Intelligent alarm clock system based on multi-dimensional dynamic data

Abstract

The invention provides an intelligent alarm clock system based on multi-dimensional dynamic data, which comprises a data acquisition module, a calculation module and an execution module, and is characterized in that the data acquisition module is used for acquiring vehicle navigation real-time road conditions, weather bureau weather data and a national legal holiday database, and the calculation module is internally provided with a calculation model; the calculation model calculates alarm time according to the data acquired by the data acquisition module; and the execution module is used for reminding a user according to the alarm time output by the calculation module. Compared with the prior art, the method can calculate the better getting-up alarm time in combination with the multi-dimensional data such as weather, holidays and festivals, real-time road conditions and the like, so that the requirements of users can be better met.

Description

Technical Field

[0001] This invention belongs to the field of alarm clock control technology, specifically relating to an intelligent alarm clock system based on multi-dimensional dynamic data. Background Technology

[0002] In traditional alarm clock settings, users typically need to manually set the alarm time. This approach cannot adapt to dynamically changing commuting environments, does not take into account the reduced traffic speed caused by extreme weather (such as heavy rain or snow), and may cause users to be late if they set the alarm time accordingly. It also does not take into account sudden traffic congestion (such as accidents or construction), resulting in unreasonable alarm times. Furthermore, it does not consider the need to set alarms during holidays or adjusted work schedules, making it difficult to meet users' needs.

[0003] Therefore, how to design an intelligent alarm clock system based on multi-dimensional dynamic data, which can combine data such as weather, holidays, and real-time traffic conditions to calculate a better alarm time, has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent alarm clock system based on multi-dimensional dynamic data to solve the above-mentioned technical problems in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A smart alarm clock system based on multi-dimensional dynamic data includes a data acquisition module, a calculation module, and an execution module. The data acquisition module is used to collect real-time traffic conditions from vehicle navigation systems, weather data from meteorological bureaus, and a database of national statutory holidays. The calculation module is equipped with a calculation model, which calculates the alarm time based on the data collected by the data acquisition module. The execution module is used to remind the user based on the alarm time output by the calculation module.

[0007] Preferably, the data acquisition module is also used to collect users' historical habit data, which includes washing time, breakfast time, and other habits.

[0008] Preferably, the calculation model includes a buffer time for correcting the alarm time, the buffer time being determined based on historical delay probabilities and current road conditions, weather, and holiday information.

[0009] Preferably, the calculation model is as follows:

[0010] T wake =T target -T prepare -T SUM -T buffe ;

[0011] Among them, T wake For alarm time, T target T represents the user's expected arrival time at their destination. prepare T is the user's time spent calculated based on historical user habits data. SUM T is the commuting time calculated based on weather conditions, holiday information, and road conditions. buffer This is the buffer time.

[0012] Preferably, the formula for calculating user time consumption is:

[0013] T prepare =T wash +T breakfast +T other ,

[0014] Among them, T wash For washing time, T breakfas For breakfast duration, T other The time spent on other user habits.

[0015] Preferably, the formula for calculating commuting time is:

[0016] T SUM =a holiday × b weather× c traffic× T SUM_T ,

[0017] Where T SUM_T The historical average commute time, a holiday For holiday correction factors, c traffic Road condition impact factor, b weather This represents the weather impact coefficient.

[0018] Preferably, the formula for calculating the buffer time is: T buffer =k ×a 2 holiday ＋ b 2 weather＋ c 2 traffic , where k is the buffer coefficient.

[0019] Preferably, during statutory holidays and when commuting is not required, a holiday =0; normal working day a holiday =1; According to meteorological bureau data, b is the clearest day. weather =1; during light rain b weather =1.1; moderate rain is b weather ==1.3; during heavy rain / snow / fog b weather ==1.5.

[0020] Preferably, when road conditions are smooth, c traffic =1; c during slow movement traffic =1.2; c during congestion traffic =1.5; c during severe congestion traffic =2.

[0021] Preferably, the execution device of the execution module includes a smartphone, smartwatch, or smart home device; the execution module also includes a vehicle pre-start module for turning on the air conditioning or defogging function according to weather conditions.

[0022] The beneficial effects of this invention are as follows:

[0023] The intelligent alarm clock system based on multi-dimensional dynamic data of the present invention can calculate a better alarm time by combining multi-dimensional data such as weather, holidays, and real-time traffic conditions, thereby better meeting the needs of users. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly described below, and the specific embodiments of the present invention will be further described in detail with reference to the drawings, wherein...

[0025] Figure 1 The control flowchart of the intelligent alarm clock system based on multi-dimensional dynamic data provided in the embodiment of the present invention is shown. Detailed Implementation

[0026] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention.

[0027] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0028] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0029] In all the examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0030] This invention provides an intelligent alarm clock system based on multi-dimensional dynamic data, comprising a data acquisition module, a calculation module, and an execution module. The data acquisition module is used to collect real-time traffic conditions from vehicle navigation systems, weather data from meteorological bureaus, and a database of national statutory holidays. The calculation module is equipped with a calculation model that calculates the alarm time based on the data collected by the data acquisition module. The execution module is used to remind the user based on the alarm time output by the calculation module.

[0031] The intelligent alarm clock system based on multi-dimensional dynamic data provided in this invention can calculate a better alarm time by combining multi-dimensional data such as weather, holidays, and real-time traffic conditions, thereby better meeting the needs of users.

[0032] Furthermore, the data acquisition module is also used to collect users' historical habit data, which includes the duration of washing up, the duration of breakfast, and other habits such as whether or not to change clothes.

[0033] Specifically, the calculation model includes a buffer time for correcting alarm times. This buffer time is determined based on historical delay probabilities and current road conditions, weather, and holiday information, thereby enabling users to better cope with unexpected situations.

[0034] Furthermore, the calculation model is as follows:

[0035] T wake =T target -T prepare -T SUM -T buffe ;

[0036] Among them, T wake For alarm time, T target T represents the user's expected arrival time at their destination. prepare T is the user's time spent calculated based on historical user habits data. SUM T calculates the commute time from the origin to the destination based on weather conditions, holiday information, and road conditions. buffer This is the buffer time.

[0037] Specifically, the formula for calculating user time consumption is as follows:

[0038] T prepare =T wash +T breakfast +T other ,

[0039] Among them, T wash For washing time, T breakfas For breakfast duration, T other The time spent on other user habits.

[0040] Specifically, the formula for calculating commuting time is:

[0041] T SUM =a holiday × b weather× c traffic× T SUM_T ,

[0042] Where T SUM_T The historical average commute time, a holiday For holiday correction factors, c traffic Road condition impact factor, b weather This represents the weather impact coefficient.

[0043] Specifically, the formula for calculating the buffer time is: T buffer =k ×a 2 holiday ＋ b 2 weather＋ c 2 traffic Where k is the buffer coefficient. The buffer coefficient can be adjusted according to the user's tolerance for lateness; the default k = 15 minutes.

[0044] Specifically, during statutory holidays and when commuting is not required, a holiday =0; normal working day a holiday =1. According to meteorological bureau data, b on sunny days weather =1; during light rain b weather =1.1; moderate rain is b weather ==1.3; during heavy rain / snow / fog b weather ==1.5; when road conditions are smooth c traffic =1; c during slow movement traffic =1.2; c during congestion traffic =1.5; c during severe congestion traffic =2.

[0045] Specifically, the execution device of the execution module includes a smartphone, smartwatch, or smart home device; the execution module also includes a vehicle pre-start module for turning on the air conditioning or defogging function according to weather conditions.

[0046] The control flow of the intelligent alarm clock system based on multi-dimensional dynamic data of the present invention is as follows: Figure 1As shown. This invention can dynamically adapt to the commuting environment, taking into account weather, holidays, and real-time traffic conditions to avoid being late due to unforeseen circumstances; it can also achieve cross-device collaboration: seamless linkage between in-vehicle navigation, mobile phones, and smart home devices (such as smart speakers and smart curtains) to enhance the user experience; it also features personalized optimization: providing precise alarm time setting suggestions based on user habits (washing, breakfast time).

[0047] While specific embodiments of the invention have been described in detail by way of examples, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of the invention. Those skilled in the art should understand that modifications can be made to the above embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.

Claims

1. A smart alarm clock system based on multi-dimensional dynamic data, characterized in that, It includes a data acquisition module, a calculation module, and an execution module. The data acquisition module is used to collect real-time traffic conditions from the vehicle navigation system, weather data from the meteorological bureau, and a database of national statutory holidays. The calculation module is equipped with a calculation model, which calculates the alarm time based on the data collected by the data acquisition module. The execution module is used to remind the user based on the alarm time output by the calculation module.

2. The intelligent alarm clock system based on multi-dimensional dynamic data according to claim 1, characterized in that, The data acquisition module is also used to collect users' historical habit data, which includes the duration of washing up, breakfast, and other habits.

3. The intelligent alarm clock system based on multi-dimensional dynamic data according to claim 2, characterized in that, The calculation model includes a buffer time for correcting alarm times, which is determined based on historical delay probabilities and current road conditions, weather, and holiday information.

4. The intelligent alarm clock system based on multi-dimensional dynamic data according to claim 3, characterized in that, The calculation model is as follows: T wake =T target -T prepare -T SUM -T buffe ; Among them, T wake For alarm time, T target T represents the user's expected arrival time at their destination. prepare T is the user's time spent calculated based on historical user habits data. SUM T is the commuting time calculated based on weather conditions, holiday information, and road conditions. buffer This is the buffer time.

5. The intelligent alarm clock system based on multi-dimensional dynamic data according to claim 4, characterized in that, The formula for calculating user time consumption is: T prepare =T wash +T breakfast +T other , Among them, T wash For washing time, T breakfas For breakfast duration, T other The time spent on other user habits.

6. The intelligent alarm clock system based on multi-dimensional dynamic data according to claim 4, characterized in that, The formula for calculating commuting time is: T SUM =a holiday × b weather× c traffic× T SUM_T , Where T SUM_T The historical average commute time, a holiday For holiday correction factors, c traffic Road condition impact factor, b weather This represents the weather impact coefficient.

7. The intelligent alarm clock system based on multi-dimensional dynamic data according to claim 6, characterized in that, The formula for calculating buffer time is: T buffer =k ×a 2 holiday ＋ b 2 weather＋ c 2 traffic , where k is the buffer coefficient.

8. The intelligent alarm clock system based on multi-dimensional dynamic data according to claim 6, characterized in that, When it is a statutory holiday and there is no need to commute holiday =0; normal working day a holiday =1; According to meteorological bureau data, b is the clearest day. weather =1; during light rain b weather =1.1; moderate rain is b weather ==1.3; during heavy rain / snow / fog b weather ==1.

5.

9. The intelligent alarm clock system based on multi-dimensional dynamic data according to claim 6, characterized in that, When the road conditions are clear traffic =1; c during slow movement traffic =1.2; c during congestion traffic =1.5; c during severe congestion traffic =2.

10. The intelligent alarm clock system based on multi-dimensional dynamic data according to any one of claims 1 to 9, characterized in that, The execution device of the execution module includes a smartphone, smartwatch, or smart home device; the execution module also includes a vehicle pre-start module for turning on the air conditioning or defogging function according to weather conditions.

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