Chemical entity identification method, device, electronic device and storage medium

Abstract

The present invention discloses a chemical entity identification method, device, electronic device, and storage medium. The method includes: receiving MSDS data to be identified for at least one chemical, and converting the MSDS data to be identified into MSDS text data to be identified in a target format; each MSDS text data to be identified includes multiple categories to be identified, and each category to be identified corresponds to a classification number; in any business processing mode, identifying the classification number of the MSDS text data to be identified, and determining business text data corresponding to the business processing mode; the business text data corresponding to the business processing mode includes text information of at least one category to be identified; inputting the business text data into an entity recognition model corresponding to the business processing mode to obtain a target business field corresponding to the business processing mode; and executing tasks corresponding to the business processing mode based on the target business field. This method can achieve rapid and accurate identification of chemical entities in MSDS data, while avoiding accidents caused by errors in manual identification.

Description

Technical Field

[0001] The natural language processing technology of the present invention relates to a field, and in particular to a chemical entity recognition method, device, electronic device and storage medium. Background Art

[0002] MSDS (Material Safety Data Sheet), also known as Material Safety Data Sheet or Chemical Safety Data Sheet, is a document used by chemical manufacturers and importers to explain the physical and chemical properties of chemicals (such as pH value, flash point, flammability, reactivity, etc.) and the possible hazards to the health of users (such as carcinogenicity, teratogenicity, etc.).

[0003] Currently, in chemical-related business areas, it is necessary to quickly identify the characteristics of chemicals in order to complete chemical-related tasks as quickly as possible. However, due to the huge amount of information in MSDS data, it is difficult for staff to quickly obtain key information from MSDS data, and manual identification is prone to errors, leading to accidents. Summary of the Invention

[0004] The present invention provides a chemical entity identification method, device, electronic device and storage medium to achieve rapid and accurate identification of chemical entities in MSDS data while avoiding accidents caused by errors in manual identification.

[0005] According to one aspect of the present invention, a method for identifying chemical entities is provided, comprising:

[0006] Receive at least one MSDS data to be identified of a chemical, and convert the MSDS data to be identified into MSDS text data to be identified in a target format; each of the MSDS text data to be identified includes multiple categories to be identified, and each category to be identified corresponds to a classification number;

[0007] Under any business processing mode, identifying the classification number of the MSDS text data to be identified, and determining the business text data corresponding to the business processing mode; the business text data corresponding to the business processing mode includes text information of at least one category to be identified;

[0008] Inputting the business text data into the entity recognition model corresponding to the business processing mode to obtain the target business field corresponding to the business processing mode;

[0009] The task corresponding to the business processing mode is executed based on the target business field.

[0010] According to another aspect of the present invention, a chemical entity recognition device is provided, comprising:

[0011] An MSDS data receiving module is configured to receive MSDS data to be identified of at least one chemical and convert the MSDS data to be identified into MSDS text data to be identified in a target format; each MSDS text data to be identified includes multiple categories to be identified, and each category to be identified corresponds to a classification number;

[0012] A business text data determination module is used to identify the classification number of the MSDS text data to be identified in any business processing mode, and determine the business text data corresponding to the business processing mode; the business text data corresponding to the business processing mode includes text information of at least one category to be identified;

[0013] A target business field identification module is used to input the business text data into the entity recognition model corresponding to the business processing mode to obtain the target business field corresponding to the business processing mode;

[0014] A task execution module is used to execute the task corresponding to the business processing mode based on the target business field.

[0015] According to another aspect of the present invention, an electronic device is provided, comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the chemical entity identification method according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the chemical entity identification method according to any embodiment of the present invention when executed.

[0020] The technical solution of the embodiment of the present invention receives MSDS data to be identified for at least one chemical and converts the MSDS data to be identified into MSDS text data to be identified in a target format; each MSDS text data to be identified includes multiple categories to be identified, and each category to be identified corresponds to a classification number; under any business processing mode, the classification number of the MSDS text data to be identified is identified to determine the business text data corresponding to the business processing mode; the business text data corresponding to the business processing mode includes text information of at least one category to be identified; the business text data is input into an entity recognition model corresponding to the business processing mode to obtain a target business field corresponding to the business processing mode; and the task corresponding to the business processing mode is executed based on the target business field. This can achieve rapid and accurate identification of chemical entities in MSDS data, thereby improving the speed of obtaining key information and avoiding accidents caused by errors in manual identification.

[0021] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 This is a flow chart of a chemical entity identification method provided in Example 1 of the present invention;

[0024] Figure 2 This is a flow chart of a chemical entity identification method provided by Example 2 of the present invention;

[0025] Figure 3 This is a schematic structural diagram of a chemical entity recognition device provided by the third embodiment of the present invention;

[0026] Figure 4 This is a structural diagram of an electronic device provided in Example 4 of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Example 1

[0030] Figure 1 This is a flow chart of a chemical entity recognition method provided by the first embodiment of the present invention. This embodiment is applicable to the case of identifying the entity characteristics of chemicals in MSDS documents. The method can be executed by a chemical entity recognition device, which can be implemented in the form of hardware and / or software. The chemical entity recognition device can be configured in a back-end server. Figure 1 As shown, the method includes:

[0031] S110, receiving MSDS data to be identified of at least one chemical, and converting the MSDS data to be identified into MSDS text data to be identified in a target format; each of the MSDS text data to be identified includes multiple categories to be identified, and each category to be identified corresponds to a classification number.

[0032] Among them, the MSDS data to be identified refers to the MSDS data of the chemical to be identified. Specifically, the MSDS data to be identified can be MSDS data in different formats and languages. The format of the MSDS data to be identified includes but is not limited to picture format, Word format, PDF format, etc., which are not limited here. The languages ​​of the MSDS data to be identified include Chinese and English. Correspondingly, the MSDS text data to be identified include Chinese text and English text. In this embodiment, the MSDS data to be identified of at least one chemical uploaded by the user is received, and the MSDS data to be identified is converted into the MSDS text data to be identified in the target format. Among them, the target format can be PDF format. It can be understood that the purpose of the conversion format is to unify the format of the MSDS data for easy processing. If the format of the MSDS data to be identified is PDF format, there is no need to perform format conversion on the MSDS data to be identified.

[0033] It should be noted that each MSDS data includes multiple categories to be identified, including but not limited to sixteen categories: chemical and company identification, hazard overview, ingredients / composition information, first aid measures, fire prevention measures, spill emergency response, handling and storage, exposure control and personal protection, and physical and chemical properties. Classification numbers include but are not limited to Arabic numerals, Roman characters, Chinese format numbers, Chinese numbers, English numbers, etc. Each category to be identified corresponds to a classification number. For example, in Chinese text, the classification number corresponding to the chemical and company identification is the first part, the classification number corresponding to the hazard overview is the second part, and the classification number corresponding to the ingredients / composition information is the third part. And so on. Each category to be identified corresponds to a classification number, and the category to be identified can be determined by identifying the classification number.

[0034] It should also be noted that the correspondence between the categories to be identified and the classification numbers is related to the template format of the MSDS. MSDS data with the same template format have the same correspondence between the categories to be identified and the classification numbers.

[0035] Based on the above embodiment, optionally, when the MSDS data to be identified is in a picture format, text recognition is performed on the MSDS data to be identified to obtain MSDS text data to be identified, and the MSDS text data to be identified is converted into a target format.

[0036] Specifically, after receiving the MSDS data of the chemical to be identified, the format of the MSDS data to be identified is determined. If the MSDS data to be identified is in image format, text recognition is performed on the MSDS data to be identified based on the image recognition model to obtain the MSDS text data to be identified. Furthermore, the format of the MSDS text data to be identified is converted to the MSDS text data to be identified in the target format. The image recognition model can be an optical character recognition (OCR) model, and specifically, it can be an OCR model based on deep learning, such as a recurrent neural network model, a convolutional neural network model, a Tesseract OCR model, an EasyOCR model, etc.

[0037] In some embodiments, optionally, the method further includes: cleaning invalid text information in the target format MSDS text data to be identified to obtain cleaned MSDS text data to be identified.

[0038] In this embodiment, invalid text information in the target format MSDS text data to be identified can be cleaned to obtain cleaned MSDS text data to be identified. Invalid text information includes, but is not limited to, legends, tables, directories, references, and the like that are not fixed in the MSDS text data to be identified. For example, using the target format of PDF as an example, invalid text information in the MSDS text data to be identified can be cleaned using the pdfplumber library.

[0039] S120. Under any business processing mode, identify the classification number of the MSDS text data to be identified and determine the business text data corresponding to the business processing mode; the business text data corresponding to the business processing mode includes text information of at least one category to be identified.

[0040] Among them, business processing modes include chemical safety testing mode, warehouse management mode, and transportation management mode. Business text data refers to the text data of the to-be-identified category corresponding to the business processing mode. It is understandable that each business processing mode requires different text data due to different business types. For example, taking the warehouse management mode as an example, the information required for warehousing business includes flash point, transport packaging, class number, fire hazard, etc. Therefore, under the warehouse management mode, the business text data corresponding to the warehouse management mode is the text information corresponding to the to-be-identified category of physical and chemical properties, transportation information, and fire prevention measures.

[0041] In this embodiment, under any business processing mode, the classification number of the MSDS text data to be identified is identified to determine the text data of each class to be identified; further, based on the class to be identified corresponding to the business processing mode, the business text information corresponding to the business processing mode is determined in the text data of each class to be identified.

[0042] It should be noted that a correspondence between business processing modes and classes to be identified is pre-established.

[0043] On the basis of the above embodiment, optionally, the MSDS text data to be identified includes multiple text pages; for each of the text pages, the classification number of the text page is identified to obtain an identification result; the identification of the classification number of the MSDS text data to be identified and the determination of the business text data corresponding to the business processing mode include: if the identification result includes multiple classification numbers, the text page is segmented to obtain a new text page; repeating the above steps, when the number of classification numbers of the identification result is one, the text data in the text page is determined as the text data of the class to be identified corresponding to the classification number; the business text data is determined based on the text data of at least one class to be identified corresponding to the business mode. It can be understood that the amount of information in the MSDS text data to be identified is huge, so each MSDS text data to be identified includes multiple text pages. Exemplarily, taking the identification of MSDS text data as text in PDF format as an example, each PDF page is a text page.

[0044] In this embodiment, for each text page, the classification number of the text page is identified to obtain a recognition result; wherein, the recognition result is the classification number in the text page. Furthermore, if the recognition result includes multiple classification numbers, it indicates that the text page includes text data of multiple categories to be identified, and the text page is segmented based on a preset segmentation logic to obtain a new text page; wherein, the preset segmentation logic can be a specific keyword or format (such as a title or a dividing line, etc.). The above steps are repeated. When the number of classification numbers in the recognition result is one, the text data in the text page is determined to be the text data of the category to be identified corresponding to the classification number. wherein, the new text page is formed by the partial text segmented from the original text page.

[0045] S130: Input the business text data into an entity recognition model corresponding to the business processing mode to obtain a target business field corresponding to the business processing mode.

[0046] Among them, the entity recognition model is an entity recognition model pre-trained for each class to be identified, and is used to extract key fields in the text data corresponding to the class to be identified. Specifically, the entity recognition model includes but is not limited to a recurrent neural network model, a convolutional neural network model, a hidden Markov model, etc., which are not limited here. In this embodiment, the business text data is input into the entity recognition model of the class to be identified corresponding to the business processing mode to extract key fields, and obtain the target business field corresponding to the business processing mode. Among them, the target business field is the key field in the text data of the class to be identified corresponding to the business processing mode. Exemplarily, assuming that the class to be identified corresponding to the business processing mode includes chemicals and corporate logos, then the target business field includes the Chinese name, English name, Chinese alias, English alias, etc. of the chemical.

[0047] S140: Execute a task corresponding to the business processing mode based on the target business field.

[0048] In this embodiment, for different business processing modes, tasks corresponding to the business processing modes are executed based on the target business fields corresponding to the business processing modes. The tasks corresponding to the business processing modes include chemical safety testing tasks, warehousing tasks, and transport document generation tasks.

[0049] On the basis of the above embodiment, optionally, the task corresponding to the business processing mode is executed based on the target business field, including: if the business processing mode is a chemical safety detection mode, performing a prohibition and restriction control detection on the target business field to obtain a detection result; if the business processing mode is a warehouse management mode, determining the storage area and storage conditions of the chemicals based on the target business field; if the business processing mode is a transportation management mode, generating a transportation document based on a preset transportation document template and the target business field.

[0050] In this embodiment, if the business processing mode is the chemical safety detection mode, the target business field is subjected to a prohibition, restriction, and control detection based on the preset regulatory database to obtain a detection result; wherein the detection result includes whether the chemical is a prohibited item, whether the chemical is a restricted item, and whether the chemical is a special controlled item. Optionally, if the business processing mode is the warehouse management mode, the target business field can be input into the warehouse recommendation model to obtain the storage area and storage conditions of the chemical to avoid safety issues caused by human misjudgment and improve the safety of the warehouse. Optionally, if the business processing mode is the transportation management mode, the target business field can be imported into the corresponding position of the preset transportation document template to generate a transportation document.

[0051] In some embodiments, optionally, the method further includes: performing feature extraction on the text data corresponding to each of the classes to be identified to obtain key fields of the MSDS text data to be identified; generating display data based on the key fields of the MSDS text data to be identified, and displaying the display data on a Web display page; the Web display page includes at least one feature item and a query control, each feature item including the key fields of the MSDS text data to be identified; on the Web display page, in response to a selection operation on the feature item, determining a target feature item; and in response to a triggering operation on the query control, jumping to a key field display page corresponding to the target feature item.

[0052] The display data refers to the characteristic items formed by the combination of key fields in the MSDS text data to be identified. Each chemical corresponds to a characteristic item. The web display page includes at least one characteristic item and a query control.

[0053] In this embodiment, the text data corresponding to each class to be identified can be input into the entity recognition model corresponding to the class to be identified for feature extraction to obtain the key fields of the MSDS text data to be identified; display data is generated based on the key fields of the MSDS text data to be identified, and the display data is displayed on the Web display page. On the Web display page, in response to the selection operation of the feature item, the target feature item is determined; in response to the triggering operation of the query control, the page jumps to the key field display page corresponding to the target feature item. The target feature item refers to the feature item selected by the selection operation. The key field display page is used to specifically display the key fields in the target feature item. For example, the CAS number and composition of the chemical can be specifically displayed on the key field display page. The technical solution of this embodiment receives MSDS data to be identified for at least one chemical and converts the MSDS data to be identified into MSDS text data in a target format; each MSDS text data to be identified includes multiple categories to be identified, each corresponding to a classification number; in any business processing mode, the classification number of the MSDS text data to be identified is identified to determine the business text data corresponding to the business processing mode; the business text data corresponding to the business processing mode includes text information of at least one category to be identified; the business text data is input into an entity recognition model corresponding to the business processing mode to obtain a target business field corresponding to the business processing mode; and the task corresponding to the business processing mode is executed based on the target business field. This method can achieve rapid and accurate identification of chemical entities in MSDS data, thereby improving the speed of obtaining key information and avoiding accidents caused by errors in manual identification.

[0054] Example 2

[0055] Figure 2This is a flowchart of a chemical entity identification method provided by Example 2 of the present invention. Based on the above embodiments, when there are multiple MSDS data to be identified, this embodiment further includes: generating target text data based on the multiple MSDS text data to be identified; accordingly, identifying the classification number of the MSDS text data to be identified and determining the text data of each class to be identified includes: identifying the classification number of the target text data and determining the text data of each class to be identified. The explanations of the terms that are the same as or corresponding to the above embodiments are not repeated here.

[0056] like Figure 2 As shown, the method includes:

[0057] S210, receiving MSDS data to be identified of at least one chemical, and converting the MSDS data to be identified into MSDS text data to be identified in a target format; each of the MSDS text data to be identified includes multiple categories to be identified, and each category to be identified corresponds to a classification number.

[0058] S220: Generate target text data based on the plurality of MSDS text data to be identified.

[0059] The target text data is a text data formed by splicing a plurality of MSDS text data to be identified, and the plurality of MSDS text data to be identified are in the same text. In this embodiment, when there are a plurality of MSDS text data to be identified, the plurality of MSDS text data to be identified can be spliced ​​to obtain the target text data.

[0060] S230. Under any business processing mode, identify the classification number of the target text data to determine the business text data corresponding to the business processing mode; the business text data corresponding to the business processing mode includes text information of at least one category to be identified.

[0061] Wherein, the target text data includes multiple text pages. In this embodiment, for each text page, the classification number of the text page is identified to obtain a recognition result; wherein, the recognition result is the classification number in the text page. Further, if the recognition result includes multiple classification numbers, it indicates that the text page includes text data of multiple categories to be identified, and the text page is segmented based on the preset segmentation logic to obtain a new text page; wherein, the preset segmentation logic can be a specific keyword or format (such as a title or a dividing line, etc.). Repeat the above steps, and when the number of classification numbers of the recognition result is one, the text data in the text page is determined to be the text data of the category to be identified corresponding to the classification number. wherein, the new text page is formed by the partial text segmented from the original text page.

[0062] S240: Input the business text data into an entity recognition model corresponding to the business processing mode to obtain a target business field corresponding to the business processing mode.

[0063] S250: Execute a task corresponding to the business processing mode based on the target business field.

[0064] The technical solution of this embodiment generates target text data based on multiple MSDS text data to be identified when there are multiple MSDS data to be identified. It can simultaneously perform entity identification of chemicals on the MSDS text data to be identified of multiple chemicals, further improving the speed of obtaining key information.

[0065] Example 3

[0066] Figure 3 This is a schematic diagram of the structure of a chemical entity recognition device provided by the third embodiment of the present invention. Figure 3 As shown, the device includes:

[0067] The MSDS data receiving module 310 is configured to receive MSDS data to be identified of at least one chemical and convert the MSDS data to be identified into MSDS text data to be identified in a target format; each MSDS text data to be identified includes multiple categories to be identified, each category to be identified corresponds to a classification number;

[0068] The business text data determination module 320 is configured to identify the classification number of the MSDS text data to be identified under any business processing mode and determine the business text data corresponding to the business processing mode; the business text data corresponding to the business processing mode includes text information of at least one category to be identified;

[0069] A target business field identification module 330 is configured to input the business text data into an entity recognition model corresponding to the business processing mode to obtain a target business field corresponding to the business processing mode;

[0070] The task execution module 340 is configured to execute the task corresponding to the business processing mode based on the target business field.

[0071] The technical solution of this embodiment receives MSDS data to be identified for at least one chemical and converts the MSDS data to be identified into MSDS text data to be identified in a target format; each MSDS text data to be identified includes multiple categories to be identified, and each category to be identified corresponds to a classification number; in any business processing mode, the classification number of the MSDS text data to be identified is identified to determine the business text data corresponding to the business processing mode; the business text data corresponding to the business processing mode includes text information of at least one category to be identified; the business text data is input into an entity recognition model corresponding to the business processing mode to obtain a target business field corresponding to the business processing mode; and the task corresponding to the business processing mode is executed based on the target business field. This can achieve rapid and accurate identification of chemical entities in MSDS data, thereby improving the speed of obtaining key information and avoiding accidents caused by errors in manual identification.

[0072] On the basis of the above embodiment, optionally, the device also includes a target text data generation module for generating target text data based on a plurality of MSDS text data to be identified when there are multiple MSDS data to be identified; correspondingly, the business text data determination module 320 is used to identify the classification number of the target text data and determine the business text data corresponding to the business processing mode.

[0073] On the basis of the above embodiment, optionally, the MSDS text data to be identified / the target text data includes multiple text pages; the business text data determination module 320 is used to identify the classification number of the text page for each of the text pages to obtain an identification result; if the identification result includes multiple classification numbers, the text page is divided to obtain a new text page; the above steps are repeated, and when the number of classification numbers of the identification result is one, the text data in the text page is determined as the text data of the class to be identified corresponding to the classification number; the business text data is determined based on the text data of at least one class to be identified corresponding to the business model.

[0074] Based on the above embodiment, optionally, the MSDS data receiving module 310 includes a format conversion unit for performing text recognition on the MSDS data to be identified when the MSDS data to be identified is in a picture format, obtaining the MSDS text data to be identified, and converting the MSDS text data to be identified into a target format.

[0075] Based on the above embodiment, the device may optionally further include a data cleaning module for cleaning invalid text information in the target format MSDS text data to be identified to obtain cleaned MSDS text data to be identified.

[0076] Based on the above embodiment, optionally, the business processing mode includes a chemical safety detection mode, a warehouse management mode and a transportation management mode; the task execution module 340 is specifically used to, if the business processing mode is a chemical safety detection mode, perform a prohibition and restriction control detection on the target business field to obtain a detection result; if the business processing mode is a warehouse management mode, determine the storage area and storage conditions of the chemical based on the target business field; if the business processing mode is a transportation management mode, generate a transportation document based on a preset transportation document template and the target business field.

[0077] On the basis of the above embodiment, optionally, the device further comprises a key field management module for performing feature extraction on the text data corresponding to each of the classes to be identified to obtain key fields of the MSDS text data to be identified;

[0078] Display data is generated based on the key fields of the MSDS text data to be identified, and the display data is displayed on a Web display page; the Web display page includes at least one feature item and a query control, each feature item includes the key fields of the MSDS text data to be identified; on the Web display page, in response to a selection operation on the feature item, a target feature item is determined; in response to a trigger operation on the query control, a jump is made to a key field display page corresponding to the target feature item.

[0079] The chemical entity recognition device provided in the embodiment of the present invention can execute the chemical entity recognition method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0080] Example 4

[0081] Figure 4 1 is a structural diagram of an electronic device provided in Embodiment 4 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0082] like Figure 4As shown, 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., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can 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. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0083] Multiple 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, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a 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.

[0084] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the 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, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the chemical entity recognition method.

[0085] In some embodiments, the chemical entity identification method can be implemented as a computer program that is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the chemical entity identification method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the chemical entity identification method in any other appropriate manner (e.g., by means of firmware).

[0086] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0087] Computer programs for implementing the chemical entity identification methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a standalone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0088] Example 5

[0089] The fifth embodiment of the present invention further provides a computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a processor to execute a chemical entity identification method, the method comprising:

[0090] Receive at least one MSDS data to be identified of a chemical, and convert the MSDS data to be identified into MSDS text data to be identified in a target format; each of the MSDS text data to be identified includes multiple categories to be identified, and each category to be identified corresponds to a classification number;

[0091] Under any business processing mode, identifying the classification number of the MSDS text data to be identified, and determining the business text data corresponding to the business processing mode; the business text data corresponding to the business processing mode includes text information of at least one category to be identified;

[0092] Inputting the business text data into the entity recognition model corresponding to the business processing mode to obtain the target business field corresponding to the business processing mode;

[0093] The task corresponding to the business processing mode is executed based on the target business field.

[0094] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0095] To provide interaction with a user, the systems and techniques described herein 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 the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0096] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, 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: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0097] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0098] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0099] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A chemical entity identification method, characterized in that: include: Receive at least one MSDS data to be identified of a chemical, and convert the MSDS data to be identified into MSDS text data to be identified in a target format; each of the MSDS text data to be identified includes multiple categories to be identified, and each category to be identified corresponds to a classification number; Under any business processing mode, the classification number of the MSDS text data to be identified is identified to determine the business text data corresponding to the business processing mode; the business text data corresponding to the business processing mode includes text information of at least one category to be identified; Inputting the business text data into the entity recognition model corresponding to the business processing mode to obtain the target business field corresponding to the business processing mode; Executing a task corresponding to the business processing mode based on the target business field; The business processing modes include chemical safety testing mode, warehouse management mode and transportation management mode; The executing the service corresponding to the service processing mode based on the target service field includes: If the business processing mode is the chemical safety detection mode, a prohibition, restriction, and control detection is performed on the target business field to obtain a detection result; wherein the detection result includes whether the chemical is a prohibited item, a restricted item, or a specially controlled item; if the business processing mode is the warehouse management mode, the storage area and storage conditions of the chemical are determined based on the target business field; if the business processing mode is the transportation management mode, a transportation document is generated based on a preset transportation document template and the target business field; The method further includes: performing feature extraction on the text data corresponding to each of the to-be-identified classes to obtain key fields of the to-be-identified MSDS text data; generating display data based on the key fields of the to-be-identified MSDS text data, and displaying the display data on a Web display page; the Web display page includes at least one feature item and a query control, each feature item including the key fields of the to-be-identified MSDS text data; on the Web display page, in response to a selection operation on the feature item, determining a target feature item; and in response to a triggering operation on the query control, jumping to a key field display page corresponding to the target feature item.

2. The method according to claim 1, characterized in that In the case where there are multiple MSDS data to be identified, the method further includes: generating target text data based on the plurality of MSDS text data to be identified; Accordingly, the step of identifying the classification number of the MSDS text data to be identified and determining the business text data corresponding to the business processing mode includes: The classification number of the target text data is identified to determine the business text data corresponding to the business processing mode.

3. The method according to claim 2, characterized in that The MSDS text data to be identified / the target text data includes a plurality of text pages; The step of identifying the classification number of the MSDS text data to be identified and determining the business text data corresponding to the business processing mode includes: For each of the text pages, identifying the classification number of the text page to obtain an identification result; If the recognition result includes multiple classification numbers, the text page is segmented to obtain new text pages; Repeating the above steps, when the number of classification numbers of the recognition result is one, determining the text data in the text page as text data of the category to be recognized corresponding to the classification number; The business text data is determined based on the text data of at least one of the to-be-identified categories corresponding to the business model.

4. The method according to claim 1, wherein The step of converting the MSDS data to be identified into MSDS text data to be identified in a target format includes: In the case that the MSDS data to be identified is in a picture format, text recognition is performed on the MSDS data to be identified to obtain MSDS text data to be identified, and the MSDS text data to be identified is converted into a target format.

5. The method according to claim 4, characterized in that The method further comprises: Invalid text information in the MSDS text data to be identified in the target format is cleaned to obtain cleaned MSDS text data to be identified.

6. A chemical entity recognition device, characterized in that: include: An MSDS data receiving module is configured to receive MSDS data to be identified of at least one chemical and convert the MSDS data to be identified into MSDS text data to be identified in a target format; each MSDS text data to be identified includes multiple categories to be identified, and each category to be identified corresponds to a classification number; A business text data determination module is used to identify the classification number of the MSDS text data to be identified in any business processing mode, and determine the business text data corresponding to the business processing mode; the business text data corresponding to the business processing mode includes text information of at least one category to be identified; A target business field identification module is used to input the business text data into the entity recognition model corresponding to the business processing mode to obtain the target business field corresponding to the business processing mode; A task execution module, configured to execute the task corresponding to the business processing mode based on the target business field; The business processing modes include chemical safety testing mode, warehouse management mode and transportation management mode; The task execution module is specifically configured to, if the business processing mode is a chemical safety detection mode, perform a prohibition, restriction, and control detection on the target business field to obtain a detection result; wherein the detection result includes whether the chemical is a prohibited item, a restricted item, or a specially controlled item; if the business processing mode is a warehouse management mode, determine the storage area and storage conditions of the chemical based on the target business field; if the business processing mode is a transportation management mode, generate a transportation document based on a preset transportation document template and the target business field; The device further includes a key field management module for performing feature extraction on the text data corresponding to each of the to-be-identified classes to obtain key fields of the to-be-identified MSDS text data; generating display data based on the key fields of the to-be-identified MSDS text data, and displaying the display data on a Web display page; the Web display page includes at least one feature item and a query control, each feature item including the key fields of the to-be-identified MSDS text data; and determining a target feature item on the Web display page in response to a selection operation on the feature item. In response to the triggering operation of the query control, jump to the key field display page corresponding to the target feature item.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the chemical entity identification method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the chemical entity identification method according to any one of claims 1 to 5 when executed.

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