CN115348315A - Health early warning information pushing method and device, storage medium and computer equipment - Google Patents
Health early warning information pushing method and device, storage medium and computer equipment Download PDFInfo
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Abstract
The invention discloses a health early warning information pushing method and device, a storage medium and computer equipment, relates to the technical field of information processing, and mainly aims to solve the problem that the success rate of pushing smoking cessation prompt information is low. The method mainly comprises the steps of responding to a cigarette lighting request sent by associated electronic cigarette equipment of a target user, and obtaining target smoking statistical data of the target user; if any one of the target smoking statistical data is larger than a corresponding preset early warning threshold value, acquiring pre-generated health analysis data; and generating health early warning information according to the health analysis data and the target smoking statistical data, and sending the health early warning information to the terminal equipment of the target user, wherein the health early warning information at least comprises one of a health state simulation image and smoking distribution data. The method is mainly used for pushing smoking cessation prompt information.
Description
Technical Field
The present invention relates to the field of information processing technologies, and in particular, to a method and an apparatus for pushing health warning information, a storage medium, and a computer device.
Background
Along with the development of science and technology, the smoking cessation means is more and more intelligent, and intelligent smoking cessation products are continuously emerging. The mainstream intelligent smoking cessation product realizes the control of the smoking frequency of the user by limiting the starting frequency and times of smoking equipment and cooperatively sending smoking time prompt information to the user. However, in the existing smoking cessation information pushing method, the information content is difficult to attach importance to the user, and the information is easy to be ignored by the user, so that the information viewing rate is low, and the information pushing success rate is low.
Disclosure of Invention
In view of this, the present invention provides a method and an apparatus for pushing health early warning information, a storage medium, and a computer device, and mainly aims to solve the problem of low success rate of pushing existing health early warning information.
According to one aspect of the invention, a method for pushing health early warning information is provided, which comprises the following steps:
responding to a cigarette lighting request sent by associated electronic cigarette equipment of a target user, and acquiring target smoking statistical data of the target user, wherein the target smoking statistical data at least comprises one of target smoking amount data, target smoking duration data and target smoking frequency data;
if any one of the target smoking statistical data is larger than a corresponding preset early warning threshold value, acquiring pre-generated health analysis data;
and generating health early warning information according to the health analysis data and the target smoking statistical data, and sending the health early warning information to the terminal equipment of the target user, wherein the health early warning information at least comprises one of a health state simulation image and smoking distribution data.
Further, before the obtaining the pre-generated health analysis data, the method further comprises:
acquiring basic information of the target user and historical smoking data of the target user;
performing prediction processing on the basic information and the historical smoking data based on a trained smoking hazard index prediction model to obtain a smoking hazard index;
and determining a target health state simulation image from a health state simulation image set according to the smoking hazard index, wherein the health state simulation image set comprises health state simulation images which have mapping relations with different smoking hazard index intervals.
Further, before the health status simulation image is an organ status simulation image, and before the target health status simulation image is determined from the health status simulation image set according to the smoking hazard index, the method further includes:
constructing organ state simulation images of different organ image color depth parameters;
configuring a corresponding hazard index range for the organ state simulation image according to the organ image color depth parameter to obtain a mapping relation between a plurality of organ state simulation images and the hazard index range, wherein the organ image color depth parameter is used for representing the degree of simulated organ damage;
and constructing a health state simulation image set based on the mapping relation between the plurality of organ state simulation images and the hazard index range.
Further, before the smoking hazard index prediction model is used for performing prediction processing on the basic information and the historical smoking data to obtain the smoking hazard index, the method further comprises the following steps:
constructing an initial smoking hazard index prediction model and a training sample set, wherein the training sample set comprises a user smoking data sample marked with a smoking hazard index;
and training the initial smoking hazard index prediction model by using the training sample set to obtain a trained smoking hazard index prediction model.
Further, before the obtaining the pre-generated health analysis data, the method further comprises:
acquiring target smoking data of the target user in a preset time period and global smoking data of the global user in the preset time period;
calculating to obtain target smoking statistical data according to the target smoking data;
calculating to obtain global smoking statistical data according to the global smoking data, wherein the global smoking statistical data comprises global smoking amount data, global smoking duration data and global smoking frequency data;
and calculating smoking distribution data according to the target smoking statistical data and the global smoking statistical data, wherein the smoking distribution data is used for representing the ordering or proportion of the target smoking statistical data in the global smoking statistical data.
Further, after the health-warning information is sent to the terminal device of the target user, the method further includes:
sending a reply instruction for rejecting the cigarette lighting request to the associated electronic cigarette device;
when the checking time of the health early warning information is monitored to be larger than a preset checking time threshold value, monitoring a repeated cigarette lighting request sent by the associated electronic equipment;
in response to the repeat lighting request, sending a reply instruction to the associated electronic vaping device agreeing to the repeat lighting request.
Further, the method further comprises:
and when the target smoking statistical data is smaller than the preset early warning threshold value and the frequency of generating the health early warning information in the preset monitoring time is smaller than the preset early warning frequency threshold value, performing negative correction on the preset early warning threshold value according to the target smoking statistical data.
According to another aspect of the present invention, there is provided a health warning information pushing device, including:
the acquisition module is used for responding to a cigarette lighting request sent by associated electronic cigarette equipment of a target user, and acquiring target smoking statistical data of the target user, wherein the target smoking statistical data at least comprises one of target smoking amount data, target smoking duration data and target smoking frequency data;
the generating module is used for acquiring pre-generated health analysis data if any one of the target smoking statistical data is larger than a corresponding preset early warning threshold value;
and the sending module is used for generating health early warning information according to the health analysis data and the target smoking statistical data and sending the health early warning information to the terminal equipment of the target user, wherein the health early warning information at least comprises one of a health state simulation image and smoking distribution data.
Further, the apparatus further comprises:
the acquisition module is further used for acquiring basic information of the target user and historical smoking data of the target user;
the prediction module is used for carrying out prediction processing on the basic information and the historical smoking data based on a trained smoking hazard index prediction model to obtain a smoking hazard index;
and the determining module is used for determining a target health state simulation image from a health state simulation image set according to the smoking hazard index, wherein the health state simulation image set comprises health state simulation images which have mapping relations with different smoking hazard index intervals.
Further, the apparatus further comprises:
the first construction module is used for constructing organ state simulation images of different organ image color depth parameters;
the matching module is used for configuring a corresponding damage index range for the organ state simulation image according to the organ image color depth parameter to obtain a mapping relation between a plurality of organ state simulation images and the damage index range, and the organ image color depth parameter is used for representing the damage degree of the simulation organ;
and the second construction module is used for constructing a health state simulation image set based on the mapping relation between the plurality of organ state simulation images and the hazard index range.
Further, the apparatus further comprises:
the third construction module is used for constructing an initial smoking hazard index prediction model and a training sample set, wherein the training sample set comprises user smoking data samples marked with smoking hazard indexes;
and the training module is used for training the initial smoking hazard index prediction model by using the training sample set to obtain a trained smoking hazard index prediction model.
Further, the apparatus further comprises:
the acquisition module is further used for acquiring target smoking data of the target user in a preset time period and global smoking data of the global user in the preset time period;
the first calculation module is used for calculating target smoking statistical data according to the target smoking data;
the second calculation module is used for calculating to obtain global smoking statistical data according to the global smoking data, wherein the global smoking statistical data comprises global smoking amount data, global smoking duration data and global smoking frequency data;
and the third calculation module is used for calculating smoking distribution data according to the target smoking statistical data and the global smoking statistical data, wherein the smoking distribution data is used for representing the ordering or proportion of the target smoking statistical data in the global smoking statistical data.
Further, the apparatus further comprises:
the sending module is further configured to send a reply instruction for rejecting the cigarette lighting request to the associated electronic cigarette device;
the monitoring module is used for monitoring a repeated cigarette lighting request sent by the associated electronic equipment when the checking time of the health early warning information is monitored to be greater than a preset checking time threshold value;
the sending module is further configured to send a reply instruction for agreeing with the repeat cigarette lighting request to the associated electronic cigarette device in response to the repeat cigarette lighting request.
Further, the apparatus further comprises:
and the correcting module is used for carrying out negative correction on the preset early warning threshold value according to the target smoking statistical data when the target smoking statistical data is smaller than the preset early warning threshold value and the times of generating the health early warning information in the preset monitoring time are smaller than the preset early warning time threshold value.
According to another aspect of the present invention, a storage medium is provided, where at least one executable instruction is stored in the storage medium, and the executable instruction causes a processor to perform an operation corresponding to the above-mentioned pushing method for health warning information.
According to still another aspect of the present invention, there is provided a computer apparatus including: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the pushing method of the health early warning information.
By the technical scheme, the technical scheme provided by the embodiment of the invention at least has the following advantages: the invention provides a pushing method and a device of health early warning information, a storage medium and computer equipment, wherein the embodiment of the invention responds to a cigarette lighting request sent by associated electronic cigarette equipment of a target user to obtain target smoking statistical data of the target user, and the target smoking statistical data at least comprises one of target smoking amount data, target smoking duration data and target smoking frequency data; if any one of the target smoking statistical data is larger than a corresponding preset early warning threshold value, acquiring pre-generated health analysis data; and generating health early warning information according to the health analysis data and the target smoking statistical data, and sending the health early warning information to the terminal equipment of the target user, wherein the health early warning information at least comprises one of a health state simulation image and smoking distribution data, so that the re-visibility and the receptivity of the user to the health early warning information are greatly improved, the viewing rate of the user to the health early warning information is improved, and the success rate of pushing the health early warning information is effectively improved.
The above description is only an overview of the technical solutions of the present invention, and the present invention can be implemented in accordance with the content of the description so as to make the technical means of the present invention more clearly understood, and the above and other objects, features, and advantages of the present invention will be more clearly understood.
Drawings
Various additional advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 shows a flowchart of a method for pushing health early warning information according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating another health-warning information pushing method provided by an embodiment of the present invention;
fig. 3 is a block diagram illustrating a health warning information pushing apparatus according to an embodiment of the present invention;
fig. 4 shows a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Wherein, artificial Intelligence (AI) is a theory, method, technology and application system for simulating, extending and expanding human Intelligence by using a digital computer or a machine controlled by the digital computer, sensing environment, acquiring knowledge and obtaining the best result by using the knowledge.
The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
Aiming at the existing smoking cessation information pushing method, the information content is difficult to attach attention of the user, the information is easy to be ignored by the user, the information viewing rate is low, and the information pushing success rate is low. An embodiment of the present invention provides a method for pushing health warning information, as shown in fig. 1, which is described by taking an example that the method is applied to a computer device such as a server, where the server may be an independent server, or a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), and a big data and artificial intelligence platform, such as an intelligent medical system, a digital medical platform, and the like. The method comprises the following steps:
101. and responding to a smoking request sent by the associated electronic smoking equipment of the target user, and acquiring target smoking statistical data of the target user.
In the embodiment of the invention, the target group for pushing the health early warning information is the use group of the electronic cigarette equipment. The target user is a user needing to push health early warning information. Associating the electronic vaping device with an electronic vaping device used by a target user. When a target user executes a smoking operation on the associated electronic smoking device, the associated electronic smoking device firstly generates a smoking request according to a smoking operation instruction and sends the smoking request to the background server, the background server responds to the received smoking request, the smoking request carries a user identification of the target user, and the background server acquires smoking statistical data corresponding to the target user, namely the target smoking statistical data, from the database according to the user identification of the target user.
The target smoking statistical data includes at least one of target smoking amount data, target smoking duration data, and target smoking frequency data. The target smoking amount data, the target smoking duration data and the target smoking frequency data can be obtained through statistical calculation based on the recorded data of the associated electronic cigarette. For example, the target smoking frequency data may be obtained by counting time intervals of the lighting trigger of the associated electronic vaping device; the target smoking duration data can be obtained by calculating the duration of single cigarette lighting and the time interval of cigarette lighting triggering; the target smoking amount data can be calculated based on the amount of tobacco burned in a unit time of a heating sheet of the associated electronic cigarette device and the target smoking duration data. The time interval corresponding to the target smoking statistical data may be 1 natural day and 1 week, and may also be customized according to specific application scenarios, which is not specifically limited in the embodiments of the present invention. Accurate data base can be provided for subsequent judgment on whether health early warning information needs to be pushed to a target user or not by acquiring target smoking amount data, target smoking duration data and target smoking frequency data, and therefore the pushing accuracy of the health early warning information is improved.
102. And if any one of the target smoking statistical data is larger than a corresponding preset early warning threshold value, acquiring pre-generated health analysis data.
In the embodiment of the invention, corresponding early warning thresholds are respectively preset aiming at target smoking amount data, target smoking duration data and target smoking frequency data, namely the target smoking amount data corresponds to a preset smoking amount early warning threshold, and the target smoking duration data corresponds to a preset smoking duration early warning threshold; the target smoking frequency data corresponds to a preset smoking frequency early warning threshold value. When any one of the target smoking amount data, the target smoking duration data and the target smoking frequency data is larger than the corresponding preset early warning threshold value, it is indicated that smoking of the target user reaches an early warning condition, and early warning needs to be performed, so that health analysis data for generating early warning information needs to be acquired. The health analysis data is generated in advance, the generated time can be after the previous smoking extinguishing action, the data is analyzed at the time point to generate the health analysis data, the timeliness of the data is guaranteed, and meanwhile the generation efficiency of the health early warning information can be improved. The data based on three dimensions of smoking amount, smoking duration and smoking frequency are judged, so that the smoking condition of a target user can be comprehensively evaluated, and the accuracy of pushing health early warning information is improved.
103. And generating health early warning information according to the health analysis data and the target smoking statistical data, and sending the health early warning information to the terminal equipment of the target user.
In the embodiment of the invention, after the health analysis data is obtained, the health analysis data is sent to the terminal equipment of the target user in the form of health early warning information. The health early warning information at least comprises one of a health state simulation image and smoking distribution data. Wherein the health state simulation image is used for displaying a simulation image of damage to health possibly caused by the target user under the current smoking condition. Such as lung lesion simulation images, blood vessel lesion simulation images, etc. The smoking profile data is used to characterize comparison data of the target user's smoking profile to the smoking profile of the global e-cigarette user, e.g. a ranking of the target user's smoking frequency among the global e-cigarette user's smoking frequencies. The target smoking statistics may be presented in the form of a data chart, such as a monthly smoking amount frequency distribution curve, a daily smoking duration distribution curve, and the like. The harm caused by smoking is more visually displayed by displaying vivid simulation images, data charts and comparison data in the global user in the health early warning information, so that the target user can pay higher attention to receiving the health early warning information, and the success rate of pushing the health early warning information is improved.
For further explanation and limitation, in an embodiment of the present invention, as shown in fig. 2, before the step 102 of obtaining the pre-generated health analysis data, the method further includes:
201. and acquiring basic information of the target user and historical smoking data of the target user.
202. And carrying out prediction processing on the basic information and the historical smoking data based on the trained smoking hazard index prediction model to obtain a smoking hazard index.
203. And determining a target health state simulation image from the health state simulation image set according to the smoking hazard index.
In the embodiment of the invention, in order to accurately analyze the health condition of the target user, basic information of the target user and historical smoking data of the target user need to be acquired. The basic information comprises information such as the age, the tobacco age, the sex and the health condition of the target user. The historical smoking data includes all historical data recorded by the associated electronic vaping device, and may also include data generated based on user smoking history survey information. After the basic information and the historical smoking data of the target user are obtained, the basic information and the historical smoking data are input into a trained smoking hazard index prediction model to obtain a smoking hazard index. The smoking hazard index is used to characterize an estimate of the extent of physical damage to the target user's current smoking status. The greater the smoking hazard index, the greater the degree of damage to the body. In order to more intuitively show the damage degree of smoking to the body, a target health state simulation image matched with the smoking hazard index is obtained from a plurality of health state simulation images in the health state simulation image set.
It should be noted that the health state simulation image set includes health state simulation images having mapping relationships with different smoking hazard index intervals. By dividing simulation images representing different predicted body damage degrees according to the interval of the smoking hazard index, not only can a single health state simulation image be displayed, but also dynamic change simulation images of the expected health state along with the aggravation of the smoking degree can be displayed. The harm degree of smoking to the health can be more directly perceived and accurate show, the degree of identity of user to health state simulation image is improved to promote the success rate of healthy early warning information propelling movement.
In one embodiment of the present invention for further explanation and limitation, before the determining a target health status simulation image from a set of health status simulation images according to the smoking hazard index in step 203, the method further comprises:
and constructing organ state simulation images of different organ image color depth parameters.
And configuring a corresponding hazard index range for the organ state simulation image according to the organ image color depth parameter to obtain a mapping relation between the plurality of organ state simulation images and the hazard index range.
And constructing a health state simulation image set based on the mapping relation between the plurality of organ state simulation images and the hazard index range.
In the embodiment of the invention, the health state simulation image is an organ state simulation image. The excessive smoking amount can cause arteriosclerosis, dark lung color and even blacken. In order to accurately show the change condition of organs, a plurality of organ state simulation images are constructed in advance, and the organ damage degree is represented by utilizing the organ image color depth parameters. Organ image color depth parameters of each organ state simulation image are different and are distributed in a continuous gradient manner. For example, 10 organ state simulation images with organ image color depth from light to dark are constructed, and organ image color depth parameters are distributed according to 1, 5, 10, 8230 \8230; 45, 50 in sequence. Further, in order to show the difference of the influence degrees of different smoking degrees on organ damage, the organ state simulation images aiming at different organ image color depth parameters are matched with corresponding hazard index ranges. For example, a mapping relation is established between the organ state simulation image with the organ image color depth parameter of 1 and the hazard index range of 0-5, a mapping relation is established between the organ state simulation image with the organ image color depth parameter of 5 and the hazard index range of 6-10, and so on.
It should be noted that the matching relationship between the smoking hazard index, and the organ state simulation image is set based on the suggestions of related experts, and can characterize the correlation between the amount of harmful substances ingested by the target user due to smoking and the degree of organ damage to a certain extent, but the organ state simulation image is only a simulation image showing the hazard of smoking to the body health, so as to warn the target user, and cannot characterize the state of the actual organ of the target user, or serve as a diagnosis and treatment basis.
In an embodiment of the present invention, for further explanation and limitation, before the predicting processing is performed on the basic information and the historical smoking data based on the smoking hazard index prediction model to obtain the smoking hazard index, the method further includes:
and constructing an initial smoking hazard index prediction model and a training sample set.
And training the initial smoking hazard index prediction model by using the training sample set to obtain a trained smoking hazard index prediction model.
In the embodiment of the invention, the harm degree of smoking to the health of the user not only depends on the smoking amount, but also is related to the age, the smoking age and the physical health condition of the target user. Therefore, an initial smoking hazard index prediction model is trained based on pre-labeled smoking data samples of a large number of users to evaluate the degree of hazard that the smoking data of the target user poses to the health of the target user. The initial smoking hazard index prediction model may be a back propagation neural network model, or may be another prediction network model, and the embodiment of the present invention is not particularly limited. The training sample set includes samples of user smoking data labeled with smoking hazard indices. Each user smoking data sample comprises basic information, smoking data, of the user.
For further explanation and limitation, in an embodiment of the present invention, before the obtaining the pre-generated health analysis data, the method further comprises:
acquiring target smoking data of the target user in a preset time period and global smoking data of a global user in the preset time period;
calculating to obtain target smoking statistical data according to the target smoking data;
calculating to obtain global smoking statistical data according to the global smoking data;
and calculating to obtain smoking distribution data according to the target smoking statistical data and the global smoking statistical data.
In the embodiment of the invention, in order to enable users to know the smoking conditions of the users, the target smoking data of the target users and the global smoking data of the global users in the same preset time period are acquired according to the performance of all smoking users. The global user refers to a user who can obtain smoking data and uses the electronic cigarette device. The preset time period may be the same as the time interval of the target smoking statistical data, and may also be customized according to the specific application scenario requirements, and the embodiment of the present invention is not specifically limited. Further, statistical calculation is carried out according to the target smoking data and the global smoking data to obtain target smoking statistical data and global smoking statistical data. The global smoking statistical data comprises global smoking amount data, global smoking duration data and global smoking frequency data. The implementation process of obtaining the global smoking statistical data by calculating according to the global smoking data is the same as the implementation process of obtaining the target smoking statistical data by calculating according to the target smoking data, and the obtaining of the target smoking statistical data by calculating according to the target smoking data is explained in step 101, which is not repeated herein. After the smoking statistics are obtained, smoking distribution data characterizing the ordering or proportion of the target smoking statistics in the global smoking statistics is calculated. For example, the ranking conditions of the target smoking amount, the target smoking duration and the target smoking frequency in the global smoking amount, the global smoking duration and the global smoking frequency are calculated respectively; and calculating the proportion of the target smoking amount to the overall smoking amount. In addition, the smoking distribution data may also be displayed in the form of a graph, for example, a histogram of smoking distribution, a pie chart of smoking distribution, and the like, which is not limited in the embodiment of the present invention.
In an embodiment of the present invention, for further explanation and limitation, after the sending the health-warning information to the terminal device of the target user, the method further includes:
and sending a reply instruction for rejecting the cigarette lighting request to the associated electronic cigarette device.
And monitoring a repeated cigarette lighting request sent by the associated electronic equipment when the checking time of the health early warning information is monitored to be greater than a preset checking time threshold value.
In response to the repeat lighting request, sending a reply instruction to the associated electronic vaping device agreeing to the repeat lighting request.
In the embodiment of the invention, in order to further ensure that the health early warning information can be checked by the target user, the cigarette lighting request of the associated electronic cigarette device is refused after the health early warning information needs to be pushed to the target user. The health early warning information is checked, and the check time meets the preset check time threshold value and serves as the limiting condition for agreeing to the cigarette lighting request, so that the target user can carefully check the health early warning information, and the success rate of pushing the health early warning information is effectively improved. The preset viewing time threshold value can be customized according to actual requirements, and embodiments of the present invention are not particularly limited.
In an embodiment of the present invention, for further explanation and limitation, the method further comprises:
and when the target smoking statistical data is smaller than the preset early warning threshold value and the times of generating the health early warning information in the preset monitoring time are smaller than the preset early warning threshold value, performing negative correction on the preset early warning threshold value according to the target smoking statistical data.
In the embodiment of the invention, when the number of times of generating the health early warning information in the preset monitoring time is less than the preset early warning number threshold, namely the number of times that the target smoking statistical data does not reach the preset early warning threshold is less than the preset early warning number threshold, the smoking condition of the target user is effectively controlled, and the preset early warning threshold is adjusted in time to further control the smoking amount of the target user. Specifically, the numerical value of the preset early warning threshold is adjusted downwards according to the target smoking statistical data. For example, if the target smoking amount in the target smoking statistical data is 10 and the original preset early warning threshold is 20, the original preset early warning threshold is negatively corrected to 10. The preset early warning time threshold and the corrected preset early warning threshold can be customized according to specific needs, and embodiments of the present invention are not particularly limited. The preset early warning threshold value is dynamically corrected based on the target smoking statistical data, the effectiveness of the preset early warning threshold value is guaranteed, the sending frequency of the health early warning information is improved, and therefore the success rate of pushing the health early warning information is improved.
The invention provides a pushing method of health early warning information, the embodiment of the invention obtains target smoking statistical data of a target user by responding to a cigarette lighting request sent by associated electronic cigarette equipment of the target user, and the target smoking statistical data at least comprises one of target smoking amount data, target smoking duration data and target smoking frequency data; if any one of the target smoking statistical data is larger than a corresponding preset early warning threshold value, acquiring pre-generated health analysis data; and generating health early warning information according to the health analysis data and the target smoking statistical data, and sending the health early warning information to the terminal equipment of the target user, wherein the health early warning information at least comprises one of a health state simulation image and smoking distribution data, so that the re-visibility and acceptance of the user to the health early warning information are greatly improved, the viewing rate of the user to the health early warning information is improved, and the success rate of pushing the health early warning information is effectively improved.
Further, as an implementation of the method shown in fig. 1, an embodiment of the present invention provides a device for pushing health warning information, and as shown in fig. 3, the device includes:
the acquisition module 31 is configured to acquire target smoking statistical data of a target user in response to a smoking request sent by an associated electronic cigarette device of the target user, where the target smoking statistical data at least includes one of target smoking amount data, target smoking duration data, and target smoking frequency data;
a generating module 32, configured to obtain pre-generated health analysis data if any one of the target smoking statistical data is greater than a corresponding preset early warning threshold;
and the sending module 33 is configured to generate health early warning information according to the health analysis data and the target smoking statistical data, and send the health early warning information to the terminal device of the target user, where the health early warning information at least includes one of a health state simulation image and smoking distribution data.
Further, the apparatus further comprises:
the obtaining module 31 is further configured to obtain basic information of the target user and historical smoking data of the target user;
the prediction module is used for carrying out prediction processing on the basic information and the historical smoking data based on a trained smoking hazard index prediction model to obtain a smoking hazard index;
and the determining module is used for determining a target health state simulation image from a health state simulation image set according to the smoking hazard index, wherein the health state simulation image set comprises health state simulation images which have mapping relations with different smoking hazard index intervals.
Further, the apparatus further comprises:
the first construction module is used for constructing organ state simulation images of different organ image color depth parameters;
the matching module is used for configuring a corresponding hazard index range for the organ state simulation image according to the organ image color depth parameters to obtain a mapping relation between a plurality of organ state simulation images and the hazard index range, and the organ image color depth parameters are used for representing the degree of damage of the simulated organ;
and the second construction module is used for constructing a health state simulation image set based on the mapping relation between the plurality of organ state simulation images and the hazard index range.
Further, the apparatus further comprises:
the third construction module is used for constructing an initial smoking hazard index prediction model and a training sample set, wherein the training sample set comprises user smoking data samples marked with smoking hazard indexes;
and the training module is used for training the initial smoking hazard index prediction model by utilizing the training sample set to obtain a trained smoking hazard index prediction model.
Further, the apparatus further comprises:
the obtaining module 31 is further configured to obtain target smoking data of the target user in a preset time period and global smoking data of the global user in the preset time period;
the first calculation module is used for calculating target smoking statistical data according to the target smoking data;
the second calculation module is used for calculating to obtain global smoking statistical data according to the global smoking data, wherein the global smoking statistical data comprises global smoking amount data, global smoking duration data and global smoking frequency data;
and the third calculation module is used for calculating smoking distribution data according to the target smoking statistical data and the global smoking statistical data, wherein the smoking distribution data is used for representing the ordering or proportion of the target smoking statistical data in the global smoking statistical data.
Further, the apparatus further comprises:
the sending module 33 is further configured to send a reply instruction for rejecting the cigarette lighting request to the associated electronic cigarette device;
the monitoring module is used for monitoring a repeated cigarette lighting request sent by the associated electronic equipment when the checking time of the health early warning information is monitored to be greater than a preset checking time threshold value;
the sending module 33 is further configured to send, in response to the repeat cigarette lighting request, a reply instruction for agreeing to the repeat cigarette lighting request to the associated electronic cigarette device.
Further, the apparatus further comprises:
and the correction module is used for carrying out negative correction on the preset early warning threshold according to the target smoking statistical data when the target smoking statistical data is smaller than the preset early warning threshold and the frequency of generating the health early warning information in the preset monitoring time is smaller than the preset early warning frequency threshold.
The invention provides a pushing device of health early warning information, which is characterized in that target smoking statistical data of a target user are obtained by responding to a smoking request sent by associated electronic smoking equipment of the target user, wherein the target smoking statistical data at least comprises one of target smoking amount data, target smoking duration data and target smoking frequency data; if any one of the target smoking statistical data is larger than a corresponding preset early warning threshold value, acquiring pre-generated health analysis data; and generating health early warning information according to the health analysis data and the target smoking statistical data, and sending the health early warning information to the terminal equipment of the target user, wherein the health early warning information at least comprises one of a health state simulation image and smoking distribution data, so that the re-visibility and the receptivity of the user to the health early warning information are greatly improved, the viewing rate of the user to the health early warning information is improved, and the success rate of pushing the health early warning information is effectively improved.
According to an embodiment of the present invention, a storage medium is provided, where the storage medium stores at least one executable instruction, and the computer executable instruction may execute the method for pushing the health warning information in any method embodiment described above.
Fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present invention, and the specific embodiment of the present invention does not limit the specific implementation of the computer device.
As shown in fig. 4, the computer apparatus may include: a processor (processor) 402, a Communications Interface 404, a memory 406, and a Communications bus 408.
Wherein: the processor 402, communication interface 404, and memory 406 communicate with each other via a communication bus 408.
A communication interface 404 for communicating with network elements of other devices, such as clients or other servers.
The processor 402 is configured to execute the program 410, and may specifically execute relevant steps in the above-mentioned pushing method embodiment of the health-warning information.
In particular, program 410 may include program code comprising computer operating instructions.
The processor 402 may be a central processing unit CPU, or an Application Specific Integrated Circuit ASIC (Application Specific Integrated Circuit), or one or more Integrated circuits configured to implement an embodiment of the present invention. The computer device includes one or more processors, which may be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
A memory 406 for storing a program 410. Memory 406 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 410 may be specifically configured to cause the processor 402 to perform the following operations:
responding to a cigarette lighting request sent by associated electronic cigarette equipment of a target user, and acquiring target smoking statistical data of the target user, wherein the target smoking statistical data at least comprises one of target smoking amount data, target smoking duration data and target smoking frequency data;
if any one of the target smoking statistical data is larger than a corresponding preset early warning threshold value, acquiring pre-generated health analysis data;
and generating health early warning information according to the health analysis data and the target smoking statistical data, and sending the health early warning information to the terminal equipment of the target user, wherein the health early warning information at least comprises one of a health state simulation image and smoking distribution data.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made without departing from the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (10)
1. A method for pushing health early warning information is characterized by comprising the following steps:
responding to a cigarette lighting request sent by associated electronic cigarette equipment of a target user, and acquiring target smoking statistical data of the target user, wherein the target smoking statistical data at least comprises one of target smoking amount data, target smoking duration data and target smoking frequency data;
if any one of the target smoking statistical data is larger than a corresponding preset early warning threshold value, acquiring pre-generated health analysis data;
and generating health early warning information according to the health analysis data and the target smoking statistical data, and sending the health early warning information to the terminal equipment of the target user, wherein the health early warning information at least comprises one of a health state simulation image and smoking distribution data.
2. The method of claim 1, wherein prior to obtaining pre-generated health analysis data, the method further comprises:
acquiring basic information of the target user and historical smoking data of the target user;
performing prediction processing on the basic information and the historical smoking data based on a trained smoking hazard index prediction model to obtain a smoking hazard index;
and determining a target health state simulation image from a health state simulation image set according to the smoking hazard index, wherein the health state simulation image set comprises health state simulation images which have mapping relations with different smoking hazard index intervals.
3. The method of claim 2, wherein the health status simulation image is an organ status simulation image, and wherein prior to determining the target health status simulation image from the set of health status simulation images based on the smoking hazard index, the method further comprises:
constructing organ state simulation images of different organ image color depth parameters;
configuring a corresponding hazard index range for the organ state simulation image according to the organ image color depth parameter to obtain a mapping relation between a plurality of organ state simulation images and the hazard index range, wherein the organ image color depth parameter is used for representing the degree of simulated organ damage;
and constructing a health state simulation image set based on the mapping relation between the plurality of organ state simulation images and the hazard index range.
4. The method of claim 2, wherein before the prediction processing is performed on the basic information and the historical smoking data based on the smoking hazard index prediction model to obtain the smoking hazard index, the method further comprises:
constructing an initial smoking hazard index prediction model and a training sample set, wherein the training sample set comprises user smoking data samples marked with smoking hazard indexes;
and training the initial smoking hazard index prediction model by using the training sample set to obtain a trained smoking hazard index prediction model.
5. The method of claim 1, wherein prior to obtaining pre-generated health analysis data, the method further comprises:
acquiring target smoking data of the target user in a preset time period and global smoking data of a global user in the preset time period;
calculating to obtain target smoking statistical data according to the target smoking data;
calculating to obtain global smoking statistical data according to the global smoking data, wherein the global smoking statistical data comprises global smoking amount data, global smoking duration data and global smoking frequency data;
and calculating smoking distribution data according to the target smoking statistical data and the global smoking statistical data, wherein the smoking distribution data are used for representing the ordering or proportion of the target smoking statistical data in the global smoking statistical data.
6. The method of claim 1, wherein after the sending the health-alert information to the terminal device of the target user, the method further comprises:
sending a reply instruction for rejecting the cigarette lighting request to the associated electronic cigarette device;
when the checking time of the health early warning information is monitored to be larger than a preset checking time threshold value, monitoring a repeated cigarette lighting request sent by the associated electronic equipment;
in response to the repeat lighting request, sending a reply instruction to the associated electronic vaping device agreeing to the repeat lighting request.
7. The method according to any one of claims 1-6, further comprising:
and when the target smoking statistical data is smaller than the preset early warning threshold value and the times of generating the health early warning information in the preset monitoring time are smaller than the preset early warning threshold value, performing negative correction on the preset early warning threshold value according to the target smoking statistical data.
8. The utility model provides a health early warning information's pusher which characterized in that includes:
the acquisition module is used for responding to a smoking request sent by associated electronic smoking equipment of a target user and acquiring target smoking statistical data of the target user, wherein the target smoking statistical data at least comprises one of target smoking amount data, target smoking duration data and target smoking frequency data;
the generating module is used for acquiring pre-generated health analysis data if any one of the target smoking statistical data is larger than a corresponding preset early warning threshold value;
and the sending module is used for generating health early warning information according to the health analysis data and the target smoking statistical data and sending the health early warning information to the terminal equipment of the target user, wherein the health early warning information at least comprises one of a health state simulation image and smoking distribution data.
9. A storage medium, wherein at least one executable instruction is stored in the storage medium, and the executable instruction causes a processor to execute operations corresponding to the method for pushing the health-warning information according to any one of claims 1 to 7.
10. A computer device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction causes the processor to execute the operation corresponding to the pushing method of the health early warning information as claimed in any one of claims 1 to 7.
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