CN117251870B - Big data analysis early warning method and system based on block chain and cloud platform thereof - Google Patents
Big data analysis early warning method and system based on block chain and cloud platform thereof Download PDFInfo
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Abstract
The invention discloses a big data analysis early warning method and system based on a blockchain and a cloud platform thereof, and relates to the technical field of big data, wherein the early warning system comprises a cloud platform, a monitoring terminal and a user interface terminal, wherein the cloud platform acquires related data acquired from the monitoring terminal, and sends a result to the user interface terminal after processing for a user to check; the technical key points are as follows: in the early warning process of the health risk state of the user, encryption storage is completed on the generated data through the blockchain storage module, on the premise of ensuring the safety of the data, the environment factors and the state factors of the user are comprehensively considered by matching with the use of the data analysis module, the accuracy of the health risk state evaluation value Hrsa generated through calculation is ensured, the parameters for acquiring the health risk state evaluation value Hrsa are subjected to secondary processing, and the reduction of the effectiveness and the accuracy of the final health risk state evaluation value Hrsa caused by errors of the data corresponding to a single time stamp is avoided.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a big data analysis and early warning method and system based on a blockchain and a cloud platform thereof.
Background
Big data technology refers to a series of technologies and methods for processing, storing, analyzing and visualizing large-scale and complex data sets, and the main objective of big data technology is to extract valuable information and insight from a large amount of data to support decision making, business optimization, innovation and other aspects.
The technical scheme pointed out in the patent with the publication number of CN113847953A and the name of a health management monitoring system based on big data analysis comprises the following steps: the system comprises a tobacco smoke detection unit, a detection abnormality early warning unit, an abnormality information data collection unit, a GPS positioning unit, a movable range acquisition unit, a stm32 singlechip, a prevention reminding unit, a wireless receiving terminal, a health condition detection unit, a central database, a health condition input unit and a health condition evaluation unit, wherein the movable range of a user is acquired through the GPS positioning unit and the movable range acquisition unit, an activity place is confirmed, the content of harmful substances in the activity place is detected by installing a tobacco smoke detection instrument in the activity place, the user is reminded of the position information with the content of the harmful substances exceeding the standard through the prevention reminding unit, prevention measures are made, and finally the user is detected healthily, so that the harm to the physical health of passive smoking crowds in intangible state is avoided;
the technical solution pointed out in the patent with the publication number of CN113804834A and the name of indoor air quality detection system based on big data analysis comprises: the system comprises a data acquisition module, a component analysis module, a biological environment control module, a correlation summarization module, an early warning feedback module and a data output module; through collecting a large amount of basic data information, carrying out symbolized calibration, formulated operation and signalized output on the basic data information, and carrying out data integration association and discrimination output, the effective feedback output of the early warning signal is realized, so that the evaluation early warning operation on the air quality is further realized while the efficient and accurate detection on the indoor air is realized, and the comprehensiveness and accuracy of the air quality detection are improved.
Aiming at the prior art, when the prior art is combined with the prior art, the traditional large data technology is used for detecting the health state of the user, only the air quality of the environment is considered to be detected, the health state of the human body is not comprehensively analyzed, namely, the air quality and the health state are combined to be analyzed, but the detected data are single and the error rate is high, so that the accuracy of the final analysis result is greatly influenced, and the deviation of the early warning prompt of the health state of the user is possibly caused.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a big data analysis early warning method and system based on a blockchain and a cloud platform thereof, in the early warning process of the health risk state of a user, encryption storage is completed on generated data in the big data through a blockchain storage module, on the premise of ensuring the safety of the data, the accuracy of a health risk state evaluation value Hrsa generated by calculation is ensured by comprehensively considering environmental factors and state factors of the user by matching with the use of the data analysis module, the parameters of the acquired health risk state evaluation value Hrsa are subjected to secondary processing, the reduction of the validity and accuracy of a final health risk state evaluation value Hrsa caused by errors of data corresponding to a single timestamp is avoided, and the problems in the background technology are solved.
In order to achieve the above purpose, the invention is realized by the following technical scheme:
the big data analysis early warning system based on the block chain comprises a cloud platform, a monitoring terminal and a user interface terminal, wherein the cloud platform acquires related data acquired from the monitoring terminal, and sends a result to the user interface terminal for a user to check after processing;
The cloud platform includes:
The data receiving module is used for acquiring related data, wherein the related data comprises air quality data, human body information data, user position data and time data, and a positioning and classifying sub-module is also arranged in the data receiving module and used for judging whether a user is indoor or outdoor according to the user position data;
the block chain storage module is used for storing related data in a block chain and synchronously running a block chain verification mechanism;
The data analysis module comprises a data characteristic extraction unit and a prediction evaluation unit;
The data feature extraction unit is used for carrying out feature extraction on related data by utilizing a big data analysis technology to obtain key features, a data analysis model is built by combining the key features by the prediction evaluation unit, an air quality evaluation coefficient Pgx and a health state evaluation coefficient Jpz are obtained through calculation, a health risk state evaluation value Hrsa is generated according to the air quality evaluation coefficient Pgx and the health state evaluation coefficient Jpz, and the health risk state evaluation value Hrsa is compared with a preset first evaluation threshold value Second evaluation threshold/>Comparison is made, and the first evaluation threshold/>Second evaluation threshold/>Judging the health risk degree of the corresponding user according to the comparison result;
the display early warning module obtains the comparison result and controls the user interface terminal to send out a corresponding early warning prompt.
Further, in the positioning and classifying sub-module, if the user is judged to be outdoors, the human body information data is obtained in real time through a bracelet end matched with the user in the monitoring terminal, and the air quality data is obtained through weather meteorological data in a network; if the user is judged to be in the room, the human body information data is obtained in real time through the bracelet end matched with the user in the monitoring terminal, and the air quality data is obtained through the sensor group matched with the monitoring terminal.
Further, the air quality data includes PM2.5 concentration, carbon dioxide concentration, and temperature and humidity, and the human body information data includes heart rate, body temperature, blood pressure, and blood oxygen saturation of the corresponding user.
Further, the user position data and the time data are obtained through a mobile phone terminal carried by the user in the monitoring terminal, the position data of the user, namely longitude and latitude coordinates, are obtained through a GPS module carried in the mobile phone terminal, a building database in navigation software is combined, the positioning and classifying sub-module is matched with the building database according to the longitude and latitude coordinates, if an indoor building exists in an area where the longitude and latitude coordinates are located, the user is judged to be indoor, and if the indoor building does not exist in the area where the longitude and latitude coordinates are located, the user is judged to be outdoor.
Further, in the blockchain storage module, the specific process of storing the related data in the blockchain is as follows:
data slicing: the original related data is divided into a plurality of data blocks;
encryption hash: each data block is encrypted through a hash function to generate a unique hash value;
Creating a block: packaging the encrypted data block and the hash value of the previous block together to form a new block;
consensus mechanism: nodes in the blockchain network agree through a consensus mechanism;
Added to the blockchain: the new block is verified and agreed upon, then the new block will be added to the end of the blockchain.
Further, in the data feature extraction unit, feature extraction is performed on the relevant data, the acquired key features include an air quality feature, a human body information feature and a time feature, wherein the air quality feature includes PM2.5 concentration, temperature and humidity in the air quality data, the human body information feature includes heart rate, body temperature and blood pressure in the human body information data, and the time feature is a time stamp in the time data.
Further, in the prediction evaluation unit, the process of calculating the air quality evaluation coefficient Pgx is as follows:
s101, obtaining PM2.5 concentration under different time stamps in T time Temperature/>Humidity/>T represents corresponding numbers of PM2.5 concentration, temperature and humidity in different time stamps within T time, and t=1, 2, … and n are positive integers;
S102, performing dimensionless treatment on PM2.5 concentration, temperature and humidity in different time stamps in T time;
S103, according to PM2.5 concentration Temperature/>Humidity/>Obtaining the average value/>, of PM2.5 concentration under different time stamps in T timeAverage value of temperature/>Average value of humidity/>The formula according to is as follows:
；
S104, according to the average value of PM2.5 concentration Average value of temperature/>Average value of humidity/>An air quality assessment coefficient Pgx is generated according to the following formula:
；
In the method, in the process of the invention, 、/>、/>3 Are preset proportionality coefficients of average value of PM2.5 concentration, average value of temperature and average value of humidity respectively, and/>＞/>＞/>3＞0，/>As a constant correction coefficient, int is a rounding function;
the process of calculating the health status assessment coefficient Jpz is as follows:
S201, acquiring heart rates under different time stamps in T time Body temperature/>Blood pressure/>T represents the corresponding numbers of heart rate, body temperature and blood pressure in different time stamps within T time, and t=1, 2, … and n are positive integers;
S102, performing dimensionless treatment on heart rate, body temperature and blood pressure in different time stamps within T time;
s103, according to heart rate Body temperature/>Blood pressure/>Obtaining the average value/>, of heart rate in different time stamps in T timeAverage value of body temperature/>Mean value of blood pressure/>The formula according to is as follows:
；
S104, according to the average value of heart rate Average value of body temperature/>Mean value of blood pressure/>The health state assessment coefficient Jpz is generated according to the following formula:
；
In the method, in the process of the invention, 、/>2、/>3 Are respectively preset proportionality coefficients of the average value of heart rate, the average value of body temperature and the average value of blood pressure, and/>＞/>3＞/>2＞0，/>As a constant correction coefficient, int is a rounding function;
The health risk status evaluation value Hrsa is generated according to the following formula:
；
In the method, in the process of the invention, For a constant correction coefficient, int is a rounding function.
Further, the health risk status evaluation value Hrsa is compared with a preset first evaluation threshold valueSecond evaluation threshold/>The results after comparison were:
If the health risk status evaluation value Hrsa is smaller than the first evaluation threshold value No early warning signal is generated; if it is the first evaluation threshold/>The health risk status evaluation value Hrsa is less than or equal to the second evaluation threshold, and a low risk signal is generated; if it is the second evaluation threshold/>< Health risk status assessment Hrsa, generating a high risk signal;
The display early warning module obtains the compared result, and if no early warning signal is generated, the display early warning module does not respond; if the low risk signal is generated, the user interface terminal is controlled by the display early warning module to send out a low risk early warning prompt; if the high risk signal is generated, the user interface terminal is controlled by the display early warning module to send out the high risk early warning prompt.
Further, the cloud platform further comprises an execution prompting module, if the cloud platform judges that the user is outdoors, a prediction model is built, a predicted air quality evaluation coefficient Pgx is generated according to future air quality characteristics predicted by weather forecast, a predicted health risk state evaluation value Hrsa curve graph is obtained by combining the current health state evaluation coefficient Jpz, when the trend of the curve graph is downward, no response is made, and when the trend of the curve graph is upward, a command for prompting the user to enter indoors is sent through a user interface terminal;
If the user is judged to be in the room, when no early warning signal is generated, no response is made, and when a high or low risk signal is generated, the user interface terminal sends an opening instruction to the indoor air purification equipment until the health risk state evaluation value Hrsa is smaller than a first evaluation threshold value Until that point.
The big data analysis and early warning method based on the block chain comprises the following steps:
Step one, acquiring related data, wherein the related data comprise air quality data, human body information data, user position data and time data, and a positioning and classifying sub-module is further configured in the data receiving module to judge whether a user is indoor or outdoor according to the user position data;
Step two, storing related data in a block chain, and synchronously operating a block chain verification mechanism;
Step three, performing feature extraction on related data by utilizing a big data analysis technology to obtain key features, building a data analysis model by combining the key features, calculating to obtain an air quality evaluation coefficient Pgx and a health state evaluation coefficient Jpz, generating a health risk state evaluation value Hrsa according to the air quality evaluation coefficient Pgx and the health state evaluation coefficient Jpz, and combining the health risk state evaluation value Hrsa with a preset first evaluation threshold value Second evaluation threshold/>Comparison is made, and the first evaluation threshold/>Second evaluation threshold/>；
If the health risk status evaluation value Hrsa is smaller than the first evaluation threshold valueNo early warning signal is generated; if it is the first evaluation threshold/>The health risk status evaluation value Hrsa is less than or equal to the second evaluation threshold, and a low risk signal is generated; if it is the second evaluation threshold/>< Health risk status assessment Hrsa, generating a high risk signal;
Step four, obtaining a comparison result, and if no early warning signal is generated, not responding; if the low risk signal is generated, the user interface terminal is controlled by the display early warning module to send out a low risk early warning prompt; if the high risk signal is generated, the user interface terminal is controlled by the display early warning module to send out a high risk early warning prompt;
If the user is judged to be outdoors, a prediction model is built, a predicted air quality evaluation coefficient Pgx is generated according to future air quality characteristics predicted by weather forecast, a predicted health risk state evaluation value Hrsa curve graph is obtained by combining the current health state evaluation coefficient Jpz, when the trend of the curve graph is downward, no response is made, and when the trend of the curve graph is upward, a command for prompting the user to enter indoors is sent out through a user interface terminal;
If the user is judged to be in the room, when no early warning signal is generated, no response is made, and when a high or low risk signal is generated, the user interface terminal sends an opening instruction to the indoor air purification equipment until the health risk state evaluation value Hrsa is smaller than a first evaluation threshold value Until that point.
A cloud platform, comprising: a plurality of user terminals, a connection network and a server;
The user terminal is connected with the server through a connecting network, server data corresponding to the user authority is obtained, and the connecting network is provided by a big data analysis and early warning system based on a block chain.
The invention provides a big data analysis and early warning method and system based on a blockchain and a cloud platform thereof, which have the following beneficial effects:
1. in the early warning process of the health risk state of the user, encryption storage is completed on the generated data through a blockchain storage module, on the premise of ensuring the safety of the data, the environment factors and the state factors of the user are comprehensively considered by matching with the use of a data analysis module, the accuracy of the health risk state evaluation value Hrsa generated through calculation is ensured, the parameters of the acquired health risk state evaluation value Hrsa are subjected to secondary treatment, the reduction of the effectiveness and accuracy of the final health risk state evaluation value Hrsa caused by errors of the data corresponding to a single timestamp is avoided, and the judgment of the subsequent early warning operation can be effectively assisted by utilizing the health risk state evaluation value Hrsa with a specific value;
2. The cloud platform is provided with a display early-warning module and an execution prompt module which are matched with each other for use, risk early-warning signals of different degrees are obtained through the display early-warning module, and the execution prompt module is used for giving more specific prompt instructions according to the risk early-warning signals in combination with different indoor or outdoor scenes where users are located so as to ensure that health risk states of corresponding users can be quickly recovered to be normal, the practicality of the whole system design is embodied, and the influence degree of external environments on the health states of the users is reduced to a certain extent.
Drawings
FIG. 1 is a schematic diagram of the overall use process of a big data analysis and early warning system based on a blockchain;
FIG. 2 is a schematic diagram of a modular architecture of a big data analysis and early warning system based on blockchain of the present invention;
FIG. 3 is an overall flow chart of the big data analysis and early warning method based on block chain.
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.
Example 1: referring to fig. 1 to 2, the invention provides a big data analysis and early warning system based on a blockchain, which comprises a cloud platform, a monitoring terminal and a user interface terminal, wherein the cloud platform acquires related data acquired from the monitoring terminal, and sends a result to the user interface terminal after processing for a user to check;
The cloud platform comprises a data receiving module, a blockchain storage module, a data analysis module, a display early warning module and an execution prompt module which are sequentially operated, wherein the data receiving module is used for acquiring related data, the related data comprise air quality data, human body information data, user position data and time data, and a positioning and classifying sub-module is further configured in the data receiving module, so that after the related data are acquired, a user is judged to be indoor or outdoor according to the user position data;
If the user is judged to be outdoors, acquiring human body information data in real time through a bracelet end matched with the user in the monitoring terminal, acquiring air quality data through weather meteorological data in a network, and classifying and defining the air quality data at the moment as outdoor environment data; if the user is judged to be in the room, the human body information data is still obtained in real time through the bracelet end matched with the user in the monitoring terminal, but the air quality data is obtained through the sensor group matched with the monitoring terminal, and the air quality data is classified and positioned into the indoor environment data at the moment;
meanwhile, the precondition for continuing to execute the subsequent operation is as follows: the personnel are required to check and ensure that the cloud platform, the monitoring terminal and the user interface terminal in the system all keep the normal working state.
Specifically, the air quality data comprise PM2.5 concentration, carbon dioxide concentration and temperature and humidity, when the air quality data are obtained directly through weather forecast outdoors, specific values of PM2.5 concentration, carbon dioxide concentration and temperature and humidity are obtained, and when the air quality data are indoors, the sensor group comprises a laser dust sensor for detecting indoor PM2.5, a gas concentration sensor for detecting indoor carbon dioxide concentration and a temperature and humidity sensor for detecting temperature and humidity;
the human body information data includes heart rate, body temperature, blood pressure and blood oxygen saturation of the corresponding user, and the user only needs to wear the bracelet end, for example: the F50S pro intelligent bracelet is provided with a GR5515 chip, so that the real-time heart rate, the body temperature, the blood pressure and the blood oxygen saturation of a wearer can be monitored in real time;
the user position data and the time data are obtained through a mobile phone terminal carried by a user in the monitoring terminal, a GPS module is usually carried in the mobile phone terminal, so that the position data of the user, namely longitude and latitude coordinates, can be obtained, a building database in navigation software is combined, the positioning and classifying sub-module is matched with the building database according to the longitude and latitude coordinates, if an indoor building exists in an area where the longitude and latitude coordinates are located, the mobile phone terminal is judged to be indoor, namely the user is indoor, if the indoor building does not exist in the area where the longitude and latitude coordinates are located, the mobile phone terminal is judged to be outdoor, namely the user is outdoor.
The block chain storage module is used for storing related data in the block chain, so that the safety and the integrity of the data are ensured, and meanwhile, the authenticity and the credibility of the data can be ensured by using a verification mechanism of the block chain;
The specific process of storing the related data in the blockchain is as follows:
data slicing: the original related data is divided into a plurality of data blocks;
encryption hash: each data block is encrypted through a hash function to generate a unique hash value;
Creating a block: packaging the encrypted data block and the hash value of the previous block together to form a new block;
consensus mechanism: nodes in the blockchain network agree through a consensus mechanism (such as rights and interests proving) to confirm the validity of the new blocks;
added to the blockchain: the new block is verified and agreed upon, then the new block will be added to the end of the blockchain as a non-tamperable permanent record;
The above-mentioned verification mechanism using the blockchain is blockchain consistency verification, because each node in the blockchain network stores a complete blockchain copy, the consistency of the whole blockchain can be verified by comparing the blockchain data among the nodes, and if the data of the nodes have differences with other nodes, an alarm can be triggered to repair.
In summary, through the hash encryption and the multi-node verification mechanism, the blockchain storage module can ensure the security, the integrity, the authenticity and the credibility of the data, so that the blockchain becomes an effective data storage mode, and the method is particularly suitable for the scene of ensuring that the data is not tampered and traceable in the application.
The data analysis module comprises a data characteristic extraction unit and a prediction evaluation unit;
The data analysis module uses a big data analysis technology to analyze and process related data stored on the blockchain in real time, evaluates the health risk state of a corresponding user by establishing a data model, and acquires a corresponding risk level;
The data feature extraction unit is used for extracting features of the related data by utilizing a big data analysis technology, and can better understand the internal rules and trends of the data by extracting key features in the related data so as to provide a basis for subsequent prediction and analysis; the extracted key features comprise PM2.5 concentration, temperature and humidity in the air quality data, wherein the PM2.5 concentration, temperature and humidity are taken as the air quality features, the heart rate, the body temperature and the blood pressure in the human body information data are taken as the human body information features, the heart rate, the body temperature and the blood pressure are taken as the time stamps in the time data, and the time stamps are taken as the time features;
The prediction evaluation unit is used for combining the key features extracted by the data feature extraction unit, building a data analysis model, calculating to obtain an air quality evaluation coefficient Pgx and a health state evaluation coefficient Jpz, generating a health risk state evaluation value Hrsa according to the air quality evaluation coefficient Pgx and the health state evaluation coefficient Jpz, and comparing the health risk state evaluation value Hrsa with a preset first evaluation threshold value Second evaluation threshold/>Comparison is made, and the first evaluation threshold/>Second evaluation threshold/>Judging the health risk degree of the corresponding user according to the comparison result;
the process of calculating the air quality evaluation coefficient Pgx is as follows:
s101, obtaining PM2.5 concentration under different time stamps in T time Temperature/>Humidity/>T represents the corresponding numbers of PM2.5 concentration, temperature and humidity at different time stamps within T time, t=1, 2, …, n is a positive integer, for example: the T time can be selected to be 1 day, and the corresponding time stamp T corresponds to each hour;
S102, performing dimensionless treatment on PM2.5 concentration, temperature and humidity in different time stamps in T time;
S103, according to PM2.5 concentration Temperature/>Humidity/>Obtaining the average value/>, of PM2.5 concentration under different time stamps in T timeAverage value of temperature/>Average value of humidity/>The formula according to is as follows:
；
S104, according to the average value of PM2.5 concentration Average value of temperature/>Average value of humidity/>An air quality assessment coefficient Pgx is generated according to the following formula:
；
In the method, in the process of the invention, 、/>、/>3 Are preset proportionality coefficients of average value of PM2.5 concentration, average value of temperature and average value of humidity respectively, and/>＞/>＞/>3＞0，/>3=2.87，/>Is a constant correction coefficient, the specific value of which can be set by user adjustment or generated by fitting an analytical function,/>The value range is between 1 and 2, and int is a rounding function.
The average value of PM2.5 concentration, average value of temperature and average value of humidity are added to realize weighted average treatment and then are added to constant correction coefficientMultiplying to complete the adjustment of the weighted average value, and finally processing by a rounding function int to obtain an integer value for subsequent calculation of the health risk status evaluation Hrsa, average value/>, of PM2.5 concentrationAverage value of temperature/>Average value of humidity/>The higher the air quality assessment coefficient Pgx, the worse the corresponding air quality state, and it is to be noted that: the high temperature and high humidity are favorable for the propagation of bacteria, mold and virus, so that the air quality is deteriorated.
The process of calculating the health status assessment coefficient Jpz is as follows:
S201, acquiring heart rates under different time stamps in T time Body temperature/>Blood pressure/>T represents the corresponding numbers of heart rate, body temperature and blood pressure in different time stamps within T time, and t=1, 2, … and n are positive integers;
S102, performing dimensionless treatment on heart rate, body temperature and blood pressure in different time stamps within T time;
s103, according to heart rate Body temperature/>Blood pressure/>Obtaining the average value/>, of heart rate in different time stamps in T timeAverage value of body temperature/>Mean value of blood pressure/>The formula according to is as follows:
；
S104, according to the average value of heart rate Average value of body temperature/>Mean value of blood pressure/>The health state assessment coefficient Jpz is generated according to the following formula:
；
In the method, in the process of the invention, 、/>2、/>3 Are respectively preset proportionality coefficients of the average value of heart rate, the average value of body temperature and the average value of blood pressure, and/>＞/>3＞/>2＞0，/>3=2.57，/>Is a constant correction coefficient, the specific value of which can be set by user adjustment or generated by fitting an analytical function,/>The value range is 1-2, int is a rounding function, and the principle of the formula is the same as that of calculating the air quality evaluation coefficient Pgx, so that detailed description is omitted, and the average value of heart rate/>Average value of body temperature/>Mean value of blood pressure/>The larger the state of health assessment coefficient Jpz, the larger the corresponding state of health is.
It should be noted that: a person skilled in the art collects a plurality of groups of sample data and sets a corresponding preset scaling factor for each group of sample data; substituting the preset proportionality coefficient, which can be the preset proportionality coefficient and the acquired sample data, into a formula, forming a ternary once equation set by any three formulas, screening the calculated coefficient, taking an average value, and obtaining a value; the magnitude of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, the magnitude of the coefficient depends on the number of sample data and the corresponding preset proportional coefficient preliminarily set by a person skilled in the art for each group of sample data, that is, the coefficient is preset according to actual practice, as long as the proportional relation between the parameter and the quantized numerical value is not influenced, and the above description is adopted for the preset proportional coefficient and the constant correction coefficient described in other formulas;
The health risk status evaluation value Hrsa is generated according to the following formula:
；
In the method, in the process of the invention, Is a constant correction coefficient, the specific value of which can be set by user adjustment or generated by fitting an analytical function,/>The value range is between 1 and 2, and int is a rounding function.
The health risk state evaluation value Hrsa is compared with a preset first evaluation threshold valueSecond evaluation threshold/>After comparison, if healthy risk status evaluation value Hrsa < first evaluation threshold/>No early warning signal is generated, which indicates that the corresponding user has no health risk; if it is the first evaluation threshold/>The health risk state evaluation value Hrsa is less than or equal to the second evaluation threshold value, a low risk signal is generated, and the corresponding user health risk is lower; if it is the second evaluation threshold/>The health risk state evaluation value Hrsa is less than the health risk state evaluation value Hrsa, a high risk signal is generated, and the corresponding user health risk is higher;
By adopting the technical scheme: in the early warning process of the health risk state of the user, encryption storage is completed on the generated data through the blockchain storage module, on the premise of ensuring the safety of the data, the environment factors and the state factors of the user are comprehensively considered by matching with the use of the data analysis module, the accuracy of the health risk state evaluation value Hrsa generated through calculation is ensured, the parameters for obtaining the health risk state evaluation value Hrsa are subjected to secondary treatment, the reduction of the effectiveness and accuracy of the final health risk state evaluation value Hrsa caused by errors of the data corresponding to a single timestamp is avoided, and the judgment of the subsequent early warning operation can be effectively assisted by utilizing the health risk state evaluation value Hrsa with a specific value.
The display early warning module obtains a comparison result, if no early warning signal is generated, the display early warning module does not respond, if a low risk signal is generated, the display early warning module controls the user interface terminal to send out a low risk early warning prompt, particularly, the early warning prompt can be carried out through an audible and visual alarm for displaying yellow light, if a high risk signal is generated, the display early warning module controls the user interface terminal to send out a high risk early warning prompt, particularly, the early warning prompt can be carried out through an audible and visual alarm for displaying red light;
executing a prompting module, if the user is judged to be outdoors, constructing a prediction model, generating a predicted air quality evaluation coefficient Pgx according to future air quality characteristics predicted by weather forecast, and combining the current health state evaluation coefficient Jpz to obtain a predicted health risk state evaluation value Hrsa curve graph, wherein when the trend of the curve graph is downward, the system does not respond, and when the trend of the curve graph is upward, a command for prompting the user to enter indoors is sent out through a user interface terminal, and the user cannot know the command for the first time, so that the command can be transmitted instead by a staff;
if the user is judged to be in a room, when no early warning signal is generated, the user interface terminal does not respond, when a high or low risk signal is generated, the user interface terminal sends an opening instruction to indoor air purification equipment, the low risk signal is used for running the air purification equipment at low power, and the high risk signal is used for running the air purification equipment at high power until the health risk state evaluation value Hrsa is smaller than a first evaluation threshold value Until that point.
The air purifying device adopts any one or a combination of a plurality of adsorption technology, negative (positive) ion technology, catalysis technology and photocatalyst technology, and is internally provided with a dehumidifier and an air conditioner so as to achieve the effect of reducing humidity and temperature, and the whole air purifying device has a conventional structure and is not repeated in the prior art.
By adopting the technical scheme: the cloud platform is provided with a display early-warning module and an execution prompt module which are matched with each other for use, risk early-warning signals of different degrees are obtained through the display early-warning module, and the execution prompt module is used for giving more specific prompt instructions according to the risk early-warning signals in combination with different indoor or outdoor scenes where users are located so as to ensure that health risk states of corresponding users can be quickly recovered to be normal, the practicality of the whole system design is embodied, and the influence degree of external environments on the health states of the users is reduced to a certain extent.
Example 2: based on embodiment 1, please refer to fig. 3, the present embodiment provides a big data analysis and early warning method based on blockchain, which comprises the following specific steps:
Step one, acquiring related data, wherein the related data comprise air quality data, human body information data, user position data and time data, and a positioning and classifying sub-module is further configured in the data receiving module to judge whether a user is indoor or outdoor according to the user position data;
Step two, storing related data in a block chain, and synchronously operating a block chain verification mechanism;
Step three, performing feature extraction on related data by utilizing a big data analysis technology to obtain key features, building a data analysis model by combining the key features, calculating to obtain an air quality evaluation coefficient Pgx and a health state evaluation coefficient Jpz, generating a health risk state evaluation value Hrsa according to the air quality evaluation coefficient Pgx and the health state evaluation coefficient Jpz, and combining the health risk state evaluation value Hrsa with a preset first evaluation threshold value Second evaluation threshold/>Comparison is made, and the first evaluation threshold/>Second evaluation threshold/>；
If the health risk status evaluation value Hrsa is smaller than the first evaluation threshold valueNo early warning signal is generated; if it is the first evaluation threshold/>The health risk status evaluation value Hrsa is less than or equal to the second evaluation threshold, and a low risk signal is generated; if it is the second evaluation threshold/>< Health risk status assessment Hrsa, generating a high risk signal;
Step four, obtaining a comparison result, and if no early warning signal is generated, not responding; if the low risk signal is generated, the user interface terminal is controlled by the display early warning module to send out a low risk early warning prompt; if the high risk signal is generated, the user interface terminal is controlled by the display early warning module to send out a high risk early warning prompt;
If the user is judged to be outdoors, a prediction model is built, a predicted air quality evaluation coefficient Pgx is generated according to future air quality characteristics predicted by weather forecast, a predicted health risk state evaluation value Hrsa curve graph is obtained by combining the current health state evaluation coefficient Jpz, when the trend of the curve graph is downward, no response is made, and when the trend of the curve graph is upward, a command for prompting the user to enter indoors is sent out through a user interface terminal;
If the user is judged to be in the room, when no early warning signal is generated, no response is made, and when a high or low risk signal is generated, the user interface terminal sends an opening instruction to the indoor air purification equipment until the health risk state evaluation value Hrsa is smaller than a first evaluation threshold value Until that point.
In the application, the related formulas are all the numerical calculation after dimensionality removal, and the formulas are one formulas for obtaining the latest real situation by software simulation through collecting a large amount of data, and the formulas are set by a person 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. Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application.
Claims (2)
1. The big data analysis early warning system based on the block chain is characterized by comprising a cloud platform, a monitoring terminal and a user interface terminal, wherein the cloud platform acquires related data acquired from the monitoring terminal, and sends a result to the user interface terminal for a user to check after processing;
The cloud platform includes:
The data receiving module is used for acquiring related data, wherein the related data comprises air quality data, human body information data, user position data and time data, and a positioning and classifying sub-module is also arranged in the data receiving module and used for judging whether a user is indoor or outdoor according to the user position data;
in the positioning and classifying sub-module, if the user is judged to be outdoors, the human body information data is obtained in real time through a bracelet end matched with the user in the monitoring terminal, and the air quality data is obtained through weather meteorological data in a network; if the user is judged to be in a room, acquiring human body information data in real time through a bracelet end matched with the user in the monitoring terminal, and acquiring air quality data through a sensor group matched with the monitoring terminal; the air quality data comprise PM2.5 concentration, carbon dioxide concentration, temperature and humidity, and the human body information data comprise heart rate, body temperature, blood pressure and blood oxygen saturation of corresponding users; the method comprises the steps that user position data and time data are obtained through a mobile phone terminal carried by a user in a monitoring terminal, the position data of the user are obtained through a GPS module carried in the mobile phone terminal, the position data comprise longitude and latitude coordinates, a building database in navigation software is combined, a positioning and classifying sub-module is matched with the building database according to the longitude and latitude coordinates, if an indoor building exists in an area where the longitude and latitude coordinates are located, the user is judged to be indoor, and if the indoor building does not exist in the area where the longitude and latitude coordinates are located, the user is judged to be outdoor;
the block chain storage module is used for storing related data in a block chain and synchronously running a block chain verification mechanism;
in the blockchain storage module, the specific process of storing related data in the blockchain is as follows:
data slicing: the original related data is divided into a plurality of data blocks;
encryption hash: each data block is encrypted through a hash function to generate a unique hash value;
Creating a block: packaging the encrypted data block and the hash value of the previous block together to form a new block;
consensus mechanism: nodes in the blockchain network agree through a consensus mechanism;
added to the blockchain: the new block is verified and agreed upon, then the new block will be added to the end of the blockchain;
The data analysis module comprises a data characteristic extraction unit and a prediction evaluation unit;
The data feature extraction unit is used for carrying out feature extraction on related data by utilizing a big data analysis technology to obtain key features, a data analysis model is built by combining the key features by the prediction evaluation unit, an air quality evaluation coefficient Pgx and a health state evaluation coefficient Jpz are obtained through calculation, a health risk state evaluation value Hrsa is generated according to the air quality evaluation coefficient Pgx and the health state evaluation coefficient Jpz, and the health risk state evaluation value Hrsa is compared with a preset first evaluation threshold value Second evaluation threshold/>Comparison is made, and the first evaluation threshold/>Second evaluation threshold/>Judging the health risk degree of the corresponding user according to the comparison result;
In the data feature extraction unit, extracting features of related data, wherein the acquired key features comprise air quality features, human body information features and time features, the air quality features comprise PM2.5 concentration, temperature and humidity in the air quality data, the human body information features comprise heart rate, body temperature and blood pressure in the human body information data, and the time features are time stamps in the time data; in the predictive evaluation unit, the process of calculating the air quality evaluation coefficient Pgx is as follows:
s101, obtaining PM2.5 concentration under different time stamps in T time Temperature/>Humidity/>T represents corresponding numbers of PM2.5 concentration, temperature and humidity in different time stamps within T time, and t=1, 2, … and n are positive integers;
S102, performing dimensionless treatment on PM2.5 concentration, temperature and humidity in different time stamps in T time;
S103, according to PM2.5 concentration Temperature/>Humidity/>Obtaining the average value/>, of PM2.5 concentration under different time stamps in T timeAverage value of temperature/>Average value of humidity/>The formula according to is as follows:
；
S104, according to the average value of PM2.5 concentration Average value of temperature/>Average value of humidity/>An air quality assessment coefficient Pgx is generated according to the following formula:
；
In the method, in the process of the invention, 、/>、/>3 Are preset proportionality coefficients of average value of PM2.5 concentration, average value of temperature and average value of humidity respectively, and/>＞/>＞/>3＞0，/>As a constant correction coefficient, int is a rounding function;
the process of calculating the health status assessment coefficient Jpz is as follows:
S201, acquiring heart rates under different time stamps in T time Body temperature/>Blood pressure/>T represents the corresponding numbers of heart rate, body temperature and blood pressure in different time stamps within T time, and t=1, 2, … and n are positive integers;
S102, performing dimensionless treatment on heart rate, body temperature and blood pressure in different time stamps within T time;
s103, according to heart rate Body temperature/>Blood pressure/>Obtaining the average value/>, of heart rate in different time stamps in T timeAverage value of body temperature/>Mean value of blood pressure/>The formula according to is as follows:
；
S104, according to the average value of heart rate Average value of body temperature/>Mean value of blood pressure/>The health state assessment coefficient Jpz is generated according to the following formula:
；
In the method, in the process of the invention, 、/>2、/>3 Are respectively preset proportionality coefficients of the average value of heart rate, the average value of body temperature and the average value of blood pressure, and/>＞/>3＞/>2＞0，/>As a constant correction coefficient, int is a rounding function;
The health risk status evaluation value Hrsa is generated according to the following formula:
；
In the method, in the process of the invention, As a constant correction coefficient, int is a rounding function;
The display early warning module acquires a comparison result and controls the user interface terminal to send out a corresponding early warning prompt;
The health risk state evaluation value Hrsa is compared with a preset first evaluation threshold value Second evaluation threshold/>The results after comparison were:
If the health risk status evaluation value Hrsa is smaller than the first evaluation threshold value No early warning signal is generated; if it is the first evaluation threshold/>The health risk status evaluation value Hrsa is less than or equal to the second evaluation threshold, and a low risk signal is generated; if it is the second evaluation threshold/>< Health risk status assessment Hrsa, generating a high risk signal;
The display early warning module obtains the compared result, and if no early warning signal is generated, the display early warning module does not respond; if the low risk signal is generated, the user interface terminal is controlled by the display early warning module to send out a low risk early warning prompt; if the high risk signal is generated, the user interface terminal is controlled by the display early warning module to send out a high risk early warning prompt;
The cloud platform further comprises an execution prompting module, if the cloud platform judges that a user is outdoors, a prediction model is built, a predicted air quality evaluation coefficient Pgx is generated according to future air quality characteristics predicted by weather forecast, a predicted health risk state evaluation value Hrsa curve graph is obtained by combining the current health state evaluation coefficient Jpz, when the trend of the curve graph is downward, no response is made, and when the trend of the curve graph is upward, a command for prompting the user to enter indoors is sent through a user interface terminal;
If the user is judged to be in the room, when no early warning signal is generated, no response is made, and when a high or low risk signal is generated, the user interface terminal sends an opening instruction to the indoor air purification equipment until the health risk state evaluation value Hrsa is smaller than a first evaluation threshold value Until that point.
2. The big data analysis early warning method based on block chain, which uses the system in claim 1, is characterized in that: the method comprises the following steps:
Step one, acquiring related data, wherein the related data comprise air quality data, human body information data, user position data and time data, and a positioning and classifying sub-module is further configured in the data receiving module to judge whether a user is indoor or outdoor according to the user position data;
If the user is judged to be outdoors, acquiring human body information data in real time through a bracelet end matched with the user in the monitoring terminal, and acquiring air quality data through weather meteorological data in a network; if the user is judged to be in a room, acquiring human body information data in real time through a bracelet end matched with the user in the monitoring terminal, and acquiring air quality data through a sensor group matched with the monitoring terminal; the air quality data comprise PM2.5 concentration, carbon dioxide concentration, temperature and humidity, and the human body information data comprise heart rate, body temperature, blood pressure and blood oxygen saturation of corresponding users; the method comprises the steps that user position data and time data are obtained through a mobile phone terminal carried by a user in a monitoring terminal, the position data of the user are obtained through a GPS module carried in the mobile phone terminal, the position data comprise longitude and latitude coordinates, a building database in navigation software is combined, matching is conducted according to the longitude and latitude coordinates and the building database, if an indoor building exists in an area where the longitude and latitude coordinates are located, the user is judged to be indoors, and if the indoor building does not exist in the area where the longitude and latitude coordinates are located, the user is judged to be outdoors;
Step two, storing related data in a block chain, and synchronously operating a block chain verification mechanism;
the specific process of storing the related data in the blockchain is as follows:
data slicing: the original related data is divided into a plurality of data blocks;
encryption hash: each data block is encrypted through a hash function to generate a unique hash value;
Creating a block: packaging the encrypted data block and the hash value of the previous block together to form a new block;
consensus mechanism: nodes in the blockchain network agree through a consensus mechanism;
added to the blockchain: the new block is verified and agreed upon, then the new block will be added to the end of the blockchain;
Step three, performing feature extraction on related data by utilizing a big data analysis technology to obtain key features, building a data analysis model by combining the key features, calculating to obtain an air quality evaluation coefficient Pgx and a health state evaluation coefficient Jpz, generating a health risk state evaluation value Hrsa according to the air quality evaluation coefficient Pgx and the health state evaluation coefficient Jpz, and combining the health risk state evaluation value Hrsa with a preset first evaluation threshold value Second evaluation threshold/>Comparison is made, and the first evaluation threshold/>Second evaluation threshold/>；
If the health risk status evaluation value Hrsa is smaller than the first evaluation threshold valueNo early warning signal is generated; if it is the first evaluation threshold/>The health risk status evaluation value Hrsa is less than or equal to the second evaluation threshold, and a low risk signal is generated; if it is the second evaluation threshold/>< Health risk status assessment Hrsa, generating a high risk signal;
Extracting the characteristics of the related data, wherein the acquired key characteristics comprise air quality characteristics, human body information characteristics and time characteristics, the air quality characteristics comprise PM2.5 concentration, temperature and humidity in the air quality data, the human body information characteristics comprise heart rate, body temperature and blood pressure in the human body information data, and the time characteristics are time stamps in the time data; in the predictive evaluation unit, the process of calculating the air quality evaluation coefficient Pgx is as follows:
s101, obtaining PM2.5 concentration under different time stamps in T time Temperature/>Humidity/>T represents corresponding numbers of PM2.5 concentration, temperature and humidity in different time stamps within T time, and t=1, 2, … and n are positive integers;
S102, performing dimensionless treatment on PM2.5 concentration, temperature and humidity in different time stamps in T time;
S103, according to PM2.5 concentration Temperature/>Humidity/>Obtaining the average value/>, of PM2.5 concentration under different time stamps in T timeAverage value of temperature/>Average value of humidity/>The formula according to is as follows:
；
S104, according to the average value of PM2.5 concentration Average value of temperature/>Average value of humidity/>An air quality assessment coefficient Pgx is generated according to the following formula:
；
In the method, in the process of the invention, 、/>、/>3 Are preset proportionality coefficients of average value of PM2.5 concentration, average value of temperature and average value of humidity respectively, and/>＞/>＞/>3＞0，/>As a constant correction coefficient, int is a rounding function;
the process of calculating the health status assessment coefficient Jpz is as follows:
S201, acquiring heart rates under different time stamps in T time Body temperature/>Blood pressure/>T represents the corresponding numbers of heart rate, body temperature and blood pressure in different time stamps within T time, and t=1, 2, … and n are positive integers;
S102, performing dimensionless treatment on heart rate, body temperature and blood pressure in different time stamps within T time;
s103, according to heart rate Body temperature/>Blood pressure/>Obtaining the average value/>, of heart rate in different time stamps in T timeAverage value of body temperature/>Mean value of blood pressure/>The formula according to is as follows:
；
S104, according to the average value of heart rate Average value of body temperature/>Mean value of blood pressure/>The health state assessment coefficient Jpz is generated according to the following formula:
；
In the method, in the process of the invention, 、/>2、/>3 Are respectively preset proportionality coefficients of the average value of heart rate, the average value of body temperature and the average value of blood pressure, and/>＞/>3＞/>2＞0，/>As a constant correction coefficient, int is a rounding function;
The health risk status evaluation value Hrsa is generated according to the following formula:
；
In the method, in the process of the invention, As a constant correction coefficient, int is a rounding function;
Step four, obtaining a comparison result, and if no early warning signal is generated, not responding; if the low risk signal is generated, the user interface terminal is controlled by the display early warning module to send out a low risk early warning prompt; if the high risk signal is generated, the user interface terminal is controlled by the display early warning module to send out a high risk early warning prompt;
If the user is judged to be outdoors, a prediction model is built, a predicted air quality evaluation coefficient Pgx is generated according to future air quality characteristics predicted by weather forecast, a predicted health risk state evaluation value Hrsa curve graph is obtained by combining the current health state evaluation coefficient Jpz, when the trend of the curve graph is downward, no response is made, and when the trend of the curve graph is upward, a command for prompting the user to enter indoors is sent out through a user interface terminal;
If the user is judged to be in the room, when no early warning signal is generated, no response is made, and when a high or low risk signal is generated, the user interface terminal sends an opening instruction to the indoor air purification equipment until the health risk state evaluation value Hrsa is smaller than a first evaluation threshold value Until that point.
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