CN116308172A - Method, device, equipment and storage medium for determining system item - Google Patents
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Abstract
The invention discloses a method, a device, equipment and a storage medium for determining a system item. The method comprises the following steps: acquiring a current department system item corresponding to a target department, and checking a department checking problem checked by the target department; and determining the target department system item based on the current department system item, the department examination problem and the target system item matching model, so that the problem system basis can be automatically determined, manual participation is not needed, the manual retrieval cost is reduced, and the working efficiency is improved.
Description
Technical Field
The present invention relates to the field of institutional search technologies, and in particular, to a method, an apparatus, a device, and a storage medium for determining an institutional entry.
Background
In the enterprise management work, when an external supervision organization performs department supervision and inspection or a department internal due-job supervision and inspection finds that a department has a problem, a system basis needs to be found for the problem found by inspection to determine whether the problem has a problem of lack of system formulation or a problem of insufficient system execution force.
However, when determining the problem system basis in each department, a manual experience search mode is generally adopted, a manual experience search mode is adopted to manufacture a corresponding search table, technicians who need to master the system entries are required to know, the search efficiency is low, the accuracy is poor, and the actual situation demands cannot be met.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for determining a system item, which are used for automatically determining a problem system basis, do not need manual participation, reduce the manual retrieval cost and improve the working efficiency.
According to one aspect of the present invention, there is provided a method for determining a system entry, comprising:
acquiring a current department system item corresponding to a target department, and checking a department checking problem checked by the target department;
and determining a target department system item based on the current department system item, the department inspection problem and a target system item matching model.
According to another aspect of the present invention, there is provided a system entry determination apparatus, comprising:
the current department system item acquisition module is used for acquiring a current department system item corresponding to a target department and a department inspection problem inspected by the target department;
and the target department system item determining module is used for determining a target department system item based on the current department system item, the department checking problem and a target system item matching model.
According to another aspect of the present invention, there is provided 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 method of determining an institutional score according to any one of the embodiments of the present invention.
According to another aspect of the present invention there is provided a computer readable storage medium storing computer instructions for causing a processor to perform a method of determining a regimen entry according to any one of the embodiments of the present invention.
According to the technical scheme, the current department system item corresponding to the target department and the department checking problem checked by the target department are obtained. And determining the target department system item based on the current department system item, the department inspection problem and the target system item matching model, solving the problems of lower manual retrieval efficiency and poorer accuracy, realizing automatic matching and determining the problem system basis without manual participation, reducing the manual retrieval cost and improving the working efficiency.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for determining a regimen entry provided in accordance with a first embodiment of the present invention;
FIG. 2 is a flow chart of a method of determining a regimen entry provided in accordance with a second embodiment of the present invention;
FIG. 3 is a block diagram of a system entry determination device provided according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device implementing a method for determining a system entry according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention 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 invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a method for determining a schedule item according to an embodiment of the present invention, where the method may be performed by a schedule item determining apparatus, which may be implemented in hardware and/or software, and the schedule item determining apparatus may be configured in an electronic device, where the condition of a related schedule item is determined according to a detected department problem. As shown in fig. 1, the method includes:
s101, acquiring a current department system entry corresponding to a target department and a department checking problem checked by the target department.
The target department may refer to a department that has an inspection problem through inspection. The current department system entry may refer to a newly formulated and/or modified department system specification for the target department. Department inspection questions may refer to questions that the target department is inspected for non-compliance with relevant department specifications.
Specifically, the collection of the external regulatory authorities supervises the inspection or organizes the internal due staff supervision to find the department inspection problem, and the collection of the current department system entries of the latest modification of the target department.
S102, determining a target department system item based on the current department system item, the department inspection problem and a target system item matching model.
The target system item matching model can be obtained through pre-training. The target system entry matching model can be used for determining a system basis corresponding to the inspection problem according to the department inspection problem retrieval. The target department system entry may refer to a system entry corresponding to a department inspection problem.
Specifically, the data processing can be performed on the current department system item and the department inspection problem, the matching processing is performed on the department inspection problem through the target system item matching model, the department system item corresponding to the department inspection problem is determined, and the target department system item is determined.
Illustratively, the determining a target department system entry based on the current department system entry and the department inspection question and target system entry matching model includes: performing problem segmentation processing on the department examination problems to obtain each examination problem statement; for each examination question sentence, carrying out convolution processing and pooling processing on the examination question sentences in sequence to obtain an examination question sentence vector; and determining a target department system item according to the check problem statement vector, the current department system item and a target system item matching model.
The check question sentence may be a natural question sentence obtained by dividing a department check question into sentences. The check question sentence vector may refer to a check question sentence vector after convolution processing and pooling processing.
Specifically, the technical scheme of the invention can preprocess the department examination questions before carrying out a matching system basis on the department examination questions, and carry out question segmentation processing on the department examination questions to obtain sentences which are preliminarily divided into a plurality of examination questions. For each inspection statement, convolution processing and pooling processing are sequentially performed on the inspection statement, so that a processed inspection statement vector can be obtained. And comparing and matching the check problem statement vector with the current department system item through a target system item matching model, so that the target department system item can be determined.
In the technical scheme of the invention, the current department system item can be preprocessed before the department inspection problem is subjected to the matching system basis. Specifically, the current department system item is subjected to convolution processing and pooling processing in sequence, and a processed current department system item vector is obtained. Based on the attention mechanism, the current department institutional entry vector is integrated into an institutional entry vector representation set. The system item vector representation set and the check question statement vector are input into a target system item matching model for comparison matching processing, and the target department system item can be determined.
Illustratively, the step of sequentially performing convolution processing and pooling processing on the inspection question sentence to obtain an inspection question sentence vector includes: performing vocabulary splitting processing on the examination question sentences to determine each examination question vocabulary contained in the examination question sentences; for each examination question vocabulary, carrying out convolution processing and pooling processing on the examination question vocabulary in sequence to obtain an examination question vocabulary vector corresponding to the examination question sentence; and performing splicing processing on each examination question vocabulary vector to obtain an examination question sentence vector corresponding to the examination question sentence.
The examination question vocabulary may refer to each question vocabulary in the examination question sentence. The check question vocabulary vector may be a check question vocabulary vector after convolution processing and pooling processing.
Specifically, the examination question sentences are subjected to vocabulary splitting processing, and each examination question vocabulary contained in the examination question sentences is obtained. And for each examination question word, carrying out convolution processing and pooling processing on the examination question word in sequence through a convolution neural network sub-module (CNN, convolutionalNeuralNetwork) to obtain an examination question word vector corresponding to the examination question sentence. And performing splicing processing on each examination question vocabulary vector to form an examination question sentence vector.
Illustratively, the determining a target department system entry based on the current department system entry, the department inspection question, and a target system entry matching model includes: determining an inspection problem expression vector and a system entry weight vector according to the current department system entry and the department inspection problem based on a collaborative attention sub-model; determining an inspection problem feature vector according to the department inspection problem based on the attention sub-model; and inputting the examination question expression vector, the system item weight vector and the examination question feature vector into a multi-layer perception sub-model to perform system item matching processing, so as to obtain a target department system item.
The inspection question expression vector may refer to a question description vector expression corresponding to the department inspection question. The institutional advancement weight vector may refer to a weight vector corresponding to the current department institutional advancement. The inspection issue feature vector may refer to an issue feature corresponding to a department inspection issue.
In particular, the target regimen entry matching model may also include a collaborative attention sub-model, an attention sub-model, and a multi-layer perception sub-model. The semantic granularity interaction is carried out on the current department system item and the department inspection problem through the collaborative attention sub-model, and the inspection problem expression vector and the system item weight vector can be obtained. And extracting and processing the characteristics of the department examination questions through the attention sub-model to obtain the examination question characteristic vectors.
And inputting the inspection problem expression vector, the system entry weight vector and the inspection problem feature vector into the multi-layer perception submodel to carry out system entry matching processing, so that a target department system entry corresponding to the inspection problem can be obtained.
Illustratively, obtaining the target department system entry through the multi-layer perceptron model may be accomplished by:
P＝MLP(E)＝MLP(concat[F，F 1 ，F 1a ])
wherein P may refer to target department system entries, E may refer to department system entry total features, MLP () may refer to multi-layer perceptron submodel, F may refer to inspection problem feature vectors, F 1 May refer to checking the problem expression vector, F 1a May refer to a system entry weight vector.
According to the technical scheme, the current department system item corresponding to the target department and the department checking problem checked by the target department are obtained. And determining the target department system item based on the current department system item, the department inspection problem and the target system item matching model, solving the problems of lower manual retrieval efficiency and poorer accuracy, realizing automatic matching and determining the problem system basis without manual participation, reducing the manual retrieval cost and improving the working efficiency.
On the basis of the above embodiments, the method further includes: and determining the type of the target system item based on the current department system item, the department examination question and the target system item matching model. Illustratively, the determining a target system item type based on the current department system item, the department inspection question, and a target system item matching model includes: based on the collaborative attention sub-model, determining a question type expression vector and a system type weight vector according to the current department system entry and the department inspection question; determining an inspection problem feature vector according to the department inspection problem based on the attention sub-model; and inputting the question type expression vector, the system type weight vector and the checking question feature vector into a multi-layer perception sub-model to perform item type matching processing, and obtaining a target system item type.
The target system item type may refer to an item type corresponding to a target department system item. The question type expression vector department examines the question description vector type expression corresponding to the question type. The system type weight vector may refer to a weight vector corresponding to a department system entry type. The inspection issue feature vector may refer to an issue feature corresponding to a department inspection issue.
Specifically, through the collaborative attention sub-model, semantic granularity interaction is performed on the current department system item and the department inspection problem, and a problem type expression vector and a system type weight vector can be obtained. And extracting and processing the characteristics of the department examination questions through the attention sub-model to obtain the examination question characteristic vectors.
And inputting the question type expression vector, the system type weight vector and the checking question feature vector into the multi-layer perception sub-model to carry out item type matching processing, so that a target system item type corresponding to the checking question can be obtained.
Illustratively, obtaining the target system entry type through the multi-layer perceptron model may be accomplished by:
U＝MLP(D)＝MLP(concat[F，F 2 ，F 2C ])
wherein U may refer to the target system entry type, D may refer to the system entry type total feature, MLP () may refer to the multi-layer perceptron model, F may refer to the inspection problem feature vector, F 2 May refer to a question type expression vector, F 2c May refer to a degree type weight vector.
The method has the advantages that the department examination problems can be summarized highly by determining the types of the target system items corresponding to the department examination problems, and the method is also used for core condensation of the target department system items, so that the target department can summarize the department examination problems conveniently, the current department system items are modified and formulated, and the rationality of the department system items is improved.
Example two
Fig. 2 is a flowchart of a method for determining a system entry according to a second embodiment of the present invention, and further discloses a training process of a target system entry matching model based on the above embodiments. As shown in fig. 2, the method includes:
s201, acquiring a department examination problem sample and a desired department system entry corresponding to the department examination problem sample.
Specifically, a historical department examination problem found from an external regulatory agency supervision examination or an organization internal due-job supervision examination is taken as a department examination problem sample, and a target department system entry determined based on a manual retrieval manner is determined as a desired department system entry.
S202, inputting the department examination problem sample and the department system item into an initialization system item matching model to perform system item matching, and obtaining a system item matching result.
The initialization of the system entry matching model may refer to a system entry matching model that is not trained by the model. And randomly setting initial values of model parameters in the initialization system entry matching model.
Specifically, a department examination problem sample and department system items are input into an initialization system item matching model, and the initialization system item matching model performs system item matching according to a training function to obtain a system item matching result.
S203, determining model loss corresponding to the initialization system item matching model based on the system item matching result and the expected department system item, and adjusting model parameters based on the model loss.
Specifically, substituting a system item matching result and an expected department system item into a model loss function to perform loss calculation, determining model loss corresponding to an initialization system item matching model, and adjusting model parameters in the initialization system item matching model according to the model loss until a preset ending condition is met. For example, the preset end condition may include the number of iterations reaching a preset number and/or the training error converging. In the case that there is a verification data set, the preset end condition may also be that the verification result of the initialization system entry matching model reaches a preset result condition.
It will be appreciated that the initialization system entry matching model may be an artificial intelligence model, such as a deep learning model or a machine learning model, etc. The selection of the model loss function may be associated with initializing a structure of the system entry matching model. Illustratively, the model loss function may include, but is not limited to, at least one of a cross entropy loss function, a mean square error, and a perceptual loss function.
S204, under the condition that a preset end condition is met, taking the initialization system item matching model as a target system item matching model.
Specifically, under the condition that the training of the initialization system item matching model meets the preset ending condition, determining that the training of the initialization system item matching model is ended, and taking the initialization system item matching model after the training is ended as the target system item matching model.
S205, acquiring a current department system entry corresponding to a target department and a department checking problem checked by the target department.
S206, determining a target department system item based on the current department system item, the department inspection problem and a target system item matching model.
It should be noted that, according to the technical scheme of the invention, the initialization system item matching model can be model trained based on the department examination problem sample and the expected system item type corresponding to the department examination problem sample, so that the target system item matching model can determine the target department system item and the target system item type based on the current department system item and the department examination problem.
According to the technical scheme provided by the embodiment of the invention, the department examination problem sample and the expected department system entry corresponding to the department examination problem sample are obtained. And inputting the department examination problem sample and the department system item into an initialized system item matching model to perform system item matching, and obtaining a system item matching result. And determining model loss corresponding to the initialization system item matching model based on the system item matching result and the expected department system item, and adjusting model parameters based on the model loss. And under the condition that a preset ending condition is met, taking the initialization system item matching model as a target system item matching model. By using the department examination question sample and the expected department system item corresponding to the department examination question sample to carry out model training, the accuracy of matching the target system item matching model can be ensured, and the accuracy of determining the target department system item is further ensured.
Example III
Fig. 3 is a schematic structural diagram of a system entry determining device according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes: a current department system entry acquisition module 301 and a target department system entry determination module 302. Wherein,,
the current department system item obtaining module 301 is configured to obtain a current department system item corresponding to a target department, and a department inspection problem that the target department is inspected. A target department system entry determination module 302, configured to determine a target department system entry based on the current department system entry, the department inspection problem, and a target system entry matching model.
According to the technical scheme, the current department system item corresponding to the target department and the department checking problem checked by the target department are obtained. And determining the target department system item based on the current department system item, the department inspection problem and the target system item matching model, solving the problems of lower manual retrieval efficiency and poorer accuracy, realizing automatic matching and determining the problem system basis without manual participation, reducing the manual retrieval cost and improving the working efficiency.
Alternatively, the target department system entry determination module 302 may include an inspection question sentence acquisition unit, an inspection question sentence vector acquisition unit, and a target department system entry determination unit. Wherein,,
the inspection question sentence acquisition unit is used for carrying out question segmentation processing on the department inspection questions to obtain each inspection question sentence;
an inspection question sentence vector obtaining unit, configured to sequentially perform convolution processing and pooling processing on each inspection question sentence to obtain an inspection question sentence vector;
and the target department system item determining unit is used for determining a target department system item according to the check question statement vector, the current department system item and a target system item matching model.
Optionally, the check question sentence vector obtaining unit may be specifically configured to:
performing vocabulary splitting processing on the examination question sentences to determine each examination question vocabulary contained in the examination question sentences;
for each examination question vocabulary, carrying out convolution processing and pooling processing on the examination question vocabulary in sequence to obtain an examination question vocabulary vector corresponding to the examination question sentence;
and performing splicing processing on each examination question vocabulary vector to obtain an examination question sentence vector corresponding to the examination question sentence.
Optionally, the target department system entry determination module 302 may be specifically configured to:
determining an inspection problem expression vector and a system entry weight vector according to the current department system entry and the department inspection problem based on a collaborative attention sub-model;
determining an inspection problem feature vector according to the department inspection problem based on the attention sub-model;
and inputting the examination question expression vector, the system item weight vector and the examination question feature vector into a multi-layer perception sub-model to perform system item matching processing, so as to obtain a target department system item.
Optionally, the device further comprises a target system entry type determining module. Wherein,,
the target system item type is used for determining the target system item type based on the current department system item, the department examination question and a target system item matching model.
Optionally, the target system entry type determining module may be specifically configured to:
based on the collaborative attention sub-model, determining a question type expression vector and a system type weight vector according to the current department system entry and the department inspection question;
determining an inspection problem feature vector according to the department inspection problem based on the attention sub-model;
and inputting the question type expression vector, the system type weight vector and the checking question feature vector into a multi-layer perception sub-model to perform item type matching processing, and obtaining a target system item type.
Optionally, the apparatus further includes: the target system item matching model training module is used for:
acquiring a department examination problem sample and an expected department system entry corresponding to the department examination problem sample;
inputting the department examination problem sample and the department system item into an initialized system item matching model to perform system item matching, and obtaining a system item matching result;
determining model loss corresponding to the initialization system item matching model based on the system item matching result and the expected department system item, and adjusting model parameters based on the model loss;
and under the condition that a preset ending condition is met, taking the initialization system item matching model as a target system item matching model.
The system item determining device provided by the embodiment of the invention can execute the system item determining method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the executing method.
Example IV
Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM12 and the RAM13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as determination of recipe schedule entries.
In some embodiments, the determination of the recipe entry may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM12 and/or the communication unit 19. When the computer program is loaded into RAM13 and executed by processor 11, one or more steps of the determination of the recipe entry described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the determination of the recipe entry in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method for determining a system entry, comprising:
acquiring a current department system item corresponding to a target department, and checking a department checking problem checked by the target department;
and determining a target department system item based on the current department system item, the department inspection problem and a target system item matching model.
2. The method of claim 1, wherein the determining a target department system entry based on the current department system entry and the department inspection question and target system entry matching model comprises:
performing problem segmentation processing on the department examination problems to obtain each examination problem statement;
for each examination question sentence, carrying out convolution processing and pooling processing on the examination question sentences in sequence to obtain an examination question sentence vector;
and determining a target department system item according to the check problem statement vector, the current department system item and a target system item matching model.
3. The method according to claim 2, wherein the sequentially performing convolution processing and pooling processing on the inspection issue sentence to obtain an inspection issue sentence vector includes:
performing vocabulary splitting processing on the examination question sentences to determine each examination question vocabulary contained in the examination question sentences;
for each examination question vocabulary, carrying out convolution processing and pooling processing on the examination question vocabulary in sequence to obtain an examination question vocabulary vector corresponding to the examination question sentence;
and performing splicing processing on each examination question vocabulary vector to obtain an examination question sentence vector corresponding to the examination question sentence.
4. The method of claim 1, wherein the determining a target department system entry based on the current department system entry, the department inspection question, and a target system entry matching model comprises:
determining an inspection problem expression vector and a system entry weight vector according to the current department system entry and the department inspection problem based on a collaborative attention sub-model;
determining an inspection problem feature vector according to the department inspection problem based on the attention sub-model;
and inputting the examination question expression vector, the system item weight vector and the examination question feature vector into a multi-layer perception sub-model to perform system item matching processing, so as to obtain a target department system item.
5. The method according to claim 1, wherein the method further comprises:
and determining the type of the target system item based on the current department system item, the department examination question and the target system item matching model.
6. The method of claim 5, wherein the determining a target institutional item type based on the current institutional item, the department exam question, and a target institutional item matching model comprises:
based on the collaborative attention sub-model, determining a question type expression vector and a system type weight vector according to the current department system entry and the department inspection question;
determining an inspection problem feature vector according to the department inspection problem based on the attention sub-model;
and inputting the question type expression vector, the system type weight vector and the checking question feature vector into a multi-layer perception sub-model to perform item type matching processing, and obtaining a target system item type.
7. The method of claim 1, further comprising, prior to the obtaining the department system entry for the target department and the department inspection question that the target department corresponds to being inspected:
acquiring a department examination problem sample and an expected department system entry corresponding to the department examination problem sample;
inputting the department examination problem sample and the department system item into an initialized system item matching model to perform system item matching, and obtaining a system item matching result;
determining model loss corresponding to the initialization system item matching model based on the system item matching result and the expected department system item, and adjusting model parameters based on the model loss;
and under the condition that a preset ending condition is met, taking the initialization system item matching model as a target system item matching model.
8. A system entry determination apparatus, comprising:
the current department system item acquisition module is used for acquiring a current department system item corresponding to a target department and a department inspection problem inspected by the target department;
and the target department system item determining module is used for determining a target department system item based on the current department system item, the department checking problem and a target system item matching model.
9. 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 institutional process determination method of any one of claims 1-7.
10. A computer readable storage medium, characterized in that it stores computer instructions for causing a processor to implement the method of determining the regimen entry of any one of claims 1-7 when executed.
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| CN202310279648.6A CN116308172A (en) | 2023-03-21 | 2023-03-21 | Method, device, equipment and storage medium for determining system item |
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| CN202310279648.6A CN116308172A (en) | 2023-03-21 | 2023-03-21 | Method, device, equipment and storage medium for determining system item |
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