Physical examination order generation method and device, equipment and storage medium

Abstract

Embodiments of the present application provide a physical examination order generation method, device and equipment and storage medium. The method comprises: obtaining target physical examination packages and target add-on package information, the target add-on package information comprising add-on packages selected by users; performing feature extraction on the target add-on package information and arranging the target add-on package according to a preset rule to generate a search characteristic value corresponding to the target add-on package information; matching the search characteristic value in a project database to obtain a matching result, the project database comprising initial physical examination packages and search characteristic values of selectable initial add-on packages corresponding to the initial physical examination packages; and in response to the matching result being a matching success, generating a target physical examination order based on the target add-on package information and the target physical examination package. The present application aims to avoid conflicts between add-on combinations and physical examination packages, and to generate a physical examination order that can meet the personalized needs of users.

Description

Methods, devices, equipment and storage media for generating physical examination orders Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method for generating physical examination orders, a device for generating physical examination orders, a computer device, and a computer-readable storage medium. Background Technology

[0002] As people's living standards continue to improve, the demand for preventive healthcare is constantly increasing, and regular physical examinations, as an important part of health care, are receiving more and more attention. In the online appointment process for physical examinations, the preset packages often cannot meet users' needs, thus requiring the provision of personalized add-ons for users to choose from and purchase. However, if users arbitrarily select add-ons, it may not be possible to meet the combination of add-ons provided in the supplier's contract, potentially leading to conflicts between the add-on combinations and the physical examination package.

[0003] For the reasons mentioned above, it is necessary to propose a method for generating physical examination orders that can meet the personalized needs of users while avoiding conflicts between additional items and physical examination packages, that is, ensuring that the combinations selected by users are within the scope supported by the contract. Summary of the Invention

[0004] This application provides a method, apparatus, computer device, and computer-readable storage medium for generating physical examination orders, aiming to avoid conflicts between additional item combinations and physical examination packages, thereby generating physical examination orders that can meet the personalized needs of users.

[0005] To achieve the above objectives, this application provides a method for generating a medical examination order, comprising:

[0006] Obtain information on the target health checkup package and the target add-on package, wherein the target add-on package information includes several add-on packages selected by the user;

[0007] Feature extraction is performed on the target add-on package information, and the target add-on packages are arranged according to preset rules to generate retrieval feature values ​​corresponding to the target add-on package information;

[0008] The search feature values ​​are used to match in the project database to obtain matching results. The project database includes several initial physical examination packages and search feature values ​​of several selectable initial supplementary packages corresponding to the initial physical examination packages.

[0009] In response to a successful match, a target medical examination order is generated based on the target add-on package information and the target medical examination package.

[0010] To achieve the above objectives, this application also provides a medical examination order generation device, comprising:

[0011] The acquisition module is used to acquire information on the target physical examination package and the target add-on package, wherein the target add-on package information includes several add-on packages selected by the user;

[0012] The feature extraction module is used to extract features from the target add-on package information, arrange the target add-on packages according to preset rules, and generate retrieval feature values ​​corresponding to the target add-on package information;

[0013] The matching module is used to match the search feature values ​​in the project database to obtain matching results. The project database includes several initial physical examination packages and search feature values ​​of several selectable initial supplementary packages corresponding to the initial physical examination packages.

[0014] The physical examination order generation module is used to generate a target physical examination order based on the target add-on package information and the target physical examination package in response to the matching result being a successful match.

[0015] In addition, to achieve the above objectives, this application also provides a computer device, which includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and, when executing the computer program, implement the steps of the physical examination order generation method provided in any of the embodiments of this application.

[0016] In addition, to achieve the above objectives, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to implement the steps of the physical examination order generation method provided in any of the embodiments of this application.

[0017] This application discloses a method, apparatus, computer device, and computer-readable storage medium for generating physical examination orders. The method includes acquiring information on a target physical examination package and target add-on packages, wherein the target add-on package information includes several add-on packages selected by the user. Further, features can be extracted from the target add-on package information, and the target add-on packages can be arranged according to preset rules to generate retrieval feature values ​​corresponding to the target add-on package information. Thus, the retrieval feature values ​​can be used to match in a project database to obtain matching results, wherein the project database includes several initial physical examination packages and retrieval feature values ​​of several selectable initial add-on packages corresponding to the initial physical examination packages. Finally, in response to a successful matching result, a target physical examination order can be generated based on the target add-on package information and the target physical examination package. This application, by extracting features from the target add-on package information and generating retrieval feature values ​​according to preset rules, makes the organization of the target add-on package information more standardized and efficient. By matching retrieval feature values ​​in the project database, physical examination packages and their add-on package combinations that meet user needs can be quickly and accurately determined. Furthermore, this method not only improves matching efficiency but also effectively reduces human error and enhances user experience. Simultaneously, by automatically generating target health checkup orders based on the matching results, it enables intelligent personalized configuration of health checkup packages, helping to meet diverse user needs and improve service accuracy and satisfaction. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 is a schematic diagram of a scenario for a method of generating a physical examination order provided in an embodiment of this application;

[0020] Figure 2 is a flowchart illustrating a method for generating a physical examination order according to an embodiment of this application;

[0021] Figure 3 is a schematic block diagram of a physical examination order generation device provided in an embodiment of this application;

[0022] Figure 4 is a schematic block diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily need to be performed in the described order. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation. Furthermore, although functional modules are divided in the device diagram, in some cases, a different module division may be used.

[0025] The term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items, as well as all possible combinations, and includes such combinations.

[0026] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0027] As shown in Figure 1, the physical examination order generation method provided in this application embodiment can be applied to the application environment shown in Figure 1. This application environment includes a terminal device 110 and a server 120, wherein the terminal device 110 can communicate with the server 120 via a network. Specifically, the server 120 can obtain target physical examination packages and target add-on package information, wherein the target add-on package information includes several add-on packages selected by the user. Further, feature extraction can be performed on the target add-on package information, and the target add-on packages can be arranged according to preset rules to generate retrieval feature values ​​corresponding to the target add-on package information. Thus, the retrieval feature values ​​can be used to match in a project database to obtain matching results, wherein the project database includes several initial physical examination packages and retrieval feature values ​​of several selectable initial add-on packages corresponding to the initial physical examination packages. Finally, in response to a successful matching result, a target physical examination order can be generated based on the target add-on package information and the target physical examination package, thereby sending the target physical examination package to the terminal device 110. The server 120 can be a standalone server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The terminal device 110 can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The terminal and server can be directly or indirectly connected via wired or wireless communication; this application does not impose any restrictions on this connection.

[0028] Please refer to Figure 2, which is a flowchart illustrating a method for generating a medical examination order according to an embodiment of this application. As shown in Figure 2, the method for generating a medical examination order can be implemented through steps S11 to S14.

[0029] Step S11: Obtain information on the target health check package and target add-on packages.

[0030] The target add-on package information includes several add-on packages selected by the user.

[0031] For example, a target health checkup package is used to characterize the set of basic health checkup services selected by the user, which typically includes a series of routine health checkup items, such as blood tests, liver function tests, and electrocardiograms. The target health checkup package can be selected by the user from multiple packages offered by the platform or service provider, or it can be a default package recommended by the system based on the user's basic information (such as age, gender, and health status). This application does not limit this selection.

[0032] For example, a target add-on package is a collection of additional health check-up items that a user selects outside of the target health check-up package. Target add-on packages are used to meet a user's personalized needs, such as screening for specific diseases or in-depth health checks.

[0033] The above implementation methods can obtain information on target health check packages and target add-on packages, laying a data foundation for accurate matching and package generation, and ensuring personalized and efficient services.

[0034] Step S12: Extract features from the target add-on package information, arrange the target add-on packages according to preset rules, and generate the retrieval feature values ​​corresponding to the target add-on package information.

[0035] Feature extraction refers to extracting key attributes from the user-selected target add-on package information and converting them into structured data that computers can understand and process. The retrieved feature value is the final unique identifier generated for database matching; its core purpose is to simplify complex add-on package information into a unified and comparable form.

[0036] For example, core keywords can be extracted from the name and content description of add-on packages, and then the information features of the add-on packages can be extracted and transformed into multi-dimensional feature vectors, such as category, price, and priority being mapped to corresponding values. Thus, the categories and attributes of add-on packages can be labeled according to preset rules, facilitating subsequent sorting and matching.

[0037] It should be noted that this application does not limit the method of feature extraction, such as natural language processing, database query, and rule engine to achieve feature extraction of the target item package.

[0038] For example, preset rules include, but are not limited to, the following: multi-level sorting (e.g., first classifying by category, then sorting by priority); rule encoding (e.g., arranging add-item bags using fixed logical rules); algorithm optimization (e.g., using sorting algorithms such as quicksort to achieve efficient arrangement).

[0039] The above implementation methods can standardize and structure the information of the add-on packages selected by the user, generate unique search feature values, ensure that complex user needs can be quickly and accurately identified and processed by the system, and provide efficient support for subsequent matching operations.

[0040] Step S13: Use the search feature values ​​to match in the project database to obtain the matching results.

[0041] The project database includes several initial physical examination packages and search feature values ​​for several optional initial supplementary packages corresponding to the initial physical examination packages.

[0042] For example, search features (such as encoded forms or hash values) can be generated from the user's selected target health check package and add-on packages. Since the search features carry detailed information about the user's needs, including category, priority, price, etc., the search features can be compared one by one with the features in the project database to obtain matching results.

[0043] The above implementation methods can utilize search feature values ​​for efficient matching, providing users with health checkup solutions that closely match their needs, ensuring the accuracy and efficiency of the service.

[0044] Step S14: In response to a successful match result, generate a target medical examination order based on the target add-on package information and the target medical examination package.

[0045] It should be understood that if the matching result is successful, it means that the target add-on package information and the target medical examination package do not conflict and can form a complete plan. Therefore, a target medical examination order can be generated based on the target add-on package information and the target medical examination package.

[0046] The above implementation methods can organically combine user needs with existing resources to generate usable target health check packages, which not only improves user satisfaction but also optimizes service efficiency.

[0047] The method for generating a physical examination order disclosed in this application includes obtaining information on a target physical examination package and a target add-on package, wherein the target add-on package information includes several add-on packages selected by the user. Further, features can be extracted from the target add-on package information, and the target add-on packages can be arranged according to preset rules to generate retrieval feature values ​​corresponding to the target add-on package information. Thus, the retrieval feature values ​​can be used to match in a project database to obtain matching results, wherein the project database includes several initial physical examination packages and retrieval feature values ​​of several selectable initial add-on packages corresponding to the initial physical examination packages. Finally, in response to a successful matching result, a target physical examination order can be generated based on the target add-on package information and the target physical examination package. This application, by extracting features from the target add-on package information and generating retrieval feature values ​​according to preset rules, makes the organization of the target add-on package information more standardized and efficient. By matching retrieval feature values ​​in the project database, physical examination packages and their add-on package combinations that meet user needs can be quickly and accurately determined. Furthermore, this method not only improves matching efficiency but also effectively reduces human error and enhances user experience. Meanwhile, by automatically generating target health check orders based on matching results, the system enables intelligent configuration of personalized health check packages, which helps meet diverse user needs and improves service accuracy and satisfaction.

[0048] Optionally, feature extraction is performed on the target add-on package information, including: preprocessing the target add-on package information to obtain preprocessed target add-on package information; and extracting features from the preprocessed target add-on package information to obtain the target identifier code corresponding to the target add-on package information.

[0049] Specifically, at least one preprocessing operation can be performed on the target add-on information, including stop word removal, punctuation removal, and special character removal, to obtain preprocessed target add-on information. This prevents invalid data from affecting subsequent feature extraction of the target add-on information. Furthermore, feature extraction operations such as encoding can be performed on the preprocessed target text to obtain the target identifier code corresponding to the target add-on information.

[0050] The above methods, through preprocessing and feature extraction of the target text, can determine the target identifier code corresponding to the target add-on information, thus providing a basis for the subsequent matching process.

[0051] Optionally, the target add-on packages are arranged according to preset rules to generate retrieval feature values ​​corresponding to the target add-on package information, including: sorting the target identifier codes according to priority order to obtain sorted target identifier codes; and dividing the sorted target identifier codes by a preset separator to generate retrieval feature values ​​corresponding to the target add-on package information.

[0052] For example, the identifiers of the target add-on packages can be sorted according to preset rules (such as priority) to ensure that the generated search feature values ​​are unique and consistent, thereby avoiding matching failures caused by different orders. For example, the target add-on packages include a cardiovascular screening package (identifier `101`, priority `2`), a tumor screening package (identifier `102`, priority `1`), and a diabetes screening package (identifier `103`, priority `3`). The sorted target identifiers include: `102` (priority 1) → `101` (priority 2) → `103` (priority 3).

[0053] For example, to generate standardized strings that are easy to store, transmit, and match, a specific symbol (such as `|` or `,`) can be predefined as a separator between identifiers. For instance, if the sorted target identifiers are `102`, `101`, and `103`, using `|` as a separator generates the string `102|101|103`, which is then used as the retrieval feature value corresponding to the target add-on information.

[0054] The above implementation methods, through the sorting of target identifier codes and the encoding of delimiters, generate retrieval feature values ​​that ensure a fast and accurate match between user needs and database information. These methods achieve automated matching while also improving system efficiency and flexibility.

[0055] Optionally, matching is performed in the project database using the retrieval feature value to obtain the matching result, including: determining the similarity value between the retrieval feature value and the project database using a cosine similarity algorithm; determining the matching result as a matching failure in response to the similarity value being greater than or equal to a preset similarity value; and determining the matching result as a matching success in response to the similarity value being less than the preset similarity value.

[0056] Cosine similarity is used to measure the degree of similarity between two feature values ​​in a multidimensional vector space. A preset similarity value is a threshold used to determine whether the matching degree between two feature values ​​meets the requirements. For example, preset similarity values ​​may be 0.9, 0.95, etc. This application embodiment does not limit this, and uses a preset similarity value of 0.9 as an example for illustration.

[0057] For example, if the similarity value is less than 0.9, the matching result is determined to be a successful match; if the similarity value is greater than or equal to 0.9, the matching result is determined to be a failed match.

[0058] The above implementation methods utilize a cosine similarity algorithm for matching, which significantly improves matching flexibility and user experience. By setting a preset similarity threshold, the matching criteria can be precisely controlled, thereby meeting different needs for strict and lenient matching.

[0059] Optionally, the similarity value between the retrieved feature value and the item database is determined by the cosine similarity algorithm, including: vectorizing the retrieved feature value using the bag-of-words model to obtain the target vector representation; traversing the item database and determining the initial similarity between the target vector representation and the item database; and determining the maximum value in the initial similarity as the similarity value.

[0060] Among them, the bag-of-words model is a text feature representation method used to transform a set of words or identifiers into numerical vectors.

[0061] For example, the bag-of-words model can be used to map the identifiers of the retrieved feature values ​​to a vocabulary, count the occurrences of each identifier, and generate a target vector representation. Further, a cosine similarity algorithm can be used to calculate the similarity between the retrieved target vector representation and the feature value vectors in the database, selecting the maximum value as the final similarity value between the retrieved feature value and the item database.

[0062] The above implementation method vectorizes the retrieval feature values ​​and the feature values ​​of the item database using a bag-of-words model, and calculates their similarity using a cosine similarity algorithm, thereby effectively achieving matching. By selecting the maximum similarity value, the system can accurately determine the closest matching result, making it suitable for flexible selection scenarios of add-on packages in health checkup packages.

[0063] Optionally, after matching the project database using the retrieval feature values ​​and obtaining the matching results, the process further includes: in response to a matching failure, generating recommended add-on package information based on the target add-on package information using an intelligent recommendation algorithm; displaying the recommended add-on package information on a display page; or, in response to a matching failure, displaying the matching results on a display page.

[0064] For example, after a search feature value fails to match the project database, a smart recommendation algorithm can be used to generate recommended add-on package information based on the target add-on package information and display it on the display page to help users quickly find add-on packages that are close to their needs; or the page can directly prompt the matching failure result to guide users to adjust their selection or get further help.

[0065] The above implementation methods not only improve the user experience in the event of a failed match, preventing user churn due to the lack of matching results, but also effectively enhance the accuracy and convenience of the service through intelligent recommendation algorithms, thereby increasing user trust and satisfaction with the system.

[0066] In this embodiment of the application, the training dataset and annotation results can be input into the CRF model for supervised learning, thereby training an iterative target deep learning model.

[0067] Optionally, a smart recommendation algorithm is used to generate recommended add-on package information based on the target add-on package information, including: obtaining historical order data and determining user behavior information based on the historical order data; analyzing the target add-on package information and user behavior information through the smart recommendation algorithm to obtain recommended add-on package information, wherein the recommended add-on package information and the target add-on package information have at least one identical add-on package.

[0068] In this embodiment, the loss function is continuously reduced by repeatedly training the target deep learning model using the stochastic gradient descent algorithm, thus obtaining the iterative target deep learning model.

[0069] Please refer to Figure 3, which is a schematic block diagram of a medical examination order generation device provided in an embodiment of this application. This medical examination order generation device can be configured in a server to execute the aforementioned medical examination order generation method.

[0070] As shown in Figure 3, the physical examination order generation device 200 includes: an acquisition module 201, a feature extraction module 202, a matching module 203, and a physical examination order generation module 204.

[0071] The acquisition module 201 is used to acquire information on the target physical examination package and the target add-on package, wherein the target add-on package information includes several add-on packages selected by the user;

[0072] The feature extraction module 202 is used to extract features from the target add-on package information, arrange the target add-on packages according to preset rules, and generate retrieval feature values ​​corresponding to the target add-on package information;

[0073] The matching module 203 is used to match the search feature values ​​in the project database to obtain matching results. The project database includes several initial physical examination packages and search feature values ​​of several selectable initial supplementary packages corresponding to the initial physical examination packages.

[0074] The physical examination order generation module 204 is used to generate a target physical examination order based on the target add-on package information and the target physical examination package in response to the matching result being a successful match.

[0075] The feature extraction module 202 is further configured to perform preprocessing operations on the target add-on package information to obtain preprocessed target add-on package information; and to perform feature extraction on the preprocessed target add-on package information to obtain the target identifier code corresponding to the target add-on package information.

[0076] The feature extraction module 202 is further configured to sort the target identifier code according to priority order to obtain the sorted target identifier code; and to divide the sorted target identifier code by a preset separator to generate the retrieval feature value corresponding to the target add-on package information.

[0077] The matching module 203 is further configured to determine the similarity value between the search feature value and the project database using a cosine similarity algorithm; determine the matching result as a matching failure in response to the similarity value being greater than or equal to a preset similarity value; and determine the matching result as a matching success in response to the similarity value being less than the preset similarity value.

[0078] The matching module 203 is further configured to vectorize the retrieved feature values ​​using a bag-of-words model to obtain a target vector representation; traverse the item database and determine the initial similarity between the target vector representation and the item database; and determine the maximum value in the initial similarity as the similarity value.

[0079] The matching module 203 is further configured to, in response to the matching result being a matching failure, generate recommended add-on package information based on the target add-on package information using an intelligent recommendation algorithm; and display the recommended add-on package information through a display page; or, in response to the matching result being a matching failure, display the matching result through the display page.

[0080] The matching module 203 is also used to acquire historical order data and determine user behavior information based on the historical order data; and to analyze the target add-on package information and the user behavior information through the intelligent recommendation algorithm to obtain the recommended add-on package information, wherein the recommended add-on package information and the target add-on package information have at least one identical add-on package.

[0081] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the above-described apparatus and its modules and units can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0082] The methods and apparatus of this application can be used in a wide variety of general-purpose or special-purpose computing system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer terminal devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0083] For example, the above-described method and apparatus can be implemented as a computer program that can run on the computer device shown in FIG4.

[0084] Please refer to Figure 4, which is a schematic diagram of a computer device provided in an embodiment of this application. The computer device may be a server.

[0085] As shown in Figure 4, the computer device includes a processor, a memory, and a network interface connected via a system bus. The memory may include volatile storage media, non-volatile storage media, and internal memory.

[0086] Non-volatile storage media can store operating systems and computer programs. These computer programs include program instructions that, when executed, cause the processor to perform any method for generating medical examination orders.

[0087] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0088] Internal memory provides an environment for the execution of computer programs in non-volatile storage media. When the computer program is executed by the processor, it enables the processor to execute any method for generating medical examination orders.

[0089] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that the structure of this computer device is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0090] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0091] In some embodiments, the processor is used to acquire target health checkup packages and target add-on package information, wherein the target add-on package information includes several add-on packages selected by the user; to extract features from the target add-on package information and arrange the target add-on packages according to preset rules to generate retrieval feature values ​​corresponding to the target add-on package information; to match the retrieval feature values ​​in a project database to obtain matching results, wherein the project database includes several initial health checkup packages and several selectable initial add-on packages corresponding to the initial health checkup packages; and in response to the matching result being successful, to generate a target health checkup order based on the target add-on package information and the target health checkup package.

[0092] In some embodiments, the processor is further configured to perform preprocessing operations on the target add-on package information to obtain preprocessed target add-on package information; and to perform feature extraction on the preprocessed target add-on package information to obtain the target identifier code corresponding to the target add-on package information.

[0093] In some embodiments, the processor is further configured to sort the target identifier codes according to priority order to obtain sorted target identifier codes; and to segment the sorted target identifier codes by a preset delimiter to generate retrieval feature values ​​corresponding to the target add-on package information.

[0094] In some implementations, the processor is further configured to determine the similarity value between the retrieved feature value and the item database using a cosine similarity algorithm; determine the matching result as a matching failure in response to the similarity value being greater than or equal to a preset similarity value; and determine the matching result as a matching success in response to the similarity value being less than the preset similarity value.

[0095] In some implementations, the processor is further configured to vectorize the retrieved feature values ​​using a bag-of-words model to obtain a target vector representation; traverse the item database and determine the initial similarity between the target vector representation and the item database; and determine the maximum value in the initial similarity as the similarity value.

[0096] In some implementations, the processor is further configured to, in response to the matching result being a match failure, generate recommended add-on package information based on the target add-on package information using an intelligent recommendation algorithm; display the recommended add-on package information through a display page; or, in response to the matching result being a match failure, display the matching result through the display page.

[0097] In some implementations, the processor is further configured to acquire historical order data and determine user behavior information based on the historical order data; and to analyze the target add-on package information and the user behavior information through the intelligent recommendation algorithm to obtain the recommended add-on package information, wherein the recommended add-on package information and the target add-on package information have at least one identical add-on package.

[0098] This application also provides a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed, implement any of the physical examination order generation methods provided in this application.

[0099] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0100] Furthermore, the computer-readable storage medium may primarily include a program storage area and a data storage area, wherein the program storage area may store the operating system, at least one application program required for a function, etc.

[0101] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for generating a physical examination order, characterized in that, The method includes: acquiring target health checkup packages and target add-on package information, wherein the target add-on package information includes several add-on packages selected by the user; extracting features from the target add-on package information and arranging the target add-on packages according to preset rules to generate retrieval feature values ​​corresponding to the target add-on package information; wherein the retrieval feature value is a unique identifier used for matching in a project database, generated by extracting attributes from the target add-on package information and converting them into structured data; using the retrieval feature value to match in the project database to obtain a matching result, wherein the project database includes several initial health checkup packages and retrieval feature values ​​of several selectable initial add-on packages corresponding to the initial health checkup packages; in response to the matching result being a successful match, generating a target health checkup order based on the target add-on package information and the target health checkup package.

2. The method according to claim 1, characterized in that, The step of extracting features from the target add-on package information includes: performing a preprocessing operation on the target add-on package information to obtain preprocessed target add-on package information; and performing feature extraction on the preprocessed target add-on package information to obtain the target identifier code corresponding to the target add-on package information.

3. The method according to claim 2, characterized in that, The step of arranging the target add-on packages according to preset rules and generating retrieval feature values ​​corresponding to the target add-on package information includes: sorting the target identifier codes according to priority order to obtain sorted target identifier codes; and dividing the sorted target identifier codes by a preset separator to generate retrieval feature values ​​corresponding to the target add-on package information.

4. The method according to claim 1, characterized in that, The step of matching the search feature value in the project database to obtain a matching result includes: determining the similarity value between the search feature value and the project database using a cosine similarity algorithm; determining the matching result as a matching failure in response to the similarity value being greater than or equal to a preset similarity value; and determining the matching result as a matching success in response to the similarity value being less than the preset similarity value.

5. The method according to claim 4, characterized in that, The step of determining the similarity value between the search feature value and the item database using the cosine similarity algorithm includes: vectorizing the search feature value using a bag-of-words model to obtain a target vector representation; traversing the item database and determining the initial similarity between the target vector representation and the item database; and determining the maximum value in the initial similarity as the similarity value.

6. The method according to claim 1, characterized in that, After matching the project database using the retrieval feature values ​​to obtain the matching result, the method further includes: in response to the matching result being a matching failure, generating recommended add-on package information based on the target add-on package information using an intelligent recommendation algorithm; displaying the recommended add-on package information on a display page; or, in response to the matching result being a matching failure, displaying the matching result on the display page.

7. The method according to claim 6, characterized in that, The step of generating recommended add-on package information based on the target add-on package information using an intelligent recommendation algorithm includes: acquiring historical order data and determining user behavior information based on the historical order data; analyzing the target add-on package information and the user behavior information using the intelligent recommendation algorithm to obtain the recommended add-on package information, wherein the recommended add-on package information and the target add-on package information have at least one identical add-on package.

8. A device for generating physical examination orders, characterized in that, The physical examination order generation device includes: an acquisition module for acquiring target physical examination packages and target add-on package information, wherein the target add-on package information includes several add-on packages selected by the user; a feature extraction module for extracting features from the target add-on package information and arranging the target add-on packages according to preset rules to generate retrieval feature values ​​corresponding to the target add-on package information; wherein the retrieval feature values ​​are unique identifiers used for matching in the project database, generated by extracting attributes from the target add-on package information and converting them into structured data; a matching module for matching in the project database using the retrieval feature values ​​to obtain matching results, wherein the project database includes several initial physical examination packages and retrieval feature values ​​of several selectable initial add-on packages corresponding to the initial physical examination packages; and a physical examination order generation module for generating a target physical examination order based on the target add-on package information and the target physical examination package in response to the matching result being successful.

9. A computer device, characterized in that, include: A memory and a processor; wherein the memory is connected to the processor and is used to store a program, and the processor is used to implement the steps of the physical examination order generation method as described in any one of claims 1-7 by running the program stored in the memory.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to implement the steps of the physical examination order generation method as described in any one of claims 1-7.

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