CN117315885A - Remote sharing alarm system for monitoring urine volume of urine bag and electrocardiograph monitor - Google Patents
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Abstract
The invention discloses a remote shared alarm system for monitoring urine volume of a urine bag and an Electrocardiograph (ECG) monitor, which relates to the technical field of medical monitoring, and is used for respectively detecting the urine bag and heartbeat of a patient, respectively constructing a urine bag detection data set and a heartbeat detection data set according to acquired detection sub-data, respectively generating a urine bag coefficient Ld and a heartbeat coefficient Xt when the urine bag coefficient Ld and the heartbeat coefficient Xt do not exceed corresponding thresholds, and respectively generating predicted values; generating abnormal characteristics when the predicted value is higher than a corresponding threshold value, sending early warning information to medical staff, and summarizing to generate a corresponding scheme library; matching the corresponding solutions from the corresponding solution library by using abnormal characteristics, outputting the corresponding solutions, carrying out data sharing and remote consultation of organization, and giving out a treatment solution; adding the corrected treatment plan into the treatment plan library, and when the patient is not treated by medical staff. The remote consultation is organized and the scheme is revised, so that when the abnormal condition of the patient occurs, a more accurate coping scheme can be obtained, and the safety of the patient is ensured.
Description
Technical Field
The invention relates to the technical field of medical monitoring, in particular to a remote sharing alarm system for monitoring urine volume of a urine bag and an electrocardiograph monitor.
Background
The remote sharing alarm system is a system capable of remotely monitoring and sharing alarm information. In the medical field, remote shared alarm systems may be used to monitor vital signs of a patient, such as electrocardiogram, blood oxygen, respiration, etc., and once an abnormal situation occurs, the system may immediately alert the healthcare personnel and initiate emergency procedures if necessary.
Critical patients are often provided with a urine bag and their heart beat status is monitored by an electrocardiograph monitor and at ordinary times cared by a specialist. However, after entering the night, the nursing medical staff or the accompanying staff needs to rest later, and at most, only can realize timing ward-looking after rest, and can not immediately arrive at the ward when the illness state of the patient is abnormal; or the medical staff can arrive at the ward in time, but the current prominent abnormality cannot be solved in time.
The remote shared alarm system for monitoring the urine volume of the urine bag and the electrocardiograph is provided.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a remote shared alarm system for monitoring urine volume of a urine bag and an electrocardiograph monitor, which is used for respectively detecting the urine bag and heartbeat of a patient to respectively construct a urine bag detection data set and a heartbeat detection data set according to acquired detection sub-data, respectively generating a urine bag coefficient Ld and a heartbeat coefficient Xt, and respectively generating predicted values when the urine bag coefficient Ld and the heartbeat coefficient Xt do not exceed corresponding thresholds; generating abnormal characteristics when the predicted value is higher than a corresponding threshold value, sending early warning information to medical staff, and summarizing to generate a corresponding scheme library; matching the corresponding solutions from the corresponding solution library by using abnormal characteristics, outputting the corresponding solutions, carrying out data sharing and remote consultation of organization, and giving out a treatment solution; adding the corrected treatment plan into the treatment plan library, and when the patient is not treated by medical staff. The remote consultation is organized and the scheme is revised, when the abnormal condition of the patient occurs, a more accurate coping scheme can be obtained, the safety of the patient is ensured, and the problem in the background technology is solved.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: the remote shared alarm system for monitoring urine volume of a urine bag and an electrocardiograph monitor comprises a detection unit, a first processing unit, a control unit, a communication unit, a scheme summarizing unit, a second processing unit, a third processing unit and an alarm unit, wherein when a patient still exists on a patient's bed at night, the detection unit firstly identifies whether medical staff exists in the patient's ward, if the medical staff does not exist, the urine bag and the heartbeat of the patient are detected respectively, and a urine bag detection data set and a heartbeat detection data set are constructed respectively according to the acquired detection sub-data; after the urine bag detection data set and the heartbeat detection data set are sent to the first processing unit, respectively generating a urine bag coefficient Ld and a heartbeat coefficient Xt, and when the current urine bag coefficient Ld and the heartbeat coefficient Xt do not exceed corresponding thresholds, predicting the urine bag coefficient Ld and the heartbeat coefficient Xt by using a trained prediction model and respectively generating predicted values; when the predicted value of the urine bag coefficient L or the heartbeat coefficient Xt is higher than a corresponding threshold value, determining abnormal sub-data and the quantity thereof, and generating abnormal characteristics by combining the abnormal degree of the abnormal sub-data, when the quantity of the abnormal sub-data exceeds the corresponding threshold value, forming a control instruction by a control unit, firstly enabling a communication unit to send early warning information to medical staff, and then enabling a scheme summarizing unit to collect corresponding schemes according to the abnormal characteristics and summarize to generate a corresponding scheme library; after the medical staff receives the early warning information, the second processing unit matches and outputs the corresponding scheme from the corresponding scheme library by using the abnormal characteristics, and the current urine bag detection data set, the heartbeat detection data set and the corresponding scheme library are combined for sharing data, so that the remote consultation is organized when the medical staff do not exist around the sickbed, and a treatment scheme is given to the patient; the third processing unit corrects the output coping schemes, adds the corrected coping schemes into the coping scheme library, and if the corrected coping schemes or the scheduled time after the treatment schemes are obtained, the alarm unit gives an alarm to the outside when the patient is not treated by the medical staff.
Further, the detection unit comprises an environment recognition module, a urine volume detection module and a heartbeat detection module, wherein after entering the night, the environment recognition module recognizes the environment of a ward where a patient is located, judges whether medical staff exists in the ward, if not, the environment recognition module detects the urine bag at fixed time intervals, at least acquires the urine volume Nv, the pH value Sj and the transparency Tm from the generated detection data, and a urine bag detection data set is established after summarizing; and then, the heartbeat detection module monitors the heartbeat data of the patient in real time at fixed time intervals, and at least acquires the heartbeat frequency Xp and the R-R interval Xw from the generated monitoring data, and the heartbeat frequency Xp and the R-R interval Xw are collected to form a heartbeat detection data set.
Further, the first processing unit includes an evaluation module, a prediction module, an analysis module and a model training module, wherein the urine bag detection data set and the heartbeat detection data set are respectively sent to the evaluation module, and the evaluation module respectively generates a urine bag coefficient Ld and a heartbeat coefficient Xt on the basis of current sub-data.
Further, the urine bag coefficient Ld is generated as follows: the urine volume Nv, the pH value Sj and the transparency Tm are obtained, and after dimensionless treatment, the following formula is adopted:
wherein alpha and beta are parameters of changeable constants, alpha is more than or equal to 0.51 and less than or equal to 0.76,0.61 and beta is more than or equal to 0.93, and a user can adjust according to actual conditions; the heartbeat coefficient Xt is generated as follows: and obtaining the heartbeat frequency Xp and the R-R interval Xw, and after dimensionless treatment, according to the following formula:
wherein, gamma is a changeable constant parameter, gamma is more than or equal to 0.91 and less than or equal to 1.36,0.68 and less than or equal to θ and less than or equal to 1.46, a user can adjust according to actual conditions, R is a correlation coefficient between heartbeat frequency Xp and R-R interval Xw, R is obtained by correlation analysis, and D is a constant correction parameter.
Further, a neural network algorithm is used for selecting sample data from a urine bag detection data set and a heartbeat detection data set, and a coefficient prediction model is built after training and testing by a model training module through the sample data; when the urine bag coefficient Ld and the heartbeat coefficient Xt do not exceed the corresponding threshold values, a coefficient prediction model is used, the urine bag detection data set and the child data in the heartbeat detection data set are used as input data, and the prediction module predicts the child data in the urine bag detection data set and the heartbeat detection data set to generate predicted values of the corresponding child data, so that predicted values of the urine bag coefficient Ld and the heartbeat coefficient Xt are generated.
Further, when at least one predicted value of the urine bag coefficient Ld and the heartbeat coefficient Xt is higher than the corresponding threshold value, the analysis module judges the parts exceeding the corresponding threshold value of the urine bag detection data set and the plurality of sub-data in the heartbeat detection data set and marks the parts as abnormal data; determining the quantity of the abnormal data, determining the abnormality degree of the sub data according to the proportion that the sub data exceeds the corresponding threshold value, and generating the abnormal characteristics by combining the abnormal data and the abnormality degree thereof.
Further, when the predicted values of the urine bag coefficient Ld and the heartbeat coefficient Xt do not exceed the corresponding threshold values, the monitoring coefficient Jxs is generated in a correlation manner, wherein the monitoring coefficient Jxs is generated as follows:
wherein F is 0.ltoreq.F 1 ≤1，0≤F 2 Not less than 1, and not less than 0.74% of F 1 +F 2 The specific value of the correction factor is less than or equal to 1.69, which is adjusted and set by a user, and C is a constant correction factor.
Further, when the monitoring coefficient Jxs exceeds the corresponding threshold, the control unit forms a control instruction, and the communication unit firstly sends early warning information to the medical staff, and then the scheme summarizing unit selects corresponding response schemes from the existing cases and treatment schemes according to the abnormal characteristics, and summarizes and outputs a response scheme library.
Further, the second processing unit comprises a remote consultation module, a data sharing module and a matching module, wherein after medical staff not in a ward receive early warning information, an abnormal feature and a corresponding scheme are used as sample data, a similarity algorithm is used for constructing a matching model, the matching model is output after training and testing of the sample data, the matching model is used by the matching module, and the corresponding scheme is matched from a corresponding scheme library according to the abnormal feature and is output; after the urine bag detection data set, the heartbeat detection data set and the response scheme which comprise the historical data and the forecast data are summarized, the data sharing module is enabled to carry out data sharing on medical staff or doctor teams responsible for the patient, and under the condition of data sharing, the remote consultation module organizes remote consultation to give a treatment scheme for the patient.
Further, the third processing unit comprises a judging module, a correcting module and an output module, wherein,
acquiring a corresponding scheme and a treatment scheme, judging the similarity of the corresponding scheme and the treatment scheme by using a trained similarity model, and marking a part which causes the similarity to be lower than a threshold value when the similarity is lower than the threshold value, determining the part as an abnormal region and outputting the abnormal region; and sending the corresponding solution carrying the abnormal region to a correction module, so that the correction module corrects the abnormal region, sending the corrected corresponding solution to a remote consultation module, verifying by the remote consultation module, and adding the corrected corresponding solution to a corresponding solution library if the verification is correct.
(III) beneficial effects
The invention provides a remote sharing alarm system for monitoring urine volume of a urine bag and an electrocardiograph, which has the following beneficial effects:
1. through generating urine bag coefficient Ld and heart beat coefficient Xt and predictive value, when there is no medical personnel in patient ward, can form prediction and monitoring to patient's state, when needs, can be timely send the notice and remind to the medical personnel who is not in the ward, make medical personnel can in time handle the unusual condition, ensure patient's healthy.
2. The state of the patient is further evaluated by acquiring the number of abnormal data and the monitoring coefficient Jxs, and when the physical state of the patient is poor, the emergency treatment is performed by timely outputting the corresponding scheme through the corresponding scheme library, so that the health state of the patient is ensured.
3. When medical staff can not timely enter a ward, through remote consultation of organization, on the basis of generated detection data, medical staff outside the ward can timely diagnose a patient, especially at night, through sending early warning information to medical staff not in the ward, the remote consultation is unfolded, and the safety of the patient is further guaranteed.
4. After the remote consultation of the organization, the output treatment scheme is used for correcting the corresponding scheme, so that a new corresponding scheme is formed, the corresponding scheme library is replaced and updated, when the abnormal condition of the patient occurs, a more accurate corresponding scheme can be obtained, and the safety of the patient is ensured.
Drawings
FIG. 1 is a schematic diagram of a first process of the remote sharing alarm system of the present invention;
FIG. 2 is a schematic diagram of a second process of the remote sharing alarm system of the present invention;
in the figure:
10. a detection unit; 11. an environment recognition module; 12. a urine volume detection module; 13. a heartbeat detection module;
20. a first processing unit; 21. an evaluation module; 22. a prediction module; 23. an analysis module; 24. a model training module; 30. a control unit; 40. a communication unit; 50. a scheme summarizing unit;
60. a second processing unit; 61. a remote consultation module; 62. a data sharing module; 63. a matching module;
70. a third processing unit; 71. a judging module; 72. a correction module; 73. an output module; 80. and an alarm unit.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides a remote shared alarm system for monitoring urine volume of urine bag and electrocardiograph monitor, which comprises a detection unit 10, a first processing unit 20, a control unit 30, a communication unit 40, a scheme summarizing unit 50, a second processing unit 60, a third processing unit 70, and an alarm unit 80, wherein,
after entering the night, when a patient still exists on the current patient bed, the detection unit 10 firstly identifies whether medical staff exists in a ward where the patient exists, and if not, the urine bag and the heartbeat of the patient are respectively detected, so that the acquired detection sub-data respectively construct a urine bag detection data set and a heartbeat detection data set;
after the urine bag detection data set and the heartbeat detection data set are sent to the first processing unit 20, respectively generating a urine bag coefficient Ld and a heartbeat coefficient Xt, and when the current urine bag coefficient Ld and the heartbeat coefficient Xt do not exceed corresponding thresholds, predicting the urine bag coefficient Ld and the heartbeat coefficient Xt by using a trained prediction model and respectively generating predicted values;
when the predicted value of the urine bag coefficient L or the heartbeat coefficient Xt is higher than a corresponding threshold value, determining abnormal sub-data and the quantity thereof, and generating abnormal characteristics by combining the abnormal degree of the abnormal sub-data, when the quantity of the abnormal sub-data exceeds the corresponding threshold value, forming a control instruction by the control unit 30, firstly enabling the communication unit 40 to send early warning information to medical staff, and then enabling the scheme summarizing unit 50 to collect corresponding response schemes according to the abnormal characteristics and summarize to generate a response scheme library;
after the medical staff receives the early warning information, the second processing unit 60 matches and outputs the corresponding schemes from the corresponding scheme library by using abnormal characteristics, and the current urine bag detection data set, the heartbeat detection data set and the corresponding scheme library are combined for sharing data, so that the remote consultation is organized when the medical staff does not exist around the sickbed, and a treatment scheme is given to the patient;
the third processing unit 70 corrects the output response plan, adds the corrected response plan to the response plan library, and if the patient is not treated by the medical staff within a predetermined time after the corrected response plan or the treatment plan is obtained, causes the alarm unit 80 to alarm to the outside.
Referring to fig. 1 and 2, the detecting unit 10 includes an environment identifying module 11, a urine volume detecting module 12 and a heartbeat detecting module 13, wherein after entering the night, the environment identifying module 11 identifies the environment of the ward where the patient is located, judges whether medical staff or accompanying staff exists in the ward, if not, the environment identifying module 11 detects the urine bag at fixed time intervals, at least obtains the urine volume Nv, the ph value Sj and the transparency Tm from the generated detection data, and establishes a urine bag detection data set after summarization; and then the heartbeat detection module 13 monitors the heartbeat data of the patient in real time at fixed time intervals, and at least acquires the heartbeat frequency Xp and the R-R interval Xw from the generated monitoring data, and the heartbeat detection data set is formed by aggregation.
When the urine bag detection data set and the heartbeat detection data set are respectively generated when medical staff do not exist around a patient during use, the physical state of the patient is monitored when the sub-data are generated through detection at fixed time intervals, and the physical condition of the patient can be monitored according to the generated detection data.
Referring to fig. 1 and 2, the first processing unit 20 includes an evaluation module 21, a prediction module 22, an analysis module 23, and a model training module 24, wherein the urine bag detection data set and the heartbeat detection data set are respectively sent to the evaluation module 21, the evaluation module 21 respectively generates a urine bag coefficient Ld and a heartbeat coefficient Xt based on the current sub-data,
wherein, the generation mode of the urine bag coefficient Ld is as follows: the urine volume Nv, the pH value Sj and the transparency Tm are obtained, and after dimensionless treatment, the following formula is adopted:
wherein, alpha and beta are parameters of changeable constants, alpha is more than or equal to 0.51 and less than or equal to 0.76,0.61 and beta is more than or equal to 0.93, and the user can adjust according to actual conditions. The heartbeat coefficient Xt is generated as follows: and obtaining the heartbeat frequency Xp and the R-R interval Xw, and after dimensionless treatment, according to the following formula:
wherein, gamma is a changeable constant parameter, gamma is more than or equal to 0.91 and less than or equal to 1.36,0.68 and less than or equal to θ and less than or equal to 1.46, a user can adjust according to actual conditions, R is a correlation coefficient between heartbeat frequency Xp and R-R interval Xw, R is obtained by correlation analysis, and D is a constant correction parameter.
When the device is used, the heart beat coefficient Xt and the urine bag coefficient Ld are formed, so that the physical state of a patient can be evaluated, and medical staff can take corresponding measures for the patient or send early warning to the outside according to the change of the heart beat coefficient Xt and the urine bag coefficient Ld when necessary.
Referring to fig. 1 and 2, using a neural network algorithm, sample data is selected from a urine bag detection data set and a heartbeat detection data set, and a coefficient prediction model is built by a model training module 24 after training and testing through the sample data;
when the urine bag coefficient Ld and the heartbeat coefficient Xt do not exceed the corresponding threshold values, using a coefficient prediction model, taking the sub-data in the urine bag detection data set and the heartbeat detection data set as input data, and predicting the sub-data in the urine bag detection data set and the heartbeat detection data set by a prediction module 22 to generate predicted values of the corresponding sub-data, so as to generate predicted values of the urine bag coefficient Ld and the heartbeat coefficient Xt;
when at least one predicted value of the urine bag coefficient Ld and the heartbeat coefficient Xt is higher than the corresponding threshold value, the analysis module 23 judges the parts exceeding the corresponding threshold value of the urine bag detection data set and a plurality of sub-data in the heartbeat detection data set and marks the parts as abnormal data; determining the quantity of the abnormal data, determining the abnormality degree of the sub data according to the proportion that the sub data exceeds the corresponding threshold value, and generating the abnormal characteristics by combining the abnormal data and the abnormality degree thereof.
When the system is used, after a coefficient prediction model is established, the changes of the urine bag coefficient Ld, the heartbeat coefficient Xt and the corresponding sub data are predicted, a predicted value is generated, when the predicted value is higher than a corresponding threshold value, the sub data generating abnormality are screened, the abnormal data and the abnormal characteristics are determined, the predicted result can be described according to the generated abnormal characteristics, and the medical staff can conveniently and quickly know the current state of the patient through the obtained abnormal characteristics.
Referring to fig. 1 and 2, when the predicted values of the urine bag coefficient Ld and the heartbeat coefficient Xt do not exceed the corresponding threshold values, a monitoring coefficient Jxs is generated in a correlation manner, wherein the monitoring coefficient Jxs is generated as follows:
wherein F is 0.ltoreq.F 1 ≤1，0≤F 2 Not less than 1, and not less than 0.74% of F 1 +F 2 The specific value of the correction factor is less than or equal to 1.69, which is adjusted and set by a user, and C is a constant correction factor.
When the monitoring coefficient Jxs exceeds the corresponding threshold, the control unit 30 forms a control instruction, and causes the communication unit 40 to send early warning information to the medical staff, and then causes the regimen summarizing unit 50 to select a corresponding coping regimen from the existing cases and treatment regimens according to the abnormal characteristics, summarize, and output a coping regimen library.
When the system is used, when predicted values of the urine bag coefficient Ld and the heartbeat coefficient Xt are normal, the monitoring coefficient Jxs is further formed, the next state of a patient can be judged and early-warned more comprehensively, when a medical staff is not in a room, if the monitoring coefficient Jxs shows that the current state of the patient is not good, a response scheme library can be quickly built according to abnormal characteristics, and when the medical staff is not in a room for processing, the response scheme library is formed for emergency.
Referring to fig. 1 and 2, the second processing unit 60 includes a remote consultation module 61, a data sharing module 62 and a matching module 63, wherein,
when medical staff not in the ward receive the early warning information, the medical staff takes the abnormal characteristics and the corresponding schemes as sample data, a similarity algorithm is used for constructing a matching model, the matching model is output after training and testing of the sample data, and the matching model is used by a matching module 63 to match and output the corresponding schemes from a corresponding scheme library according to the abnormal characteristics;
after the urine bag detection data set, the heartbeat detection data set and the response scheme containing the historical data and the predicted data are summarized, the data sharing module 62 is enabled to carry out data sharing to the medical staff or the doctor team responsible for the patient, and under the condition of data sharing, the remote consultation module 61 organizes remote consultation to give a treatment scheme for the patient.
When the medical treatment device is used, the matching model generated through training can rapidly select the corresponding scheme from the corresponding scheme library when abnormal characteristics exist, so that emergency treatment is performed by rapidly selecting the corresponding scheme when no medical care personnel exist in a ward; furthermore, by constructing a remote consultation system, after sending a notification to medical staff who are not in a ward on the basis of detection data and corresponding prediction data, a treatment scheme can be given to a patient through remote consultation, the process of remote treatment is completed, and the treatment efficiency is improved.
Referring to fig. 1 and 2, the third processing unit 70 includes a judging module 71, a correcting module 72 and an output module 73, wherein the judging module 71 is configured to judge the similarity of the treatment plan and the treatment plan by using the trained similarity model, and when the similarity is lower than a threshold value, mark a portion that causes the similarity to be lower than the threshold value, determine an abnormal region and output the abnormal region;
the response scheme carrying the abnormal region is sent to the correction module 72, the correction module 72 corrects the abnormal region, the corrected response scheme is sent to the remote consultation module 61, the remote consultation module 61 performs verification, and if the verification is correct, the corrected response scheme is added to the response scheme library.
When the treatment method is used, after the treatment plan and the treatment plan are acquired, the output treatment plan is corrected, so that a new treatment plan is formed, replacement and updating are formed on the corresponding plan library, and when the abnormal condition of a patient occurs, a more accurate treatment plan can be obtained.
The above contents are combined:
through generating urine bag coefficient Ld and heart beat coefficient Xt and predictive value, when there is no medical personnel in patient ward, can form prediction and monitoring to patient's state, when needs, can be timely send the notice and remind to the medical personnel who is not in the ward, make medical personnel can in time handle the unusual condition, ensure patient's healthy.
The state of the patient is further evaluated by acquiring the number of abnormal data and the monitoring coefficient Jxs, and when the physical state of the patient is poor, the emergency treatment is performed by timely outputting the corresponding scheme through the corresponding scheme library, so that the health state of the patient is ensured.
When medical staff can not timely enter a ward, through remote consultation of organization, on the basis of generated detection data, medical staff outside the ward can timely diagnose a patient, especially at night, through sending early warning information to medical staff not in the ward, the remote consultation is unfolded, and the safety of the patient is further guaranteed.
After the remote consultation of the organization, the output treatment scheme is used for correcting the corresponding scheme, so that a new corresponding scheme is formed, the corresponding scheme library is replaced and updated, when the abnormal condition of the patient occurs, a more accurate corresponding scheme can be obtained, and the safety of the patient is ensured.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application.
Claims (10)
1. A remote shared alarm system for monitoring urine volume of urine bag and electrocardiograph monitor, which is characterized in that: comprises a detection unit (10), a first processing unit (20), a control unit (30), a communication unit (40), a proposal summarizing unit (50), a second processing unit (60), a third processing unit (70) and an alarm unit (80), wherein,
after entering the night, when a patient still exists on the current patient bed, a detection unit (10) firstly identifies whether medical staff exists in a ward where the patient exists, and if not, the urine bag and the heartbeat of the patient are detected respectively, so that the acquired detection sub-data respectively construct a urine bag detection data set and a heartbeat detection data set;
after the urine bag detection data set and the heartbeat detection data set are sent to the first processing unit (20), respectively generating a urine bag coefficient Ld and a heartbeat coefficient Xt, and when the current urine bag coefficient Ld and the heartbeat coefficient Xt do not exceed corresponding thresholds, predicting the urine bag coefficient Ld and the heartbeat coefficient Xt by using a trained prediction model and respectively generating predicted values;
when the predicted value of the urine bag coefficient L or the heartbeat coefficient Xt is higher than a corresponding threshold value, determining abnormal sub-data and the quantity thereof, and generating abnormal characteristics by combining the abnormal degree of the abnormal sub-data, when the quantity of the abnormal sub-data exceeds the corresponding threshold value, forming a control instruction by a control unit (30), firstly enabling a communication unit (40) to send early warning information to medical staff, and then enabling a scheme summarizing unit (50) to collect corresponding response schemes according to the abnormal characteristics and summarize to generate a response scheme library;
after the medical staff receives the early warning information, the second processing unit (60) matches and outputs the corresponding scheme from the corresponding scheme library according to the abnormal characteristics, and the current urine bag detection data set, the heartbeat detection data set and the corresponding scheme library are combined for sharing data, so that the organization remotely consultates when the medical staff does not exist around the sickbed, and a treatment scheme is given to the patient;
the output coping scheme is corrected by the third processing unit (70), the corrected coping scheme is added into the coping scheme library, and if the corrected coping scheme or the scheduled time after the treatment scheme is obtained, the alarm unit (80) is caused to give an alarm to the outside when the patient is not treated by the medical staff.
2. The remote shared alert system for monitoring urine volume of a urine bag and an electrocardiograph according to claim 1, wherein: the detection unit (10) comprises an environment recognition module (11), a urine volume detection module (12) and a heartbeat detection module (13), wherein,
after entering the night, the environment recognition module (11) recognizes the environment of a ward where a patient is located, judges whether medical staff exists in the ward, if not, the environment recognition module (11) detects the urine bag at fixed time intervals, at least acquires the urine volume Nv, the pH value Sj and the transparency Tm from the generated detection data, and a urine bag detection data set is established after summarizing;
and then, the heartbeat detection module (13) monitors the heartbeat data of the patient in real time at fixed time intervals, and at least acquires the heartbeat frequency Xp and the R-R interval Xw from the generated monitoring data, and the heartbeat detection data set is formed by summarization.
3. The remote shared alert system for monitoring urine volume of a urine bag and an electrocardiograph according to claim 2, wherein: the first processing unit (20) comprises an evaluation module (21), a prediction module (22), an analysis module (23) and a model training module (24), wherein the urine bag detection data set and the heartbeat detection data set are respectively sent to the evaluation module (21), and the urine bag coefficient Ld and the heartbeat coefficient Xt are respectively generated by the evaluation module (21) on the basis of current sub-data.
4. A remote shared alert system for monitoring urine volume of a urine bag and an electrocardiograph according to claim 3, wherein: the urine bag coefficient Ld is generated as follows: the urine volume Nv, the pH value Sj and the transparency Tm are obtained, and after dimensionless treatment, the following formula is adopted:
wherein alpha and beta are parameters of changeable constants, alpha is more than or equal to 0.51 and less than or equal to 0.76,0.61 and beta is more than or equal to 0.93, and a user can adjust according to actual conditions; the heartbeat coefficient Xt is generated as follows: and obtaining the heartbeat frequency Xp and the R-R interval Xw, and after dimensionless treatment, according to the following formula:
wherein, gamma is a changeable constant parameter, gamma is more than or equal to 0.91 and less than or equal to 1.36,0.68 and less than or equal to θ and less than or equal to 1.46, a user can adjust according to actual conditions, R is a correlation coefficient between heartbeat frequency Xp and R-R interval Xw, R is obtained by correlation analysis, and D is a constant correction parameter.
5. The remote shared alert system for monitoring urine volume of a urine bag and an electrocardiograph according to claim 4, wherein: using a neural network algorithm to select sample data from a urine bag detection data set and a heartbeat detection data set, and establishing a coefficient prediction model by a model training module (24) after training and testing through the sample data;
when the urine bag coefficient Ld and the heartbeat coefficient Xt do not exceed the corresponding threshold values, a coefficient prediction model is used, sub-data in the urine bag detection data set and the heartbeat detection data set are used as input data, and a prediction module (22) predicts the sub-data in the urine bag detection data set and the heartbeat detection data set to generate predicted values of the corresponding sub-data, and further predicted values of the urine bag coefficient Ld and the heartbeat coefficient Xt are generated.
6. The remote shared alert system for monitoring urine volume of a urine bag and an electrocardiograph according to claim 5, wherein: when at least one predicted value of the urine bag coefficient Ld and the heartbeat coefficient Xt is higher than a corresponding threshold value, judging the parts exceeding the corresponding threshold value of the urine bag detection data set and a plurality of sub-data in the heartbeat detection data set by an analysis module (23), and marking the parts as abnormal data;
determining the quantity of the abnormal data, determining the abnormality degree of the sub data according to the proportion that the sub data exceeds the corresponding threshold value, and generating the abnormal characteristics by combining the abnormal data and the abnormality degree thereof.
7. The remote shared alert system for monitoring urine volume of a urine bag and an electrocardiograph according to claim 6, wherein: when the predicted values of the urine bag coefficient Ld and the heartbeat coefficient Xt do not exceed the corresponding threshold values, a monitoring coefficient Jxs is generated in a correlation manner, wherein the monitoring coefficient Jxs is generated as follows:
wherein F is 0.ltoreq.F 1 ≤1，0≤F 2 Not less than 1, and not less than 0.74% of F 1 +F 2 The specific value of the correction factor is less than or equal to 1.69, which is adjusted and set by a user, and C is a constant correction factor.
8. The remote shared alert system for monitoring urine volume of a urine bag and an electrocardiograph according to claim 7, wherein: when the monitoring coefficient Jxs exceeds the corresponding threshold value, a control instruction is formed by the control unit (30), the communication unit (40) is firstly caused to send out early warning information to medical staff, and then the scheme summarizing unit (50) is caused to select corresponding response schemes from the existing cases and treatment schemes according to abnormal characteristics, summarize and output a response scheme library.
9. The remote shared alert system for monitoring urine volume of a urine bag and an electrocardiograph according to claim 8, wherein: the second processing unit (60) comprises a remote consultation module (61), a data sharing module (62) and a matching module (63), wherein,
when medical staff not in a ward receive the early warning information, the abnormal characteristics and the corresponding schemes are used as sample data, a similarity algorithm is used for constructing a matching model, the matching model is output after training and testing of the sample data, and the matching model is used by a matching module (63) to match and output the corresponding schemes from a corresponding scheme library according to the abnormal characteristics;
after the urine bag detection data set, the heartbeat detection data set and the response proposal containing the historical data and the forecast data are summarized, the data sharing module (62) carries out data sharing to medical staff or doctor team responsible for the patient, and under the condition of data sharing, the remote consultation module (61) organizes remote consultation to give a treatment proposal for the patient.
10. The remote shared alert system for monitoring urine volume of a urine bag and an electrocardiograph according to claim 9, wherein: the third processing unit (70) comprises a judging module (71), a correcting module (72) and an output module (73), wherein,
acquiring a corresponding scheme and a treatment scheme, judging the similarity of the corresponding scheme and the treatment scheme by a judging module (71) by using a trained similarity model, and marking a part which causes the similarity to be lower than a threshold value when the similarity is lower than the threshold value, determining the part as an abnormal region and outputting the abnormal region;
the response scheme carrying the abnormal region is sent to the correction module (72), the correction module (72) corrects the abnormal region, the corrected response scheme is sent to the remote consultation module (61), the remote consultation module (61) verifies the response scheme, and if the verification is correct, the corrected response scheme is added to the response scheme library.
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