CN114168651B - System for counting xerophthalmia patient distribution group by utilizing cloud computing - Google Patents
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- 208000005494 xerophthalmia Diseases 0.000 title claims abstract description 22
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 34
- 238000004891 communication Methods 0.000 claims abstract description 32
- 238000012545 processing Methods 0.000 claims abstract description 32
- 238000001914 filtration Methods 0.000 claims description 21
- 238000004364 calculation method Methods 0.000 claims description 20
- 208000003556 Dry Eye Syndromes Diseases 0.000 claims description 16
- 206010013774 Dry eye Diseases 0.000 claims description 16
- 230000001105 regulatory effect Effects 0.000 claims description 15
- 238000007635 classification algorithm Methods 0.000 claims description 12
- 238000003066 decision tree Methods 0.000 claims description 11
- 230000000087 stabilizing effect Effects 0.000 claims description 8
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- 238000012800 visualization Methods 0.000 claims description 3
- 230000000007 visual effect Effects 0.000 abstract description 3
- 206010006784 Burning sensation Diseases 0.000 description 1
- 208000022873 Ocular disease Diseases 0.000 description 1
- 206010034960 Photophobia Diseases 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/248—Presentation of query results
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
- G06F18/24155—Bayesian classification
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/24323—Tree-organised classifiers
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention discloses a system for counting xerophthalmia patient distribution groups by utilizing cloud computing; the system comprises a cloud server, wherein a first communication module is connected to the cloud server in a communication way, and a hospital system is electrically connected to the first communication module; the cloud server is in communication connection with a second communication module, the second communication module is electrically connected with a processing module, the processing module is electrically connected with a control module, the control module is electrically connected with an algorithm module, the algorithm module is electrically connected with a map module, and the map module is electrically connected with a display module; according to the invention, the information of xerophthalmia patients in each hospital is acquired through the cloud server and cloud computing, then the addresses of xerophthalmia patients are counted and classified through the algorithm module, and then the distribution display on a map is realized by combining with an ECharts front-end visual generating tool, so that the observation is facilitated.
Description
Technical Field
The invention belongs to the technical field of distribution systems, and particularly relates to a system for counting xerophthalmia patient distribution groups by utilizing cloud computing.
Background
Dry eye is an ocular disease with impaired ocular tear secretion, often accompanied by itching, foreign body sensation, burning sensation, photophobia, blurred vision, and vision fluctuation, but in order to perform calculation processing on dry eye patients, it is necessary to perform calculation processing by using some system, and in order to perform calculation on the distribution of dry eye patients, the distribution of dry eye patients in cities is clarified, however, various problems still exist in various systems for calculation of dry eye distribution groups on the market.
However, in the prior art, calculation distribution is not performed on dry eye patients, calculation is performed on the distribution situation of the dry eye patients, so that the distribution situation of the dry eye patients is unclear, follow-up detection and auxiliary treatment results cannot be served, and the system for counting the distribution group of the dry eye patients by utilizing cloud computing is quite inconvenient.
Disclosure of Invention
The invention aims to provide a system for counting dry eye patient distribution groups by utilizing cloud computing, so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: a system for counting xerophthalmia patient distribution groups by utilizing cloud computing comprises a cloud server, wherein a first communication module is connected to the cloud server in a communication way, a hospital system is electrically connected to the first communication module, the hospital system comprises medical systems of all hospitals in an area, the cloud computing system is used for extracting xerophthalmia patient information in the medical systems of all hospitals, and then the first communication module is used for uploading data information and transmitting the data information to the cloud server;
the cloud server is in communication connection with a second communication module, the second communication module is electrically connected with a processing module, the processing module is electrically connected with a control module, the control module is electrically connected with an algorithm module, the algorithm module is electrically connected with a map module, the map module is electrically connected with a display module, and the control module is electrically connected with the map module and the display module; the second communication module is used for receiving and transmitting the big data information searched by the cloud server, the processing module is used for effectively processing the transmitted data information and improving the accuracy of the data information, the algorithm module is used for calculating the data information and counting the data information, the map module is used for integrating the data information counted and processed by the algorithm module onto a map and realizing a distribution image of map points, and the display module is used for effectively displaying the map information and the data information.
Preferably, the control module is electrically connected with a voltage regulating module, the voltage regulating module is electrically connected with a power supply module, the voltage regulating module is used for regulating the power supply module and keeping the stability of voltage, and the power supply module is used for supplying power to the system so that the system can realize stable operation.
Preferably, the voltage regulating module comprises a voltage reducing circuit, a rectifying circuit, a voltage stabilizing circuit, a filtering circuit and an anti-surge circuit, wherein the voltage reducing circuit is used for reducing the high voltage of the power supply module into low voltage, the rectifying circuit is used for converting the low voltage after voltage reduction into direct-current voltage, the voltage stabilizing circuit is used for stably regulating the voltage, stabilizing the voltage and preventing voltage fluctuation, the filtering circuit is used for filtering alternating-current voltage in the direct-current voltage, and the anti-surge circuit is used for absorbing voltage peaks and preventing the voltage peaks from damaging subsequent electronic components.
Preferably, the control module is electrically connected with an auxiliary module, and the auxiliary module comprises a keyboard, a control key, a clock crystal oscillator circuit, a reset circuit, an indicator lamp, an alarm and a storage module.
Preferably, the keyboard is used for realizing operation and adjustment processing, the control key is used for realizing on-off starting of the equipment, the indicator lamp is used for realizing displaying the running state of the equipment, and the alarm is used for realizing reminding after calculation processing.
Preferably, the clock crystal oscillator circuit is used for realizing delay control, timing processing and waveform output of the system and realizing control and regulation of the system, the reset circuit is used for realizing reset operation when the system fails, the storage module is used for realizing storage of data information, and the storage module comprises a ROM storage module for storing the running program body of the system, a RAM storage module for storing the data information and a cache module for caching the data.
Preferably, the processing module includes a data information receiving circuit, a data information filtering circuit, a data information converting circuit and a data information gain circuit, wherein the data information receiving circuit is used for receiving transmitted data information, the data information filtering circuit is used for filtering the data information and effectively removing clutter, the data information filtering circuit adopts a digital filter, the data information converting circuit is used for performing analog-to-digital conversion on the data information, and the data information gain circuit is used for amplifying the data information.
Preferably, the algorithm module adopts a decision tree model or a Bayesian classification algorithm, the algorithm module realizes statistics and classification of the addresses of xerophthalmia patients through the decision tree model or the Bayesian classification algorithm, and the statistics classification carries out effective classification calculation according to communities.
Preferably, the decision tree model adopts an ID3 algorithm, and the calculation formula of the ID3 algorithm is as follows:
wherein p (x) i ) Is category x i The probability of occurrence, n is the number of classifications,
the conditional entropy of Y under the condition of X means the small or large information amount of this variable of Y after the information of X, and the calculation formula is as follows:
the Bayesian classification algorithm comprises the following calculation steps:
the first step is to set each data sample to describe the values of n attributes by using an n-dimensional feature vector, namely: x= { X1, X2, …, xn }, assuming m classes, denoted C1, C2, …, cm, respectively;
the second step, given an unknown data sample X, when the naive Bayesian classification assigns the unknown sample X to the class Ci, it is certain that
P(Ci|X)>P(Cj|X)1≤j≤m，j≠i，
And according to the Bayesian theorem
Since P (X) is constant for all classes, maximizing the posterior probability P (ci|x) can translate to maximizing the prior probability P (x|ci) P (Ci);
third, when the training dataset has many attributes and tuples, the overhead of computing P (X|Ci) may be very large, it is generally assumed that the values of the attributes are independent of each other, so
The prior probabilities P (x1|ci), P (x2|ci), …, P (xn|ci) are derived from the training dataset;
according to the method, for a sample X of an unknown class, the probability P (X|Ci) P (Ci) of the X belonging to each class Ci can be calculated, and then the class with the highest probability is selected as the class.
Preferably, the map module adopts an ECharts front end visualization generating tool, downloads a target map, and inputs data information calculated by statistics of the algorithm module to finish distribution display of the data information on the map.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the information of xerophthalmia patients in each hospital is acquired through the cloud server and cloud computing, the address of the xerophthalmia patients is counted and classified through the algorithm module, the classification statistics of the patients according to different classification requirements is realized, the distribution display on a map is realized by combining with an ECharts front end visual generating tool, the observation is convenient, and the statistics and classification of the address of the xerophthalmia patients are realized through the algorithm module through a decision tree model or a Bayesian classification algorithm, so that the statistics computing processing of the address of the xerophthalmia patients can be effectively realized, the computing efficiency and accuracy are improved, and the distribution on the map is convenient.
Drawings
FIG. 1 is a schematic diagram of a system architecture of the present invention;
fig. 2 is a flowchart illustrating a calculation step of the bayesian classification algorithm according to the present invention.
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 technical solution: a system for counting xerophthalmia patient distribution groups by utilizing cloud computing comprises a cloud server, wherein a first communication module is connected to the cloud server in a communication way, a hospital system is electrically connected to the first communication module, the hospital system comprises medical systems of all hospitals in an area, the cloud computing system is used for extracting xerophthalmia patient information in the medical systems of all hospitals, and then the first communication module is used for uploading data information and transmitting the data information to the cloud server;
the cloud server is in communication connection with a second communication module, the second communication module is electrically connected with a processing module, the processing module is electrically connected with a control module, the control module is electrically connected with an algorithm module, the algorithm module is electrically connected with a map module, the map module is electrically connected with a display module, and the control module is electrically connected with the map module and the display module; the second communication module is used for receiving and transmitting the big data information searched by the cloud server, the processing module is used for effectively processing the transmitted data information and improving the accuracy of the data information, the algorithm module is used for calculating the data information and counting the data information, the map module is used for integrating the data information counted and processed by the algorithm module onto a map and realizing a distribution image of map points, and the display module is used for effectively displaying the map information and the data information.
In order to achieve effective power supply operation of the device and keep stable operation of the device, in this embodiment, preferably, the control module is electrically connected with a voltage regulating module, the voltage regulating module is electrically connected with a power supply module, the voltage regulating module is used for achieving adjustment of the power supply module and keeping stability of voltage, and the power supply module is used for achieving power supply of the system, so that the system can achieve stable operation.
In order to achieve adjustment of the power supply voltage, so that the power supply voltage can be suitable for operation of equipment, in this embodiment, preferably, the voltage adjustment module includes a step-down circuit, a rectifying circuit, a voltage stabilizing circuit, a filtering circuit and an anti-surge circuit, the step-down circuit is used for achieving reduction of high voltage of the power supply module into low voltage, the rectifying circuit is used for achieving conversion of the low voltage after the step-down into direct current voltage, the voltage stabilizing circuit is used for achieving stable adjustment of the voltage, keeping stability of the voltage, preventing voltage fluctuation, the filtering circuit is used for achieving filtering of alternating current voltage in the direct current voltage, and the anti-surge circuit is used for achieving absorption of voltage peaks and preventing damage of the voltage peaks to subsequent electronic components.
In order to realize operation control of the system and improve operation convenience of equipment, in this embodiment, preferably, the control module is electrically connected with an auxiliary module, and the auxiliary module includes a keyboard, a control key, a clock crystal oscillator circuit, a reset circuit, an indicator light, an alarm and a storage module.
In order to realize controlling and adjusting the running process of the equipment and realizing the reminding function, in this embodiment, preferably, the keyboard is used for realizing operation and adjusting treatment, the control key is used for realizing on-off starting of the equipment, the indicator light is used for realizing displaying the running state of the equipment, and the alarm is used for realizing reminding after calculation treatment.
In order to realize stable data output to equipment, reset operation to equipment and classified storage to the data information of setting, prevent data information storage confusion, in this embodiment, preferably, the clock crystal oscillator circuit is used for realizing delay control, timing processing and generating waveform output to the system, realize control and regulation to the system, the reset circuit is used for realizing reset operation when the system breaks down, the storage module is used for realizing storage to the data information, the storage module includes ROM storage module of storage system operation program body, RAM storage module of storage data information and buffer module of buffer data.
In order to achieve processing of data information, improve accuracy of the data information and facilitate computing, in this embodiment, preferably, the processing module includes a data information receiving circuit, a data information filtering circuit, a data information converting circuit and a data information gain circuit, where the data information receiving circuit is used to receive transmitted data information, the data information filtering circuit is used to implement filtering processing of the data information and effectively remove clutter, the data information filtering circuit adopts a digital filter, the data information converting circuit is used to implement analog-to-digital conversion of the data information, and the data information gain circuit is used to implement amplifying processing of the data information.
In order to realize effective and rapid statistics and classification processing on data information, in this embodiment, preferably, the algorithm module adopts a decision tree model or a bayesian classification algorithm, the algorithm module performs statistics and classification on addresses of dry eye patients through the decision tree model or the bayesian classification algorithm, and the statistics classification performs effective classification computation according to communities, the decision tree model adopts an ID3 algorithm, and the computation formula of the ID3 algorithm is as follows:
wherein p (x) i ) Is category x i The probability of occurrence, n is the number of classifications,
the conditional entropy of Y under the condition of X means the small or large information amount of this variable of Y after the information of X, and the calculation formula is as follows:
the Bayesian classification algorithm comprises the following calculation steps:
the first step is to set each data sample to describe the values of n attributes by using an n-dimensional feature vector, namely: x= { X1, X2, …, xn }, assuming m classes, denoted C1, C2, …, cm, respectively;
the second step, given an unknown data sample X, when the naive Bayesian classification assigns the unknown sample X to the class Ci, it is certain that
P(Ci|X)>P(Cj|X)1≤j≤m，j≠i，
And according to the Bayesian theorem
Since P (X) is constant for all classes, maximizing the posterior probability P (ci|x) can translate to maximizing the prior probability P (x|ci) P (Ci);
third, when the training dataset has many attributes and tuples, the overhead of computing P (X|Ci) may be very large, it is generally assumed that the values of the attributes are independent of each other, so
The prior probabilities P (x1|ci), P (x2|ci), …, P (xn|ci) are derived from the training dataset;
according to the method, for a sample X of an unknown class, the probability P (X|Ci) P (Ci) of the X belonging to each class Ci can be calculated, and then the class with the highest probability is selected as the class.
In order to integrate the calculated data information into a map for display, and facilitate viewing, in this embodiment, preferably, the map module adopts an ECharts front end visualization generating tool, downloads a target map, and then inputs the data information calculated by the algorithm module, so as to complete the distributed display of the data information on the map.
The working principle and the using flow of the invention are as follows: when the system is used, the cloud server extracts address information of xerophthalmia patients of each hospital system through the first communication module, the cloud server transmits the address information of the xerophthalmia patients to the control module through the second communication module after acquiring the address information of the xerophthalmia patients, the control module receives, filters, converts and amplifies data information through the processing module, accuracy of the data information is improved, then the control module realizes control adjustment through the auxiliary module, then transmits the data information to the algorithm module, the decision tree model or the Bayesian classification algorithm in the algorithm module is used for calculating, counting and classifying the data information, the data information is input into the map module after counting and classifying the data information is completed, the data information is combined with the ECharts front-end visual generating tool, distribution conditions of the xerophthalmia patients in an area are processed, display and display are performed through the display module, power supply operation of the system is realized through the voltage adjusting module and the power supply module, and stability of the system is maintained.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. A system for counting xerophthalmia patient distribution groups by utilizing cloud computing comprises a cloud server, and is characterized in that: the cloud server is connected with a first communication module in a communication way, the first communication module is electrically connected with a hospital system, the hospital system comprises medical systems of all hospitals in an area, the cloud computing system is used for extracting xerophthalmia patient information in the medical systems of all hospitals, and then the first communication module is used for uploading data information and transmitting the data information to the cloud server;
the cloud server is in communication connection with a second communication module, the second communication module is electrically connected with a processing module, the processing module is electrically connected with a control module, the control module is electrically connected with an algorithm module, the algorithm module is electrically connected with a map module, the map module is electrically connected with a display module, and the control module is electrically connected with the map module and the display module; the second communication module is used for receiving and transmitting the big data information searched by the cloud server, the processing module is used for effectively processing the transmitted data information and improving the accuracy of the data information, the algorithm module is used for calculating the data information and counting the data information, the map module is used for integrating the data information counted by the algorithm module onto a map and realizing a distribution image of map points, and the display module is used for effectively displaying the map information and the data information;
the processing module comprises a data information receiving circuit, a data information filtering circuit, a data information converting circuit and a data information gain circuit, wherein the data information receiving circuit is used for receiving transmitted data information, the data information filtering circuit is used for filtering the data information and effectively removing clutters, the data information filtering circuit adopts a digital filter, the data information converting circuit is used for performing analog-to-digital conversion on the data information, and the data information gain circuit is used for amplifying the data information;
the algorithm module adopts a decision tree model or a Bayesian classification algorithm, the algorithm module realizes statistics and classification of the address of the xerophthalmia patient through the decision tree model or the Bayesian classification algorithm, and the statistics classification carries out effective classification calculation according to communities;
the decision tree model adopts an ID3 algorithm, and the calculation formula of the ID3 algorithm is as follows:
wherein p (x) i ) Is category x i The probability of occurrence, n is the number of classifications,
the conditional entropy of Y under the condition of X means the small or large information amount of this variable of Y after the information of X, and the calculation formula is as follows:
the Bayesian classification algorithm comprises the following calculation steps:
the first step is to set each data sample to describe the values of n attributes by using an n-dimensional feature vector, namely: x= { X1, X2, …, xn }, assuming m classes, denoted C1, C2, …, cm, respectively;
the second step, given an unknown data sample X, when the naive Bayesian classification assigns the unknown sample X to the class Ci, it is certain that
P(Ci|X)>P(Cj|X)1≤j≤m，j≠i，
And according to the Bayesian theorem
Since P (X) is constant for all classes, maximizing the posterior probability P (ci|x) can translate to maximizing the prior probability P (x|ci) P (Ci);
third, when the training dataset has many attributes and tuples, the overhead of computing P (X|Ci) may be very large, it is generally assumed that the values of the attributes are independent of each other, so
The prior probabilities P (x1|ci), P (x2|ci), …, P (xn|ci) are derived from the training dataset;
fourth, according to the method, for a sample X of an unknown class, the probability P (X|Ci) P (Ci) of the X belonging to each class Ci can be calculated respectively, and then the class with the highest probability is selected as the class;
the map module adopts an ECharts front end visualization generating tool, and the data information calculated by the algorithm module is input by downloading a target map, so that the distribution display of the data information on the map is completed.
2. The system for counting dry eye patient distribution clusters using cloud computing as set forth in claim 1, wherein: the control module is electrically connected with a voltage regulating module, the voltage regulating module is electrically connected with a power supply module, the voltage regulating module is used for regulating the power supply module and keeping the stability of voltage, and the power supply module is used for supplying power to the system so that the system can realize stable operation.
3. A system for counting dry eye patient distribution clusters using cloud computing as claimed in claim 2, wherein: the voltage regulating module comprises a voltage reducing circuit, a rectifying circuit, a voltage stabilizing circuit, a filtering circuit and an anti-surge circuit, wherein the voltage reducing circuit is used for reducing high voltage of the power supply module into low voltage, the rectifying circuit is used for converting the low voltage after voltage reduction into direct-current voltage, the voltage stabilizing circuit is used for stably regulating the voltage, stabilizing the voltage and preventing voltage fluctuation, the filtering circuit is used for filtering alternating-current voltage in the direct-current voltage, and the anti-surge circuit is used for absorbing voltage peaks and preventing the voltage peaks from damaging subsequent electronic components.
4. The system for counting dry eye patient distribution clusters using cloud computing as set forth in claim 1, wherein: the control module is electrically connected with an auxiliary module, and the auxiliary module comprises a keyboard, a control key, a clock crystal oscillator circuit, a reset circuit, an indicator lamp, an alarm and a storage module.
5. The system for counting dry eye patient distribution clusters using cloud computing as set forth in claim 4, wherein: the keyboard is used for realizing operation and adjustment processing, the control key is used for realizing on-off starting of equipment, the indicator lamp is used for realizing displaying of the running state of the equipment, and the alarm is used for reminding after calculation processing.
6. The system for counting dry eye patient distribution clusters using cloud computing as set forth in claim 4, wherein: the clock crystal oscillator circuit is used for realizing delay control, timing processing and waveform output of the system and realizing control and regulation of the system, the reset circuit is used for realizing reset operation when the system fails, the storage module is used for realizing storage of data information, and the storage module comprises a ROM storage module for storing a running program body of the system, a RAM storage module for storing the data information and a cache module for caching the data.
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