CN116506205A - Data processing method and system of intelligent medical platform - Google Patents
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Abstract
The invention discloses a data processing method and a system of an intelligent medical platform, which relate to the technical field of data processing, wherein the processing method comprises the following steps: in the data transmission process, the processing end acquires the early warning coefficient in real time, compares the early warning coefficient with the early warning threshold value, stops transmitting data when the early warning coefficient is smaller than the early warning threshold value, and caches the data in the memory and sends out an early warning signal. According to the invention, the medical data is processed through the machine learning algorithm and then transmitted to the database, the processing end acquires the early warning coefficient in real time in the data transmission process, compares the early warning coefficient with the early warning threshold value, stops transmitting the data when the early warning coefficient is smaller than the early warning threshold value, and the processing end caches the data in the memory and sends out the early warning signal, so that the data can be cached in the memory of the device in time before the abnormality occurs in the device in the data transmission process, thereby effectively avoiding the data loss and guaranteeing the integrity of a data chain.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a data processing method and system of an intelligent medical platform.
Background
The intelligent medical platform is a novel solution which applies artificial intelligence and big data technology in the medical field in recent years, along with the increasing of the amount of medical data, the traditional medical service can not meet the demand of big data age for medical service, therefore, the intelligent medical platform is generated, and the platform provides more accurate and faster diagnosis and treatment decision for doctors by integrating and analyzing the information of medical records, medical data, diagnosis and treatment schemes and the like of patients;
because of the large variety of data in the smart medical platform, the amount of data is large, and thus the smart medical platform needs to process and analyze the medical data in the platform through the data processing system.
The prior art has the following defects:
the existing data processing system firstly analyzes and processes the collected data and then transmits the data to a database for storage, and because the medical data has wide sources and large data volume, the existing data processing system is usually only provided with safety software for protection (preventing data leakage or network terminals from being occupied by hackers) in the process of transmitting the data to the database, however, when the network is attacked successfully (the hacking technology is higher than the software protection technology) or other faults occur in the equipment in the data transmission process, the data transmission is stopped, partial data is lost during recovery, and a complete data chain cannot be formed, so that unnecessary loss is caused.
Disclosure of Invention
The invention aims to provide a data processing method and system of an intelligent medical platform, which are used for solving the defects in the background technology.
In order to achieve the above object, the present invention provides the following technical solutions: a data processing method of an intelligent medical platform, the processing method comprising the steps of:
s1: the acquisition end acquires medical data from various data sources based on a cloud computing technology, processes the medical data through a machine learning algorithm and then transmits the processed medical data to a database;
s2: in the data transmission process, a processing end acquires early warning coefficients in real time;
s3: comparing the early warning coefficient with an early warning threshold, stopping transmitting the data when the early warning coefficient is smaller than the early warning threshold, and caching the data in a memory by a processing end and sending out an early warning signal;
s4: after receiving the early warning signal, maintenance personnel maintain the equipment, and when the equipment is recovered to be used, the data cached in the memory is continuously transmitted to the database;
s5: the intelligent medical platform performs data interaction and data sharing based on the Internet.
In a preferred embodiment, in step S2, acquiring the early warning coefficient includes the steps of:
s2.1: in the data transmission process, network parameters and equipment parameters are collected, wherein the network parameters comprise network jitter, network packet loss rate and network flow rate increase rate, and the equipment parameters comprise equipment heat dissipation rate and current fluctuation rate;
s2.2: the network jitter, the network packet loss rate and the network flow rate increase rate are respectively calibrated to be w x 、w y 、w z The heat dissipation rate and the current fluctuation rate of the equipment are respectively calibrated as sb r 、d b ；
S2.3: the network jitter, the network packet loss rate, the network flow rate increase rate, the equipment heat dissipation rate and the current fluctuation rate are calculated through formulas, and then early warning coefficients are established, wherein the expressions are as follows:
wherein xs y G is the early warning coefficient 1 、g 2 、g 3 、g 4 、g 5 The ratio coefficients of the network jitter, the network packet loss rate, the network flow rate increase rate, the equipment heat dissipation rate and the current fluctuation rate are respectively, and g 3 ＞g 1 ＞g 2 ＞g 4 ＞g 5 ＞0。
In a preferred embodiment, the early warning factor sx is established y Then, an early warning threshold sx is set g The early warning coefficient sx y And the early warning threshold xs g Comparing, when the early warning coefficient xs y Not less than the early warning threshold xs g When the system judges that the current data transmission environment is stable, the system does not make a callSection, when the early warning coefficient xs y > early warning threshold xs g And when the system judges that the data transmission environment is unstable, the system makes adjustment.
In a preferred embodiment, the patient flow rate and the running number of the equipment in the hospital are obtained, and the correction value is calculated by a formula, wherein the expression is:
j z ＝a 1 ·r i +a 2 ·s i
wherein j is z Is the correction value, r i 、s i Quantity of person flow and operation of device, respectively, a 1 、a 2 Proportional coefficients of human flow and number of operations of the apparatus, respectively, and 0 < a 1 ＜a 2 。
In a preferred embodiment, the correction value j is calculated z After that, through the correction value j z Correcting early warning threshold sx g Will early warn the threshold xs g After reduction, the system is stabilized, and the expression is:
wherein xs jz To correct the threshold.
In a preferred embodiment, the correction value j is used z Correcting the early warning threshold xs g Obtaining a correction threshold xs jz Afterwards, the early warning coefficient sx is calculated y And correction threshold xs jz Comparing, when the early warning coefficient xs y Not less than correction threshold xs jz When the system judges that the current data transmission environment is stable, the system does not adjust, and when the early warning coefficient xs is high y < correction threshold xs jz And when the system judges that the data transmission environment is unstable, the system makes adjustment.
In a preferred embodiment, step S3 further comprises the steps of:
s3.1: stopping the transmission of the subsequent data after transmitting the previous complete data chain to the database, wherein the step is to ensure the integrity of the previous transmission data chain;
s3.2: after disconnecting the network of the equipment, stopping the transmitted data caching in a local memory of the equipment;
s3.3: maintaining early warning coefficient sx by manager y Not less than early warning threshold sx g And then, recovering the equipment network, and continuously uploading the data cached in the memory to the database.
The invention also provides a data processing system of the intelligent medical platform, which comprises an acquisition module, a data transmission module, an early warning module, a cache module, a management module and a sharing module;
the acquisition module acquires medical data from various data sources based on a cloud computing technology, the data transmission module processes the medical data through a machine learning algorithm and then transmits the medical data to the database, in the data transmission process, the early warning module compares the early warning coefficient with an early warning threshold value and judges whether to send an early warning signal according to a comparison result, when the early warning coefficient is smaller than the early warning threshold value, the data are cached by the caching module, when the equipment is recovered for use, the cached data in the memory are continuously transmitted to the database, the management module sends the early warning signal to maintenance personnel, the maintenance personnel maintains the equipment after receiving the early warning signal, and the sharing module carries out data interaction and data sharing based on the Internet.
In the technical scheme, the invention has the technical effects and advantages that:
1. according to the invention, the medical data is processed through the machine learning algorithm and then transmitted to the database, the processing end acquires the early warning coefficient in real time in the data transmission process, compares the early warning coefficient with the early warning threshold value, and stops transmitting when the early warning coefficient is smaller than the early warning threshold value, and the processing end caches the data in the memory and sends out the early warning signal, so that the data can be cached in the memory of the device in time before the abnormality occurs in the device in the data transmission process, thereby effectively avoiding the data loss and guaranteeing the integrity of a data chain;
2. the invention establishes the early warning coefficient after calculating the network jitter, the network packet loss rate, the network flow rate increase rate, the equipment heat dissipation rate and the current fluctuation rate through formulas, comprehensively processes the multi-source data, effectively improves the data processing efficiency, andat the early warning coefficient xs y < early warning threshold xs g When the data transmission scheme is adjusted in time, medical data leakage and loss are avoided, and the integrity of a data chain is effectively ensured;
3. the invention passes through the correction value j z Correcting the early warning threshold xs g Obtaining a correction threshold sx jz The system early warning method and the system can avoid the system early warning error in special scenes, effectively improve the early warning precision of the system and further ensure the running stability of the system.
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For a clearer description of embodiments of the present application or of the solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments described in the present invention, and that other drawings may be obtained according to these drawings for a person skilled in the art.
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. 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.
Example 1
Referring to fig. 1, the data processing method of the intelligent medical platform according to the embodiment includes the following steps:
the acquisition end acquires medical data from various data sources based on a cloud computing technology, the medical data is processed through a machine learning algorithm and then is transmitted to a database, in the data transmission process, the processing end acquires early warning coefficients in real time and compares the early warning coefficients with an early warning threshold value, when the early warning coefficients are smaller than the early warning threshold value, the data stops transmitting, the processing end caches the data in a memory and sends out early warning signals, maintenance staff maintains equipment after receiving the early warning signals, when the equipment is recovered to be used, the cached data in the memory is continuously transmitted to the database, and the intelligent medical platform carries out data interaction and data sharing based on the Internet to promote the cooperation and optimization of the medical data so as to improve the efficiency and quality of medical services.
According to the data transmission method and device, the medical data are transmitted to the database after being processed through the machine learning algorithm, the early warning coefficient is acquired in real time by the processing end in the data transmission process, the early warning coefficient is compared with the early warning threshold value, when the early warning coefficient is smaller than the early warning threshold value, the data are stopped from being transmitted, the processing end caches the data in the memory and sends out early warning signals, so that the data can be cached in the memory of the device in time before the abnormality occurs in the device in the data transmission process, the data loss is effectively avoided, and the integrity of a data chain is guaranteed.
In this embodiment, the collecting end collects medical data from a plurality of data sources based on a cloud computing technology specifically includes the following steps:
(1) Determining a data source: according to the requirements of the intelligent medical platform, determining which data sources need to collect medical data, such as an electronic medical record system, a checking and checking system, a pharmacy, a community health service center and the like of a hospital;
(2) Configuring a data acquisition device: selecting proper data acquisition equipment, such as a sensor, an RFID tag, a two-dimensional code scanner and the like, according to the type of data to be acquired;
(3) Establishing a data transmission channel: and a data transmission channel is established through a network, and the acquired data is transmitted to a cloud server, so that the high efficiency and accuracy of the whole data processing flow are realized.
Specifically, the medical data is transmitted to the database after being processed by the machine learning algorithm, and the medical data is processed by the machine learning algorithm specifically comprises the following steps:
(1) Data preprocessing: firstly, medical data needs to be preprocessed, including data cleaning, data denoising, data normalization and the like, so that subsequent processing and analysis can be performed;
(2) Feature extraction: for different medical data, a proper feature extraction algorithm is required to be selected, and the most representative and distinguishing features are extracted from the medical data so as to facilitate subsequent classification and prediction;
(3) Model training: selecting a proper machine learning algorithm, such as a decision tree, a support vector machine, a neural network and the like, and performing model training; in the training process, the data set is divided into a training set and a testing set, and the methods of cross verification and the like are carried out to avoid the problems of over fitting, under fitting and the like;
(4) Model evaluation: evaluating the trained model, including calculating indexes such as accuracy, recall rate, F1 value and the like, so as to judge the prediction performance of the model;
(5) Model application: and applying the trained model to new medical data to classify and predict.
Processing the medical data through a machine learning algorithm and transmitting the medical data to a database comprises the following steps:
(1) Data preprocessing: preprocessing the collected medical data, including data cleaning, denoising, normalization, feature selection and other operations, so as to improve the data quality and the accuracy of the model;
(2) Modeling data: selecting a proper machine learning algorithm, and establishing a machine learning model according to the processed data type and the application scene;
(3) Model training: training the established machine learning model, and optimizing the model to improve the precision and generalization capability of the model;
(4) Model evaluation: evaluating the trained machine learning model, and determining whether the model is available according to an evaluation result;
(5) And (3) data transmission: transmitting the processed medical data to a database for subsequent data query and application;
(6) And (3) data storage: storing the transmitted medical data in a database for subsequent data querying and application;
(7) Model updating: and updating the established machine learning model aiming at new medical data and application scenes so as to improve the accuracy and usability of the model.
The specific logic of the step (4) is as follows: data set preparation: suitable data sets are selected, including training sets, validation sets, and test sets, and appropriate evaluation criteria are selected based on the size and characteristics of the data sets.
(4.1) model prediction: and predicting the trained model by using the test set to generate a prediction result.
(4.2) model evaluation: and calculating evaluation indexes such as accuracy, precision, recall rate, F1 value and the like according to the prediction result and the real label of the test set.
(4.3) analysis of results: analyzing the evaluation result, and checking the performance and potential misjudgment conditions of the model under different categories.
(4.4) model adjustment: and according to the analysis result, the model is adjusted, such as adjusting model parameters, selecting new features and the like, so as to improve the performance and the robustness of the model.
(4.5) repeating steps (4.1) to (4.4): if multiple iterative adjustments are required, the above steps are repeated until a satisfactory result is obtained.
(4.6) model application: and finally, determining whether the model is available according to the evaluation result, and applying the model to an actual scene.
Example 2
In the above embodiment 1, in the data transmission process, the processing end acquires the early warning coefficient in real time, compares the early warning coefficient with the early warning threshold, and when the early warning coefficient is smaller than the early warning threshold, the data stops transmitting, and the processing end caches the data in the memory and sends out the early warning signal.
The processing end acquires the early warning coefficient in real time and comprises the following steps:
in the data transmission process, network parameters and equipment parameters are collected, wherein the network parameters comprise network jitter, network packet loss rate and network flow rate increase rate, and the equipment parameters comprise equipment heat dissipation rate and current fluctuation rate;
the network jitter, the network packet loss rate and the network traffic growth rate are respectively calibrated as
w x 、w y 、w z The heat dissipation rate and the current fluctuation rate of the equipment are respectively calibrated as sb r 、d b ；
The network jitter, the network packet loss rate, the network flow rate increase rate, the equipment heat dissipation rate and the current fluctuation rate are calculated through formulas, and then early warning coefficients are established, wherein the expressions are as follows:
wherein xs y G is the early warning coefficient 1 、g 2 、g 3 、g 4 、g 5 The ratio coefficients of the network jitter, the network packet loss rate, the network flow rate increase rate, the equipment heat dissipation rate and the current fluctuation rate are respectively, and g 3 ＞g 1 ＞g 2 ＞g 4 ＞g 5 In the proportionality coefficient, g 1 、g 2 、g 3 The specific value of g is obtained by the person skilled in the art from a historical database 4 、g 5 The specific values of (2) are set by those skilled in the art according to the model of the apparatus, and are not limited herein.
Establishing an early warning coefficient sx y Then, an early warning threshold sx is set g The early warning coefficient xs y And the early warning threshold xs g Comparing, when the early warning coefficient xs y Not less than the early warning threshold xs g When the system judges that the current data transmission environment is stable, the system does not adjust, and when the early warning coefficient xs is high y < early warning threshold xs g And when the system judges that the data transmission environment is unstable, the system makes adjustment.
Wherein, the system judges that the data transmission environment is unstable, and the making of the adjustment comprises the following steps:
(1) Stopping the transmission of the subsequent data after transmitting the previous complete data chain to the database, wherein the step is to ensure the integrity of the previous transmission data chain;
(2) After disconnecting the network of the equipment, stopping the transmitted data caching in a local memory of the equipment;
(3) Maintaining the early warning coefficient xs by an administrator y Not less than the early warning threshold xs g After that, resume settingAnd (5) preparing a network, and continuously uploading the data cached in the memory to a database.
According to the method, the early warning coefficient is built after network jitter, network packet loss rate, network flow rate increase rate, equipment heat dissipation rate and current fluctuation rate are calculated through the formula, the multisource data is comprehensively processed, the processing efficiency of the data is effectively improved, and the early warning coefficient xs is obtained y < early warning threshold xs g And when the data transmission scheme is adjusted in time, the medical data is prevented from being leaked and lost, and the integrity of a data chain is effectively ensured.
Example 3
In embodiment 2, the system is mainly based on the early warning coefficient xs y And early warning threshold
xs g In the practical application process, we find that the management method in embodiment 2 can actually play a good role in early warning in daily use and effectively guarantee the integrity of the data link.
However, when a special scene is encountered, the numerical value of the network parameter or some of the device parameters is increased rapidly, so that the system can continuously send out early warning signals and stop transmitting data to the database when the system is in the special scene, which not only causes trouble to an administrator and increases the workload, but also reduces the data transmission efficiency, therefore, the following scheme is proposed for the special scene to improve:
acquiring the flow rate of people and the running number of equipment in a hospital, and calculating to obtain a correction value through a formula, wherein the expression is as follows:
j z ＝a 1 ·r i +a 2 ·s i
wherein j is z Is the correction value, r i 、s i Quantity of person flow and operation of device, respectively, a 1 、a 2 Proportional coefficients of human flow and number of operations of the apparatus, respectively, and 0 < a 1 ＜a 2 。
When the flow of people and the running number of equipment in a hospital are increased (such as influenza, etc.), the network and the current of the hospital are affected, for example, the flow of people is large to indicate that the number of people seeing a doctor is increased, the generated medical data is more, the network occupancy rate is increased, and the network jitter, the network packet loss rate and the network flow increase rate are all increased; the increased number of devices operated results in increased hospital power usage and thus increased current ripple.
Thus, the correction value j is calculated z After that, it is necessary to pass the correction value j z Correcting the early warning threshold xs g Will early warn the threshold xs g After reduction, the system is stabilized, and the expression is:
wherein xs jz To correct the threshold value, by correcting value j z Correcting the early warning threshold xs g Obtaining a correction threshold xs jz Afterwards, the early warning coefficient xs is calculated y And correction threshold xs jz Comparing, when the early warning coefficient xs y Not less than correction threshold xs jz When the system judges that the current data transmission environment is stable, the system does not adjust, and when the early warning coefficient xs is high y < correction threshold xs jz And when the system judges that the data transmission environment is unstable, the system makes adjustment.
In the present embodiment, the correction value j is passed through z Correcting the early warning threshold xs g Obtaining a correction threshold
xs jz The system early warning method and the system can avoid the system early warning error in special scenes, effectively improve the early warning precision of the system and further ensure the running stability of the system.
Example 4
The data processing system of the intelligent medical platform comprises an acquisition module, a data transmission module, an early warning module, a cache module, a management module and a sharing module;
wherein:
and the acquisition module is used for: acquiring medical data from a plurality of data sources based on cloud computing technology;
and a data transmission module: medical data are processed through a machine learning algorithm and then transmitted to a database;
and the early warning module is used for: in the data transmission process, acquiring an early warning coefficient in real time, comparing the early warning coefficient with an early warning threshold value, and judging whether to send out an early warning signal according to a comparison result;
and a cache module: caching data when the early warning coefficient is smaller than the early warning threshold value, and continuously transmitting the cached data in the memory to the database when the equipment is recovered to be used;
and a management module: after receiving the early warning signal, maintenance personnel perform maintenance on the equipment;
and a sharing module: and data interaction and data sharing are performed based on the Internet, so that cooperation and optimization of medical data are promoted, and the efficiency and quality of medical services are improved.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
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. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present application are all or partially produced. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
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. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided in the present application, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
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. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A data processing method of an intelligent medical platform is characterized in that: the processing method comprises the following steps:
s1: the acquisition end acquires medical data from various data sources based on a cloud computing technology, processes the medical data through a machine learning algorithm and then transmits the processed medical data to a database;
s2: in the data transmission process, a processing end acquires early warning coefficients in real time;
s3: comparing the early warning coefficient with an early warning threshold, stopping transmitting the data when the early warning coefficient is smaller than the early warning threshold, and caching the data in a memory by a processing end and sending out an early warning signal;
s4: after receiving the early warning signal, maintenance personnel maintain the equipment, and when the equipment is recovered to be used, the data cached in the memory is continuously transmitted to the database;
s5: the intelligent medical platform performs data interaction and data sharing based on the Internet.
2. The data processing method of the intelligent medical platform according to claim 1, wherein: in step S2, the obtaining the early warning coefficient includes the following steps:
s2.1: in the data transmission process, network parameters and equipment parameters are collected, wherein the network parameters comprise network jitter, network packet loss rate and network flow rate increase rate, and the equipment parameters comprise equipment heat dissipation rate and current fluctuation rate;
s2.2: network jitter,The network packet loss rate and the network flow rate increase rate are respectively calibrated as w x 、w y 、w z The heat dissipation rate and the current fluctuation rate of the equipment are respectively calibrated as sb r 、d b ；
S2.3: the network jitter, the network packet loss rate, the network flow rate increase rate, the equipment heat dissipation rate and the current fluctuation rate are calculated through formulas, and then early warning coefficients are established, wherein the expressions are as follows:
wherein xs y G is the early warning coefficient 1 、g 2 、g 3 、g 4 、g 5 The ratio coefficients of the network jitter, the network packet loss rate, the network flow rate increase rate, the equipment heat dissipation rate and the current fluctuation rate are respectively, and g 3 ＞g 1 ＞g 2 ＞g 4 ＞g 5 ＞0。
3. The data processing method of the intelligent medical platform according to claim 2, wherein: establishing the early warning coefficient xs y Then, setting a pre-warning threshold xs g The early warning coefficient xs y And the early warning threshold xs g Comparing, when the early warning coefficient xs y Not less than the early warning threshold xs g When the system judges that the current data transmission environment is stable, the system does not adjust, and when the early warning coefficient xs is high y < early warning threshold xs g And when the system judges that the data transmission environment is unstable, the system makes adjustment.
4. A method for processing data of an intelligent medical platform according to claim 3, wherein: acquiring the flow rate of people and the running number of equipment in a hospital, and calculating to obtain a correction value through a formula, wherein the expression is as follows:
j z ＝a 1 ·r i +a 2 ·s i
wherein j is z Is the correction value, r i 、s i Human flow and device respectivelyQuantity of runs, a 1 、a 2 Proportional coefficients of human flow and number of operations of the apparatus, respectively, and 0 < a 1 ＜a 2 。
5. The data processing method of the intelligent medical platform according to claim 4, wherein: calculating the correction value j z After that, through the correction value j z Correcting the early warning threshold xs g Will early warn the threshold xs g After reduction, the system is stabilized, and the expression is:
wherein xs jz To correct the threshold.
6. The data processing method of the intelligent medical platform according to claim 5, wherein: through correction value j z Correcting the early warning threshold xs g Obtaining a correction threshold xs jz Afterwards, the early warning coefficient xs is calculated y And correction threshold xs jz Comparing, when the early warning coefficient xs y Not less than correction threshold xs jz When the system judges that the current data transmission environment is stable, the system does not adjust, and when the early warning coefficient xs is high y < correction threshold xs jz And when the system judges that the data transmission environment is unstable, the system makes adjustment.
7. The data processing method of the intelligent medical platform according to claim 6, wherein: step S3 further comprises the steps of:
s3.1: stopping the transmission of the subsequent data after transmitting the previous complete data chain to the database, wherein the step is to ensure the integrity of the previous transmission data chain;
s3.2: after disconnecting the network of the equipment, stopping the transmitted data caching in a local memory of the equipment;
s3.3: maintaining the early warning coefficient xs by an administrator y Not less than the early warning threshold xs g After that, resumeAnd the complex equipment network continuously uploads the data cached in the memory to the database.
8. A data processing system of an intelligent medical platform for implementing the processing method of any one of claims 1-7, characterized in that: the system comprises an acquisition module, a data transmission module, an early warning module, a cache module, a management module and a sharing module;
the acquisition module acquires medical data from various data sources based on a cloud computing technology, the data transmission module processes the medical data through a machine learning algorithm and then transmits the medical data to the database, in the data transmission process, the early warning module compares the early warning coefficient with an early warning threshold value and judges whether to send an early warning signal according to a comparison result, when the early warning coefficient is smaller than the early warning threshold value, the data are cached by the caching module, when the equipment is recovered for use, the cached data in the memory are continuously transmitted to the database, the management module sends the early warning signal to maintenance personnel, the maintenance personnel maintains the equipment after receiving the early warning signal, and the sharing module carries out data interaction and data sharing based on the Internet.
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