CN117994021A - Auxiliary configuration method, device, equipment and medium for asset verification mode - Google Patents
Auxiliary configuration method, device, equipment and medium for asset verification mode Download PDFInfo
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Abstract
The invention discloses an auxiliary configuration method, device, equipment and medium for an asset verification mode. The method comprises the following steps: acquiring asset data to be classified in at least one picture form; inputting the asset data to be classified into a pre-constructed asset classification model to respectively obtain asset tags matched with the asset data to be classified; aggregating asset data to be classified with the same asset tag to obtain an asset group matched with the asset tag, and outputting the asset group to display equipment; and ordering the asset groups based on the touch operation of the user to obtain an asset group list, and respectively configuring a verification mode for the asset groups based on a preset rule. By the technical scheme, the auxiliary configuration of the asset verification mode can be realized, the configuration speed of the asset verification mode is improved, and further the working efficiency of the asset verification mode configuration work is improved.
Description
Technical Field
The present invention relates to the field of asset verification, and in particular, to an auxiliary configuration method, apparatus, device, and medium for a method of verifying an asset.
Background
Asset verification refers to the process by which an enterprise discards or disposes of fixed assets, intangible assets, and the like. In the traditional asset verification mode, an asset verification application form is usually required to be filled manually, and is subjected to approval and verification by a plurality of departments, so that the process is complicated, the efficiency is low, human errors and omission are easy to occur, and the time is long. Meanwhile, since the asset verification involves problems related to aspects such as affairs, tax and the like, the value of the asset needs to be accurately estimated and calculated, otherwise, the financial condition of an enterprise may be adversely affected.
However, in practical application, on one hand, due to the complexity of the types of the assets, the number is huge, the time span is longer, and the assets gradually become devalued along with the time extension; on the other hand, the verification and the verification of the data are complex in logic, the difference is difficult to trace, enterprises rely on a manual statistics and verification mode to provide the data, time and labor are consumed, the accuracy of the data is difficult to be ensured, and a large risk is easily caused. In summary, when the existing asset verification method is used for configuring the asset verification mode, the configuration speed is low, and therefore the working efficiency of the asset verification mode configuration work is affected.
Disclosure of Invention
The invention provides an auxiliary configuration method, device, equipment and medium for an asset verification mode, which can solve the problem that the existing asset verification mode is low in configuration speed and further low in working efficiency.
In a first aspect, an embodiment of the present invention provides an auxiliary configuration method for an asset verification method, where the method includes:
acquiring asset data to be classified in at least one picture form;
inputting the asset data to be classified into a pre-constructed asset classification model to respectively obtain asset tags matched with the asset data to be classified;
aggregating asset data to be classified with the same asset tag to obtain an asset group matched with the asset tag, and outputting the asset group to display equipment;
and ordering the asset groups based on the touch operation of the user to obtain an asset group list, and respectively configuring a verification mode for the asset groups based on a preset rule.
In a second aspect, an embodiment of the present invention provides an auxiliary configuration apparatus for an asset verification method, where the apparatus includes:
The data acquisition module is used for acquiring at least one picture form of asset data to be classified;
the tag acquisition module is used for inputting the asset data to be classified into a pre-constructed asset classification model to respectively obtain asset tags matched with the asset data to be classified;
the asset grouping module is used for aggregating asset data to be classified with the same asset tag to obtain an asset grouping matched with the asset tag, and outputting the asset grouping to the display equipment;
And the verification mode configuration module is used for sequencing the asset groups based on the touch operation of the user to obtain an asset group list, and respectively configuring verification modes for the asset groups based on a preset rule.
In a third aspect, an embodiment of the present invention provides an electronic device, 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 an auxiliary configuration method of an asset verification method according to any one of the embodiments of the present invention.
In a fourth aspect, an embodiment of the present invention provides a computer readable storage medium, where computer instructions are stored, where the computer instructions are configured to cause a processor to implement an auxiliary configuration method for an asset accounting method according to any one of the embodiments of the present invention when executed.
According to the technical scheme, at least one picture type of asset data to be classified is firstly obtained, then the asset data to be classified is input into a pre-built asset classification model, asset tags matched with the asset data to be classified are respectively obtained, the asset data to be classified with the same asset tags are aggregated to obtain asset groups matched with the asset tags, the asset groups are output to display equipment, finally, the asset groups are ordered based on touch operation of a user to obtain an asset group list, and verification modes are respectively configured for the asset groups based on preset rules, so that the problem that the configuration speed of the conventional asset verification mode is low, further the configuration work efficiency of the asset verification mode is low is solved, the auxiliary configuration of the asset verification mode is realized, the configuration speed of the asset verification mode is improved, and further the configuration work efficiency of the asset verification mode is improved.
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 an auxiliary configuration method for an asset verification method according to a first embodiment of the present invention;
FIG. 2 is a flow chart of an auxiliary configuration method for an asset verification method according to an embodiment of the invention;
FIG. 3 is a schematic structural diagram of an auxiliary configuration device for an asset verification method according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device implementing an auxiliary configuration method of an asset verification method 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 an auxiliary configuration method of an asset verification mode, which is provided in an embodiment of the present invention, and the embodiment may be suitable for a case of performing auxiliary configuration on an asset verification mode, where the method may be performed by an auxiliary configuration device of an asset verification mode, and the auxiliary configuration device of an asset verification mode may be implemented in a form of hardware and/or software, and the auxiliary configuration device of an asset verification mode may be configured in a terminal or a server having an auxiliary configuration function of an asset verification mode.
As shown in fig. 1, the method includes:
s110, acquiring asset data to be classified in at least one picture form.
The property data to be classified can be a picture file of a mortgage of a bad loan; specifically, in one specific implementation manner of this embodiment, the mortgage of the bad loan refers to a mortgage of the loan, in which the borrower cannot return the interest on schedule and in quantity, and the bad loan is mainly classified into five grades of "normal, concerned, secondary, suspicious and lost", and after the bad loan is formed, the person is required to be responsible for the special person to collect and withdraw, and then choose a method to cancel the loan; further, the recovery rate of the bad asset is the ratio of the treated bad asset to all the bad assets, and the improvement of the recovery rate of the bad asset is beneficial to promoting the effective utilization of resources, the reasonable distribution of funds and the flow of more funds to the facing sun industry. Since the borrower does not have the ability to return the loan on schedule, the mortgage of the loan related to the bad loan, the property under the name of the liability-related person, and the like need to be checked and disposed and recovered, and accordingly, in this embodiment, the property data to be classified may be a picture of the mortgage of the loan related to the bad loan, the property under the name of the liability-related person.
S120, inputting the asset data to be classified into a pre-constructed asset classification model to obtain asset tags matched with the asset data to be classified.
Specifically, when we need to classify a set of asset data to be classified, these data can be input into a pre-built asset classification model; the asset classification model is trained based on a deep learning algorithm and a large-scale data set, and can automatically learn and identify asset characteristics of different types; further, in the model, the asset data to be classified is subjected to a series of processing and conversion to extract useful characteristic information, wherein the characteristic information may include data on basic properties, transaction records, market performances and the like of the asset, and then the model compares and matches the characteristic information with known asset tags to determine the most suitable asset class; further, in this process, the asset classification model analyzes and determines the input data based on existing knowledge and experience, and considers the influence of various factors, such as risk level, profit potential, fluidity, etc. of the assets to ensure accuracy and reliability of classification, and finally, the model outputs asset tags matched with the asset data to be classified so that we can better understand and manage the assets.
Wherein, the asset tag can be the name of the asset type which can appear in each asset to be classified; for example, if the asset data to be classified is a house picture, the asset tag related to the asset data to be classified may be "real estate", "learning district house", "city district house", "second house" or "transition house", and the like, and specifically, the attribute of the asset tag matched with the asset data to be classified by the asset data to be classified itself is related to the type of the asset tag input in the model training process.
Optionally, after inputting the asset data to be classified into the pre-constructed asset classification model, respectively obtaining each asset tag matched with the asset data to be classified, the method further includes: detecting whether each asset data to be classified has a matched asset tag or not; if at least one asset data to be classified is detected to have no matched asset tag, generating prompt information and sending the asset data to be classified and the prompt information to display equipment for display so as to prompt related staff.
In this embodiment, if it is detected that at least one asset data to be classified does not have a matched asset tag, that is, each asset tag input in the model training process is not matched with the asset data to be classified, at this time, the system generates prompt information and sends the asset data to be classified and the prompt information to the display device for display, and after receiving the prompt information, the relevant staff can create a new asset tag matched with the current asset data to be classified through the text input interface for binding and storing the new asset tag and the current asset data to be classified.
It should be noted that, on the basis of the above steps, if there is a behavior of creating a new asset tag, the new asset tag and the current asset data to be classified are updated by the asset classification model as a set of new training data, that is, when the new asset data to be classified is input and the new asset tag are related, the asset classification model outputs the asset tag as an asset tag matched with the current asset data. Those skilled in the art should know that the model cycle training method should be a mature prior art, and this embodiment will not be described in detail.
And S130, aggregating the asset data to be classified with the same asset tag to obtain an asset group matched with the asset tag, and outputting the asset group to display equipment.
Specifically, after classifying the asset data to be classified, we need to aggregate the asset data to be classified with the same asset tag to obtain an asset group matching the asset tag. This process may be implemented in a variety of ways, for example using a data mining algorithm, a clustering algorithm, or a machine learning algorithm, etc.; further, once we have obtained the asset groupings matching the asset tags, we can output these groupings to the display device so that the user can better understand and manage the assets; the display device can be a computer screen, a mobile phone screen, a tablet computer screen or the like, and is specifically dependent on the requirements and the use scenes of the user; illustratively, on a display device, we can use various charts and graphs to present information of asset groupings, such as bar graphs, line graphs, pie charts, and the like. The charts and the graphs can help a user to quickly know the information such as the scale, the distribution, the change trend and the like of the asset group, so that an investment strategy and a risk management strategy can be formulated better; in addition, we can use data visualization tools to further enhance the presentation of asset groupings. These tools can transform data into a more intuitive and lively form, such as maps, thermodynamic diagrams, scatter plots, etc., to better present information about geographical distribution, relevance, and anomalies of asset groupings. In a word, the asset data to be classified with the same asset tag is aggregated to obtain the asset group matched with the asset tag, and the asset group is output to the display device, so that a user can be helped to better understand and manage the assets, and the accuracy of investment decision and the efficiency of risk management are improved.
It should be noted that, when the asset classification model provided in this embodiment performs data processing on the asset data to be classified, only one unique tag that best matches the asset data to be classified is output, that is, in this embodiment, each asset data to be classified has only one asset tag that matches the unique tag, so that the problem that repeated configuration of the verification mode is performed due to repeated occurrence of the asset data to be classified in different asset groups is avoided, and configuration efficiency is improved.
And S140, sorting the asset groups based on the touch operation of the user to obtain an asset group list, and respectively configuring a verification mode for the asset groups based on a preset rule.
Specifically, the method for respectively configuring the verification and cancellation mode for each asset group based on the preset rule comprises the following steps: responding to the verification mode configuration operation of the user on each asset group, pushing a preset verification mode list to the display equipment; wherein the verification and approval mode list comprises at least one verification and approval mode; and responding to the selection operation of any one of the verification modes in the verification mode list by a user, and performing aggregation storage on the selected verification mode serving as the verification mode matched with the current asset group and the asset group.
For example, when processing the asset group, we can configure the verification mode according to the requirement and operation of the user, when the user performs verification mode configuration operation on each asset group, we can push a preset verification mode list to the display device, where the verification mode list includes at least one verification mode, such as forced verification, self-service verification, auction verification, and the like, and the user can view and select any verification mode in the verification mode list through the display device. Once a user selects a certain verification mode, the selected verification mode can be used as a verification mode matched with the current asset group and is stored together with the asset group; it should be noted that, specific verification methods included in the verification method list may be added or modified by a worker according to actual needs, and the types and the number of verification methods are not limited in this embodiment.
Optionally, after configuring the verification mode for each asset group based on the preset rule, the method includes: and generating an asset verification table based on each asset group and verification modes matched with each asset group, and sending the asset verification table to a display device for display.
According to the technical scheme, at least one picture type asset data to be classified is firstly obtained, then each asset data to be classified is input into a pre-built asset classification model, each asset tag matched with each asset data to be classified is obtained respectively, the asset data to be classified with the same asset tag is aggregated to obtain asset groups matched with the asset tags, the asset groups are output to display equipment, finally each asset group is ordered based on point touch operation of a user to obtain an asset group list, and a verification mode is configured for each asset group based on a preset rule, so that auxiliary configuration of the asset verification mode is achieved, the configuration speed of the asset verification mode is improved, and further the working efficiency of the asset verification mode configuration work is improved.
Example two
Fig. 2 is a flowchart of an auxiliary configuration method of an asset verification method according to a second embodiment of the present invention, which is supplemented based on the above embodiment, specifically, in this embodiment, a method before each asset tag matching each asset data to be classified is obtained by inputting each asset data to be classified into a pre-constructed asset classification model.
As shown in fig. 2, the method includes:
s210, acquiring asset data to be classified in at least one picture form.
S220, acquiring a plurality of target asset data from a target asset data set constructed in advance.
Optionally, before acquiring the plurality of target asset data from the pre-constructed target asset data set, the method further includes: acquiring a plurality of data to be processed, carrying out data cleaning on each data to be processed by a preset data cleaning method to obtain each data to be sampled, and forming a data set to be sampled; and processing the data set to be sampled by a preset undersampling method to obtain the target asset data set matched with the data set to be sampled.
In particular, in data analysis and processing, it is often necessary to clean and sample a plurality of data to be processed to obtain a more accurate and useful data set. The following is an example of acquiring a plurality of data to be processed and processing by a preset data cleaning method and undersampling method:
1) And acquiring a plurality of data to be processed. The data may come from different data sources, such as databases, files, networks, etc. In acquiring data, factors such as the format, quality and integrity of the data need to be considered to ensure the reliability and availability of the data.
2) And carrying out data cleaning on each piece of data to be processed by a preset data cleaning method. Data cleaning refers to processing data to remove noise, missing values, outliers and the like, thereby improving the quality and usability of the data. Common data cleansing methods include filling missing values, removing outliers, correcting erroneous data, and the like. In the data cleaning, we need to select the proper method according to the specific situation and make proper adjustment and optimization.
3) After the data cleaning is completed, all the data to be sampled can be obtained, and a data set to be sampled is formed. The data set to be sampled refers to the data set after being cleaned and processed, and the data set includes data which needs to be sampled and analyzed.
4) And processing the data set to be sampled by a preset undersampling method to obtain a target asset data set matched with the data set to be sampled. Undersampling refers to the process of retaining a portion of a sample in a dataset while removing another portion of the sample. By undersampling, we can reduce the size of the dataset, thereby improving computational efficiency and accuracy. Common undersampling methods include random undersampling, hierarchical undersampling, clustered undersampling, and the like. In undersampling, we need to choose the appropriate method according to the specific situation and make appropriate adjustments and optimizations.
Through the steps, a plurality of data to be processed can be obtained, and the data are processed through a preset data cleaning method and an undersampling method to obtain a target asset data set matched with the data set to be sampled. This process may help us better understand and analyze the data to make more accurate and useful decisions.
S230, generating a training sample set according to the target asset pictures in the target asset data and asset tags corresponding to the target asset pictures, and training a deep learning model by using the training sample set to obtain the asset classification model.
In particular, when we need to classify a large number of target assets, we can use a deep learning model to improve the accuracy and efficiency of classification. The following is an example of generating a training sample set according to the target asset pictures in each target asset data and the asset tags corresponding to the target asset pictures, and training a deep learning model by using the training sample set to obtain the asset classification model:
1) And acquiring a target asset picture in each target asset data and an asset tag corresponding to the target asset picture. These target asset pictures may come from different data sources, such as databases, files, networks, etc. When obtaining the target asset picture, the format, quality and integrity of the picture need to be considered to ensure the reliability and usability of the picture.
2) And generating a training sample set according to the target asset pictures in the target asset data and the asset tags corresponding to the target asset pictures. The training sample set refers to a sample set for training a deep learning model, wherein a large number of target asset pictures and corresponding asset tags are contained. In generating the training sample set, we need to choose the appropriate method according to the specific situation and make the appropriate adjustments and optimizations.
3) After the training sample set is generated, the training sample set can be used for training the deep learning model to obtain the asset classification model. The deep learning model is a machine learning model based on a neural network, and can automatically learn and extract characteristics and information in a target asset picture, and classify the target asset according to the characteristics and information. In training the deep learning model, we need to select the appropriate model architecture and training parameters, and make the appropriate adjustments and optimizations.
Through the steps, a training sample set can be generated according to the target asset pictures in the target asset data and the asset tags corresponding to the target asset pictures, and the training sample set is used for training the deep learning model to obtain the asset classification model. This process may help us better understand and analyze the target asset data to make more accurate and useful decisions.
S240, inputting the asset data to be classified into a pre-constructed asset classification model to obtain asset tags matched with the asset data to be classified.
S250, aggregating the asset data to be classified with the same asset tag to obtain an asset group matched with the asset tag, and outputting the asset group to display equipment.
S260, sorting the asset groups based on the touch operation of the user to obtain an asset group list, and respectively configuring a verification mode for the asset groups based on a preset rule.
According to the technical scheme, at least one picture type asset data to be classified is firstly obtained, then a plurality of target asset data are obtained from a pre-built target asset data set, a training sample set is generated according to target asset pictures in the target asset data and asset tags corresponding to the target asset pictures, a deep learning model is trained by using the training sample set to obtain an asset classification model, then each asset data to be classified is input into the pre-built asset classification model to respectively obtain each asset tag matched with each asset data to be classified, the asset data to be classified with the same asset tag are aggregated to obtain asset groups matched with the asset tag, the asset groups are output to a display device, finally each asset group is ordered based on point touch operation of a user to obtain an asset group list, a verification mode is respectively configured for each asset group based on a preset rule, auxiliary configuration of the asset verification mode is achieved, the configuration speed of the asset verification mode is improved, and the working efficiency of the configuration work of the asset verification mode is further improved.
Example III
Fig. 3 is a schematic structural diagram of an auxiliary configuration device for an asset verification method according to a third embodiment of the present invention.
As shown in fig. 3, the apparatus includes:
A data acquisition module 310, configured to acquire asset data to be classified in at least one picture form;
the tag obtaining module 320 is configured to input each asset data to be classified into a pre-constructed asset classification model, and obtain each asset tag matched with each asset data to be classified;
an asset grouping module 330, configured to aggregate asset data to be classified having the same asset tag to obtain an asset group matched with the asset tag, and output the asset group to a display device;
the verification mode configuration module 340 is configured to sort the asset groups based on the touch operation of the user to obtain an asset group list, and configure verification modes for the asset groups based on preset rules, respectively.
According to the technical scheme, at least one picture type asset data to be classified is firstly obtained, then each asset data to be classified is input into a pre-built asset classification model, each asset tag matched with each asset data to be classified is obtained respectively, the asset data to be classified with the same asset tag is aggregated to obtain asset groups matched with the asset tags, the asset groups are output to display equipment, finally each asset group is ordered based on point touch operation of a user to obtain an asset group list, and a verification mode is configured for each asset group based on a preset rule, so that auxiliary configuration of the asset verification mode is achieved, the configuration speed of the asset verification mode is improved, and further the working efficiency of the asset verification mode configuration work is improved.
On the basis of the above embodiment, the auxiliary configuration device of the asset verification mode further includes:
The model training module is used for acquiring a plurality of target asset data from a pre-built target asset data set before inputting the asset data to be classified into a pre-built asset classification model to respectively obtain asset tags matched with the asset data to be classified; generating a training sample set according to the target asset pictures in the target asset data and the asset labels corresponding to the target asset pictures, and training the deep learning model by using the training sample set to obtain the asset classification model.
On the basis of the above embodiment, the model training module is further configured to obtain a plurality of data to be processed before obtaining a plurality of target asset data from a target asset data set constructed in advance, and perform data cleaning on each data to be processed by a preset data cleaning method to obtain each data to be sampled, and form a data set to be sampled; and processing the data set to be sampled by a preset undersampling method to obtain the target asset data set matched with the data set to be sampled.
On the basis of the above embodiment, the auxiliary configuration device of the asset verification mode further includes:
The prompt generation module is used for detecting whether the asset data to be classified has the matched asset tag after inputting the asset data to be classified into a pre-constructed asset classification model to obtain the asset tags matched with the asset data to be classified respectively; if at least one asset data to be classified is detected to have no matched asset tag, generating prompt information and sending the asset data to be classified and the prompt information to display equipment for display so as to prompt related staff.
Based on the above embodiment, the verification and cancellation mode configuration module 340 includes:
The list pushing unit is used for pushing a preset verification and approval mode list to the display equipment in response to verification and approval mode configuration operation of the user on each asset group; wherein the verification and approval mode list comprises at least one verification and approval mode;
And the verification and cancellation mode determining unit is used for responding to the selection operation of any verification and cancellation mode in the verification and cancellation mode list by a user, and taking the selected verification and cancellation mode as the verification and cancellation mode matched with the current asset group, and carrying out aggregation storage on the verification and cancellation mode and the asset group.
On the basis of the above embodiment, the auxiliary configuration device of the asset verification mode further includes:
And the table pushing module is used for generating an asset verification table based on each asset group and the verification mode matched with each asset group after the verification mode is respectively configured for each asset group based on the preset rule, and sending the asset verification table to the display equipment for display.
The auxiliary configuration device of the asset verification mode provided by the embodiment of the invention can execute the auxiliary configuration method of the asset verification mode provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution 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 RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 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 an auxiliary configuration method for an asset verification approach.
Accordingly, the method comprises the following steps:
acquiring asset data to be classified in at least one picture form;
inputting the asset data to be classified into a pre-constructed asset classification model to respectively obtain asset tags matched with the asset data to be classified;
aggregating asset data to be classified with the same asset tag to obtain an asset group matched with the asset tag, and outputting the asset group to display equipment;
and ordering the asset groups based on the touch operation of the user to obtain an asset group list, and respectively configuring a verification mode for the asset groups based on a preset rule.
In some embodiments, an auxiliary configuration method of an asset verification approach 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 ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of an auxiliary configuration method of an asset verification approach described above may be performed. Alternatively, in other embodiments, processor 11 may be configured by any other suitable means (e.g., by means of firmware) to perform an auxiliary configuration method of an asset verification approach.
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.
Claims (10)
1. An auxiliary configuration method of an asset verification mode is characterized by comprising the following steps:
acquiring asset data to be classified in at least one picture form;
inputting the asset data to be classified into a pre-constructed asset classification model to respectively obtain asset tags matched with the asset data to be classified;
aggregating asset data to be classified with the same asset tag to obtain an asset group matched with the asset tag, and outputting the asset group to display equipment;
and ordering the asset groups based on the touch operation of the user to obtain an asset group list, and respectively configuring a verification mode for the asset groups based on a preset rule.
2. The method of claim 1, further comprising, prior to inputting each asset data to be classified into the pre-constructed asset classification model, respectively obtaining each asset tag matching each asset data to be classified:
acquiring a plurality of target asset data from a pre-constructed target asset data set;
generating a training sample set according to the target asset pictures in the target asset data and the asset labels corresponding to the target asset pictures, and training the deep learning model by using the training sample set to obtain the asset classification model.
3. The method of claim 2, further comprising, prior to acquiring the plurality of target asset data from the pre-constructed target asset data set:
Acquiring a plurality of data to be processed, carrying out data cleaning on each data to be processed by a preset data cleaning method to obtain each data to be sampled, and forming a data set to be sampled;
And processing the data set to be sampled by a preset undersampling method to obtain the target asset data set matched with the data set to be sampled.
4. The method of claim 1, further comprising, after inputting each asset data to be classified into the pre-constructed asset classification model, respectively obtaining each asset tag matching each asset data to be classified:
detecting whether each asset data to be classified has a matched asset tag or not;
if at least one asset data to be classified is detected to have no matched asset tag, generating prompt information and sending the asset data to be classified and the prompt information to display equipment for display so as to prompt related staff.
5. The method of claim 1, wherein configuring the countering mode for each asset group based on a preset rule comprises:
responding to the verification mode configuration operation of the user on each asset group, pushing a preset verification mode list to the display equipment; wherein the verification and approval mode list comprises at least one verification and approval mode;
and responding to the selection operation of any one of the verification modes in the verification mode list by a user, and performing aggregation storage on the selected verification mode serving as the verification mode matched with the current asset group and the asset group.
6. The method of claim 1, wherein after configuring the accounting means for each asset group based on the preset rule, respectively, comprising:
and generating an asset verification table based on each asset group and verification modes matched with each asset group, and sending the asset verification table to a display device for display.
7. An auxiliary configuration device for an asset verification method, comprising:
The data acquisition module is used for acquiring at least one picture form of asset data to be classified;
the tag acquisition module is used for inputting the asset data to be classified into a pre-constructed asset classification model to respectively obtain asset tags matched with the asset data to be classified;
the asset grouping module is used for aggregating asset data to be classified with the same asset tag to obtain an asset grouping matched with the asset tag, and outputting the asset grouping to the display equipment;
And the verification mode configuration module is used for sequencing the asset groups based on the touch operation of the user to obtain an asset group list, and respectively configuring verification modes for the asset groups based on a preset rule.
8. The method of claim 7, wherein the countering mode configuration module comprises:
The list pushing unit is used for pushing a preset verification and approval mode list to the display equipment in response to verification and approval mode configuration operation of the user on each asset group; wherein the verification and approval mode list comprises at least one verification and approval mode;
And the verification and cancellation mode determining unit is used for responding to the selection operation of any verification and cancellation mode in the verification and cancellation mode list by a user, and taking the selected verification and cancellation mode as the verification and cancellation mode matched with the current asset group, and carrying out aggregation storage on the verification and cancellation mode and the asset group.
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 an asset validation method of any of claims 1-6.
10. A computer readable storage medium storing computer instructions for causing a processor to perform the method of facilitating configuration of an asset accounting manner of any of claims 1-6.
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