CN117291427A - Project risk determining method, device and storage medium - Google Patents

Project risk determining method, device and storage medium Download PDF

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CN117291427A
CN117291427A CN202311378676.XA CN202311378676A CN117291427A CN 117291427 A CN117291427 A CN 117291427A CN 202311378676 A CN202311378676 A CN 202311378676A CN 117291427 A CN117291427 A CN 117291427A
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risk
text data
project
target item
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李堃
蔡一欣
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China United Network Communications Group Co Ltd
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China United Network Communications Group Co Ltd
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    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G06Q10/103—Workflow collaboration or project management

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Abstract

The application discloses a project risk determining method, a project risk determining device and a storage medium, and relates to the technical field of text recognition. The method comprises the following steps: acquiring target item text data, wherein the target item text data is used for describing, recording and transmitting information of items; determining candidate item risks corresponding to the target item text data based on the risk identification model; and determining the item risk with the weight value larger than a preset threshold value in the candidate item risk group as the target item risk of the target item text data, wherein the weight value is used for representing the influence degree of the item risk. The method can accurately identify the project risk existing in the project, and meanwhile, the accuracy of project risk identification can be effectively improved.

Description

Project risk determining method, device and storage medium
Technical Field
The present disclosure relates to the field of text recognition technologies, and in particular, to a method and apparatus for determining risk of an item, and a storage medium.
Background
The project risk refers to the possibility that the project is affected normally or directly lost in the project construction process, and common risks include progress risk, quality risk, technical risk, personnel risk, cost risk and the like. Wherein the higher the risk, the higher the probability of the problem occurring. Therefore, it is extremely important to identify the possibility of risk occurrence in the project through corresponding management means.
At present, a method for identifying the risk of the project in the project management link mainly comprises the steps of evaluating and analyzing project data by an expert, and identifying the risk in an empirical summary mode. All the methods mainly rely on an artificial mode to actively discover risks, and risks existing in projects cannot be discovered timely.
Disclosure of Invention
The application provides a method, a device and a storage medium for determining risk of an item, which are used for accurately identifying the risk existing in a text of the item.
In order to achieve the above purpose, the present application adopts the following technical scheme:
in a first aspect, a method for determining risk of an item is provided, including: acquiring target item text data, wherein the target item text data is used for describing, recording and transmitting information of items; determining candidate item risks corresponding to the target item text data based on the risk identification model; and determining the item risk with the weight value larger than a preset threshold value in the candidate item risk group as the target item risk of the target item text data, wherein the weight value is used for representing the influence degree of the item risk.
Based on the method, a server acquires target item text data in an item platform, identifies the acquired target item text data based on a risk identification model, obtains candidate item risks corresponding to the target item text data, and finally determines the candidate item risks with the item risk weights greater than a preset threshold in the candidate item model as target item risks corresponding to the target item text data. The method provided by the invention can accurately identify the project risk existing in the target project text. Compared with the existing method, the method provided by the application can automatically identify the risk existing in the project, and meanwhile, the method can effectively improve the accuracy of project risk identification.
In a possible implementation manner, the method further includes: acquiring a plurality of historical item text data and item risks corresponding to the plurality of historical item text data; performing feature extraction on the plurality of historical item text data and item risks corresponding to the plurality of historical item text data according to a Bert algorithm to obtain a plurality of item text feature data; training the text feature data of a plurality of projects based on the DNN algorithm to obtain a risk identification model.
In a possible implementation manner, the determining, based on the risk recognition model, the candidate item risk corresponding to the target item text data includes: inputting target item text data into a risk identification model, and outputting a vector matrix corresponding to the target item text data, wherein the vector matrix comprises at least one dimension, and one dimension corresponds to one item risk; and converting each dimension of the at least one dimension into item risks, and determining candidate item risks corresponding to the target item text data.
In a possible implementation manner, the determining the item risk with the weight value greater than the preset threshold value in the candidate item risk group as the target item risk of the target item text data includes: for the vector matrix, determining a weight value of each dimension in at least one dimension according to a preset rule; and taking the item risk with the weight value larger than the preset threshold value as the target item risk of the item text data.
In a possible implementation manner, the method further includes: and updating parameters of the Bert algorithm and the risk identification model according to the item text data and the target item risk.
In a second aspect, an item risk determination device is provided, applied to a chip or a system on a chip in the item risk determination device, and may be a functional module of the item risk determination device for implementing the method of the first aspect or any of the possible designs of the first aspect. The device may implement the functions performed by the item risk determination device in the aspects or in the possible designs, and the functions may be implemented by hardware executing corresponding software. The hardware or software comprises one or more modules corresponding to the functions. Such as: the device comprises an acquisition unit, a determination unit and a processing unit.
The acquisition unit is used for acquiring target item text data, wherein the target item text data is used for describing, recording and transmitting information of the items;
the determining unit is used for determining candidate item risks corresponding to the target item text data based on the risk identification model;
the determining unit is further configured to determine, as a target item risk of the target item text data, an item risk in the candidate item risk group, where a weight value is greater than a preset threshold, and the weight value is used to characterize an influence degree of the item risk.
In a possible implementation manner, the acquiring unit is further configured to acquire a plurality of historical item text data and item risks corresponding to the plurality of historical item text data; the device also comprises a processing unit, a processing unit and a processing unit, wherein the processing unit is used for extracting characteristics of a plurality of historical project text data and project risks corresponding to the plurality of historical project text data according to the Bert algorithm to obtain a plurality of project text characteristic data; and the processing unit is also used for training the text feature data of the plurality of items based on the DNN algorithm to obtain a risk identification model.
In a possible implementation manner, the determining unit is specifically configured to input the target item text data into the risk recognition model, output a vector matrix corresponding to the target item text data, where the vector matrix includes at least one dimension, and one dimension corresponds to one item risk; and converting each dimension of the at least one dimension into item risks, and determining candidate item risks corresponding to the target item text data.
In a possible implementation manner, the determining unit is specifically configured to determine, for the vector matrix, a weight value of each dimension in at least one dimension according to a preset rule; and taking the item risk with the weight value larger than the preset threshold value as the target item risk of the item text data.
In a possible implementation manner, the processing unit is further configured to update parameters of the Bert algorithm and the risk identification model according to the item text data and the target item risk.
In a third aspect, an item risk determination device is provided, which may be an item risk determination device or a chip or a system on a chip in an item risk determination device. The apparatus may implement the functions performed by the item risk determination apparatus in the above aspects or in each possible design, where the functions may be implemented by hardware, for example: in one possible design, the apparatus may include: a processor and a communication interface, the processor being operable to support the project risk determination apparatus to implement the functions involved in the first aspect or any one of the possible designs of the first aspect.
In yet another possible design, the item risk determination device may further include a memory for holding computer-executable instructions and data necessary for the item risk determination device. When the apparatus is running, the processor executes the computer-executable instructions stored by the memory to cause the apparatus to perform the project risk determination method of the first aspect or any one of the possible designs of the first aspect.
In a fourth aspect, a computer readable storage medium is provided, which may be a readable non-volatile storage medium, storing computer instructions or a program which, when run on a computer, cause the computer to perform the project risk determination method of the first aspect or any one of the possible designs of the aspects.
In a fifth aspect, there is provided a computer program product comprising instructions which, when run on a computer, cause the computer to perform the project risk determination method of the first aspect or any of the possible designs of the aspects.
In a sixth aspect, an item risk determination apparatus is provided, which may be an item risk determination apparatus or a chip or a system on a chip in an item risk determination apparatus, the apparatus comprising one or more processors and one or more memories. The one or more memories are coupled to the one or more processors, the one or more memories being configured to store computer program code comprising computer instructions that, when executed by the one or more processors, cause the item risk determination apparatus to perform the item risk determination method as described above in the first aspect or any of the possible designs of the first aspect.
In a seventh aspect, a chip system is provided, comprising a processor and a communication interface, which chip system may be used to implement the functions performed by the item risk determination device of the first aspect or any of the possible designs of the first aspect. In one possible design, the chip system further includes a memory for holding program instructions and/or data. The chip system may be composed of a chip, or may include a chip and other discrete devices, without limitation.
Drawings
Fig. 1 is a schematic structural diagram of an item risk determining system according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an item risk determining apparatus 200 according to an embodiment of the present application;
fig. 3 is a schematic flow chart of a method for determining risk of an item according to an embodiment of the present application;
FIG. 4 is a flowchart of another method for determining risk of an item according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an item risk determining apparatus 50 according to an embodiment of the present application.
Detailed Description
In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the disclosure described herein may be capable of operation in sequences other than those illustrated or described herein. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with aspects of embodiments of the present application as detailed in the accompanying claims.
It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, and/or components.
RPA (Robotic Process Automation) is an automated technique that uses software robots to simulate the operation of a human user to perform repetitive, regular and highly predictable tasks. The RPA may automate various business processes including data entry, data processing, data analysis, report generation, and the like. The RPA is widely applied to application scenes of project management, and the project repeatability filling work including data management, file management and the like is completed through the technology, so that the project team efficiency can be improved, and the time cost can be saved.
At present, a method for identifying the risk of the project in the project management link mainly comprises the steps of evaluating and analyzing project data by an expert, and identifying the risk in an empirical summary mode. All the methods mainly rely on an artificial mode to actively discover risks, and risks existing in projects cannot be discovered timely.
In view of this, the application proposes a method for determining a risk of an item in combination with an RPA technology, a server obtains text data of a target item in an item platform, identifies the obtained text data of the target item based on a risk identification model, obtains a candidate risk of the target item corresponding to the text data of the target item, and finally determines a candidate risk of the candidate item having a weight greater than a preset threshold as a target risk corresponding to the text data of the target item. The method provided by the invention can accurately identify the project risk existing in the target project text. Compared with the existing method, the method provided by the application can automatically identify the risk existing in the project, and meanwhile, the method can effectively improve the accuracy of project risk identification.
The project risk determining method provided by the embodiment of the application is described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic structural diagram of an item risk determining system according to an embodiment of the present application. As shown in fig. 1, the project risk determination system may include a project platform and a server. The project platform is in communication with the server.
The project platform stores key resources and key resources in the project. For example, the key resource and the key resource include a plurality of item text data.
The server is used for collecting a plurality of item text data stored by the item platform. For example, the server may collect text data related to the project, including text data of a project declaration, a contract, a technical requirement specification, etc., on the project platform through the RPA tool, and store a plurality of project text data. The server may include a project information management module, a model library, a risk identification and analysis module, and an early warning notification module.
The project information management module is used for storing a plurality of project text data. The model library stores a plurality of preset models and training models. For example, a DNN model for training. The risk recognition and analysis module is used for storing the risk recognition model obtained through training. And the early warning notification module is used for notifying the user of the model item risk identified by the item text data model.
In some embodiments, the server may be a single server, or may be a server cluster formed by a plurality of servers. In some implementations, the server cluster may also be a distributed cluster. The present disclosure is not limited to a specific implementation of the server.
Wherein when the server is configured with a database, the database may store the server-generated candidate item risk and target item risk.
It should be noted that, the execution subject of the project risk determination method provided in the present disclosure is a server, and may be a chip or a system on a chip in the server, and the like, without limitation.
The application scenario of the project risk determination system is not limited by the embodiment of the application. The system architecture and the service scenario described in the embodiments of the present application are for more clearly describing the technical solution of the embodiments of the present application, and do not constitute a limitation on the technical solution provided in the embodiments of the present application, and those skilled in the art can know that, with the evolution of the network architecture and the appearance of the new service scenario, the technical solution provided in the embodiments of the present application is also applicable to similar technical problems.
In an example, the embodiments of the present application further provide an item risk determining apparatus 200 (hereinafter, for convenience of description, simply referred to as determining apparatus), which may be used to perform the method of the embodiments of the present application.
For example, as shown in fig. 2, a schematic diagram of a determining apparatus 200 according to an embodiment of the present application is provided. The determining device 200 may include a processor 201, a communication interface 202, and a communication line 203.
Further, the determining device 200 may further include a memory 204. The processor 201, the memory 204, and the communication interface 202 may be connected by a communication line 203.
The processor 201 is a CPU, general-purpose processor, network processor (network processor, NP), digital signal processor (digital signal processing, DSP), microprocessor, microcontroller, programmable logic device (programmable logic device, PLD), or any combination thereof. The processor 201 may also be other devices with processing functions, such as, without limitation, circuits, devices, or software modules.
Communication interface 202 is used to communicate with other devices or other communication networks. The communication interface 202 may be a module, a circuit, a communication interface, or any device capable of enabling communication.
A communication line 203 for transmitting information between the components included in the determination device 200.
Memory 204 for storing instructions. Wherein the instructions may be computer programs.
The memory 204 may be, but is not limited to, a read-only memory (ROM) or other type of static storage device capable of storing static information and/or instructions, a random access memory (random access memory, RAM) or other type of dynamic storage device capable of storing information and/or instructions, an EEPROM, a CD-ROM (compact disc read-only memory) or other optical disk storage, an optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, etc.
It should be noted that the memory 204 may exist separately from the processor 201 or may be integrated with the processor 201. Memory 204 may be used to store instructions or program code or some data, etc. The memory 204 may be located inside the determination device 200 or outside the determination device 200, without limitation. The processor 201 is configured to execute instructions stored in the memory 204 to implement the project risk determining method provided in the following embodiments of the present application.
In one example, processor 201 may include one or more CPUs, such as CPU0 and CPU1 in fig. 2.
As an alternative implementation, the determining device 200 includes a plurality of processors, for example, the processor 207 may be included in addition to the processor 201 in fig. 2.
As an alternative implementation, the determining apparatus 200 further comprises an output device 205 and an input device 206. Illustratively, the input device 206 is a keyboard, mouse, microphone, or joystick device, and the output device 205 is a display screen, speaker (spaker), or the like.
It should be noted that the determining apparatus 200 may be a desktop computer, a portable computer, a web server, a mobile phone, a tablet computer, a wireless terminal, an embedded device, a chip system, or a device having a similar structure as in fig. 2. Further, the constituent structure shown in fig. 2 is not limited, and may include more or less components than those shown in fig. 2, or may combine some components, or may be arranged differently, in addition to those shown in fig. 2.
In the embodiment of the application, the chip system may be formed by a chip, and may also include a chip and other discrete devices.
Further, actions, terms, etc. referred to between embodiments of the present application may be referred to each other without limitation. In the embodiment of the present application, the name of the message or the name of the parameter in the message, etc. interacted between the devices are only an example, and other names may also be adopted in the specific implementation, and are not limited.
In order to clearly describe the technical solutions of the embodiments of the present application, in the embodiments of the present application, the words "first", "second", etc. are used to distinguish the same item or similar items having substantially the same function and effect. It will be appreciated by those of skill in the art that the words "first," "second," and the like do not limit the amount and order of execution, and that the words "first," "second," and the like do not necessarily differ.
In this application, the terms "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.
In the present application, "at least one" means one or more, and "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a alone, a and B together, and B alone, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b, or c may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or plural.
The method for determining the risk of the project according to the embodiment of the present application will be described below with reference to the system for determining the risk of the project shown in fig. 1. In which, the terms and the like related to the embodiments of the present application may refer to each other without limitation. In the embodiment of the present application, the name of the message or the name of the parameter in the message, etc. interacted between the devices are only an example, and other names may also be adopted in the specific implementation, and are not limited. The actions involved in the embodiments of the present application are just an example, and other names may be used in specific implementations, for example: the "included" in the embodiments of the present application may also be replaced by "carried on" or the like.
As shown in fig. 3, a method for determining risk of an item according to an embodiment of the present application includes:
s301, acquiring text data of a target item.
Wherein the target item text data is used to describe, record and communicate information of the item. For example, the target project text data may include text data of project declarations, technical requirement specifications, and the like.
In one possible implementation, the server obtains one or more item text data stored by the item platform using an RPA tool and stores the obtained data.
S302, determining candidate item risks corresponding to the target item text data based on the risk identification model.
The risk recognition model is used for determining one or more item risks corresponding to the target item text data, and is obtained by training a plurality of historical item text data and the item risks corresponding to the historical item text data. Specifically, reference may be made to the following descriptions of S401 to S403, which are not repeated here.
Candidate item risk is a potential event that negatively affects an item. For example, candidate item risks may include technical risks, market risks, progress risks, quality risks, personnel risks, cost risks, and the like. For example, technical risks refer to risks that developers face high technical difficulty in developing technical solutions and the like in the project development process. The personnel risk refers to the risks of unsmooth cooperation, personnel loss and the like of the staff of the departments in the project completed by the cooperation of the departments.
In a possible implementation manner, according to the description of S301, the server inputs the obtained target item text data into the risk identification model, and outputs a vector matrix corresponding to the target item text data, where the vector matrix includes at least one dimension, and one dimension corresponds to one item risk. Then, the server converts each dimension of the at least one dimension into one or more item risks, and determines the one or more item risks as candidate item risks corresponding to the target item text data.
In one example, the input project text data may be a project technical requirement specification, and the vector matrix corresponding to the output project text is a one-dimensional or multi-dimensional digital combination. For example, the one-dimensional vector output may be [1,2,3], and the item risk corresponding to the vector is a technical risk. The output multidimensional vector can be [1,2,3] … [ a, b, c ], and risks corresponding to the multidimensional vector are technical risks, market risks and the like.
S303, determining the item risk with the weight value larger than a preset threshold value in the candidate item risk group as the target item risk of the target item text data.
The weight value is used for representing the influence degree of the project risk, and is a value of each dimension in the vector matrix.
In a possible implementation manner, the server determines, for the vector matrix corresponding to the target item text data determined in S302, a weight value of each dimension in at least one dimension in the vector matrix according to a preset rule. And then, the server takes the item risk with the weight value larger than the preset threshold value as the target item risk of the item text data.
In one example, the above vectors [1,2,3] are determined according to a predetermined rule]The value of (2) isI.e. the weight corresponding to the technical risk is +.>If the preset threshold is 3, the method comprises the following steps of ++>The item risk is determined to be the target item risk for the target item text data.
It should be noted that, if the server identifies the risk of the target item in the target text data, the early warning notification module carries out early warning and reminding on the related staff.
Based on the technical scheme of fig. 3, the embodiment of the application provides a project risk determining method, which includes that target project text data in a project platform is obtained through a server, the obtained target project text data are identified based on a risk identification model, candidate project risks corresponding to the target project text data are obtained, and finally, candidate project risks with weight greater than a preset threshold in the candidate project risks are determined to be target project risks corresponding to the target project text data. The method provided by the invention can accurately identify the project risk existing in the target project text. Compared with the existing method, the method provided by the application can automatically identify the risk existing in the project, and meanwhile, the method can effectively improve the accuracy of project risk identification.
In some embodiments, as shown in FIG. 4, the method further includes S401-S403.
S401, acquiring a plurality of historical item text data and item risks corresponding to the plurality of historical item text data.
The historical item text data and the item risk corresponding to the historical item text data are the same as the item text data in S301.
And S402, carrying out feature extraction on the plurality of historical item text data and item risks corresponding to the plurality of historical item text data according to a Bert algorithm to obtain a plurality of item text feature data.
Wherein, the feature extraction of the text data of a plurality of historical projects means that the text data related to the projects in the target project text is extracted.
In one possible implementation, the server first divides the historical project text data into training set data and test set data, and deletes text data that is hollow in the training set data and test set data, and deletes text data that is not related to the project. And then, the server performs feature extraction on training set data and test set data corresponding to the historical project text data according to the Bert algorithm, and extracts text data related to the project in the target project text.
In one example, if the item description of the historical item text data is "the item group should sufficiently estimate the purchasing period of each material, purchasing demands should be issued to purchasing departments in advance at each key point. The method has the advantages that the advance purchasing is added, the provider selects, and the server can conduct feature extraction on the text to obtain a plurality of core semantic phrases, such as materials, periods and key points. And, the server deletes the core semantic phrase which is irrelevant to the description of the project text data, such as 'full', 'demand' and 'purchasing department'.
S403, training the text feature data of the plurality of items based on the DNN algorithm to obtain a risk identification model.
In a possible implementation manner, the server trains training sets corresponding to the item risks corresponding to the item text feature data and the historical item text data according to the Keras neural network framework and the DNN algorithm to obtain a risk identification model. The server can also adjust and optimize the risk recognition model obtained through training according to the plurality of historical item text data and the test set corresponding to the item risk corresponding to the plurality of historical item text data.
It should be noted that, the server may update the risk identification model by using the plurality of item text data and the plurality of item risks corresponding to each item text data in the plurality of item text data within a preset time, for example, the preset time may be 30 days. The server may also adjust parameters of the Bert algorithm using the plurality of item text data and a plurality of item risks corresponding to each item text data in the plurality of item text data.
Based on the technical scheme of fig. 4, the server in the embodiment of the present application trains a plurality of historical item text data and a plurality of item risks corresponding to each item text data in the plurality of historical item text data through a preset algorithm to obtain a risk identification model, and periodically updates the risk identification model. Meanwhile, the server adjusts parameters of the Bert algorithm according to the plurality of item text data and a plurality of item risks corresponding to each item text data in the plurality of item text data. Compared with the existing method, the trained risk identification model can automatically identify the item risk of the item text data, and timeliness and accuracy of item risk identification can be effectively improved.
The embodiment of the present application may divide the functional modules or functional units of the project risk determining apparatus according to the above method example, for example, each functional module or functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing module. The integrated modules may be implemented in hardware, or in software functional modules or functional units. The division of the modules or units in the embodiments of the present application is merely a logic function division, and other division manners may be implemented in practice.
In the case of dividing the respective functional modules with the respective functions, fig. 5 shows a schematic configuration of an item risk determination device 50, which item risk determination device 50 can be used to perform the functions involved in the above-described embodiments. The item risk determination device 50 shown in fig. 5 may include: an acquisition unit 501, a determination unit 502, and a processing unit 503.
An acquisition unit 501 for acquiring target item text data for describing, recording, and delivering information of items;
a determining unit 502, configured to determine a candidate item risk corresponding to the target item text data based on the risk identification model;
the determining unit 502 is further configured to determine, as the target item risk of the target item text data, an item risk in the candidate item risk group having a weight value greater than a preset threshold, where the weight value is used to characterize an influence degree of the item risk.
In a possible implementation manner, the obtaining unit 501 is further configured to obtain a plurality of historical item text data and item risks corresponding to the plurality of historical item text data;
the device further comprises a processing unit 503, configured to perform feature extraction on a plurality of historical item text data and item risks corresponding to the plurality of historical item text data according to a Bert algorithm, so as to obtain a plurality of item text feature data;
the processing unit 503 is further configured to train the text feature data of the plurality of items based on the DNN algorithm, to obtain a risk recognition model.
In a possible implementation manner, the determining unit 502 is specifically configured to input the target item text data into the risk recognition model, output a vector matrix corresponding to the target item text data, where the vector matrix includes at least one dimension, and one dimension corresponds to one item risk; and converting each dimension of the at least one dimension into item risks, and determining candidate item risks corresponding to the target item text data.
In a possible implementation manner, the determining unit 502 is specifically configured to determine, for a vector matrix, a weight value of each dimension in at least one dimension according to a preset rule; and taking the item risk with the weight value larger than the preset threshold value as the target item risk of the item text data.
In a possible implementation, the processing unit 503 is further configured to update parameters of the Bert algorithm and the risk recognition model according to the item text data and the target item risk.
As yet another possible implementation, the processing unit 503 in fig. 5 may be replaced by a processor, which may integrate the functionality of the processing unit 503.
Further, when the processing unit 503 is replaced by a processor, the item risk determining apparatus 50 according to the embodiment of the present application may be the item risk determining apparatus 200 shown in fig. 2.
Embodiments of the present application also provide a computer-readable storage medium. All or part of the flow in the above method embodiments may be implemented by a computer program to instruct related hardware, where the program may be stored in the above computer readable storage medium, and when the program is executed, the program may include the flow in the above method embodiments. The computer readable storage medium may be an internal storage unit of the communication device (including the data transmitting end and/or the data receiving end) of any of the foregoing embodiments, for example, a hard disk or a memory of the communication device. The computer readable storage medium may be an external storage device of the terminal apparatus, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash card (flash card), or the like, which are provided in the terminal apparatus. Further, the computer readable storage medium may further include both an internal storage unit and an external storage device of the communication apparatus. The computer-readable storage medium is used to store the computer program and other programs and data required by the communication device. The above-described computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.
It should be noted that the terms "first" and "second" and the like in the description, claims and drawings of the present application are used for distinguishing between different objects and not for describing a particular sequential order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
From the foregoing description of the embodiments, it will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of functional modules is illustrated, and in practical application, the above-described functional allocation may be implemented by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to implement all or part of the functions described above.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another apparatus, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and the parts displayed as units may be one physical unit or a plurality of physical units, may be located in one place, or may be distributed in a plurality of different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application 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 may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a readable storage medium. Based on such understanding, the technical solution of the embodiments of the present application may be essentially or a part contributing to the prior art or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, including several instructions for causing a device (may be a single-chip microcomputer, a chip or the like) or a processor (processor) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.
The foregoing is merely a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any changes or substitutions within the technical scope of the present disclosure should be covered in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (12)

1. A method of item risk determination, the method comprising:
acquiring target item text data, wherein the target item text data is used for describing, recording and transmitting information of an item;
determining candidate item risks corresponding to the target item text data based on a risk identification model;
and determining the item risk with the weight value larger than a preset threshold value in the candidate item risk group as the target item risk of the target item text data, wherein the weight value is used for representing the influence degree of the item risk.
2. The method according to claim 1, wherein the method further comprises:
acquiring a plurality of historical item text data and item risks corresponding to the plurality of historical item text data;
performing feature extraction on the plurality of historical item text data and item risks corresponding to the plurality of historical item text data according to a Bert algorithm to obtain a plurality of item text feature data;
training the text feature data of the plurality of items based on a DNN algorithm to obtain the risk identification model.
3. The method according to claim 1 or 2, wherein determining a candidate item risk corresponding to the target item text data based on a risk recognition model comprises:
inputting the target item text data into a risk identification model, and outputting a vector matrix corresponding to the target item text data, wherein the vector matrix comprises at least one dimension, and one dimension corresponds to one item risk;
and converting each dimension of the at least one dimension into a project risk, and determining a candidate project risk corresponding to the target project text data.
4. The method of claim 3, wherein determining the item risk in the candidate item risk group having a weight value greater than a preset threshold as the target item risk of the target item text data comprises:
for the vector matrix, determining a weight value of each dimension in the at least one dimension according to a preset rule;
and taking the item risk with the weight value larger than a preset threshold value as a target item risk of the item text data.
5. The method according to claim 1, wherein the method further comprises:
and updating parameters of the Bert algorithm and the risk identification model according to the item text data and the target item risk.
6. An item risk determination apparatus, the apparatus comprising:
the acquisition unit is used for acquiring target item text data, wherein the target item text data is used for describing, recording and transmitting information of an item;
the determining unit is used for determining candidate item risks corresponding to the target item text data based on the risk identification model;
the determining unit is further configured to determine, as a target item risk of the target item text data, an item risk in the candidate item risk group, where a weight value is greater than a preset threshold, and the weight value is used to characterize an influence degree of the item risk.
7. The apparatus of claim 6, wherein the device comprises a plurality of sensors,
the acquiring unit is further used for acquiring a plurality of historical project text data and project risks corresponding to the plurality of historical project text data;
the device also comprises a processing unit, a processing unit and a processing unit, wherein the processing unit is used for extracting features of the plurality of historical project text data and project risks corresponding to the plurality of historical project text data according to a Bert algorithm to obtain a plurality of project text feature data;
the processing unit is further used for training the text feature data of the plurality of items based on a DNN algorithm to obtain the risk identification model.
8. The apparatus according to claim 6 or 7, wherein the determining unit is specifically configured to:
inputting the target item text data into a risk identification model, and outputting a vector matrix corresponding to the target item text data, wherein the vector matrix comprises at least one dimension, and one dimension corresponds to one item risk;
and converting each dimension of the at least one dimension into a project risk, and determining a candidate project risk corresponding to the target project text data.
9. The apparatus according to claim 8, wherein the determining unit is specifically configured to:
for the vector matrix, determining a weight value of each dimension in the at least one dimension according to a preset rule;
and taking the item risk with the weight value larger than a preset threshold value as a target item risk of the item text data.
10. The apparatus of claim 6, wherein the processing unit is further configured to:
and updating parameters of the Bert algorithm and the risk identification model according to the item text data and the target item risk.
11. A computer readable storage medium having instructions stored therein which, when executed, implement the method of any of claims 1-5.
12. An item risk determination apparatus, comprising: a processor coupled to a memory for storing one or more programs, the one or more programs comprising computer-executable instructions, which when executed by the apparatus, cause the apparatus to perform the method of any of claims 1-5.
CN202311378676.XA 2023-10-23 2023-10-23 Project risk determining method, device and storage medium Pending CN117291427A (en)

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