Intelligent nursing system and method based on clinical big data

Abstract

The invention relates to the technical field of clinical nursing management, and discloses an intelligent nursing system and method based on clinical big data, and the system comprises a clinical nursing node supervision module, a clinical nursing personnel allocation module, and a clinical nursing execution module. According to current nursing time point information, in combination with an intelligent search algorithm and target patient clinical nursing plan information, patient clinical nursing project information is accurately extracted, clinical nursing projects are efficiently customized and supervised for patients at different clinical nursing time nodes, and the service quality and efficiency of clinical nursing are improved; the optimal clinical nursing personnel is accurately and scientifically screened based on the clinical nursing project information of the target patient, the working state of the clinical nursing personnel and the spatial distance information between the clinical nursing personnel and the patient, intelligent allocation of the clinical nursing personnel is achieved, and the response speed and scientificity of clinical nursing are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of clinical nursing management, and specifically to an intelligent nursing system and method based on clinical big data. Background Art

[0002] Clinical nursing is an important branch of nursing science, referring to a series of medical nursing activities carried out by professional nursing staff applying nursing theory and skills in clinical practice around the needs of patients. Its core contents include routine nursing operations, emergency and critical care, nursing management, and multi-department collaboration, etc. The clinical nursing platform is to realize the scientific and intelligent management of clinical nursing based on information technology; the existing clinical nursing platforms cannot achieve customized supervision of patients' clinical nursing items, nor can they achieve efficient and scientific allocation of the required nursing staff for patients.

[0003] The Chinese invention patent with the publication number of CN117457147B discloses a personalized nursing plan method and system for rehabilitation patients. By mining real-time data streams based on real-time monitoring device data and electronic health records, dynamically monitoring the health status, and generating a real-time health status report, the personalization and precision of the rehabilitation plan are realized; the above technical solutions cannot achieve scientific supervision of the process of the patient's nursing plan and scientific allocation of nursing staff. Summary of the Invention

[0004] (I) Technical Problems to be Solved To solve the problems that the existing clinical nursing platforms cannot achieve customized supervision of patients' clinical nursing items and cannot achieve efficient and scientific allocation of the required nursing staff for patients, and to achieve the purposes of customized search for patients' clinical nursing plan information, accurate supervision of patients' clinical nursing time nodes, precise acquisition of patients' clinical nursing item information at specific times, scientific allocation of the optimal clinical nursing staff, and high-quality execution of clinical nursing operations.

[0005] (II) Technical Solutions The present invention is realized through the following technical solutions: an intelligent nursing method based on clinical big data, and the method includes the following steps: S1. Collect data at the current nursing time point and text data of patients' nursing characteristics; S2. Based on the text data of patients' nursing characteristics and the text data of the hospital's clinical nursing plan for patients, perform search processing on the characteristic information of the clinical nursing plan of patients to generate target text data of the clinical nursing plan for patients; S3. According to the data at the current nursing time point and the target text data of the clinical nursing plan for patients, perform judgment processing on the clinical nursing time nodes of patients to generate judgment data of the clinical nursing time nodes for target patients; when the time node is not reached, directly end the current clinical nursing supervision operation; S4. When reaching the time node, extract and process the clinical nursing item information of the patient based on the current nursing time point data and the target patient's clinical nursing plan text data to generate the target patient's clinical nursing item text data; S5. Collect the nursing characteristic text data of the target clinical nurse and measure the spatial distance between the clinical nurse and the patient with the patient's nursing characteristic text data to construct the distance data between the target clinical nurse and the patient; S6. Screen and process the optimal clinical nurse required by the patient based on the nursing characteristic text data of the target clinical nurse and the distance data between the target clinical nurse and the patient to generate the characteristic text data of the optimal clinical nurse for the patient; S7. Construct the patient's clinical nursing command data and execute the patient's clinical nursing operation.

[0006] Preferably, the operation steps for collecting the current nursing time point data and the patient's nursing characteristic text data are as follows: S11. Online collect the time point information of the geographical location of the target patient being nursed through the time measurement module and generate the current nursing time point data , where the unit composition includes year, month, day, hour, and minute; Online collect the identity information and nursing address information of the target patient being nursed through the nursing management platform and generate the patient's nursing characteristic text data , and the patient's nursing characteristic text data includes the patient's name, ID number, contact phone number, and nursing room address information.

[0007] Preferably, the operation steps for searching and processing the clinical nursing plan characteristic information of the patient based on the patient's nursing characteristic text data and the hospital patient's clinical nursing plan text data to generate the target patient's clinical nursing plan text data are as follows: S21. Establish a set of hospital patient clinical nursing plan text data , ; where represents the hospital patient clinical nursing plan text data corresponding to the th patient being nursed, represents the maximum value of the number of patients being nursed; the hospital patient clinical nursing plan text data represents the clinical nursing plan information at different time points formulated for each hospital patient registered on the nursing management platform; the clinical nursing plan information includes the patient's body temperature monitoring nursing plan information, respiratory monitoring nursing plan information, blood pressure monitoring nursing plan information, drug administration monitoring nursing plan information, eating monitoring nursing plan information, excretion monitoring nursing plan information, dressing change monitoring nursing plan information, and blood glucose monitoring nursing plan information; S22. Match the patient care feature text data with the hospital patient clinical care plan text data set in the hospital patient clinical care plan text data to perform keyword matching for patient care features, and search for the corresponding hospital patient clinical care plan text data of the patient care feature text data , and generate target patient clinical care plan text data through data identification ; The specific operation steps for generating the target patient clinical care plan text data are as follows: S221. Initialize the algorithm parameters, population size N, and maximum number of iterations T; S222. Initialize the population, calculate the fitness, and determine the patient care plan search pathfinder and the patient care plan search follower; S223. Update the position of the patient care plan search pathfinder in the search space of the hospital patient clinical care plan text data set according to the position formula , where t represents the current iteration generation of the algorithm; represents the position of the patient care plan search pathfinder in the search space of the hospital patient clinical care plan text data set after the t-th iteration, represents the position of the patient care plan search pathfinder in the search space of the hospital patient clinical care plan text data set after the (t - 1)-th iteration, represents the position of the patient care plan search pathfinder in the search space of the hospital patient clinical care plan text data set after the (t + 1)-th iteration, represents the step size factor for the movement of the patient care plan search pathfinder, which takes values in [1, 2] and follows a uniform distribution; S224. Update the position of the patient care plan search follower in the search space of the hospital patient clinical care plan text data set according to the position formula , where represents the position of the patient care plan search follower in the search space of the hospital patient clinical care plan text data set after the t-th iteration, represents the position of the patient care plan search follower The position in the search space of the hospital patient clinical care plan text data set ; Indicates the position of other patient care plan search followers after the t-th iteration in the search space of the hospital patient clinical care plan text data set in the search space of the hospital patient clinical care plan text data set ; the position of the patient care plan search follower The movement of the patient care plan search follower is not only related to the position of the patient care plan search pathfinder but also affected by the positions of other patient care plan search followers ; Indicates the position distance parameter between patient care plan search followers in the search space of the hospital patient clinical care plan text data set ; Indicates the position distance parameter between the patient care plan search pathfinder and the patient care plan search follower in the search space of the hospital patient clinical care plan text data set ; , ; Indicates the interaction coefficient between patient care plan search followers Indicates the attraction coefficient of the patient care plan search pathfinder to the patient care plan search follower , Both take values in [1, 2] and follow a uniform distribution; is the step size factor for the movement of the patient care plan search follower and other patient care plan search followers is the step size factor for the movement of the patient care plan search follower and the patient care plan search pathfinder , Both are random numbers in the range of [0, 1]; S225. Calculate the fitness value of the patient care feature text data and all the hospital patient clinical care plan text data , and update and search for the hospital patient clinical care plan text data in the search space of the hospital patient clinical care plan text data set that is most matched with the patient care feature text data to obtain the global optimal value; S226. When the maximum number of iterations is reached, output the hospital patient clinical care plan text data that is most matched with the patient care feature text data , and generate the target patient clinical care plan text data through data identification ​, the target patient clinical care plan text data represents the clinical care plan information at different time points formulated for the target care patient.

[0008] Preferably, based on the current care time point data and the target patient clinical care plan text data, perform judgment processing on the clinical care time nodes of the patient to generate target patient clinical care time node judgment data; when the time node has not been reached, the operation steps to directly end the current clinical care supervision operation are as follows: S31. Compare the time value of the current care time point data with the time points in the target patient clinical care plan text data to generate target patient clinical care time node judgment data based on the numerical comparison result of the time points. ; When and the time value comparison is successful, indicating that the target care patient has reached the formulated clinical care time node, then output the target patient clinical care time node judgment data as "reached the time node"; When and the time value comparison is not successful, indicating that the target care patient has not reached the formulated clinical care time node, then output the target patient clinical care time node judgment data as "not reached the time node", and at this time, directly end the current clinical care supervision operation.

[0009] Preferably, when reaching the time node, the operation steps to extract the clinical care item information of the patient based on the current care time point data and the target patient clinical care plan text data to generate target patient clinical care item text data are as follows: S41. When the target patient clinical care time node judgment data is "reached the time node", use the BERT language model algorithm to search and extract the specific item information of the clinical care plan at the time node corresponding to the current care time point data from the target patient clinical care plan text data according to the time information corresponding to the current care time point data , and generate target patient clinical care item text data through data identification. , the text data of the target patient's clinical nursing items represents the specific nursing item information of the target nursing patient at the current nursing time node. The text data of the target patient's clinical nursing items includes body temperature monitoring item information, respiratory monitoring item information, blood pressure monitoring item information, medication nursing item information, feeding nursing item information, excretion nursing item information, dressing change nursing item information, and blood glucose monitoring item information.

[0010] Preferably, the operation steps of collecting the text data of the nursing characteristics of the target clinical nursing staff, measuring the spatial distance between the clinical nursing staff and the patient with the text data of the patient's nursing characteristics, and constructing the distance data between the target clinical nursing staff and the patient are as follows: S51. Import the text data of the target patient's clinical nursing items into the data input dialog box of the nursing management platform. The nursing management platform searches for the nursing characteristic information of the nursing staff that matches the clinical nursing items required by the target nursing patient according to the clinical nursing item information corresponding to the text data of the target patient's clinical nursing items and generates a set of text data of the nursing characteristics of the target clinical nursing staff through data identification , where represents the text data of the nursing characteristics of the target clinical nursing staff corresponding to the th clinical nursing staff collected, represents the maximum value of the number of clinical nursing staff; the text data of the nursing characteristics of the target clinical nursing staff includes the identity characteristic information, contact information, current nursing work status information, and the nursing room address information where the current nursing operation is located of the clinical nursing staff. The current nursing work status includes being in the nursing work status and not being in the nursing work status; S52. Import the text data of the nursing characteristics of the target clinical nursing staff in the set of text data of the nursing characteristics of the target clinical nursing staff into the departure location dialog box of the map navigation software in an orderly manner according to the nursing staff number, and import the text data of the patient's nursing characteristics alone into the destination dialog box of the map navigation software. The map navigation software measures the spatial distance values between different clinical nursing staff and the target nursing patient online and constructs a set of distance data between the target clinical nursing staff and the patient , where represents the distance data between the th clinical nursing staff and the target nursing patient corresponding to the target clinical nursing staff and the patient, and the unit of is meter.

[0011] Preferably, the operation steps for screening the optimal clinical caregiver required by the patient based on the target clinical caregiver's nursing characteristic text data and the distance data between the target clinical caregiver and the patient to generate the optimal clinical caregiver characteristic text data for the patient are as follows: S61. Use the Boyer-Moore search algorithm to search for the nursing characteristic information of the clinical caregiver who simultaneously meets the conditions of not being in the nursing work state and having the smallest spatial distance value from the target cared patient in the set of the target clinical caregiver's nursing characteristic text data and the nursing characteristic text data of the target clinical caregiver in and the set of the distance data between the target clinical caregiver and the patient and the distance data between the target clinical caregiver and the patient in and generate the optimal clinical caregiver characteristic text data for the patient through data identification .

[0012] Preferably, the operation steps for constructing the patient clinical care command data and executing the patient clinical care operation are as follows: S71. Combine the current care time point data , the patient nursing characteristic text data , the target patient clinical care plan text data , the target patient clinical care time node judgment data , the target patient clinical care item text data , and the optimal clinical caregiver characteristic text data for the patient through data combination to construct the patient clinical care command data , where ; S72. The nursing management platform pushes the patient clinical care command data to the mobile terminal of the target clinical caregiver through the Internet of Things communication network and prompts the target clinical caregiver to execute the patient clinical care operation. The mobile terminal includes any one of a smart phone, a smart bracelet, and a smart tablet.

[0013] An intelligent nursing system based on clinical big data, which is used for the intelligent nursing method based on clinical big data. The system includes a clinical care node supervision module, a clinical caregiver allocation module, and a clinical care execution module; The clinical care node supervision module includes a current care time point information collection unit, a patient nursing characteristic information collection unit, a hospital patient clinical care plan information storage unit, a patient clinical care plan information search unit, a patient clinical care time node judgment unit, and a patient clinical care item information extraction unit; The current nursing time point information collection unit collects current nursing time point data through a time measurement module; the patient nursing characteristic information collection unit collects patient nursing characteristic text data through a nursing management platform; the hospital patient clinical nursing plan information storage unit is used to store hospital patient clinical nursing plan text data; the patient clinical nursing plan information search unit performs search processing on the patient's clinical nursing plan characteristic information based on the patient nursing characteristic text data and the hospital patient clinical nursing plan text data stored based on clinical big data, and generates target patient clinical nursing plan text data; the patient clinical nursing time node judgment unit performs patient clinical nursing time node judgment processing based on the current nursing time point data and the target patient clinical nursing plan text data, and generates target patient clinical nursing time node judgment data; the patient clinical nursing item information extraction unit performs patient clinical nursing item information extraction processing based on the current nursing time point data and the target patient clinical nursing plan text data, and generates target patient clinical nursing item text data; The clinical nursing staff deployment module includes a target clinical nursing staff nursing characteristic information collection unit, a clinical nursing staff and patient spatial distance measurement unit, and a patient optimal clinical nursing staff object screening unit; The target clinical nursing staff nursing characteristic information collection unit collects target clinical nursing staff nursing characteristic text data through a nursing management platform; the clinical nursing staff and patient spatial distance measurement unit performs clinical nursing staff and patient spatial distance measurement processing based on the target clinical nursing staff nursing characteristic text data and the patient nursing characteristic text data, and constructs target clinical nursing staff and patient spacing data; the patient optimal clinical nursing staff object screening unit performs screening processing on the optimal clinical nursing staff required by the patient based on the target clinical nursing staff nursing characteristic text data and the target clinical nursing staff and patient spacing data, and generates target patient optimal clinical nursing staff characteristic text data; The clinical nursing execution module includes a patient clinical nursing command information construction unit and a patient clinical nursing operation execution unit; The patient clinical nursing command information construction unit constructs patient clinical nursing command data through data processing by combining the current nursing time point, patient nursing characteristic information, target patient clinical nursing plan information, target patient clinical nursing time node judgment information, target patient clinical nursing item information, and target patient optimal clinical nursing staff characteristic information; the patient clinical nursing operation execution unit, through the nursing management platform, pushes the patient clinical nursing command data to the mobile terminal of the target clinical nursing staff through the Internet of Things communication network, and prompts the target clinical nursing staff to execute the patient clinical nursing operation.

[0014] (III) Beneficial effects The present invention provides an intelligent nursing system and method based on clinical big data. It has the following beneficial effects: First, the time measurement module and the nursing management platform accurately collect the current nursing time point information and the patient's nursing characteristic information, providing reliable data support for the scientific supervision of the patient's clinical nursing operations; based on the patient's nursing characteristic information, combined with the intelligent recognition algorithm and the hospital patient's clinical nursing plan information stored in the clinical big data, the efficient search for the patient's clinical nursing plan characteristic information is realized, and the customized search for the patient's clinical nursing plan information is achieved; according to the current nursing time point information, combined with the intelligent search algorithm and the target patient's clinical nursing plan information, the accurate judgment of the patient's clinical nursing time node is carried out, realizing the dynamic supervision of the patient's clinical nursing time point and improving the reliability of the patient's clinical nursing; according to the current nursing time point information, combined with the intelligent search algorithm and the target patient's clinical nursing plan information, the accurate extraction of the patient's clinical nursing project information is carried out, realizing the efficient customized supervision of the clinical nursing project for the patient at different clinical nursing time nodes and improving the service quality and efficiency of the clinical nursing.

[0015] Second, the nursing management platform collects the nursing characteristic information of the target clinical nursing staff, providing real data support for the scientific allocation of clinical nursing staff; based on the clinical nursing project information of the target patient, the working status of the clinical nursing staff, and the spatial distance information between the clinical nursing staff and the patient, the optimal clinical nursing staff is accurately and scientifically selected, realizing the intelligent allocation of clinical nursing staff and improving the response speed and scientificity of the clinical nursing.

[0016] Third, based on the current nursing time point, the patient's nursing characteristic information, the target patient's clinical nursing plan information, the target patient's clinical nursing time node judgment information, the target patient's clinical nursing project information, and the patient's optimal clinical nursing staff characteristic information, the patient's clinical nursing command data is scientifically constructed, and at the same time, combined with the nursing management platform and the mobile terminal, the patient's clinical nursing operations are autonomously and efficiently executed, realizing the accurate and efficient collection of clinical nursing information and the precise execution of clinical nursing operations, and improving the satisfaction and applicability of the clinical nursing. Brief Description of the Drawings

[0017] Figure 1 It is a schematic diagram of the modules of the intelligent nursing system based on clinical big data provided by the present invention; Figure 2 It is a flowchart of the intelligent nursing method based on clinical big data provided by the present invention. Detailed Embodiments

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] The embodiments of the intelligent nursing system and method based on clinical big data are as follows: Embodiment 1: Please refer to Figure 1 - Figure 2 , an intelligent nursing method based on clinical big data, the method includes the following steps: S1. Collect data at the current nursing time point and patient care feature text data; S2. Perform search processing on the clinical care plan feature information of the patient based on the patient care feature text data and the hospital patient clinical care plan text data to generate target patient clinical care plan text data; S3. Perform judgment processing on the clinical care time nodes of the patient based on the data at the current nursing time point and the target patient clinical care plan text data to generate target patient clinical care time node judgment data; when the time node is not reached, directly end the current clinical care supervision operation; S4. When the time node is reached, extract the clinical care item information of the patient according to the data at the current nursing time point and the target patient clinical care plan text data to generate target patient clinical care item text data; S5. Collect the target clinical care personnel care feature text data and measure the spatial distance between the clinical care personnel and the patient with the patient care feature text data to construct the distance data between the target clinical care personnel and the patient; S6. Screen the optimal clinical care personnel required by the patient based on the target clinical care personnel care feature text data and the distance data between the target clinical care personnel and the patient to generate the optimal clinical care personnel feature text data of the patient; S7. Construct the patient clinical care command data and execute the patient clinical care operation.

[0020] Further, please refer to Figure 1 - Figure 2 , the operation steps of collecting data at the current nursing time point and patient care feature text data are as follows: S11. Online collect the time point information of the geographical location where the target nursing patient is located through the time measurement module and generate the current nursing time point data , where the unit composition includes year, month, day, hour, and minute; Online collect the identity information and nursing address information of the target nursing patients through the nursing management platform, and generate patient nursing characteristic text data , and the patient nursing characteristic text data includes the patient's name, ID number, contact phone number and nursing room address information.

[0021] Based on the patient nursing characteristic text data and the hospital patient clinical nursing plan text data, perform a search and processing of the clinical nursing plan characteristic information of the patient, and the operation steps for generating the target patient clinical nursing plan text data are as follows: S21. Establish a collection of hospital patient clinical nursing plan text data , ; where represents the hospital patient clinical nursing plan text data corresponding to the th nursing patient, represents the maximum value of the number of nursing patients; the hospital patient clinical nursing plan text data represents the clinical nursing plan information at different time points formulated for each hospital nursing patient registered on the nursing management platform; the clinical nursing plan information includes the patient's body temperature monitoring nursing plan information, respiratory monitoring nursing plan information, blood pressure monitoring nursing plan information, administration monitoring nursing plan information, eating monitoring nursing plan information, excretion monitoring nursing plan information, dressing change monitoring nursing plan information and blood glucose monitoring nursing plan information; S22. Match the patient nursing characteristic text data with the hospital patient clinical nursing plan text data in the hospital patient clinical nursing plan text data set to perform a keyword match of the patient nursing characteristics, search for the hospital patient clinical nursing plan text data corresponding to the patient nursing characteristic text data , and generate the target patient clinical nursing plan text data through data identification; the specific operation steps for executing the generation of the target patient clinical nursing plan text data are as follows: S221. Initialize the algorithm parameters, the population size N, and the maximum number of iterations T; S222. Initialize the population, calculate the fitness, and determine the patient nursing plan search pathfinder and the patient nursing plan search follower; S223. Update the position of the patient nursing plan search pathfinder in the search space of the hospital patient clinical nursing plan text data set according to the position formula , where t represents the current iteration generation of the algorithm; represents the position of the patient nursing plan search pathfinder after the t-th iteration in the hospital patient clinical nursing plan text data set , The position in the search space, represents the patient care plan search pathfinder after the (t - 1)-th iteration in the hospital patient clinical care plan text data set The position in the search space, represents the patient care plan search pathfinder after the (t + 1)-th iteration in the hospital patient clinical care plan text data set The position in the search space, represents the step size factor by which the patient care plan search pathfinder moves, which takes values in the range [1, 2] and follows a uniform distribution; S224. According to the position formula Update the position of the patient care plan search follower in the hospital patient clinical care plan text data set in the search space, where represents the patient care plan search follower after the t-th iteration in the hospital patient clinical care plan text data set The position in the search space, represents the patient care plan search follower after the (t + 1)-th iteration in the hospital patient clinical care plan text data set The position in the search space; represents the other patient care plan search followers after the t-th iteration in the hospital patient clinical care plan text data set The position in the search space. The position of the patient care plan search follower moves not only related to the position of the patient care plan search pathfinder but also affected by the positions of other patient care plan search followers , represents the position distance parameter between patient care plan search followers in the hospital patient clinical care plan text data set in the search space, represents the position distance parameter between the patient care plan search pathfinder and the patient care plan search follower in the hospital patient clinical care plan text data set in the search space, , ; represents the interaction coefficient between patient care plan search followers, represents the attraction coefficient of the patient care plan search pathfinder to the patient care plan search follower, , Both take values in [1, 2] and follow a uniform distribution; is the step size factor for the patient care plan search follower to move with other patient care plan search followers, is the step size factor for the patient care plan search follower and the patient care plan search pathfinder to move, and are all random numbers in the range of [0, 1]; S225. Calculate the patient care feature text data and the fitness values of all hospital patient clinical care plan text data , and update and search for the hospital patient clinical care plan text data that best matches the patient care feature text data in the search space of the hospital patient clinical care plan text data set to obtain the global optimal value; Global optimal value; S226. When the maximum number of iterations is reached, output the hospital patient clinical care plan text data that best matches the patient care feature text data , and generate the target patient clinical care plan text data through data identification. The target patient clinical care plan text data represents the clinical care plan information at different time points for the target care patient.

[0022] Based on the current care time point data and the target patient clinical care plan text data, perform judgment processing on the clinical care time nodes of the patient to generate the target patient clinical care time node judgment data; when it is not yet the time node, directly end the operation steps of this clinical care supervision task as follows: S31. Compare the time values of the current care time point data with the time points in the target patient clinical care plan text data , and generate the target patient clinical care time node judgment data according to the result of the numerical comparison of the time points; When and are successfully compared in terms of time values, indicating that the target care patient has reached the designated clinical care time node, then output the target patient clinical care time node judgment data as having reached the time node; When and are not successfully compared in terms of time values, indicating that the target care patient has not reached the designated clinical care time node, then output the target patient clinical care time node judgment data as not having reached the time node, and directly end this clinical care supervision task at this time.

[0023] When the time node is reached, the patient's clinical nursing project information is extracted and processed according to the current nursing time point data and the target patient's clinical nursing plan text data. The operation steps for generating the target patient's clinical nursing project text data are as follows: S41. When the target patient's clinical nursing time node judgment data When the time node is reached, the BERT language model algorithm is used according to the current nursing time point data The corresponding time information is from the target patient clinical care plan text data Search and extract current nursing time point data The specific project information of the clinical nursing plan at the corresponding time node, and the target patient clinical nursing project text data is generated through data identification The target patient clinical nursing project text data represents the specific nursing project information of the target nursing patient at the current nursing time node. The target patient clinical nursing project text data includes body temperature monitoring project information, respiratory monitoring project information, blood pressure monitoring project information, medication nursing project information, eating nursing project information, excretion nursing project information, dressing change nursing project information and blood sugar monitoring project information.

[0024] Through the cooperation between the current nursing time point information collection unit and the patient nursing characteristic information collection unit, the time measurement module and the nursing management platform are used to accurately collect the current nursing time point information and the patient nursing characteristic information, so as to provide reliable data support for the scientific supervision of the patient's clinical nursing work; the patient clinical nursing plan information search unit, based on the patient nursing characteristic information combined with the intelligent recognition algorithm and the hospital patient clinical nursing plan information based on the clinical big data storage, efficiently searches for the patient's clinical nursing plan characteristic information, and realizes customized search for the patient's clinical nursing plan information; the patient clinical nursing time node judgment unit, based on the current nursing time point information combined with the intelligent search algorithm and the target patient's clinical nursing plan information, accurately judges the patient's clinical nursing time node, realizes dynamic supervision of the patient's clinical nursing time point, and improves the reliability of the patient's clinical nursing; the patient clinical nursing project information extraction unit, based on the current nursing time point information combined with the intelligent search algorithm and the target patient's clinical nursing plan information, accurately extracts the patient's clinical nursing project information, realizes efficient customized supervision of clinical nursing projects for patients at different clinical nursing time nodes, and improves the service quality and efficiency of clinical nursing.

[0025] For further information, see Figure 1 - Figure 2 , collect the target clinical nursing staff nursing feature text data and measure the clinical nursing staff and patient spatial distance with the patient nursing feature text data, and construct the target clinical nursing staff and patient distance data as follows: S51. Import the text data of the clinical care items of the target patient into the data input dialog box of the nursing management platform. The nursing management platform searches for the nursing characteristic information of the nursing staff that matches the clinical care items required by the target nursing patient based on the text data of the clinical care items of the target patient and generates a set of target clinical nursing staff nursing characteristic text data through data identification , ; where represents the target clinical nursing staff nursing characteristic text data corresponding to the th collected clinical nursing staff, represents the maximum value of the number of clinical nursing staff; the target clinical nursing staff nursing characteristic text data includes the identity characteristic information, contact information, current nursing work status information, and the nursing room address information where the current nursing operation is located of the clinical nursing staff. The current nursing work status includes being in the nursing work status and not being in the nursing work status; S52. Import the target clinical nursing staff nursing characteristic text data in the set of target clinical nursing staff nursing characteristic text data into the departure location dialog box of the map navigation software in an orderly manner according to the nursing staff number, and import the patient nursing characteristic text data into the destination dialog box of the map navigation software separately. The map navigation software measures the spatial distance values between different clinical nursing staff and the target nursing patient online and constructs a set of target clinical nursing staff - patient spacing data , where represents the target clinical nursing staff - patient spacing data corresponding to the th clinical nursing staff and the target nursing patient, and the unit of is meters.

[0026] The operation steps for screening the optimal clinical nursing staff required by the patient based on the target clinical nursing staff nursing characteristic text data and the target clinical nursing staff - patient spacing data to generate the optimal clinical nursing staff characteristic text data of the patient are as follows: S61. Use the Boyer - Moore search algorithm to search for the nursing work status keyword and the spatial spacing value in the target clinical nursing staff nursing characteristic text data in the set of target clinical nursing staff nursing characteristic text data and the target clinical nursing staff - patient spacing data in the set of target clinical nursing staff - patient spacing data and the target clinical nursing staff - patient spacing data in the set of target clinical nursing staff - patient spacing data Search for the nursing characteristic information of clinical nurses who simultaneously meet the conditions of not being in a nursing work state and having the smallest spatial distance value from the target nursing patient, and generate the optimal clinical nurse characteristic text data for the patient through data identification 。

[0027] Through the target clinical nurse nursing characteristic information acquisition unit, the nursing management platform is used to collect the nursing characteristic information of the target clinical nurse, providing real data support for the scientific allocation of clinical nurses; the clinical nurse-patient spatial distance measurement unit and the optimal clinical nurse object screening unit for patients cooperate with each other to accurately and scientifically screen out the optimal clinical nurses based on the clinical nursing project information of the target patient, the working status of clinical nurses, and the spatial distance information between clinical nurses and patients, realizing the intelligent allocation of clinical nurses and improving the response speed and scientificity of clinical nursing

[0028] Furthermore, please refer to Figure 1 - Figure 2 to construct the patient clinical nursing command data and the operation steps for performing the patient clinical nursing operation are as follows S71. Combine the current nursing time point data , the patient nursing characteristic text data , the target patient clinical nursing plan text data , the target patient clinical nursing time node judgment data , the target patient clinical nursing project text data , and the optimal clinical nurse characteristic text data for the patient through data combination to construct the patient clinical nursing command data , where ; S72. The nursing management platform pushes the patient clinical nursing command data to the mobile terminal of the target clinical nurse through the Internet of Things communication network and prompts the target clinical nurse to perform the patient clinical nursing operation. The mobile terminal includes any one of a smart phone, a smart bracelet, and a smart tablet

[0029] Through the cooperation of the patient clinical nursing command information construction unit and the patient clinical nursing operation execution unit, based on the current nursing time point, patient nursing characteristic information, target patient clinical nursing plan information, target patient clinical nursing time node judgment information, target patient clinical nursing project information, and optimal clinical nurse characteristic information for the patient, the patient clinical nursing command data is scientifically constructed, and at the same time, in combination with the nursing management platform and the mobile terminal, the patient clinical nursing operation is autonomously and efficiently executed, realizing the accurate and efficient collection of clinical nursing information and the precise execution of clinical nursing operations, and improving the satisfaction and applicability of clinical nursing

[0030] Example 2: Please refer to Figure 1 - Figure 2 An intelligent nursing system based on clinical big data, for an intelligent nursing method based on clinical big data. The system includes a clinical nursing node supervision module, a clinical nursing staff allocation module, and a clinical nursing execution module; The clinical nursing node supervision module includes a current nursing time point information collection unit, a patient nursing characteristic information collection unit, a hospital patient clinical nursing plan information storage unit, a patient clinical nursing plan information search unit, a patient clinical nursing time node judgment unit, and a patient clinical nursing item information extraction unit; The current nursing time point information collection unit collects current nursing time point data through a time measurement module; the patient nursing characteristic information collection unit collects patient nursing characteristic text data through a nursing management platform; the hospital patient clinical nursing plan information storage unit is used to store hospital patient clinical nursing plan text data; the patient clinical nursing plan information search unit performs a search process on the patient's clinical nursing plan characteristic information based on the patient nursing characteristic text data and the hospital patient clinical nursing plan text data stored based on clinical big data, and generates target patient clinical nursing plan text data; the patient clinical nursing time node judgment unit performs a judgment process on the patient's clinical nursing time node based on the current nursing time point data and the target patient clinical nursing plan text data, and generates target patient clinical nursing time node judgment data; the patient clinical nursing item information extraction unit performs an extraction process on the patient's clinical nursing item information according to the current nursing time point data and the target patient clinical nursing plan text data, and generates target patient clinical nursing item text data; The clinical nursing staff allocation module includes a target clinical nursing staff nursing characteristic information collection unit, a clinical nursing staff and patient space distance measurement unit, and a patient optimal clinical nursing staff object screening unit; The target clinical nursing staff nursing characteristic information collection unit collects target clinical nursing staff nursing characteristic text data through a nursing management platform; the clinical nursing staff and patient space distance measurement unit performs a measurement process on the space distance between the clinical nursing staff and the patient based on the target clinical nursing staff nursing characteristic text data and the patient nursing characteristic text data, and constructs target clinical nursing staff and patient spacing data; the patient optimal clinical nursing staff object screening unit performs a screening process on the optimal clinical nursing staff required by the patient based on the target clinical nursing staff nursing characteristic text data and the target clinical nursing staff and patient spacing data, and generates patient optimal clinical nursing staff characteristic text data; The clinical nursing execution module includes a patient clinical nursing command information construction unit and a patient clinical nursing operation execution unit; The patient clinical care command information construction unit constructs patient clinical care command data through data processing by combining the current care time point, patient care characteristic information, target patient clinical care plan information, target patient clinical care time node judgment information, target patient clinical care item information, and patient optimal clinical care staff characteristic information; the patient clinical care operation execution unit, the nursing management platform pushes the patient clinical care command data to the mobile terminal of the target clinical care staff through the Internet of Things communication network, and prompts the target clinical care staff to execute the patient clinical care operation.

[0031] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Intelligent nursing method based on clinical big data, characterized by: The method comprises the following steps: S1, collect current nursing time point data and patient nursing characteristic text data; S2. Search and process the patient's clinical nursing plan characteristic information to generate clinical nursing plan text data for the target patient; S3. Perform clinical nursing time node judgment processing on the patient and generate clinical nursing time node judgment data for the target patient; if the time node has not been reached, directly end this clinical nursing supervision operation; S4. When the time node is reached, the patient's clinical nursing project information is extracted and processed to generate the target patient's clinical nursing project text data; S5, collecting nursing feature text data of target clinical nursing staff and performing spatial distance measurement processing between clinical nursing staff and patients on the nursing feature text data of patients, and constructing distance data between target clinical nursing staff and patients; S6. Screening and processing the optimal clinical nursing staff required by the patient, and generating characteristic text data of the optimal clinical nursing staff for the patient; S7. Construct patient clinical nursing command data and perform patient clinical nursing tasks.

2. The intelligent nursing method based on clinical big data according to claim 1 is characterized in that: The S1 comprises the following steps: S11. Collect the time point information of the target nursing patient's geographical location online through the time measurement module, and generate the current nursing time point data ,in The units of include year, month, day, hour and minute; Collect the identity information and address information of the target nursing patients online through the nursing management platform, and generate patient nursing feature text data The patient care feature text data includes the patient's name, ID number, contact number and nursing room address information.

3. The intelligent nursing method based on clinical big data according to claim 2 is characterized in that: The S2 comprises the following steps: S21. Establish a text data set of clinical nursing plans for hospital patients , ;in Indicates Text data of hospital patient clinical care plans corresponding to each nursing patient, Indicates the maximum number of patients being cared for; S22, the With the As stated in Perform keyword matching of patient care characteristics and search for the The corresponding , and generate the target patient clinical care plan text data through data identification ; Execute and generate the clinical nursing plan text data of the target patient The specific steps are as follows: S221, initialize algorithm parameters, population size N, maximum number of iterations T; S222, initializing the population, calculating fitness, and determining a patient care plan search pathfinder and a patient care plan search follower; S223, in the Update the position of the patient care plan search pathfinder in the search space of ; S224, update the patient care plan to search for followers in the The position in the search space of S225, calculating the With all the said The fitness value of Update the search space to find the The best match for Global optimum; S226, when the maximum number of iterations is met, output The best match for , and generate the target patient clinical care plan text data through data identification .

4. The intelligent nursing method based on clinical big data according to claim 3 is characterized in that: The S3 comprises the following steps: S31, the With the The time points in the experiment are compared with each other, and the clinical nursing time node judgment data of the target patients are generated according to the time point comparison results. ; when and If the time value comparison is successful, the output is To reach the time node; when and If the time value is not successfully compared, the output is Since the time node has not been reached, the clinical nursing supervision task will be terminated directly at this time.

5. The intelligent nursing method based on clinical big data according to claim 4 is characterized in that: The S4 comprises the following steps: S41, when the When the time node is reached, the BERT language model algorithm is used as described The corresponding time information is from the Search for extracts described The specific project information of the clinical nursing plan at the corresponding time node, and the target patient clinical nursing project text data is generated through data identification .

6. The intelligent nursing method based on clinical big data according to claim 5 is characterized in that: The S5 comprises the following steps: S51, the Import the data input dialog box of the nursing management platform. The nursing management platform The corresponding clinical nursing project information searches for the nursing characteristic information of the nursing staff that matches the clinical nursing projects required by the target nursing patients, and generates the nursing characteristic text data set of the target clinical nursing staff through data identification. , ;in Indicates the collected Nursing characteristic text data of target clinical nurses corresponding to clinical nurses, Indicates the maximum number of clinical nursing staff; the target clinical nursing staff nursing feature text data includes the clinical nursing staff's identity feature information, contact information, current nursing work status information and nursing room address information where the current nursing work is located, and the current nursing work status includes being in the nursing work state and not being in the nursing work state; S52, respectively As stated in Import the departure point dialog box of the map navigation software in order according to the nursing staff number, and the Import the destination dialog box of the map navigation software separately. The map navigation software measures the spatial distance between different clinical nursing staff and the target nursing patients online, and constructs a data set of the distance between the target clinical nursing staff and the patients. ,in Indicates The target clinical nursing staff and patient distance data corresponding to the clinical nursing staff and the target nursing patients, The unit is meter.

7. The intelligent nursing method based on clinical big data according to claim 6 is characterized in that: The S6 comprises the following steps: S61, using the Boyer-Moore search algorithm according to the nursing work status keywords and spatial spacing values ​​in the As stated in and stated As stated in Search for the nursing characteristic information of clinical nurses who are not in nursing work and have the smallest spatial distance value with the target nursing patient, and generate the optimal clinical nurse characteristic text data of the patient through data identification. .

8. The intelligent nursing method based on clinical big data according to claim 7 is characterized in that: The S7 comprises the following steps: S71, the , , , , , After data combination, patient clinical care command data is constructed ; S72, the nursing management platform will It is pushed to the mobile terminal of the target clinical nurse through the Internet of Things communication network, and prompts the target clinical nurse to perform clinical nursing tasks for the patient.

9. An intelligent nursing system based on clinical big data, used to implement the intelligent nursing method based on clinical big data as claimed in any one of claims 1 to 8, characterized in that: The system includes a clinical nursing node supervision module, a clinical nursing staff deployment module, and a clinical nursing execution module.

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