CN118035445A

CN118035445A - Work order classification method and device, electronic equipment and storage medium

Info

Publication number: CN118035445A
Application number: CN202410197522.9A
Authority: CN
Inventors: 曹甜甜; 刘波; 徐超
Original assignee: Beijing Softong Intelligent Technology Co ltd
Current assignee: Beijing Softong Intelligent Technology Co ltd
Priority date: 2024-02-22
Filing date: 2024-02-22
Publication date: 2024-05-14

Abstract

The invention discloses a worksheet classification method, a worksheet classification device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a work order to be processed, a historical work order and a work order type corresponding to the historical work order; processing the work order to be processed to obtain at least two text vectors matched with the work order to be processed, and determining a text vector to be searched matched with the work order to be processed according to each text vector; and determining a target historical work order matched with the text vector to be searched in each historical work order, and taking the work order type of the target historical work order as a classification result of the work order to be processed. According to the technical scheme, the work orders to be processed and the historical work orders are represented in a vectorization mode, and the work orders are classified rapidly, efficiently and accurately in a vector retrieval mode.

Description

Work order classification method and device, electronic equipment and storage medium

Technical Field

The present invention relates to the field of data processing technologies, and in particular, to a method and apparatus for classifying worksheets, an electronic device, and a storage medium.

Background

Text classification is the most common task in natural language processing and is also a task with wider application scenarios. The text classification task is simple, a text, which may be sentences, articles or even a document content, is given, and the machine uses the classification model to classify the text, and gives a final classification label result.

The worksheets reported through various channels are generally classified, and the traditional manner is that the reporting personnel needs to manually select the corresponding classifications, and the classifications are usually relatively large in number and have multi-level characteristics, so that manual selection is usually time-consuming.

The prior art is usually based on historical worksheets and corresponding classifications and gives a traditional artificial intelligent model for training, the model needs more data, the trained model cannot be suitable for all projects, and different projects need retraining, so that time and labor are consumed. After the large model is developed at present, the problem of universality can be solved through the large model, a prompt word is usually written and submitted to the large model for classification, but the large model also has length limitation, the large model is unrealistic for scenes with the classification quantity reaching thousands, the large model has randomness, and the accuracy of processing classification tasks of the magnitude is not required.

Disclosure of Invention

The invention provides a work order classification method, a device, electronic equipment and a storage medium, which are used for realizing quick, efficient and accurate work order classification.

In a first aspect, an embodiment of the present invention provides a method for classifying worksheets, where the method includes:

Acquiring a work order to be processed, a historical work order and a work order type corresponding to the historical work order;

Processing the work order to be processed to obtain at least two text vectors matched with the work order to be processed, and determining a text vector to be searched matched with the work order to be processed according to each text vector;

And determining a target historical work order matched with the text vector to be searched in each historical work order, and taking the work order type of the target historical work order as a classification result of the work order to be processed.

In a second aspect, an embodiment of the present invention further provides a worksheet classification device, where the worksheet classification device includes:

The work order acquisition module is used for acquiring the work order to be processed, the historical work order and the work order type corresponding to the historical work order;

the work order processing module is used for processing the work order to be processed to obtain at least two text vectors matched with the work order to be processed, and determining a text vector to be retrieved matched with the work order to be processed according to each text vector;

and the work order classification result determining module is used for determining a target historical work order matched with the text vector to be searched in each historical work order, and taking the work order type of the target historical work order as the classification result of the work order to be processed.

In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the worksheet classification method according to any of the embodiments of the present invention when the processor executes the program.

In a fourth aspect, embodiments of the present invention also provide a storage medium storing computer-executable instructions that, when executed by a computer processor, are configured to perform a work order classification method according to any of the embodiments of the present invention.

According to the technical scheme, the work orders to be processed and the historical work orders are represented through vectorization, and the work orders are classified rapidly, efficiently and accurately in a vector retrieval mode.

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 worksheet sorting method according to a first embodiment of the present invention;

Fig. 2 is a schematic structural diagram of a worksheet sorting device according to a second embodiment of the present invention;

Fig. 3 is a schematic structural diagram of an electronic device according to a third 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.

In the technical scheme, the acquisition, storage and use of the personal information data of the user of the work order follow the information security technical personal information security specification.

Example 1

Fig. 1 is a flowchart of a method for classifying a work order according to an embodiment of the present invention, where the method may be applied to a work order classification device, and the method may be performed by the work order classification device, and the work order classification device may be implemented in hardware and/or software, and the work order classification device may be configured in any electronic device with network communication and computation.

As shown in fig. 1, the method includes:

S110, acquiring a work order to be processed, a historical work order and a work order type corresponding to the historical work order.

In the embodiment of the application, the work order can describe the content of the work order through a section of text, the work order to be processed is the work order needing to be classified, and the history work order can be the processed work order and has the corresponding work order type.

Wherein the worksheet types comprise complaint worksheets, opinion worksheets, advice worksheets, consultation worksheets, repair worksheets and the like.

Specifically, the work order may be reported in real time through the internet or obtained from a database, for example, the service system may receive the consultation type work order reported from the mobile terminal of the device or the client terminal of the computer. The application is not limited as to the manner in which the work order is obtained.

As an optional but non-limiting implementation manner, after obtaining the work order to be processed, the method further includes:

And removing the entity field and/or transferring the text of the work order to be processed.

It should be noted that, the to-be-processed worksheet includes information such as name, unit, address, contact, reporting content, etc., and the entity field represents materialized information such as name, unit, address, contact, etc. of the worksheet reporter, however, the worksheet classification to be processed is to determine the worksheet type according to the reporting content. Therefore, the information of the name, the unit, the address, the contact information and the like of the reporter of the work order to be processed, which are not related to the work order classification, can be removed, and the report content is reserved, so that the effective information is more outstanding, and the work order classification is facilitated.

Secondly, text transcription can be carried out on the report content of the work order to be processed, and the literal description of the report content is transcribed into a text with preference. For example, by taking a report content as a recommended class worksheet of recommended milk powder as an example, the recommended class worksheet of recommended milk powder can be transcribed into a worksheet of a preferred recommended milk powder brand, a worksheet of a preferred recommended milk powder performance or a worksheet of a preferred recommended milk powder suitable for the age.

As an optional but non-limiting implementation, before acquiring the work order to be processed, the method further includes:

Entity field removal and/or text transcription are performed on the historical worksheet.

In addition, information, such as names, units, addresses, contact ways and the like of the report persons of the historical work order report information, which are not related to the work order classification, and/or text transcription are removed in the same way. It should be noted that, after some work orders to be processed complete the classification task, the history data information may be provided as a history work order. In addition, for each work order classification task, entity field removal and/or text transcription of the historical work order are not needed, so that the work order classification retrieval time can be effectively reduced.

S120, processing the work order to be processed to obtain at least two text vectors matched with the work order to be processed, and determining a text vector to be retrieved matched with the work order to be processed according to each text vector.

In the embodiment of the application, the text vector is the result of vectorization of the work order. The text vector matched with the work order to be processed represents the report content of the work order to be processed as a numerical vector, namely, a numerical form of the text, so that the report content is conveniently provided for a computer to process and analyze.

The text vector to be retrieved is a result after processing each text vector matched with the work order to be processed, and is used for representing the vector value of the work order to be processed.

According to the text with at least two different expression contents of the work order to be processed after removing the entity field and/or text transcription, each text can be processed to obtain at least two text vectors matched with the work order to be processed. And processing each text vector to obtain a text vector to be retrieved, which is matched with the work order to be processed.

As an optional but non-limiting implementation manner, processing the to-be-processed work order to obtain at least two text vectors matched with the to-be-processed work order, including:

processing the work orders to be processed according to the vectorization model to obtain at least two candidate work orders matched with the work orders to be processed;

And carrying out vectorization processing on each candidate work order according to the vectorization model to obtain at least two text vectors matched with the work order to be processed.

In the embodiment of the application, the candidate worksheets represent the results of processing the report content of the worksheets to be processed according to the vectorization model, the text vectors represent the results of vectorizing the candidate worksheets according to the vectorization model, and the text vectors can be used for being provided for computer processing and analysis.

The vectorization model may be a large model, which generally refers to a neural network model with large-scale parameters and depth, and may process complex natural language tasks. Meanwhile, the vectorization model can also map text into a vector representation of a fixed length.

According to the vectorization model, vectorization processing is carried out on each candidate work order, specifically, word segmentation processing can be carried out on the work order to be processed, numbers and punctuations in the work order to be processed are replaced by spaces, a vocabulary of the work order to be processed is constructed, each word has an index, the size of the vocabulary is used as the dimension of a text vector, and each element of the text vector represents the occurrence frequency or the number of times of the word in the text. The vocabulary may be ordered based on frequency of occurrence or custom ordered according to business needs. The vocabulary order may be flexibly set, and the present embodiment is not limited.

As an optional but non-limiting implementation, determining a text vector to be retrieved matching the work order to be processed according to each text vector includes:

and carrying out average value processing on each text vector, and determining the text vector to be retrieved, which is matched with the work order to be processed.

The text vector to be searched represents the result of average value processing of each text vector matched with the work order to be processed. The mean processing of each text vector may use an arithmetic mean, a weighted mean, and a median method. According to different data distribution and scenes, a proper mean value processing method is selected, so that the data characteristics of the work order to be processed can be reflected. The method of the average processing is not limited in this embodiment.

As an optional but non-limiting implementation, after performing entity field removal and/or text transcription on the historical worksheet, further comprises:

processing the historical work orders according to the vectorization model to obtain at least two historical candidate work orders matched with the historical work orders;

carrying out vectorization processing on each historical candidate work order according to the vectorization model to obtain at least two text vectors matched with the historical work orders;

and carrying out mean value processing on the text vectors matched with each historical work order, and determining the historical text vectors.

In the embodiment of the application, the history candidate worksheet represents the result of processing the report content of the history worksheet according to the vectorization model, and the history text vector represents the result of average value processing of each text vector matched with the history worksheet.

And processing the historical candidate worksheets by the same vectorization processing method to obtain at least two text vectors matched with the historical worksheets. And simultaneously, processing the text vectors matched with each historical work order by using the same average value processing method to obtain the historical text vectors. Through mean processing, each historical text vector reflects the data characteristics of the corresponding historical worksheet.

S130, determining a target historical work order matched with the text vector to be searched in each historical work order, and taking the work order type of the target historical work order as a classification result of the work order to be processed.

In the embodiment of the application, the target historical worksheet represents a worksheet matched with a text vector to be searched in the historical worksheet, and the text vector to be searched represents the characteristic information of the worksheet to be processed. Therefore, by determining the target history worksheet matched with the to-be-processed worksheet in the history worksheets, the worksheet type of the target history worksheet can be used as the classification result of the to-be-processed worksheet.

As an optional but non-limiting implementation manner, determining a target historical work order matched with the text vector to be retrieved in each historical work order, and taking the work order type of the target historical work order as a classification result of the work order to be processed, including:

Searching in each historical text vector, and determining a target historical text vector matched with the text vector to be searched;

Determining a target history work order according to the target history text vector;

and taking the work order type of the target historical work order as a classification result of the work order to be processed.

In the embodiment of the application, the target historical text vector represents the historical text vector matched with the text vector to be searched, and the target historical worksheet represents the historical worksheet matched with the text vector to be searched.

And determining a corresponding target historical worksheet by acquiring the target historical text vectors of which the similarity with the text vector to be retrieved is greater than a certain threshold value or the first k of the similarity sequences. The work order types of the target historical work orders can be counted from high to low according to the occurrence frequency, a similar work order list matched with the work orders to be processed is generated, and the similar work order list is used as a classification result of the work orders to be processed.

Specifically, the work order classification task is performed on the work orders to be processed, and new work orders reported in real time through various channels can be obtained, for example, the work orders of urban infrastructure emergencies. The work orders processed in the last three years are used as historical work orders and stored in a database, the acquired new work orders are used as work orders to be processed, vector retrieval is carried out in the database, similar work order lists matched with the work orders to be processed are returned, classification results corresponding to the similar work order lists are ordered according to the occurrence times of the work order types corresponding to the target historical work orders from high to low, and the first five work order types with the largest occurrence times are returned to be used as classification results of the work orders to be processed. The application is not limited to returning the first several values of the similar list of worksheets as the classification result of the worksheets to be processed.

After the classification result of the work order to be processed is determined, the classification result can be displayed at the front end in a form of a table or a graph, so that service personnel can quickly and intuitively identify the type of the work order. Meanwhile, the work order classification can avoid the work order from circulating in different systems, reduce the work order processing time and improve the customer satisfaction.

Example two

Fig. 2 is a schematic structural diagram of a worksheet sorting device according to a second embodiment of the present invention. As shown in fig. 2, the apparatus includes:

The work order obtaining module 310 is configured to obtain a work order to be processed, a historical work order, and a work order type corresponding to the historical work order;

the work order processing module 320 is configured to process a work order to be processed to obtain at least two text vectors matched with the work order to be processed, and determine a text vector to be retrieved matched with the work order to be processed according to each text vector;

And the worksheet classification result determining module 330 is configured to determine, from each historical worksheet, a target historical worksheet matching the text vector to be retrieved, and use a worksheet type of the target historical worksheet as a classification result of the worksheet to be processed.

Optionally, the worksheet acquiring module 310 includes:

and the work order processing unit is used for removing the entity field and/or transferring the text of the work order to be processed.

Optionally, the worksheet acquiring module 310 further includes:

And the history work order processing unit is used for removing entity fields and/or transferring texts on the history work order.

Optionally, the work order processing module 320 includes:

The candidate work order determining unit is used for processing the work order to be processed according to the vectorization model to obtain at least two candidate work orders matched with the work order to be processed;

And the text vector determining unit is used for carrying out vectorization processing on each candidate work order according to the vectorization model to obtain at least two text vectors matched with the work order to be processed.

Optionally, the work order processing module 320 includes:

And the average processing unit is used for carrying out average processing on each text vector and determining a text vector to be retrieved, which is matched with the work order to be processed.

Optionally, the work order processing module 320 further includes:

The historical candidate work order determining unit is used for processing the historical work orders according to the vectorization model to obtain at least two historical candidate work orders matched with the historical work orders;

The vectorization processing unit is used for vectorizing each historical candidate work order according to the vectorization model to obtain at least two text vectors matched with the historical work order;

And the historical text vector determining unit is used for carrying out average value processing on each text vector matched with the historical worksheet to determine a historical text vector.

Optionally, the worksheet classification result determining module 330 includes:

The vector retrieval unit is used for retrieving in each historical text vector and determining a target historical text vector matched with the text vector to be retrieved;

The target history work order determining unit is used for determining a target history work order according to the target history text vector;

and the classification result determining unit is used for taking the work order type of the target historical work order as the classification result of the work order to be processed.

The worksheet classification device provided by the embodiment of the invention can execute the worksheet classification method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

Example III

Fig. 3 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. 3, 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 (central processor), a Graphics Processing Unit (GPU), various dedicated 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 the work order classification method.

In some embodiments, the work order classification method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the 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 the work order sorting method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the work order classification method 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

1. A method of worksheet classification, comprising:

2. The method of claim 1, further comprising, after obtaining the work order to be processed:

3. The method of claim 1, wherein processing the work order to be processed to obtain at least two text vectors matching the work order to be processed comprises:

4. A method according to claim 3, wherein determining a text vector to be retrieved that matches the work order to be processed from each text vector comprises:

5. The method of claim 1, further comprising, prior to obtaining the work order to be processed:

6. The method of claim 5, further comprising, after performing entity field removal and/or text transcription on the historical worksheet:

and carrying out mean value processing on each text vector matched with the historical worksheet to determine a historical text vector.

7. The method of claim 6, wherein determining a target history work order matching the text vector to be retrieved in each history work order and taking the work order type of the target history work order as the classification result of the work order to be processed comprises:

8. A worksheet sorting apparatus, comprising:

9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the work order classification method of any of claims 1-7 when the program is executed by the processor.

10. A storage medium storing computer executable instructions which, when executed by a computer processor, are adapted to perform the work order classification method of any one of claims 1-7.