CN110742595A - Abnormal blood pressure monitoring system based on cognitive cloud system - Google Patents
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Abstract
The invention provides an abnormal blood pressure monitoring system based on a cognitive cloud system. The method comprises the following steps: the acquisition module is used for acquiring original blood pressure data; the data screening module is used for setting a blood pressure data range, screening the blood pressure standardized data through the blood pressure data range and screening abnormal blood pressure standardized data; the diagnostic report generating module is used for acquiring local historical abnormal blood pressure standardized data and historical disease names, establishing an abnormal blood pressure data table according to the local historical abnormal blood pressure data and the historical disease names, searching the corresponding disease names from the abnormal blood pressure data table according to the abnormal blood pressure standardized data, and generating corresponding diagnostic reports; and the early warning module is used for sending early warning information to the user. According to the invention, the abnormal blood pressure of the user is determined by collecting and analyzing the blood pressure data of the user, then the abnormal blood pressure of the user is diagnosed according to the local historical blood pressure diagnosis data, and a corresponding diagnosis report is generated.
Description
Technical Field
The invention relates to the technical field of computers, in particular to an abnormal blood pressure monitoring system based on a cognitive cloud system.
Background
The traditional sphygmomanometer is difficult to realize the cognition, analysis, prediction and judgment of the blood pressure due to the factors of complex operation, static data and susceptibility to environmental infection.
The electronic blood pressure meter which is started in recent years is deeply concerned by wide consumers because of the intellectualization, the capability of monitoring dynamic data and the capability of preventing interference. The intelligent sphygmomanometer mainly utilizes multiple communication means to upload measurement data of the electronic sphygmomanometer to the cloud end through intelligent processing, so that a user and medical personnel of the intelligent sphygmomanometer can monitor the measurement data of the user at any time and any place in real time, and the user and the medical personnel can check the measurement data through the cloud end such as a platform.
However, the intelligent sphygmomanometer cannot really recognize the hypertension data, cannot give real intelligent analysis, prediction and judgment, is more difficult to give professional solutions, and cannot help clinical diagnosis of abnormal blood pressure.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
In view of this, the invention provides an abnormal blood pressure monitoring system based on a cognitive cloud system, and aims to solve the technical problems that the prior art cannot realize real cognition on hypertension data and cannot give real intelligent analysis, prediction and judgment.
The technical scheme of the invention is realized as follows:
in one aspect, the present invention provides an abnormal blood pressure monitoring system based on a cognitive cloud system, including:
the acquisition module is used for acquiring original blood pressure data;
the data screening module is used for setting a data standardization format and a blood pressure data range, converting the blood pressure original data into blood pressure standardization data through the data standardization format, screening the blood pressure standardization data through the blood pressure data range, and screening abnormal blood pressure standardization data;
the diagnostic report generating module is used for acquiring local historical abnormal blood pressure standardized data and historical disease names, establishing an abnormal blood pressure data table according to the local historical abnormal blood pressure data and the historical disease names, searching the corresponding disease names from the abnormal blood pressure data table according to the abnormal blood pressure standardized data, and generating corresponding diagnostic reports;
and the early warning module is used for setting a blood pressure standardized data threshold value and sending early warning information to the user when the blood pressure standardized data is greater than the blood pressure standardized data threshold value.
On the basis of the above technical solution, preferably, the data filtering module includes a user blood pressure model establishing module, configured to acquire user information, where the information includes: and establishing a blood pressure model of the user according to the user information and the blood pressure standardized data, and extracting the user information and the corresponding blood pressure standardized data from the blood pressure model of the user to be used as combined data.
On the basis of the above technical solution, preferably, the data screening module includes an abnormal blood pressure determining module for setting a blood pressure data range, determining the blood pressure standardized data in the combined data according to the blood pressure data range, and determining the blood pressure standardized data as normal blood pressure standardized data when the blood pressure standardized data satisfies the blood pressure data range; and when the blood pressure standardized data does not meet the blood pressure data range, judging the blood pressure standardized data as abnormal blood pressure standardized data, and marking the user information corresponding to the abnormal blood pressure standardized data.
On the basis of the above technical solution, preferably, the data screening module further includes a blood pressure grading module, configured to establish an abnormal blood pressure grade judgment table according to local historical abnormal blood pressure standardized data, and set an early warning grade of abnormal blood pressure, where the grade includes: the first stage, the second stage and the third stage are used for grading the abnormal blood pressure standardized data according to the abnormal blood pressure grade judgment table, comparing the grade of the abnormal blood pressure standardized data with the early warning grade of the abnormal blood pressure, and sending an early warning signal to the early warning module when the grade of the abnormal blood pressure standardized data is greater than the early warning grade of the abnormal blood pressure.
On the basis of the above technical solution, preferably, the diagnosis report generating module further includes a matching tag generating module, configured to obtain a local historical disease tag, local historical abnormal blood pressure standardized data, and a historical solution corresponding to the symptom tag, and establish a data matching tag according to the local historical disease tag, the local historical abnormal blood pressure standardized data, and the historical solution corresponding to the symptom tag.
On the basis of the above technical solution, preferably, the diagnosis report generation module includes a data matching module, establishes a gradient lifting tree algorithm, matches the abnormal blood pressure data with the data matching tag according to the gradient lifting tree algorithm, and generates a corresponding diagnosis report according to a matching result.
On the basis of the above technical solution, preferably, the acquisition module includes a gradient lifting tree algorithm unit, and the gradient lifting tree algorithm is:
wherein L (y, f (x)) is a loss function, y represents a disease corresponding to abnormal blood pressure, and pk(x) Represents ykProbability of 1, f (x) represents a predetermined function model, ykRepresents a certain class of knowledge and solution corresponding to the disease corresponding to abnormal blood pressure, K is different, and K represents a certain class of knowledge and solution.
Still further preferably, the cognitive cloud system-based blood pressure monitoring device comprises:
the acquisition unit is used for acquiring original blood pressure data;
the data screening unit is used for setting a data standardization format and a blood pressure data range, converting the blood pressure original data into blood pressure standardization data through the data standardization format, screening the blood pressure standardization data through the blood pressure data range, and screening abnormal blood pressure standardization data;
the diagnostic report generating unit is used for acquiring local historical abnormal blood pressure standardized data and historical disease names, establishing an abnormal blood pressure data table according to the local historical abnormal blood pressure data and the historical disease names, searching the corresponding disease names from the abnormal blood pressure data table according to the abnormal blood pressure standardized data, and generating corresponding diagnostic reports;
and the early warning unit is used for setting a blood pressure standardized data threshold value and sending early warning information to the user when the blood pressure standardized data is greater than the blood pressure standardized data threshold value.
Compared with the prior art, the abnormal blood pressure monitoring system based on the cognitive cloud system has the following beneficial effects that:
(1) the abnormal blood pressure is judged by collecting the blood pressure data and quantitatively analyzing the collected blood pressure, and after the abnormal blood pressure is judged, diseases corresponding to the abnormal blood pressure and problems possibly occurring can be matched according to the abnormal blood pressure and a corresponding report is generated and sent to a user;
(2) through the gradient lifting tree algorithm, abnormal blood pressure is matched with the blood pressure problem solution in the knowledge base, through the method, cognitive inference and prediction can be accurately and efficiently carried out on the abnormal blood pressure, a corresponding diagnosis report is generated and sent to a user, and the user can more visually recognize the state of an illness.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block diagram of a first embodiment of an abnormal blood pressure monitoring system based on a cognitive cloud system according to the present invention;
FIG. 2 is a block diagram of an abnormal blood pressure monitoring system based on a cognitive cloud system according to a second embodiment of the present invention;
FIG. 3 is a block diagram of an abnormal blood pressure monitoring system based on a cognitive cloud system according to a third embodiment of the present invention;
FIG. 4 is a diagram of a hardware functional module at the front section of an abnormal blood pressure monitoring system based on a cognitive cloud system according to the present invention;
FIG. 5 is a diagram of a middle recognition software of an abnormal blood pressure monitoring system based on a cognitive cloud system according to the present invention;
fig. 6 is a block diagram of an abnormal blood pressure monitoring device of the abnormal blood pressure monitoring system based on the cognitive cloud system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1, fig. 1 is a block diagram illustrating a first embodiment of an abnormal blood pressure monitoring system based on a cognitive cloud system according to the present invention. Wherein, the abnormal blood pressure monitoring system based on the cognitive cloud system comprises: an acquisition module 10, a blood pressure grading module 20, a diagnosis module 30 and an early warning module 40.
The acquisition module 10 is used for acquiring original blood pressure data;
the data screening module 20 is used for setting a data standardization format and a blood pressure data range, converting the blood pressure original data into blood pressure standardization data through the data standardization format, screening the blood pressure standardization data through the blood pressure data range, and screening abnormal blood pressure standardization data;
a diagnosis report generation module 30, configured to obtain local historical abnormal blood pressure standardized data and historical disease names, establish an abnormal blood pressure data table according to the local historical abnormal blood pressure data and the historical disease names, look up corresponding disease names from the abnormal blood pressure data table according to the abnormal blood pressure standardized data, and generate corresponding diagnosis reports;
and the early warning module 40 is used for setting a blood pressure standardized data threshold value and sending early warning information to the user when the blood pressure standardized data is greater than the blood pressure standardized data threshold value.
It should be understood that the application scenario of the abnormal blood pressure monitoring system based on the cognitive cloud system proposed by the embodiment of the present invention may be a blood pressure measuring device, wherein the acquisition module 10 acquires the acquisition of blood pressure data. Namely, the cuff pressure in the pressurizing process is obtained by using a pressure sensor, after sampling, the blood pressure value is obtained through calculation and analysis by a microprocessor, then the blood pressure value is stored according to a user-defined protocol format and is wirelessly transmitted to a mobile equipment terminal with Bluetooth, GPRS and WIFI functions, and the human-computer interaction and wireless communication technology is introduced into the design of a blood pressure monitoring system. Because the acquired data needs to be uploaded to a mobile terminal (cloud) for intelligent analysis, the hardware needs to have a GPRS (general packet radio service), WiFi (wireless fidelity) or Bluetooth function, or the hardware needs to have a corresponding module, so that the automation of the blood pressure measurement process can be realized, the measured data can be sent to a mobile equipment terminal in a wireless transmission mode and stored for the convenience of future inquiry of a patient, and the change of the health of the patient can be known at any time; the data screening module 20 performs quantitative analysis and screening on the collected blood pressure, and classifies the abnormal blood pressure after the abnormal blood pressure is screened out; the diagnosis report generating module 30 is used for acquiring some data about blood pressure knowledge from the network, and then generating a corresponding blood pressure diagnosis report according to the classified blood pressure acquired from the blood pressure classifying module 20, and the early warning module 40 is used for providing timely early warning for the user when detecting that the abnormal blood pressure value is too high.
It should be understood that, in this embodiment, the system will collect the blood pressure raw data of the user, which are some numerical values, and the system will determine whether these data belong to the hypertension data or the hypotension data according to these numerical values, after collecting the data, the system will set a data threshold range, compare the blood pressure raw data with the data threshold range, and mark the raw data as normal data when the raw data meets the data threshold range; and when the original data does not meet the data threshold range, marking the original data as abnormal data and sending an early warning to a user.
It should be understood that after the data is collected, the collected original data is structured to obtain the blood pressure physiological structured standard data, meanwhile, the system sets a standardized data threshold range, analyzes the blood pressure physiological structured standard data according to the standardized data threshold range, marks the blood pressure physiological structured standard data as abnormal blood pressure data when the blood pressure physiological structured standard data does not meet the standardized data threshold range, and establishes an abnormal blood pressure body model; and when the blood pressure physiological structural standard data meet the standardized data threshold range, the blood pressure is in a normal range, and a normal blood pressure body model is established.
It should be understood that in the information society, information can be divided into two broad categories. One type of information can be represented by data or a uniform structure, which we refer to as structured data, such as numbers, symbols; and another kind of information cannot be represented by numbers or a uniform structure, such as text, images, sound, web pages, and the like, which we refer to as unstructured data. The structured data belongs to unstructured data, and is a special case of unstructured data. In this embodiment, the system may preset a standard of a data format for performing unified management on data.
It should be understood that, after the system establishes the blood pressure ontology model, it will establish an abnormal blood pressure level judgment table according to the local historical abnormal blood pressure standardized data, and set the early warning level of the abnormal blood pressure, where the level includes: the method comprises a first stage, a second stage and a third stage, wherein abnormal blood pressure standardized data are classified according to the abnormal blood pressure grade judgment table, then patient knowledge and corresponding expert suggestion knowledge which may appear in different blood pressures are obtained from a network, a knowledge base is established according to the patient knowledge and the corresponding expert suggestion knowledge which may appear in different blood pressures, namely blood pressure patient knowledge which may correspond to abnormal changes of blood pressures in different crowds, different age stages of the crowds and different states is subjected to patient entity label description and expert knowledge description.
It should be understood that the system will also obtain historical diagnostic data from the local historical diagnostic record, including: the method comprises the steps of establishing an abnormal blood pressure data table according to local historical abnormal blood pressure standardized data and historical disease names, searching corresponding disease names from the abnormal blood pressure data table according to the abnormal blood pressure standardized data, and generating a corresponding diagnosis report.
It should be understood that the system will obtain a local historical condition label, local historical abnormal blood pressure standardized data and a historical solution corresponding to the symptom label, and establish a data matching label according to the local historical condition label, the local historical abnormal blood pressure standardized data and the historical solution corresponding to the symptom label.
It should be understood that after the data matching labels are established, the knowledge and the solution of different blood pressures are obtained from the network, the gradient lifting tree algorithm is established, the diseases corresponding to abnormal blood pressure are matched with the knowledge and the solution of different blood pressures through the gradient lifting tree algorithm, and meanwhile, an abnormal blood pressure cognitive analysis report and an auxiliary diagnosis comprehensive report are generated.
It should be understood that the gradient lifting tree algorithm is:
wherein L (y, f (x)) is a loss function, y represents a disease corresponding to abnormal blood pressure, and pk(x) Represents ykProbability of 1, f (x) represents a predetermined function model, ykRepresents a certain class of knowledge and solution corresponding to the disease corresponding to abnormal blood pressure, K is different, and K represents a certain class of knowledge and solution.
Further, as shown in fig. 2, a structural block diagram of a second embodiment of the abnormal blood pressure monitoring system based on the cognitive cloud system is provided based on the above embodiments, in this embodiment, the data filtering module 20 further includes:
a user blood pressure model establishing module 201, configured to obtain user information, where the information includes: establishing a blood pressure model of the user according to the user information and blood pressure standardized data, and extracting the user information and corresponding blood pressure standardized data from the blood pressure model of the user to be used as combined data;
the abnormal blood pressure judging module 202 is used for setting a blood pressure data range, judging the blood pressure standardized data in the combined data through the blood pressure data range, and judging the blood pressure standardized data as normal blood pressure standardized data when the blood pressure standardized data meets the blood pressure data range; when the blood pressure standardized data does not meet the blood pressure data range, judging the blood pressure standardized data as abnormal blood pressure standardized data, and marking user information corresponding to the abnormal blood pressure standardized data;
a blood pressure grading module 203, configured to establish an abnormal blood pressure grade judgment table according to local historical abnormal blood pressure standardized data, where the grade includes: and the first stage, the second stage and the third stage are used for judging the abnormal blood pressure standardized data according to the abnormal blood pressure grade judging table and early warning a user corresponding to the abnormal blood pressure standardized data.
It should be noted that after acquiring the acquired blood pressure data, the acquired raw data is structured to obtain blood pressure structured standard data, and then the system acquires user information, where the information includes: the gender, age and occupation can reduce the disease state search range of the user and reduce the search time for related disease states through the information, meanwhile, the system can establish a blood pressure model of the user according to the information, extract the user information and corresponding blood pressure standardized data from the blood pressure model of the user to be used as combined data, judge the blood pressure standardized data in the combined data through the blood pressure data range by setting the blood pressure data range, and judge the blood pressure standardized data to be normal blood pressure standardized data when the blood pressure standardized data meets the blood pressure data range; when the blood pressure standardized data does not meet the range of the blood pressure data, judging that the blood pressure standardized data is abnormal blood pressure standardized data, marking user information corresponding to the abnormal blood pressure standardized data, simultaneously sending an early warning signal to an early warning module, and after judging that the blood pressure data of a user is abnormal data, establishing an abnormal blood pressure level judgment table by a system according to local historical abnormal blood pressure standardized data, wherein the level comprises: the higher the level is, the more serious the level is, then the abnormal blood pressure standard data can be judged according to the abnormal blood pressure level judgment table, and early warning information is sent to a user through an early warning module.
Further, as shown in fig. 3, a structural block diagram of a third embodiment of the abnormal blood pressure monitoring system based on the cognitive cloud system according to the present invention is provided based on the above embodiments, in this embodiment, the diagnostic report generating module 30 further includes:
the matching tag generation module 301 is configured to obtain a local historical disease tag, local historical abnormal blood pressure standardized data, and a historical solution corresponding to the symptom tag, and establish a data matching tag according to the local historical disease tag, the local historical abnormal blood pressure standardized data, and the historical solution corresponding to the symptom tag.
The data matching module 302 is configured to establish a gradient lifting tree algorithm, match the abnormal blood pressure data with the data matching tag according to the gradient lifting tree algorithm, and generate a corresponding diagnosis report according to a matching result.
A gradient lifting tree algorithm unit 303, where the gradient lifting tree algorithm is:
wherein L (y, f (x)) is a loss function, y represents a disease corresponding to abnormal blood pressure, and pk(x) Represents ykProbability of 1, f (x) represents a predetermined function model, ykRepresents a certain class of knowledge and solution corresponding to the disease corresponding to abnormal blood pressure, K is different, and K represents a certain class of knowledge and solution.
It should be understood that the system is outside the steps of obtaining local historical abnormal blood pressure standardized data and historical disease names, establishing an abnormal blood pressure data table according to the local historical abnormal blood pressure data and the historical disease names, searching the abnormal blood pressure data table according to the abnormal blood pressure standardized data to obtain corresponding disease names, and generating corresponding diagnosis reports. The method can also obtain a local historical disease label, local historical abnormal blood pressure standardized data and a historical solution corresponding to the symptom label, establish a data matching label according to the local historical disease label, the local historical abnormal blood pressure standardized data and the historical solution corresponding to the symptom label, match the abnormal blood pressure data with the data matching label through a gradient lifting tree algorithm, and generate a corresponding diagnosis report according to a matching result.
It should be understood that the blood pressure monitoring recognition system mentioned in this embodiment is composed of front-end hardware, middle-stage cognitive software, and back-stage emergency hardware.
The front-end hardware is mainly used for collecting blood pressure data, namely, the cuff air pressure in the pressurizing process is obtained by using a pressure sensor, after sampling, the blood pressure value is obtained through calculation and analysis by a microprocessor, then the blood pressure value is stored according to a self-defined protocol format and is wirelessly transmitted to a mobile equipment terminal with Bluetooth, GPRS and WIFI functions, and the human-computer interaction and wireless communication technology is introduced into the design of a blood pressure monitoring system. Because the collected data need to be uploaded to a mobile terminal (cloud) for intelligent analysis, the hardware needs to have a GPRS function, a WiFi function or a Bluetooth function, or the hardware needs to have a corresponding module, so that the automation of the blood pressure measurement process can be realized, the measured data can be sent to a mobile equipment terminal in a wireless transmission mode and stored to be convenient for a patient to inquire in the future, and the change of the health of the patient can be conveniently known at any time.
The front-stage hardware functional module is as shown in fig. 4:
pressure sensor and signal conditioning circuit: the responsible object is to measure the air pressure value in the inflatable cuff, convert the air pressure value into a signal and transmit the signal to the control unit;
a drive circuit: the responsible object is to help the output signal of the main control unit to amplify and transfer to the control power element, so as to realize the function of inflating and deflating the cuff;
the main control unit: the intelligent sphygmomanometer is specially responsible for the coordination and synchronization of the work of each functional unit in the whole measurement process. Meanwhile, the pressure sensor also has the capability of data processing, and can realize the conversion between the pressure value and the display value;
liquid crystal display and function key unit: the method helps a user to provide a good man-machine interaction environment, and results can be displayed on the liquid crystal panel according to the requirements of the user;
a system power supply unit: this unit is primarily intended to supply the entire system.
The cognitive system in the middle section recognition software is used for collecting blood pressure in the implementation, judging the collected data according to the stored expert knowledge and generating a corresponding diagnosis report. The cognitive system is typically composed of a human-machine interface, a knowledge base, an inference engine, an interpreter, and a comprehensive database, as shown in FIG. 5.
It should be understood that the cognitive technology is a technology that mimics the human brain, can independently complete or assist human tasks, assist human decision making, and can automatically plan, reason and learn, and that a blood pressure measurement device is a cognitive blood pressure system according to the definition of cognition, and needs to have certain human characteristics and capabilities, such as the ability to sense blood pressure, the ability to intelligently process blood pressure signals, the ability to store and deduce, predict, and even make decisions. And the capability of finding abnormal signal solutions and the like are also required. That is, understanding and adapting to the environment, with certain conscious intelligence capabilities of thinking, reasoning, memory, imagination, learning, information processing, knowledge application, priority change, and the like.
The knowledge base is mainly used for storing the specialized knowledge of expert systems in different fields, and the data (or signal) solving process of the system simulates the thinking mode of experts through the knowledge in the knowledge base, so that the knowledge base is the key point for whether the expert system is superior or not, namely the quality and the quantity of the knowledge in the knowledge base determine the quality level of the expert system. Generally speaking, a knowledge base in the cognitive system and an expert system program are mutually independent, and a user can improve the performance of the expert system by changing and perfecting the knowledge content in the knowledge base; the comprehensive database is used for initial data of the field or the problem and intermediate data or information obtained in the reasoning process; the inference machine is used for adopting rules and controlling programs of strategies, so that the whole cognitive system can work coordinately in a logic mode, and the inference machine repeatedly matches the rules in the knowledge base according to the conditions or known data of the current problem to obtain a new conclusion so as to obtain a problem solving result; the interpretation mechanism is used for interpreting the behavior of the cognitive system to the user, and the interpreter explains the conclusion and the solving process according to the data sample collected by the user, so that the system has more human emotion; the integrated database is dedicated to storing raw data, intermediate results and final conclusions required in the reasoning process, often as a temporary storage area.
The back-end emergency hardware is mainly used for reminding and calling for help service. The method comprises the steps of deducing an acquired data structure through a preset special abnormal condition rule, giving reminding and calling for help, recording abnormal information into a database by the system if abnormal conditions occur in the system after data acquisition every time, then displaying the abnormal data on a display screen, and reminding a user of checking specific abnormal information.
It should be understood that the following is an example, a 56 year old middle aged person, measured blood pressure data results in a systolic blood pressure of 143mmHg, a diastolic blood pressure of 106mmHg, and a heart rate of 84 beats/minute, and the system and the platform automatically determine a first-class hypertension and a normal heart rate according to the monitored data results. The patient can select labels such as 'vision loss', 'retinal hemorrhage', 'fundus hemorrhage' and the like according to the symptom labels provided by the system at this time, and the device can further infer that the user may have hypertensive eye disease. The system can give comprehensive information such as common symptoms, specific causes, complications, treatment methods, diet conditioning, nursing methods, prognosis conditions and the like of hypertensive ophthalmopathy. The user can make a preliminary decision as to whether a medical visit is needed. If the doctor needs to be seen, the system can give out the information of seeing a doctor of a nearby hospital, a pharmacy and the like. If the user measures severe hypertension (such as 186mmHg systolic pressure, 138mmHg diastolic pressure, 97 heart rate/min), the system will alert the guardian to the abnormal blood pressure until the guardian handles and gives an alarm.
It should be understood that, for the characteristics of the blood pressure abnormal data sample label including a lot of information and strong relevance, a gradient lifting tree algorithm (GBDT algorithm) may be selected to implement the label matching process. If one person is 30 years old, we first fit the person by 20 years old and find the loss to be 10 years old, we fit the loss to be left by 6 years old and find the gap to be 4 years old, and the third round we fit the gap to be left by 3 years old and have the gap to be only one year old. If the iteration turns are not finished, the following iteration can be continued, and the fitting years error is reduced in each iteration turn.
The above description is only for illustrative purposes and does not limit the technical solutions of the present application in any way.
Through the above description, it is easy to find that this embodiment provides an abnormal blood pressure monitoring system based on a cognitive cloud system, including: the acquisition module is used for acquiring original blood pressure data; the data screening module is used for setting a data standardization format and a blood pressure data range, converting the blood pressure original data into blood pressure standardization data through the data standardization format, screening the blood pressure standardization data through the blood pressure data range, and screening abnormal blood pressure standardization data; the diagnostic report generating module is used for acquiring local historical abnormal blood pressure standardized data and historical disease names, establishing an abnormal blood pressure data table according to the local historical abnormal blood pressure data and the historical disease names, searching the corresponding disease names from the abnormal blood pressure data table according to the abnormal blood pressure standardized data, and generating corresponding diagnostic reports; and the early warning module is used for setting a blood pressure standardized data threshold value and sending early warning information to the user when the blood pressure standardized data is greater than the blood pressure standardized data threshold value. According to the embodiment, the abnormal blood pressure of the user is determined by collecting and analyzing the blood pressure data of the user, then the abnormal blood pressure of the user is diagnosed according to the local historical blood pressure diagnosis data, and a corresponding diagnosis report is generated.
In addition, the embodiment of the invention also provides abnormal blood pressure monitoring equipment based on the cognitive cloud system. As shown in fig. 6, the abnormal blood pressure monitoring device based on the cognitive cloud system includes: the system comprises an acquisition unit 10, a data screening unit 20, a diagnosis report generation unit 30 and an early warning unit 40.
The acquisition unit 10 is used for acquiring original blood pressure data;
a data screening unit 20, configured to set a data standardization format and a blood pressure data range, convert the blood pressure raw data into blood pressure standardization data through the data standardization format, and screen the blood pressure standardization data through the blood pressure data range to screen out abnormal blood pressure standardization data;
a diagnosis report generating unit 30, configured to obtain local historical abnormal blood pressure standardized data and historical disease names, create an abnormal blood pressure data table according to the local historical abnormal blood pressure data and the historical disease names, look up corresponding disease names from the abnormal blood pressure data table according to the abnormal blood pressure standardized data, and generate corresponding diagnosis reports;
and the early warning unit 40 is used for setting a blood pressure standardized data threshold value and sending early warning information to the user when the blood pressure standardized data is greater than the blood pressure standardized data threshold value.
In addition, it should be noted that the above-described embodiments of the apparatus are merely illustrative, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of the modules to implement the purpose of the embodiments according to actual needs, and the present invention is not limited herein.
In addition, the technical details that are not described in detail in this embodiment can be referred to the abnormal blood pressure monitoring system based on the cognitive cloud system provided in any embodiment of the present invention, and are not described herein again.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (8)
1. An abnormal blood pressure monitoring system based on a cognitive cloud system, which is characterized by comprising:
the acquisition module is used for acquiring original blood pressure data;
the data screening module is used for setting a data standardization format and a blood pressure data range, converting the blood pressure original data into blood pressure standardization data through the data standardization format, screening the blood pressure standardization data through the blood pressure data range, and screening abnormal blood pressure standardization data;
the diagnostic report generating module is used for acquiring local historical abnormal blood pressure standardized data and historical disease names, establishing an abnormal blood pressure data table according to the local historical abnormal blood pressure data and the historical disease names, searching the corresponding disease names from the abnormal blood pressure data table according to the abnormal blood pressure standardized data, and generating corresponding diagnostic reports;
and the early warning module is used for setting a blood pressure standardized data threshold value and sending early warning information to the user when the blood pressure standardized data is greater than the blood pressure standardized data threshold value.
2. The cognitive cloud system-based monitoring system for abnormal blood pressure as claimed in claim 1, wherein the data filtering module comprises a user blood pressure model building module for obtaining user information, the information comprising: and establishing a blood pressure model of the user according to the user information and the blood pressure standardized data, and extracting the user information and the corresponding blood pressure standardized data from the blood pressure model of the user to be used as combined data.
3. The cognitive cloud system-based blood pressure monitor system of claim 2, wherein: the data screening module comprises an abnormal blood pressure judging module which is used for setting a blood pressure data range, judging the blood pressure standardized data in the combined data through the blood pressure data range, and judging the blood pressure standardized data as normal blood pressure standardized data when the blood pressure standardized data meets the blood pressure data range; and when the blood pressure standardized data does not meet the blood pressure data range, judging the blood pressure standardized data as abnormal blood pressure standardized data, and marking the user information corresponding to the abnormal blood pressure standardized data.
4. The cognitive cloud system-based blood pressure monitor system of claim 3, wherein: the data screening module also comprises a blood pressure grading module which is used for establishing an abnormal blood pressure grade judgment table according to the local historical abnormal blood pressure standardized data and setting the early warning grade of the abnormal blood pressure, wherein the grade comprises the following steps: the first stage, the second stage and the third stage are used for grading the abnormal blood pressure standardized data according to the abnormal blood pressure grade judgment table, comparing the grade of the abnormal blood pressure standardized data with the early warning grade of the abnormal blood pressure, and sending an early warning signal to the early warning module when the grade of the abnormal blood pressure standardized data is greater than the early warning grade of the abnormal blood pressure.
5. The cognitive cloud system-based blood pressure monitor system of claim 4, wherein: the diagnosis report generation module also comprises a matching label generation module which is used for acquiring a local historical disease label, local historical abnormal blood pressure standardized data and a historical solution corresponding to the symptom label, and establishing a data matching label according to the local historical disease label, the local historical abnormal blood pressure standardized data and the historical solution corresponding to the symptom label.
6. The cognitive cloud system-based blood pressure monitor system of claim 5, wherein: the diagnosis report generation module comprises a data matching module used for establishing a gradient lifting tree algorithm, matching the abnormal blood pressure data with the data matching labels according to the gradient lifting tree algorithm, and generating a corresponding diagnosis report according to the matching result.
7. The cognitive cloud system-based blood pressure monitor system of claim 6, wherein: the acquisition module comprises a gradient lifting tree algorithm unit, wherein the gradient lifting tree algorithm unit comprises the following steps:
wherein L (y, f (x)) is a loss function, y represents a disease corresponding to abnormal blood pressure, and pk(x) Represents ykProbability of 1, f (x) represents a predetermined function model, ykRepresents a certain class of knowledge and solution corresponding to the disease corresponding to abnormal blood pressure, K is different, and K represents a certain class of knowledge and solution.
8. An abnormal blood pressure monitoring device based on a cognitive cloud system, which is characterized by comprising:
the acquisition unit is used for acquiring original blood pressure data;
the data screening unit is used for setting a data standardization format and a blood pressure data range, converting the blood pressure original data into blood pressure standardization data through the data standardization format, screening the blood pressure standardization data through the blood pressure data range, and screening abnormal blood pressure standardization data;
the diagnostic report generating unit is used for acquiring local historical abnormal blood pressure standardized data and historical disease names, establishing an abnormal blood pressure data table according to the local historical abnormal blood pressure data and the historical disease names, searching the corresponding disease names from the abnormal blood pressure data table according to the abnormal blood pressure standardized data, and generating corresponding diagnostic reports;
and the early warning unit is used for setting a blood pressure standardized data threshold value and sending early warning information to the user when the blood pressure standardized data is greater than the blood pressure standardized data threshold value.
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