CN111755121A - Method and device for evaluating physical development trend of juveniles - Google Patents
Method and device for evaluating physical development trend of juveniles Download PDFInfo
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Abstract
The invention discloses a method and a device for evaluating the physical development trend of minors, wherein the method comprises the following steps: acquiring the basic characteristic data of the minor individuals and the physical development index data of the individuals on two or more dates; carrying out conversion processing on physical development index data of an individual, and obtaining a data combination of the evaluation value of the individual development level through statistical calculation; and carrying out logic analysis according to the data assembly, and outputting the development trend grade of each development index of the individual at the last measurement time point and the development prediction condition at the subsequent time. Compared with the prior art and the scheme, the standardization method and the standardization device can replace the empirical manual judgment of professionals to a certain extent, and users can not have professional knowledge bases, so that the cost can be effectively saved, the efficiency is improved, and the standardization method and the standardization device are further applied to services such as real-time assessment and interpretation of growth and development conditions of minors, health state early warning and the like.
Description
Technical Field
The invention relates to the field of development evaluation, in particular to a method and a device for evaluating the physical development trend of minors.
Background
Physical development is an important index reflecting the physical health condition of minors. The medical institution and the parents pay more and more attention to the physical development indexes of minors in China and other countries. And evaluating the physical development index, wherein the evaluation comprises evaluating the development level of a single measurement and evaluating the development trend of multiple measurement data.
Trend assessment is more important for continuous measurement of minors than for assessment of developmental level in a single measurement. Because the level assessment only represents the current development level of the assessed individual, the level may be reflected by bad health factors, or may be influenced by individual and environmental factors such as heredity, regions and the like; the latter condition is healthy, and if not clearly identified, misleads the health management measures. The development trend evaluation of the continuous data can scientifically reflect the growth and development tracks of the evaluated individuals and can be used for identifying individual factors and unhealthy factors.
At present, the commonly used growth and development tendency evaluation mode is to draw a normal growth curve by using a physical development evaluation standard issued by the world health organization or the Chinese health department, then draw an individual growth curve in the normal map by using continuous measurement data of an evaluated individual, and manually evaluate the growth and development tendency condition by a professional for growth and development evaluation while displaying the level evaluation result of single data.
The manual evaluation mode consumes a large amount of manpower and time, and has high cost and low efficiency; the manual evaluation mode is limited by the professional ability of an evaluator, and the stability of an evaluation result cannot be guaranteed; meanwhile, the manual evaluation needs professional knowledge as a basis, and cannot be popularized to become a self-service health management scheme for the masses. Although efficient and automatic methods and devices exist for evaluating the development level of single data, no method or device can effectively and automatically evaluate and monitor the growth and development trend of continuous data.
Generally, an automated alternative to manual evaluation work requiring complex intelligence is to use an artificial intelligence device to perform big data learning and construct an automatic processing model. However, big data learning of artificial intelligent devices has its own limitations, including: the black box method is not standardized, the technical maturity is limited, the training data source cost is high, the data is contaminated by mixed factors, and the like.
Therefore, a simple and universal method and apparatus for evaluating growth and development trend are needed.
Disclosure of Invention
The invention aims to provide a method and a device for evaluating the physical development trend of minors, which are expected to replace the manual evaluation work of professionals to a certain extent, are simple, convenient, scientific, standardized and easy to popularize.
In order to achieve the above object, a method for evaluating a physical development tendency of a minor includes:
s1, acquiring the basic characteristic data of the minor individuals and the physical development index data of the individuals on two or more dates; wherein the base feature data comprises: individual territory, sex, birth date, premature gestational age, infant stage feeding mode; the physical development indexes comprise: height, length, weight, head circumference, chest circumference, arm length, sitting height, arm circumference, height-to-weight, length-to-weight, and BMI;
s2, converting the physical development index data of the individual according to the individual basic feature data, and calculating a plurality of horizontal evaluation average values to obtain a data assembly of the individual development horizontal evaluation value;
and S3, performing logic comparison analysis on the calculated level evaluation value data combination by using preset early warning parameters, and outputting the development trend grade of each development index of the individual at the last measurement time point and the development prediction condition at the subsequent time.
According to an embodiment of the present invention, further, wherein the evaluation method, for any physical development index, can be analyzed and evaluated separately.
Further, according to an embodiment of the present invention, the conversion process of step S2 includes:
converting the development index data of the individual into a development level evaluation value in the same basic characteristic population by using the physical development standard and/or normal model data of the minors released by each related scientific research group and/or academic organization; abnormal data elimination is carried out on the development level evaluation value according to a preset statistical rule; the same basic characteristics are determined by the development standard and/or the grouping condition of the normal data and are matched with the acquired individual basic characteristic data.
According to an embodiment of the present invention, further, the calculating of step S2 includes:
and carrying out average value calculation and discrete data processing on the development level evaluation value of each time point of the individual to be evaluated so as to obtain a data assembly of the development level evaluation value of the individual.
According to another embodiment of the present invention, further wherein the calculating of step S2 includes:
an iterative calculation is used: sequencing all development level evaluation values of individuals to be evaluated according to measurement time, and then performing iterative computation one by one; cleaning the development level evaluation value calculated in the current time by using the weighted average value calculated in the previous iteration period as a reference condition; after cleaning, continuously carrying out weighted average numerical calculation; the weighting weight uses a preset weight distribution method; the weight-based formulation is based on a professional knowledge base and universal juvenile developmental characteristics, including the age and/or age of the individual to be assessed at the time of measurement.
Further, according to an embodiment of the present invention, wherein the calculating of the final output evaluation value data combination in step S2 includes:
evaluating all development level evaluation values and measuring time of the development level evaluation values after data cleaning; a weighted average value y calculated from the remaining data except the last data ranked in the measurement time; the weighted average value z calculated for all data.
According to an embodiment of the present invention, further, the outputting of the grade of the developmental trend in step S3 includes:
subtracting the last development level evaluation value in the measurement time from the average value of the development level evaluation values, and comparing the obtained difference value with a preset early warning parameter to give a quality grade; wherein the difference comprises a sign; the preset early warning parameters are preset according to actual needs and aiming at any physical development index.
According to an embodiment of the present invention, further, wherein the predicting the development condition in step S3 includes:
calculating and outputting a development level evaluation value prediction range of the evaluated minor individuals from any future to adult date by using the average value of the development level evaluation values and preset early warning parameters; the preset early warning parameters are preset according to actual needs and aiming at any physical development index;
using the method of converting the development level evaluation value in step S2, reversely calculating and outputting the measurement value prediction range of the evaluated physical development index item of the individual at any future time point; the prediction range of the measured value also needs to be restricted and adjusted according to the biological growth and development rule; any one of the time points is constrained by the age range of the transformed developmental level assessment method.
In order to achieve the above object, another embodiment of the present invention provides an apparatus for evaluating a physical development tendency of a minor person, comprising:
the data acquisition module is used for acquiring basic characteristic data of an individual to be evaluated and physical development index data of the individual and preprocessing the data; wherein the base feature data includes, but is not limited to: individual territory, sex, birth date, premature gestational age, infant stage feeding mode; the physical development indicator data include, but are not limited to: height, body length, weight, head circumference, chest circumference, arm length, sitting height, and upper arm circumference;
the evaluation calculation module is used for converting the basic characteristic data and the physical development index data and processing to obtain a development level evaluation value data combination which can be used for logic evaluation; the processing mainly comprises the steps of carrying out average numerical calculation and data cleaning on the horizontal evaluation value;
and the evaluation conclusion module is used for carrying out logic judgment analysis on the evaluation value data assembly output by the evaluation calculation module by using the preset early warning parameters, and outputting the development trend grade of any development index of the individual at the last measurement time point and the development prediction condition at the subsequent time.
According to an embodiment of the present invention, further wherein the evaluation conclusion module includes:
the parameter control unit is used for presetting early warning parameters aiming at any physical development index according to actual needs; the early warning parameters are used for judging the grade of the development trend and calculating the development prediction result;
the trend judging unit is used for comparing the last development level evaluation value ranked in the measurement time with the average value of the development level evaluation values under any physical development index of an individual to be evaluated, and outputting a development trend grade value according to the early warning parameter;
the prediction unit is used for converting and outputting a measurement value prediction range of an individual to be evaluated at any future time by using the average value and the early warning parameter of the evaluation value of the development level and a method for converting the evaluation value of the development level under any physical development index; the arbitrary time is constrained by the age range covered by the method used to transform the developmental level estimates.
The method and the device for evaluating the physical development trend of the minor can replace the manual judgment of professionals to a certain extent through automatic operation and evaluation, can complete self-service evaluation and rapid preliminary screening of the growth and development conditions of the minor individuals, and can be further applied to the services of health preliminary screening, health early warning and the like of the growth and development conditions of large-scale crowds.
The method and the device for evaluating the physical development trend of the minor are preferably used for carrying out standardized conversion and horizontal evaluation on the measured data by using the physical development standard and/or the normal data of the minor released by each related scientific research group and/or academic organization, and compared with a method for building a big data model by self, the method and the device not only avoid complicated early-stage normal data collection and artificial intelligence model training and verification, but also utilize authoritative big data and statistical significance of the development standard and/or the normal data, and are more rigorous.
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Fig. 1 is a schematic flow chart of a method for evaluating a physical development trend of a minor person according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart illustrating a process of processing physical development index data to obtain a development level evaluation value data assembly in the method for evaluating a physical development tendency of a minor person according to an embodiment of the present invention.
Fig. 3 is a schematic structural diagram of an apparatus for evaluating a physical development tendency of a minor person according to another embodiment of the present invention.
FIG. 4 is a schematic diagram illustrating an interaction interface of a data acquisition module of an evaluation apparatus according to an embodiment of the present invention.
Fig. 5 is a schematic diagram showing an evaluation result of the evaluation apparatus according to the embodiment of the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings. The embodiments described by referring to the drawings are exemplary only for the purpose of illustrating the invention and are not to be construed as limiting the invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, belong to the scope of the present invention.
As shown in fig. 1, an embodiment of the first aspect of the present invention provides a method for evaluating a physical development tendency of a minor.
S1, acquiring the basic characteristic data of the minor individuals and the physical development index data of the individuals on two or more dates; wherein the base feature data comprises: individual territory, sex, birth date, premature gestational age, infant stage feeding mode; the physical development indexes comprise: height, length, weight, head circumference, chest circumference, arm length, sitting height, arm circumference, height-to-weight, length-to-weight, and BMI.
In the evaluation method proposed in this embodiment, further, analysis and evaluation can be performed individually for any physical development index. That is, for example: the user can only input the weight data of the person to be evaluated, but not the height data at the same time; similarly, the user can also input the weight data and the height data at different times respectively, and the evaluation method can perform trend evaluation aiming at different final time points of different index items respectively.
It should be noted that, in step S1 of the above embodiment, the individual basic feature data to be acquired is an optimal combination, but in other embodiments, the simplest manner may be to set a default value for the basic feature data, so as to omit the collection of the basic feature data. For example: the default does not distinguish the regional characteristics of China, foreign countries and the like; the default infant stage is pure breast feeding; optionally, the term preterm birth age is not imported, but rather, the term preterm birth is used for evaluation; if the birth date is not provided, the absolute time is not provided when the physical development index data is input, and the measurement time relative to the birth date, namely the day age, needs to be provided; meanwhile, if gender is not provided, only an assessment scheme that does not distinguish gender can be used in a subsequent step.
Further, in step S1 of the above embodiment, the physical development index data to be obtained may be different according to different requirements of different items, such as weight items, and the simplest way may be to provide the measurement time and weight value and unit with daily precision, while the composite index of BMI, height and weight, etc. needs to provide the height and weight value and unit at the same measurement time.
And S2, converting the physical development index data of the individual according to the individual basic feature data, and calculating a plurality of horizontal evaluation average values to obtain a data assembly of the individual development horizontal evaluation value.
Further, the calculation of step S2 includes: and carrying out average value calculation and discrete data processing on the development level evaluation value of each time point of the individual to be evaluated so as to obtain a data assembly of the development level evaluation value of the individual.
In step S2 of this embodiment, the calculation process preferably uses an iterative calculation mode, as shown in fig. 2, and includes the following sub-processes.
S211, preprocessing the physical development index data, preferably, the method includes: and (3) unified conversion of units, filtering abnormal values which do not conform to natural rules, converting the absolute measurement time into the day-age time relative to the birth date, and correcting the day-age time of the premature infant. The method for calculating the age of day and correcting the early childbirth is a method commonly used in the art and well known to those skilled in the art, and therefore will not be described herein.
S212, performing development level evaluation value conversion on the physical development index data, preferably, converting the individual development index data into the development level evaluation value in the same basic characteristic population by using the physical development standard and/or normal mode data of the minors issued by each related scientific research group and/or academic organization; the same basic characteristics are determined by the development standard and/or the grouping condition of the normal data and are matched with the acquired individual basic characteristic data.
Further, in this embodiment, the juvenile physical development standard and/or normative data are based on the following four sets of standard data: the system comprises the data of a preterm fenton curve, the data of standard growth and development of children aged 0-5 years by the world health organization, the data of standard growth and development of Chinese children aged 0-7 years released by the Chinese health department, and the data of growth curves of the teenagers aged 0-18 years, such as height, weight and BMI, released by the Chinese related academic organization. The standardized data is selected for use according to the specific adaptation condition of the data to be evaluated; those skilled in the art will know the applicable characteristics of the above standards and the matching conditions of the standards and the individual basic feature data, and will not be described herein. One of ordinary skill in the art may select other standardized data to use and derive other embodiments that fall within the scope of the invention.
Further, in the present embodiment, the individual development index data is expressed by using a z-score normalized value as the development level evaluation value in the population with the basic characteristics obtained by the above-mentioned development standard and/or normative data conversion. The method of calculating the z-score normalization value is well known to those skilled in the art and will not be described herein. Other values, such as percentiles, may be selected by one of ordinary skill in the art as the developmental level assessment to obtain other embodiments, and all such embodiments are within the scope of the present invention. However, the z-score normalization method is the best choice for the proposed method, especially in the subsequent calculation of the mean value, which is determined by its statistical significance.
And S213, carrying out first abnormal data elimination on the development level evaluation value, preferably, adopting a rule that data outside the range of +/-5 times of standard deviation is eliminated.
S214, the converted data are sorted according to the measuring time, and further, are sorted according to the time sequence from first to last, namely the measuring time is earlier, and the subsequent iterative operation is carried out first.
It should be noted that, after the measurement data of the height-specific weight and the height-specific weight are sorted according to the measurement time sequence, the measurement data are actually sorted from the height to the height. The numerical ordering is shown in the evaluation result display form in another embodiment, which is not beyond the scope of the present invention.
S215, performing iterative computation on the sorted development level evaluation value data, specifically:
cleaning a development level evaluation value calculated in the current time, namely a z-score standardized value, by using a weighted average value calculated in the previous iteration period as a reference condition; after cleaning, carrying out weighted average numerical calculation on all the data entering into operation again, and entering into the next iteration cycle.
Wherein the weighted weights use a preset weight distribution method, and the weight distribution formula is formulated based on a professional knowledge base and universal juvenile development characteristics, including the age and/or the day age of the individual to be evaluated at the time of measurement.
Further, in this embodiment, the weight distribution method used by the weight during weighted average calculation specifically includes:
taking all data which have entered into operation in the current iteration cycle as a judgment set:
at the initial stage of iteration, the total amount of data is 1, and no weight adjustment is involved;
when the total data amount is 2, all the data weights are the same;
when the total data amount is more than 2, if the age of the evaluated person at the last measurement time is not more than 3 years old, the data weight of the birth date is 2 times that of the data of other dates;
when the total amount of data is more than 2, if the age of the evaluated person at the last measurement time exceeds 3 years, but does not exceed 14 years, all data are weighted the same;
when the total data amount is more than 2, if the age of the evaluated person exceeds 14 years at the last measurement time, the data weight of the birthday day later than 14 years is 3 times of the weight of other data;
in other embodiments, other weight distribution methods may be adopted, or no weight adjustment may be performed, all of which fall within the scope of the present invention.
Further, the cleaning rule in step S215, that is, the second abnormal data removing rule, is: horizontal evaluation values differing by more than 2 from the weighted average value calculated in the previous iteration cycle will be skipped. In other embodiments, other culling rules may be used, and are within the scope of the invention.
S216, outputting a developmental level evaluation value data assembly, preferably including:
evaluating all development level evaluation values and measuring time of the development level evaluation values after data cleaning;
a weighted average value y calculated from the remaining data except the last data ranked in the measurement time; the weighted average value z calculated for all data.
And S3, performing logic comparison analysis on the level evaluation value data combination obtained by calculation in the step S2 by using preset early warning parameters, and outputting the development trend grade of each development index of the individual at the last measurement time point and the current development prediction condition at the subsequent time.
Furthermore, the preset early warning parameters can be preset aiming at any physical development index according to actual needs. Specifically, in this embodiment, the warning parameter is preset to a default value of 0.6.
Further, in the present embodiment, the development trend quality grade values are represented by five numbers of-2, -1, 0, 1 and 2; the larger the absolute value of the trend is, the more abnormal the trend is represented; positive numbers indicate a trend of developing faster, negative numbers indicate a trend of developing slower.
Further, the rule for judging the grade of the development trend specifically includes:
subtracting the weighted average value y output in step S2 from the last development level evaluation value ranked at the measurement time, determining the sign of the rank value according to the sign of the difference, and comparing the absolute value of the difference with the preset early warning parameter:
the absolute value of the difference is less than or equal to the early warning parameter, and the grade value of the quality is 0;
the absolute value of the difference is more than 2 times of the early warning parameter, and the grade value of the quality is 2 or-2;
in addition to the above, the merit value is 1 or-1.
Further, the predicting of the development in step S3 includes:
using the weighted average value z and the warning parameter outputted in step S2, a prediction interval is calculated to output an assessment value of the development level of the minor individual to be assessed at any date from the future to the adult, that is, a normalized value of z-score. Adding an early warning parameter value to z to obtain an interval upper limit value; subtracting the early warning parameter value from z to obtain a lower limit value of the interval; the interval is a closed interval.
Further, using the physical development standard and/or the normative data used for evaluating the development level in the step S2, calculating and outputting a measurement value prediction range of the evaluated physical development index item of the individual at any future time point; any of the time points described are constrained by the age range covered by the developmental criteria and/or normative data. The specific calculation method is that the starting and ending values of the prediction interval of the z-score standardized value are sleeved into a development standard and/or a normal model data model and are respectively converted and calculated into actual measurement values; the calculation method is well known to those skilled in the art and will not be described herein.
It should be noted that, the prediction range of the measured value needs to be restricted and adjusted according to a biological growth and development rule, and the specific development rule includes: the lower limit values of height, body length, arm length and head circumference can not be lower than the existing actual measurement data.
It should be noted that, for the composite development index of height, weight and length, when calculating the predicted measurement value, the prediction calculation of the height and length items is performed in advance, and then the prediction calculation of the item is performed, which is equivalent to two calculation processes, and this does not exceed the protection scope of the present invention.
Further, in the evaluation method according to the present invention, the conversion method of the age group range of the height and the length and the overlapping period of the age groups belongs to the knowledge of those skilled in the art, and will not be described.
As shown in fig. 3, the device for evaluating the physical development tendency of a minor person according to an embodiment of the second aspect of the present invention includes the following structure:
the data acquisition module is used for acquiring basic characteristic data of an individual to be evaluated and physical development index data of the individual and preprocessing the data; wherein the base feature data includes, but is not limited to: individual territory, sex, birth date, premature gestational age, infant stage feeding mode; the physical development indicator data include, but are not limited to: height, body length, weight, head circumference, chest circumference, arm length, sitting height, and upper arm circumference; wherein, part of the development index data can be combined and processed into a plurality of composite development indexes, such as height and weight on the same date, and can be combined and processed into height-based weight and BMI indexes;
the evaluation calculation module is used for converting the basic characteristic data and the physical development index data and processing to obtain a development level evaluation value data combination which can be used for logic evaluation; the processing mainly comprises the steps of carrying out average numerical calculation and data cleaning on the horizontal evaluation value;
and the evaluation conclusion module is used for carrying out logic judgment analysis on the evaluation value data assembly output by the evaluation calculation module by using the preset early warning parameters, and outputting the development trend grade of any development index of the individual at the last measurement time point and the development prediction condition at the subsequent time.
As a preferred embodiment of the present invention, wherein the evaluation calculation module includes:
the first computing unit is used for converting the development index data into a development level evaluation value in the crowd with the same basic characteristics; abnormal data elimination is carried out on the development level evaluation value according to a preset statistical rule;
the second computing unit is used for carrying out operation processing on the development level evaluation value output by the first computing unit and outputting an evaluation value data assembly; the data assembly includes: evaluating all development level evaluation values and measuring time of the development level evaluation values after data cleaning; the average value y calculated by the rest data except the last data ranked in the measurement time; the calculated average z for all data.
As a preferred embodiment of the present invention, wherein the evaluation conclusion module comprises:
the parameter control unit is used for presetting early warning parameters aiming at any physical development index according to actual needs; the early warning parameters are used for judging the grade of the development trend and calculating the development prediction result;
the trend judging unit is used for comparing the last development level evaluation value ranked in the measurement time with the average value y under any physical development index of the individual to be evaluated, referring to the early warning parameter and outputting the development trend grade value;
the prediction unit is used for converting and outputting a measurement value prediction range of an individual to be evaluated at any future time by using the average value z and the early warning parameter and the development standard and/or the normal mode data used for converting the development level evaluation value under any physical development index; the any time is constrained by the age range covered by the developmental criteria and/or normative data used.
The specific logical operation process of the above evaluation calculation module and the evaluation conclusion module is described in detail in the embodiments of the first aspect of the present invention.
As a preferred embodiment of the present invention, the data acquisition module further includes a data storage function, so that multiple step-by-step data entry of the person to be evaluated can be realized, but this is not necessary in other embodiments of the present invention.
As a preferred embodiment of the present invention, the device for evaluating the physical development tendency of the minors provides an evaluation service to the outside in a server API interface manner. Namely, data is submitted to the data acquisition module in an API request mode, and then the evaluation conclusion module returns evaluation conclusion data.
In other embodiments of the present invention, the above API request mode may be replaced by a client and/or a user terminal interactive interface: FIG. 4 is a schematic diagram of a user terminal interface showing the most concise content for the first user in a friendly and simple interface; fig. 5 shows a schematic diagram of an evaluation conclusion of the ue.
The client and/or the user terminal interface can also support various input modes such as voice, intelligent hardware equipment and the like; the output developmental trend evaluation conclusion and the developmental situation prediction can be correspondingly displayed in various modes such as characters, images, graphs, charts, voice and the like.
In other embodiments of the present invention, the apparatus mentioned in the present invention can also be implemented by a composite structure formed by the client and/or the user terminal and the API interface.
In other embodiments of the invention, a plurality of user terminals can be connected with the network server through the network in a communication way, and the data of the same person to be evaluated is input and maintained, and the evaluation conclusion is checked; including but not limited to wired networks, wireless networks, bluetooth communications, infrared communications, etc.
In other embodiments of the present invention, the whole device can also be implemented in a form of completely using a user terminal, so that the development tendency evaluation function proposed by the present invention is implemented without depending on network communication.
The client and/or user terminal of some embodiments may also be stored on a computer-readable storage medium when sold or used as a stand-alone product; the storage medium may be an erasable memory or a read-only memory, and includes a usb disk, a magnetic disk, or an optical disk.
It should be noted that the terms "first," "second," and the like in the description of the present invention are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The above embodiments of the present invention are implemented by software, and in other embodiments, the embodiments may also be implemented by hardware or firmware, or implemented by any two or three of hardware, software, and firmware; in addition, each functional unit in the above embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module; in addition, the communication modes between modules and units include, but are not limited to, wired network, wireless network, bluetooth communication, infrared communication, and the like.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A method for evaluating a physical development trend of a minor, comprising:
s1, acquiring the basic characteristic data of the minor individuals and the physical development index data of the individuals on two or more dates; wherein the base feature data comprises: individual territory, sex, birth date, premature gestational age, infant stage feeding mode; the physical development indexes comprise: height, length, weight, head circumference, chest circumference, arm length, sitting height, arm circumference, height-to-weight, length-to-weight, and BMI;
s2, converting the physical development index data of the individual according to the individual basic feature data, and calculating a plurality of horizontal evaluation average values to obtain a data assembly of the individual development horizontal evaluation value;
and S3, performing logic comparison analysis on the calculated level evaluation value data combination by using preset early warning parameters, and outputting the development trend grade of each development index of the individual at the last measurement time point and the development prediction condition at the subsequent time.
2. The method of claim 1, wherein the evaluation method comprises: for any physical development index, analysis and evaluation are carried out separately.
3. The method according to claim 1, wherein the conversion process of step S2 includes:
converting the development index data of the individual into a development level evaluation value in the same basic characteristic population by using the physical development standard and/or normal model data of the minors released by each related scientific research group and/or academic organization; abnormal data elimination is carried out on the development level evaluation value according to a preset statistical rule; the same basic characteristics are determined by the development standard and/or the grouping condition of the normal data and are matched with the acquired individual basic characteristic data.
4. The method of claim 1, wherein the calculating of step S2 includes:
and carrying out average value calculation and discrete data processing on the development level evaluation value of each time point of the individual to be evaluated so as to obtain a data assembly of the development level evaluation value of the individual.
5. The method of claim 1, wherein the calculating of step S2 includes:
an iterative calculation is used: sequencing all development level evaluation values of individuals to be evaluated according to measurement time, and then performing iterative computation one by one; cleaning the development level evaluation value calculated in the current time by using the weighted average value calculated in the previous iteration period as a reference condition; after cleaning, continuously carrying out weighted average numerical calculation; the weighting weight uses a preset weight distribution method; the weight-based formulation is based on a professional knowledge base and universal juvenile developmental characteristics, including the age and/or age of the individual to be assessed at the time of measurement.
6. The method of claim 1, wherein the calculating of the final output evaluation value data combination in step S2 includes:
evaluating all development level evaluation values and measuring time of the development level evaluation values after data cleaning; a weighted average value y calculated from the remaining data except the last data ranked in the measurement time; the weighted average value z calculated for all data.
7. The method according to any one of claims 1 to 6, wherein the outputting of the grade of the developmental trend goodness in step S3 comprises:
subtracting the last development level evaluation value in the measurement time from the average value of the development level evaluation values, and comparing the obtained difference value with a preset early warning parameter to give a quality grade; wherein the difference comprises a sign; the preset early warning parameters are preset according to actual needs and aiming at any physical development index.
8. The method according to any one of claims 1 to 6, wherein the step S3 of predicting the development condition comprises:
calculating and outputting a development level evaluation value prediction range of the evaluated minor individuals from any future to adult date by using the average value of the development level evaluation values and preset early warning parameters; the preset early warning parameters are preset according to actual needs and aiming at any physical development index;
using the method of converting the development level evaluation value in step S2, reversely calculating and outputting the measurement value prediction range of the evaluated physical development index item of the individual at any future time point; the prediction range of the measured value also needs to be restricted and adjusted according to the biological growth and development rule; any one of the time points is constrained by the age range of the transformed developmental level assessment method.
9. An apparatus for evaluating a physical development tendency of a minor, comprising:
the data acquisition module is used for acquiring basic characteristic data of an individual to be evaluated and physical development index data of the individual and preprocessing the data; wherein the base feature data includes, but is not limited to: individual territory, sex, birth date, premature gestational age, infant stage feeding mode; the physical development indicator data include, but are not limited to: height, body length, weight, head circumference, chest circumference, arm length, sitting height, and upper arm circumference;
the evaluation calculation module is used for converting the basic characteristic data and the physical development index data and processing to obtain a development level evaluation value data combination which can be used for logic evaluation; the processing mainly comprises the steps of carrying out average numerical calculation and data cleaning on the horizontal evaluation value;
and the evaluation conclusion module is used for carrying out logic judgment analysis on the evaluation value data assembly output by the evaluation calculation module by using the preset early warning parameters, and outputting the development trend grade of any development index of the individual at the last measurement time point and the development prediction condition at the subsequent time.
10. The method of claim 9, wherein the evaluation conclusion module comprises:
the parameter control unit is used for presetting early warning parameters aiming at any physical development index according to actual needs; the early warning parameters are used for judging the grade of the development trend and calculating the development prediction result;
the trend judging unit is used for comparing the last development level evaluation value ranked in the measurement time with the average value of the development level evaluation values under any physical development index of an individual to be evaluated, and outputting a development trend grade value according to the early warning parameter;
the prediction unit is used for converting and outputting a measurement value prediction range of an individual to be evaluated at any future time by using the average value and the early warning parameter of the evaluation value of the development level and a method for converting the evaluation value of the development level under any physical development index; the arbitrary time is constrained by the age range covered by the method used to transform the developmental level estimates.
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