponding to a main link node with the matching degree larger than or equal to a preset first matching degree threshold value as a candidate resource estimated link;
and calculating a second matching degree of the current theme feature dimension label and each subordinate link node in the candidate resource estimation link.
In the embodiment of the invention, the first matching degree and the second matching degree can be calculated by adopting the following similarity formula:
Wherein Item (r 1) is a historical topic label vector in the multi-path resource estimation link, item (r 2) is the current topic label vector, similarity is Similarity between the historical topic label vector and the current topic label vector, in addition, item (r 1) may also be a historical topic feature dimension label vector in the multi-path resource estimation link, item (r 2) may also be the current topic feature dimension label vector, and Similarity may also be Similarity between the historical topic feature dimension label vector and the current topic feature dimension label vector.
And S7, extracting historical activity topic texts meeting the matching conditions in the multipath resource estimation link according to the first matching degree and the second matching degree, and obtaining the estimated resources of the current application topic activities according to the historical activity topic texts meeting the matching conditions.
In detail, the embodiment of the invention extracts the resource estimated links corresponding to the main link nodes with the matching degree larger than or equal to the preset first matching degree threshold value to serve as candidate resource estimated links; extracting a subordinate link node with the second matching degree being greater than or equal to a preset second matching degree threshold value, and extracting a historical activity topic text corresponding to a subordinate link node corresponding to the subordinate link node as a reference activity topic case text; and summarizing all the texts of the reference activity topic cases to obtain estimated resources of the current application topic activities.
Fig. 2 is a functional block diagram of an activity resource estimation device according to an embodiment of the present invention.
The activity resource predicting device 100 of the present invention may be installed in an electronic device. Depending on the implementation, the activity resource estimation device 100 may include a historical activity topic case processing module 101, a multi-path resource estimation link establishment module 102, and a resource estimation module 103. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
The historical activity topic case processing module 101 is configured to obtain historical activity topic cases, and perform a first classification on the historical activity topic cases according to preset historical topic tags to obtain historical activity topic cases under each of the historical topic tags; extracting text information of the historical activity topic cases to obtain historical activity topic texts under each historical activity topic label; and performing second classification on the historical activity topic text under each historical topic label according to the preset historical topic feature dimension label to obtain the historical activity topic text of each historical topic feature dimension under each historical topic label.
In the embodiment of the invention, the historical activity theme cases can be all activity cases held in the history. The activity cases include, but are not limited to, activity topic names, activity overall flows, activity participation numbers, activity resource consumption and other activity information. For example, the historical activity topic case may be a promotional topic that is built when an enterprise makes an enterprise product campaign.
In the embodiment of the invention, the historical theme label can be obtained by analyzing and inducing the historical theme label in advance according to the activity theme name of the historical activity theme case. For example, if the active subject name of a certain historical active subject case is "XX product release meeting", the corresponding historical subject label may be a release meeting subject label, and if the active subject name of a certain historical active subject case is "XX exhibition", the corresponding historical subject label may be an exhibition meeting subject label, etc.
In detail, the first classification of the historical activity topic cases according to the preset historical topic label, to obtain each historical activity topic case under the historical topic label, includes:
Acquiring activity information of the historical activity topic case;
extracting an activity theme name from the activity information;
acquiring a historical topic label to which each historical activity topic case belongs according to a preset corresponding relation between the activity topic name and the historical topic label;
And classifying the historical activity topic cases with the same historical topic label into one type to obtain the historical activity topic case under each historical topic label.
According to the embodiment of the invention, text information in the historical activity theme case is identified through an OCR text recognition technology; and converting the identified text information into a standardized text by using the set text formats such as text types, text sizes and the like, and storing the standardized text to obtain the historical activity topic text under each historical topic label.
In the embodiment of the invention, the history theme feature dimension label can be other activity information except the activity theme name, such as an activity overall flow, the number of the participants in the activity, activity resource consumption and the like.
The second classification in the embodiment of the present invention is similar to the first classification method described above, and will not be described herein.
The multi-path resource estimation link establishment module 102 is configured to establish multi-path resource estimation links with the historical topic labels, the historical topic feature dimension labels, and the historical activity topic text of each historical topic feature dimension label under each historical topic label as link nodes.
In the embodiment of the invention, the multipath resource estimation link comprises a resource estimation link structure composed of a link node, a directional link connection and a vector of characters contained in the link node, wherein the directional link connection is determined by the vector in the link node.
Specifically, the constructing a multi-path resource estimation link by using the historical topic label, the historical topic feature dimension label and the historical activity topic text of each historical topic feature dimension label under each historical topic label as a link node includes:
Converting the historical theme label, the historical theme feature dimension label and the historical activity theme text into vectors to obtain a historical theme label vector, a historical theme feature dimension label vector and a historical activity theme text vector;
Constructing a main link node by using the historical topic tag vector;
Constructing subordinate link nodes by using the historical subject feature dimension label vector;
Constructing a subordinate link node of the subordinate link node by using the historical active subject text vector attached to the subordinate link node;
And connecting the main link node, the subordinate link node and the subordinate link node by utilizing a link connection according to a link node-to-link correspondence rule to obtain the multipath resource estimated link.
And one or more link connecting lines are connected among the link nodes.
Further, the connecting the main link node, the subordinate link node and the subordinate link node by using a link connection according to a link node-to-link correspondence rule to obtain the multipath resource estimated link, including:
constructing the co-occurrence relation of all the link nodes in the multipath resource pre-estimated link according to the corresponding rule among the link nodes;
and according to the co-occurrence relation, utilizing a link connection line to link all the link nodes to obtain the multipath resource estimated link.
In the embodiment of the present invention, the co-occurrence relationship may be understood as a correspondence relationship between two link nodes.
Further, the embodiment of the invention can calculate the weight O of the directional link connection between the main link node and the subordinate link node in the multipath resource estimation link through the following formula:
V j is the historical subject feature dimension label vector included by the subordinate link node in the multipath resource estimation link, and V i is the historical subject label vector included by the main link node in the multipath resource estimation link.
In the embodiment of the present invention, the method for calculating the weight of the directional link connection between the slave link node and the subordinate link node in the multipath link node is consistent with the method for calculating the weight of the directional link connection between the main link node and the slave link node in the multipath resource estimation link, and will not be described herein.
According to the embodiment of the invention, the link node text can be converted into the vector through a Word2vec Word vector conversion model.
The resource estimation module 103 is configured to extract a current topic label and a current topic feature dimension label from a current application topic activity; calculating a first matching degree of the current theme label and each main link node in the multipath resource estimation link, and calculating a second matching degree of the current theme feature dimension label and each subordinate link node in the multipath resource estimation link; and extracting historical activity topic texts meeting the matching conditions in the multipath resource estimation link according to the first matching degree and the second matching degree, and obtaining the estimated resources of the current application topic activities according to the historical activity topic texts meeting the matching conditions.
The embodiment of the invention obtains the current theme label by extracting the activity theme names of the current application theme activities, and extracts all activity information except the activity theme names of the current application theme activities as the current theme feature dimension label.
In the embodiment of the present invention, the matching degree may be a similarity between two tags. For example, in the embodiment of the present invention, the matching degree may be a similarity between the current theme label and a main link node in the multipath resource estimation link.
In detail, the step of calculating a first matching degree between the current topic label and each main link node in the multi-path resource estimation link, and calculating a second matching degree between the current topic feature dimension label and each subordinate link node in the multi-path resource estimation link, and extracting a historical activity topic text meeting a matching condition in the multi-path resource estimation link according to the first matching degree and the second matching degree, and obtaining an estimated resource of a current application topic activity according to the historical activity topic text meeting the matching condition, wherein the method comprises the steps of:
Calculating a first matching degree of the current theme label and each main link node in the multipath resource pre-estimated link;
extracting a resource estimated link corresponding to a main link node with the matching degree larger than or equal to a preset first matching degree threshold value as a candidate resource estimated link;
and calculating a second matching degree of the current theme feature dimension label and each subordinate link node in the candidate resource estimation link.
In the embodiment of the invention, the first matching degree and the second matching degree can be calculated by adopting the following similarity formula:
Wherein Item (r 1) is a historical topic label vector in the multi-path resource estimation link, item (r 2) is the current topic label vector, similarity is Similarity between the historical topic label vector and the current topic label vector, in addition, item (r 1) may also be a historical topic feature dimension label vector in the multi-path resource estimation link, item (r 2) may also be the current topic feature dimension label vector, and Similarity may also be Similarity between the historical topic feature dimension label vector and the current topic feature dimension label vector.
In detail, the embodiment of the invention extracts the resource estimated links corresponding to the main link nodes with the matching degree larger than or equal to the preset first matching degree threshold value to serve as candidate resource estimated links; extracting a subordinate link node with the second matching degree being greater than or equal to a preset second matching degree threshold value, and extracting a historical activity topic text corresponding to a subordinate link node corresponding to the subordinate link node as a reference activity topic case text; and summarizing all the texts of the reference activity topic cases to obtain estimated resources of the current application topic activities.
Fig. 3 is a schematic structural diagram of an electronic device for implementing an activity resource estimation method according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as an activity resource predictor, stored in the memory 11 and executable on the processor 10.
The processor 10 may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, and connects various components of the entire electronic device using various interfaces and lines, and executes various functions of the electronic device and processes data by running or executing programs or modules (e.g., executing an activity resource estimation program, etc.) stored in the memory 11, and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only for storing application software installed in an electronic device and various data, such as codes of an activity resource predictor, etc., but also for temporarily storing data that has been output or is to be output.
The communication bus 12 may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
The communication interface 13 is used for communication between the electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
Fig. 3 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for supplying power to the respective components, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The activity resource predictor stored in the memory 11 of the electronic device 1 is a combination of instructions which, when executed in the processor 10, may implement:
acquiring historical activity topic cases, and performing first classification on the historical activity topic cases according to preset historical topic labels to obtain historical activity topic cases under each type of the historical topic labels;
extracting text information of the historical activity topic cases to obtain historical activity topic texts under each historical activity topic label;
performing second classification on the historical activity topic text under each historical topic label according to a preset historical topic feature dimension label to obtain the historical activity topic text of each historical topic feature dimension under each historical topic label;
Constructing a multipath resource estimation link by taking the historical topic labels, the historical topic feature dimension labels and the historical activity topic text of each historical topic feature dimension label under each historical topic label as link nodes;
extracting a current topic label and a current topic feature dimension label from a current application topic activity;
Calculating a first matching degree of the current theme label and each main link node in the multipath resource estimation link, and calculating a second matching degree of the current theme feature dimension label and each subordinate link node in the multipath resource estimation link;
And extracting historical activity topic texts meeting the matching conditions in the multipath resource estimation link according to the first matching degree and the second matching degree, and obtaining the estimated resources of the current application topic activities according to the historical activity topic texts meeting the matching conditions.
In particular, the specific implementation method of the above instructions by the processor 10 may refer to the description of the relevant steps in the corresponding embodiment of the drawings, which is not repeated herein.
Further, the modules/units integrated in the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
acquiring historical activity topic cases, and performing first classification on the historical activity topic cases according to preset historical topic labels to obtain historical activity topic cases under each type of the historical topic labels;
extracting text information of the historical activity topic cases to obtain historical activity topic texts under each historical activity topic label;
performing second classification on the historical activity topic text under each historical topic label according to a preset historical topic feature dimension label to obtain the historical activity topic text of each historical topic feature dimension under each historical topic label;
Constructing a multipath resource estimation link by taking the historical topic labels, the historical topic feature dimension labels and the historical activity topic text of each historical topic feature dimension label under each historical topic label as link nodes;
extracting a current topic label and a current topic feature dimension label from a current application topic activity;
Calculating a first matching degree of the current theme label and each main link node in the multipath resource estimation link, and calculating a second matching degree of the current theme feature dimension label and each subordinate link node in the multipath resource estimation link;
And extracting historical activity topic texts meeting the matching conditions in the multipath resource estimation link according to the first matching degree and the second matching degree, and obtaining the estimated resources of the current application topic activities according to the historical activity topic texts meeting the matching conditions.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The blockchain (Blockchain), essentially a de-centralized database, is a string of data blocks that are generated in association using cryptographic methods, each of which contains information from a batch of network transactions for verifying the validity (anti-counterfeit) of its information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Wherein artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) is the theory, method, technique, and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend, and expand human intelligence, sense the environment, acquire knowledge, and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (8)
1. An activity resource estimation method, characterized in that the method comprises:
acquiring historical activity topic cases, and performing first classification on the historical activity topic cases according to preset historical topic labels to obtain historical activity topic cases under each type of the historical topic labels;
extracting text information of the historical activity topic cases to obtain historical activity topic texts under each historical activity topic label;
Performing second classification on historical activity topic texts under each historical topic label according to a preset historical topic feature dimension label to obtain historical activity topic texts of each historical topic feature dimension under each historical topic label, wherein the historical topic feature dimension label comprises an activity overall process, activity participation numbers and activity resource consumption;
Converting the historical topic label into a corresponding historical topic label vector, and constructing a main link node by using the historical topic label and the corresponding historical topic label vector; converting the historical theme feature dimension labels into corresponding historical theme feature dimension label vectors, and constructing subordinate link nodes by utilizing the historical theme feature dimension labels and the corresponding historical theme feature dimension label vectors; constructing a subordinate link node of the subordinate link node by utilizing the historical activity subject text affiliated to the subordinate link node; connecting the main link node, the subordinate link node and the subordinate link node by using a link connecting line according to a link node-to-link correspondence rule to obtain a multipath resource estimated link;
Extracting a current topic label and a current topic feature dimension label from a current application topic activity, wherein the current topic label is obtained by extracting an activity topic name of the current application topic activity, and the current topic feature dimension label is all activity information of the current application topic activity except the activity topic name;
Calculating a first matching degree of the current theme label and each main link node in the multipath resource estimation link, and calculating a second matching degree of the current theme feature dimension label and each subordinate link node in the multipath resource estimation link;
Extracting a resource estimated link corresponding to a main link node with the first matching degree larger than or equal to a preset first matching degree threshold value as a candidate resource estimated link; extracting a subordinate link node with the second matching degree being greater than or equal to a preset second matching degree threshold value, and extracting a historical activity topic text corresponding to a subordinate link node corresponding to the subordinate link node as a reference activity topic case text; summarizing all the texts of the reference activity topic cases to obtain estimated resources of the current application topic activities;
The step of connecting the main link node, the subordinate link node and the subordinate link node by using link connection lines according to a link node-to-link correspondence rule to obtain the multipath resource estimated link comprises the following steps: constructing the co-occurrence relation of all the link nodes in the multipath resource pre-estimated link according to the corresponding rule among the link nodes; and according to the co-occurrence relation, utilizing a link connection line to link all the link nodes to obtain the multipath resource estimated link.
2. The activity resource prediction method as set forth in claim 1, wherein the first classifying the historical activity topic cases according to a preset historical topic label to obtain a historical activity topic case under each of the historical topic labels includes:
Acquiring activity information of the historical activity topic case;
extracting an activity theme name from the activity information;
acquiring a historical topic label to which each historical activity topic case belongs according to a preset corresponding relation between the activity topic name and the historical topic label;
the historical activity topic cases with the same historical topic label are classified into one type, and the historical activity topic case under each historical topic label is obtained.
3. The activity resource estimation method according to claim 1, wherein the extracting text information of the historical activity topic case to obtain the historical activity topic text under each of the historical activity topic tags includes:
Recognizing characters in the historical activity theme cases through an OCR character recognition technology;
and converting the recognized characters into standardized characters by using the set character types and character size character formats, and storing the standardized characters to obtain the historical activity theme text.
4. The method of active resource estimation according to claim 1, wherein the calculating a first matching degree between the current topic label and each main link node in the multi-path resource estimation link, and the calculating a second matching degree between the current topic feature dimension label and each subordinate link node in the multi-path resource estimation link, includes:
Calculating a first matching degree of the current theme label and each main link node in the multipath resource pre-estimated link;
Extracting multipath resource estimated links corresponding to the main link nodes with the matching degree larger than or equal to a preset first matching degree threshold value as candidate estimated links;
and calculating a second matching degree of the current theme feature dimension label and each subordinate link node in the candidate estimated link.
5. The method of active resource estimation according to claim 4, wherein the calculating a first matching degree between the current topic label and each main link node in the multipath resource estimation link includes:
Calculating a first matching degree of the current theme label and each main link node in the multipath resource estimation link by adopting the following similarity formula:
；
Wherein, Estimating historical topic label vectors in the links for the multipath resources,For the current subject tag vector,And the similarity between the historical topic label vector and the current topic label vector is obtained.
6. An activity resource estimation apparatus for implementing the activity resource estimation method according to any one of claims 1 to 5, wherein the apparatus includes:
The historical activity topic case processing module is used for acquiring historical activity topic cases, and carrying out first classification on the historical activity topic cases according to preset historical topic labels to obtain the historical activity topic cases under each historical topic label; extracting text information of the historical activity topic cases to obtain historical activity topic texts under each historical activity topic label; performing second classification on the historical activity topic text under each historical topic label according to a preset historical topic feature dimension label to obtain the historical activity topic text of each historical topic feature dimension under each historical topic label;
The multi-path resource estimation link establishment module is used for establishing multi-path resource estimation links by taking the historical topic labels, the historical topic feature dimension labels and the historical activity topic text of each historical topic feature dimension label under each historical topic label as link nodes;
The resource estimation module is used for extracting a current theme label and a current theme feature dimension label from the current application theme activity; calculating a first matching degree of the current theme label and each main link node in the multipath resource estimation link, and calculating a second matching degree of the current theme feature dimension label and each subordinate link node in the multipath resource estimation link; and extracting historical activity topic texts meeting the matching conditions in the multipath resource estimation link according to the first matching degree and the second matching degree, and obtaining the estimated resources of the current application topic activities according to the historical activity topic texts meeting the matching conditions.
7. An electronic device, the electronic device comprising:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the activity resource estimation method according to any one of claims 1 to 5.
8. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the activity resource estimation method according to any one of claims 1 to 5.
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