CN115412858B - Data recommendation method and system based on wearable device - Google Patents
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Abstract
The invention provides a data recommendation method and system based on wearable equipment, and belongs to the technical field of Internet of things and data recommendation. The system comprises a central server, wearable equipment and an intelligent chip meal card; when the accumulated activation times InvM counted by the central server and activated by the intelligent chip meal card in a first preset time period exceeds a first preset time PreSetM, the central server acquires the current positions of N target users wearing wearable equipment and the activation states of the intelligent chip meal card configured by the M target users in the first preset time period PreSetT, determines K users to be reminded in the N target users, and sends data recommendation information, wherein the data recommendation information is used for indicating the K users to be reminded to go to the target positions in a second prediction time period after the first preset time period PreSetT. According to the technical scheme, real-time data recommendation can be achieved based on wearable equipment and the Internet of things data analysis technology.
Description
Technical Field
The invention belongs to the technical field of Internet of things and data recommendation, and particularly relates to a data recommendation method and system based on wearable equipment.
Background
With the development of the technology of the internet of things, wearable devices are gradually popularized from professional devices originally belonging to professionals to conventional devices which can be configured by common people. The wearable device can also perform data interaction with various portable mobile terminals, such as smart phones, laptops and the like. Wearable devices such as smart bracelets, smart watches have been widely used by people for monitoring the self-movement condition, the sleep state and the like, or for monitoring the safety of old people, children and the like at home. The wearable device can also perform data interaction with the background server and the cloud through a wireless network based on a data application program, receive a reminding message sent by the cloud or the background server, or send self data to the cloud or the background server for trend analysis, for example, send positioning data to realize trajectory analysis, send physiological state data to perform motion state analysis, and the like.
Taking wearable wrist-watch or wearable bracelet as an example, along with the improvement of people's living standard, more parents have been disposed wearable wrist-watch or wearable bracelet at school children, through self mobile terminal and its radio communication to realize safety monitoring and communicate with in time, the head of a family or any notice of school all can in time send to wearable wrist-watch or wearable bracelet.
However, the inventor has noted that the above applications are only simple single point communications or unified group messaging. Under the campus environment, the existing wearable equipment and the existing campus specific equipment are fully utilized, personalized data recommendation is given after data collection and analysis in a specific time period and a specific place are realized, and no specific technical scheme is given in the prior art.
Disclosure of Invention
In order to solve the technical problem, the invention provides a data recommendation method and system based on wearable equipment.
In a first aspect of the invention, a data recommendation method based on a wearable device is provided, wherein N target users in M target users wear the wearable device, and the M target users are configured with smart chip meal cards;
the method is executed by a central server, and the wearable device and the smart chip meal card are communicated with the central server;
the wearable device is configured with a data positioning program, and the current position of the wearable device is sent to the central server through the data positioning program.
When the method is specifically executed, the method comprises the following steps:
s1: when detecting that the accumulated activation times InvM of the smart chip meal card exceeds a first preset time Preset within a first preset time period Preset, acquiring current positions of N target users wearing the wearable device and activation states of the smart chip meal card configured by the M target users within the first preset time period Preset;
s2: determining K users to be reminded in the N target users, wherein the intelligent chip meal card configured by the K users to be reminded is not activated within the first preset time period PreSetT, and the distance difference value between the current position of the K users to be reminded and other positions of other users activated within the first preset time period PreSetT of the intelligent chip meal card is greater than a first preset distance value PreSetDInv;
s3: sending data recommendation information to the K users to be reminded, wherein the data recommendation information is used for indicating the K users to be reminded to move to a target position in a second prediction time period after the first preset time period Preset, and the distance difference between the target position and other positions of other users, which are activated by the intelligent chip meal card in the first preset time period Preset, is smaller than a second preset distance value Preset Dou;
wherein M, N, K are positive integers, and M > N > K >1.
The wearable equipment is one of intelligent wrist-watch, intelligent bracelet, intelligent glasses or its arbitrary combination.
The wearable device and the intelligent chip meal card can be paired;
the central server is provided with a near-field card reading terminal, and when the intelligent chip meal card approaches the near-field card reading terminal, the intelligent chip meal card is activated; and when the smart chip meal card is activated, sleeping the wearable equipment paired with the smart chip meal card within the first preset time period Preset.
Specifically, first wearable devices worn by N target users in the M target users are paired with a first smart chip meal card;
when the first smart chip meal card is activated within the first preset time period Preset, the first wearable device is dormant within the first preset time period Preset.
The central server obtains the length of a first preset time period Preset, the accumulated activation times InvM and the current positions of N target users wearing the wearable equipment, and predicts a plurality of first predicted numbers of the target users reaching the target positions in a plurality of time periods in the future;
and when the first prediction quantity is smaller than a preset upper limit value, determining the second prediction time period.
In a second aspect of the present invention, to implement the method of the first aspect, a wearable device-based data recommendation system is provided, where the system includes a central server, a wearable device, and a smart-chip meal card;
the wearable device and the intelligent chip meal card are communicated with the central server;
the wearable device is configured with a data positioning program, and the wearable device sends the current position to the central server through the data positioning program;
the central server is provided with a near field card reading terminal, and when the intelligent chip meal card approaches the near field card reading terminal, the central server monitors that the intelligent chip meal card is activated;
the central server counts the number of activated cumulative activation times InvM of the intelligent chip meal card in a first preset time period Preset;
when the accumulated activation times InvM exceed a first preset time PreSetM, the central server acquires the current positions of N target users wearing the wearable device and the activation states of the smart chip dining cards configured by the M target users within the first preset time period PreSetT;
the central server determines K users to be reminded in the N target users and sends data recommendation information to the K users to be reminded, wherein the data recommendation information is used for indicating the K users to be reminded to move to a target position in a second prediction time period after the first preset time period Preset;
the intelligent chip meal card configured by the K users to be reminded is not activated within the first preset time period Preset, and the distance difference value between the current position of the K users to be reminded and other positions of other users activated within the first preset time period Preset of the intelligent chip meal card is greater than a first preset distance value PresetDInv;
wherein M, N, K are positive integers, and M > N > K >1.
In a specific configuration, N target users in the M target users wear the wearable device, and the M target users are configured with smart chip meal cards.
First wearable devices worn by N target users in the M target users are paired with the first smart chip meal card; when the first smart chip meal card is activated within the first preset time period PresetT, the first wearable device is dormant within the first preset time period PresetT. The central server obtains the length of a first preset time period Preset, the accumulated activation times InvM and the current positions of N target users wearing the wearable equipment, and predicts a plurality of first predicted numbers of the target users reaching the target positions in a plurality of time periods in the future;
and when the first prediction quantity is smaller than a preset upper limit value, determining the second prediction time period.
The technical scheme of the invention changes the current situation that the prior art can only execute simple single-point communication or message unified mass-sending function, but can fully utilize the existing wearable equipment and the existing campus specific equipment to realize personalized data recommendation after data collection and analysis in specific time periods and specific places.
Further embodiments and improvements of the present invention will be further described with reference to the accompanying drawings and specific embodiments.
Drawings
Fig. 1 is a schematic flowchart illustrating steps of a data recommendation method based on a wearable device according to an embodiment of the present invention;
FIG. 2 is a logic flow diagram of a process of FIG. 1 when a wearable device-based data recommendation method is implemented automatically using a computer program;
FIG. 3 is a schematic diagram of a wearable device-based data recommendation system according to an embodiment of the present invention;
fig. 4 is a schematic diagram illustrating interaction control when the wearable device is paired with the smart chip card in the system of fig. 3.
Detailed Description
The invention is further described with reference to the following drawings and detailed description.
Fig. 1 is a schematic flowchart illustrating steps of a data recommendation method based on a wearable device according to an embodiment of the present invention.
In fig. 1, the method comprises steps S1-S3, and the method is performed by a central server scheduling wireless communication data transmitted by a wearable device and a smart card.
In a specific embodiment, the method is applied to a campus environment, and particularly to a dining room environment for a campus dining time period.
Of course, the invention can also be applied to other scenes with intensive people or people gathering in time periods, such as factories, industrial parks, office parks, and the like;
specifically, the hardware basis for executing the method is as follows:
m target users are provided with intelligent chip meal cards, and N target users in the M target users wear the wearable equipment.
Taking the above method applied to the campus environment, especially to the dining room environment of the campus dining time period as an example, the target users here may be students at school, teachers and other people having meals in the school dining room.
It can be understood that, in most cases, students, teachers and other people having meals in school dining halls are provided with smart chip meal cards, that is, M target users are provided with smart chip meal cards;
on the basis, some persons can configure a smart watch, a smart bracelet, smart glasses or other wearable devices.
The wearable device is configured with a data positioning program, and the current position of the wearable device is sent to the central server through the data positioning program.
As one example, the data location procedure may be a GPS based location APP or LBS based location procedure.
As another example, a wearable device does not necessarily rely solely on GPS to provide location services. Some wearable devices use a-GPS, location beacons, identified network access points, etc. to determine location and provide location-based services, such as navigation. Thus, the wearable device may also provide location services in areas and locations where traditional GPS-based location may not be available.
Of course, unlike a smart-chip meal card, a wearable device is not mandatory to configure the device, and thus, it can be understood that N target users among M target users wear the wearable device, i.e., M ≧ N >1.
In one embodiment, the central server is configured with a near field card reading terminal, and the smart chip meal card is activated when the smart chip meal card approaches the near field card reading terminal.
It can be understood that the near-field card reading terminal is arranged at a target location, for example, a school dining room, and when a target person has a meal, the card needs to be swiped for consumption, that is, when the smart chip meal card approaches the near-field card reading terminal, the smart chip meal card is activated, and each activation is sensed and counted by the central server.
Typically, upon reaching a meal time period (e.g., lunch 11-00.
When the number of target people is large and the guide is lacked, due to the fact that dining positions and dish preparation positions of a dining room are limited, if the number of people reaching the target position (a dining room) in the same time period is large, a congestion phenomenon can occur, and user experience is affected.
In the prior art, the peak section of dining is informed in advance through experience values, and the target personnel are advocated to have dinner by peak, for example, warm prompt: 12:00-12:30 divided into meal peak periods. However, such a static uniform reminding method is not effective. For example, reminder 12:00-12: after the peak period of dining 30, the number of people having dinner in the next day may be rather small, and other periods may be crowded, and the environmental change cannot be sensed in real time.
Therefore, the invention provides an improved technical scheme, which makes full use of the existing wearable equipment and the existing campus specific equipment, realizes personalized data recommendation after data collection and analysis in specific time periods and specific places, and can dynamically sense environmental changes in real time and send out reminding information.
Specifically, referring to fig. 1, the steps S1 to S3 are specifically performed as follows:
s1: when detecting that the accumulated activation times InvM of the smart chip meal card exceeds a first preset time Preset within a first preset time period Preset, acquiring current positions of N target users wearing the wearable device and activation states of the smart chip meal card configured by the M target users within the first preset time period Preset;
as a specific example, the first preset time period PreSetT may be a regular meal time period, such as 7:00-9: 00. 11:00-13: 00. 17:00-19:00;
of course, the above is only an illustrative example, and different schools and industrial parks may open meals in other periods, so that the first preset time period PreSetT is determined by those skilled in the art according to actual scenarios, and the embodiment does not specifically limit the first preset time period PreSetT.
The first preset number of times PreSetM may also be determined based on the maximum number of people queued while the current dining scene can provide, the maximum number of people having a meal, and the like, for example, if the current dining room has 500 dining positions, the first preset number of times PreSetM may be set to 500 × 75% =375;
as a further preference, the first preset number of times PreSetM is set differently based on different first preset time periods PreSetT, i.e. the first preset number of times PreSetM is set dynamically.
For example, when the first preset time period PreSetT is set to the first time period PreSetT1, the first preset number of times PreSetM is set to PreSetM1;
for example, the first time period PreSetT1 is 7:00-9:00, the first preset number of times PreSetM is set to PreSetM1=500 × 85% =425;
when the first preset time period PreSetT is set to the second time period PreSetT2, the first preset number of times PreSetM is set to PreSetM2;
for example, the second period PreSetT2 is 11:00-13:00, the first preset number of times, preSetM, is set to PreSetM2=500 × 75% =375;
when the first preset time period PreSetT is set to the third time period PreSetT3, the first preset number of times PreSetM is set to PreSetM3;
for example, the third time period PreSetT3 is 17:00-19:00, the first preset number of times PreSetM is set to PreSetM3=500 × 60% = 300;
when the accumulated activation times InvM of the intelligent chip meal card exceeds the first preset times PresetM within the first preset time period PresetT, the fact that the number of target users who reach a restaurant at the moment is close to a preset upper limit means that other target users who do not reach the restaurant need to be guided in time;
it is understood that the present embodiment supports sending a guidance notification to a target user who is wearing the wearable device.
Acquiring current positions of N target users wearing the wearable device and activation states of the intelligent chip meal cards configured by the M target users within the first preset time period Preset;
s2: determining K users to be reminded in the N target users, wherein the intelligent chip meal card configured by the K users to be reminded is not activated within the first preset time period PreSetT, and the distance difference value between the current position of the K users to be reminded and other positions of other users activated within the first preset time period PreSetT of the intelligent chip meal card is greater than a first preset distance value PreSetDInv;
it can be seen that, in step S2, the determined K users to be reminded are not already having a meal by swiping the card (the smart chip meal card has not been activated within the first preset time period PreSetT) and have not yet reached the restaurant range (the difference between the current location of the user to be reminded and the distances of other locations of other users who have been activated within the first preset time period PreSetT of the smart chip meal card is greater than a first preset distance value PreSetDInv);
s3: sending data recommendation information to the K users to be reminded, wherein the data recommendation information is used for indicating the K users to be reminded to move to a target position in a second prediction time period after the first preset time period Preset, and the distance difference between the target position and other positions of other users, which are activated by the intelligent chip meal card in the first preset time period Preset, is smaller than a second preset distance value Preset Dou;
wherein M, N, K are positive integers, and M > N > K >1.
In step S3, K users to be reminded have been identified, and at this time, a recommendation message is sent to the K users to be reminded to indicate the target users that the current time period is recommended not to go to the restaurant, but the current time period is recommended to go to the target location only within a second prediction time period, for example, after 5 minutes and 10 minutes (according to the prediction result in a specific time period), the restaurant is recommended.
Preferably, the central server obtains the length of a first preset time period PreSetT, the cumulative number of activations invam, and current locations of N target users wearing the wearable device, and predicts a plurality of first predicted numbers of target users reaching the target locations in a plurality of time periods in the future; and when the first prediction quantity is smaller than a preset upper limit value, determining the second prediction time period.
As an example, the central server may count the cumulative number of activations invam in the current period, the current locations of the N target users wearing the wearable device, predict the number of people going to the target location, the number of people already located within the target location range, the number of people about to leave the target location range in different periods based on the length of the first preset time period PreSetT (which may be predicted based on the average meal time for determining the number of meals) and thereby determine a plurality of first predicted numbers of target users arriving (located) at the target location range within 5 minutes, 10 minutes, and 30 minutes in the future;
assuming that a first predicted number of target users reaching the destination location within 5 minutes in the future is Y5; a first predicted number of target users reaching the destination location within 10 minutes in the future is Y10; assuming that a first predicted number of target users reaching the destination location within 30 minutes in the future is Y30;
setting a preset upper limit value to be PresetM 75%;
if Y10, Y30 and Y10 are all smaller than a preset upper limit value, taking the future 5 minutes as the second prediction time period;
that is to say, when a plurality of first prediction amounts are smaller than a preset upper limit value, taking the earliest prediction time period in a plurality of prediction time periods corresponding to the plurality of first prediction amounts as the second prediction time period, and reminding the K users to be reminded of going to a destination position in the second prediction time period;
of course, if only one first prediction number is smaller than the preset upper limit value, only one determined second prediction time period exists.
Therefore, in the embodiment of fig. 1, a specific part of target crowds needing to send the data recommendation message is determined in a specific time period, so that staff drainage in a specific place is realized, staff congestion is avoided, and user experience is improved.
The method described in fig. 1 may also be implemented based on automated programming of computer program instructions. Fig. 2 is a flowchart of a program logic of the wearable device-based data recommendation method in fig. 1 implemented automatically by using a computer program.
Meanwhile, as mentioned above, different schools and industrial parks may open meals in other time periods, and therefore, the first preset time period PreSetT is determined by a person skilled in the art according to an actual scenario, which is not particularly limited in this embodiment.
For this reason, in the program flow given in fig. 2, the specific first preset time period PreSetT is not defined any more, but the recommended cycle duration cordit is set;
for example, setting the recommendation cycle duration cordit =30 minutes, if the cumulative activation time InvM of the smart chip mess card caused by card swiping in a restaurant is detected to exceed the first preset time PreSetM within 30 minutes from the current time node, the data recommendation method described in fig. 1 is started.
Specifically, the computer flow depicted in FIG. 2 is summarized in the form of a pseudo-code language as follows (step designations are omitted in FIG. 2):
SS1, initializing cumulative activation times InvM =0; setting a recommended period duration cordidt;
SS2; judging whether the intelligent chip mess card is detected to be close to the near field card reading terminal or not, if so, invM + + and Timer + +;
otherwise, returning to the step SS1;
and (4) SS3: judging whether Timer = cordidt is true, if yes, entering a step SS4;
otherwise, timer + +, return to step SS2;
and (4) SS: judging whether InvM > PresetM is true, if so, entering a step SS5;
if not, returning to the step SS1;
and SS5: acquiring current positions of N target users wearing the wearable equipment and activation states of the intelligent chip meal cards configured by the N target users within a set recommended period length cordiT;
and SS6: k users to be reminded in the N target users are determined;
and (7) SS: and sending data recommendation information to the K users to be reminded.
It can be understood that, in step SS6, the smart card configured by the K users to be reminded is not activated within the set recommended period duration cordit, and the distance difference between the current position of the K users to be reminded and the other positions of the other users that the smart card is activated within the set recommended period duration cordit is greater than a first preset distance value PreSetDInv;
it can be understood that, in step SS7, the data recommendation information is used to indicate that the K users to be reminded move to the destination positions in the second predicted time period after the current time node, and a distance difference between the destination positions and other positions of other users that the smart chip rice card has been activated within the set recommendation cycle duration cordit is smaller than a second preset distance value PreSetDou.
A specific optimization algorithm implemented as a computer program, wherein the settings of PreSetM are related to the size of M, N, wherein:
or,
wherein,
indicates the length of the first preset time period PreSetT in minutes;
represents rounding down;
represents rounding up; maxP represents the maximum number of meals or maximum meal location that the target location can provide.
Of course, as described in the foregoing embodiment of fig. 1, the first preset times PreSetM may also be set differently based on different first preset time periods PreSetT, that is, the first preset times PreSetM is dynamically set, and the first preset times PreSetM is determined by multiplying MaxP by a preset ratio value, where the preset ratio value dynamically changes with the difference of the first preset time periods PreSetT.
Fig. 3 is a schematic composition diagram of a wearable device-based data recommendation system according to an embodiment of the present invention.
In fig. 3, the system includes a central server, a wearable device, and a smart chip meal card; the wearable device and the intelligent chip meal card are communicated with the central server;
the wearable device is configured with a data positioning program, and the wearable device sends the current position to the central server through the data positioning program;
the central server is provided with a near field card reading terminal, and when the intelligent chip meal card approaches the near field card reading terminal, the central server monitors that the intelligent chip meal card is activated.
The central server counts the number of activated cumulative activation times InvM of the intelligent chip meal card in a first preset time period Preset;
when the accumulated activation times InvM exceeds a first preset time PresetM, the central server acquires the current positions of N target users wearing the wearable device and the activation states of the smart chip dining cards configured by the M target users within the first preset time period PresetT;
the central server determines K users to be reminded in the N target users and sends data recommendation information to the K users to be reminded, wherein the data recommendation information is used for indicating the K users to be reminded to move to a target position in a second prediction time period after the first preset time period Preset;
the intelligent chip meal card configured by the K users to be reminded is not activated within the first preset time period Preset, and the distance difference value between the current position of the K users to be reminded and other positions of other users activated within the first preset time period Preset of the intelligent chip meal card is greater than a first preset distance value PresetDInv;
wherein M, N, K are positive integers, and M > N > K >1.
It is understood that the hardware architecture described in fig. 3 may implement the method embodiment described in fig. 1, and may also implement the method flow described in fig. 2, and the description is not repeated here.
Similarly, the central server obtains the length of a first preset time period PreSetT, the accumulated activation times InvM, and the current positions of N target users wearing the wearable device, and predicts a plurality of first predicted numbers of target users reaching the target positions in a plurality of time periods in the future; and when the first prediction quantity is smaller than a preset upper limit value, determining the second prediction time period.
Preferably, the central server obtains the length of a first preset time period PreSetT, the accumulated activation times InvM, and the current positions of N target users wearing the wearable device, and predicts a plurality of first predicted numbers of target users reaching the target position in a plurality of time periods in the future; and when the first prediction quantity is smaller than a preset upper limit value, determining the second prediction time period.
As an example, the central server may count the cumulative number of activations invam in the current period, the current locations of the N target users wearing the wearable device, predict the number of people going to the target location, the number of people already within the target location range, the number of people about to leave the target location range in different periods based on the length of the first preset time period PreSetT (which may be predicted by determining the average meal time for the number of meals based on historical data), thereby determining a plurality of first predicted numbers of target users reaching the target location within 5 minutes, 10 minutes, and 30 minutes in the future;
when a plurality of first prediction quantities are smaller than a preset upper limit value, taking the earliest prediction time period in a plurality of prediction time periods corresponding to the plurality of first prediction quantities as the second prediction time period, and reminding the K users to be reminded of going to a target position in the second prediction time period;
of course, if only one first prediction number is smaller than the preset upper limit value, only one determined second prediction time period exists.
The wearable device is worn by N target users in the M target users, and the M target users are configured with smart chip meal cards.
With continued reference to fig. 4, fig. 4 is a schematic diagram illustrating interaction control when the wearable device is paired with the smart-chip card in the system of fig. 3.
First wearable devices worn by N target users in the M target users are paired with the first smart chip meal card;
when the first smart chip meal card is activated within the first preset time period Preset, the first wearable device is dormant within the first preset time period Preset.
It is understood that when the first wearable device is paired with the first smart chip meal card and the first smart chip meal card is activated within the first preset time period PreSetT, the current location of the first smart chip meal card may represent the current location of the first wearable device.
At this time, the first wearable device is not required to send the current position of the first wearable device to the central server through a data positioning program.
This is because, when the first smart chip card is activated within the first preset time period PreSetT, it means that the first smart chip card has been located near the target location for which the location has been sensed by the central server for the current time period.
At this time, the first wearable device is dormant within the first preset time period PreSetT, so that the endurance performance of the first wearable device can be remarkably improved, and the user experience is further improved.
Compared with the prior art, the invention has the advantages that:
(1) The existing wearable equipment and the existing campus specific equipment are fully utilized, other data servers or hardware equipment do not need to be additionally arranged, implementation and arrangement are easy, and hardware cost is reduced;
(2) The specific part of target crowds needing to send the data recommendation message can be determined in a specific time period, so that personnel drainage in a specific place is realized, personnel congestion is avoided, and user experience is improved;
(3) The cruising performance of the wearable equipment is obviously improved.
It will of course be understood that embodiments of the invention may achieve one of the effects alone, and that combinations of embodiments of the invention may achieve all of the effects described above, but that it is not required that each and every embodiment of the invention achieve all of the advantages and effects described above, since each and every embodiment of the invention constitutes a separate technical solution and contributes one or more of the prior art.
The present invention is not limited to the specific module structure described in the prior art. The prior art mentioned in the background section and the detailed description section can be used as part of the invention to understand the meaning of some technical features or parameters. The scope of the present invention is defined by the claims.
Claims (9)
1. A data recommendation method based on wearable equipment is characterized in that N target users in M target users wear the wearable equipment, and the M target users are provided with intelligent chip meal cards;
characterized in that the method comprises the following steps:
s1: when detecting that the accumulated activation times InvM of the smart chip meal card exceeds a first preset time Preset within a first preset time period Preset, acquiring the current positions of N target users wearing the wearable device and the activation states of the smart chip meal card configured by the M target users within the first preset time period Preset;
s2: determining K users to be reminded in the N target users, wherein the intelligent chip meal card configured by the K users to be reminded is not activated within the first preset time period PreSetT, and the distance difference between the current position of the K users to be reminded and other positions of other users activated within the first preset time period PreSetT of the intelligent chip meal card is greater than a first preset distance value PreSetDInv;
s3: sending data recommendation information to the K users to be reminded, wherein the data recommendation information is used for indicating the K users to be reminded to move to a target position in a second prediction time period after the first preset time period Preset, and the distance difference between the target position and other positions of other users, which are activated by the intelligent chip meal card in the first preset time period Preset, is smaller than a second preset distance value Preset Dou;
the method is executed by a central server, and the wearable device and the smart chip meal card are communicated with the central server;
the central server is provided with a near field card reading terminal, and when the intelligent chip meal card approaches the near field card reading terminal, the intelligent chip meal card is activated;
wherein M, N, K are positive integers, and M > N > K >1.
2. The wearable device-based data recommendation method of claim 1,
the wearable equipment is one of intelligent wrist-watch, intelligent bracelet, intelligent glasses or its arbitrary combination.
3. The wearable device-based data recommendation method of claim 1,
the wearable device is configured with a data positioning program, and the current position of the wearable device is sent to the central server through the data positioning program.
4. The wearable device-based data recommendation method of claim 1,
first wearable devices worn by N target users in the M target users are paired with a first smart chip meal card; when the first smart chip meal card was activated within the first preset time period, preSetT, the first wearable device is dormant.
5. The wearable device-based data recommendation method of claim 1,
the central server acquires the length of a first preset time period Preset, the accumulated activation times InvM and the current positions of N target users wearing the wearable equipment, and predicts a plurality of first predicted numbers of the target users reaching the target positions in a plurality of time periods in the future;
and when the first prediction quantity is smaller than a preset upper limit value, determining the second prediction time period.
6. A data recommendation system based on wearable equipment comprises a central server, the wearable equipment and a smart chip meal card;
the method is characterized in that:
the wearable device and the intelligent chip meal card are communicated with the central server;
the wearable device is configured with a data positioning program, and the wearable device sends the current position to the central server through the data positioning program;
the central server is provided with a near field card reading terminal, and when the intelligent chip meal card approaches the near field card reading terminal, the central server monitors that the intelligent chip meal card is activated;
the central server counts the number InvM of activated cumulative activation times of the intelligent chip meal card in a first preset time period PresetT;
when the accumulated activation times InvM exceed a first preset time Preset, the central server acquires the current positions of N target users wearing the wearable device and the activation states of the smart chip dining cards configured by the M target users within the first preset time period Preset;
the central server determines K users to be reminded in the N target users and sends data recommendation information to the K users to be reminded, wherein the data recommendation information is used for indicating the K users to be reminded to move to a target position in a second prediction time period after the first preset time period Preset;
the intelligent chip meal card configured by the K users to be reminded is not activated within the first preset time period Preset, and the distance difference value between the current position of the K users to be reminded and other positions of other users activated within the first preset time period Preset of the intelligent chip meal card is greater than a first preset distance value PresetDInv;
wherein M, N, K are positive integers, and M > N > K >1.
7. The wearable device-based data recommendation system of claim 6, wherein:
n target users in M target users wear the wearable equipment, and the M target users are provided with intelligent chip meal cards.
8. The wearable device-based data recommendation system of claim 6, wherein:
first wearable devices worn by N target users in the M target users are paired with the first smart chip meal card; when the first smart chip meal card is activated within the first preset time period PresetT, the first wearable device is dormant within the first preset time period PresetT.
9. The wearable device-based data recommendation system of claim 6, wherein:
the central server obtains the length of a first preset time period Preset, the accumulated activation times InvM and the current positions of N target users wearing the wearable equipment, and predicts a plurality of first predicted numbers of the target users reaching the target positions in a plurality of time periods in the future;
and when the first prediction quantity is smaller than a preset upper limit value, determining the second prediction time period.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5797809A (en) * | 1994-02-24 | 1998-08-25 | Hyuga; Makoto | Golf course guidance method, guidance device, and management system |
| CN105371848A (en) * | 2015-11-05 | 2016-03-02 | 广东欧珀移动通信有限公司 | Indoor navigation method and user terminal |
| CN109726920A (en) * | 2018-12-29 | 2019-05-07 | 滨州学院 | An intelligent management system for students based on big data |
| CN112016880A (en) * | 2019-05-29 | 2020-12-01 | 广东小天才科技有限公司 | Learning task supervision reminding implementation method and system |
| CN112073573A (en) * | 2020-07-14 | 2020-12-11 | 奇酷互联网络科技（深圳）有限公司 | Reminding method and device applied to intelligent wearable device |
| CN112364841A (en) * | 2020-12-21 | 2021-02-12 | 杭州雄伟科技开发股份有限公司 | Navigation method and system for dining position of restaurant |
| CN113642389A (en) * | 2021-07-06 | 2021-11-12 | 西安商汤智能科技有限公司 | Meal guide method and system, electronic device, computer storage medium |
-
2022
- 2022-11-02 CN CN202211364311.7A patent/CN115412858B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5797809A (en) * | 1994-02-24 | 1998-08-25 | Hyuga; Makoto | Golf course guidance method, guidance device, and management system |
| CN105371848A (en) * | 2015-11-05 | 2016-03-02 | 广东欧珀移动通信有限公司 | Indoor navigation method and user terminal |
| CN109726920A (en) * | 2018-12-29 | 2019-05-07 | 滨州学院 | An intelligent management system for students based on big data |
| CN112016880A (en) * | 2019-05-29 | 2020-12-01 | 广东小天才科技有限公司 | Learning task supervision reminding implementation method and system |
| CN112073573A (en) * | 2020-07-14 | 2020-12-11 | 奇酷互联网络科技（深圳）有限公司 | Reminding method and device applied to intelligent wearable device |
| CN112364841A (en) * | 2020-12-21 | 2021-02-12 | 杭州雄伟科技开发股份有限公司 | Navigation method and system for dining position of restaurant |
| CN113642389A (en) * | 2021-07-06 | 2021-11-12 | 西安商汤智能科技有限公司 | Meal guide method and system, electronic device, computer storage medium |
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