Safety supervision method and safety early warning system for Internet nursing

Abstract

The invention discloses a safety supervision method and a safety early warning system for internet nursing, and relates to the technical field of digital communication, and the method comprises the steps: receiving a nursing demand of a customer; performing one-way nursing safety supervision verification, obtaining first verification information, and generating an internet nursing task; connecting the nursing server and receiving nursing service feedback; performing one-way nursing safety supervision verification, obtaining second verification information, and generating an internet nursing request; setting a bidirectional matching framework; identifying potential risk points in the nursing service process, setting a nursing risk prediction layer, performing callback training, and establishing a safety supervision model; and determining an optimal distribution combination, performing safety supervision on the Internet nursing platform in combination with the safety supervision model, and judging whether a safety early warning signal is sent out or not. The technical problems of low response speed and low nursing service matching efficiency during nursing supervision in the prior art are solved, and the technical effect of improving the nursing supervision efficiency and the nursing service matching efficiency is achieved.

Description

Technical Field

[0001] The present invention relates to the field of digital communication technology, and in particular to a safety supervision method and a safety early warning system for Internet nursing. Background Art

[0002] With the rapid growth of Internet nursing demand, especially in the fields of telemedicine, elderly care and chronic disease management, Internet nursing platforms have been widely used. However, with the increasing diversification and complexity of nursing needs, the safety supervision of the nursing process faces increasing challenges. Traditional nursing supervision methods usually rely on manual monitoring and feedback, which have problems such as slow response speed and low efficiency in matching nursing services, making it difficult to effectively ensure the quality and safety of nursing services. Summary of the invention

[0003] The present application provides a safety supervision method and a safety early warning system for Internet nursing, which are used to solve the technical problems of slow response speed and low nursing service matching efficiency in nursing supervision in the existing technology.

[0004] In view of the above problems, the present application provides a safety supervision method and a safety early warning system for Internet nursing.

[0005] In a first aspect of the present application, a safety supervision method for Internet nursing is provided, the method is applied to a safety supervision system for Internet nursing, the safety supervision system for Internet nursing is communicatively connected with a safety early warning system, the method comprising: Connect to the client, receive customer care needs, and the customer care needs correspond to the vital signs monitoring service, wound care service, drug management service, and rehabilitation guidance service of the Internet nursing platform; based on the customer care needs, perform one-way nursing safety supervision verification, obtain first verification information, and generate an Internet nursing task; connect to the nursing service end, receive nursing service feedback, and the nursing service feedback corresponds to the vital signs monitoring resource allocation global variables, wound care resource allocation global variables, drug management resource allocation global variables, and rehabilitation guidance resource allocation global variables of the Internet nursing platform; based on the nursing service feedback, perform one-way nursing safety supervision verification, obtain second verification information, and generate an Internet nursing request; based on the first verification information The second verification information is matched on the Internet nursing platform to set up a two-way matching architecture for Internet nursing tasks and Internet nursing requests; in a data storage unit that is communicatively connected to the Internet nursing platform, the Internet nursing instance with the error code mark added is located, the potential risk points in the nursing service process are identified, a nursing risk prediction layer is set, and callback training is performed in combination with the first verification information and the second verification information to establish a safety supervision model; the two-way matching architecture is connected to the nursing risk prediction layer to determine the best allocation combination, and the Internet nursing platform is safety supervised in combination with the safety supervision model, and it is determined whether to issue a safety warning signal, and the safety warning signal is used to activate the safety warning system.

[0006] A second aspect of the present application provides a safety supervision system for Internet nursing, wherein the safety supervision system for Internet nursing is communicatively connected to a safety early warning system, and the system comprises: A nursing demand receiving unit, the nursing demand receiving unit is connected to a client to receive a customer's nursing demand, wherein the customer's nursing demand corresponds to a vital sign monitoring service, a wound care service, a drug management service, and a rehabilitation guidance service of an Internet nursing platform; a first safety supervision verification unit, the first safety supervision verification unit performs a one-way nursing safety supervision verification based on the customer's nursing demand, obtains first verification information, and generates an Internet nursing task; a service feedback receiving unit, the service feedback receiving unit is connected to a nursing service end to receive nursing service feedback, wherein the nursing service feedback corresponds to a vital sign monitoring resource allocation global variable, a wound care resource allocation global variable, a drug management resource allocation global variable, and a rehabilitation guidance resource allocation global variable of an Internet nursing platform; a second safety supervision verification unit, the second safety supervision verification unit performs a one-way nursing safety supervision verification based on the nursing service feedback, obtains second verification information, and generates an Internet nursing request; a matching unit, which matches the Internet nursing platform based on the first verification information and the second verification information, and sets a two-way matching architecture for Internet nursing tasks and Internet nursing requests; a callback training unit, which locates the Internet nursing instance with an error code mark added in a data storage unit that is communicatively connected to the Internet nursing platform, identifies potential risk points in the nursing service process, sets a nursing risk prediction layer, and performs callback training in combination with the first verification information and the second verification information to establish a safety supervision model; a safety supervision unit, which connects the two-way matching architecture with the nursing risk prediction layer, determines the best allocation combination, performs safety supervision on the Internet nursing platform in combination with the safety supervision model, and determines whether to issue a safety warning signal, wherein the safety warning signal is used to activate the safety warning system.

[0007] The third aspect of the present application provides a safety warning system, including a processor, a buzzer and an LED display, which are used to jointly execute a computer program. When the processor executes the computer program, the steps of a safety supervision method for Internet nursing described in any one of the first aspects are implemented.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application connects to the client, receives customer care needs, and customer care needs correspond to the vital signs monitoring service, wound care service, drug management service, and rehabilitation guidance service of the Internet nursing platform; based on customer care needs, performs one-way nursing safety supervision verification, obtains first verification information, and generates Internet nursing tasks; connects to the nursing service end, receives nursing service feedback, and nursing service feedback corresponds to the vital signs monitoring resource allocation global variables, wound care resource allocation global variables, drug management resource allocation global variables, and rehabilitation guidance resource allocation global variables of the Internet nursing platform; based on nursing service feedback, performs one-way nursing safety supervision verification, obtains second verification information, and generates Internet nursing requests; based on The first verification information and the second verification information are matched on the Internet nursing platform, and a two-way matching architecture of Internet nursing tasks and Internet nursing requests is set; in the data storage unit connected to the Internet nursing platform, the Internet nursing instance with the error code mark is located, the potential risk points in the nursing service process are identified, and the nursing risk prediction layer is set, and callback training is performed in combination with the first verification information and the second verification information to establish a safety supervision model; the two-way matching architecture is connected to the nursing risk prediction layer, the best allocation combination is determined, and the Internet nursing platform is safely supervised in combination with the safety supervision model, and it is determined whether to issue a safety warning signal, and the safety warning signal is used to activate a safety warning system. The present invention solves the technical problems of slow response speed and low nursing service matching efficiency in the prior art when performing nursing supervision. Through the nursing risk prediction layer based on the rule-based two-way matching architecture and the machine learning model, the customer needs and nursing staff qualifications are intelligently matched, resource allocation is optimized, and the nursing process is monitored in real time in combination with the safety supervision model, risks are identified and warning signals are issued, so as to achieve the technical effect of improving the efficiency of nursing supervision and the matching efficiency of nursing services. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic diagram of a safety supervision method for Internet nursing provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a security supervision system for Internet nursing provided in an embodiment of the present application.

[0011] Explanation of the reference numerals: nursing demand receiving unit 11, first safety supervision verification unit 12, service feedback receiving unit 13, second safety supervision verification unit 14, matching unit 15, callback training unit 16, safety supervision unit 17. DETAILED DESCRIPTION

[0012] This application provides a security supervision method and a security early warning system for Internet nursing, aiming to solve the technical problems of slow response speed and low nursing service matching efficiency in nursing supervision in the existing technology. Through a rule-based two-way matching architecture and a nursing risk prediction layer of a machine learning model, it intelligently matches customer needs with nursing staff qualifications, optimizes resource allocation, and combines a security supervision model to monitor the nursing process in real time, identify risks and issue early warning signals, thereby achieving the technical effect of improving nursing supervision efficiency and nursing service matching efficiency.

[0013] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0014] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0015] Embodiment 1, as Figure 1 As shown, the present application provides a safety supervision method for Internet nursing, the method is applied to a safety supervision system for Internet nursing, the safety supervision system for Internet nursing is communicatively connected with a safety early warning system, and the method includes: Step S100: Connect to the client and receive customer care needs, which correspond to the vital signs monitoring service, wound care service, drug management service, and rehabilitation guidance service of the Internet care platform.

[0016] In the embodiment of the present application, the client is first connected to the client through the Internet. Here, the client refers to the device used by the nursing object, such as a smart phone, a tablet computer, etc. Then the customer's nursing needs are received, and the specific nursing needs of the nursing object are obtained from the client. These needs come from the information entered by the customer in the client interface. These nursing needs correspond to the four main services on the Internet nursing platform, namely, vital signs monitoring service, wound care service, drug management service and rehabilitation guidance service.

[0017] Among them, the vital signs monitoring service monitors the customer's vital signs data in real time, such as heart rate, blood pressure, body temperature, etc., through wearable devices such as smart watches and heart rate belts. The wound care service receives wound information uploaded by the customer. The drug management service receives the time and dosage information of the customer's medication. The rehabilitation guidance service receives the implementation status of the rehabilitation plan, such as the amount of exercise or rehabilitation exercises completed by the customer.

[0018] Step S200: Based on the customer care needs, perform one-way care safety supervision verification, obtain first verification information, and generate an Internet care task.

[0019] Furthermore, in the method provided in the embodiment of the application, based on the customer care needs, one-way care safety supervision verification is performed to obtain first verification information, and further includes: After receiving the customer care demand, the first single read operation is triggered to read the customer's basic information, wherein the customer's basic information includes customer identity information, health status information, geographic location information, and auxiliary device usage information. The auxiliary device defined by the auxiliary device usage information is a wearable device, and the auxiliary device usage information includes the device model, device serial number, and device usage status. Through the customer care demand, a one-way care safety supervision verification is performed in combination with the customer's basic information to obtain the first verification information.

[0020] In an embodiment of the present application, after receiving the customer's care needs, the first single read operation is triggered, and this operation is completed through a database query. The basic information of the customer is retrieved from the database through the query operation, including customer identity information, health status, geographic location information, and auxiliary device usage information. The customer's auxiliary device is a wearable device, such as a smart bracelet or a heart rate monitor. The model, serial number and current usage status of these devices will be recorded. Then, through the data parsing and extraction method, the raw data read from the database is parsed and classified to extract various types of basic customer information, such as the customer's health records, identity information, and geographic location. This step ensures that structured and accurate basic data is obtained.

[0021] Then comes the data fusion and matching stage, in which the customer's care needs are matched and fused with the extracted basic information. By using data fusion algorithms, the customer's care needs, such as vital sign monitoring services or medication management services, are matched with their health status and auxiliary device status to ensure that these care needs are feasible. For example, check whether the customer's device can support the requested service, or determine whether the customer's geographical location is within the coverage of the care service.

[0022] Once the match is completed, the one-way nursing safety supervision verification phase begins. In this phase, verification is performed through the rule engine. The rule engine verifies the safety of the customer's needs based on the pre-set nursing standards. For example, it verifies whether the customer's nursing needs are consistent with their medical records and whether the equipment status can safely support nursing services.

[0023] After the verification is completed, the first verification information is generated.

[0024] Furthermore, the method provided in the application embodiment also includes: The wearable device has a built-in physiological signal acquisition module, which is used to upload the monitored data to the Internet nursing platform in real time through the BLE protocol; evaluate the vital signs of the target customer, determine whether it is in an abnormal mode, and automatically trigger the alternative allocation combination with the highest matching degree with the target customer, wherein the matching indicators of the target customer include the urgency of nursing needs and the availability of nursing services, and the nursing service availability includes equipment availability and drug availability.

[0025] In the embodiment of the present application, the physiological signal acquisition module built into the wearable device is used to monitor the customer's vital signs data in real time, such as heart rate, body temperature, blood pressure, etc. These data are transmitted via the BLE protocol, and ensure that the monitored vital signs data can be uploaded to the Internet nursing platform in real time. The data transmission in this process mainly relies on the short-range wireless communication technology of the BLE protocol to achieve real-time data transmission.

[0026] Once the data is uploaded to the platform, the vital signs status assessment module is activated. This module will use preset health standards, such as normal heart rate range, blood pressure standard value, etc. to evaluate the customer's vital signs data to determine whether the customer is in an abnormal mode. If the monitoring data exceeds the normal range, this abnormal mode is automatically identified. For example, if the customer's heart rate is too high or the body temperature is abnormal, it is immediately marked as an abnormal state.

[0027] When an anomaly is detected, the best alternative allocation combination with the target customer is automatically triggered based on the preset matching indicators. The matching indicators include the urgency of care needs and the availability of care services. The urgency of care needs assesses whether the customer's health condition requires immediate care; the availability of care services determines whether there are available equipment and drugs to support the corresponding care services based on the resource situation.

[0028] By combining equipment availability and drug availability, the current nursing resources are evaluated. For example, if the vital signs monitoring equipment is still working effectively and the relevant drugs are sufficient in stock, the nursing service combination with the highest matching degree is triggered first. When these conditions are met, the nearest nursing resource is automatically allocated and the task is sent to the nursing staff or nursing service end to ensure a quick response to the customer's urgent care needs.

[0029] Step S300: Connect to the nursing service end and receive nursing service feedback, where the nursing service feedback corresponds to the vital sign monitoring resource allocation global variables, wound care resource allocation global variables, drug management resource allocation global variables, and rehabilitation guidance resource allocation global variables of the Internet nursing platform.

[0030] In the embodiment of the present application, the nursing service end is connected to receive nursing service feedback information. The nursing service end refers to a terminal system or device that provides actual nursing services, which maintains communication with the Internet nursing platform to feedback resource allocation information used in the nursing process.

[0031] Connect to the nursing service end through the network and start receiving nursing service feedback from the nursing service end. Nursing service feedback is the key data for obtaining the execution of nursing tasks, which covers the resource allocation of four major nursing services. These four types of resource allocation global variables correspond to vital signs monitoring, wound care, drug management, and rehabilitation guidance services.

[0032] Among them, the global variable of vital sign monitoring resource allocation means that when the nursing task requested by the customer involves vital sign monitoring, the nursing service will return the allocated monitoring resources, such as the usage of heart rate monitors or blood pressure monitoring equipment. Receive these feedbacks and record the allocation and working status of the equipment to ensure the smooth completion of the monitoring task.

[0033] The global variable of wound care resource allocation means that if the client's care needs involve wound care, the nursing service end will feedback the allocated care supplies, drugs, and care procedures. By receiving these feedbacks, it monitors whether the allocated resources are sufficient, whether they are used correctly according to the plan, and ensures the safety of the care process.

[0034] The global variable of drug management resource allocation refers to the distribution of drugs fed back by the nursing service end for drug management services, such as the drugs that customers should take, drug dosage, medication time, etc. By recording these feedback data, it is confirmed whether the drug management task is carried out according to the predetermined plan.

[0035] The global variable of rehabilitation guidance resource allocation means that if the nursing task involves rehabilitation guidance, the nursing service end will return the allocated rehabilitation equipment and guidance resources, such as the use of rehabilitation training equipment, the progress of rehabilitation courses, etc. The execution of the rehabilitation process is monitored through feedback to ensure that the customer can get the correct rehabilitation guidance.

[0036] Step S400: Based on the nursing service feedback, perform one-way nursing safety supervision verification, obtain second verification information, and generate an Internet nursing request.

[0037] Furthermore, in the method provided in the embodiment of the application, based on the nursing service feedback, one-way nursing safety supervision verification is performed to obtain second verification information, and further includes: After receiving the nursing service feedback, trigger a second single read operation to read the basic information of the caregiver, which includes the caregiver's identity information, professional qualification information, service history record, customer evaluation information and current work status information; Through the nursing service feedback, combined with the basic information of the caregiver, a one-way nursing safety supervision verification is performed to obtain the second verification information.

[0038] In an embodiment of the present application, after receiving the feedback information from the nursing service end, the second single read operation is first triggered to retrieve the basic information of the caregiver related to the nursing service from the database of the platform. The purpose of this step is to ensure that the caregivers providing nursing services are equipped with legal qualifications and good service records. Specifically, the basic information of the caregiver includes the identity information of the caregiver (such as name and identity authentication information, used to confirm whether the identity of the caregiver is legal), professional qualification information (such as a practicing certificate or certification, to ensure that the caregiver is equipped with the professional ability to perform specific nursing tasks), service history records (including the tasks performed by the caregiver in the past and their performance), customer evaluation information (previous customer feedback on the caregiver's services), and current work status information (whether the caregiver is performing other tasks).

[0039] The basic information of these caregivers is matched and verified with the feedback information of nursing services to form a one-way nursing safety supervision verification. Through this verification, it is ensured that the nursing service is completed by a qualified caregiver and that the service process complies with the nursing standards of the platform. For example, it is verified whether the identity and qualifications of the caregiver match the nursing tasks performed, and whether the caregiver's service history and customer evaluations indicate that he or she is capable of performing the current tasks.

[0040] Once the verification is complete, a second verification message is generated.

[0041] Step S500: Based on the first verification information and the second verification information, matching is performed on the Internet nursing platform to set a two-way matching architecture for Internet nursing tasks and Internet nursing requests.

[0042] In an embodiment of the present application, a matching operation is first performed on the Internet nursing platform based on the first verification information (data obtained from the customer care needs verification) and the second verification information (nurse information obtained from the nursing service feedback verification) to set up a two-way matching architecture for Internet nursing tasks and Internet nursing requests.

[0043] Specifically, after obtaining the first verification information, the information contains the results of the legality and rationality verification of the customer's care needs, such as the customer's health status, type of care needs, equipment status, etc. The second verification information covers the identity verification of the caregiver, professional qualification review, service compliance assessment, etc. These two types of verification information represent the key data of the demand side (customer) and the service side (caregiver), respectively.

[0044] Next, the two sets of data are intelligently matched through a two-way matching algorithm. First, we look for qualified and experienced caregivers based on the customer's care needs (such as vital signs monitoring, medication management, wound care, etc.), and ensure that the caregiver's current working status allows him to accept new tasks. In addition, we match available equipment and drug resources based on the customer's urgency and type of needs to ensure that the care tasks can be carried out smoothly.

[0045] During the matching process, preset matching rules and scoring mechanisms are used, such as giving priority to assigning experienced caregivers and high-quality equipment to high-urgency nursing tasks. The matching degree is calculated based on these rules, and the best combination of caregivers and resources is selected to ensure that the customer's needs can receive the most appropriate response.

[0046] Once the match is completed, the corresponding Internet nursing tasks and Internet nursing requests are generated. Internet nursing tasks refer to the task list generated by the platform for the caregivers, which lists in detail the nursing operations to be performed, equipment usage instructions and nursing schedules, etc.; Internet nursing requests refer to the service confirmation information generated by the platform for the customer, through which the customer confirms the received nursing service content, time and caregiver information.

[0047] Step S600: In a data storage unit that is communicatively connected to the Internet nursing platform, locate the Internet nursing instance with the error code mark added, identify potential risk points in the nursing service process, set up a nursing risk prediction layer, and perform callback training in combination with the first verification information and the second verification information to establish a safety supervision model.

[0048] In an embodiment of the present application, first, the recorded nursing instances are located through a data storage unit that is communicatively connected to the Internet nursing platform. In order to identify anomalies or potential risk points that may occur in the nursing service process, these nursing instances are analyzed, and error code tags are added according to the rules or the manifestation of actual problems. For example, if a nursing task is not completed according to the predetermined process, or the equipment status does not meet the requirements, an error code is added to the corresponding instance. Next, based on the marked instances, the potential risk points in the nursing service process are analyzed. This step uses a risk identification algorithm to detect anomalies in the nursing process, such as task execution delays, equipment failures, or service quality deviations. These risk points will be classified and recorded for further analysis and prediction.

[0049] In order to better manage and prevent these potential risks, a nursing risk prediction layer is set up. This prediction layer collects and analyzes marked nursing instances, and uses historical data and real-time feedback to predict possible risks in the future. For example, based on past error codes and time series data of nursing instances, possible failures or delays of a certain type of nursing service or equipment are predicted. In this process, callback training is performed by combining the first verification information obtained in the early stage (the verification results of customer needs and health status) with the second verification information (the verification results of the nurse's identity, qualifications and service execution). Callback training refers to re-entering the first and second verification information into the model to further optimize the accuracy of the nursing risk prediction and safety supervision model. This training can continuously adjust and improve the model, more accurately identify potential risks in nursing, and provide effective response plans.

[0050] Ultimately, more comprehensive oversight of nursing services will be provided through the establishment of a safety supervision model.

[0051] Furthermore, the method provided in the application embodiment also includes: A camera access request is issued, and based on the camera access permission received from the client, wound photos are collected and received, and wound features are identified using image recognition technology; based on the wound features, a wound risk prediction model is generated using transfer learning technology to obtain wound healing progress and predicted infection risks; based on the wound healing progress and predicted infection risks, nursing qualification constraints are added, and the nursing qualification constraints include a nursing level threshold and an emergency tolerance range.

[0052] In the embodiment of the present application, a camera access request is first issued to the client through the device permission management technology. This request is made to the client device through the API. When the client agrees to the permission on the device, the device camera is activated to collect images, especially to take pictures of the wound area that needs care.

[0053] After the photo collection is completed, the image data is securely uploaded to the Internet care platform using encrypted transmission protocols such as HTTPS to ensure the security and integrity of the data during transmission. This process is achieved through image collection and encrypted transmission technology to ensure that the customer's privacy is protected.

[0054] Once wound photos are uploaded to the platform, they are analyzed using a convolutional neural network model. The CNN model extracts feature information from the image layer by layer to identify key features of the wound, such as wound size, shape, color change, blurriness of the edges, etc. After the image feature recognition is completed, transfer learning technology is used to fine-tune the wound risk assessment model based on the pre-trained deep learning model. Transfer learning can quickly adapt to new tasks, and even if data is limited, it can generate a model suitable for specific care scenarios through fine-tuning. The model predicts the healing progress of the wound based on the identified wound features and assesses the possible risk of infection. For example, it predicts whether the wound is likely to be infected in the next few days or whether there are signs of delayed healing.

[0055] Next, based on the risk assessment results of the wound, the rule engine is used to add nursing qualification constraints. The rule engine sets specific nursing requirements based on the risk level of the wound. For example, if the wound has a high risk of infection, it is limited to a nurse with advanced nursing qualifications to handle this task. At the same time, the rule engine also considers the quality and matching of nursing services to ensure that the right people undertake the corresponding nursing tasks.

[0056] To ensure the speed of nursing response in emergency situations, the nursing level threshold and emergency tolerance range are dynamically adjusted based on the risk assessment results of the wound. If the infection risk of the wound is high, the nursing response time is shortened and the task priority of the nursing staff is adjusted. For example, nursing staff who are closer to the client and have advanced nursing qualifications are given priority to handle wound care tasks.

[0057] Furthermore, in the method provided in the embodiment of the application, locating the Internet nursing instance with the error code mark added in the data storage unit in communication connection with the Internet nursing platform, further includes: Taking the geographic location information in the customer basic information as the center and the coverage area limited by a preset time as the radius, M nursing service terminals are connected; traversing the target area, N clients are determined, and a short-time communication network between the clients and the nursing service terminals is established, and the short-time communication network includes M×N short-time communication channels; based on the M×N short-time communication channels in the short-time communication network, the data flow in the short-time communication channel is analyzed, and an error code mark is set, which cannot be illegally erased, and includes a nursing response delay mark, a nursing operation violation mark, and an equipment failure mark.

[0058] In the embodiment of the present application, firstly, based on the geographical location information of the customer, the customer is used as the center point to determine a service range with a coverage area limited by a preset time as the radius. The size of this coverage area is determined by the nursing response time or the urgency of the service. For example, in an emergency nursing scenario, the coverage area is set to be smaller to facilitate a quick response.

[0059] After determining the coverage area, connect M nursing service terminals, that is, nursing staff or nursing equipment terminals with response capabilities in the area. Through communication protocols such as MQT, connections are established with these nursing service terminals to form a temporary network structure.

[0060] Then, N clients are traversed in the target area, that is, the nursing service demanders in the area are identified. These clients may be personal devices of the clients, such as smartphones, wearable devices, etc., which play the role of information uploading and monitoring in the nursing process. A short-term communication network is established between the client and the nursing service end. This communication network is temporarily established to achieve fast and efficient data exchange in the nursing service process.

[0061] The short-term communication network includes M×N short-term communication channels, each of which corresponds to a connection between a nursing server and a client. Real-time data in the nursing process, such as equipment status and nursing staff's operation information, are received through these channels.

[0062] Once the short-term communication network is established, the data streams in the short-term communication channel are analyzed. These data streams include all operations and equipment status information during the nursing process, such as the time nodes of nursing operations, the operating status of equipment, and the response speed of nursing services. Using data stream analysis technology, by monitoring this information, error code tags are automatically set.

[0063] Error code tags cannot be illegally erased, ensuring that all abnormal operations can be recorded and traced. Common error code tags include nursing response delay tags, nursing operation violation tags, and equipment failure tags.

[0064] The nursing response delay mark is set automatically when the nursing service response time exceeds the preset standard. The nursing operation violation mark is set when the nursing staff does not operate according to the preset nursing specifications. The equipment failure mark is recorded if the equipment used in the nursing process fails or is abnormal.

[0065] Furthermore, the method provided in the application embodiment also includes: Add a frequency record of the nursing operation violation mark in the error code mark, and set U distributed quality rating nodes in the short-term communication network; based on the U distributed quality rating nodes, set a penalty mechanism, the penalty mechanism is used to capture the average number of indicators and peak number of indicators in the time series and lock the nursing level or reduce the nursing level; at the same time, map and connect the V nursing service terminals determined by the penalty mechanism to tighten the emergency tolerance range.

[0066] In the embodiment of the present application, the frequency of nursing operation violations is first recorded and analyzed by log recording and time series data analysis technology. Each nursing operation violation, such as failure to complete the task on time or failure to follow the operating specifications, will generate a violation mark and record it in the system log. These mark data form a time series, including the time, type and associated nursing service end of each violation.

[0067] Next, multiple distributed quality rating nodes are deployed in the short-term communication network. These nodes use data collection and monitoring technology to collect operation data from each nursing service end in real time. The data includes the response time, completion status and operation quality of the nursing task. Each node performs preliminary processing on the received data and calculates the quality score of the nursing service. Specifically, after receiving the data from the nursing service end, each node first performs a data cleaning operation to remove noise and outliers in the data to ensure the accuracy of the data. For example, if the time record of a nursing task is incomplete or there are obvious errors, the node will automatically identify it and mark it as invalid data. Next, the node will perform data standardization, such as normalizing the response time of the nursing task to ensure that the data between different nursing tasks are comparable. After preliminary processing, the node analyzes the data and generates a nursing service quality score. The calculation of the quality score is based on multiple indicators, including response time, task completion rate and violation behavior. When scoring the response time, the actual response time of each nursing task is compared with the preset standard response time. The shorter the response time, the higher the score. When scoring the task completion rate, the completion of the nursing tasks is counted. The higher the rate of tasks completed on time and according to standards, the higher the score. When scoring violations, the violations in each nursing task are recorded. The number and severity of violations will directly affect the score. The score is calculated through a weighted algorithm. Each indicator will be given different weights according to its importance to the quality of care, which is pre-set by technical experts. For example, response time and task completion rate may have higher weights, while violations will significantly reduce the score. Finally, the node summarizes the scores of each nursing service end and uses them as the basis for subsequent adjustments and penalty mechanisms.

[0068] When analyzing the collected violation data, time series analysis and statistical techniques are used to calculate the average number of indicators and the peak number of indicators. These indicators measure the average frequency of nursing operation violations in a specific time period and the maximum number of violations in a certain time window. If the average or peak indicator exceeds the preset threshold, the penalty mechanism will be triggered. The rule engine performs automated decisions based on the results of time series analysis. When violations occur frequently, the rule engine will decide whether to implement a penalty mechanism for the nursing staff. The penalty mechanism includes nursing level locking or nursing level reduction, which limits the nursing service end to only handle lower-level nursing tasks, or completely prohibits it from handling high-level nursing tasks to ensure that the service quality is not affected by continuous violations. At the same time, this penalty result is mapped to the specific nursing service end through a distributed task scheduling algorithm. The punished nursing service end will be excluded from the allocation of high-level or emergency tasks to avoid adverse effects on high-risk nursing tasks due to operational violations.

[0069] In order to strengthen the management of emergency tasks and tighten the emergency tolerance range, the dynamic resource allocation and priority adjustment technology can be used to shorten the response time window of the nursing service end when handling emergency tasks, or directly limit its authority to receive emergency tasks. This ensures that violations will not have a negative impact on the response speed and quality of emergency nursing tasks, thereby improving the safety and reliability of the overall nursing service.

[0070] Step S700: Connect the two-way matching architecture with the nursing risk prediction layer to determine the optimal allocation combination, and combine the security supervision model to perform security supervision on the Internet nursing platform, and determine whether to issue a safety warning signal, and the safety warning signal is used to activate the safety warning system.

[0071] In an embodiment of the present application, the best allocation combination is determined by connecting the two-way matching architecture with the nursing risk prediction layer. The task of the two-way matching architecture is to match the first verification information (customer nursing needs, health status, etc.) with the second verification information (nursing staff qualifications, service history, etc.). The two-way matching architecture ensures that the customer's nursing needs can be met by suitable nursing staff or equipment. At the same time, the nursing risk prediction layer predicts the risks that the current nursing task may face by analyzing the risk data in previous nursing operations. The prediction layer uses historical data and machine learning models to evaluate the potential risks of nursing tasks. For example, for high-risk tasks, the prediction layer may prompt to give priority to experienced nursing staff. Once the two-way matching architecture is connected to the nursing risk prediction layer, the best allocation combination begins to be determined. The allocation combination is determined based on matching, risk assessment, and resource availability. Among them, the matching degree is to give priority to nursing staff or equipment that highly matches customer needs.

[0072] Risk assessment is to adjust the allocation strategy according to the prompts of the nursing risk prediction layer to ensure that high-risk nursing tasks are assigned to more experienced or qualified nursing staff. Resource availability is to consider the current status and availability of nursing staff or equipment at the same time to ensure the efficient use of resources.

[0073] After determining the best allocation combination, the entire Internet nursing platform is monitored for security in combination with the security supervision model. The role of the security supervision model is to determine whether the nursing service meets the standards by analyzing the real-time data (such as operation data, response time, equipment status, etc.) that occurs during the nursing process. For example, if an abnormal situation occurs during the execution of a nursing task, the security supervision model will immediately capture these abnormalities.

[0074] Once the safety supervision model detects potential risks or abnormal situations, it determines whether a safety warning signal needs to be issued. This warning signal is triggered based on a pre-set threshold, such as when the abnormal frequency in the nursing operation exceeds the predetermined safety threshold. These warning signals are used to activate the safety warning system.

[0075] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects: This application connects to the client, receives customer care needs, and customer care needs correspond to the vital signs monitoring service, wound care service, drug management service, and rehabilitation guidance service of the Internet nursing platform; based on customer care needs, performs one-way nursing safety supervision verification, obtains first verification information, and generates Internet nursing tasks; connects to the nursing service end, receives nursing service feedback, and nursing service feedback corresponds to the vital signs monitoring resource allocation global variables, wound care resource allocation global variables, drug management resource allocation global variables, and rehabilitation guidance resource allocation global variables of the Internet nursing platform; based on nursing service feedback, performs one-way nursing safety supervision verification, obtains second verification information, and generates Internet nursing requests; based on The first verification information and the second verification information are matched on the Internet nursing platform, and a two-way matching architecture of Internet nursing tasks and Internet nursing requests is set; in the data storage unit connected to the Internet nursing platform, the Internet nursing instance with the error code mark is located, the potential risk points in the nursing service process are identified, and the nursing risk prediction layer is set, and callback training is performed in combination with the first verification information and the second verification information to establish a safety supervision model; the two-way matching architecture is connected to the nursing risk prediction layer, the best allocation combination is determined, and the Internet nursing platform is safely supervised in combination with the safety supervision model, and it is determined whether to issue a safety warning signal, and the safety warning signal is used to activate a safety warning system. The present invention solves the technical problems of slow response speed and low nursing service matching efficiency in the prior art when performing nursing supervision. Through the nursing risk prediction layer based on the rule-based two-way matching architecture and the machine learning model, the customer needs and nursing staff qualifications are intelligently matched, resource allocation is optimized, and the nursing process is monitored in real time in combination with the safety supervision model, risks are identified and warning signals are issued, so as to achieve the technical effect of improving the efficiency of nursing supervision and the matching efficiency of nursing services.

[0076] Embodiment 2, based on the same inventive concept as a security supervision method for Internet nursing in the above embodiment, Figure 2 As shown, the present application provides a safety supervision system for Internet nursing. The system and method embodiments in the embodiments of the present application are based on the same inventive concept, wherein the safety supervision system for Internet nursing is communicatively connected with a safety early warning system, and the system includes: A nursing demand receiving unit 11, wherein the nursing demand receiving unit 11 is connected to a client to receive a customer's nursing demand, wherein the customer's nursing demand corresponds to a vital sign monitoring service, a wound care service, a drug management service, and a rehabilitation guidance service of an Internet nursing platform; a first safety supervision verification unit 12, wherein the first safety supervision verification unit 12 performs a one-way nursing safety supervision verification based on the customer's nursing demand, obtains first verification information, and generates an Internet nursing task; a service feedback receiving unit 13, wherein the service feedback receiving unit 13 is connected to a nursing service end to receive nursing service feedback, wherein the nursing service feedback corresponds to a vital sign monitoring resource allocation global variable, a wound care resource allocation global variable, a drug management resource allocation global variable, and a rehabilitation guidance resource allocation global variable of an Internet nursing platform; a second safety supervision verification unit 14, wherein the second safety supervision verification unit 14 performs a one-way nursing safety supervision verification based on the nursing service feedback, obtains a second verification information, and generates an Internet nursing task. information, and generates an Internet nursing request; a matching unit 15, the matching unit 15 matches the Internet nursing platform based on the first verification information and the second verification information, and sets a two-way matching architecture for Internet nursing tasks and Internet nursing requests; a callback training unit 16, the callback training unit 16 locates the Internet nursing instance with an error code mark added in a data storage unit that is communicatively connected to the Internet nursing platform, identifies potential risk points in the nursing service process, sets a nursing risk prediction layer, and performs callback training in combination with the first verification information and the second verification information to establish a safety supervision model; a safety supervision unit 17, the safety supervision unit 17 connects the two-way matching architecture with the nursing risk prediction layer, determines the best allocation combination, and performs safety supervision on the Internet nursing platform in combination with the safety supervision model, and determines whether to issue a safety warning signal, the safety warning signal being used to activate the one safety warning system.

[0077] Furthermore, the system is also used to implement the following functions: After receiving the customer care demand, the first single read operation is triggered to read the customer's basic information, wherein the customer's basic information includes customer identity information, health status information, geographic location information, and auxiliary device usage information. The auxiliary device defined by the auxiliary device usage information is a wearable device, and the auxiliary device usage information includes the device model, device serial number, and device usage status. Through the customer care demand, a one-way care safety supervision verification is performed in combination with the customer's basic information to obtain the first verification information.

[0078] Furthermore, the system is also used to implement the following functions: After receiving the nursing service feedback, a second single read operation is triggered to read the basic information of the caregiver, which includes the caregiver's identity information, professional qualification information, service history record, customer evaluation information and current work status information; through the nursing service feedback, combined with the caregiver's basic information, a one-way nursing safety supervision verification is performed to obtain the second verification information.

[0079] Furthermore, the system is also used to implement the following functions: The wearable device has a built-in physiological signal acquisition module, which is used to upload the monitored data to the Internet nursing platform in real time through the BLE protocol; evaluate the vital signs of the target customer, determine whether it is in an abnormal mode, and automatically trigger the alternative allocation combination with the highest matching degree with the target customer, wherein the matching indicators of the target customer include the urgency of nursing needs and the availability of nursing services, and the nursing service availability includes equipment availability and drug availability.

[0080] Furthermore, the system is also used to implement the following functions: A camera access request is issued, and based on the camera access permission received from the client, wound photos are collected and received, and wound features are identified using image recognition technology; based on the wound features, a wound risk prediction model is generated using transfer learning technology to obtain wound healing progress and predicted infection risks; based on the wound healing progress and predicted infection risks, nursing qualification constraints are added, and the nursing qualification constraints include a nursing level threshold and an emergency tolerance range.

[0081] Furthermore, the system is also used to implement the following functions: Taking the geographic location information in the customer basic information as the center and the coverage area limited by a preset time as the radius, M nursing service terminals are connected; traversing the target area, N clients are determined, and a short-time communication network between the clients and the nursing service terminals is established, and the short-time communication network includes M×N short-time communication channels; based on the M×N short-time communication channels in the short-time communication network, the data flow in the short-time communication channel is analyzed, and an error code mark is set, which cannot be illegally erased, and includes a nursing response delay mark, a nursing operation violation mark, and an equipment failure mark.

[0082] Furthermore, the system is also used to implement the following functions: Add a frequency record of the nursing operation violation mark in the error code mark, and set U distributed quality rating nodes in the short-term communication network; based on the U distributed quality rating nodes, set a penalty mechanism, the penalty mechanism is used to capture the average number of indicators and peak number of indicators in the time series and lock the nursing level or reduce the nursing level; at the same time, map and connect the V nursing service terminals determined by the penalty mechanism to tighten the emergency tolerance range.

[0083] Embodiment 3, a safety warning system, including a processor, a buzzer and an LED display, is used to execute a computer program in a linked manner, and the processor implements the steps of a safety supervision method for Internet nursing in embodiment 1 when executing the computer program. The buzzer is used to sound an alarm, and the LED display is used to display specific warning information.

[0084] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0085] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0086] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A safety supervision method for Internet nursing, characterized in that: The method is applied to a safety supervision system for Internet nursing, wherein the safety supervision system for Internet nursing is communicatively connected with a safety early warning system, and the method comprises: Connecting to the client and receiving the customer's care needs, which correspond to the vital signs monitoring service, wound care service, drug management service, and rehabilitation guidance service of the Internet care platform; Based on the customer's nursing needs, one-way nursing safety supervision verification is performed, first verification information is obtained, and an Internet nursing task is generated; Connecting to the nursing service end to receive nursing service feedback, wherein the nursing service feedback corresponds to the vital sign monitoring resource allocation global variable, wound care resource allocation global variable, drug management resource allocation global variable, and rehabilitation guidance resource allocation global variable of the Internet nursing platform; Based on the nursing service feedback, one-way nursing safety supervision verification is performed, second verification information is obtained, and an Internet nursing request is generated; Based on the first verification information and the second verification information, matching is performed on the Internet nursing platform to set a two-way matching architecture of Internet nursing tasks and Internet nursing requests; In a data storage unit that is communicatively connected to the Internet nursing platform, locate the Internet nursing instance with the added error code mark, identify the potential risk points in the nursing service process, set the nursing risk prediction layer, and perform callback training in combination with the first verification information and the second verification information to establish a safety supervision model; The two-way matching architecture is connected to the nursing risk prediction layer to determine the best allocation combination, and the Internet nursing platform is supervised for safety in combination with the safety supervision model, and it is determined whether to issue a safety warning signal, and the safety warning signal is used to activate the safety warning system.

2. A safety supervision method for Internet nursing according to claim 1, characterized in that: Based on the customer care needs, one-way care safety supervision verification is performed to obtain first verification information, and the method further includes: After receiving the customer care demand, trigger a first single read operation to read the customer basic information, the customer basic information includes customer identity information, health status information, geographic location information and auxiliary device usage information, the auxiliary device defined in the auxiliary device usage information is a wearable device, and the auxiliary device usage information includes device model, device serial number, and device usage status; Through the customer care needs, combined with the customer basic information, a one-way care safety supervision verification is performed to obtain the first verification information.

3. A safety supervision method for Internet nursing as claimed in claim 2, characterized in that: Based on the nursing service feedback, one-way nursing safety supervision verification is performed to obtain second verification information, and the method includes: After receiving the nursing service feedback, trigger a second single read operation to read the basic information of the caregiver, which includes the caregiver's identity information, professional qualification information, service history record, customer evaluation information and current work status information; Through the nursing service feedback, combined with the basic information of the caregiver, a one-way nursing safety supervision verification is performed to obtain the second verification information.

4. A safety supervision method for Internet nursing as claimed in claim 2, characterized in that: The wearable device has a built-in physiological signal acquisition module, which is used to upload the monitored data to the Internet nursing platform in real time via the BLE protocol; Assess the target customer's vital sign status, determine whether it is in an abnormal mode, and automatically trigger the alternative allocation combination that best matches the target customer, wherein the target customer's matching indicators include the urgency of nursing needs and the availability of nursing services, and the nursing service availability includes equipment availability and drug availability.

5. A safety supervision method for Internet nursing as claimed in claim 4, characterized in that: The method comprises: Sending a camera access request, and upon receiving the camera access permission from the client, collecting and receiving wound photos, and identifying wound features using image recognition technology; Based on the wound characteristics, a wound risk prediction model is generated using transfer learning technology to obtain wound healing progress and predict infection risk; Based on the wound healing progress and predicted infection risk, nursing qualification constraints are added, and the nursing qualification constraints include a nursing level threshold and an emergency tolerance range.

6. A safety supervision method for Internet nursing as claimed in claim 5, characterized in that: In a data storage unit that is in communication with the Internet nursing platform, locating the Internet nursing instance to which the error code mark is added, the method further comprises: Taking the geographical location information in the basic customer information as the center and the coverage area limited by the preset time as the radius, connecting M nursing service terminals; Traversing the target area, determining N clients, and establishing a short-term communication network between the clients and the nursing service end, wherein the short-term communication network includes M×N short-term communication channels; Based on the M×N short-time communication channels in the short-time communication network, the data stream in the short-time communication channel is analyzed, and an error code mark is set. The error code mark cannot be illegally erased, including a nursing response delay mark, a nursing operation violation mark, and an equipment failure mark.

7. A safety supervision method for Internet nursing as claimed in claim 6, characterized in that: The method comprises: Adding a frequent record of nursing operation violation marks in the error code mark, and setting U distributed quality rating nodes in the short-term communication network; Based on the U distributed quality rating nodes, a penalty mechanism is set, wherein the penalty mechanism is used to capture the average number of times index and the peak number of times index in the time series and to lock the nursing level or reduce the nursing level; At the same time, the V nursing service terminals determined by the penalty mechanism are mapped and connected to tighten the emergency tolerance range.

8. A safety supervision system for Internet nursing, characterized in that: A method for implementing a safety supervision method for Internet nursing according to any one of claims 1 to 7, wherein the safety supervision system for Internet nursing is communicatively connected to a safety early warning system, comprising: A nursing demand receiving unit, which is connected to a client and receives a customer's nursing demand, wherein the customer's nursing demand corresponds to a vital sign monitoring service, a wound care service, a drug management service, and a rehabilitation guidance service of an Internet nursing platform; A first safety supervision verification unit, which performs a one-way nursing safety supervision verification based on the customer's nursing needs, obtains first verification information, and generates an Internet nursing task; A service feedback receiving unit, the service feedback receiving unit is connected to the nursing service end and receives nursing service feedback, wherein the nursing service feedback corresponds to the vital sign monitoring resource allocation global variable, the wound care resource allocation global variable, the drug management resource allocation global variable, and the rehabilitation guidance resource allocation global variable of the Internet nursing platform; A second safety supervision verification unit, which performs a one-way nursing safety supervision verification based on the nursing service feedback, obtains second verification information, and generates an Internet nursing request; A matching unit, wherein the matching unit performs matching on the Internet nursing platform based on the first verification information and the second verification information, and sets a two-way matching architecture of Internet nursing tasks and Internet nursing requests; A callback training unit, wherein the callback training unit locates the Internet nursing instance with the error code mark added in the data storage unit in communication connection with the Internet nursing platform, identifies the potential risk points in the nursing service process, sets the nursing risk prediction layer, and performs callback training in combination with the first verification information and the second verification information to establish a safety supervision model; A safety supervision unit connects the two-way matching architecture with the nursing risk prediction layer, determines the best allocation combination, and performs safety supervision on the Internet nursing platform in combination with the safety supervision model, and determines whether to issue a safety warning signal, wherein the safety warning signal is used to activate the safety warning system.

9. A safety warning system, comprising a processor, a buzzer and an LED display, for linking and executing a computer program, characterized in that: When the processor executes the computer program, the steps of a safety supervision method for Internet nursing described in any one of claims 1 to 7 are implemented.

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