CN114898877B - Processing method and system suitable for multi-dimensional health data - Google Patents
Processing method and system suitable for multi-dimensional health data Download PDFInfo
- Publication number
- CN114898877B CN114898877B CN202210489972.6A CN202210489972A CN114898877B CN 114898877 B CN114898877 B CN 114898877B CN 202210489972 A CN202210489972 A CN 202210489972A CN 114898877 B CN114898877 B CN 114898877B
- Authority
- CN
- China
- Prior art keywords
- health data
- type
- dimension
- acquisition frequency
- dimension health
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000036541 health Effects 0.000 title claims abstract description 511
- 238000003672 processing method Methods 0.000 title abstract description 8
- 238000012545 processing Methods 0.000 claims abstract description 24
- 238000000034 method Methods 0.000 claims description 41
- 238000005259 measurement Methods 0.000 claims description 6
- 230000009467 reduction Effects 0.000 claims description 4
- 239000000523 sample Substances 0.000 claims 1
- 230000036760 body temperature Effects 0.000 description 38
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 21
- 210000004369 blood Anatomy 0.000 description 21
- 239000008280 blood Substances 0.000 description 21
- 229910052760 oxygen Inorganic materials 0.000 description 21
- 239000001301 oxygen Substances 0.000 description 21
- 230000000968 intestinal effect Effects 0.000 description 20
- 208000036649 Dysbacteriosis Diseases 0.000 description 15
- 208000027244 Dysbiosis Diseases 0.000 description 15
- 210000000577 adipose tissue Anatomy 0.000 description 15
- 230000007140 dysbiosis Effects 0.000 description 15
- 230000006870 function Effects 0.000 description 12
- 238000004590 computer program Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 7
- 230000005540 biological transmission Effects 0.000 description 6
- 230000037182 bone density Effects 0.000 description 5
- 238000004891 communication Methods 0.000 description 3
- 230000003247 decreasing effect Effects 0.000 description 3
- 201000009240 nasopharyngitis Diseases 0.000 description 3
- 230000004044 response Effects 0.000 description 3
- 238000013480 data collection Methods 0.000 description 2
- 238000003745 diagnosis Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000003860 sleep quality Effects 0.000 description 2
- 208000002720 Malnutrition Diseases 0.000 description 1
- 208000010428 Muscle Weakness Diseases 0.000 description 1
- 206010028372 Muscular weakness Diseases 0.000 description 1
- 206010035664 Pneumonia Diseases 0.000 description 1
- 206010037660 Pyrexia Diseases 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000037396 body weight Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 235000005911 diet Nutrition 0.000 description 1
- 230000000378 dietary effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000007499 fusion processing Methods 0.000 description 1
- 210000001035 gastrointestinal tract Anatomy 0.000 description 1
- 230000003862 health status Effects 0.000 description 1
- 208000021760 high fever Diseases 0.000 description 1
- 230000001071 malnutrition Effects 0.000 description 1
- 235000000824 malnutrition Nutrition 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 208000015380 nutritional deficiency disease Diseases 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Classifications
-
- 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
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y20/00—Information sensed or collected by the things
- G16Y20/40—Information sensed or collected by the things relating to personal data, e.g. biometric data, records or preferences
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y40/00—IoT characterised by the purpose of the information processing
- G16Y40/20—Analytics; Diagnosis
-
- 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
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Biomedical Technology (AREA)
- Computing Systems (AREA)
- Medical Informatics (AREA)
- General Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Epidemiology (AREA)
- Pathology (AREA)
- Primary Health Care (AREA)
- Databases & Information Systems (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
The invention provides a processing method and a processing system suitable for multidimensional health data, which are characterized in that the type of a historical case and the frequency data of each type of case are input to a device end through a user; the equipment end determines the number of the types of the multidimensional health data according to the types of the historical cases and the multidimensional health data corresponding to each type of case; the equipment end determines a first acquisition frequency of each dimension health data according to the frequency data of each type of case and the multi-dimension health data corresponding to each type of case; generating each dimension health data distribution coefficient according to the first acquisition frequency and the product of each dimension health data acquisition occupation space; generating a storage space allocation scheme according to the total storage space, the number of the multi-dimensional health data types and the allocation coefficient of each dimensional health data type; the storage space is distributed more reasonably, and the data is more convenient to call.
Description
Technical Field
The present invention relates to data processing technologies, and in particular, to a method and a system for processing multidimensional health data.
Background
With the continuous development of technology, various intelligent devices are continuously gushed out, for example: the intelligent bracelet, the intelligent watch, the intelligent body weight instrument and the like can detect health data of multiple dimensions of a human body in real time, comprehensively process the health data of the multiple dimensions, display the health status of the human body in a digital mode, and transmit the health data of the multiple dimensions to a doctor for checking when the user feels that the body is untimely, so that follow-up diagnosis is convenient.
However, in the prior art, all multidimensional health data of a user can be collected and stored in real time in a unified way, but the corresponding space is insufficient, so that the health data required by the user cannot be stored, and the storage space cannot be allocated according to the actual condition of the patient, so that part of the storage space is wasted and part of the storage space is insufficient.
Therefore, how to allocate the storage space according to the own situation of each user is a problem to be solved.
Disclosure of Invention
The embodiment of the invention provides a processing method and a processing system suitable for multidimensional health data, which enable storage space to be utilized more reasonably and enable the acquisition frequency to be more fit with the requirements of users by intelligently distributing the storage space of equipment according to different physical conditions of each user and generating corresponding health data acquisition frequency.
In a first aspect of an embodiment of the present invention, a processing method applicable to multi-dimensional health data is provided, including:
the user inputs the type of the historical case and the frequency data of each type of case to the equipment end;
the equipment end determines the number of the types of the multidimensional health data according to the types of the historical cases and the multidimensional health data corresponding to each type of case;
the equipment end determines a first acquisition frequency of each dimension health data according to the frequency data of each type of case and the multi-dimension health data corresponding to each type of case;
generating each dimension health data distribution coefficient according to the first acquisition frequency and the product of each dimension health data acquisition occupation space;
and generating a storage space allocation scheme according to the total storage space, the number of the multi-dimensional health data types and the allocation coefficient of each dimensional health data type.
Optionally, in one possible implementation manner of the first aspect, the step of determining, at the device side, the first collection frequency of each dimension health data according to the number of times data of each type of case and the multi-dimension health data corresponding to each type of case specifically includes:
Setting the initial acquisition frequency of the multidimensional health data corresponding to each type of case as the frequency data of the corresponding type of case to obtain a plurality of initial acquisition frequencies of the multidimensional health data;
summing the plurality of initial acquisition frequencies of each dimension health data to obtain an initial total acquisition frequency of each dimension health data;
shifting the initial total acquisition frequency of each dimension health data to the reference acquisition frequency of each dimension health data to generate a first acquisition frequency of each dimension health data;
the first acquisition frequency of each dimensional health data is obtained by the following formula,
wherein,,
first acquisition frequency, w, of health data in ith dimension i A first acquisition frequency weight value for the ith dimension health data,/for>
For the reference acquisition frequency of the ith dimension health data, n is the upper limit value of the number of the historical case types, +.>
E is a constant value for the initial acquisition frequency of the ith dimensional health data in the d-th type of case.
Optionally, in a possible implementation manner of the first aspect, in the step of generating each dimension health data distribution coefficient according to the product of the first acquisition frequency and each dimension health data acquisition occupation space, specifically includes:
Generating each dimension health data distribution coefficient according to the first acquisition frequency of each dimension health data and the product of each dimension health data acquisition occupation space;
the allocation coefficient for each dimension health data type is obtained by the following formula,
wherein S is i Assigning coefficients, k, to the ith dimension health data type i Assigning a weight value of the coefficient to the i-th dimension health data type,
first acquisition frequency, y, for ith dimension health data i And acquiring occupied space for the ith dimension health data type each time.
Optionally, in a possible implementation manner of the first aspect, in the step of generating the storage space allocation scheme according to the total storage space, the number of multi-dimensional health data types, and the allocation coefficient of each dimensional health data type, specifically includes:
generating a multi-dimensional health data type total distribution coefficient according to the number of the multi-dimensional health data types and the distribution coefficient of each dimensional health data type;
generating the percentage of each dimension health data type according to the ratio of the multi-dimension health data type total distribution coefficient to each dimension health data type distribution coefficient;
generating a storage space allocation scheme of each dimension health data type according to the product of the total storage space and the percentage of each dimension health data type;
The storage space allocation scheme for each dimension health data type is obtained by the following formula,
wherein,,
storage space allocated for the ith dimension health data type, T is total storage space, S i Assigning coefficients to the ith dimension health data type, v being multi-dimensional healthUpper limit value of data type number S g Assigning coefficients for the type of healthy data of dimension g, < >>
Assigning coefficients, q, to a total of multi-dimensional health data types i And (3) a storage space weight value allocated for the ith dimension health data type.
Optionally, in one possible implementation manner of the first aspect, the method further includes:
if the dimension health data type is newly added, determining the initial acquisition frequency of the dimension health data in the newly added case according to the attribute of the newly added case;
the initial acquisition frequency of the newly-added dimension health data shifts the reference acquisition frequency of the newly-added dimension health data to obtain a second acquisition frequency of the newly-added dimension health data type;
generating a new dimension health data type distribution coefficient according to the second acquisition frequency and the occupied space of each acquisition of the new dimension health data type;
generating the remaining storage space of each dimension health data type according to the storage space allocated by each dimension health data type and the difference value of the storage space occupied by each dimension health data type;
Summing the remaining storage spaces according to the health data types of each dimension to generate total remaining storage space;
determining the newly increased capacity of the newly increased dimension health data type according to the total remaining storage space, the newly increased dimension health data type distribution coefficient and the multi-dimension health data type total distribution coefficient;
the newly added capacity of the newly added dimension health data type is obtained by the following formula,
wherein T is New type New capacity for newly added dimension health data type, m New type For newly added dimension healthThe weight value of the newly added capacity of the data type, p is the upper limit value of the number of the multi-dimensional healthy data types,
storage space allocated for the ith dimension health data type,/->
Storage space occupied for the ith dimension health data type,/->
E is the second acquisition frequency weight value of the newly added dimension health data, which is the total remaining storage space,/L>
For the reference acquisition frequency of the newly added dimension health data, G is the initial acquisition frequency of the newly added dimension health data in the newly added type of cases, y New type And acquiring occupied space for each time of the newly added dimension health data type.
Optionally, in one possible implementation manner of the first aspect, the method further includes:
the equipment end performs offset processing on the first acquisition frequency of each dimension health data according to the remaining storage space of each dimension health data type to obtain a third acquisition frequency, and transmits the multi-dimension health data to the doctor end according to the third acquisition frequency;
The method comprises the steps that a user inputs a current case type to a device end, and the device end determines a multidimensional health data type corresponding to the current case type according to the current case type;
the equipment end determines a first acquisition frequency of each dimension health data corresponding to the current case type according to the multi-dimension health data type corresponding to the current case type;
the equipment end obtains a first degree difference value of the multi-dimensional health data type according to the difference value of the measured value of the multi-dimensional health data type corresponding to the current case type and the preset value of the multi-dimensional health data type;
adjusting the first acquisition frequency according to the first degree difference value and a preset value of the multi-dimensional health data type to obtain a fourth acquisition frequency;
the equipment end collects the multidimensional health data based on the fourth collection frequency and encrypts and transmits the multidimensional health data to the doctor end.
Optionally, in one possible implementation manner of the first aspect, in the step of adjusting the first acquisition frequency according to the first degree difference value and the preset value of the multi-dimensional health data type to obtain the fourth acquisition frequency, the method specifically includes:
obtaining a first degree difference percentage according to the ratio of the first degree difference to a preset value of the multi-dimensional health data type;
Performing offset processing on the first acquisition frequency according to the first degree difference percentage to obtain a fourth acquisition frequency of each dimension health data corresponding to the current case type;
the fourth acquisition frequency is obtained by the following formula,
wherein,,
fourth acquisition frequency of ith dimension health data corresponding to current case type, ++>
For the first acquisition frequency of the ith dimension health data corresponding to the current case type, ++>
For the measurement value of the ith dimension health data type corresponding to the current case type,/for the measurement value of the ith dimension health data type corresponding to the current case type>
The ith dimension health number corresponding to the current case typeDepending on the preset value of the type of the data,
and a fourth acquisition frequency weight value of the ith dimension health data corresponding to the current case type.
Optionally, in one possible implementation manner of the first aspect, the method further includes:
according to a fifth acquisition frequency of the ith dimension health data actively input by the doctor end;
obtaining a frequency adjustment trend according to the difference value of the fifth acquisition frequency and the fourth acquisition frequency;
adjusting a fourth acquisition frequency weight value of the ith dimension health data corresponding to the current case type according to the frequency adjustment trend to obtain an adjusted fourth acquisition frequency weight value;
The adjusted fourth acquisition frequency weight value is obtained by the following formula,
wherein,,
fifth acquisition frequency for ith dimension health data, +.>
Fourth acquisition frequency for ith dimension health data, +.>
For the adjusted fourth acquisition frequency weight, < +.>
A fourth acquisition frequency weight value delta for the ith dimension health data 1 Trend adjustment value, delta, for acquisition frequency increase 2 The frequency reduction trend adjustment value is collected.
Optionally, in a possible implementation manner of the first aspect, in the step of collecting, at the device side, the multi-dimensional health data based on the fourth collection frequency and encrypting and transmitting the multi-dimensional health data to the doctor side, the method specifically includes:
after the equipment receives the sending request, the equipment acquires the multi-dimensional health data based on the fourth acquisition frequency to generate first data to be sent;
the equipment end determines the data null moment of the first data to be transmitted according to the fourth acquisition frequency;
the equipment terminal randomly generates data at the data null moment of the first data to be transmitted to obtain virtual data;
the equipment end encrypts and transmits the virtual data to the doctor end by utilizing the random number and the numerical value of the fourth acquisition frequency through the hash function.
In a second aspect of an embodiment of the present invention, a processing system suitable for multidimensional health data includes:
The input module is used for inputting the types of the historical cases and the frequency data of each type of cases to the equipment end by a user;
the data determining module is used for determining the number of the types of the multidimensional health data and the multidimensional health data corresponding to each type of case by the equipment end according to the historic case types;
the frequency determining module is used for determining a first acquisition frequency of each dimension health data according to the frequency data of each type of case and the multi-dimension health data corresponding to each type of case by the equipment end;
the generation module is used for generating each dimension health data distribution coefficient according to the product of the first acquisition frequency and each dimension health data acquisition occupation space;
and the distribution module is used for generating a storage space distribution scheme according to the total storage space, the number of the multi-dimensional health data types and the distribution coefficient of each dimensional health data type.
In a third aspect of an embodiment of the present invention, there is provided an electronic device including: a memory, a processor and a computer program stored in the memory, the processor running the computer program to perform the first aspect of the invention and the methods that the first aspect may relate to.
In a fourth aspect of embodiments of the present invention, there is provided a readable storage medium having stored therein a computer program for implementing the method of the first aspect and the various possible aspects of the first aspect when executed by a processor.
The invention provides a processing method and a processing system suitable for multi-dimensional health data. The collection frequency of each dimension health data can be intelligently obtained according to the type of the historical case of the user and the frequency data of each type of case, and corresponding storage space is allocated according to the collection frequency of each dimension health data, so that the space allocation corresponding to the multi-dimension health data collection is more reasonable and is more suitable for the actual situation of the user; according to the technical scheme provided by the invention, if the dimension health data types are newly added, the residual space is generated according to the space and the allocation space used by each dimension health data at present, and the residual space is allocated to the dimension health data type storage space, so that the space allocation is more fit with the requirements of users, and the availability and the rationality of space allocation are improved.
According to the technical scheme provided by the invention, the acquisition frequency of the corresponding dimensional health data is increased according to the current physical condition of the user, and the acquisition frequency of the other dimensional health data is decreased or unchanged, so that the acquisition evaluation rate is more suitable for the actual condition of the user, and the physical condition of the user can be better monitored.
According to the technical scheme provided by the invention, the system has the function of autonomous learning iteration, the doctor actively adjusts the multi-dimensional health data acquisition frequency, the frequency of the doctor adjustment can be actively recorded to adjust the weight, so that the next time the acquisition frequency is output more accords with the requirements of the doctor, the system has the function of autonomous learning adjustment, and the behavior data of the doctor can be recorded to continuously calibrate the next time data, so that the system more accords with the actual scene.
According to the technical scheme, the data are randomly generated by utilizing the null value in the time interval of the acquisition frequency, so that the data are changed into virtual data, even if the user individual multidimensional health data are intercepted and still can not be read, the safety of the user data is improved, the hash function is utilized for encryption transmission to a doctor, the safety of the user multidimensional health data is fully ensured, the asymmetry of the hash function is adopted to ensure the safety of the data, and even if the multidimensional health data are intercepted and stolen, the corresponding data are also mixed data, and the safety of the multidimensional health data is greatly improved.
Drawings
Fig. 1 is a schematic view of an application scenario of the technical scheme provided by the invention;
FIG. 2 is a flow chart of a first embodiment of a method for processing multi-dimensional health data;
FIG. 3 is a flow chart of a second embodiment of a method for processing multi-dimensional health data;
FIG. 4 is a schematic diagram of a processing system suitable for multi-dimensional health data;
fig. 5 is a schematic hardware structure of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, 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.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein.
It should be understood that, in various embodiments of the present invention, the sequence number of each process does not mean that the execution sequence of each process should be determined by its functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
It should be understood that in the present invention, "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements that are expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present invention, "plural" means two or more. "and/or" is merely an association relationship describing an association object, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. "comprising A, B and C", "comprising A, B, C" means that all three of A, B, C comprise, "comprising A, B or C" means that one of the three comprises A, B, C, and "comprising A, B and/or C" means that any 1 or any 2 or 3 of the three comprise A, B, C.
It should be understood that in the present invention, "B corresponding to a", "a corresponding to B", or "B corresponding to a" means that B is associated with a, from which B can be determined. Determining B from a does not mean determining B from a alone, but may also determine B from a and/or other information. The matching of A and B is that the similarity of A and B is larger than or equal to a preset threshold value.
As used herein, "if" may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to detection" depending on the context.
The technical scheme of the invention is described in detail below by specific examples. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.
As shown in fig. 1, a schematic view of a scenario of a technical solution provided by the present invention includes a device side and a doctor side, where the device side is connected with the doctor side, may be a wireless connection or a wired connection, and the wireless connection may be a bluetooth connection or a network connection, and the device side may be an intelligent watch, an intelligent bracelet, or other intelligent device capable of collecting multidimensional health data of a user, where the device side is not limited, and the device side is one or more; the doctor end can be a notebook computer, a mobile phone, a tablet and other intelligent equipment, and is not limited herein, and the doctor end can be one or more; the equipment end collects multi-dimensional health data of a user and encrypts and transmits the collected data to the doctor end, the doctor end can send a collection frequency adjustment request to the equipment end through the initiative input of a doctor so as to adjust the collection frequency, wherein the multi-dimensional health data can be grip strength, sleep quality, body temperature, heart rate, body fat, blood oxygen, swing amplitude measured by a gyroscope and the like, and the corresponding doctor can evaluate the physical condition of the user according to the grip strength, sleep quality, body temperature, heart rate, body fat, blood oxygen and the measured value of the gyroscope.
The invention provides a processing method suitable for multi-dimensional health data, as shown in fig. 2, comprising the following steps:
step S110, the user inputs the type of the historical case and the frequency data of each type of case to the equipment side. According to the technical scheme provided by the invention, the user inputs the past historical case types and the frequency data of each type of case to the equipment end, so that the actual acquisition frequency can be conveniently generated according to the actual situation of the user, for example: the user has once had two case types of cold and intestinal flora imbalance, the cold is over 10 times and the intestinal flora imbalance is over 1 time, and the data are input into the equipment end, so that the data of the times of 10 times and 1 time are the data of the times of each type of case, and the actual acquisition frequency can be conveniently generated subsequently.
Step S120, the equipment end determines the number of the types of the multidimensional health data and the multidimensional health data corresponding to each type of case according to the historic case types. According to the technical scheme provided by the invention, the equipment side can determine the number of the types of the multidimensional health data in the historical case type and the multidimensional health data corresponding to each type of case according to the historical case type, so that the first acquisition frequency can be conveniently and subsequently obtained; for example: the equipment end can automatically determine dimension health data related to the cold according to the history case types of the cold: for example, body temperature, grip strength, heart rate, blood oxygen saturation, dimensional health data related to dysbacteriosis in the intestinal tract: for example, the body fat rate and the grip strength can be obtained, the number of the corresponding obtained influencing factors is 4 and 2, the number of the corresponding multidimensional health data types is 5, and the body temperature, the grip strength, the heart rate, the blood oxygen saturation and the body fat rate are respectively prepared for generating the acquisition frequency suitable for the user.
Step S130, the equipment end determines a first acquisition frequency of each dimension health data according to the frequency data of each type of case and the multi-dimension health data corresponding to each type of case. According to the technical scheme provided by the invention, it can be understood that the equipment end can change the body temperature of the user, such as the body temperature of a high fever user can be increased according to the times of the past cold cases of the user, such as 10 times, and the multidimensional health data corresponding to the cold cases, such as the body temperature, the grip strength, the heart rate and the blood oxygen saturation, the method has the advantages that the blood oxygen saturation is correspondingly reduced due to pneumonia and the like caused by fever and the heart rate is correspondingly accelerated, so that the grip strength is reduced due to the fact that the user is debilitated, and according to the times of common cold cases such as 10 times, the acquisition frequency of the body temperature, grip strength, heart rate and blood oxygen saturation under the common cold cases is correspondingly determined to be 10 times; for example, according to the number of times of intestinal dysbacteriosis of 1 time in the past, the intestinal dysbacteriosis can cause the body fat rate of a user to be reduced correspondingly to cause muscular weakness of malnutrition to affect grip strength because of dietary absorption of the user, the body fat rate of the intestinal dysbacteriosis and the acquisition frequency of the grip strength are correspondingly determined to be 1 time through the 1 time of intestinal dysbacteriosis, the first acquisition frequency of health data in each dimension can be correspondingly determined, for example, the grip strength is 10+1=11 times, the initial total acquisition frequency of the grip strength is 11 times, and the reference acquisition frequency is subjected to offset processing according to the initial total acquisition frequency to obtain the first acquisition frequency of the grip strength, wherein the reference acquisition frequency is a preset initial value quantified by a system according to various cases, so that corresponding storage contents can be conveniently distributed for the health data in each dimension.
In one possible implementation manner, the step S130 specifically includes:
setting the initial acquisition frequency of the multidimensional health data corresponding to each type of case as the frequency data of the corresponding type of case to obtain a plurality of initial acquisition frequencies of the multidimensional health data. According to the technical scheme provided by the invention, the initial acquisition frequency of the multidimensional health data under the historic case type is set as the frequency data of each type of case, for example: two case types of cold and intestinal dysbacteriosis are past by users, namely the cold is over 10 times, the intestinal dysbacteriosis is over 1 time, and dimensional health data related to the cold are corresponding to the cold: the method comprises the steps of setting initial acquisition frequency of body temperature to be 10 times/day, setting initial acquisition frequency of grip strength to be 10 times/day, setting initial acquisition frequency of heart rate to be 10 times/day, and setting initial acquisition frequency of blood oxygen saturation to be 10 times/day; intestinal dysbacteriosis is over 1 time, corresponding to dimensional health data related to intestinal dysbacteriosis: the body fat rate may be a body fat rate, the initial collection frequency corresponding to the collection of the body fat rate is 1 time/day, the initial collection frequency of the collection of the grip strength is 1 time/day, the time may be day, the month or a fixed period of time is not limited herein, and the initial collection frequencies corresponding to the collection of the grip strength are 1 time/day and 10 times/day.
And summing the plurality of initial acquisition frequencies of the health data of each dimension to obtain an initial total acquisition frequency of the health data of each dimension. According to the technical scheme provided by the invention, as each case possibly corresponds to the multidimensional health data, the health data corresponding to a certain dimension possibly reappears in a plurality of cases, a plurality of initial acquisition frequencies are accumulated, so that the subsequent generation of the first acquisition frequency of the health data of each dimension is facilitated, for example: two case types of cold and intestinal dysbacteriosis are past by users, namely the cold is over 10 times, the intestinal dysbacteriosis is over 1 time, and dimensional health data related to the cold are corresponding to the cold: the method comprises the steps of setting initial acquisition frequency of body temperature to be 10 times/day, setting initial acquisition frequency of grip strength to be 10 times/day, setting initial acquisition frequency of heart rate to be 10 times/day, and setting initial acquisition frequency of blood oxygen saturation to be 10 times/day; intestinal dysbacteriosis is over 1 time, corresponding to dimensional health data related to intestinal dysbacteriosis: the body fat rate and the grip strength can be obtained, the initial collection frequency of the corresponding collected body fat is 1 time/day, the initial collection frequency of the grip strength is 1 time/day, wherein the initial collection frequency of the grip strength appears in common cold and intestinal dysbacteriosis, the corresponding initial collection frequencies are 10 times/day and 1 time/day, the initial total collection frequency of the grip strength obtained by summation is 11 times/day, and the first collection frequency can be conveniently generated according to the actual situation of a user.
And shifting the initial total acquisition frequency of each dimension health data to the reference acquisition frequency of each dimension health data to generate a first acquisition frequency of each dimension health data. According to the technical scheme provided by the invention, the reference acquisition frequency of each dimension health data is increased or reduced according to the initial total acquisition frequency of each dimension health data, so that the first acquisition frequency of each dimension health data is correspondingly generated, for example: the initial total acquisition frequency of the grip strength of the user is 11 times/day, and the reference acquisition frequency of the grip strength health dimension is adjusted, wherein the reference acquisition frequency of the health data of each dimension is a preset value of the standard acquisition frequency of the health data of each dimension obtained by quantifying the data of the past case type at the equipment end, and the standard acquisition frequency can be uniform fixed value data, for example, 1 time/day, and can be the initial acquisition frequency set according to the importance degree of the health data of each dimension, and is not limited herein; and shifting the reference acquisition frequency of the grip strength health dimension by 11 times/day to obtain the first acquisition frequency of the fit user.
The first acquisition frequency of each dimensional health data is obtained by the following formula,
Wherein,,
first acquisition frequency, w, of health data in ith dimension i A first acquisition frequency weight value for the ith dimension health data,/for>
For the reference acquisition frequency of the ith dimension health data, n is the upper limit value of the number of the historical case types, +.>
For the initial acquisition frequency of the ith dimensional health data in the (d) th type of cases, E is a constant value, w d And->
Proportional by->
The initial total acquisition frequency of the health data of each dimension can be obtained, < >>
And->
In proportion, the constant value E may be set manually. The first acquisition frequency of each dimension health data calculated in the above manner is more fit to the actual situation of the user.
According to the technical scheme provided by the invention, the first acquisition frequency of each dimension health data fitting the actual situation of the user can be generated through the past cases of the user.
And step 140, generating each dimension health data distribution coefficient according to the product of the first acquisition frequency and each dimension health data acquisition occupation space. According to the technical scheme provided by the invention, each dimension health data occupation space is generated according to the product of the first acquisition frequency and each dimension health data occupation space, for example: the user has past two case types of cold and intestinal flora imbalance, the cold is over 10 times, the intestinal flora imbalance is over 1 time, and the first acquisition frequency of corresponding multidimensional health data can be as follows: the first acquisition frequency of the body temperature is 20 times/day, the first acquisition frequency of the grip strength is 10 times/day, the first acquisition frequency of the heart rate is 200 times/day, the initial acquisition frequency of the blood oxygen saturation is 20 times/day, the first acquisition frequency of the body fat is 20 times/day, the corresponding body temperature is 1KB in occupied space for each acquisition, the corresponding grip strength is 1KB in occupied space for each acquisition, the corresponding heart rate is 1KB in occupied space for each acquisition, the corresponding blood oxygen saturation is 1KB in occupied space for each acquisition, the corresponding body fat is 1KB in occupied space for each acquisition, the corresponding body temperature is 20KB in occupied space, the corresponding grip strength is 10KB in occupied space, the corresponding heart rate is 200KB in occupied space, the corresponding blood oxygen saturation is 20KB in occupied space, and the corresponding body fat is 20KB in occupied space.
In one possible implementation manner, the step S140 specifically includes:
and generating each dimension health data distribution coefficient according to the first acquisition frequency of each dimension health data and the product of each dimension health data acquisition occupation space. According to the technical scheme provided by the invention, the occupied space of each dimension health data is generated according to the product of the first acquisition frequency and the occupied space of each dimension health data, for example: the user has past two case types of cold and intestinal flora imbalance, the cold is over 10 times, the intestinal flora imbalance is over 1 time, and the first acquisition frequency of corresponding multidimensional health data can be as follows: the first acquisition frequency of the body temperature is 20 times/day, the first acquisition frequency of the grip strength is 10 times/day, the first acquisition frequency of the heart rate is 200 times/day, the initial acquisition frequency of the blood oxygen saturation is 20 times/day, the corresponding acquisition and distribution coefficient of the body temperature is 1KB each time, the corresponding grip strength is 1KB each time, the corresponding heart rate is 1KB each time, the corresponding blood oxygen saturation is 1KB each time, the corresponding body temperature distribution coefficient is 20KB, the corresponding grip strength distribution coefficient is 10KB, the corresponding heart rate distribution coefficient is 200KB, and the corresponding blood oxygen saturation distribution coefficient is 20KB.
The allocation coefficient for each dimension health data type is obtained by the following formula,
wherein S is i Assigning coefficients, k, to the ith dimension health data type i Assigning a weight value of the coefficient to the i-th dimension health data type,
first acquisition frequency, y, for ith dimension health data i For each acquisition of the occupied space of the ith dimension health data type, the weight value k of the occupied space of the ith dimension health data type i The method can be set manually and is set according to different dimension health data types.
According to the technical scheme provided by the invention, the occupied space is fixed according to each dimension health data collection, for example: the body temperature is 20 ℃, the number of characters and the number of numbers of corresponding contents are fixed in corresponding occupied storage space, and the distribution coefficient of each dimension health data type is obtained according to the first acquisition frequency and the fixed occupied storage space of each dimension health data, so that the storage space of each dimension health data type can be conveniently distributed subsequently.
And step S150, a storage space allocation scheme is generated according to the total storage space, the number of the multi-dimensional health data types and the allocation coefficient of each dimensional health data type. The technical scheme provided by the invention is that for example, according to 1024KB of total storage space (namely, the total storage space of equipment), the number of multi-dimensional health data types is as follows: the total multidimensional health data corresponding to and relevant to two case types of cold and intestinal dysbacteriosis are body temperature, grip strength, heart rate, blood oxygen saturation and body fat, 5 types of corresponding multidimensional health data are 5 types, and the distribution coefficient of each type of multidimensional health data is as follows: the body temperature distribution coefficient is 20KB, the corresponding grip strength distribution coefficient is 10KB, the corresponding heart rate distribution coefficient is 200KB, the corresponding blood oxygen saturation distribution coefficient is 20KB, the product of the ratio occupied by each dimension health data type and the total storage space generates a storage space distribution scheme, and the storage space distribution scheme is more fit for the actual situation of a user.
In one possible implementation manner, as shown in fig. 3, step S150 specifically includes:
step S1501, generating a multi-dimensional health data type total distribution coefficient according to the multi-dimensional health data type number and each dimensional health data type distribution coefficient. The technical scheme provided by the invention is as follows: all past multidimensional health data can be obtained according to past historical cases of patients, the body temperature distribution coefficient is 20KB, the corresponding grip strength distribution coefficient is 10KB, the corresponding heart rate distribution coefficient is 200KB, the corresponding blood oxygen saturation distribution coefficient is 20KB, all occupied memory spaces are summed to 20+10+200+20=250 KB, and the total distribution coefficient corresponding to the type of the multidimensional health data is 250KB, so that a storage space distribution scheme can be conveniently generated subsequently.
Step S1502, generating a percentage of each dimension health data type according to a ratio of the multi-dimension health data type total distribution coefficient to the each dimension health data type distribution coefficient. The technical scheme provided by the invention is as follows: according to the body temperature distribution coefficient being 20KB, the corresponding grip strength occupation space being 10KB, the corresponding heart rate distribution coefficient being 200KB, the corresponding blood oxygen saturation distribution coefficient being 20KB, summing all occupied memory spaces to 20+10+200+20=250 KB, it will be understood that the ratio of the corresponding multi-dimensional health data type total distribution coefficient to the each dimensional health data type distribution coefficient is, for example: the ratio of the body temperature distribution coefficient to the total distribution coefficient is 20/250, the percentage of the corresponding each dimension health data type is 20/250, and the data percentage is generated for the subsequent generation of the storage space distribution scheme.
Step S1503, generating a storage space allocation scheme of each dimension health data type according to the product of the total storage space and the percentage of each dimension health data type. According to the technical scheme provided by the invention, the storage space allocation scheme of each dimension health data type is generated according to the product of the total storage space and the percentage of each dimension health data type, and it can be understood that the total storage space is 1024KB and 1024 multiplied by 20/250 is the storage space allocated for the body temperature health data type.
The storage space allocation scheme for each dimension health data type is obtained by the following formula,
wherein,,
storage space allocated for the ith dimension health data type, T is total storage space, S i Assigning coefficients for the ith dimension health data type, v is the upper limit value of the number of the multi-dimension health data types, S g Assigning coefficients for the type of healthy data of dimension g, < >>
Assigning coefficients, q, to a total of multi-dimensional health data types i And (3) a storage space weight value allocated for the ith dimension health data type.
According to the technical scheme provided by the invention, the storage space of each dimension health data type can be reasonably allocated according to the actual situation of the user, so that the storage space allocation is more reasonable, and the storage is classified according to the health dimension, so that the subsequent traversal and the calling are convenient, and the response speed of the calling is improved.
In one possible implementation manner, the technical scheme provided by the invention further comprises:
if the dimension health data type is newly added, determining the initial acquisition frequency of the dimension health data in the newly added case according to the attribute of the newly added case. The technical scheme provided by the invention is that if new types of cases are added, for example: the multi-dimensional health data corresponding to the newly-increased fracture case is bone density, the newly-increased dimensional health data type is bone density, the method can quantify according to the attribute of the fracture case according to the previous recurrence data to obtain the initial acquisition frequency of the fracture, namely, the initial acquisition frequency of the fracture case is set according to the occurrence probability of the previous fracture case, and the standard acquisition frequency of the newly-increased dimensional health data can be subjected to offset processing to obtain the second acquisition frequency of the newly-increased dimensional health data type, so that the storage space of the newly-increased dimensional health data type can be conveniently allocated.
And the initial acquisition frequency of the newly-added dimension health data shifts the reference acquisition frequency of the newly-added dimension health data to obtain a second acquisition frequency of the newly-added dimension health data type. According to the technical scheme provided by the invention, the initial acquisition frequency of the fracture is obtained by quantifying according to the attribute of the fracture case and the previous recurrence data, namely, the standard value obtained by quantifying according to the occurrence probability of the previous fracture case, namely, the initial acquisition frequency of the fracture case set by a system, the standard acquisition frequency of the newly-added dimension health data is subjected to offset processing to obtain the second acquisition frequency of the newly-added dimension health data type, and the storage space of the newly-added dimension health data type is allocated subsequently.
And generating a new dimension health data type distribution coefficient according to the second acquisition frequency and the occupied space of each acquisition of the new dimension health data type. According to the technical scheme provided by the invention, the newly-added dimension health data type occupation space is generated according to the second acquisition frequency and the newly-added dimension health data type occupation space, for example, the second acquisition frequency of bone density is 1 time/day, the corresponding bone density occupation space is 1KB, and then the bone density distribution coefficient is 1KB.
And generating the remaining storage space of each dimension health data type according to the storage space allocated by each dimension health data type and the difference value of the storage space occupied by each dimension health data type. According to the technical scheme provided by the invention, the remaining storage space of each dimension health data type is generated according to the storage space allocated by each dimension health data type and the difference value of the storage space occupied by each dimension health data type, and it can be understood that the remaining space of each dimension health data type is correspondingly obtained according to the storage space allocated by each dimension health data type and the difference value of the occupied space.
And summing the remaining storage space of each dimension health data type to generate total remaining storage space. According to the technical scheme provided by the invention, the total remaining storage space is obtained by summing the remaining storage spaces of each dimension health data type, so that the total remaining storage space is conveniently distributed subsequently.
And determining the newly increased capacity of the newly increased dimension health data type according to the total remaining storage space, the newly increased dimension health data type distribution coefficient and the multi-dimension health data type total distribution coefficient. According to the technical scheme provided by the invention, the sum of the occupied space of the newly added case type and the total occupied space of the multi-dimensional health data type is used for obtaining the calculated total occupied space, and the newly added capacity of the newly added dimensional health data type is obtained by multiplying the percentage obtained by the ratio of the occupied space of the newly added case type to the calculated total occupied space and the total residual storage space.
The newly added capacity of the newly added dimension health data type is obtained by the following formula,
wherein T is New type New capacity for newly added dimension health data type, m New type For the newly increased capacity weight value of the newly increased dimension health data type, p is the upper limit value of the number of the multi-dimension health data types,
storage space allocated for the ith dimension health data type,/->
Storage space occupied for the ith dimension health data type,/->
E is the second acquisition frequency weight value of the newly added dimension health data, which is the total remaining storage space,/L>
For the reference acquisition frequency of the newly added dimension health data, G is the initial acquisition frequency of the newly added dimension health data in the newly added type of cases, y New type Space is occupied for newly added dimension health data types, < >>
And T is New type Inversely proportional->
And T is New type Proportional by->
And obtaining a second acquisition frequency.
According to the technical scheme provided by the invention, the storage space can be allocated for the newly added dimension health data type, and the memory is reallocated according to the current residual space, so that the space allocation is more reasonable, and the subsequent data calling is facilitated.
In one possible implementation manner, the technical scheme provided by the invention further comprises:
the equipment end performs offset processing on the first acquisition frequency of each dimension health data according to the remaining storage space of each dimension health data type to obtain a third acquisition frequency, and transmits the multi-dimension health data to the doctor end according to the third acquisition frequency. According to the technical scheme, under the condition that a user does not have any pain, the equipment side can carry out offset processing on the first acquisition frequency of each dimension health data according to the residual storage space of each dimension health data type to obtain the third acquisition frequency, and the multi-dimension health data is transmitted to the doctor side according to the third acquisition frequency, namely the equipment is the first acquisition frequency for acquisition, but when the equipment sends a doctor, the third acquisition frequency is sent, the sending of the third acquisition frequency is related to the residual space, the acquisition and transmission frequency are adjusted according to the residual size of a memory, the smaller the residual storage space of each dimension health data type is, the smaller the first acquisition frequency of each dimension health data is, and the larger the residual storage space of each dimension health data type is, the larger the first acquisition frequency of each dimension health data is, so that the third acquisition frequency is obtained.
The third acquisition frequency is obtained by the following formula,
wherein,,
for the third acquisition frequency, +.>
Storage space allocated for dimension J health data type,>
storage space occupied for the type J dimension health data,>
for the total remaining storage space, p is the upper limit value of the number of multi-dimensional healthy data types, ++>
Is the weight value of the third acquisition frequency. The technical proposal provided by the invention can be according to the residual memory of the equipment in normal stateAnd the frequency of data acquisition is adjusted, the equipment stores a lot and transmits a corresponding reduction under a normal state, the acquisition frequency is improved if the residual storage amount is less because the comprehensive data amount is sufficient, and the patient can maintain the healthy data in a sufficient state in multiple dimensions at any time because the comprehensive data amount is less, so that the physical condition of the user can be well known through the subsequent data acquisition amount of the user.
The method comprises the steps that a user inputs a current case type to a device side, and the device side determines a multidimensional health data type corresponding to the current case type according to the current case type. According to the technical scheme provided by the invention, the equipment end can monitor in real time according to the type of the current case input by the user, for example, the user has a cold at present, and the corresponding multidimensional health data type of the cold influence can be determined according to the cold.
The equipment end determines the first acquisition frequency of each dimension health data corresponding to the current case type according to the multi-dimension health data type corresponding to the current case type. According to the technical scheme, when a user catches a cold at present, the corresponding multidimensional health data type body temperature, grip strength, heart rate and blood oxygen saturation affected by the cold are determined according to the cold, the first acquisition frequency of the body temperature, grip strength, heart rate and blood oxygen saturation is correspondingly determined, and the current acquisition frequency corresponding to the multidimensional health data type is acquired through the multidimensional health data type.
The equipment end obtains a first degree difference value of the multi-dimensional health data type according to the difference value of the measured value of the multi-dimensional health data type corresponding to the current case type and the preset value of the multi-dimensional health data type. According to the technical scheme provided by the invention, the equipment end obtains a first degree difference value of the multi-dimensional health data type according to the difference value of the measured value of the multi-dimensional health data type corresponding to the current case suffered by the user and the preset value of the dimensional health data type, for example, the current user suffers from cold, and it can be understood that the monitoring of the related multi-dimensional health data type of the cold needs to be enhanced, and the degree value is obtained through the current measured value and the preset value, for example: the lower body temperature is 37 ℃, the preset body temperature value (standard value) is 36 ℃, and the first degree difference value of the corresponding body temperature is 1 ℃.
And adjusting the first acquisition frequency according to the first degree difference value and a preset value of the multi-dimensional health data type to obtain a fourth acquisition frequency. According to the technical scheme provided by the invention, the first acquisition frequency is adjusted according to the first degree difference value and the preset value of the multi-dimensional health data type to obtain the fourth acquisition frequency, and it can be understood that the size of the adjusted acquisition frequency is determined according to the size of the difference by the first degree difference value of the body temperature being 1 ℃, the acquisition frequency is larger as the corresponding difference is larger, and the data acquisition or extraction is only carried out on the time period from the next moment to the moment when the user suffers from cold.
The equipment end collects the multidimensional health data based on the fourth collection frequency and encrypts and transmits the multidimensional health data to the doctor end. According to the technical scheme provided by the invention, the equipment end collects the multidimensional health data based on the fourth collection frequency and encrypts and transmits the multidimensional health data to the doctor end, and the safety of the multidimensional health data of the user is ensured by encrypting the data after the data is collected.
In one possible implementation manner, in the step of adjusting the first acquisition frequency according to the first degree difference value and the preset value of the multi-dimensional health data type to obtain the third acquisition frequency, the method specifically includes:
And obtaining a first degree difference percentage according to the ratio of the first degree difference to the preset value of the multi-dimensional health data type. The technical scheme provided by the invention is as follows: and obtaining the first degree difference percentage of the body temperature by 1/36 according to the ratio of the first degree difference of the body temperature to the preset value (standard value) of the body temperature of 36 ℃.
And performing offset processing on the first acquisition frequency according to the first degree difference percentage to obtain a fourth acquisition frequency of each dimension health data corresponding to the current case type. The technical scheme provided by the invention is as follows: and carrying out offset treatment on the first acquisition frequency of the body temperature for 20 times per day according to the first degree difference percentage 1/36 of the body temperature to obtain an offset value, and accumulating the first acquisition frequency.
The fourth acquisition frequency is obtained by the following formula,
wherein, among them,
fourth acquisition frequency of ith dimension health data corresponding to current case type, ++>
For the first acquisition frequency of the ith dimension health data corresponding to the current case type, ++>
For the measurement value of the ith dimension health data type corresponding to the current case type,/for the measurement value of the ith dimension health data type corresponding to the current case type>
For the preset value of the ith dimension health data type corresponding to the current case type, ++ >
A fourth acquisition frequency weight value of the ith dimension health data corresponding to the current case type,
and->
Proportional by->
And obtaining the offset value of the first acquisition frequency of the ith dimension health data corresponding to the current case type.
The technical scheme provided by the invention can be based on the actual situation of the user in reality, for example: when the cold is febrile, the data in the corresponding dimension are monitored, collected in a key mode, the collection frequency is increased, and only the data from the moment to the moment of cold is collected, so that the requirements of users are met.
In one possible implementation manner, the technical scheme provided by the invention further comprises:
and according to the fifth acquisition frequency of the ith dimension health data actively input by the doctor. According to the technical scheme provided by the invention, a doctor may feel that the acquisition frequency of the health data in a certain dimension output by the system is too large or too small, and the fifth acquisition frequency is obtained by adjusting according to the actual situation.
And obtaining a frequency adjustment trend according to the difference value of the fifth acquisition frequency and the fourth acquisition frequency. According to the technical scheme provided by the invention, the trend of increasing or decreasing is obtained according to the difference value of the fifth acquisition frequency and the fourth acquisition frequency which are adjusted by a doctor.
And adjusting a fourth acquisition frequency weight value of the ith dimension health data corresponding to the current case type according to the frequency adjustment trend to obtain an adjusted fourth acquisition frequency weight value. According to the technical scheme provided by the invention, the four acquisition frequency weight values can be subjected to the memorial adjustment according to the increasing trend or the decreasing trend, so that the next time the system outputs the adjustment frequency, the requirement of a doctor is met, and the system has the function of independently learning iterative data.
The adjusted fourth acquisition frequency weight value is obtained by the following formula,
wherein,,
fifth acquisition frequency for ith dimension health data, +.>
Fourth acquisition frequency for ith dimension health data, +.>
For the adjusted fourth acquisition frequency weight, < +.>
A fourth acquisition frequency weight value delta for the ith dimension health data 1 Trend adjustment value, delta, for acquisition frequency increase 2 The frequency reduction trend adjustment value is collected.
According to the technical scheme provided by the invention, the training learning of the model is realized by the iterative function of autonomous learning, so that the acquisition frequency of the system output is more suitable for the requirements of doctors, and the real-time diagnosis or the check of the doctors on the user data is facilitated.
In one possible implementation manner, the technical scheme provided by the invention specifically includes the steps of acquiring and encrypting the multidimensional health data based on the fourth acquisition frequency at the equipment end and transmitting the multidimensional health data to the doctor end:
After the equipment receives the sending request, the equipment acquires the multi-dimensional health data based on the fourth acquisition frequency to generate first data to be sent. According to the technical scheme provided by the invention, when a user catches a cold, the user can select multidimensional health data such as body temperature and the like related to the cold, and after the equipment receives a sending request, the multidimensional health data is collected based on the fourth collection frequency to generate first data to be sent.
And the equipment end determines the data null moment of the first data to be transmitted according to the fourth acquisition frequency. According to the technical scheme provided by the invention, the temperature data is acquired once every 3 seconds according to the fourth acquisition frequency, and then the temperature data is assumed to be 36 ℃ when the current time is 13:00:00, the temperature data is acquired at 36.1 ℃ when the previous time is 12:59:57, and the data at the corresponding time points of 12:59:58 and 12:59:59 are null, namely the data null time points of 12:59:58 and 12:59:59 are determined.
And the equipment terminal randomly generates data at the data null moment of the first data to be transmitted to obtain virtual data. According to the technical scheme provided by the invention, the equipment terminal randomly generates the numerical value at the null value moment on the basis of the original data according to the null value moment of the first data to be transmitted, and can select one or more of the null value moments to be generated, for example: once every 3 seconds, assuming that when the current time is 13:00:00 to collect body temperature data is 36 ℃, and the next time is 12:59:57 to collect body temperature data is 36.1 ℃, corresponding to 12:59:58, 12:59:59, the data at the time is null, that is, the data null time is determined to be 12:59:58, 12:59:59, the value of the corresponding 12:59:59:59 can be null, for example, 37 ℃, the last time is 12:59:54 to collect body temperature data is 36.1 ℃, the value of the corresponding data null time is 12:59:55 and 12:59:56, the value of the corresponding 12:59:55 is a random value, for example, 38 ℃, the value of the corresponding 12:59:56 is 39 ℃, the corresponding generated data is 13:00:00-36 ℃, 12:59:59-58-37 ℃, the value of the corresponding 12:59:57-36.1 ℃, the frequency is generated according to the random frequency interval of 12:59:55-36.1 ℃ and the value of the corresponding 12:59:55:59:55 is random frequency interval of the random frequency is generated according to the random frequency interval of the data of the 12:59:59:59-37), generating a sequence distributed according to time corresponds to virtual data to improve the safety of user data, generating an identification code by a fourth acquisition frequency, transmitting the identification code to a doctor end, wherein a doctor has a corresponding identification table of the identification code, the doctor can find the identification code with the same value in the identification table according to the identification code to obtain the fourth acquisition frequency, and input the fourth acquisition frequency into the doctor end to pick out correct multidimensional health data in the virtual data, for example, the identification code generated by the fourth acquisition frequency can be a binary code, the corresponding identification table is a comparison table of the binary code, for example, the fourth acquisition frequency is acquired 1 time every 3 seconds, the corresponding identification code is 11, the fourth acquisition frequency is acquired 1 time every 6 seconds, the corresponding identification code is 110, 1 time corresponds to 1 second in the identification table of the doctor, 10 times corresponds to 2 seconds, 11 times corresponds to 3 seconds, 100 times corresponds to 4 seconds, and so on, the corresponding doctor can obtain a fourth acquisition frequency with a value of 3 seconds in the identification table according to 11, and the corresponding doctor side extracts correct multidimensional health data according to the fourth acquisition frequency from the first sequence in the sequences distributed according to the time, wherein the first sequence is the sequence closest to the current time, and the actual multidimensional health data of the user is selected.
The equipment end encrypts and transmits the virtual data to the doctor end by utilizing the random number and the numerical value of the fourth acquisition frequency through the hash function. According to the technical scheme provided by the invention, the equipment end encrypts each dimension health data by utilizing the random number and the numerical value of the third acquisition frequency through the hash function, the security of the user data is improved due to the asymmetry of encryption and the randomness of the random number, meanwhile, the keys correspondingly generated by the numerical value of the third acquisition frequency of each dimension health data are different, the transmission keys of each dimension health data are correspondingly generated, and the security of the multidimensional health data is improved due to the keys in each dimension.
The transmission key of each dimensional health data is obtained by the following formula,
wherein, E is i As the transmission key of the i-th dimension health data, hash () is a hash function, θ is a random number, |is concatenation,
and is the fourth acquisition frequency.
According to the technical scheme provided by the invention, the data null moment is determined through the acquisition frequency, the random number is inserted to generate the virtual data, the safety of the user data is improved, the personal health data of the user cannot be obtained through decoding even if the data is stolen, and the safety of the user data is improved through fusion processing of the virtual data and the transmission data and encryption of each dimension health data through a hash function.
In order to better implement the processing method suitable for multi-dimensional health data provided by the present invention, the present invention further provides a processing system suitable for multi-dimensional health data, as shown in fig. 4, including:
the input module is used for inputting the types of the historical cases and the frequency data of each type of cases to the equipment end by a user;
the data determining module is used for determining the number of the types of the multidimensional health data and the multidimensional health data corresponding to each type of case by the equipment end according to the historic case types;
the frequency determining module is used for determining a first acquisition frequency of each dimension health data according to the frequency data of each type of case and the multi-dimension health data corresponding to each type of case by the equipment end;
the generation module is used for generating the occupied space of each dimension health data according to the product of the first acquisition frequency and the occupied space of each dimension health data acquired each time;
and the distribution module is used for generating a storage space distribution scheme according to the total storage space, the number of the multi-dimensional health data types and the occupied space of each dimensional health data type.
As shown in fig. 5, a schematic hardware structure of an electronic device according to an embodiment of the present invention is shown, where the electronic device 50 includes: a processor 51, a memory 52 and a computer program; wherein the method comprises the steps of
A memory 52 for storing the computer program, which memory may also be a flash memory (flash). Such as application programs, functional modules, etc. implementing the methods described above.
A processor 51 for executing the computer program stored in the memory to implement the various steps performed by the apparatus in the method described above. Reference may be made in particular to the description of the embodiments of the method described above.
Alternatively, the memory 52 may be separate or integrated with the processor 51.
When the memory 52 is a device separate from the processor 51, the apparatus may further include:
a bus 53 for connecting the memory 52 and the processor 51.
The present invention also provides a readable storage medium having stored therein a computer program for implementing the methods provided by the various embodiments described above when executed by a processor.
The readable storage medium may be a computer storage medium or a communication medium. Communication media includes any medium that facilitates transfer of a computer program from one place to another. Computer storage media can be any available media that can be accessed by a general purpose or special purpose computer. For example, a readable storage medium is coupled to the processor such that the processor can read information from, and write information to, the readable storage medium. In the alternative, the readable storage medium may be integral to the processor. The processor and the readable storage medium may reside in an application specific integrated circuit (Application Specific Integrated Circuits, ASIC for short). In addition, the ASIC may reside in a user device. The processor and the readable storage medium may reside as discrete components in a communication device. The readable storage medium may be read-only memory (ROM), random-access memory (RAM), CD-ROMs, magnetic tape, floppy disk, optical data storage device, etc.
The present invention also provides a program product comprising execution instructions stored in a readable storage medium. The at least one processor of the device may read the execution instructions from the readable storage medium, and execution of the execution instructions by the at least one processor causes the device to perform the methods provided by the various embodiments described above.
In the above embodiment of the apparatus, it should be understood that the processor may be a central processing unit (english: central Processing Unit, abbreviated as CPU), or may be other general purpose processors, digital signal processors (english: digital Signal Processor, abbreviated as DSP), application specific integrated circuits (english: application Specific Integrated Circuit, abbreviated as ASIC), or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in a processor for execution.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (7)
1. A method for processing multi-dimensional health data, comprising:
the user inputs the type of the historical case and the frequency data of each type of case to the equipment end;
the equipment end determines the number of the types of the multidimensional health data according to the types of the historical cases and the multidimensional health data corresponding to each type of case;
the equipment end determines a first acquisition frequency of each dimension health data according to the frequency data of each type of case and the multi-dimension health data corresponding to each type of case;
generating each dimension health data distribution coefficient according to the first acquisition frequency and the product of each dimension health data acquisition occupation space;
generating a storage space allocation scheme according to the total storage space, the number of the multi-dimensional health data types and the allocation coefficient of each dimensional health data type;
the step of determining, at the device side, a first acquisition frequency of each dimension health data according to the frequency data of each type of case and the multi-dimension health data corresponding to each type of case specifically includes:
setting the initial acquisition frequency of the multidimensional health data corresponding to each type of case as the frequency data of the corresponding type of case to obtain a plurality of initial acquisition frequencies of the multidimensional health data;
Summing the plurality of initial acquisition frequencies of each dimension health data to obtain an initial total acquisition frequency of each dimension health data;
shifting the initial total acquisition frequency of each dimension health data to the reference acquisition frequency of each dimension health data to generate a first acquisition frequency of each dimension health data;
the first acquisition frequency of each dimensional health data is obtained by the following formula,
wherein,,
first acquisition frequency, w, of health data in ith dimension i A first acquisition frequency weight value for the ith dimension health data,/for>
The standard acquisition frequency of the i-th dimension health data is used, n is the upper limit value of the number of the historical case types,
the initial acquisition frequency of the ith dimension health data in the d type of cases is set as a constant value;
the step of generating each dimension health data distribution coefficient according to the product of the first acquisition frequency and each dimension health data acquisition occupation space specifically comprises the following steps:
generating each dimension health data distribution coefficient according to the first acquisition frequency of each dimension health data and the product of each dimension health data acquisition occupation space;
the allocation coefficient for each dimension health data type is obtained by the following formula,
Wherein S is i Assigning coefficients, k, to the ith dimension health data type i Assigning a weight value of the coefficient to the i-th dimension health data type,
first acquisition frequency, y, for ith dimension health data i Each time the occupied space is acquired for the first dimension health data type;
the step of generating a storage space allocation scheme according to the total storage space, the number of the multi-dimensional health data types and the allocation coefficient of each dimensional health data type specifically comprises the following steps:
generating a multi-dimensional health data type total distribution coefficient according to the number of the multi-dimensional health data types and the distribution coefficient of each dimensional health data type;
generating the percentage of each dimension health data type according to the ratio of the multi-dimension health data type total distribution coefficient to each dimension health data type distribution coefficient;
generating a storage space allocation scheme of each dimension health data type according to the product of the total storage space and the percentage of each dimension health data type;
the storage space allocation scheme for each dimension health data type is obtained by the following formula,
wherein,,
storage space allocated for the ith dimension health data type, T is total storage space, S i Assigning coefficients for the ith dimension health data type, v is the upper limit value of the number of the multi-dimension health data types, S g Assigning coefficients for the type of healthy data of dimension g, < >>
Assigning coefficients, q, to a total of multi-dimensional health data types i And (3) a storage space weight value allocated for the ith dimension health data type.
2. The method as recited in claim 1, further comprising:
if the dimension health data type is newly added, determining the initial acquisition frequency of the dimension health data in the newly added case according to the attribute of the newly added case;
the initial acquisition frequency of the newly-added dimension health data shifts the reference acquisition frequency of the newly-added dimension health data to obtain a second acquisition frequency of the newly-added dimension health data type;
generating a new dimension health data type distribution coefficient according to the second acquisition frequency and the occupied space of each acquisition of the new dimension health data type;
generating the remaining storage space of each dimension health data type according to the storage space allocated by each dimension health data type and the difference value of the storage space occupied by each dimension health data type;
summing the remaining storage spaces according to the health data types of each dimension to generate total remaining storage space;
Determining the newly increased capacity of the newly increased dimension health data type according to the total remaining storage space, the newly increased dimension health data type distribution coefficient and the multi-dimension health data type total distribution coefficient;
the newly added capacity of the newly added dimension health data type is obtained by the following formula,
wherein T is New type New capacity for newly added dimension health data type, m New type For the newly increased capacity weight value of the newly increased dimension health data type, p is the upper limit value of the number of the multi-dimension health data types,
storage space allocated for the ith dimension health data type,/->
Storage space occupied for the ith dimension health data type,/->
E is the second acquisition frequency weight value of the newly added dimension health data, which is the total remaining storage space,/L>
For the reference acquisition frequency of the newly added dimension health data, G is the initial acquisition frequency of the newly added dimension health data in the newly added type of cases, y New type And acquiring occupied space for each time of the newly added dimension health data type.
3. The method as recited in claim 2, further comprising:
the equipment end performs offset processing on the first acquisition frequency of each dimension health data according to the remaining storage space of each dimension health data type to obtain a third acquisition frequency, and transmits the multi-dimension health data to the doctor end according to the third acquisition frequency;
The method comprises the steps that a user inputs a current case type to a device end, and the device end determines a multidimensional health data type corresponding to the current case type according to the current case type;
the equipment end determines a first acquisition frequency of each dimension health data corresponding to the current case type according to the multi-dimension health data type corresponding to the current case type;
the equipment end obtains a first degree difference value of the multi-dimensional health data type according to the difference value of the measured value of the multi-dimensional health data type corresponding to the current case type and the preset value of the multi-dimensional health data type;
adjusting the first acquisition frequency according to the first degree difference value and a preset value of the multi-dimensional health data type to obtain a fourth acquisition frequency;
the equipment end collects the multidimensional health data based on the fourth collection frequency and encrypts and transmits the multidimensional health data to the doctor end.
4. The method of claim 3, wherein the step of,
the step of adjusting the first acquisition frequency according to the first degree difference value and the preset value of the multi-dimensional health data type to obtain a fourth acquisition frequency specifically includes:
obtaining a first degree difference percentage according to the ratio of the first degree difference to a preset value of the multi-dimensional health data type;
Performing offset processing on the first acquisition frequency according to the first degree difference percentage to obtain a fourth acquisition frequency of each dimension health data corresponding to the current case type;
the fourth acquisition frequency is obtained by the following formula,
wherein,,
fourth acquisition frequency of ith dimension health data corresponding to current case type, ++>
For the first acquisition frequency of the ith dimension health data corresponding to the current case type, ++>
For the measurement value of the ith dimension health data type corresponding to the current case type,/for the measurement value of the ith dimension health data type corresponding to the current case type>
For the preset value of the first dimension health data type corresponding to the current case type, ++>
And a fourth acquisition frequency weight value of the ith dimension health data corresponding to the current case type.
5. The method as recited in claim 4, further comprising:
according to a fifth acquisition frequency of the ith dimension health data actively input by the doctor end;
obtaining a frequency adjustment trend according to the difference value of the fifth acquisition frequency and the fourth acquisition frequency;
adjusting a fourth acquisition frequency weight value of the ith dimension health data corresponding to the current case type according to the frequency adjustment trend to obtain an adjusted fourth acquisition frequency weight value;
The adjusted fourth acquisition frequency weight value is obtained by the following formula,
wherein,,
fifth acquisition frequency for ith dimension health data, +.>
Fourth acquisition frequency for ith dimension health data, +.>
For the adjusted fourth acquisition frequency weight, < +.>
A fourth acquisition frequency weight value delta for the ith dimension health data 1 Trend adjustment value, delta, for acquisition frequency increase 2 The frequency reduction trend adjustment value is collected.
6. The method of claim 5, wherein the step of determining the position of the probe is performed,
the step of collecting the multidimensional health data based on the fourth collecting frequency at the equipment end and encrypting and transmitting the multidimensional health data to the doctor end specifically comprises the following steps:
after the equipment receives the sending request, the equipment acquires the multi-dimensional health data based on the fourth acquisition frequency to generate first data to be sent;
the equipment end determines the data null moment of the first data to be transmitted according to the fourth acquisition frequency;
the equipment terminal randomly generates data at the data null moment of the first data to be transmitted to obtain virtual data;
the equipment end encrypts and transmits the virtual data to the doctor end by utilizing the random number and the numerical value of the fourth acquisition frequency through the hash function.
7. A processing system for multidimensional health data, comprising:
The input module is used for inputting the types of the historical cases and the frequency data of each type of cases to the equipment end by a user;
the data determining module is used for determining the number of the types of the multidimensional health data and the multidimensional health data corresponding to each type of case by the equipment end according to the historic case types;
the frequency determining module is used for determining a first acquisition frequency of each dimension health data according to the frequency data of each type of case and the multi-dimension health data corresponding to each type of case by the equipment end;
the generation module is used for generating each dimension health data distribution coefficient according to the product of the first acquisition frequency and each dimension health data acquisition occupation space;
the distribution module is used for generating a storage space distribution scheme according to the total storage space, the number of the multi-dimensional healthy data types and the distribution coefficient of each dimensional healthy data type;
the step of determining, at the device side, a first acquisition frequency of each dimension health data according to the frequency data of each type of case and the multi-dimension health data corresponding to each type of case specifically includes:
setting the initial acquisition frequency of the multidimensional health data corresponding to each type of case as the frequency data of the corresponding type of case to obtain a plurality of initial acquisition frequencies of the multidimensional health data;
Summing the plurality of initial acquisition frequencies of each dimension health data to obtain an initial total acquisition frequency of each dimension health data;
shifting the initial total acquisition frequency of each dimension health data to the reference acquisition frequency of each dimension health data to generate a first acquisition frequency of each dimension health data;
the first acquisition frequency of each dimensional health data is obtained by the following formula,
wherein,,
first acquisition frequency, w, of health data in ith dimension i A first acquisition frequency weight value for the ith dimension health data,/for>
The standard acquisition frequency of the i-th dimension health data is used, n is the upper limit value of the number of the historical case types,
the initial acquisition frequency of the ith dimension health data in the d type of cases is set as a constant value;
the step of generating each dimension health data distribution coefficient according to the product of the first acquisition frequency and each dimension health data acquisition occupation space specifically comprises the following steps:
generating each dimension health data distribution coefficient according to the first acquisition frequency of each dimension health data and the product of each dimension health data acquisition occupation space;
the allocation coefficient for each dimension health data type is obtained by the following formula,
Wherein S is i Assigning coefficients, k, to the ith dimension health data type i Assigning a weight value of the coefficient to the i-th dimension health data type,
first acquisition frequency, y, for ith dimension health data i Each time the occupied space is acquired for the first dimension health data type;
the step of generating a storage space allocation scheme according to the total storage space, the number of the multi-dimensional health data types and the allocation coefficient of each dimensional health data type specifically comprises the following steps:
generating a multi-dimensional health data type total distribution coefficient according to the number of the multi-dimensional health data types and the distribution coefficient of each dimensional health data type;
generating the percentage of each dimension health data type according to the ratio of the multi-dimension health data type total distribution coefficient to each dimension health data type distribution coefficient;
generating a storage space allocation scheme of each dimension health data type according to the product of the total storage space and the percentage of each dimension health data type;
the storage space allocation scheme for each dimension health data type is obtained by the following formula,
wherein,,
storage space allocated for the ith dimension health data type, T is total storage space, S i Assigning coefficients for the ith dimension health data type, v is the upper limit value of the number of the multi-dimension health data types, S g Assigning coefficients for the type of healthy data of dimension g, < >>
Assigning coefficients, q, to a total of multi-dimensional health data types i And (3) a storage space weight value allocated for the ith dimension health data type.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210489972.6A CN114898877B (en) | 2022-05-06 | 2022-05-06 | Processing method and system suitable for multi-dimensional health data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210489972.6A CN114898877B (en) | 2022-05-06 | 2022-05-06 | Processing method and system suitable for multi-dimensional health data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114898877A CN114898877A (en) | 2022-08-12 |
| CN114898877B true CN114898877B (en) | 2023-07-04 |
Family
ID=82720198
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210489972.6A Active CN114898877B (en) | 2022-05-06 | 2022-05-06 | Processing method and system suitable for multi-dimensional health data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114898877B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009087189A (en) * | 2007-10-02 | 2009-04-23 | Nec Corp | Data stream analysis system, and device, method and program used therefor |
| CN101667224A (en) * | 2008-09-05 | 2010-03-10 | 深圳易拓科技有限公司 | Portable health data storing device, health data detecting device and health data storing platform |
| CN103124203A (en) * | 2011-11-17 | 2013-05-29 | 京信通信系统（中国）有限公司 | Maintenance management method and device for time division multiplexing (TDM) channel parameters |
| CN107168809A (en) * | 2017-04-20 | 2017-09-15 | 中国人民解放军海军工程大学 | Synthetic aperture sonar multi-sensor data sharing method under cluster environment |
| CN107315840A (en) * | 2017-07-20 | 2017-11-03 | 郑州云海信息技术有限公司 | The management method and device of memory space in database |
| CN111540471A (en) * | 2020-05-12 | 2020-08-14 | 西安交通大学医学院第一附属医院 | Health state tracking and early warning method and system based on user health data |
| CN112667123A (en) * | 2020-12-15 | 2021-04-16 | 歌尔光学科技有限公司 | Health detection project customization method and device, wearable device and storage medium |
| CN113205656A (en) * | 2021-07-05 | 2021-08-03 | 环球数科集团有限公司 | Distributed forest fire monitoring and early warning data acquisition system and method |
| CN114154112A (en) * | 2021-11-19 | 2022-03-08 | 上海寒武纪信息科技有限公司 | Data processing device, chip and board card |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103034585B (en) * | 2011-09-28 | 2017-09-05 | 泰克科技（中国）有限公司 | The collection Memory Allocation of digital storage oscilloscope |
| US11226987B2 (en) * | 2015-10-23 | 2022-01-18 | Oracle International Corporation | System and method for in-place data writes to reduce fragmentation in a multidimensional database environment |
| US10204147B2 (en) * | 2016-04-05 | 2019-02-12 | Fractal Industries, Inc. | System for capture, analysis and storage of time series data from sensors with heterogeneous report interval profiles |
-
2022
- 2022-05-06 CN CN202210489972.6A patent/CN114898877B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009087189A (en) * | 2007-10-02 | 2009-04-23 | Nec Corp | Data stream analysis system, and device, method and program used therefor |
| CN101667224A (en) * | 2008-09-05 | 2010-03-10 | 深圳易拓科技有限公司 | Portable health data storing device, health data detecting device and health data storing platform |
| CN103124203A (en) * | 2011-11-17 | 2013-05-29 | 京信通信系统（中国）有限公司 | Maintenance management method and device for time division multiplexing (TDM) channel parameters |
| CN107168809A (en) * | 2017-04-20 | 2017-09-15 | 中国人民解放军海军工程大学 | Synthetic aperture sonar multi-sensor data sharing method under cluster environment |
| CN107315840A (en) * | 2017-07-20 | 2017-11-03 | 郑州云海信息技术有限公司 | The management method and device of memory space in database |
| CN111540471A (en) * | 2020-05-12 | 2020-08-14 | 西安交通大学医学院第一附属医院 | Health state tracking and early warning method and system based on user health data |
| CN112667123A (en) * | 2020-12-15 | 2021-04-16 | 歌尔光学科技有限公司 | Health detection project customization method and device, wearable device and storage medium |
| CN113205656A (en) * | 2021-07-05 | 2021-08-03 | 环球数科集团有限公司 | Distributed forest fire monitoring and early warning data acquisition system and method |
| CN114154112A (en) * | 2021-11-19 | 2022-03-08 | 上海寒武纪信息科技有限公司 | Data processing device, chip and board card |
Non-Patent Citations (1)
| Title |
|---|
| 多维数组动态存储空间的开辟;黄中强;;微型机与应用(第12期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114898877A (en) | 2022-08-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11562222B2 (en) | Systems and methods of identity analysis of electrocardiograms | |
| CN107491166B (en) | Method for adjusting parameters of virtual reality equipment and virtual reality equipment | |
| KR20160125544A (en) | User-oriented healthcare big data service method, computer program and system | |
| CN106228019B (en) | medical cloud data network analysis method | |
| Surantha et al. | Intelligent sleep monitoring system based on microservices and event-driven architecture | |
| CN114898877B (en) | Processing method and system suitable for multi-dimensional health data | |
| KR102055717B1 (en) | Method and Apparatus for Managing Pain | |
| US20200167658A1 (en) | System of Portable Real Time Neurofeedback Training | |
| Benish | Mutual information as an index of diagnostic test performance | |
| CN113288082A (en) | Medical rehabilitation system based on human body diagnosis data | |
| Zhang et al. | Development and research of exercise heart rate monitoring system under the concept of Internet of things telemedicine and mobile health | |
| US20120047191A1 (en) | Comparing boolean functions representing sensor data | |
| Rahman et al. | SCC Health: A framework for online estimation of disease severity for the smart and connected community | |
| CN105469341A (en) | Medical health intervention system and intervention method based on system | |
| Ryzhakov et al. | Electrocardiogram Signals Digital Processing in a Distributed Computing System | |
| CN112151178A (en) | Intelligent health cloud platform based on Java language development | |
| CN114465828B (en) | Case data processing method for medical system | |
| Selvan et al. | Healthcare Application System with Cyber-Security Using Machine Learning Techniques | |
| Bai | [Retracted] Designing and Implementing a Terminal Platform to Manage and Detect the Health of Exercise Parameters Based on Internet Objects | |
| Zhang et al. | Complex brain activity analysis and recognition based on multiagent methods | |
| TWI774997B (en) | Risk assessment method and system, service system and computer program product | |
| CN116471002B (en) | Industrial Internet data transmission method and system | |
| Gabrielyan et al. | Approaches for Processing and Storing Data from Wearable Medical Devices in Health Monitoring Systems | |
| CN116110602B (en) | Information processing method and system applied to medical community | |
| CN114708970A (en) | Pain comprehensive assessment data processing method and system suitable for big data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |