Identification system, method, electronic device, storage medium and program product

Abstract

The present invention discloses an identification system, method, electronic device, storage medium and program product. The system includes a monitoring device and an identification device, which are connected to each other; the identification device includes a distribution curve determination module, an identification determination module and an identification module; the monitoring device is used to monitor weight parameters; the distribution curve determination module is used to obtain the weight parameters monitored daily from the monitoring device, and determine multiple distribution curves based on the weight parameters monitored daily; the identification determination module is used to determine the possibility identification of preset dietary habits corresponding to each time period in a day based on the multiple distribution curves; the identification module is used to determine the identification result of the preset dietary habits based on the first statistical result and evaluation model of the possibility identification of the preset dietary habits within a first preset period. Using this system, it is possible to accurately identify whether the user has a preset dietary habit.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of dietary habit recognition, and in particular to a recognition system, method, electronic device, storage medium, and program product. Background Art

[0002] With the continuous progress of society and the improvement of living standards, people's consumption level has been met, but it has also caused people's diet to become more and more irregular. As the pressure of life increases and the pace of life accelerates, many people will unconsciously develop unhealthy eating habits such as overeating, but they cannot accurately identify whether they have bad eating habits such as overeating.

[0003] In the prior art, there is only a scheme of simply monitoring the user's weight value to determine the user's health status, or a scheme of generating a diet plan based on the weight information.

[0004] The solutions of the prior art cannot be used to identify whether a user has a bad eating habit of overeating. Summary of the invention

[0005] The present invention provides an identification system, method, electronic device, storage medium and program product, which obtain a preset dietary habit possibility mark according to a distribution curve corresponding to a weight parameter, and determine the identification result of the preset dietary habit based on the statistical results of the preset dietary habit possibility mark and an evaluation model, so as to solve the defect that the prior art can only determine the user's health status by monitoring the weight value, but cannot identify whether the user has bad dietary habits.

[0006] According to one aspect of the present invention, there is provided an identification system, comprising a monitoring device and an identification device, wherein the monitoring device and the identification device are connected; the identification device comprises a distribution curve determination module, an identification determination module and an identification module;

[0007] The monitoring device is used to monitor a weight parameter, the weight parameter is monitored within a preset daily monitoring time period, and the weight parameter is a weight value with a timestamp;

[0008] The distribution curve determination module is used to obtain the weight parameters monitored daily from the monitoring device, and determine multiple distribution curves based on the weight parameters monitored daily, and the weight parameters monitored in one day correspond to one distribution curve;

[0009] The identification determination module is used to determine the preset dietary habit possibility identification corresponding to each time period in a day based on the multiple distribution curves, wherein each time period is obtained by segmenting the daily monitoring time period;

[0010] The recognition module is used to determine the recognition result of the preset eating habit based on the first statistical result of the possibility identification of the preset eating habit in the first preset period and the evaluation model.

[0011] According to another aspect of the present invention, there is provided an identification method, which is performed by an identification system and includes:

[0012] Obtaining the weight parameters monitored daily, and determining multiple distribution curves based on the weight parameters monitored daily, wherein the weight parameters monitored daily correspond to one distribution curve;

[0013] Determine a preset dietary habit possibility indicator corresponding to each time period of the day based on the multiple distribution curves, wherein each time period is obtained by segmenting the daily monitoring time period;

[0014] Based on the first statistical result of the possibility identification of the preset eating habit in the first preset period and the evaluation model, the recognition result of the preset eating habit is determined.

[0015] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising: at least one processor; and

[0016] a memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the identification method described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions for causing a processor to execute a

[0019] Implement the identification method described in any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the identification method provided in any embodiment of the present invention is implemented.

[0021] The technical solution of the embodiment of the present invention solves the problem that the prior art is unable to identify eating habits based on weight conditions through the distribution curve corresponding to weight parameters and the possibility identification of eating habits, and achieves the beneficial effect of accurately identifying whether the user has bad eating habits.

[0022] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 A schematic diagram of the structure of an identification system provided in Embodiment 1 of the present invention;

[0025] Figure 2 A schematic diagram of the structure of an identification system provided in Embodiment 2 of the present invention;

[0026] Figure 3 A schematic diagram of a flow chart of an identification method provided in Embodiment 3 of the present invention;

[0027] Figure 4 A schematic diagram of the structure of an electronic device for the identification method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only an embodiment of a part of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work should belong to the scope of protection of the present invention. It should be understood that the various steps recorded in the method implementation of the present invention can be performed in different orders and / or in parallel. In addition, the method implementation may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0029] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] It should be noted that the modifications of "one" and "plurality" mentioned in the present invention are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0032] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes, and are not used to limit the scope of these messages or information.

[0033] Embodiment 1

[0034] Figure 1 This is a structural diagram of an identification system provided in Embodiment 1 of the present invention. The system can be used to identify whether a user has bad eating habits. The system can be implemented by software and / or hardware. The system includes an identification device to identify the bad eating habits of a target group.

[0035] like Figure 1 As shown, an identification system provided by the first embodiment of the present invention includes a monitoring device 110 and an identification device 120, wherein the monitoring device 110 and the identification device 120 are connected; the identification device 120 includes a distribution curve determination module 121, an identification determination module 122 and an identification module 123;

[0036] Monitoring device 110, used to monitor a weight parameter, the weight parameter is monitored within a preset daily monitoring time period, and the weight parameter is a weight value with a timestamp;

[0037] The distribution curve determination module 121 is used to obtain the weight parameters monitored daily from the monitoring device 110, and determine multiple distribution curves based on the weight parameters monitored daily, and the weight parameters monitored daily correspond to one distribution curve;

[0038] An identification determination module 122 is used to determine a preset dietary habit possibility identification corresponding to each time period in a day based on the multiple distribution curves, wherein each time period is obtained by segmenting the daily monitoring time period;

[0039] The recognition module 123 is used to determine the recognition result of the preset eating habit based on the first statistical result of the possibility identification of the preset eating habit in the first preset period and the evaluation model.

[0040] The monitoring device 110 may be any device capable of monitoring weight parameters, and for example, the monitoring device 110 may be a smart weight scale. The data form of the weight parameter is [weight value, timestamp], the unit of the weight value is kg, and the timestamp may be accurate to minutes. For example, a weight parameter may be [45kg, 8:30], which indicates that the user's weight is 45kg as measured at 8:30.

[0041] In this embodiment, the monitoring device 110 can also store the monitored weight parameters, and the monitoring device 110 can be connected to the identification device 120 to send the monitored weight parameters to the identification device 120. The monitoring device 110 and the identification device 120 can be connected by a solid line or wirelessly, which is not specifically limited here.

[0042] In this embodiment, the monitoring device 110 can monitor the user's weight parameter within a preset daily monitoring time period. The preset daily monitoring time period can be a pre-set time period, and the preset daily monitoring time period can be set arbitrarily. For example, the preset daily monitoring time period can be 7:30 to 22:30, that is, the user's weight parameter can be monitored every minute within the time period from 7:30 to 22:30 every day.

[0043] In this embodiment, the distribution curve determination module 121 may be a module for determining a distribution curve. The distribution curve determination module 121 is used to determine a corresponding distribution curve according to the weight parameter obtained by daily monitoring. The weight parameter obtained by daily monitoring can determine a distribution curve, and multiple distribution curves can be determined here. Further, a distribution curve is composed of multiple weight parameter points connected together, the horizontal coordinate of a weight parameter point is the timestamp of a weight parameter, and the vertical coordinate of a weight parameter point is the weight value of a weight parameter.

[0044] Among them, the distribution curve determination module 121 can determine the distribution curve in a manner as follows: taking determining a distribution curve as an example for explanation, using time as the horizontal axis and weight value as the vertical axis to construct a coordinate system, and based on multiple weight parameters monitored within a preset daily monitoring time period in one day, multiple weight parameter points are determined in the coordinate system, that is, one weight parameter corresponds to one weight parameter point, and a distribution curve can be obtained by connecting multiple weight parameter points, and the weight value corresponding to each timestamp can be known based on the distribution curve.

[0045] In this embodiment, the identification determination module 122 may be a module for determining a preset dietary habit possibility identification. The preset dietary habits may be pre-set bad dietary habits. For example, the preset dietary habits may be overeating dietary habits. The preset dietary habit possibility identification may be understood as an identification for characterizing the possibility of the preset dietary habits. For example, whether there is a possibility of overeating may be determined based on the overeating dietary habit possibility identification. The preset dietary habit possibility identification may be a digital identification or a character identification, which is not specifically limited here.

[0046] The identification determination module 122 may be used to determine the preset dietary habit possibility identification corresponding to each time period of the day based on the maximum peak and the nearest trough in each distribution curve and the weight parameter in each time period.

[0047] Wherein, each time period can be obtained by segmenting the preset daily monitoring time period according to the preset time interval. The preset time interval can be a preset time interval. For example, the preset time interval can be 30 minutes, that is, the daily monitoring time period is divided into multiple 30-minute time periods. The number of time periods can be determined according to the preset daily monitoring time period and the preset time interval. For example, if the preset daily monitoring time period is 10:30 to 13:30 and the preset time interval is 30 minutes, the preset daily monitoring time period can be divided into 6 time periods, each of which is 30 minutes.

[0048] Furthermore, the identification determination module 122 is specifically used to perform a determination operation on each distribution curve to obtain a preset dietary habit possibility identification corresponding to each time period of the day, the determination operation including: for a distribution curve, determining a maximum peak and a nearest trough with the largest change trend on the distribution curve, the nearest trough being the trough closest to the maximum peak; determining a difference between a weight value corresponding to the maximum peak and a weight value corresponding to the nearest trough on the distribution curve, and determining a first timestamp corresponding to the maximum peak and a second timestamp corresponding to the nearest trough; determining a plurality of weight values ​​within a target time period with the first timestamp as the start time and the second timestamp as the end time on each distribution curve; calculating a standard deviation of the plurality of weight values; and determining a preset dietary habit possibility identification corresponding to each time period of the day based on the difference and the standard deviation.

[0049] In this embodiment, after performing the above determination operation based on a distribution curve, the preset dietary habit possibility identifier corresponding to each time period in a day can be obtained, and after performing the above determination operation on each distribution curve respectively, the preset dietary habit possibility identifier corresponding to each time period in a day corresponding to each distribution curve can be obtained. Each time period in a day can have a preset dietary habit possibility identifier corresponding to it.

[0050] The first timestamp can be understood as the timestamp corresponding to the maximum peak, the second timestamp can be understood as the timestamp corresponding to the most recent trough, and the target time period can be understood as a time period starting with the first timestamp and ending with the second timestamp.

[0051] Furthermore, the identification determination module 122 is specifically used for: if the difference satisfies the preset conditions, setting the preset dietary habit possibility identification corresponding to the target time period on the one distribution curve to a first preset value; if the difference does not satisfy the preset conditions, setting the preset dietary habit possibility identification corresponding to the target time period on the one distribution curve to a second preset value; determining the preset dietary habit possibility identification corresponding to other time periods within a day except the target time period as the second preset value; wherein the preset conditions include that the difference is greater than or equal to a preset multiple of the standard deviation, and the difference is greater than or equal to a third preset value.

[0052] It is understandable that the above process is a specific process of determining the preset dietary habit possibility mark corresponding to each time period in a day according to the difference and the standard deviation. According to the above method, the preset dietary habit possibility mark corresponding to each time period in a day can be calculated.

[0053] Among them, the first preset value can be any preset value different from the second preset value. For example, the first preset value can be 1; the second preset value can be any preset value different from the first preset value. For example, the second preset value can be 0; the preset multiple can be any preset multiple. For example, the preset multiple can be 2; the third preset value can be any preset value. For example, the third preset value can be 2kg.

[0054] Exemplarily, the identification module 122 is specifically used to: if the difference between the body weight calculated based on the maximum peak and the most recent trough on a distribution curve is greater than or equal to 2 times the standard deviation, and the difference is greater than or equal to 2kg, then the preset eating habit possibility identifier corresponding to the target time period on the distribution curve is set to 1; if the difference between the body weight calculated based on the maximum peak and the most recent trough on a distribution curve is less than 2 times the standard deviation, then the preset eating habit possibility identifier corresponding to the target time period on the distribution curve is set to 0; if the difference between the body weight calculated based on the maximum peak and the most recent trough on a distribution curve is greater than or equal to 2 times the standard deviation, but the difference is less than 2kg, then the preset eating habit possibility identifier corresponding to the target time period on the distribution curve is set to 0; the preset eating habit possibility identifiers corresponding to other time periods in a day except the target time period are determined to be 0.

[0055] In this embodiment, the identification module 123 can be a module for identifying whether there are preset eating habits. The identification module 123 can be used to input the first statistical result of the possibility identification of the preset eating habits within the first preset period into the evaluation model to obtain the identification result of the preset eating habits.

[0056] The first preset period may be a preset time period, for example, every 3 days may be a preset period, a week may be a preset period, or a month may be a preset period. The first statistical result may be a statistical result of the preset dietary possibility identifier within the first preset period. For example, if every 3 days is a first preset period, the result obtained by statistically analyzing the preset dietary habit possibility identifier corresponding to each time period within each of the 3 days may be used as the first statistical result.

[0057] It should be noted that the evaluation model is the final model obtained after the state algorithm model is trained and tested based on the statistical results of the preset diet possibility identification in other preset periods. The specific content of the statistical results can be set according to needs and is not specifically limited.

[0058] The specific function of the identification module 123 is described in detail in the second embodiment and will not be described in detail here.

[0059] A recognition system provided in the first embodiment of the present invention first monitors a weight parameter through a monitoring device, wherein the weight parameter is obtained by monitoring within a preset daily monitoring time period, and the weight parameter is a weight value with a timestamp; then, a distribution curve determination module in the recognition device obtains the weight parameter monitored daily from the monitoring device, and multiple distribution curves are determined based on the weight parameter monitored daily, and the weight parameter obtained by monitoring one day corresponds to one distribution curve; then, a possible identification of a preset dietary habit corresponding to each time period in a day is determined based on the multiple distribution curves by an identification determination module in the recognition device, wherein each time period is obtained after segmenting the daily monitoring time period; finally, the recognition module in the recognition device determines the recognition result of the preset dietary habit based on the first statistical result and evaluation model of the possible identification of the preset dietary habit within a first preset period. By using the above system, it is possible to identify a user with a preset dietary habit through an identification device, so as to solve the defect that the prior art can only determine the health status of the user by monitoring the weight value, but cannot identify whether the user has bad dietary habits.

[0060] Embodiment 2

[0061] Figure 2 This is a schematic diagram of the structure of an identification system provided by the second embodiment of the present invention. The second embodiment is optimized on the basis of the above embodiments. For details not yet provided in this embodiment, please refer to the first embodiment, which will not be described in detail here.

[0062] In this embodiment, the identification module 123 includes an input submodule 1231 and a determination submodule 1232. The input submodule 1231 is used to input the cumulative sum of all preset dietary habit possibility identifiers obtained within a first preset period and the characteristic parameters of all distribution curves obtained within the first preset period as the first statistical results into the evaluation model, and the evaluation model is a model constructed based on the second statistical results of the preset dietary habit possibility identifiers within a second preset period, and the second preset period is different from the first preset period; the determination submodule 1232 is used to determine the recognition result of the preset dietary habit according to the output value of the evaluation model.

[0063] The second preset period may be a time period different from the first preset period, and the second statistical result may be a statistical result of the possibility identification of the preset eating habits within the second preset period, and the second statistical result is different from the first statistical result.

[0064] The characteristic parameters of the distribution curve may include the distribution frequency of the preset dietary habit possibility identification or the proportion of different preset dietary habit possibility identifications. For example, the number of occurrences and distribution of the preset dietary habit possibility identification of 1 within a month, the number of occurrences and distribution of the preset dietary habit possibility identification of 0 within a month may be counted, and the proportion of the possibility identification of the preset dietary habit possibility identification of 1 within a month to all the preset dietary habit possibility identifications may also be counted, and the proportion of the possibility identification of the preset dietary habit possibility identification of 0 within a month to all the preset dietary habit possibility identifications may also be counted.

[0065] Among them, the statistical contents corresponding to the first statistical result and the second statistical result are the same. It can be understood that if the cumulative sum of all preset dietary habit possibility identifiers obtained within the second preset period and the distribution frequency of all preset dietary habit possibility identifiers obtained within the second preset period are used as the second statistical result, then the cumulative sum of all preset dietary habit possibility identifiers obtained within the first preset period and the distribution frequency of all preset dietary habit possibility identifiers obtained within the first preset period can be used as the input of the evaluation model.

[0066] It should be noted that the process of constructing an evaluation model based on the second statistical result is not the focus of the present invention and will not be introduced in detail here.

[0067] Furthermore, the determination submodule 1232 is specifically used for: if the output value of the evaluation model is a first value, then determining that the recognition result of the preset eating habits is that there is no preset eating habits; if the output value of the evaluation model is a second value, then determining that the recognition result of the preset eating habits is that there are slight preset eating habits; if the output value of the evaluation model is a third value, then determining that the recognition result of the preset eating habits is that there are more obvious preset eating habits; if the output value of the evaluation model is a fourth value, then determining that the recognition result of the preset eating habits is that there are more serious preset eating habits; wherein, the first value, the second value, the third value and the fourth value are values ​​that increase in sequence.

[0068] Exemplarily, when the preset eating habit is overeating, if the output value of the evaluation model is 0, then the recognition result of the preset eating habit can be determined as an eating habit without overeating; if the output value of the evaluation model is 1, then the recognition result of the preset eating habit can be determined as an eating habit with slight overeating; if the output value of the evaluation model is 2, then the recognition result of the preset eating habit can be determined as an eating habit with more obvious overeating; if the output value of the evaluation model is 3, then the recognition result of the preset eating habit can be determined as an eating habit with more serious overeating.

[0069] Furthermore, the system also includes a result push device 130, which is connected to the recognition device 120; the result push device 130 is used to push the recognition result of the preset eating habit to the user and remind the user.

[0070] The result push device 130 may be any device with a message push function, and no specific limitation is made here. The result push device 130 and the identification device 120 may be connected to each other or wirelessly connected.

[0071] In this embodiment, the recognition result is pushed to the user through the result pushing device 130. The user can understand whether he or she has bad eating habits according to the recognition result pushed by the system, so as to adjust his or her eating habits.

[0072] The second embodiment of the present invention provides a recognition system, which refines the recognition module. By using the system, it is possible to accurately obtain the recognition result of eating habits based on the statistical results and the evaluation model.

[0073] Embodiment 3

[0074] Figure 3 This is a flow chart of an identification method provided by Embodiment 3 of the present invention. This method can be applied to identifying whether a user has bad eating habits. This method can be performed by the identification system provided by Embodiment 1 or Embodiment 2 of the present invention. The identification system can be implemented by hardware and software. Figure 3 As shown, the method specifically comprises the following steps:

[0075] S110, obtaining the weight parameters monitored daily, and determining a plurality of distribution curves based on the weight parameters monitored daily, wherein the weight parameters monitored daily correspond to one distribution curve.

[0076] Wherein, one of the distribution curves is composed of a plurality of weight parameter points connected together, the abscissa of one of the weight parameter points is a timestamp of one of the weight parameter points, and the ordinate of one of the weight parameter points is a weight value of one of the weight parameter points.

[0077] S120, determining a preset dietary habit possibility indicator corresponding to each time period of the day based on the multiple distribution curves, wherein each time period is obtained by segmenting the daily monitoring time period.

[0078] Each time period is obtained by segmenting the preset daily monitoring time period according to preset time intervals.

[0079] Furthermore, by performing a determination operation on each distribution curve, a preset dietary habit possibility indicator corresponding to each time period within a day can be obtained.

[0080] Specifically, the determination operation includes: for a distribution curve, determining the maximum peak and the nearest trough with the largest change trend on the distribution curve, the nearest trough being the trough closest to the maximum peak; determining the difference between the weight value corresponding to the maximum peak and the weight value corresponding to the nearest trough on the distribution curve, and determining the first timestamp corresponding to the maximum peak and the second timestamp corresponding to the nearest trough; determining multiple weight values ​​within a target time period with the first timestamp as the starting time and the second timestamp as the ending time on each distribution curve; calculating the standard deviation of the multiple weight values; and determining a preset dietary habit possibility identifier corresponding to each time period within a day based on the difference and the standard deviation.

[0081] Furthermore, a preset dietary habit possibility identifier corresponding to each time period within a day is determined based on the difference and the standard deviation, including: if the difference meets a preset condition, setting the preset dietary habit possibility identifier corresponding to the target time period on the one distribution curve to a first preset value; if the difference does not meet the preset condition, setting the preset dietary habit possibility identifier corresponding to the target time period on the one distribution curve to a second preset value; and determining the preset dietary habit possibility identifiers corresponding to other time periods within a day except the target time period to a second preset value; wherein the preset conditions include that the difference is greater than or equal to a preset multiple of the standard deviation, and the difference is greater than or equal to a third preset value.

[0082] S130: Determine a recognition result of the preset eating habit based on a first statistical result of the possibility identification of the preset eating habit within a first preset period and an evaluation model.

[0083] In this embodiment, based on the first statistical result of the preset dietary habit possibility identification within the first preset period and the evaluation model, the method of determining the recognition result of the preset dietary habit is: the cumulative sum of all preset dietary habit possibility identifications obtained within the first preset period and the characteristic parameters of all distribution curves obtained within the first preset period are input into the evaluation model as the first statistical result; the recognition result of the preset dietary habit is determined according to the output value of the evaluation model.

[0084] The evaluation model is a model constructed based on a second statistical result of the possibility identification of the preset eating habits within a second preset period, and the second preset period is a time period different from the first preset period.

[0085] Further, determining the recognition result of the preset dietary habits according to the output value of the evaluation model includes: if the output value of the evaluation model is a first value, determining the recognition result of the preset dietary habits as not having the preset dietary habits; if the output value of the evaluation model is a second value, determining the recognition result of the preset dietary habits as having slight preset dietary habits; if the output value of the evaluation model is a third value, determining the recognition result of the preset dietary habits as having obvious preset dietary habits; if the output value of the evaluation model is a fourth value, determining the recognition result of the preset dietary habits as having serious preset dietary habits;

[0086] The first value, the second value, the third value and the fourth value are values ​​that increase in sequence.

[0087] Furthermore, the method also includes: pushing the recognition result of the preset eating habits to the user and reminding the user.

[0088] An identification method provided in the third embodiment of the present invention first obtains the weight parameters monitored daily, and determines multiple distribution curves based on the weight parameters monitored daily, wherein the weight parameters monitored daily correspond to one distribution curve; then, based on the multiple distribution curves, a preset dietary habit possibility mark corresponding to each time period of the day is determined, wherein each time period is obtained by segmenting the daily monitoring time period; finally, based on the first statistical result and evaluation model of the preset dietary habit possibility mark within the first preset period, the identification result of the preset dietary habit is determined. The above method obtains the preset dietary habit possibility mark according to the distribution curve corresponding to the weight parameter, and determines the identification result of the preset dietary habit based on the statistical result and evaluation model of the preset dietary habit possibility mark, so as to solve the defect that the prior art can only determine the health status of the user by monitoring the weight value, but cannot identify whether the user has bad dietary habits.

[0089] Embodiment 4

[0090] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0091] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0092] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0093] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the recognition method.

[0094] In some embodiments, the identification method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps in the identification method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the identification method in any other appropriate manner (e.g., by means of firmware).

[0095] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0096] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0097] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0098] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0099] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0100] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0101] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0102] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A recognition system, characterized in that: The system includes a monitoring device and an identification device, wherein the monitoring device and the identification device are connected; the identification device includes a distribution curve determination module, a mark determination module and an identification module; The monitoring device is used to monitor a weight parameter, the weight parameter is monitored within a preset daily monitoring time period, and the weight parameter is a weight value with a timestamp; The distribution curve determination module is used to obtain the weight parameters monitored daily from the monitoring device, and determine multiple distribution curves based on the weight parameters monitored daily, and the weight parameters monitored in one day correspond to one distribution curve; The identification determination module is used to determine the preset dietary habit possibility identification corresponding to each time period in a day based on the multiple distribution curves, wherein each time period is obtained by segmenting the daily monitoring time period; The recognition module is used to determine the recognition result of the preset eating habit based on the first statistical result and the evaluation model of the possibility identification of the preset eating habit in the first preset period; The preset dietary habit possibility identifier is a digital identifier or a character identifier used to represent the possibility of the preset dietary habit; The identification determination module is specifically used to perform a determination operation on each distribution curve to obtain a preset dietary habit possibility identification corresponding to each time period in a day, and the determination operation includes: For a distribution curve, determining a maximum peak with the largest change trend and a nearest trough on the distribution curve, wherein the nearest trough is a trough closest to the maximum peak; Determine a difference between a weight value corresponding to a maximum peak and a weight value corresponding to a most recent trough on the distribution curve, and determine a first timestamp corresponding to the maximum peak and a second timestamp corresponding to the most recent trough; Determine, on each distribution curve, a plurality of weight values ​​within a target time period starting from the first timestamp and ending at the second timestamp; calculating a standard deviation of the plurality of weight values; Determine a preset dietary habit possibility indicator corresponding to each time period in a day according to the difference and the standard deviation; The identification module includes an input submodule and a determination submodule. The input submodule is used to input the cumulative sum of all preset dietary habit possibility identifiers obtained by statistics within a first preset period and the characteristic parameters of all distribution curves obtained by statistics within the first preset period as the first statistical result into the evaluation model, wherein the evaluation model is a model constructed based on the second statistical result of the preset dietary habit possibility identifiers within a second preset period, and the second preset period is different from the first preset period; The determination submodule is used to determine the recognition result of the preset eating habit according to the output value of the evaluation model.

2. The system according to claim 1, characterized in that The distribution curve is composed of a plurality of weight parameter points connected together, the abscissa of a weight parameter point is a timestamp of the weight parameter, and the ordinate of a weight parameter point is a weight value of the weight parameter.

3. The system according to claim 1, characterized in that The identification determination module is specifically used for: If the difference satisfies a preset condition, setting the preset dietary habit possibility indicator corresponding to the target time period on the one distribution curve to a first preset value; If the difference does not satisfy the preset condition, setting the preset dietary habit possibility indicator corresponding to the target time period on the one distribution curve to a second preset value; Determine the preset dietary habit possibility identifier corresponding to other time periods in a day except the target time period as a second preset value; The preset conditions include that the difference is greater than or equal to a preset multiple of the standard deviation, and the difference is greater than or equal to a third preset value.

4. The system according to claim 1, characterized in that The determination submodule is specifically used for: If the output value of the evaluation model is the first value, determining that the recognition result of the preset dietary habit is that there is no preset dietary habit; If the output value of the evaluation model is a second value, determining that the recognition result of the preset eating habit is that there is a slight amount of the preset eating habit; If the output value of the evaluation model is a third value, determining that the recognition result of the preset eating habit is that the preset eating habit is more obvious; If the output value of the evaluation model is a fourth value, determining that the recognition result of the preset eating habit is that the preset eating habit is relatively serious; The first value, the second value, the third value and the fourth value are values ​​that increase in sequence.

5. The system according to claim 1, characterized in that The system further comprises a result pushing device, wherein the result pushing device is connected to the identification device; The result push device is used to push the recognition result of the preset eating habit to the user and remind the user.

6. A recognition method, characterized in that: The method is performed by the recognition system according to claim 1, and the method comprises: Obtaining a weight parameter monitored daily, and determining a plurality of distribution curves based on the weight parameter monitored daily, wherein a weight parameter monitored daily corresponds to one distribution curve, wherein the weight parameter is monitored within a preset daily monitoring time period, and the weight parameter is a weight value with a timestamp; Determine a preset dietary habit possibility indicator corresponding to each time period of the day based on the multiple distribution curves, wherein each time period is obtained by segmenting the daily monitoring time period; Determining a recognition result of the preset eating habit based on a first statistical result and an evaluation model of the possibility identification of the preset eating habit within a first preset period; The preset dietary habit possibility identifier is a digital identifier or a character identifier used to represent the possibility of the preset dietary habit; The determining of the possibility identifier of the preset dietary habits corresponding to each time period of the day based on the multiple distribution curves is specifically to perform a determination operation on each distribution curve to obtain the possibility identifier of the preset dietary habits corresponding to each time period of the day, and the determination operation includes: For a distribution curve, determining a maximum peak with the largest change trend and a nearest trough on the distribution curve, wherein the nearest trough is a trough that is closest to the maximum peak; Determine a difference between a weight value corresponding to a maximum peak and a weight value corresponding to a most recent trough on the distribution curve, and determine a first timestamp corresponding to the maximum peak and a second timestamp corresponding to the most recent trough; Determine, on each distribution curve, a plurality of weight values ​​within a target time period starting from the first timestamp and ending at the second timestamp; calculating a standard deviation of the plurality of weight values; Determine a preset dietary habit possibility indicator corresponding to each time period in a day according to the difference and the standard deviation; The method of determining the recognition result of the preset eating habit based on the first statistical result and the evaluation model of the possibility identification of the preset eating habit in the first preset period is: The cumulative sum of all preset dietary habit possibility identifiers obtained within a first preset period and the characteristic parameters of all distribution curves obtained within the first preset period are input into the evaluation model as the first statistical result; wherein the evaluation model is a model constructed based on the second statistical result of the preset dietary habit possibility identifiers within a second preset period, and the second preset period is a time period different from the first preset period; The recognition result of the preset eating habit is determined according to the output value of the evaluation model.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the identification method according to claim 6.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the identification method of claim 6 when executed.

9. A computer program product, characterized in that The computer program product comprises a computer program which, when executed by a processor, implements the identification method according to claim 6.

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