Method, device, equipment and storage medium for generating standard report

Abstract

The present application discloses a method, device, equipment and storage medium for generating a standard report, which relates to the field of artificial intelligence technology. A specific implementation of the method includes: obtaining a target report; inputting the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report; according to the text structure information, obtaining corresponding standard text information from a preset association relationship library; generating a standard report according to the standard text information.

Description

Technical Field

[0001] The present application relates to the field of computer technology, specifically to the field of artificial intelligence technology, and in particular to a method, apparatus, device and storage medium for generating a standard report. Background Art

[0002] With the rapid development of mobile Internet, accompanied by continuous breakthroughs in various technologies and the improvement of consumers' service experience requirements under consumption upgrades, traditional service industries are actively integrating into the Internet era. Various platforms are connecting systems, data and services with offline service agencies to provide consumers with more accurate, fast and convenient offline services.

[0003] In the related art, different suppliers push reports to the platform for users to view in different formats, which makes it inconvenient for the platform to manage different suppliers. This situation increases the reading and analysis costs of users, and the platform cannot effectively summarize and analyze the results of multiple reports for users and provide users with more targeted suggestions. Summary of the invention

[0004] The embodiments of the present application provide a method, apparatus, device and storage medium for generating a standard report.

[0005] In a first aspect, an embodiment of the present application provides a method for generating a standard report, the method comprising: obtaining a target report; inputting the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report; obtaining corresponding standard text information from a preset association relationship library based on the text structure information; and generating a standard report based on the standard text information.

[0006] In some embodiments, a preset association relationship library is determined based on the following steps: obtaining multiple initial reports; establishing association relationships between the multiple initial reports and the standard text information in response to the similarity between the multiple initial reports and the standard text information satisfying a preset similarity threshold; and constructing a preset association relationship library based on the association relationships.

[0007] In some embodiments, in response to the similarity between multiple initial reports and standard text information satisfying a preset similarity threshold, an association relationship between the multiple initial reports and the standard text information is established, including: extracting keywords from the multiple initial reports and the standard text information respectively; in response to the similarity between the keywords in the multiple initial reports and the standard text information satisfying a preset similarity threshold, an association relationship between the multiple initial reports and the standard text information is established.

[0008] In some embodiments, in response to the similarity between keywords in multiple initial reports and standard text information satisfying a preset similarity threshold, an association relationship between multiple initial reports and standard text information is established, including: obtaining the priority of the keywords in the initial reports and the standard text information; in response to the similarity between keywords with priorities greater than a preset priority threshold satisfying a preset similarity threshold, an association relationship between multiple initial reports and standard text information is established.

[0009] In some embodiments, the keywords in the standard text information include at least one medical standard term.

[0010] In some embodiments, the target report is input into a pre-trained text recognition model to obtain text structure information corresponding to the target report, including: in response to the format of the target report being a target format, the target report is input into a pre-trained text recognition model to obtain text structure information corresponding to the target report.

[0011] In some embodiments, the method for generating a standard report further includes: in response to the format of the target report not being in the target format, converting the format of the target report into the target format using a conversion method corresponding to the format of the target report.

[0012] In a second aspect, an embodiment of the present application provides a device for generating a standard report, the device comprising: a report acquisition module for acquiring a target report; an information acquisition module for inputting the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report; an information acquisition module for acquiring corresponding standard text information from a preset association relationship library based on the text structure information; and a report generation module for generating a standard report based on the standard text information.

[0013] In some embodiments, the device for generating a standard form also includes: a report acquisition module, which is also used to acquire multiple initial reports; a relationship establishment module, which is used to establish an association relationship between multiple initial reports and standard text information in response to the similarity between the multiple initial reports and the standard text information satisfying a preset similarity threshold; and a relationship library construction module, which is used to construct a preset association relationship library based on the association relationship.

[0014] In some embodiments, the relationship establishment module includes: a keyword extraction unit, used to extract keywords from multiple initial reports and standard text information respectively; a relationship establishment unit, used to establish an association relationship between multiple initial reports and standard text information in response to the similarity between the keywords in the multiple initial reports and standard text information satisfying a preset similarity threshold.

[0015] In some embodiments, the relationship establishing unit is further used to: obtain the priority of keywords in the initial report and the standard text information; in response to the similarity between keywords with priorities greater than a preset priority threshold satisfying a preset similarity threshold, establish an association relationship between multiple initial reports and standard text information.

[0016] In some embodiments, the keywords in the standard text information include at least one medical standard term.

[0017] In some embodiments, the information acquisition module is further used to: in response to the format of the target report being the target format, input the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report.

[0018] In some embodiments, the apparatus for generating a standard form further includes: a format conversion module for converting the format of the target report into the target format using a conversion method corresponding to the format of the target report in response to the format of the target report not being the target format.

[0019] In a third aspect, an embodiment of the present application provides an electronic device comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in the first aspect.

[0020] In a fourth aspect, an embodiment of the present application provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the method described in the first aspect.

[0021] The method, apparatus, device and storage medium for generating a standard report provided by the embodiment of the present application first obtains a target report; then inputs the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report; then obtains corresponding standard text information from a preset association library based on the text structure information; then generates a standard report based on the standard text information. The pre-trained text recognition model can be used to obtain text structure information corresponding to the target report; then, based on the text structure information, the corresponding standard text information is obtained from a preset association library to generate a standard report, thereby achieving standardization of non-standard reports. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0023] Figure 1is an exemplary system architecture diagram to which the present application may be applied;

[0024] Figure 2 is a flow chart of an embodiment of a method for generating a standard report according to the present application;

[0025] Figure 3 is a flow chart of an embodiment of a method for generating a standard report according to the present application;

[0026] Figure 4 is a schematic structural diagram of an embodiment of a device for generating a standard report according to the present application;

[0027] Figure 5 It is a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present application. DETAILED DESCRIPTION

[0028] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It should also be noted that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings.

[0029] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0030] Figure 1 An exemplary system architecture 100 is shown to which an embodiment of a method for generating a standard report or an apparatus for generating a standard report of the present application can be applied.

[0031] like Figure 1 As shown, system architecture 100 may include servers 101 and 102, network 103, and electronic device 104. Network 103 is used to provide a medium for communication links between servers 101 and 102 and electronic device 104. Network 103 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0032] The user can use the servers 101 and 102 to interact with the electronic device 104 via the network 103, such as target reporting.

[0033] Servers 101 and 102 may be hardware or software. When servers 101 and 102 are hardware, they may be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When servers 101 and 102 are software, they may be implemented as multiple software or software modules (for example, for providing distributed services), or as a single software or software module. No specific limitation is made here.

[0034] The electronic device 104 can provide various services. For example, the electronic device 104 can obtain a target report; input the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report; obtain corresponding standard text information from a preset association relationship library based on the text structure information; and generate a standard report based on the standard text information.

[0035] It should be noted that the electronic device 104 can be hardware or software. When the electronic device 104 is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or it can be implemented as a single server; or the electronic device 104 can be an electronic product that interacts with the user through one or more methods such as a keyboard, touchpad, touch screen, remote control, voice interaction or handwriting device, such as a PC (Personal Computer), a mobile phone, a smart phone, a PDA (Personal Digital Assistant), a wearable device, a PPC (Pocket PC), a tablet computer, a smart car machine, a smart TV, a smart speaker, a tablet computer, a laptop portable computer and a desktop computer, etc. When the electronic device 104 is software, it can be implemented as multiple software or software modules (for example, to provide distributed services), or it can be implemented as a single software or software module. No specific limitation is made here.

[0036] It should be noted that the method for generating a standard report provided in the embodiment of the present application is generally executed by the electronic device 104 , and accordingly, the device for generating a standard report is generally disposed in the electronic device 104 .

[0037] It should be understood that Figure 1 The number of electronic devices, networks and servers in the embodiment is only for illustration. Any number of electronic devices, networks and servers may be provided as required.

[0038] Continue to refer Figure 2 , which shows a flowchart 200 of an embodiment of a method for generating a standard report according to the present application. The method for generating a standard report may include the following steps:

[0039] Step 201, obtaining a target report.

[0040] In this embodiment, the execution subject of the method for generating a standard report (eg Figure 1 The electronic device 104 shown) can be connected to a network (eg Figure 1 The network 103 shown in FIG. 104 ) obtains the terminal device (eg Figure 1The target report can be a report formed by medical-related data provided by different suppliers, such as a physical examination report.

[0041] Step 202: input the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report.

[0042] In this embodiment, the execution subject may input the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report. The text recognition model is used to obtain text structure information corresponding to the target report, and the text structure information may be stored in a key-value pair format.

[0043] In this embodiment, the above-mentioned text recognition model can be determined based on the following steps: obtaining text structure information and corresponding behavior text labels; then, using the text structure information and the corresponding text labels for training to obtain a text recognition model. During training, the execution subject can use the text structure information as the input of the text recognition model, and the text label corresponding to the text structure information as the expected output to obtain the text recognition model. The above-mentioned machine learning model can be a probability model, classification model or other classifier in the existing technology or future development technology. For example, the machine learning model can include any of the following: decision tree model (XGBoost), logistic regression model (LR), deep neural network model (DNN).

[0044] Step 203: Acquire corresponding standard text information from a preset association relationship library according to the text structure information.

[0045] In this embodiment, the execution subject can obtain the corresponding standard text information from a preset association relationship library according to the text structure information. The preset association relationship library can be used to store the corresponding relationship between the standard text information and the text structure information.

[0046] In this embodiment, the execution entity may obtain a plurality of initial reports and standard reports, and establish association relationships between the plurality of initial reports and standard reports; and then, establish an association relationship library in advance according to the association relationships.

[0047] In one example, the target report may include a table, and the association relationship may also include: the field category to which the text in the cell belongs, that is, the field category to which the text structure information belongs. The field category may be used to characterize the category of the text structure information stored in the cell, such as department, project, part, etc.

[0048] Step 204: Generate a standard report based on the standard text information.

[0049] In this embodiment, the above execution entities may integrate the standard text information together to generate a standard report.

[0050] In one example, according to the position information of the text structure information in the target report, the standard text information corresponding to the text structure information is displayed at the position of the text structure information to form a standard report. The above standard report can be data related to medical treatment relative to the target report, and the standard report can be used to measure whether the data in the target report meets the relevant medical standards.

[0051] It should be noted that the standard report is relative to the target report, and includes the standard text information corresponding to the text structure information in the target report.

[0052] The data involved in this embodiment (eg, target report, physical examination report, standard report, etc.) are obtained with the user's consent or through compliant means.

[0053] In this embodiment, after generating the standard report, the method for generating the standard report may further include: sending a standard report to a server (eg Figure 1 The servers 101 and 102 shown in the figure send reminder information to remind you to check the standard report. The above reminder information can be sent via SMS, interface highlighting, etc.

[0054] In this embodiment, after the standard report is generated, the standard report may also be classified according to the supplier of the target report.

[0055] The method for generating a standard report provided by the embodiment of the present application first obtains a target report; then inputs the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report; then obtains corresponding standard text information from a preset association library based on the text structure information; then generates a standard report based on the standard text information. The pre-trained text recognition model can be used to obtain text structure information corresponding to the target report; then, based on the text structure information, the corresponding standard text information is obtained from a preset association library to generate a standard report, thereby achieving standardization of non-standard reports.

[0056] like Figure 3 , a flowchart 300 of an embodiment of a method for generating a standard report according to the present application is shown. The method for generating a standard report may include the following steps:

[0057] Step 301, obtaining a target report.

[0058] In this embodiment, the execution subject of the method for generating a standard report (eg Figure 1 The electronic device 104 shown) can be connected to a network (eg Figure 1 The network 103 shown in FIG. 104 ) obtains the terminal device (eg Figure 1 The target report can be a report formed by medical-related data provided by different suppliers.

[0059] Step 302: input the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report.

[0060] In this embodiment, the execution subject may input the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report. The text recognition model is used to obtain text structure information corresponding to the target report, and the text structure information may be stored in a key-value pair format.

[0061] In this embodiment, the above-mentioned text recognition model can be determined based on the following steps: obtaining text structure information and corresponding behavior text labels; then, using the text structure information and the corresponding text labels for training to obtain a text recognition model. During training, the execution subject can use the text structure information as the input of the text recognition model, and the text label corresponding to the text structure information as the expected output to obtain the text recognition model. The above-mentioned machine learning model can be a probability model, classification model or other classifier in the existing technology or future development technology. For example, the machine learning model can include any of the following: decision tree model (XGBoost), logistic regression model (LR), deep neural network model (DNN).

[0062] Step 303: Obtain multiple initial reports.

[0063] In this embodiment, the execution subject can be connected via a network (eg Figure 1 The network 103 shown in FIG. 104 ) obtains the terminal device (eg Figure 1 The aforementioned initial reports may be reports formed by medical-related data provided by different suppliers, and the multiple initial reports may include target reports.

[0064] Step 304: in response to the similarity between the multiple initial reports and the standard text information satisfying a preset similarity threshold, establishing an association relationship between the multiple initial reports and the standard text information.

[0065] In this embodiment, the execution subject may respectively establish association relationships between the multiple initial reports and the standard text information when the similarities between the multiple initial reports and the standard text information meet a preset similarity threshold.

[0066] In one example, the similarity between initial report A, initial report B, initial report C and the standard text information meets a preset similarity threshold, and an association relationship between initial report A and the standard text information, an association relationship between initial report B and the standard text information, and an association relationship between initial report C and the standard text information can be established.

[0067] It should be noted that the preset similarity threshold may be determined according to the user's demand for standard text information or the standard accuracy of the standard text information.

[0068] Step 305: construct a preset association relationship library according to the association relationship.

[0069] In this embodiment, the execution entity may construct a preset association relationship library according to the association relationship generated in step 305 , and the preset association relationship library may be used to obtain standard text information corresponding to the text structure information through indexing.

[0070] It should be noted that the above-mentioned multiple initial reports may be initial reports provided by different suppliers. The association relationship may include an association relationship between the initial report of the supplier and the standard report.

[0071] It should be noted that the execution order of steps 301 to 305 can be arbitrarily combined, for example, steps 303 to 305 can be executed before step 302, for example, steps 303 to 305 can be executed before step 301, or steps 303 to 305 can be executed between step 301 and step 302. Step 303 can also be executed simultaneously with step 301 or step 303 can be executed simultaneously with step 302.

[0072] Step 306: Obtain corresponding standard text information from a preset association relationship library according to the text structure information.

[0073] In this embodiment, the execution subject can obtain the corresponding standard text information from a preset association relationship library according to the text structure information. The preset association relationship library can be used to store the corresponding relationship between the standard text information and the text structure information.

[0074] Step 307: Generate a standard report based on the standard text information.

[0075] In this embodiment, the above execution entities may integrate the standard text information together to generate a standard report.

[0076] In this embodiment, the specific operations of steps 301, 302, 306 and 307 are the same as Figure 2 The operations of steps 201, 202, 203 and 204 in the illustrated embodiment are substantially the same and will not be described in detail herein.

[0077] from Figure 3 It can be seen that Figure 2 Compared with the corresponding embodiment, the method 300 for generating a standard report in this embodiment obtains multiple initial reports, and when the similarity between the multiple initial reports and the standard text information meets the preset similarity threshold, establishes an association relationship between the multiple initial reports and the standard text information to construct an association relationship library, so that the pre-trained text recognition model can be used to obtain the text structure information corresponding to the target report; thereafter, the corresponding standard text information is obtained from the preset association relationship library according to the text structure information to generate a standard report, thereby realizing the standardization of non-standard reports.

[0078] In some optional implementations of this embodiment, the preset association relationship library is determined based on the following steps: obtaining multiple initial reports; in response to the similarity between the multiple initial reports and the standard text information satisfying a preset similarity threshold, establishing an association relationship between the multiple initial reports and the standard text information; and constructing a preset association relationship library based on the association relationship.

[0079] In this implementation, the execution subject may obtain multiple initial reports; when the similarity between the text structure information in the multiple initial reports and the standard text information meets a preset similarity threshold, an association relationship between the multiple initial reports and the standard text information is established; and then, based on the association relationship, a preset association relationship library is constructed. The initial report is relative to the standard report, and the initial report may be a report before being converted into a standard report. The initial report may include multiple text structure information, and the multiple initial reports may include the target report. The association relationship may be used to characterize the similarity between the initial report and the standard text information.

[0080] It should be noted that the similarity threshold can be set according to the degree of conversion from the initial report to the standard report.

[0081] In this implementation, an association relationship between the initial reports and the standard text information may be established based on the similarity between the text structure information in the plurality of initial reports and the standard text information, so as to construct an association relationship library.

[0082] In some optional implementations of the present embodiment, in response to the similarity between multiple initial reports and standard text information satisfying a preset similarity threshold, an association relationship between the multiple initial reports and the standard text information is established, including: extracting keywords from the multiple initial reports and the standard text information respectively; in response to the similarity between the keywords in the multiple initial reports and the standard text information satisfying a preset similarity threshold, an association relationship between the multiple initial reports and the standard text information is established.

[0083] In this implementation, the above-mentioned execution entity can first extract keywords from multiple initial reports and standard text information; then, when the similarity between the keywords in the multiple initial reports and standard text information meets the preset similarity threshold, an association relationship between the multiple initial reports and standard text information is established.

[0084] Here, extracting keywords from the multiple initial reports and standard text information respectively may include: extracting keywords from the multiple initial reports and standard text information respectively by TF-IDF (term frequency-inverse document frequency); or extracting keywords from the multiple initial reports and standard text information by a text extraction model. The above keywords may be words that can standardize the content of the initial report.

[0085] In an example, the above keyword may be a keyword ranked in top 3 priorities among multiple keywords.

[0086] It should be noted that the degree of conversion of the initial report into the standard report may be set or randomly set by the user.

[0087] In this implementation, the execution subject may establish an association relationship between the initial reports and the standard text information based on the similarity between the text structure information in the multiple initial reports and the keywords in the standard text information.

[0088] In some optional implementations of the present embodiment, in response to the similarity between the keywords in multiple initial reports and standard text information satisfying a preset similarity threshold, an association relationship between the multiple initial reports and standard text information is established, including: obtaining the priority of the keywords in the initial reports and the standard text information; in response to the similarity between the keywords with priorities greater than a preset priority threshold satisfying a preset similarity threshold, an association relationship between the multiple initial reports and the standard text information is established.

[0089] In this implementation, the above-mentioned execution entity can obtain the priority of keywords in the initial report and standard text information; when the similarity between keywords with priorities greater than a preset priority threshold meets the preset similarity threshold, an association relationship between multiple initial reports and standard text information is established.

[0090] In an example, the above keyword may be a keyword ranked in top 3 priorities among multiple keywords.

[0091] It should be noted that the degree of conversion of the initial report into the standard report may be set or randomly set by the user.

[0092] In this implementation, the execution entity may establish an association relationship between the initial reports and the standard text information based on the similarity between the text structure information in the multiple initial reports and the keywords in the standard text information with a priority greater than a preset priority threshold.

[0093] In some optional implementations of this embodiment, the keywords in the standard text information include at least one medical standard term.

[0094] In this implementation, the keywords in the standard text information may include at least one medical standard term. The medical standard term may be a term recorded in a medical book.

[0095] In one example, the medical standard terminology may include: an examination item name and an examination site name.

[0096] It should be noted that standard text information can also be constructed according to departments.

[0097] In one example, the electronic device (eg, Figure 1 Corresponding standard text information is displayed on the screen of the electronic device 104) as shown, so that the user can browse the corresponding data.

[0098] In one example, the execution entity may also cluster standard reports, for example, cluster standard reports from the same supplier, cluster the same detection site, etc.; or cluster the same detection site in standard reports from the same supplier to achieve multi-dimensional clustering.

[0099] It should be noted that the above multi-dimensional clustering can be multiple different dimensions, for example, supplier, part, test item, etc.

[0100] In some optional implementations of this embodiment, the target report is input into a pre-trained text recognition model to obtain text structure information corresponding to the target report, including: in response to the format of the target report being a target format, the target report is input into a pre-trained text recognition model to obtain text structure information corresponding to the target report.

[0101] In this implementation, when the target report is in the target format, the execution subject inputs the target report into the pre-trained text recognition model to obtain text structure information corresponding to the target report. The above format is the presentation format of the target report, for example, PDF (Portable Document Format), JPG (Joint Photographic Experts Group), image file storage format (Portable Network Graphic Format, PNG), image file format (Bitmap, BMP), text document (txt), word.

[0102] In one example, if the format of the initial report is PDF format, a PDF parser is used to convert the format of the initial report into a string format, so that the format of the standard report is consistent with the format of the converted initial report.

[0103] It should be noted that the format is not limited to the above formats, but may also include display text formats, such as font size, font color, font spacing, etc.

[0104] In some optional implementations of this embodiment, the method for generating a standard report further includes: in response to the format of the target report not being the target format, converting the format of the target report into the target format using a conversion method corresponding to the format of the target report.

[0105] In this implementation, when the format of the target report is not the target format, the execution subject may convert the format of the target report into the target format using a conversion method corresponding to the format of the target report.

[0106] In one example, if the format of the initial report is PDF format, a PDF parser is used to convert the format of the initial report into a string format, so that the format of the standard report is consistent with the format of the converted initial report.

[0107] Further references Figure 4 As an implementation of the methods shown in the above figures, the present application discloses an embodiment of a device for generating a standard report. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0108] like Figure 4As shown, the embodiment of the present application provides a device 400 for generating a standard report, and the device 400 includes: a report acquisition module 401, an information acquisition module 402, an information acquisition module 403 and a report generation module 404. Among them, the report acquisition module 401 is used to obtain a target report; the information acquisition module 402 is used to input the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report; the information acquisition module 403 is used to obtain corresponding standard text information from a preset association relationship library according to the text structure information; the report generation module 404 is used to generate a standard report according to the standard text information.

[0109] In the apparatus 400 for generating a standard report in this embodiment, the specific processing of the report acquisition module 401, the information acquisition module 402, the information acquisition module 403 and the report generation module 404 and the technical effects thereof can be referred to in Figure 2 This corresponds to step 201 to step 204 in the embodiment.

[0110] In some embodiments, the device for generating standard reports also includes: a report acquisition module 401, which is also used to acquire multiple initial reports; a relationship establishment module, which is used to establish an association relationship between multiple initial reports and standard text information in response to the similarity between the multiple initial reports and the standard text information satisfying a preset similarity threshold; and a relationship library construction module, which is used to construct a preset association relationship library based on the association relationship.

[0111] In some embodiments, the relationship establishment module includes: a keyword extraction unit, used to extract keywords from multiple initial reports and standard text information respectively; a relationship establishment unit, used to establish an association relationship between multiple initial reports and standard text information in response to the similarity between the keywords in the multiple initial reports and standard text information satisfying a preset similarity threshold.

[0112] In some embodiments, the relationship establishing unit is further used to: obtain the priority of keywords in the initial report and the standard text information; in response to the similarity between keywords with priorities greater than a preset priority threshold satisfying a preset similarity threshold, establish an association relationship between multiple initial reports and standard text information.

[0113] In some embodiments, the keywords in the standard text information include at least one medical standard term.

[0114] In some embodiments, the information obtaining module 402 is further used to: in response to the format of the target report being the target format, input the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report.

[0115] In some embodiments, the apparatus for generating a standard report further includes: a format conversion module for converting the format of the target report into the target format using a conversion method corresponding to the format of the target report in response to the format of the target report not being the target format.

[0116] like Figure 5 , is a block diagram of an electronic device according to a method for generating a standard report according to an embodiment of the present application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, 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 application described and / or required herein.

[0117] like Figure 5 As shown, the electronic device includes: one or more processors 501, a memory 502, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In other embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 501 is taken as an example.

[0118] The memory 502 is a non-transitory computer-readable storage medium provided in the present application. The memory stores instructions executable by at least one processor to enable at least one processor to perform the method for generating a standard report provided in the present application. The non-transitory computer-readable storage medium of the present application stores computer instructions, which are used to enable a computer to perform the method for generating a standard report provided in the present application.

[0119] The memory 502 is a non-transient computer-readable storage medium that can be used to store non-transient software programs, non-transient computer executable programs and modules, such as program instructions / modules corresponding to the method for generating a standard report in the embodiment of the present application (for example, the attached Figure 4The processor 501 executes various functional applications and data processing of the server by running the non-transient software programs, instructions and modules stored in the memory 502, that is, the method for generating a standard report in the above method embodiment is implemented.

[0120] The memory 502 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the blockchain-based information processing electronic device, etc. In addition, the memory 502 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 502 may optionally include a memory remotely arranged relative to the processor 501, and these remote memories may be connected to the blockchain-based information processing electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0121] The electronic device of the method for generating a standard report may further include: an input device 503 and an output device 504. The processor 501, the memory 502, the input device 503 and the output device 504 may be connected via a bus or other means. Figure 5 The example of connecting through bus is taken in the following.

[0122] The input device 503 can receive input digital or character information, and generate key signal input related to user settings and function control of blockchain-based information processing electronic devices, such as touch screens, keypads, mice, trackpads, touchpads, indicator rods, one or more mouse buttons, trackballs, joysticks and other input devices. The output device 504 may include a display device, an auxiliary lighting device (e.g., LED) and a tactile feedback device (e.g., a vibration motor), etc. The display device may include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display and a plasma display. In some embodiments, the display device may be a touch screen.

[0123] Various implementations of the systems and techniques described herein can be realized in digital electronic circuit systems, integrated circuit systems, dedicated ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including 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.

[0124] These computer programs (also referred to as programs, software, software applications, or code) include machine instructions for programmable processors and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or means (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0125] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer 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 computer. 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).

[0126] The systems and techniques described herein may 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 a 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 may 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), and the Internet.

[0127] A computer system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other.

[0128] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution disclosed in this application can be achieved, and this document is not limited here.

[0129] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for generating a standard report, comprising: Get target report; Inputting the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report; According to the text structure information, corresponding standard text information is obtained from a preset association relationship library; Generate a standard report based on the standard text information; The preset association relationship library is determined based on the following steps: Obtain multiple initial reports; In response to the similarity between the multiple initial reports and the standard text information satisfying a preset similarity threshold, establishing an association relationship between the multiple initial reports and the standard text information; According to the association relationship, construct the preset association relationship library; The establishing of association relationships between the multiple initial reports and the standard text information in response to the similarity between the multiple initial reports and the standard text information satisfying a preset similarity threshold includes: extracting keywords from the plurality of initial reports and standard text information respectively; Obtaining priorities of keywords in the initial report and standard text information; In response to the similarity between the keywords with priorities greater than a preset priority threshold satisfying a preset similarity threshold, an association relationship between the plurality of initial reports and the standard text information is established.

2. The method according to claim 1, wherein: The keywords in the standard text information include at least one medical standard term.

3. The method according to claim 1, wherein: The step of inputting the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report includes: In response to the format of the target report being the target format, the target report is input into a pre-trained text recognition model to obtain text structure information corresponding to the target report.

4. The method according to claim 3, further comprising: In response to the format of the target report not being the target format, the format of the target report is converted into the target format using a conversion method corresponding to the format of the target report.

5. A device for generating a standard report, comprising: A report acquisition module is used to acquire target reports; An information acquisition module, used for inputting the target report into a pre-trained text recognition model to obtain text structure information corresponding to the target report; An information acquisition module, used to acquire corresponding standard text information from a preset association relationship library according to the text structure information; A report generation module, used for generating a standard report according to the standard text information; Wherein, the device further comprises: The report acquisition module is also used to acquire multiple initial reports; a relationship establishing module, configured to establish an association relationship between the plurality of initial reports and the standard text information in response to the similarity between the plurality of initial reports and the standard text information satisfying a preset similarity threshold; A relationship library construction module, used to construct the preset association relationship library according to the association relationship; Wherein, the relationship establishment module includes: A keyword extraction unit, used to extract keywords from the plurality of initial reports and standard text information respectively; A relationship establishing unit is used to obtain the priorities of keywords in the initial reports and standard text information; in response to the similarity between keywords with priorities greater than a preset priority threshold meeting a preset similarity threshold, establish an association relationship between the multiple initial reports and the standard text information.

6. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium storing computer instructions, on which a computer program is stored, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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