Intelligent supervision method and system based on medical health

Abstract

The invention discloses an intelligent supervision method and system based on medical treatment and public health, and relates to the technical field of medical supervision. A target object needing medical treatment and public health supervision is selected, full-process medical data related to medical treatment and public health of the target object are collected, different medical data carriers of the full-process medical data are identified, and the target object is subjected to medical treatment and public health supervision; each medical data carrier controls acquisition of medical data of a respective corresponding stage in the whole-process medical data, data mining of the whole-process medical data is carried out to obtain medical data features in each medical stage, and standard data features of the medical data features in each medical stage are associated to obtain standard data features of the medical data features in each medical stage; the method comprises the steps of performing feature analysis on a to-be-analyzed medical data feature group of each medical stage to obtain a medical and health problem of each medical stage, generating a corresponding risk early warning prompt, processing the corresponding medical and health problem after receiving the risk early warning prompt, and performing feature analysis on the to-be-analyzed medical data feature group of each medical stage. And supervising execution objects for processing the medical and health problems, and evaluating the processing efficiency of the execution objects.

Description

Technical Field

[0001] The present invention relates to the field of medical supervision technology, and in particular to a smart supervision method and system based on medical care and health. Background Art

[0002] With the development of society and the continuous improvement of medical standards, medical and health safety issues have received increasing attention. Traditional medical and health supervision methods mainly rely on manual inspections and paper records, which have low supervision efficiency. The collection of data information at each link in the entire process of medical and health supervision often has improper operations, which has affected the accuracy of medical data at each medical stage. In addition, the medical risks at each medical stage in the entire process of medical supervision cannot be located and resolved in a timely manner, which greatly affects the level of supervision of medical and health supervision.

[0003] Therefore, in order to improve the supervision level of all stages in the whole process of medical and health supervision, better reduce medical and health risks, and ensure the smooth and safe operation of the medical system, it is necessary to study an efficient and intelligent medical and health supervision method. Summary of the invention

[0004] In order to solve the above problems, the purpose of the present invention is to provide a smart supervision method and system based on medical and health care.

[0005] The purpose of the present invention can be achieved by the following technical solution: A smart supervision method based on medical and health care, comprising the following steps:

[0006] Step S1: Select the target object of medical and health supervision, collect the full-process medical data of the target object related to medical and health, and identify different medical data carriers corresponding to the full-process medical data, so that each medical data carrier controls the collection of the corresponding partial stage medical data in the full-process medical data;

[0007] Step S2: Data mining is performed on the full-process medical data of the target object, thereby obtaining the medical data features of the target object at each medical stage, and the medical data features at each medical stage are associated with corresponding standard data features, thereby generating a medical data feature group to be analyzed at each medical stage;

[0008] Step S3: Perform feature analysis on the feature group of medical data to be analyzed at each medical stage, thereby obtaining the medical and health problems at each medical stage, and generating risk warning prompts for the corresponding medical stage;

[0009] Step S4: After receiving the risk warning prompt, the corresponding medical and health issues are processed, and the execution objects that process the medical and health issues are supervised in real time, and the efficiency of the execution objects in processing the medical and health issues is evaluated.

[0010] Furthermore, the process of selecting the target object of medical and health supervision and collecting the full-process medical data of the target object related to medical and health includes:

[0011] A data collection period is set, and the full-process medical data related to medical care and health care of a target object is collected during the data collection period. The full-process medical data consists of stage medical data of different medical stages;

[0012] The different medical stages include outpatient stage, instrument examination stage, hospitalization stage, surgery stage, rehabilitation stage and follow-up visit stage, and the medical data of different stages include outpatient data, instrument examination data, hospitalization data, surgery data, rehabilitation data and follow-up data.

[0013] Furthermore, different medical data carriers corresponding to the full-process medical data are identified, and each medical data carrier controls the collection of medical data corresponding to a respective part of the full-process medical data, including:

[0014] Identify the medical data carriers of the target object at different medical stages of the full-process medical data, and build a data control node for each medical data carrier. Each data control node is used to control the collection of medical data at some stages of the full-process medical data.

[0015] Set up the respective standard operating procedures for each medical data carrier when collecting medical data at the corresponding stage, obtain the actual operating procedures of each medical data carrier when collecting medical data at the corresponding stages of the full process of medical data, and enter the standard operating procedures and actual operating procedures of each medical data carrier into their respective data control nodes;

[0016] The data control node determines whether there is an abnormal collection event for each corresponding medical data carrier, and performs corresponding control. When the actual operation process of the medical data carrier is inconsistent with the standard operation process, it is determined that there is an abnormal collection event. Otherwise, it is determined that there is no abnormal collection event. When an abnormal collection event occurs on a medical data carrier, a corresponding early warning prompt is generated, and the medical data carrier adjusts its actual operation process when collecting medical data at the corresponding stage until it meets the standard operation process. When there is no abnormal collection event, no operation is performed.

[0017] Furthermore, the process of conducting data mining on the full-process medical data of the target object and obtaining the medical data characteristics of the target object at each medical stage includes:

[0018] Construct a data mining program for data mining of the whole process medical data, import the whole process medical data into the data mining program and divide it into stage medical data of several medical stages, start the data mining program, perform cluster analysis on the stage medical data of the target object in each medical stage, and then obtain several cluster data groups corresponding to the stage medical data in each medical stage;

[0019] Each cluster data group corresponds to a partial medical data feature of the current medical stage, and the proportion of each cluster data group in the medical data of the corresponding stage is counted, and the proportion value is used as the feature key value of the corresponding cluster data group. The feature key value is used to determine whether the corresponding cluster data group is a component element required for the corresponding medical data feature of the corresponding medical stage, and the component element is the partial medical data feature corresponding to the cluster data group;

[0020] The feature key value corresponding to the cluster data group is denoted as TZ, and a screening threshold is set, and the screening threshold is denoted as μ. Some medical data features corresponding to all cluster data groups with TZ≥μ are screened out and integrated to generate medical data features corresponding to each medical stage, and all cluster data groups with TZ<μ are eliminated.

[0021] Furthermore, the process of associating the medical data features at each medical stage with the corresponding standard data features and then generating the medical data feature group to be analyzed at each medical stage includes:

[0022] Obtain the target object's historical full-process medical data, and perform data analysis, feature extraction, and feature screening to derive the target object's standard data features at each medical stage. Integrate the target object's medical data features and standard data features at each medical stage to generate a set of medical data features to be analyzed for the target object at each medical stage.

[0023] Furthermore, the process of performing feature analysis on the feature group of medical data to be analyzed at each medical stage, thereby obtaining the medical and health problems at each medical stage, and generating risk warning prompts for the corresponding medical stage includes:

[0024] Constructing a feature analysis network for feature analysis of the feature groups of medical data to be analyzed, inputting the feature groups of medical data to be analyzed in all medical stages into the feature analysis network, and using the feature analysis network to calculate the data fitting degree of each feature group of medical data to be analyzed;

[0025] The data fit is denoted as NH 数据 , then NH 数据 =D 相似 / D 数据组 , where D 相似D is the data volume corresponding to the data part with similar and consistent medical data features and normative data features in each medical data feature group to be analyzed. 数据组 For the amount of data corresponding to each medical data feature group to be analyzed, a fitting threshold is set and recorded as τ;

[0026] NH 数据 All the medical data feature groups to be analyzed <τ are marked as the hidden danger feature data groups of the corresponding medical stage. 数据 No operation is performed on the medical data feature groups to be analyzed that are ≥τ, and all the hidden danger feature data groups are input into the set medical database. The medical database is used to record all medical and health problems occurring at different medical stages. Each medical and health problem corresponds to a retrieval fit degree in the medical database;

[0027] According to the rule that the data fit is equal to the retrieval fit, the hidden danger feature data group corresponding to each medical stage is matched in the medical database to obtain all the medical and health problems corresponding to each medical stage. The frequency of the medical and health problems corresponding to the target object in each medical stage is counted, and the warning level judgment value is set. According to the numerical relationship between the frequency and the warning level judgment value, the risk warning prompts for the corresponding medical stage are generated. The risk warning prompts include blue warning prompts, yellow warning prompts, orange warning prompts and red warning prompts.

[0028] Furthermore, after receiving the risk warning prompt, the corresponding medical and health problems are processed, and the execution object of processing the medical and health problems is supervised in real time, and the process of evaluating the efficiency of the execution object in processing the medical and health problems includes:

[0029] Receive all risk warning prompts generated by the target object, and handle all medical and health problems existing in the corresponding medical stage of the target object in order of the warning priority corresponding to the risk warning prompt from high to low, conduct real-time supervision on all execution objects that handle medical and health problems, regard the process of each execution object handling medical and health problems as a task event, and obtain the task execution information of the task event, which includes the task completion degree and task processing time;

[0030] The execution object executes the corresponding task event, and evaluates the processing efficiency of the corresponding execution object in handling medical and health issues based on the task execution information. The execution object whose processing efficiency meets the preset efficiency is identified as the resident task object for handling medical and health issues, and the execution object whose processing efficiency does not meet the preset efficiency is replaced.

[0031] Furthermore, a smart supervision system based on medical and health care includes:

[0032] The medical data collection module is used to select the target object of medical and health supervision, collect the full-process medical data related to the target object and identify the different medical data carriers corresponding to the full-process medical data, and each medical data carrier controls the collection of the corresponding part of the full-process medical data;

[0033] The medical data processing module is used to perform data mining on the full-process medical data of the target object, thereby obtaining the medical data features of the target object at each medical stage, and associating the medical data features at each medical stage with the corresponding standard data features, thereby generating a group of medical data features to be analyzed at each medical stage;

[0034] The medical data analysis module is used to perform feature analysis on the feature groups of medical data to be analyzed at each medical stage, thereby obtaining the medical and health problems at each medical stage and generating risk warning prompts for the corresponding medical stage;

[0035] The medical and health supervision module is used to handle corresponding medical and health issues after receiving risk warning prompts, conduct real-time supervision on the execution objects of handling medical and health issues, and evaluate the efficiency of the execution objects in handling medical and health issues.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. Through supervising the target objects related to medical and health care and collecting their full-process medical data, different medical data carriers corresponding to the full-process medical data are identified. Each medical data carrier controls the collection of medical data at its corresponding stage, thus ensuring the accuracy of data collection at each process stage related to medical and health care.

[0038] 2. After data mining the entire medical data process, the medical data characteristics of each medical stage are obtained, and after associating the medical data characteristics of each medical stage with the standard data characteristics, the corresponding medical data feature group to be analyzed is generated. After feature analysis of the medical data feature group to be analyzed, the medical and health problems in each medical stage are obtained, and corresponding risk warning prompts are generated and handed over to the executor. The executor arranges the processing priority of the medical and health problems corresponding to different medical stages, and supervises the executor in real time to evaluate its processing efficiency of medical and health problems, so as to realize the timely detection and discovery of medical and health problems that cause risk hazards in the entire process of medical and health supervision, and deal with them in a timely manner, so as to improve the efficiency of medical and health supervision and effectively reduce medical and health risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0040] like Figure 1 As shown, a smart supervision method based on medical and health care includes the following steps:

[0041] Step S1: Select the target object of medical and health supervision, collect the full-process medical data of the target object related to medical and health, and identify different medical data carriers corresponding to the full-process medical data, so that each medical data carrier controls the collection of the corresponding partial stage medical data in the full-process medical data;

[0042] Step S2: Data mining is performed on the full-process medical data of the target object, thereby obtaining the medical data features of the target object at each medical stage, and the medical data features at each medical stage are associated with corresponding standard data features, thereby generating a medical data feature group to be analyzed at each medical stage;

[0043] Step S3: Perform feature analysis on the feature group of medical data to be analyzed at each medical stage, thereby obtaining the medical and health problems at each medical stage, and generating risk warning prompts for the corresponding medical stage;

[0044] Step S4: After receiving the risk warning prompt, the corresponding medical and health issues are processed, and the execution objects that process the medical and health issues are supervised in real time, and the efficiency of the execution objects in processing the medical and health issues is evaluated.

[0045] It should be further explained that, in the specific implementation process, the process of selecting the target object of medical and health supervision and collecting the full-process medical data related to medical and health of the target object includes:

[0046] Select the target object of medical and health supervision, which is a major hospital, community health service station and medical clinic, set a data collection period, and collect the full-process medical data related to medical and health of a target object during the data collection period, wherein the collection of the full-process medical data related to medical and health of the target object is carried out through various types of medical information equipment deployed by the target object, and the various types of medical information equipment include hospital information terminals, electronic medical record equipment, laboratory information management equipment, radiological information management equipment and drug management equipment;

[0047] The full-process medical data consists of stage medical data of different medical stages;

[0048] The different medical stages include outpatient stage, instrument examination stage, hospitalization stage, surgery stage, rehabilitation stage and follow-up visit stage;

[0049] The medical data at different stages include outpatient data, instrument examination data, hospitalization data, surgery data, rehabilitation data and follow-up data;

[0050] The correspondence between the medical stage and the medical data of the stage is as follows:

[0051] Outpatient stage—outpatient data;

[0052] Instrument inspection phase—instrument inspection data;

[0053] Hospitalization stage—hospitalization data;

[0054] Surgical stage—surgical data;

[0055] Rehabilitation stage – rehabilitation data;

[0056] Follow-up stage—follow-up data.

[0057] It should be further explained that, in the specific implementation process, different medical data carriers corresponding to the full-process medical data are identified, and each medical data carrier controls the collection of the corresponding partial stage medical data in the full-process medical data, including:

[0058] Identify the medical data carriers of different medical stages of the full-process medical data corresponding to the target object, and construct a corresponding data control node for each medical data carrier. The data control node corresponding to each medical data carrier is used to control the collection of medical data at some stages of the full-process medical data;

[0059] The medical data carrier includes an operation terminal corresponding to the consulting doctor in the outpatient stage, various medical examination instruments corresponding to the instrument examination stage, such as CT machines, MRI equipment, X-ray equipment, and angiography equipment, etc.; it also includes handheld recorders used by the treating nurses and the doctors making rounds to record hospitalization-related information in the hospitalization stage, various surgical operation equipment and surgical information storage equipment in the surgical stage, an information recording terminal used by the nursing staff to record the patient's condition and recovery status in the rehabilitation stage, and a mobile device terminal used by the patient to record his or her own physical condition in the follow-up visit stage;

[0060] Set up the respective standard operating procedures for each medical data carrier when collecting medical data at the corresponding stage, and enter the standard operating procedures in the form of digitized text into the corresponding data control nodes constructed respectively, obtain the actual operating procedures of each medical data carrier when collecting medical data at the corresponding stages of the full process of medical data, and enter the actual operating procedures in the form of digitized text into the respective data control nodes;

[0061] The data control node determines whether there is an abnormal collection event for each corresponding medical data carrier, and performs corresponding control. When the actual operation process of the medical data carrier is inconsistent with the operation specification process, it is determined that there is an abnormal collection event. Otherwise, it is determined that there is no abnormal collection event.

[0062] When an abnormal collection event occurs on a medical data carrier, a corresponding warning prompt is generated, and the medical data carrier adjusts its actual operating procedures for medical data collection at the corresponding stage until it complies with the standard operating procedures. When there is no abnormal collection event, no operation is performed.

[0063] It should be further explained that, in the specific implementation process, the process of data mining the full-process medical data of the target object and then obtaining the medical data characteristics of the target object at each medical stage includes:

[0064] Constructing a data mining program, wherein the data mining program is used to perform data mining on the full-process medical data of the target object, and the data mining program imports the full-process medical data and divides it into stage medical data of several medical stages;

[0065] Obtain the amount of medical data corresponding to each medical stage, and record the amount of data as D;

[0066] Setting a mining computing power interval corresponding to the data mining program, wherein the mining computing power interval includes a first computing power interval, a second computing power interval, and a third computing power interval, and each mining computing power interval is associated with a program computing power of a corresponding numerical value;

[0067] The first computing power interval, the second computing power interval, and the third computing power interval are respectively recorded as Ω1, Ω2, and Ω3, and the values ​​of the program computing power of the first computing power interval, the second computing power interval, and the third computing power interval are respectively recorded as SL1, SL2, and SL3;

[0068] In terms of numerical value, SL1<SL2<SL3, where Ω1=[d1, d2], Ω2=(d2, d3], Ω3=(d3, d4], 0<d1<d2<d3<d4;

[0069] When D∈Ω1, the program computing power of the data mining program is set to SL1;

[0070] When D∈Ω2, the program computing power of the data mining program is set to SL2;

[0071] When D∈Ω3, the program computing power of the data mining program is set to SL3;

[0072] Start the data mining program, perform cluster analysis on the stage medical data of the target object in each medical stage, and then obtain several cluster data groups corresponding to the stage medical data in each medical stage, each cluster data group corresponds to a partial medical data feature of the current medical stage, count the proportion of each cluster data group in the medical data of the corresponding stage, and use the proportion value as the feature key value of the corresponding cluster data group;

[0073] The feature key value is used to determine whether the corresponding clustered data group is a component element required by the corresponding medical data feature of the corresponding medical stage, and the component element is a part of the medical data feature corresponding to the clustered data group; the determination process is as follows:

[0074] The feature key value corresponding to the cluster data group is denoted as TZ, and a screening threshold is set, and the screening threshold is denoted as μ. Some medical data features corresponding to all cluster data groups with TZ≥μ are screened out and integrated to generate medical data features corresponding to each medical stage, and all cluster data groups with TZ<μ are eliminated.

[0075] It should be further explained that, in the specific implementation process, the process of associating the medical data features at each medical stage with the corresponding standard data features and then generating the medical data feature group to be analyzed at each medical stage includes:

[0076] Obtain the historical full-process medical data of the target object, perform data analysis, feature extraction and feature screening on the historical full-process medical data, and then obtain the standard data features of the target object at each medical stage, integrate the medical data features of the target object at each medical stage with the standard data features, and then generate a medical data feature group to be analyzed for the target object at each medical stage;

[0077] The feature group of medical data to be analyzed at different medical stages is recorded as Z[type], where type represents the type of medical stage, and Z[type]=<Z-A,Z-B> , where ZA represents the medical data characteristics under the current type of medical stage, and ZB represents the standard data characteristics under the current type of medical stage.

[0078] Among them, by performing data analysis on the historical full-process medical data, the historical clustering data group under each medical stage is obtained, and by performing feature extraction on the historical clustering data groups of all medical stages under the same historical full-process medical data, the historical medical data features of each medical stage are obtained, and the screening conditions for the historical medical data features under standardized conditions are set to filter out the standardized data features corresponding to each medical stage.

[0079] It should be further explained that, in the specific implementation process, the process of performing feature analysis on the feature group of medical data to be analyzed at each medical stage, thereby obtaining the medical and health problems at each medical stage, and generating risk warning prompts for the corresponding medical stage includes:

[0080] Obtain a feature group of medical data to be analyzed for the target object at each medical stage;

[0081] Constructing a feature analysis network, wherein the feature analysis network is used to perform feature analysis on the feature groups of the medical data to be analyzed at each medical stage, wherein the feature analysis comprises: inputting the feature groups of the medical data to be analyzed at all medical stages into the feature analysis network, and calculating the data fitting degree of each feature group of the medical data to be analyzed by the feature analysis network;

[0082] The data fit is denoted as NH 数据 , then NH 数据 =D 相似 / D 数据组 , where D 相似 D is the data volume corresponding to the data part with similar and consistent medical data features and normative data features in each medical data feature group to be analyzed. 数据组 The amount of data corresponding to each data portion of the medical data feature group to be analyzed;

[0083] Set the fitting threshold and record it as τ;

[0084] NH 数据 All the medical data feature groups to be analyzed <τ are marked as the hidden danger feature data groups of the corresponding medical stage;

[0085] About NH 数据 No operation is performed on all the medical data feature groups to be analyzed that are ≥τ;

[0086] Input all the hidden danger feature data groups into a set medical database, wherein the medical database is used to record all medical and health problems that may occur at different medical stages, and each medical and health problem corresponds to a retrieval fit degree in the medical database;

[0087] According to the rule that the data fitting degree is equal to the search fitting degree, the hidden danger feature data group corresponding to each medical stage is matched in the medical database to obtain all the medical and health problems corresponding to each medical stage;

[0088] Count the frequency of medical and health problems corresponding to the target object in each medical stage, and record the frequency as X, set the warning level judgment value, the warning level judgment value includes K1, K2 and K3, wherein 0<K1<K2<K3, and generate the risk warning prompt of the corresponding medical stage according to the numerical relationship between the frequency and the warning level judgment value;

[0089] The risk warning prompts include blue warning prompts, yellow warning prompts, orange warning prompts and red warning prompts;

[0090] When 0<X≤K1, the risk warning prompt generated in the corresponding medical stage is a blue warning prompt;

[0091] When K1<X≤K2, the risk warning generated at the corresponding medical stage is a yellow warning;

[0092] When K2<X≤K3, the risk warning generated at the corresponding medical stage is an orange warning;

[0093] When X>K3, the risk warning generated in the corresponding medical stage is a red warning.

[0094] Among them, in terms of the severity of risk problems and warning priorities corresponding to risk warning prompts, there are: red warning prompts > orange warning prompts > yellow warning prompts > blue warning prompts.

[0095] It should be further explained that, in the specific implementation process, after receiving the risk warning prompt, the corresponding medical and health problems are handled, and the execution objects of handling medical and health problems are supervised in real time, and the efficiency of handling medical and health problems by the execution objects is evaluated. The process includes:

[0096] Receive all risk warnings generated by the target object, and handle all medical and health problems existing in the corresponding medical stage of the target object in order of the risk warning priorities from high to low;

[0097] Real-time supervision is performed on all execution objects that handle medical and health issues, and the process of each execution object handling medical and health issues is regarded as a task event, and task execution information of the task event is obtained, wherein the task execution information includes task completion degree and task processing time;

[0098] The execution object executes the corresponding task event, and evaluates the processing efficiency of the corresponding execution object in handling medical and health issues based on the task execution information. The execution object whose processing efficiency meets the preset efficiency is identified as the resident task object for handling medical and health issues, and the execution object whose processing efficiency does not meet the preset efficiency is replaced.

[0099] It should be noted that an expected degree of completion and an expected processing time are set for each task event. When the task completion degree of the execution object processing the corresponding task event meets the expected degree of completion and the task processing time meets the expected processing time, the processing efficiency of the current execution object is evaluated to meet the preset efficiency. Otherwise, the processing efficiency of the execution object is evaluated to not meet the preset efficiency.

[0100] The present invention also provides a smart supervision system based on medical and health care, the system comprising:

[0101] The medical data collection module is used to select the target object of medical and health supervision, collect the full-process medical data related to the target object and identify the different medical data carriers corresponding to the full-process medical data, and each medical data carrier controls the collection of the corresponding part of the full-process medical data;

[0102] The medical data processing module is used to perform data mining on the full-process medical data of the target object, thereby obtaining the medical data features of the target object at each medical stage, and associating the medical data features at each medical stage with the corresponding standard data features, thereby generating a group of medical data features to be analyzed at each medical stage;

[0103] The medical data analysis module is used to perform feature analysis on the feature groups of medical data to be analyzed at each medical stage, thereby obtaining the medical and health problems at each medical stage and generating risk warning prompts for the corresponding medical stage;

[0104] The medical and health supervision module is used to handle corresponding medical and health issues after receiving risk warning prompts, conduct real-time supervision on the execution objects of handling medical and health issues, and evaluate the efficiency of the execution objects in handling medical and health issues.

[0105] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A smart supervision method based on medical and health care, characterized in that: The following steps are included Step S1: Select the target object of medical and health supervision, collect the full-process medical data of the target object related to medical and health, and identify different medical data carriers corresponding to the full-process medical data, so that each medical data carrier controls the collection of the corresponding partial stage medical data in the full-process medical data; Step S2: Data mining is performed on the full-process medical data of the target object, thereby obtaining the medical data features of the target object at each medical stage, and the medical data features at each medical stage are associated with corresponding standard data features, thereby generating a medical data feature group to be analyzed at each medical stage; Step S3: Perform feature analysis on the feature group of medical data to be analyzed at each medical stage, thereby obtaining the medical and health problems at each medical stage, and generating risk warning prompts for the corresponding medical stage; Step S4: After receiving the risk warning prompt, the corresponding medical and health issues are processed, and the execution objects that process the medical and health issues are supervised in real time, and the efficiency of the execution objects in processing the medical and health issues is evaluated.

2. According to claim 1, a smart supervision method based on medical care is characterized in that: The process of selecting the target object for medical and health supervision and collecting the full-process medical data related to the target object and medical and health includes: A data collection period is set, and the full-process medical data related to medical care and health care of a target object is collected during the data collection period. The full-process medical data consists of stage medical data of different medical stages; The different medical stages include outpatient stage, instrument examination stage, hospitalization stage, surgery stage, rehabilitation stage and follow-up visit stage, and the medical data of different stages include outpatient data, instrument examination data, hospitalization data, surgery data, rehabilitation data and follow-up data.

3. According to claim 2, a smart supervision method based on medical care is characterized in that: The process of identifying different medical data carriers corresponding to the full-process medical data and having each medical data carrier control the collection of medical data at the corresponding stages of the full-process medical data includes: Identify the medical data carriers of the target object at different medical stages of the full-process medical data, and build a data control node for each medical data carrier. Each data control node is used to control the collection of medical data at some stages of the full-process medical data. Set up the respective standard operating procedures for each medical data carrier when collecting medical data at the corresponding stage, obtain the actual operating procedures of each medical data carrier when collecting medical data at the corresponding stages of the full process of medical data, and enter the standard operating procedures and actual operating procedures of each medical data carrier into their respective data control nodes; The data control node determines whether there is an abnormal collection event for each corresponding medical data carrier, and performs corresponding control. When the actual operation process of the medical data carrier is inconsistent with the standard operation process, it is determined that there is an abnormal collection event. Otherwise, it is determined that there is no abnormal collection event. When an abnormal collection event occurs on a medical data carrier, a corresponding early warning prompt is generated, and the medical data carrier adjusts its actual operation process when collecting medical data at the corresponding stage until it meets the standard operation process. When there is no abnormal collection event, no operation is performed.

4. The intelligent supervision method based on medical care and health care according to claim 3 is characterized in that: The process of mining the full-process medical data of the target object and obtaining the medical data characteristics of the target object at each medical stage includes: Construct a data mining program for data mining of the whole process medical data, import the whole process medical data into the data mining program and divide it into stage medical data of several medical stages, start the data mining program, perform cluster analysis on the stage medical data of the target object in each medical stage, and then obtain several cluster data groups corresponding to the stage medical data in each medical stage; Each cluster data group corresponds to a partial medical data feature of the current medical stage, and the proportion of each cluster data group in the medical data of the corresponding stage is counted, and the proportion value is used as the feature key value of the corresponding cluster data group. The feature key value is used to determine whether the corresponding cluster data group is a component element required for the corresponding medical data feature of the corresponding medical stage, and the component element is the partial medical data feature corresponding to the cluster data group; The feature key value corresponding to the cluster data group is denoted as TZ, and a screening threshold is set, and the screening threshold is denoted as μ. Some medical data features corresponding to all cluster data groups with TZ≥μ are screened out and integrated to generate medical data features corresponding to each medical stage, and all cluster data groups with TZ<μ are eliminated.

5. The intelligent supervision method based on medical care and health care according to claim 4 is characterized in that: The process of associating the medical data features at each medical stage with the corresponding standard data features and then generating the medical data feature group to be analyzed at each medical stage includes: Obtain the target object's historical full-process medical data, and perform data analysis, feature extraction, and feature screening to derive the target object's standard data features at each medical stage. Integrate the target object's medical data features and standard data features at each medical stage to generate a set of medical data features to be analyzed for the target object at each medical stage.

6. The intelligent supervision method based on medical care and health care according to claim 5 is characterized in that: The process of performing feature analysis on the feature group of medical data to be analyzed at each medical stage, thereby obtaining the medical and health problems at each medical stage, and generating risk warning prompts for the corresponding medical stage includes: Constructing a feature analysis network for feature analysis of the feature groups of medical data to be analyzed, inputting the feature groups of medical data to be analyzed in all medical stages into the feature analysis network, and using the feature analysis network to calculate the data fitting degree of each feature group of medical data to be analyzed; The data fit is denoted as NH 数据 , then NH 数据 =D 相似 / D 数据组 , where D 相似 D is the data volume corresponding to the data part with similar and consistent medical data features and normative data features in each medical data feature group to be analyzed. 数据组 For the amount of data corresponding to each medical data feature group to be analyzed, a fitting threshold is set and recorded as τ; NH 数据 All the medical data feature groups to be analyzed <τ are marked as the hidden danger feature data groups of the corresponding medical stage. 数据 No operation is performed on the medical data feature groups to be analyzed that are ≥τ, and all the hidden danger feature data groups are input into the set medical database. The medical database is used to record all medical and health problems occurring at different medical stages. Each medical and health problem corresponds to a retrieval fit degree in the medical database; According to the rule that the data fit is equal to the retrieval fit, the hidden danger feature data group corresponding to each medical stage is matched in the medical database to obtain all the medical and health problems corresponding to each medical stage. The frequency of the medical and health problems corresponding to the target object in each medical stage is counted, and the warning level judgment value is set. According to the numerical relationship between the frequency and the warning level judgment value, the risk warning prompts for the corresponding medical stage are generated. The risk warning prompts include blue warning prompts, yellow warning prompts, orange warning prompts and red warning prompts.

7. The intelligent supervision method based on medical care and health care according to claim 6 is characterized in that: After receiving the risk warning, the corresponding medical and health problems are handled, and the execution objects of handling the medical and health problems are supervised in real time, and the process of evaluating the execution objects' handling efficiency of the medical and health problems includes: Receive all risk warning prompts generated by the target object, and handle all medical and health problems existing in the corresponding medical stage of the target object in order of the warning priority corresponding to the risk warning prompt from high to low, conduct real-time supervision on all execution objects that handle medical and health problems, regard the process of each execution object handling medical and health problems as a task event, and obtain the task execution information of the task event, which includes the task completion degree and task processing time; The execution object executes the corresponding task event, and evaluates the processing efficiency of the corresponding execution object in handling medical and health issues based on the task execution information. The execution object whose processing efficiency meets the preset efficiency is identified as the resident task object for handling medical and health issues, and the execution object whose processing efficiency does not meet the preset efficiency is replaced.

8. A smart supervision system based on medical and health care, used to implement the smart supervision method according to any one of claims 1 to 7, characterized in that: The system includes: The medical data collection module is used to select the target object of medical and health supervision, collect the full-process medical data related to the target object and identify the different medical data carriers corresponding to the full-process medical data, and each medical data carrier controls the collection of the corresponding part of the full-process medical data; The medical data processing module is used to perform data mining on the full-process medical data of the target object, thereby obtaining the medical data features of the target object at each medical stage, and associating the medical data features at each medical stage with the corresponding standard data features, thereby generating a group of medical data features to be analyzed at each medical stage; The medical data analysis module is used to perform feature analysis on the feature groups of medical data to be analyzed at each medical stage, thereby obtaining the medical and health problems at each medical stage and generating risk warning prompts for the corresponding medical stage; The medical and health supervision module is used to handle corresponding medical and health issues after receiving risk warning prompts, conduct real-time supervision on the execution objects of handling medical and health issues, and evaluate the efficiency of the execution objects in handling medical and health issues.

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