Business processing method, device, electronic device and computer-readable medium

Abstract

This application discloses a business processing method, apparatus, electronic device, and computer-readable medium, relating to the field of computer technology. A specific implementation method includes obtaining a user identifier and corresponding historical detection data in response to a preset mechanism being triggered; determining a customization dimension based on the historical detection data; determining the similarity between the customization dimension and process nodes of each preset dimension of a service plan output model; determining a target process node based on the similarity, and then adding the customization dimension to the execution program of the target process node to generate a corresponding service plan and output the service plan. This method can quickly and accurately provide users with personalized services, improving service efficiency.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a business processing method, device, electronic device, and computer-readable medium. Background Art

[0002] At present, in the scenario of basic long-term care, the contents of the long-term care service lists stipulated by various places are mainly general service items proposed based on local service capabilities and service status. The levels of nursing staff are uneven, and the efficient quality of nursing services cannot be guaranteed. Summary of the Invention

[0003] In view of this, the embodiments of the present application provide a business processing method, device, electronic device and computer-readable medium, which can solve the technical problems of uneven nursing service levels and low service efficiency in existing nursing services.

[0004] To achieve the above objectives, according to one aspect of an embodiment of the present application, a service processing method is provided, including:

[0005] In response to a preset mechanism being triggered, obtaining a user identifier and corresponding historical detection data;

[0006] Determine customization dimensions based on historical test data;

[0007] Determine the similarity between the customized dimension and the process nodes of each preset dimension of the service plan output model;

[0008] The target process node is determined based on the similarity, and the customized dimension is added to the execution program of the target process node to generate a corresponding service plan and output the service plan.

[0009] Optionally, based on historical detection data, determine the customized dimensions, including:

[0010] Get the current detection data corresponding to the user ID;

[0011] Generate user profiles based on historical and current test data;

[0012] Determine service dimensions based on user portraits;

[0013] Compare the service dimensions with the preset dimensions to determine the custom dimensions.

[0014] Optionally, generate a user profile, including:

[0015] Extract high-level features and low-level features from historical detection data and current detection data, and then generate fusion features;

[0016] Generate user profiles based on fused features.

[0017] Optionally, before obtaining the user identifier and the corresponding historical detection data, the method further includes:

[0018] Get the number of identifiers that mark the same user;

[0019] In response to the number of identifications exceeding a preset threshold, a preset mechanism is triggered.

[0020] Optionally, determining a target process node based on similarity includes:

[0021] In response to the similarity being lower than a preset threshold, determining a business type of the customized dimension, and determining a corresponding group according to the business type;

[0022] Get the full dimensions in the group and determine the target process node based on the similarity between the custom dimension and each process node of the full dimension.

[0023] Optionally, determining a target process node based on similarity includes:

[0024] In response to the similarity being lower than a preset threshold, determining a business type corresponding to the customized dimension, and then determining detailed data corresponding to the preset dimension based on the business type;

[0025] Determine the target field item based on the similarity between the custom dimension and each field item corresponding to the detailed data;

[0026] The process node corresponding to the target field item is determined as the target process node.

[0027] Optionally, generate a corresponding service plan, including:

[0028] Get the idle time corresponding to the user ID;

[0029] Grouping the current detection data according to preset dimensions to generate grouped data;

[0030] Determine the service duration corresponding to the packet data, and then determine the service time corresponding to the packet data based on the idle time and the service duration;

[0031] Generate a corresponding service plan based on the group data and service time.

[0032] Optionally, output a service plan including:

[0033] Determine the target execution user and send the service plan to the terminal corresponding to the target execution user;

[0034] Determining the target execution user includes: decomposing the service plan into service tasks according to preset dimensions; determining the task service time corresponding to each service task, and then determining the corresponding target execution user according to the preset dimensions and the task service time.

[0035] In addition, the present application also provides a service processing device, including:

[0036] an acquiring unit, configured to acquire a user identifier and corresponding historical detection data in response to a preset mechanism being triggered;

[0037] a customization dimension determination unit, configured to determine the customization dimension based on historical detection data;

[0038] a similarity determination unit configured to determine the similarity between the customized dimension and the process nodes of each preset dimension of the service plan output model;

[0039] The service plan output unit is configured to determine the target process node according to the similarity, and then add the customized dimension to the execution program of the target process node to generate a corresponding service plan and output the service plan.

[0040] Optionally, the custom dimension determination unit is further configured to:

[0041] Get the current detection data corresponding to the user ID;

[0042] Generate user profiles based on historical and current test data;

[0043] Determine service dimensions based on user portraits;

[0044] Compare the service dimensions with the preset dimensions to determine the custom dimensions.

[0045] Optionally, the custom dimension determination unit is further configured to:

[0046] Extract high-level features and low-level features from historical detection data and current detection data, and then generate fusion features;

[0047] Generate user profiles based on fused features.

[0048] Optionally, the acquiring unit is further configured to:

[0049] Get the number of identifiers that mark the same user;

[0050] In response to the number of identifications exceeding a preset threshold, a preset mechanism is triggered.

[0051] Optionally, the service plan output unit is further configured to:

[0052] In response to the similarity being lower than a preset threshold, determining a business type of the customized dimension, and determining a corresponding group according to the business type;

[0053] Get the full dimensions in the group and determine the target process node based on the similarity between the custom dimension and each process node of the full dimension.

[0054] Optionally, the service plan output unit is further configured to:

[0055] In response to the similarity being lower than a preset threshold, determining a business type corresponding to the customized dimension, and then determining detailed data corresponding to the preset dimension based on the business type;

[0056] Determine the target field item based on the similarity between the custom dimension and each field item corresponding to the detailed data;

[0057] The process node corresponding to the target field item is determined as the target process node.

[0058] Optionally, the service plan output unit is further configured to:

[0059] Get the idle time corresponding to the user ID;

[0060] Grouping the current detection data according to preset dimensions to generate grouped data;

[0061] Determine the service duration corresponding to the packet data, and then determine the service time corresponding to the packet data based on the idle time and the service duration;

[0062] Generate a corresponding service plan based on the group data and service time.

[0063] Optionally, the service plan output unit is further configured to:

[0064] Determine the target execution user and send the service plan to the terminal corresponding to the target execution user;

[0065] Among them, determining the target execution user is configured to: decompose the service plan into service tasks according to preset dimensions; determine the task service time corresponding to each service task, and then determine the corresponding target execution user according to the preset dimensions and the task service time.

[0066] In addition, the present application also provides a business processing electronic device, including: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by one or more processors, the one or more processors implement the business processing method as described above.

[0067] In addition, the present application also provides a computer-readable medium on which a computer program is stored, and when the program is executed by a processor, the business processing method as described above is implemented.

[0068] One embodiment of the above invention has the following advantages or beneficial effects: in response to a preset mechanism being triggered, the present application obtains a user identifier and corresponding historical detection data; determines a customization dimension based on the historical detection data; determines the similarity between the customization dimension and the process nodes of each preset dimension of the service plan output model; determines a target process node based on the similarity, and then adds the customization dimension to the execution program of the target process node to generate a corresponding service plan and output the service plan. This allows for the rapid and accurate provision of personalized services to users, improving service efficiency.

[0069] The further effects of the above-mentioned non-conventional optional manner will be described below in conjunction with specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The accompanying drawings are provided to facilitate a better understanding of the present application and do not constitute an undue limitation on the present application.

[0071] Figure 1 This is a schematic diagram of the main process of a business processing method provided according to an embodiment of the present application;

[0072] Figure 2 This is a schematic diagram of the main process of a business processing method provided according to an embodiment of the present application;

[0073] Figure 3 This is a schematic diagram of the main flow of a business processing method provided according to an embodiment of the present application;

[0074] Figure 4 This is a schematic diagram of an application scenario of a business processing method provided according to an embodiment of the present application;

[0075] Figure 5 is a schematic diagram of the main units of the service processing device according to an embodiment of the present application;

[0076] Figure 6 is an exemplary system architecture diagram to which embodiments of the present application may be applied;

[0077] Figure 7 It is a structural diagram of a computer system of a terminal device or server suitable for implementing an embodiment of the present application. DETAILED DESCRIPTION

[0078] The following describes exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those skilled in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description. The acquisition, storage, use, processing, etc. of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0079] Figure 1 This is a schematic diagram of the main process of the business processing method provided according to an embodiment of the present application. Figure 1 As shown, the business processing method includes:

[0080] Step S101 : in response to a preset mechanism being triggered, obtaining a user identification and corresponding historical detection data.

[0081] In this embodiment, the execution subject of the business processing method (for example, it can be a server) can detect whether the preset mechanism is triggered to determine whether to obtain the user identification and the corresponding historical detection data. Specifically, before obtaining the user identification and the corresponding historical detection data, the method also includes: obtaining the number of identifications that mark the same user; in response to the number of identifications exceeding the preset threshold, triggering the preset mechanism. For example, when the number of identifications marked by a user (for example, an insured person of an insurance institution) exceeds the preset threshold, for example, more than 5 identifications, and the identification can be, for example, gastritis, hepatitis, heart failure, poor coagulation function, allergies, muscle weakness, etc., then the execution subject can trigger the preset mechanism to obtain the user identification corresponding to the user, the user identification can be, for example, the user name or user number, etc., and the embodiment of the present application does not specifically limit the user identification. In addition, the execution subject can obtain the corresponding historical detection data based on the user identification. The historical detection data can, for example, be the user's information in the following examples: Figure 4 The detection end shown generates data when performing physical examinations before the current time. The historical detection data includes health detection data of various organs of the body, mental health detection data, endocrine detection data, etc. The embodiment of this application does not specifically limit the content of the historical detection data.

[0082] Step S102: Determine the customization dimension based on historical detection data.

[0083] For example, historical test data may contain an identifier corresponding to a custom dimension, such as DZ. The presence of this identifier in the historical test data can be used to determine whether the custom dimension exists. Custom dimensions may include, for example, a dimension for a specific organ indicator (e.g., an endocrine indicator for organ A) or a dimension for the size of a nodule in organ B.

[0084] Step S103 : determining the similarity between the process nodes of the customized dimension and each preset dimension of the service plan output model.

[0085] The execution entity can determine the attributes of the process nodes for each preset dimension of the service plan output model. These attributes can include the business type or detailed data for each preset dimension. Business types can include internal medicine business, surgical business, general medicine business, etc. Detailed data can include disease type, disease severity classification, qualified indicator range data, etc. for each preset dimension. This embodiment of the application does not specifically limit the detailed data.

[0086] The execution entity may calculate the similarity between the customized dimension and the business type or detailed data in the attributes of the process nodes of each preset dimension of the service plan output model to obtain each similarity. Specifically, the similarity may be calculated between the business type or detailed data corresponding to the customized dimension and the business type or detailed data in the attributes of the process nodes of each preset dimension of the service plan output model by word embedding to obtain each similarity. For example, the business type corresponding to the customized dimension may be converted into a first business type vector by word embedding, and the business type in the attributes of the process nodes of each preset dimension of the service plan output model may be converted into each second business type vector by word embedding, and the similarity between the first business type vector and each second business type vector may be calculated to obtain the similarity between the customized dimension and the process nodes of each preset dimension of the service plan output model.

[0087] Step S104 , determining a target process node based on the similarity, and then adding the customized dimension to the execution program of the target process node to generate a corresponding service plan, and outputting the service plan.

[0088] Specifically, the target process node is determined based on the similarity, including: in response to the similarity being lower than a preset threshold, determining the business type corresponding to the custom dimension, and then determining the detailed data corresponding to the preset dimension based on the business type; determining the target field item based on the similarity between the custom dimension and each field item corresponding to the detailed data; and determining the process node corresponding to the target field item as the target process node.

[0089] When the similarity between the customized dimension and the process nodes of each preset dimension of the service plan output model is lower than the preset threshold, the detailed data corresponding to the preset dimension corresponding to the business type (such as thyroid-endocrinology business) can be obtained through the business type corresponding to the customized dimension (such as thyroid-endocrinology business). The embodiment of the present application does not specifically limit the detailed data. Customized dimensions, such as human thyroid function. The target field item is determined by the similarity between the customized dimension (such as thyroid function) and the detailed data (such as thyroid hormone detection value, thyroid stimulating hormone detection value). For example, if the customized dimension is hypothyroidism, the similar detailed data needs to have the characteristics that the thyroid hormone detection value is lower than the preset threshold and the thyroid stimulating hormone detection value is higher than the preset threshold, and the field item with this feature is determined as the target field item. The process node corresponding to the target field item is determined as the target process node.

[0090] Specifically, a corresponding service plan is generated, including: obtaining the free time corresponding to the user identifier. For example, the user identifier can be the number of the nurse involved in the nursing. The executing entity can query the free time corresponding to the number of the nurse involved in the nursing. The free time can be, for example, the duty time of the nurse involved in the nursing; the current detection data is grouped according to preset dimensions (preset dimensions, such as acupoint health care, dietary conditioning, daily life guidance, music conditioning, risk warnings) to generate grouped data. Specifically, it can be to call the preset data-dimension key-value pair library to determine the corresponding preset dimension according to the current detection data, and then obtain each group data according to the corresponding preset dimension; determine the service time corresponding to the group data, for example, determine the service time required for the nurses involved in the care in the dimensions of acupoint health care, dietary conditioning, daily life guidance, music conditioning, and risk warning, and then determine the service time corresponding to the group data according to the idle time and service time. Specifically, it can be to determine the service time corresponding to the group data from the idle time according to the service time. For example, if the idle time is business 8:00-11:00 and the service time is 1 hour, then the corresponding service time of the group data can be 8:00-9:00 in the morning, or 9:00-10:00 in the morning, or 10:00-11:00 in the morning; generate a corresponding service plan (for example, acupoint health care, nurse A, 8:00-9:00 in the morning) according to the group data (for example, acupoint health care) and service time (for example, 8:00-9:00 in the morning).

[0091] Specifically, outputting a service plan includes: determining a target execution user (e.g., nurse A), sending a service plan (e.g., acupoint health care, nurse A, 8:00-9:00 a.m.) to a terminal (e.g., a mobile phone, tablet computer, or desktop computer) corresponding to the target execution user (e.g., nurse A), and the embodiment of the present application does not specifically limit the type of terminal; wherein, determining a target execution user includes: decomposing a service plan into service tasks according to a preset dimension, a service plan may be discontinuous, and the execution subject may decompose the service plan into a corresponding number of service tasks according to the discontinuous nodes of the service plan, for example, if the service plan is acupoint health care, nurse A, 8:00-8:12, 8:15-8:30, 8:36-9:00 a.m., then the service tasks decomposed into The task can be a service plan of acupoint health care, nurse A, 8:00-8:12 in the morning, acupoint health care, nurse A, 8:15-8:30, acupoint health care, nurse A, 8:36-9:00; determine the task service time corresponding to each service task, such as 8:00-8:12, 8:15-8:30, 8:36-9:00 in the morning, and then determine the corresponding target execution user according to the preset dimension and task service time. Specifically, the target execution user can include multiple users, for example, multiple nurses. The free time of the nurses who can perform acupoint health care is matched with 8:00-8:12, 8:15-8:30, 8:36-9:00 in the morning, and the matched one or more nurses are determined as the target execution users.

[0092] This embodiment obtains a user ID and corresponding historical test data in response to a preset mechanism being triggered; determines a customization dimension based on the historical test data; determines the similarity between the customization dimension and the process nodes of each preset dimension in the service plan output model; determines a target process node based on the similarity; then adds the customization dimension to the execution program of the target process node to generate and output a corresponding service plan. This allows for the rapid and accurate provision of personalized services to users, improving service efficiency.

[0093] Figure 2 This is a schematic diagram of the main flow of a business processing method provided according to an embodiment of the present application. Figure 2 As shown, the business processing method includes:

[0094] Step S201: in response to a preset mechanism being triggered, obtaining a user identification and corresponding historical detection data.

[0095] When the number of identifiers marked on a user exceeds a preset threshold, a preset mechanism is triggered. The preset mechanism is to obtain the user identifier, such as the user number, and obtain the historical detection data corresponding to the user identifier.

[0096] Step S202: Acquire current detection data corresponding to the user identification.

[0097] The current test data includes health test data of various organs of the user's (e.g., insured person), mental health test data, and endocrine test data.

[0098] Step S203: Generate a user portrait based on historical detection data and current detection data.

[0099] The execution entity can merge the historical detection data and the current detection data to generate deduplicated merged data, and then generate a user profile based on the merged data.

[0100] Specifically, generating a user profile involves extracting high-level and low-level features from historical and current detection data to generate fused features. Specifically, extracting high-level and low-level features from the merged data to generate fused features of the high-level and low-level features. User profiles are generated based on the fused features. High-level features are abstract, while low-level features are concrete. High-level features can be extracted using the deep neural network of the feature extraction network based on the merged data, while low-level features can be extracted using the shallow neural network of the feature extraction network based on the merged data.

[0101] Step S204: Determine the service dimension based on the user portrait.

[0102] By analyzing user portraits, we can determine user tags, such as stomach disease, liver disease, heart disease, endocrine disease, acupoint health care, dietary conditioning, daily life guidance, music conditioning, risk warnings, etc. Each user tag represents a service dimension.

[0103] Step S205 : Compare the service dimension with the preset dimension to determine the customized dimension.

[0104] The executing entity can compare the service dimensions (such as stomach disease, liver disease, heart disease, endocrine disease, acupoint health care, dietary conditioning, daily life guidance, music conditioning, and risk warnings) with the preset dimensions (such as endocrine disease, acupoint health care, dietary conditioning, daily life guidance, music conditioning, and risk warnings) to determine the customized dimensions, such as stomach disease, liver disease, heart disease, and endocrine disease.

[0105] Step S206 : determining the similarity between the process nodes of the customized dimension and each preset dimension of the service plan output model.

[0106] For example, the execution entity can determine the similarity of the attributes corresponding to the process nodes of customized dimensions, such as gastric disease monitoring, liver disease monitoring, heart disease monitoring, endocrine disease monitoring and each preset dimension of the service plan output model (such as acupoint health care, dietary conditioning, daily life guidance, music conditioning and risk warning) (the attribute can include, for example, detailed data of each preset dimension, business type, etc.). For example, the similarity between gastric disease monitoring and the detailed data of dietary conditioning (for example, it can include stomach monitoring data during dietary conditioning) can be calculated to obtain the corresponding similarity, or the similarity between gastric disease and stomach monitoring data during acupoint health care can be calculated to obtain the corresponding similarity. When performing the similarity calculation, the gastric disease can be converted into a first vector by word embedding, and the detailed data, business type, etc. of each preset dimension can be converted into each second vector and third vector. The similarity between the first vector and each second vector and each third vector can be calculated respectively to obtain the similarity between the customized dimension and the process nodes of each preset dimension of the service plan output model.

[0107] Step S207 , determining the target process node according to the similarity, and then adding the customized dimension to the execution program of the target process node to generate a corresponding service plan, and outputting the service plan.

[0108] The obtained similarities are sorted in descending order, and the process nodes corresponding to the first N similarities arranged in sequence are determined as target process nodes. The number of target process nodes can be one or more. Custom dimensions, such as gastric disease monitoring, liver disease monitoring, heart disease monitoring, and endocrine disease monitoring, are partially or completely added to the execution program of the target process node to generate a corresponding service plan. For example, the target process node: gastric disease conditioning diet-aerobic exercise, gastric disease monitoring is added to the target process node to obtain the service plan: stomach conditioning diet-aerobic exercise-gastric disease monitoring. This can provide users with personalized services quickly and accurately, and improve service efficiency.

[0109] Figure 3 This is a schematic diagram of the main flow of a business processing method provided according to an embodiment of the present application. Figure 3 As shown, the business processing method includes:

[0110] Step S301: in response to a preset mechanism being triggered, obtaining a user identification and corresponding historical detection data.

[0111] When a user (e.g., an insured person of an insurance institution) is tagged with more than a preset threshold, for example, more than five identifiers, such as gastritis, hepatitis, heart failure, poor coagulation function, allergies, and muscle weakness, the execution entity can trigger a preset mechanism to obtain the user identifier corresponding to the user, and then obtain the user identifier and the corresponding historical test data.

[0112] Step S302: Acquire current detection data corresponding to the user identification.

[0113] The current detection data may be the user's body detection data within a preset time period, and may include, for example, organ detection data, endocrine detection data, etc. The embodiment of the present application does not specifically limit the time and content of the current detection data.

[0114] Step S303: Generate a user portrait based on historical detection data and current detection data.

[0115] The execution entity may use historical detection data alone to generate a first user profile, and use current detection data to generate a second user profile.

[0116] Step S304: Determine the service dimension based on the user portrait.

[0117] Determine the service dimension based on the first user profile and the second user profile. Specifically, a first user tag corresponding to the first user profile and a second user tag corresponding to the second user profile may be determined, and the service dimension may be determined based on the first user tag and the second user tag. Specifically, the first user tag and the second user tag may be deduplicated, and the deduplicated user tags may be used as the corresponding service dimension.

[0118] Step S305 : Compare the service dimension with the preset dimension to determine the customized dimension.

[0119] Specifically, the service dimension and the preset dimension may be deduplicated, and the remaining dimensions in the service dimension may be determined as customized dimensions.

[0120] Step S306 : determining the similarity between the process nodes of the customized dimension and each preset dimension of the service plan output model.

[0121] The similarity between the process nodes of the customized dimension and each preset dimension of the service plan output model is calculated by word embedding.

[0122] Step S307 : In response to the similarity being lower than a preset threshold, determining the business type of the customized dimension, and determining the corresponding grouping according to the business type.

[0123] When the similarity between the process nodes of the customized dimension and each preset dimension of the service plan output model is below a preset threshold, for example, below 0.5, the execution entity can determine the corresponding business type from the dimension-business type key-value pair database based on the customized dimension, and then determine the corresponding group based on the business type. Specifically, the customized dimension can be grouped into groups belonging to the same business type.

[0124] Step S308: Obtain the full dimension in the group, and determine the target process node based on the similarity between the customized dimension and each process node of the full dimension.

[0125] A group can include one or more dimensions. After determining the group corresponding to a custom dimension, the execution entity can obtain all dimensions in that group. The full dimension refers to the set of all dimensions in the group. The custom dimension is converted into a custom vector through word embedding, and each process node of the full dimension is converted into a full vector. The similarity between the custom vector and each full vector is calculated to obtain the similarity between the custom dimension and each process node of the full dimension. The target process node is determined based on the obtained similarity. The number of target process nodes can be one or more.

[0126] Step S309: Add the customized dimension to the execution program of the target process node to generate a corresponding service plan and output the service plan.

[0127] Determine the position of the custom dimension in the target process node, and then insert the custom dimension into the corresponding position of the execution program of the target process node according to the position to generate a corresponding service plan and output it.

[0128] This enables users to be provided with personalized services quickly and accurately, thereby improving service efficiency.

[0129] Figure 4 This is a schematic diagram of an application scenario of a business processing method provided according to an embodiment of the present application. The business processing method of the embodiment of the present application can be applied to the care scenario of the insured person. Figure 4As shown, the insured scans the QR code on the e-body machine's testing terminal through the user terminal to undergo testing, and the test data is uploaded to the e-body machine cloud service platform. The test data is used in the e-body machine cloud platform's knowledge base to generate health indicators in five dimensions, including acupoint health care for the twelve meridians, dietary adjustments, daily living guidance, music therapy, and risk warnings. Finally, combined with the baseline test data, a test report is generated. The long-term care insured's health indicators are synchronized to the long-term care cloud platform via the server and linked to the insured. The insured can view their test report through the app or the e-body machine. The long-term care cloud platform service generates a care plan for the insured using the care engine. Based on the standard care plan, the care engine checks whether the insured has the health indicators detected by the e-body machine. If the insured has the test data for acupoint health care, music therapy, daily living guidance, etc., the engine adds the corresponding care services to the care plan. The facility manager then makes appropriate adjustments and generates the final care plan. The facility manager breaks down the care plan into care tasks and assigns them to caregivers (such as nurses). Caregivers view and receive nursing tasks through the institution's app. On the task details page, they can view all nursing service items. For services linked to health test data, they can click on the detailed instructions to view health service tips for that service. For example, for acupoint massage, they can see the specific acupoint selection method and massage instructions. With the insured person's consent, the caregiver can view the insured person's detailed test report, gaining a more detailed understanding of the insured person's physical condition and, through health warnings, paying closer attention to the insured person during care.

[0130] The embodiment of the present application can install a health detection e-body machine in the nursing service agency. The insured person scans the code to conduct the test, and a test report is generated after the test. During the nursing task on the second day, the caregiver can see the insured person's health care service items, and through health instructions, perform professional acupoint massage on the insured person under health guidance, and improve the insured person's physical condition through traditional Chinese medicine massage techniques, greatly improving the quality of nursing services. By combining the health detection e-body machine with long-term care (i.e., long-term care) services, the service level of nursing staff can be improved, the overall quality of nursing services can be improved, the physical condition of the insured person can be effectively promoted, and the long-term care fund expenditure can be saved from the side.

[0131] Figure 5 Schematic diagram of the main units of the service processing device according to the embodiment of the present application. Figure 5 As shown, the business processing device 500 includes an acquisition unit 501, a customization dimension determination unit 502, a similarity determination unit 503 and a service plan output unit 503.

[0132] The acquisition unit 501 is configured to acquire a user identifier and corresponding historical detection data in response to a preset mechanism being triggered;

[0133] A customization dimension determination unit 502 is configured to determine a customization dimension based on historical detection data;

[0134] A similarity determination unit 503 is configured to determine the similarity between the customized dimension and the process nodes of each preset dimension of the service plan output model;

[0135] The service plan output unit 504 is configured to determine a target process node according to the similarity, and then add the customized dimension to the execution program of the target process node to generate a corresponding service plan and output the service plan.

[0136] In some embodiments, the customized dimension determination unit 502 is further configured to: obtain current detection data corresponding to the user identifier; generate a user portrait based on historical detection data and current detection data; determine the service dimension based on the user portrait; compare the service dimension with the preset dimension to determine the customized dimension.

[0137] In some embodiments, the customized dimension determination unit 502 is further configured to: extract high-level features and low-level features from historical detection data and current detection data, and then generate fused features; and generate a user profile based on the fused features.

[0138] In some embodiments, the acquisition unit 501 is further configured to: acquire the number of identifiers that mark the same user; and trigger a preset mechanism in response to the number of identifiers exceeding a preset threshold.

[0139] In some embodiments, the service plan output unit 504 is further configured to: in response to the similarity being lower than a preset threshold, determine the business type of the customized dimension, and determine the corresponding grouping according to the business type; obtain the full dimension in the grouping, and determine the target process node according to the similarity between the customized dimension and each process node of the full dimension.

[0140] In some embodiments, the service plan output unit 504 is further configured to: in response to the similarity being lower than a preset threshold, determine the business type corresponding to the custom dimension, and then determine the detailed data corresponding to the preset dimension based on the business type; determine the target field item based on the similarity between the custom dimension and each field item corresponding to the detailed data; and determine the process node corresponding to the target field item as the target process node.

[0141] In some embodiments, the service plan output unit 504 is further configured to: obtain the idle time corresponding to the user identifier; group the current detection data according to preset dimensions to generate grouped data; determine the service duration corresponding to the grouped data, and then determine the service time corresponding to the grouped data based on the idle time and the service duration; generate a corresponding service plan based on the grouped data and the service time.

[0142] In some embodiments, the service plan output unit 504 is further configured to: determine the target execution user and send the service plan to the terminal corresponding to the target execution user; wherein, determining the target execution user is configured to: decompose the service plan into service tasks according to preset dimensions; determine the task service time corresponding to each service task, and then determine the corresponding target execution user according to the preset dimensions and task service time.

[0143] It should be noted that the business processing method and business processing device of the present application have a corresponding relationship in terms of specific implementation content, so the repeated content will not be explained again.

[0144] Figure 6 An exemplary system architecture 600 is shown to which the service processing method or service processing apparatus according to the embodiments of the present application can be applied.

[0145] like Figure 6 As shown, system architecture 600 may include terminal devices 601, 602, 603, a network 604, and a server 605. Network 604 is used to provide a medium for communication links between terminal devices 601, 602, 603 and server 605. Network 604 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0146] Users can use terminal devices 601, 602, and 603 to interact with server 605 via network 604 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 601, 602, and 603, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0147] The terminal devices 601, 602, and 603 may be various electronic devices having a credit authorization inquiry processing screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0148] Server 605 may be a server that provides various services, such as a background management server (for example only) that supports tasks submitted by users using terminal devices 601, 602, and 603. The background management server may scan and process received automatic tasks, determine the number of executable automatic tasks, and switch scanning modes based on the number of executable automatic tasks to conserve system resources and increase the processing speed of a large number of automatic tasks.

[0149] It should be noted that the business processing method provided in the embodiment of the present application is generally executed by the server 605, and accordingly, the business processing device is generally set in the server 605.

[0150] It should be understood that Figure 6 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0151] Reference below Figure 7 , which shows a structural diagram of a computer system 700 of a terminal device suitable for implementing an embodiment of the present application. Figure 7 The terminal device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0152] like Figure 7 As shown, the computer system 700 includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage unit 708 into a random access memory (RAM) 703. Various programs and data required for the operation of the computer system 700 are also stored in the RAM 703. The CPU 701, ROM 702, and RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0153] The following components are connected to the I / O interface 705: an input section 706 including a keyboard, a mouse, and the like; an output section 707 including displays such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card or a modem. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. Removable media 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, and the like, is installed in the drive 710 as needed, so that computer programs read therefrom can be installed into the storage section 708 as needed.

[0154] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 709, and / or installed from a removable medium 711. When the computer program is executed by the central processing unit (CPU) 701, the above-mentioned functions defined in the system of the present application are executed.

[0155] It should be noted that the computer-readable medium described in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. Computer-readable storage media can include, for example, but are not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or devices, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, or any suitable combination thereof.

[0156] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of the boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0157] The units involved in the embodiments described in this application can be implemented by software or hardware. The units described can also be set in a processor. For example, it can be described as: a processor includes an acquisition unit, a customization dimension determination unit, a similarity determination unit, and a service plan output unit. In some cases, the names of these units do not constitute a limitation on the units themselves.

[0158] As another aspect, the present application also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently and not be assembled into the device. The above computer-readable medium carries one or more programs. When the above one or more programs are executed by a device, the device is triggered in response to a preset mechanism, obtains a user identifier and corresponding historical detection data; determines a customized dimension based on the historical detection data; determines the similarity between the customized dimension and the process nodes of each preset dimension of the service plan output model; determines the target process node based on the similarity, and then adds the customized dimension to the execution program of the target process node to generate a corresponding service plan and output the service plan.

[0159] According to the technical solutions of the embodiments of the present application, personalized services can be provided to users quickly and accurately, thereby improving service efficiency.

[0160] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A business processing method, characterized in that: include: In response to a preset mechanism being triggered, obtaining a user identifier and corresponding historical detection data; Determining the customization dimension based on the historical detection data includes: obtaining current detection data corresponding to the user identifier; generating a user profile based on the historical detection data and the current detection data, including: extracting high-level features and low-level features from the historical detection data and the current detection data, thereby generating fused features, and generating a user profile based on the fused features; determining a service dimension based on the user profile; and comparing the service dimension with a preset dimension to determine the customization dimension; Determining the similarity between the customized dimension and the process nodes of each preset dimension of the service plan output model; Determining a target process node based on the similarity includes: in response to the similarity being lower than a preset threshold, determining a business type of the customized dimension, and determining a corresponding group based on the business type; wherein, based on the customized dimension, a corresponding business type is determined from a dimension-business type key-value pair database, and the corresponding group is determined based on the business type; Obtaining the full dimensions in the group, and determining the target process node based on the similarity between the customized dimension and each process node of the full dimension; wherein, after determining the group corresponding to the customized dimension, obtaining the full dimensions in the group, where the full dimensions refer to the set of all dimensions in the group; The customized dimension is then added to the execution program of the target process node to generate a corresponding service plan, and the service plan is output.

2. The method according to claim 1, characterized in that Before obtaining the user identifier and the corresponding historical detection data, the method further includes: Get the number of identifiers that mark the same user; In response to the number of identifications exceeding a preset threshold, a preset mechanism is triggered.

3. The method according to claim 1, characterized in that Determining the target process node according to the similarity includes: In response to the similarity being lower than a preset threshold, determining a business type corresponding to the customized dimension, and then determining detailed data corresponding to the preset dimension according to the business type; Determine a target field item based on the similarity between the customized dimension and each field item corresponding to the detailed data; The process node corresponding to the target field item is determined as the target process node.

4. The method according to claim 1, wherein The generating of the corresponding service plan includes: Obtaining the idle time corresponding to the user ID; Grouping the current detection data according to preset dimensions to generate grouped data; determining a service duration corresponding to the packet data, and further determining a service time corresponding to the packet data according to the idle time and the service duration; A corresponding service plan is generated according to the grouping data and the service time.

5. The method according to claim 1, wherein The outputting of the service plan includes: Determine the target execution user, and send the service plan to the terminal corresponding to the target execution user; The determining of the target execution user includes: decomposing the service plan into service tasks according to preset dimensions; determining the task service time corresponding to each of the service tasks, and then determining the corresponding target execution user according to the preset dimensions and the task service time.

6. A business processing device, characterized in that: include: an acquiring unit, configured to acquire a user identifier and corresponding historical detection data in response to a preset mechanism being triggered; The customized dimension determination unit is configured to determine the customized dimension based on the historical detection data, including: obtaining current detection data corresponding to the user identifier; generating a user profile based on the historical detection data and the current detection data, including: extracting high-level features and low-level features from the historical detection data and the current detection data, thereby generating fused features, and generating a user profile based on the fused features; determining a service dimension based on the user profile; and comparing the service dimension with a preset dimension to determine the customized dimension. a similarity determination unit configured to determine the similarity between the customized dimension and the process nodes of each preset dimension of the service plan output model; The service plan output unit is configured to determine the target process node according to the similarity, including: in response to the similarity being lower than a preset threshold, determining the business type of the customized dimension, and determining the corresponding grouping according to the business type; wherein, based on the customized dimension, the corresponding business type is determined from the dimension-business type key-value pair database to determine the corresponding grouping according to the business type; obtaining the full dimension in the grouping, and determining the target process node according to the similarity between the customized dimension and each process node of the full dimension; wherein, after determining the grouping corresponding to the customized dimension, obtaining the full dimension in the grouping, and the full dimension refers to the set of all dimensions in the grouping; and then adding the customized dimension to the execution program of the target process node to generate a corresponding service plan and output the service plan.

7. A business processing electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, 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 5.

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