An artificial intelligence-based prevention and control supervision method for adolescent pseudomyopia

Abstract

The present invention relates to a method for preventing and controlling pseudomyopia in adolescents based on artificial intelligence, and relates to the technical field of myopia prevention and control, which includes: obtaining human body parameter information and current time point of adolescents; matching adjustment strategies from a preset adjustment database according to the human body parameter information; determining supervisor identity information according to the current time point; determining a notification terminal based on the supervisor identity information and prompting the notification terminal with the adjustment strategy. The present invention has the effect of facilitating effective prevention and control of pseudomyopia in adolescents.

Description

Technical Field

[0001] The present invention relates to the technical field of myopia prevention and control, and in particular to an artificial intelligence-based prevention and control supervision method for juvenile pseudomyopia. Background Art

[0002] Myopia refers to the situation where parallel light rays enter the eye and focus in front of the retina without adjustment, causing distant objects to appear blurry while nearby objects are relatively clear. Myopia is mainly caused by poor eye posture and long-term use of the eyes. With the increase in close-range work scenes, the number of people with myopia is also increasing.

[0003] Among the current myopic population, the proportion of adolescents is increasing. Since adolescents need to use their eyes at close range for a long time when studying, poor sitting posture during long periods of study can easily lead to continuous contraction and spasm of the ciliary muscle, which increases the thickness of the lens and prevents it from relaxing normally, causing blurred vision and pseudomyopia. At present, the prevention and control of pseudomyopia in adolescents generally involves guardians supervising and guiding the sitting posture of adolescents when studying, so that adolescents can maintain a correct sitting posture to study, and controlling the time adolescents spend using electronic products and their daily routines, thereby preventing and controlling the development of pseudomyopia in adolescents.

[0004] At present, pseudomyopia in adolescents is generally prevented and controlled by guardians. However, different adolescents have different eye habits, resulting in different prevention and control needs. Guardians cannot always be around adolescents to supervise and guide them, making it inconvenient to effectively prevent and control pseudomyopia in adolescents. Summary of the invention

[0005] In order to facilitate the effective prevention and control of juvenile pseudomyopia, the present invention provides an artificial intelligence-based prevention and control supervision method for juvenile pseudomyopia.

[0006] In the first aspect, the present invention provides a method for preventing and controlling pseudomyopia in adolescents based on artificial intelligence, which adopts the following technical solution:

[0007] A method for preventing and controlling pseudomyopia in adolescents based on artificial intelligence, comprising:

[0008] Obtain the adolescent's body parameter information and current time point;

[0009] Matching an adjustment strategy from a preset adjustment database according to human body parameter information;

[0010] Determine supervisory identity information based on the current point in time;

[0011] Based on the supervision identity information, the notification terminal is determined and the adjustment strategy is prompted to the notification terminal.

[0012] Optionally, the method for obtaining human body parameter information includes:

[0013] Obtain the equipment number and testing parameters of the testing equipment;

[0014] Determine the equipment detection type information based on the equipment number;

[0015] Based on the device detection category information, the detection parameters are classified to form single category detection information, where the single category detection information is eye category detection information, sports category detection information or body fat category detection information;

[0016] Based on a single type of detection information, similar information is combined to form eye condition information, exercise condition information and body fat condition information;

[0017] The eye condition information, exercise condition information and body fat condition information are combined and used as human body parameter information.

[0018] Optionally, the method for forming the eye condition information, exercise condition information and body fat condition information includes:

[0019] Retrieving a single type detection value and a single type detection individual value based on the single type detection information;

[0020] Determine whether the number of detected values ​​of a single type is only one;

[0021] If yes, the single type of detection value is used as eye condition information, exercise condition information or body fat condition information;

[0022] If not, retrieve the device usage time value and data type information based on the single type detection information;

[0023] According to the correspondence between the data type information and the preset data type reference parameter value, a data type reference parameter value corresponding to the data type information is determined;

[0024] The comprehensive value of the same type of data is determined based on the equipment usage time value and the data type benchmark parameter value analysis, and the comprehensive value of the same type of data is used as eye condition information, exercise condition information or body fat condition information.

[0025] Optionally, methods for determining the comprehensive value of the same type of data include:

[0026] Calculate the product value between the device usage time value and the data type benchmark parameter value as the single device parameter value;

[0027] The device usage time values ​​are sorted in reverse order to obtain a sequence number, and the maximum value of the device usage time value is selected and defined as the maximum time value;

[0028] Determine whether the maximum time value is greater than a preset time reference value;

[0029] If it is greater than, the single device parameter value corresponding to the maximum time value is used as the primary parameter initial value, and the device usage time value other than the maximum time value is used as the remaining time value, and the single device parameter value corresponding to the remaining time value is used as the secondary parameter value;

[0030] Analyze the remaining time value and the secondary parameter value to determine the remaining time parameter impact value;

[0031] Calculate the sum of the initial value of the main parameter and the influence value of the remaining time parameter and use it as the parameter adjustment value of the same data, and use the parameter adjustment value of the same data as the comprehensive value of the same data;

[0032] If it is not greater than, then the time sorting influence value corresponding to the sequence number is determined according to the correspondence between the sequence number and the preset time sorting influence value;

[0033] Calculate the product value between the time sorting impact value and the single device parameter value and use it as the single device parameter adjustment value;

[0034] The sum of the parameter adjustment values ​​of each single device in a single type of detection information is calculated and used as the same type of comprehensive parameter value, and the same type of comprehensive parameter value is used as the same type of data comprehensive value.

[0035] Optionally, a method for determining the remaining time parameter impact value includes:

[0036] Calculate the difference between the remaining time value and the time base value and use it as the remaining time deviation value;

[0037] According to the correspondence between the remaining time deviation value and the preset remaining time deviation initial impact value, the remaining time deviation initial impact value corresponding to the remaining time deviation value is determined;

[0038] Calculate the difference between the remaining time value and the maximum time value and use it as the maximum time deviation value;

[0039] According to the correspondence between the maximum time deviation value and the preset maximum time deviation impact value, the maximum time deviation impact value corresponding to the maximum time deviation value is determined;

[0040] The sum of the initial impact value of the remaining time deviation and the maximum time deviation impact value is calculated and used as the remaining time deviation adjustment impact value, and the remaining time deviation adjustment impact value is used as the remaining time parameter impact value.

[0041] Optionally, the method further includes steps after combining the same type of detection information based on the single type to form eye condition information, exercise condition information and body fat condition information, which are as follows:

[0042] According to the correspondence between the single type detection information and the preset type usage reference time value, the type usage reference time value corresponding to the single type detection information is determined;

[0043] Determine whether the equipment usage time value is greater than the type usage benchmark time value;

[0044] If yes, continue to output the eye condition information, exercise condition information or body fat condition information;

[0045] If not, the difference between the device usage time value and the category usage reference time value is calculated and used as the usage time deviation value;

[0046] Determine the time deviation cause information based on the usage time deviation value and the current time point analysis;

[0047] Determine the deviation output information based on the time deviation cause information analysis and output the deviation output information.

[0048] Optionally, a method for determining the time deviation cause information includes:

[0049] Retrieve historical deviation values ​​based on the current time point;

[0050] Perform curve processing based on the historical deviation value in the same period to form a historical deviation curve;

[0051] Analyze the deviation between the historical deviation curve and the preset deviation reference curve to form curve deviation information;

[0052] Selecting a reference time value based on the deviation between the current time point and a preset time point to select curve deviation information and form selected time deviation information;

[0053] Similar deviation information is formed by matching the selected time deviation information with the curve deviation information;

[0054] Retrieving the estimated deviation interval based on similar deviation information;

[0055] Determine whether the usage time deviation value is within the estimated deviation range;

[0056] If yes, the preset usage abnormal information is output and used as the time deviation reason information;

[0057] If not, the preset device abnormality information is output as the time deviation cause information.

[0058] Optionally, a method for determining the deviation output information includes:

[0059] Determine whether the time deviation reason information is usage abnormality information;

[0060] If yes, the single device parameter value is used as the deviation output information;

[0061] If not, retrieve the device operation time value and the internal operation location point of the device based on the location device usage information;

[0062] Retrieve the type of operation benchmark time value and required operation position point based on the equipment number;

[0063] Calculate the difference between the equipment operation time value and the type operation benchmark time value and use it as the operation time deviation value;

[0064] Analyze the deviation between the internal operating position point of the equipment and the required operating position point and use it as the operating deviation position point;

[0065] The equipment abnormality information is determined based on the running time deviation value and the running deviation position point analysis, and the equipment abnormality information is used as the deviation output information.

[0066] Optionally, a method for determining device abnormality information includes:

[0067] According to the correspondence between the running deviation position point and the preset position deviation time reference value, the position deviation time reference value corresponding to the running deviation position point is determined;

[0068] Calculate the difference between the running time deviation value and the position deviation time reference value and use it as the position time deviation value;

[0069] Based on the position time deviation value, a positive value is selected as the selected time deviation value, and the running deviation position point corresponding to the selected time deviation value is used as the selected deviation position point;

[0070] According to the correspondence between the selected deviation position point and the preset single position abnormality information, the single position abnormality information corresponding to the selected deviation position point is determined;

[0071] Determine whether there is only one deviation position point selected;

[0072] If yes, the single location abnormality information is used as the device abnormality information;

[0073] If not, then based on the selected deviation position points, the mutually influential ones are selected as the selected influential position points, and the selected deviation position points except the selected influential position points are selected as the selected remaining position points, and the single position abnormality information corresponding to the selected remaining position points is selected as the selected remaining position abnormality information;

[0074] According to the correspondence between the selected influencing position point and the preset position influencing abnormality information, the position influencing abnormality information corresponding to the selected influencing position point is determined, and the position influencing abnormality information is combined with the selected remaining position abnormality information as the equipment abnormality information.

[0075] Optionally, the method further includes the following steps after determining the notification terminal based on the supervision identity information and prompting the notification terminal with the adjustment strategy:

[0076] Retrieve supervisory identity feedback time value and supervisory identity feedback benchmark information based on supervisory identity information;

[0077] After the supervisor identity feedback time value has passed, query the situation corresponding to the supervisor identity feedback benchmark information received and use it as feedback situation information;

[0078] Determine whether the feedback information is normal feedback;

[0079] If yes, continue to obtain human body parameter information;

[0080] If not, then according to the correspondence between the supervisor identity information and the preset subject reinforcement education initial information, the subject reinforcement education initial information corresponding to the supervisor identity information is determined;

[0081] Obtain the time value corresponding to the time value of the supervisory identity feedback in real time and use it as the feedback time deviation value;

[0082] According to the corresponding relationship between the feedback time deviation value and the preset feedback time deviation influence information, the feedback time deviation influence information corresponding to the feedback time deviation value is determined;

[0083] The subject reinforcement education initial information is combined with the feedback time deviation impact information to form the subject reinforcement education adjustment information, and output to the notification terminal.

[0084] In summary, the present invention includes at least one of the following beneficial technical effects:

[0085] 1. By acquiring human body parameter information and the current time point, matching the adjustment strategy through the human body parameter information, and determining the supervisor identity information through the current time point analysis, the adjustment strategy is sent to the notification terminal corresponding to the supervisor identity information for prompting, so as to formulate different youth pseudomyopia prevention and control plans based on the human body parameter information and determine different supervisor identities through the current time point to supervise the youth, so as to facilitate the effective prevention and control of youth pseudomyopia;

[0086] 2. By acquiring the device number and detection parameters and retrieving the device detection type information, the detection parameters of different devices are classified according to the device detection type information to form a single type of detection information, and then the single type of detection information is combined in the same category to form eye condition information, exercise condition information and body fat condition information, and then combined as human body parameter information to improve the accuracy of the obtained human body parameter information;

[0087] 3. Retrieve single-category detection values ​​and single-category detection individual values ​​through single-category detection information, and determine whether there is only one single-category detection individual value. When there is only one, use the single-category detection value as the eye condition information, exercise condition information or body fat condition information. When there is more than one, retrieve the device usage time value and data type information through single-category detection information, determine the data type benchmark parameter value through data type information query, determine the comprehensive value of the same type of data through device usage time value and data type benchmark parameter value analysis and use it as the eye condition information, exercise condition information or body fat condition information, thereby improving the accuracy of the acquired eye condition information, exercise condition information or body fat condition information. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 This is a flow chart of a method for the prevention, control and supervision of pseudomyopia in adolescents based on artificial intelligence according to an embodiment of the present application;

[0089] Figure 2 is a flow chart of a method for obtaining human body parameter information according to an embodiment of the present application;

[0090] Figure 3 is a flow chart of a method for forming eye condition information, exercise condition information and body fat condition information according to an embodiment of the present application;

[0091] Figure 4 It is a method flow chart of the steps after determining the notification terminal based on the supervisory identity information and prompting the notification terminal with the adjustment strategy according to the embodiment of the present application. DETAILED DESCRIPTION

[0092] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0093] An artificial intelligence-based prevention and control supervision method for pseudomyopia in adolescents, which obtains and analyzes human body parameter information and the current time point, thereby determining the prevention and control plan for different adolescents and the supervisory identity, and then providing supervisory prompts for supervision, which is convenient for effective prevention and control of pseudomyopia in adolescents. The human body parameter information is analyzed through the detection parameters of different devices, and a comprehensive analysis is performed when there are multiple devices, and the device status is automatically diagnosed in real time, thereby improving the accuracy of the human body parameter information.

[0094] The embodiment of the present invention discloses a method for preventing and controlling pseudomyopia in adolescents based on artificial intelligence. Figure 1 , a prevention and control supervision method for pseudomyopia in adolescents based on artificial intelligence includes:

[0095] Step S100: Obtaining the body parameter information of the teenager and the current time point.

[0096] Among them, human body parameter information refers to the real-time personal eye use and physical fitness parameter information of adolescents, including information such as the adolescents' eye use, exercise conditions and body fat conditions. Eye use conditions include the duration of eye use, eye use distance and lighting conditions when using the eyes. Exercise conditions include the type of exercise, exercise time, exercise intensity and other conditions. Body fat conditions include body fat percentage, body fat distribution and other conditions.

[0097] The current time point refers to the time point corresponding to the current time. By obtaining the human body parameter information and the current time point, it is convenient for subsequent use.

[0098] Step S200: matching an adjustment strategy from a preset adjustment database according to human body parameter information.

[0099] The adjustment database refers to a database that can match the adjustment strategy based on the human body parameter information. The adjustment database can be obtained through pre-input by the operator, or it can be obtained and stored by inputting the human body parameter information in real time and training the neural network, so it will not be described in detail here. The adjustment strategy refers to the strategy plan that needs to be adjusted to the real-time situation of the teenager. The adjustment strategy includes stopping eye use, adjusting eye posture, exercising and other adjustment plans.

[0100] Step S300: Determine supervisor identity information according to the current time point.

[0101] Among them, the supervisory identity information refers to the information of the subject person who supervises the prevention and control of pseudomyopia in adolescents. The supervisory identity information can be offline guardians, teachers, eye prevention and control coaches, and online artificial intelligence, etc. By analyzing the current time point, the supervisory identity information can be determined for subsequent use.

[0102] Furthermore, in order to improve the accuracy of the obtained supervisory identity information, the method for determining the supervisory identity information includes:

[0103] Step S310: Retrieve daily identity information for supervision based on the current time point.

[0104] Among them, the supervisory daily identity information refers to the supervisory identity information corresponding to the current time point under normal daily circumstances. The supervisory daily identity information is retrieved at the current time point to facilitate subsequent use.

[0105] Step S320: Retrieve the daily identity reference location area based on the supervised daily identity information.

[0106] Among them, the daily identity reference position area refers to the position range that the teenager needs to be in when supervising their daily identity. The daily identity reference position area is retrieved by supervising the daily identity information to facilitate subsequent use.

[0107] Step S330: Obtain the current location of the teenager.

[0108] The current location point refers to the location point of the teenager at the current time, and the current location point is detected and obtained through devices such as watches worn by the teenager.

[0109] Step S340: Determine whether the current location point is located within the daily identity reference location area. If yes, execute step S350; if no, execute step S360.

[0110] Among them, by judging whether the current location point is located in the daily identity reference location area, it is judged whether the supervisory subject corresponding to the daily identity information can supervise the teenagers.

[0111] Step S350: Use the daily supervisory identity information as the supervisory identity information.

[0112] Among them, if the current location point is located in the daily identity reference location area, it means that the supervisory subject corresponding to the daily identity information can supervise the teenagers at this time, so the supervisory daily identity information is used as the supervisory identity information.

[0113] Step S360: Calculate the distance between the daily identity reference location area and the current location point and use it as the current location deviation distance value.

[0114] Among them, the current position deviation distance value refers to the distance deviation value between the location of the teenager and the supervisory position area of ​​the supervisory subject corresponding to the daily identity information. The distance value between the daily identity reference position area and the current position point is calculated and used as the current position deviation distance value, which is convenient for subsequent use.

[0115] Step S370: Determine identity adjustment information based on the current position deviation distance value and the supervisory daily identity information, and use the identity adjustment information as the supervisory identity information.

[0116] Among them, identity adjustment information refers to the identity information after the supervisory identity is adjusted. By analyzing the current position deviation distance value and the supervisory daily identity information, the identity adjustment information is determined and used as the supervisory identity information, thereby improving the accuracy of the obtained supervisory identity information.

[0117] Furthermore, in order to improve the accuracy of the obtained identity adjustment information, the method for determining the identity adjustment information includes:

[0118] Step S371: Retrieve the daily identity moving distance value based on the supervised daily identity information.

[0119] Among them, the daily identity movement distance value refers to the distance value that the subject corresponding to the daily identity can move. The daily identity movement distance value is retrieved by monitoring the daily identity information to facilitate subsequent use.

[0120] Step S372: Determine whether the current position deviation distance value is less than the daily identity movement distance value. If yes, execute step S373; if no, execute step S375.

[0121] Among them, by judging whether the current position deviation distance value is less than the daily identity movement distance value, it is judged whether adjustment can be made by moving the subject corresponding to the daily identity.

[0122] Step S373: Determine the deviation distance adjustment prompt information corresponding to the current position deviation distance value according to the correspondence between the current position deviation distance value and the preset deviation distance adjustment prompt information.

[0123] The deviation distance adjustment prompt information refers to the prompt information used to prompt the subject corresponding to the daily identity to make movement adjustments. The deviation distance adjustment prompt information is obtained by querying from a database storing the current position deviation distance value and the deviation distance adjustment prompt information, and the database is obtained after the operator pre-enters. If the current position deviation distance value is less than the daily identity movement distance value, it means that the adjustment can be made by moving the subject corresponding to the daily identity at this time. Therefore, the deviation distance adjustment prompt information is determined by querying the current position deviation distance value, which is convenient for subsequent use.

[0124] Step S374: The deviation distance-based adjustment prompt information is combined with the supervised daily identity information and used as identity adjustment information.

[0125] Among them, the identity adjustment information is formed by merging the deviation distance adjustment prompt information with the daily identity information of supervision, thereby improving the accuracy of the obtained identity adjustment information.

[0126] Step S375: Retrieve nearby identity information and daily identity reference location points based on the monitored daily identity information.

[0127] Among them, nearby identity information refers to the supervisory identity information located near the subject corresponding to the supervisory daily identity, and the daily identity reference position point refers to the reference position point where the subject corresponding to the supervisory daily identity is located. If the current position deviation distance value is not less than the daily identity movement distance value, it means that the adjustment cannot be made by moving the subject corresponding to the daily identity at this time. Therefore, the nearby identity information and the daily identity reference position point are retrieved through the supervisory daily identity information for subsequent use.

[0128] Step S376: Retrieve nearby identity position points and nearby identity movable reference values ​​based on nearby identity information.

[0129] Among them, the nearby identity location point refers to the location point of the subject corresponding to the nearby identity at the current time, and the nearby identity movable reference value refers to the benchmark probability reference value corresponding to the ability of the subject corresponding to the nearby identity to move. The nearby identity location point and the nearby identity movable reference value are retrieved through the nearby identity information to facilitate subsequent use.

[0130] Step S377: Calculate the distance between the nearby identity location point and the current location point and use it as the nearby identity distance value.

[0131] Among them, the nearby identity distance value refers to the distance value between the subject corresponding to the nearby identity and the teenager. The distance value between the nearby identity location point and the current location point is calculated and used as the nearby identity distance value, which is convenient for subsequent use.

[0132] Step S378: Determine the nearby identity distance influence value corresponding to the nearby identity distance value according to the correspondence between the nearby identity distance value and the preset nearby identity distance influence value.

[0133] Among them, the nearby identity distance impact value refers to the value of the degree of influence of the distance between the subject corresponding to the nearby identity and the teenager on the movement, and the nearby identity distance impact value is obtained by querying from a database storing nearby identity distance values ​​and nearby identity distance impact values. The nearby identity distance impact value is determined by querying the nearby identity distance value, which is convenient for subsequent use.

[0134] Step S379: Calculate the sum of the identity movable reference value and the nearby identity distance influence value and use it as the nearby identity comprehensive reference value.

[0135] Among them, the comprehensive reference value of nearby identities refers to the comprehensive probability reference value of the subject corresponding to the nearby identity for mobile supervision. The sum of the identity movable reference value and the nearby identity distance influence value is calculated and used as the comprehensive reference value of the nearby identity for subsequent use.

[0136] Step S37A: based on the nearby identity comprehensive reference value, a maximum value is selected as the nearby identity selection reference value, and the nearby identity information corresponding to the nearby identity selection reference value is used as the selected identity information.

[0137] Among them, the nearby identity selection reference value refers to the reference value after selecting the nearby identity comprehensive reference value. The maximum value of the nearby identity comprehensive reference value is selected and used as the nearby identity selection reference value, and the selected identity information is defined to facilitate subsequent use.

[0138] Step S37B: Combining the selected identity information with the nearby identity distance value to form selected identity comprehensive information, and using the selected identity comprehensive information as identity adjustment information.

[0139] The selected identity information is combined with the nearby identity distance value to form the selected identity comprehensive information, and the selected identity comprehensive information is used as the identity adjustment information, thereby improving the accuracy of the obtained identity adjustment information.

[0140] Step S400: determining a notification terminal based on the supervisory identity information and prompting the notification terminal by adjusting the strategy.

[0141] The notification terminal refers to the terminal corresponding to the supervisory identity information, which can be a mobile phone, computer or other terminal that can receive information. The corresponding notification terminal is determined by the supervisory identity information, and the adjustment strategy is sent to the notification terminal and prompted, so that teenagers can be supervised by different supervisory identities at different time points, which is convenient for effective prevention and control of pseudomyopia in teenagers.

[0142] exist Figure 1 In step S100, in order to further ensure the rationality of the human body parameter information, it is necessary to further analyze and calculate the human body parameter information. Figure 2 The steps shown are explained in detail.

[0143] Reference Figure 2 , the method for obtaining human body parameter information includes the following steps:

[0144] Step S110: Obtain the device number and detection parameters of the detection device.

[0145] Among them, the detection equipment refers to the equipment used to detect the eyes, exercise and body fat conditions of teenagers. The detection equipment can be computers, tablets, treadmills, body fat meters and other equipment. The equipment number refers to a single number corresponding to different equipment. The equipment number is directly queried and retrieved from the detection equipment. The detection parameters refer to the parameters obtained by the equipment used to detect the eyes, exercise and body fat conditions of teenagers. The detection parameters are obtained by reading different devices. For example, when the detection parameter is an eye parameter, the computer in the detection device is queried to determine the usage time and use it as the detection parameter.

[0146] Step S120: Determine device detection type information according to the device number.

[0147] Among them, the equipment detection type information refers to the parameter type information obtained by detecting the type of each device. The equipment detection type information includes eye type, sports type and body fat type. The equipment detection type information is obtained by querying from a database storing the equipment number and equipment detection type information. The database is obtained after the operator pre-enters. The equipment detection type information is determined by querying the equipment number to facilitate subsequent use.

[0148] Step S130: Classifying detection parameters of different devices based on the device detection category information to form a single category of detection information.

[0149] Among them, the single type of detection information is eye type detection information, sports type detection information or body fat type detection information. Single type detection information refers to detection information of one of the types of eye, sports and body fat. The detection parameters of different detection devices are classified by the device detection type information, and the classified results are used as single type detection information for subsequent use.

[0150] Step S140: Based on the single type of detection information, similar combinations are performed to form eye condition information, exercise condition information and body fat condition information.

[0151] Among them, the eye condition information refers to the current eye condition information of the adolescent, the exercise condition information refers to the current exercise condition information of the adolescent, and the body fat condition information refers to the current body fat condition information of the adolescent. By combining single type of detection information with the same type of information, the eye condition information, exercise condition information and body fat condition information are formed to facilitate subsequent use.

[0152] Step S150: combining the eye condition information, exercise condition information and body fat condition information and using them as human body parameter information.

[0153] Among them, the accuracy of the obtained human body parameter information is improved by combining the eye condition information, exercise condition information and body fat condition information and using them as human body parameter information.

[0154] exist Figure 2 In step S140, in order to further ensure the rationality of the eye condition information, exercise condition information and body fat condition information, it is necessary to further analyze and calculate the eye condition information, exercise condition information and body fat condition information separately. Figure 3 The steps shown are explained in detail.

[0155] Reference Figure 3 The method for forming the eye condition information, exercise condition information and body fat condition information comprises the following steps:

[0156] Step S141: Retrieve a single type detection value and a single type detection individual value based on the single type detection information.

[0157] Among them, the single-category detection value refers to the specific detection value in a single category, the single-category detection individual value refers to the individual value of the detection equipment in a single category, and the single-category detection information includes the single-category detection value and the single-category detection individual value. The single-category detection value and the single-category detection individual value are retrieved through the single-category detection information to facilitate subsequent use.

[0158] For example, when the single-type detection information is eye-use detection information, the single-type detection value can be the eye-use reference value detected by the mobile phone and the eye-use reference value detected by the computer, and the single-type detection individual value is two at this time. When the single-type detection information is exercise detection information, the single-type detection value can be the exercise reference value detected by the treadmill, and the single-type detection individual value is one at this time. When the single-type detection information is body fat detection information, the single-type detection value can be the body fat reference value detected by the body fat meter, and the single-type detection individual value is one at this time.

[0159] Step S142: Determine whether the single type detection value is only one. If yes, execute step S143; if no, execute step S144.

[0160] Here, by judging whether the single-type detection value is only one, it is judged whether it is necessary to further analyze the acquired single-type detection value.

[0161] Step S143: using a single type of detection value as eye condition information, exercise condition information or body fat condition information.

[0162] Among them, when the single-type detection value is only one, it means that there is no need to further analyze the obtained single-type detection value at this time, so the single-type detection value is used as eye condition information, exercise condition information or body fat condition information.

[0163] Step S144: Retrieve the device usage time value and data type information based on the single type detection information.

[0164] Among them, the single type detection information includes the device usage time value and data type information. The device usage time value refers to the time value of the device being used, and the data type information refers to the type information of the detection data of the detection device in a single type. When the single type detection value is not only one, it means that the acquired single type detection value needs to be further analyzed at this time, so the device usage time value and data type information are retrieved through the single type detection information for subsequent use.

[0165] Step S145: determining a data type reference parameter value corresponding to the data type information according to a correspondence between the data type information and a preset data type reference parameter value.

[0166] The data type reference parameter value refers to the reference parameter value corresponding to the general parameter in a unit time when the data type is adjusted to the data type. The data type reference parameter value is obtained by querying from a database storing data type information and data type reference parameter values. The database is obtained after pre-input by the operator. The data type reference parameter value is determined by querying the data type information to facilitate subsequent use.

[0167] For example, when the data type information is eye use type, the device benchmark parameter value is the eye use benchmark reference value corresponding to unit time during normal use; when the data type information is sports type, the device benchmark parameter value is the sports benchmark reference value corresponding to unit time during normal use.

[0168] Step S146: Determine the comprehensive value of the same type of data based on the device usage time value and the data type benchmark parameter value analysis, and use the comprehensive value of the same type of data as eye condition information, exercise condition information or body fat condition information.

[0169] Among them, the comprehensive value of the same data refers to the parameter value after a single quantitative comprehensive analysis of the parameters of different detection devices in a single data type. By analyzing the equipment usage time value and the data type benchmark parameter value, the comprehensive value of the same data is determined and used as the eye condition information, exercise condition information or body fat condition information, thereby improving the accuracy of the acquired eye condition information, exercise condition information or body fat condition information.

[0170] exist Figure 3 In step S146 shown, in order to further ensure the rationality of the comprehensive value of the same type of data, it is necessary to perform further separate analysis and calculation on the comprehensive value of the same type of data, which is specifically described in detail through the following steps.

[0171] The method for determining the comprehensive value of the same data includes the following steps:

[0172] Step S1461: Calculate the product value between the device usage time value and the data type reference parameter value as the single device parameter value.

[0173] Among them, the single-device parameter value refers to the parameter value corresponding to a single device after a period of use. The product value between the device usage time value and the data type benchmark parameter value is calculated and used as the single-device parameter value for convenience in subsequent use.

[0174] Step S1462: sort the device usage time values ​​in reverse order to obtain a sequence number, filter out the maximum value of the device usage time value and define it as the maximum time value.

[0175] Among them, the sequence number refers to the sequence number corresponding to the sorting according to the usage time, and the maximum time value refers to the maximum time value of the usage time of the same type of data device. By sorting the device usage time values ​​from large to small, and obtaining the sequence number and the maximum time value respectively, it is convenient for subsequent use.

[0176] Step S1463: Determine whether the maximum time value is greater than a preset time reference value. If it is greater, execute step S1464; if it is not greater, execute step S1467.

[0177] The time reference value refers to the minimum time value that the device needs to use when in use. The time reference value is obtained after being pre-input by the operator. By judging whether the maximum time value is greater than the preset time reference value, it is judged whether the use of the device meets the use standard.

[0178] Step S1464: the single device parameter value corresponding to the maximum time value is used as the primary parameter initial value, the device usage time value other than the maximum time value is used as the remaining time value, and the single device parameter value corresponding to the remaining time value is used as the secondary parameter value.

[0179] Among them, when the maximum time value is greater than the preset time reference value, it means that the use of the equipment at this time meets the use standard, so the main parameter initial value, remaining time value and secondary parameter value are defined respectively to facilitate subsequent use.

[0180] Step S1465: Analyze the remaining time value and the secondary parameter value to determine the remaining time parameter impact value.

[0181] The remaining time parameter impact value refers to the impact degree value when the parameter corresponding to the remaining time produces an impact. By analyzing the remaining time value and the secondary parameter value, the remaining time parameter impact value is determined to facilitate subsequent use.

[0182] Step S1466: Calculate the sum of the initial value of the main parameter and the influence value of the remaining time parameter and use it as the same data parameter adjustment value, and use the same data parameter adjustment value as the same data comprehensive value.

[0183] Among them, the same data parameter adjustment value refers to the adjustment value after adjusting the initial value of the main parameter. The sum of the initial value of the main parameter and the remaining time parameter influence value is calculated and used as the same data parameter adjustment value, and the same data parameter adjustment value is used as the same data comprehensive value, thereby improving the accuracy of the obtained same data comprehensive value.

[0184] Step S1467: Determine the time sorting influence value corresponding to the sequence number according to the correspondence between the sequence number and the preset time sorting influence value.

[0185] Among them, the time sorting impact value refers to the impact degree value generated according to the time sorting result. The time sorting impact value is obtained by querying from a database storing sequence numbers and time sorting impact values. The database is obtained and stored through experiments. The time sorting impact value is determined by querying the sequence number to facilitate subsequent use.

[0186] Step S1468: Calculate the product value between the time sorting impact value and the single device parameter value and use it as the single device parameter adjustment value.

[0187] Among them, the single device parameter adjustment value refers to the parameter value after the time-ordered adjustment of the parameter corresponding to a single device. The product value between the time-ordered impact value and the single device parameter value is calculated and used as the single device parameter adjustment value for convenience in subsequent use.

[0188] Step S1469: Calculate the sum of the parameter adjustment values ​​of each single device in the single type of detection information and use it as the same type of comprehensive parameter value, and use the same type of comprehensive parameter value as the same type of data comprehensive value.

[0189] Among them, the same comprehensive parameter value refers to the comprehensive parameter value corresponding to all devices of a single type. The sum of the parameter adjustment values ​​of each single device is calculated and used as the same comprehensive parameter value, and the same comprehensive parameter value is used as the same data comprehensive value, thereby improving the accuracy of the obtained same data comprehensive value.

[0190] In step S1465, in order to further ensure the rationality of the remaining time parameter influence value, it is necessary to perform further separate analysis and calculation on the remaining time parameter influence value, which is specifically described in detail through the following steps.

[0191] The method for determining the remaining time parameter influence value comprises the following steps:

[0192] Step S14651: Calculate the difference between the remaining time value and the time reference value and use it as the remaining time deviation value.

[0193] The remaining time deviation value refers to the deviation value when there is a deviation in the remaining time. The difference between the remaining time value and the time reference value is calculated and used as the remaining time deviation value for convenience in subsequent use.

[0194] Step S14652: According to the correspondence between the remaining time deviation value and the preset remaining time deviation initial impact value, the remaining time deviation initial impact value corresponding to the remaining time deviation value is determined.

[0195] The remaining time deviation initial impact value refers to the initial impact value generated when there is a deviation in the remaining time. The remaining time deviation initial impact value is obtained by querying from a database storing the remaining time deviation value and the remaining time deviation initial impact value. The database is obtained and stored through experiments. The remaining time deviation initial impact value is determined by querying the remaining time deviation value to facilitate subsequent use.

[0196] Step S14653: Calculate the difference between the remaining time value and the maximum time value and use it as the maximum time deviation value.

[0197] The maximum time deviation value refers to the deviation value when there is a deviation between the remaining time and the maximum time. By calculating the difference between the remaining time value and the maximum time value and using it as the maximum time deviation value, it is convenient for subsequent use.

[0198] Step S14654: Determine the maximum time deviation impact value corresponding to the maximum time deviation value according to the correspondence between the maximum time deviation value and the preset maximum time deviation impact value.

[0199] The maximum time deviation impact value refers to the impact value generated when there is a deviation between the remaining time and the maximum time. The maximum time deviation impact value is obtained by querying from a database storing the maximum time deviation value and the maximum time deviation impact value. The database is obtained and stored through experiments. The maximum time deviation impact value is determined by querying the maximum time deviation value to facilitate subsequent use.

[0200] Step S14655: Calculate the sum of the initial impact value of the remaining time deviation and the maximum time deviation impact value and use it as the remaining time deviation adjustment impact value, and use the remaining time deviation adjustment impact value as the remaining time parameter impact value.

[0201] Among them, the remaining time deviation adjustment impact value refers to the impact degree value after adjusting the impact caused by the existence of deviation in the remaining time. The sum of the remaining time deviation initial impact value and the maximum time deviation impact value is calculated and used as the remaining time deviation adjustment impact value, and the remaining time deviation adjustment impact value is used as the remaining time parameter impact value, so as to improve the accuracy of the obtained remaining time parameter impact value.

[0202] exist Figure 2 After step S140 shown, in order to further ensure the rationality of the eye condition information, exercise condition information and body fat condition information, it is necessary to combine the same type of detection information based on a single type to form the eye condition information, exercise condition information and body fat condition information, and then perform further separate analysis and calculation, which is explained in detail through the steps shown below.

[0203] The steps after combining the same type of detection information based on the single type to form eye condition information, exercise condition information and body fat condition information include the following steps:

[0204] Step S151: determining a category usage reference time value corresponding to the single category detection information according to a correspondence relationship between the single category detection information and a preset category usage reference time value.

[0205] The type usage reference time value refers to the minimum time value required for the use of the equipment type. The type usage reference time value is obtained by querying from a database storing single type detection information and type usage reference time values. The database is obtained after pre-input by the operator. The type usage reference time value is determined by querying the single type detection information to facilitate subsequent use.

[0206] Step S152: Determine whether the device usage time value is greater than the category usage reference time value. If yes, execute step S153; if no, execute step S154.

[0207] Among them, by judging whether the equipment usage time value is greater than the type usage reference time value, it is judged whether there is an abnormality in the usage time.

[0208] Step S153: Continue to output eye condition information, exercise condition information or body fat condition information.

[0209] Among them, when the device usage time value is greater than the type usage benchmark time value, it means that there is no abnormality in the usage time at this time, so the eye condition information, exercise condition information or body fat condition information continues to be output.

[0210] Step S154: Calculate the difference between the device usage time value and the category usage reference time value and use it as the usage time deviation value.

[0211] Among them, the usage time deviation value refers to the deviation value when there is a deviation in the usage time. When the equipment usage time value is not greater than the type usage benchmark time value, it means that there is an abnormality in the usage time at this time. Therefore, the difference between the equipment usage time value and the type usage benchmark time value is calculated and used as the usage time deviation value for subsequent use.

[0212] Step S155: analyzing and determining the time deviation reason information based on the usage time deviation value and the current time point.

[0213] The time deviation reason information refers to the reason information corresponding to the deviation in the usage time. By analyzing the usage time deviation value and the current time point, the time deviation reason information is determined to facilitate subsequent use.

[0214] Step S156: Determine the deviation output information according to the time deviation reason information analysis, and output the deviation output information.

[0215] Among them, the deviation output information refers to the information output according to the cause of the deviation. By analyzing the time deviation cause information, the deviation output information is determined and output, so that when there is an abnormality in the use time, early warning and other operations are performed to facilitate understanding of the abnormality.

[0216] In step S155, in order to further ensure the rationality of the time deviation reason information, it is necessary to further analyze and calculate the time deviation reason information separately, which is specifically described in detail through the following steps.

[0217] The method for determining the time deviation cause information includes the following steps:

[0218] Step S1551: Retrieve the historical deviation value based on the current time point.

[0219] Among them, the historical synchronous deviation value refers to the deviation value existing at the time point corresponding to the current time point in the historical time. The historical synchronous deviation value is retrieved through the current time point to facilitate subsequent use.

[0220] Step S1552: Perform curve processing based on the historical contemporaneous deviation value to form a historical contemporaneous deviation curve.

[0221] Among them, the historical synchronous deviation curve refers to the deviation curve formed in the historical time when there is a deviation at the time point corresponding to the current time point. The historical synchronous deviation values ​​are processed into curves, and the historical synchronous deviation values ​​are connected according to the changes in time to form a historical synchronous deviation curve, which is convenient for subsequent use.

[0222] Step S1553: Analyze the deviation between the historical deviation curve for the same period and the preset deviation reference curve to form curve deviation information.

[0223] The deviation reference curve refers to the reference curve corresponding to the time point corresponding to the current time point when there is no deviation. The deviation reference curve is obtained after the operator pre-enters. The curve deviation information refers to the deviation information when the curve has deviation. The curve deviation information is formed by analyzing the deviation between the historical deviation curve of the same period and the preset deviation reference curve, which is convenient for subsequent use.

[0224] Step S1554: Selecting a reference time value based on the deviation between the current time point and a preset time point to select curve deviation information and form selected time deviation information.

[0225] Among them, the deviation selection reference time value refers to the reference interval time value when selecting, the deviation selection reference time value is obtained after pre-input by the operator, and the selection time deviation information refers to the deviation information within the selection time. By analyzing and calculating the current time point and the preset deviation selection reference time value, the selection interval time before the current time point and the interval deviation selection reference time value is obtained, and then the curve deviation information is selected according to the selection interval time, so as to form the selection time deviation information, which is convenient for subsequent use.

[0226] Step S1555: Generate similar deviation information based on matching the selected time deviation information with the curve deviation information.

[0227] Among them, similar deviation information refers to deviation information that is similar to the selected time deviation information. By matching the selected time deviation information with the curve deviation information, and taking the curve deviation information corresponding to the maximum matching similarity result as the similar deviation information, it is convenient for subsequent use.

[0228] Step S1556: Retrieve the estimated deviation interval based on the similarity deviation information.

[0229] Among them, the estimated deviation interval refers to the estimated interval for estimating the deviation. The estimated deviation interval is retrieved through similar deviation information to facilitate subsequent use.

[0230] Step S1557: Determine whether the usage time deviation value is within the estimated deviation interval. If yes, execute step S1558; if no, execute step S1559.

[0231] Among them, by judging whether the usage time deviation value is within the estimated deviation range, it is judged whether the deviation is caused by the device.

[0232] Step S1558: Output the preset usage abnormality information and use it as the time deviation reason information.

[0233] Among them, the abnormal use information refers to the abnormal information that causes deviation when used by teenagers, and the abnormal use information is obtained after being pre-input by the operator. When the use time deviation value is within the estimated deviation range, it means that the deviation is not caused by the device at this time, so the preset abnormal use information is output and used as the time deviation cause information, thereby improving the accuracy of the obtained time deviation cause information.

[0234] Step S1559: Output the preset device abnormality information and use it as the time deviation cause information.

[0235] Among them, the equipment abnormality information refers to the abnormality information caused by the equipment, and the equipment abnormality information is obtained after being pre-input by the operator. When the use time deviation value is not within the estimated deviation interval, it means that the deviation is caused by the equipment at this time, so the preset equipment abnormality information is output and used as the time deviation cause information, thereby improving the accuracy of the obtained time deviation cause information.

[0236] In step S156, in order to further ensure the rationality of the deviation output information, it is necessary to perform further separate analysis and calculation on the deviation output information, which is specifically described in detail through the following steps.

[0237] The method for determining the deviation output information comprises the following steps:

[0238] Step S1561: Determine whether the time deviation reason information is usage abnormality information. If yes, execute step S1562; if no, execute step S1563.

[0239] Among them, whether the time deviation cause information is usage abnormality information is judged, so as to judge whether an early warning is needed.

[0240] Step S1562: Use the single device parameter value as deviation output information.

[0241] Among them, when the time deviation reason information is abnormal use information, it means that no early warning is needed at this time, so the single device parameter value is used as the deviation output information, thereby continuing to jump to step S160.

[0242] Step S1563: Retrieve the device operation time value and the internal operation location point of the device based on the location device usage information.

[0243] Among them, the location equipment usage information includes the equipment operation time value and the internal operation position point of the equipment. The equipment operation time value refers to the time value corresponding to the continuous operation of the equipment, and the internal operation position point of the equipment refers to the position point where each operating component inside the equipment is located. When the time deviation reason information is not abnormal usage information, it means that an early warning is required at this time, so the equipment operation time value and the internal operation position point of the equipment are retrieved through the location equipment usage information to facilitate subsequent use.

[0244] Step S1564: retrieve the type of operation reference time value and the required operation position point based on the equipment number.

[0245] Among them, the type operation benchmark time value refers to the maximum operation time value of the equipment type when it is running, and the required operation position point refers to the position point corresponding to the internal components that must run when the equipment type is running. The type operation benchmark time value and the required operation position point are queried and retrieved through the equipment number to facilitate subsequent use.

[0246] Step S1565: Calculate the difference between the device operation time value and the category operation reference time value and use it as the operation time deviation value.

[0247] The running time deviation value refers to the deviation value when the running time has a deviation. The difference between the equipment running time value and the type running reference time value is calculated and used as the running time deviation value for convenience of subsequent use.

[0248] Step S1566: Analyze the deviation between the internal operating position point of the equipment and the required operating position point and use it as the operating deviation position point.

[0249] The operation deviation position point refers to the position point corresponding to the deviation of the internal operating components. The deviation between the internal operating position point of the equipment and the required operating position point is analyzed and used as the operation deviation position point for convenience of subsequent use.

[0250] Step S1567: Determine the equipment abnormality information based on the running time deviation value and the running deviation position point analysis, and use the equipment abnormality information as deviation output information.

[0251] Among them, equipment abnormality information refers to early warning information for warning when equipment has abnormalities. By analyzing the running time deviation value and the running deviation position point, the equipment abnormality information is determined and used as deviation output information, thereby improving the accuracy of the obtained deviation output information.

[0252] In step S1567, in order to further ensure the rationality of the device abnormality information, it is necessary to perform further separate analysis and calculation on the device abnormality information, which is specifically described in detail through the following steps.

[0253] The method for determining device abnormal information includes the following steps:

[0254] Step S15671: Determine the position deviation time reference value corresponding to the running deviation position point according to the correspondence between the running deviation position point and the preset position deviation time reference value.

[0255] The position deviation time reference value refers to the reference deviation value of the time deviation caused by the running deviation position. The position deviation time reference value is obtained by querying from a database storing running deviation position points and position deviation time reference values. The database is obtained after pre-input by the operator. The position deviation time reference value is determined by querying the running deviation position point, which is convenient for subsequent use.

[0256] Step S15672: Calculate the difference between the running time deviation value and the position deviation time reference value and use it as the position time deviation value.

[0257] Among them, the position time deviation value refers to the deviation value corresponding to the deviation time of the running deviation position. The difference between the running time deviation value and the position deviation time reference value is calculated and used as the position time deviation value for subsequent use.

[0258] Step S15673: Based on the position time deviation value, a positive value is selected as the selected time deviation value, and the running deviation position point corresponding to the selected time deviation value is used as the selected deviation position point.

[0259] Among them, the selected time deviation value refers to the deviation value of the deviation time of the running deviation position with abnormal deviation. By selecting a positive position time deviation value as the selected time deviation value and defining the selected deviation position point, it is convenient for subsequent use.

[0260] Step S15674: Determine the single position abnormality information corresponding to the selected deviation position point according to the correspondence between the selected deviation position point and the preset single position abnormality information.

[0261] The single position abnormality information refers to the warning information for a single warning of the abnormality caused by the selected deviation position. The single position abnormality information is obtained by querying from a database storing the selected deviation position point and the single position abnormality information. The database is obtained after the operator pre-enters. The single position abnormality information is determined by querying the selected deviation position point to facilitate subsequent use.

[0262] Step S15675: Determine whether there is only one deviation position point selected. If yes, execute step S15676; if no, execute step S15677.

[0263] Here, by judging whether there is only one deviation position point, it is judged whether different positions affect each other.

[0264] Step S15676: Use the single location abnormality information as the device abnormality information.

[0265] Among them, when only one deviation position point is selected, it means that there is no mutual influence between different positions at this time, so the single position abnormality information is used as the equipment abnormality information, thereby improving the accuracy of the acquired equipment abnormality information.

[0266] Step S15677: Based on the selected deviation position points, mutually influencing ones are selected as the selected influencing position points, and the selected deviation position points except the selected influencing position points are selected as the selected remaining position points, and the single position anomaly information corresponding to the selected remaining position points is selected as the selected remaining position anomaly information.

[0267] Among them, the selected influencing position points refer to the position points that influence each other. When more than one deviation position point is selected, it means that different positions influence each other at this time. Therefore, the selected influencing position points are selected, and the selected remaining position points and the selected remaining position abnormality information are defined respectively for subsequent use.

[0268] Step S15678: Determine the position impact abnormality information corresponding to the selected impact position point according to the correspondence between the selected impact position point and the preset position impact abnormality information, and combine the position impact abnormality information with the selected remaining position abnormality information as the equipment abnormality information.

[0269] Among them, the position influence abnormality information refers to the warning information for warning the abnormality caused by the mutual influence of the position, and the position influence abnormality information is obtained by querying from a database storing the selected influence position points and the position influence abnormality information, and the database is obtained after the operator pre-enters. The position influence abnormality information is determined by querying the selected influence position points, and the position influence abnormality information is combined with the selected remaining position abnormality information as the equipment abnormality information, thereby improving the accuracy of the acquired equipment abnormality information.

[0270] exist Figure 1 After step S400, in order to further ensure the rationality of determining the notification terminal based on the supervision identity information and adjusting the strategy after prompting the notification terminal, it is necessary to perform further separate analysis and calculation on determining the notification terminal based on the supervision identity information and adjusting the strategy after prompting the notification terminal. Figure 4 The steps shown are explained in detail.

[0271] Reference Figure 4 The steps after determining the notification terminal based on the supervision identity information and prompting the notification terminal with the adjustment strategy include the following steps:

[0272] Step S410: Retrieve supervisory identity feedback time value and supervisory identity feedback benchmark information based on supervisory identity information.

[0273] Among them, the supervisory identity feedback time value refers to the maximum time value required for the supervisory identity to provide feedback, and the supervisory identity feedback benchmark information refers to the benchmark information fed back when the supervisory identity provides feedback. The supervisory identity feedback time value and the supervisory identity feedback benchmark information are queried and retrieved through the supervisory identity information to facilitate subsequent use.

[0274] Step S420: After the supervisory identity feedback time value has passed, query the situation corresponding to the received supervisory identity feedback benchmark information and use it as feedback situation information.

[0275] Among them, feedback situation information refers to the situation information of whether the supervisory identity provides feedback. After the feedback time value of the supervisory identity has passed, the situation corresponding to the feedback benchmark information of the received supervisory identity is queried, and the query result is used as the feedback situation information for subsequent use.

[0276] Step S430: Determine whether the feedback information is normal feedback. If yes, execute step S440; if no, execute step S450.

[0277] Among them, normal feedback refers to feedback from the supervisory identity. Whether the feedback information is normal feedback is judged to determine whether the supervisory identity provides feedback.

[0278] Step S440: Continue to obtain human body parameter information.

[0279] Among them, when the feedback information is normal feedback, it means that the supervisory identity is giving feedback at this time, so the human body parameter information continues to be obtained.

[0280] Step S450: According to the correspondence between the supervisory identity information and the preset subject reinforcement education initial information, the subject reinforcement education initial information corresponding to the supervisory identity information is determined.

[0281] Among them, the initial information of the subject's intensive education refers to the initial warning information for the intensive education of the supervisory identity. The initial information of the subject's intensive education is obtained by querying from a database that stores the supervisory identity information and the initial information of the subject's intensive education. The database is obtained after the operator pre-enters. The initial information of the subject's intensive education includes knowledge popularization and psychological counseling. The knowledge popularization involves knowledge such as the causes, hazards and correction methods of pseudomyopia. Psychological counseling is about solving emotional problems such as anxiety and irritability that may occur during the correction process. When the feedback information is normal feedback, it means that the supervisory identity is giving feedback at this time. Therefore, the initial information of the subject's intensive education is determined by querying the supervisory identity information, which is convenient for subsequent use.

[0282] Step S460: acquiring in real time the time value corresponding to the time value fed back by the supervisory identity and using it as the feedback time deviation value.

[0283] Among them, the feedback time deviation value refers to the deviation time value between the supervisory identity and the feedback time value when the supervisory identity has not provided feedback. The feedback time deviation value starts counting the time after the supervisory identity feedback time value has passed, and uses the counting result as the feedback time deviation value for subsequent use.

[0284] Step S470: Determine feedback time deviation influence information corresponding to the feedback time deviation value according to the corresponding relationship between the feedback time deviation value and the preset feedback time deviation influence information.

[0285] The feedback time deviation impact information refers to the impact information caused by the deviation of the feedback time, and the feedback time deviation impact information is obtained by querying from a database storing feedback time deviation values ​​and feedback time deviation impact information, and the database is obtained and stored through operator experiments. The feedback time deviation impact information is determined by querying the feedback time deviation value, which is convenient for subsequent use.

[0286] Step S480: combining the subject reinforcement education initial information with the feedback time deviation impact information to form the subject reinforcement education adjustment information, and outputting it to the notification terminal.

[0287] Among them, the subject's reinforced education adjustment information refers to the early warning information after the supervisory identity is strengthened and educated according to the feedback time deviation. The subject's reinforced education adjustment information is formed by merging the subject's initial information with the feedback time deviation impact information, and outputting it to the notification terminal, thereby timely reminding the supervisory identity information for timely supervision.

[0288] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A method for the prevention and control of pseudomyopia in adolescents based on artificial intelligence, characterized in that: include: Obtain the adolescent's body parameter information and current time point; Matching an adjustment strategy from a preset adjustment database according to human body parameter information; Determine the actual supervisory personnel information based on the current time point; Determine the notification terminal based on the actual supervisor information and adjust the strategy to prompt the notification terminal; Methods for obtaining human body parameter information include: Obtain the equipment number and testing parameters of the testing equipment; Determine the equipment detection type information based on the equipment number; Based on the device detection category information, the detection parameters are classified to form single category detection information, where the single category detection information is eye category detection information, sports category detection information or body fat category detection information; Based on a single type of detection information, similar information is combined to form eye condition information, exercise condition information and body fat condition information; Combine eye condition information, exercise condition information and body fat condition information as human body parameter information; The method for forming the eye condition information, exercise condition information and body fat condition information includes: Retrieving a single type detection value and a single type detection individual value based on the single type detection information; Determine whether the number of detected values ​​of a single type is only one; If yes, the single type of detection value is used as eye condition information, exercise condition information or body fat condition information; If not, retrieve the device usage time value and data type information based on the single type detection information; According to the correspondence between the data type information and the preset data type reference parameter value, a data type reference parameter value corresponding to the data type information is determined; The comprehensive value of the same type of data is determined based on the equipment usage time value and the data type benchmark parameter value analysis, and the comprehensive value of the same type of data is used as eye condition information, exercise condition information or body fat condition information.

2. The artificial intelligence-based prevention and control supervision method for juvenile pseudomyopia according to claim 1 is characterized in that: Methods for determining the comprehensive value of the same data include: Calculate the product value between the device usage time value and the data type benchmark parameter value as the single device parameter value; The device usage time values ​​are sorted in reverse order to obtain a sequence number, and the maximum value of the device usage time value is selected and defined as the maximum time value; Determine whether the maximum time value is greater than a preset time reference value; If it is greater than, the single device parameter value corresponding to the maximum time value is used as the primary parameter initial value, and the device usage time value other than the maximum time value is used as the remaining time value, and the single device parameter value corresponding to the remaining time value is used as the secondary parameter value; Analyze the remaining time value and the secondary parameter value to determine the remaining time parameter impact value; Calculate the sum of the initial value of the main parameter and the influence value of the remaining time parameter and use it as the parameter adjustment value of the same data, and use the parameter adjustment value of the same data as the comprehensive value of the same data; If it is not greater than, then the time sorting influence value corresponding to the sequence number is determined according to the correspondence between the sequence number and the preset time sorting influence value; Calculate the product value between the time sorting impact value and the single device parameter value and use it as the single device parameter adjustment value; The sum of the parameter adjustment values ​​of each single device in a single type of detection information is calculated and used as the same type of comprehensive parameter value, and the same type of comprehensive parameter value is used as the same type of data comprehensive value.

3. The artificial intelligence-based prevention and control supervision method for juvenile pseudomyopia according to claim 2 is characterized in that: Methods for determining the impact value of the remaining time parameter include: Calculate the difference between the remaining time value and the time base value and use it as the remaining time deviation value; According to the correspondence between the remaining time deviation value and the preset remaining time deviation initial impact value, the remaining time deviation initial impact value corresponding to the remaining time deviation value is determined; Calculate the difference between the remaining time value and the maximum time value and use it as the maximum time deviation value; According to the correspondence between the maximum time deviation value and the preset maximum time deviation impact value, the maximum time deviation impact value corresponding to the maximum time deviation value is determined; The sum of the initial impact value of the remaining time deviation and the maximum time deviation impact value is calculated and used as the remaining time deviation adjustment impact value, and the remaining time deviation adjustment impact value is used as the remaining time parameter impact value.

4. The artificial intelligence-based prevention and control supervision method for juvenile pseudomyopia according to claim 1 is characterized in that: The method further includes steps after combining the same type of detection information based on the single type to form eye condition information, exercise condition information and body fat condition information, which are as follows: According to the correspondence between the single type detection information and the preset type usage reference time value, the type usage reference time value corresponding to the single type detection information is determined; Determine whether the equipment usage time value is greater than the type usage benchmark time value; If yes, continue to output the eye condition information, exercise condition information or body fat condition information; If not, the difference between the device usage time value and the category usage reference time value is calculated and used as the usage time deviation value; Determine the time deviation cause information based on the usage time deviation value and the current time point analysis; Determine the deviation output information based on the time deviation cause information analysis and output the deviation output information.

5. The artificial intelligence-based prevention and control supervision method for juvenile pseudomyopia according to claim 4 is characterized in that: Methods for determining the time deviation cause information include: Retrieve historical deviation values ​​based on the current time point; Perform curve processing based on the historical deviation value in the same period to form a historical deviation curve; Analyze the deviation between the historical deviation curve and the preset deviation reference curve to form curve deviation information; Selecting a reference time value based on the deviation between the current time point and a preset time point to select curve deviation information and form selected time deviation information; Similar deviation information is formed by matching the selected time deviation information with the curve deviation information; Retrieving the estimated deviation interval based on similar deviation information; Determine whether the usage time deviation value is within the estimated deviation range; If yes, the preset usage abnormal information is output and used as the time deviation reason information; If not, the preset device abnormality information is output as the time deviation cause information.

6. The artificial intelligence-based prevention and control supervision method for pseudomyopia in adolescents according to claim 5 is characterized in that: The method for determining the deviation output information includes: Determine whether the time deviation reason information is usage abnormality information; If yes, the single device parameter value is used as the deviation output information; If not, retrieve the device operation time value and the internal operation location point of the device based on the location device usage information; Retrieve the type of operation benchmark time value and required operation position point based on the equipment number; Calculate the difference between the equipment operation time value and the type operation benchmark time value and use it as the operation time deviation value; Analyze the deviation between the internal operating position point of the equipment and the required operating position point and use it as the operating deviation position point; The equipment abnormality information is determined based on the running time deviation value and the running deviation position point analysis, and the equipment abnormality information is used as the deviation output information.

7. The artificial intelligence-based prevention and control supervision method for juvenile pseudomyopia according to claim 6 is characterized in that: Methods for determining device abnormality information include: According to the correspondence between the running deviation position point and the preset position deviation time reference value, the position deviation time reference value corresponding to the running deviation position point is determined; Calculate the difference between the running time deviation value and the position deviation time reference value and use it as the position time deviation value; Based on the position time deviation value, a positive value is selected as the selected time deviation value, and the running deviation position point corresponding to the selected time deviation value is used as the selected deviation position point; According to the correspondence between the selected deviation position point and the preset single position abnormality information, the single position abnormality information corresponding to the selected deviation position point is determined; Determine whether there is only one deviation position point selected; If yes, the single location abnormality information is used as the device abnormality information; If not, then based on the selected deviation position points, the mutually influential ones are selected as the selected influential position points, and the selected deviation position points except the selected influential position points are selected as the selected remaining position points, and the single position abnormality information corresponding to the selected remaining position points is selected as the selected remaining position abnormality information; According to the correspondence between the selected influencing position point and the preset position influencing abnormality information, the position influencing abnormality information corresponding to the selected influencing position point is determined, and the position influencing abnormality information is combined with the selected remaining position abnormality information as the equipment abnormality information.

8. The artificial intelligence-based prevention and control supervision method for juvenile pseudomyopia according to claim 1 is characterized in that: The method also includes the following steps after determining the notification terminal based on the actual supervisor information and prompting the notification terminal by adjusting the strategy: Based on the actual supervisory personnel information, retrieve the supervisory identity feedback time value and supervisory identity feedback benchmark information; After the supervisor identity feedback time value has passed, query the situation corresponding to the supervisor identity feedback benchmark information received and use it as feedback situation information; Determine whether the feedback information is normal feedback; If yes, continue to obtain human body parameter information; If not, then according to the correspondence between the actual supervisor information and the preset subject reinforcement education initial information, the subject reinforcement education initial information corresponding to the actual supervisor information is determined; Obtain the time value corresponding to the time value of the supervisory identity feedback in real time and use it as the feedback time deviation value; According to the corresponding relationship between the feedback time deviation value and the preset feedback time deviation influence information, the feedback time deviation influence information corresponding to the feedback time deviation value is determined; The subject reinforcement education initial information is combined with the feedback time deviation impact information to form the subject reinforcement education adjustment information, and output to the notification terminal.

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