Adaptive fence construction method, device, equipment and medium for wearable student card

Abstract

The present application relates to a method, apparatus, device, and medium for constructing an adaptive fence for a wearable student card, and relates to the field of intelligent wearable technology. The method comprises constructing a preliminary electronic fence; obtaining a starting area and an ending area; obtaining the distance from the starting area to the ending area; constructing a preliminary electronic fence based on the starting area, the ending area, and the distance from the starting area to the ending area; and adaptively constructing an optimized electronic fence based on the distance information of the preliminary electronic fence. Setting the electronic fence area based on the student's activity trajectory or actual needs not only protects the student's range of activity, but also improves the effectiveness of guardianship.

Description

Technical Field

[0001] The present application relates to the field of smart wearable technology, and in particular to a method, device, equipment and medium for constructing an adaptive fence for a wearable student card. Background Art

[0002] As society continues to develop, student safety issues are becoming increasingly serious. The safety of students in school has become a focal point for the country, society, schools, and parents, particularly for underage students. Currently, several smartwatches with child-centric positioning services have emerged, offering real-time positioning, electronic fencing, and intelligent alerts. However, existing electronic fencing systems typically use circular or square areas. As children age and need to attend school, and if the distance between school and home is significant, using only a circular or square area to define the area while ensuring both the school and home are within the defined perimeter inevitably expands. This can result in many areas the child cannot access, negatively impacting their travel and personal safety. Furthermore, if the defined area is too small, encompassing only the neighborhood or school area, an alert will be triggered if the child leaves the area, even though the child is not in danger. This can cause information distress to parents, restrict the child's freedom, and negatively impact the physical and mental development of adolescents. These issues urgently need to be addressed. Summary of the Invention

[0003] The main purpose of this application is to provide a method, device, equipment and medium for constructing an adaptive fence for a wearable student card, aiming to improve the limitations of the setting area of ​​the electronic fence in the existing student card.

[0004] In order to achieve the above-mentioned purpose of the invention, the present application proposes a method for constructing an adaptive fence for a wearable student card, comprising:

[0005] Get the starting area and the ending area;

[0006] Obtaining distance information from the starting area to the end area;

[0007] Constructing and generating a preliminary electronic fence based on the starting area, the ending area, and the distance information from the starting area to the ending area;

[0008] Based on the distance information of the preliminary electronic fence, an optimized electronic fence is adaptively constructed.

[0009] Furthermore, the adaptively constructing and optimizing the electronic fence based on the distance information of the preliminary electronic fence includes:

[0010] Acquire tag information according to the distance information, wherein the tag information is information of businesses on both sides of the distance from the starting area to the end area;

[0011] Inputting the label information into a label classification model to obtain a label classification result;

[0012] Obtaining an accessible area according to the label classification result;

[0013] The accessible area is added to the preliminary electronic fence to construct the optimized electronic fence.

[0014] Furthermore, before the step of inputting the tag information into the tag classification model to obtain the tag classification result, the step includes:

[0015] Obtaining the label information from the big data, and annotating the label information with the label classification result to obtain an annotation result;

[0016] Storing the labeling results in a database to obtain a label information database;

[0017] Establishing the label classification model to divide the data in the label information database into training data and test data;

[0018] Inputting the training data into the label classification model for training until a training accuracy threshold or a preset number of training times is reached;

[0019] Inputting the test data into the trained label classification model for testing until a test accuracy threshold or a preset number of tests is reached;

[0020] When the test accuracy threshold or the preset number of tests is reached, the label classification model is obtained.

[0021] Furthermore, after the step of adding the accessible area to the preliminary electronic fence to construct the optimized electronic fence, the following steps are included:

[0022] The area of ​​the optimized electronic fence where no tag information is obtained is set as an unknown area;

[0023] Based on the unknown area, an optimized electronic fence is constructed.

[0024] Furthermore, the step of constructing a further optimized electronic fence based on the unknown area of ​​the optimized electronic fence includes:

[0025] The step of constructing a re-optimized electronic fence based on the unknown area includes:

[0026] Obtaining business information in the unknown area;

[0027] According to the merchant information, obtaining the area division information of the merchant for the wearable student card of the same type, wherein the area division information includes an accessible area and an absolutely prohibited area;

[0028] Counting the number of the accessible areas and the number of the absolutely prohibited areas respectively;

[0029] Determining, according to the number of the accessible areas and the absolutely prohibited areas, whether to add the merchant information to the accessible areas or to add the merchant to the absolutely prohibited areas;

[0030] If the merchant information is added to the accessible area, the merchant information is added to the re-optimized electronic fence to construct the re-optimized electronic fence.

[0031] Furthermore, the determining whether to add the merchant information to the accessible area or to add the merchant to the absolutely prohibited area according to the number of the accessible area and the absolutely prohibited area includes:

[0032] If the number of the accessible areas is greater than the number of the absolutely prohibited areas, the merchant is added to the accessible areas;

[0033] If the number of the accessible areas is less than the number of the absolutely prohibited areas, the merchant is added to the absolutely prohibited areas.

[0034] Furthermore, if the number of the accessible areas is less than the number of the absolutely prohibited areas, after the step of adding the merchant to the absolutely prohibited areas, the method further includes:

[0035] Determining whether the user has entered the absolutely prohibited area;

[0036] If the user enters the absolutely prohibited area, a red warning message is sent to the user's associated terminal;

[0037] If the user does not enter the absolutely prohibited area, a yellow warning message is sent to the user's associated terminal.

[0038] An adaptive fence construction device for a wearable student card, comprising:

[0039] The area acquisition module is used to obtain the starting area and the end area;

[0040] A distance acquisition module is used to acquire the distance from the starting area to the end area;

[0041] A preliminary electronic fence module is configured to construct and generate a preliminary electronic fence based on the starting area, the ending area, and the distance information from the starting area to the ending area;

[0042] The electronic fence optimization module adaptively constructs an optimized electronic fence based on the distance information of the preliminary electronic fence.

[0043] The present application also provides a computer device including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the adaptive fence construction method for the wearable student card described in any one of the above items are implemented.

[0044] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the adaptive fence construction method for a wearable student card described in any one of the above are implemented.

[0045] The present application relates to a method, apparatus, device, and medium for constructing an adaptive fence for a wearable student card, and relates to the field of intelligent wearable technology. The method comprises constructing a preliminary electronic fence; obtaining a starting area and an ending area; obtaining the distance from the starting area to the ending area; constructing a preliminary electronic fence based on the starting area, the ending area, and the distance from the starting area to the ending area; and adaptively constructing an optimized electronic fence based on the distance information of the preliminary electronic fence. Setting the electronic fence area based on the student's activity trajectory or actual needs not only protects the student's range of activity, but also improves the effectiveness of guardianship. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a flow chart of an embodiment of a method for constructing an adaptive fence for a wearable student card in this application;

[0047] Figure 2 This is a schematic structural diagram of an embodiment of an adaptive fence construction device for a wearable student card of the present application;

[0048] Figure 3 This is a schematic block diagram of the structure of an embodiment of the device of the present application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0050] Reference Figure 1 An embodiment of the present application provides a method for constructing an adaptive fence for a wearable student card, including steps S10-S40. The steps of the method for constructing an adaptive fence for a wearable student card are described in detail as follows.

[0051] S10: Obtain the starting area and the ending area.

[0052] In this embodiment, the user sets a starting area and an end area based on their needs. The starting area can be the area surrounding the user's residence, and the end area can be the area surrounding the user's work or school. The starting and end areas are obtained based on the user-set locations, and distance information for the starting and end areas is obtained. The user can also set a preset area for the starting and end areas based on their needs. Specifically, in one embodiment, the preset area is 100 square meters, with a length of 10 meters and a width of 10 meters. The preset area can be set by the user based on their specific needs. When the user is traveling alone, there is no need for real-time monitoring by family members or associated persons. When the user is outside the initial electronic fence, the alarm system intervenes in real-time processing, ensuring the user's safety while reducing the burden on the user's associated persons.

[0053] S20: Obtain distance information from the starting area to the end area.

[0054] In this embodiment, distance information from the starting area to the end area is obtained, and the starting area and the end area are areas set by the user in the preliminary electronic fence. The distance information includes location information, merchant information, etc. The distance information can be obtained according to the positioning method used by the positioning terminal, and the positioning method includes one or more of satellite positioning, Wi-Fi positioning, Bluetooth positioning, UWB positioning or base station positioning. A preliminary electronic fence is constructed between the starting area and the end area based on the distance information, and the area in the distance information is identified and judged, and the area accessible to the user is added to the preliminary electronic fence. The size of the preliminary electronic fence can be set by the user's own needs, or the preliminary electronic fence can be automatically generated according to the system. The system offline plans a preset area for the starting area, the end area, and the area from the starting area to the end area, and finally synthesizes the preset areas of the three areas to construct the preliminary electronic fence.

[0055] S30: Constructing and generating a preliminary electronic fence based on the starting area, the ending area, and the distance information from the starting area to the ending area.

[0056] In this embodiment, the preliminary electronic fence is an area set based on the distance information between two locations selected by the user. When the user is in the starting area, the distance information is used to obtain the user's current location. The user then sets the location of the end area, obtains the location information of the end area and the distance information from the starting area to the end area, and sets the preliminary electronic fence based on the location information of the starting area, the end area, and the distance information from the starting area to the end area. The starting area and the end area can be the area where the user lives or studies, but are not limited to these areas. The user can set them according to their own circumstances. If the user is outside the area, the student card will send an alarm to the user's associated person, ensuring the user's safety.

[0057] S40: Adaptively construct an optimized electronic fence based on the distance information of the preliminary electronic fence.

[0058] In this embodiment, the optimized electronic fence obtains merchant information from the aforementioned starting and ending areas and labels the merchant information. Specifically, in one embodiment, the starting and ending areas may include, for example, bookstores, stationery stores, internet cafes, and hotels. Based on the labeling model, bookstores and stationery stores are classified as accessible areas, while internet cafes and hotels are classified as inaccessible areas. Based on the classification results, accessible areas are added to the preliminary electronic fence, inaccessible areas are set as strictly prohibited areas, and businesses without clearly defined boundaries are classified as unknown areas, completing the optimization of the preliminary electronic fence. The labeling model classification utilizes a neural network model to obtain the classification results. The neural network model is based on a mathematical model of neurons. An artificial neural network (ANN) is a scan of the first-order characteristics of the human brain system. Simply put, it is a mathematical model represented by network topology, node characteristics, and learning rules. The optimized electronic fence further expands the user's range of activities, further updates the user's location information, and ensures the user's personal safety.

[0059] In one implementation, the adaptively constructing and optimizing the electronic fence based on the distance information of the preliminary electronic fence S40 includes:

[0060] Acquire tag information according to the distance information, wherein the tag information is information of businesses on both sides of the distance from the starting area to the end area;

[0061] Inputting the label information into a label classification model to obtain a label classification result;

[0062] Obtaining an accessible area according to the label classification result;

[0063] The accessible area is added to the preliminary electronic fence to construct the optimized electronic fence.

[0064] In this embodiment, the distance information obtained from the starting area to the end area refers to the merchant information between the starting area and the end area, and the label information refers to the marking of the merchant information. Specifically, in one embodiment, for example, there are stationery stores, bookstores, etc. between home and school, and such merchants are marked as learning merchants. For example, there are game halls, toy stores, etc., and such merchants are marked as entertainment merchants. According to the label information, a neural network model is used to classify all merchant information into accessible areas, absolutely prohibited areas, and unknown areas. Merchants classified as accessible areas are added to the preliminary electronic fence, and the preliminary electronic fence is further optimized.

[0065] In one implementation, before the step of inputting the tag information into the tag classification model to obtain the tag classification result, the following steps are included:

[0066] Obtaining the label information from the big data, and annotating the label information with the label classification result to obtain an annotation result;

[0067] Storing the labeling results in a database to obtain a label information database;

[0068] Establishing the label classification model, copying the data in the label information database into training data and test data;

[0069] Inputting the training data into the label classification model for training until a training accuracy threshold or a preset number of training times is reached;

[0070] Inputting the test data into the trained label classification model for testing until a test accuracy threshold or a preset number of tests is reached;

[0071] When the test accuracy threshold or the preset number of tests is reached, the label classification model is obtained.

[0072] In this embodiment, a label database is obtained from the database of the system. The label database is the classification data of all merchant information. The classification data of merchant information includes catering, entertainment, learning merchants, etc. The training set can be constructed in Python or MATLAB. The training set (Training data) is the data set we use to build the model, such as the parameters w and b of the univariate linear regression, the weight w and bias b of the neural network. The training data is input into the training set for training until the training accuracy threshold and the preset number of training times are reached, and the training process of the label model is completed. The training data is for the label database. When the model training is completed, the test data is input into the trained model to obtain the test results. The quality of the label model is judged according to the test results. After the test of the model is completed, the label classification model is obtained, and the label information of all merchants from the starting area to the end area set by the current user is input. The classification result is obtained from the label classification model that has completed the test. The classification result is data that classifies all businesses from the starting area to the end area set by the current user as accessible, absolutely prohibited, and unknown areas. Specifically, in this embodiment, the training accuracy threshold is 98%, and the number of training times is 200,000 times; the test accuracy threshold is 98%, and the number of tests is 100,000 times. The above values ​​can be set according to the system. The test set is used to test the generalization ability of the model. If the test accuracy is not high (the generalization ability is not strong), then the optimal model constructed by the training set and the validation set is a failure. The test set (Test data) is often used to compare the pros and cons of different models. For example, in many Kaggle competitions, the competition initially only gives training sets and validation sets. When the competition is about to end, the test data set is used to determine whose model is better. The accuracy of merchant classification is further improved through label model classification, and the effectiveness of monitoring user safety is improved.

[0073] In one embodiment, after the step of adding the accessible area to the preliminary electronic fence and constructing the optimized electronic fence, the following steps are included:

[0074] The area of ​​the optimized electronic fence where no tag information is obtained is set as an unknown area;

[0075] Based on the unknown area, an optimized electronic fence is constructed.

[0076] In this embodiment, the re-optimization of the electronic fence further optimizes the merchant information within the optimized electronic fence that is classified as an unknown area by the tag model. Specifically, in one embodiment, when the distance data locates the user entering an unknown area, such as a pet store, this information is sent to the user's associated person. The information includes merchant information, the user's stay time, and so on. Based on the acquired merchant information, the student card obtains from big data the number of similar merchant information that has been set as accessible areas and absolutely prohibited areas by similar student cards. The similar student cards are student cards with the same function as the student card in this embodiment. The user uses the settings for the merchant on the similar student cards as a reference to determine whether to add the unknown area to the accessible area. In addition, when the distance data from the school card backend locates the user entering an area classified as an absolutely prohibited area by the tag model, the student card immediately sends a red alert to the user's associated person at the starting time and sends the user's location information to the user's associated person to remind them and ensure their safety. The re-optimization of the electronic fence further expands the scope of user protection and is set according to the user's own needs, making it more user-friendly.

[0077] In one implementation, constructing a re-optimized electronic fence based on the unknown area includes:

[0078] Obtaining business information in the unknown area;

[0079] Acquire, based on the merchant information, the area division information of the merchant for the wearable student card of the same type, wherein the area division information includes an accessible area and an absolutely prohibited area;

[0080] Counting the number of the accessible areas and the number of the absolutely prohibited areas respectively;

[0081] Determining, according to the number of the accessible areas and the absolutely prohibited areas, whether to add the merchant information to the accessible areas or to add the merchant to the absolutely prohibited areas;

[0082] If the merchant information is added to the accessible area, the merchant information is added to the re-optimized electronic fence to construct the re-optimized electronic fence.

[0083] In this embodiment, the unknown area refers to the merchants without specific label information between the starting area and the end area when the preliminary electronic fence is optimized. Specifically, in one embodiment, when the route information recognizes that the user has entered the unknown area, the positioning terminal immediately locates the user's specific location, obtains the merchant information of the unknown area, and sends the information to the user's associated person's terminal to remind the associated person of the user's specific location. The associated person determines whether the user is in danger in the unknown area. At the same time, the student card will also obtain data showing that the same type of merchant information is set as a safe area or an unsafe area in the same type of student card. The safe area and the unsafe area are set as an accessible area and an absolutely prohibited area in the student card. The user's associated person can also judge the situation based on the data. Whether there is danger in the unknown area, if the associated person believes that the area is safe, the area can be set as an accessible area. After the setting is completed, when the user enters the area again, no warning information will be sent to the associated person's terminal. The scope of the optimized electronic fence has also been further expanded and optimized. The re-optimization of the electronic fence can be automatically updated and optimized at a fixed time period. The update time can be the system default time, or it can be set by the user, such as updating every week or every month, so as to obtain the re-optimization of the electronic fence more accurately. For example, when a store moves away, or when the statistical data of each terminal changes, it is also necessary to keep pace with the times and further dynamically update the re-optimized fence, which reduces the pressure of the associated person's supervision and ensures the safety of the user.

[0084] In one implementation, determining whether to add the merchant information to the accessible area or to add the merchant to the absolutely prohibited area based on the number of the accessible area and the absolutely prohibited area includes:

[0085] If the number of the accessible areas is greater than the number of the absolutely prohibited areas, the merchant is added to the accessible areas;

[0086] If the number of the accessible areas is less than the number of the absolutely prohibited areas, the merchant is added to the absolutely prohibited areas.

[0087] In this embodiment, the user determines whether to add a merchant to the accessible area or the absolutely prohibited area based on the number of merchants marked as accessible areas or absolutely prohibited areas on similar wearable student cards. The merchant can be a merchant of the same type or a merchant that is located. The number of merchants marked as accessible areas and absolutely prohibited areas on similar wearable student cards is obtained from big data. When the number of accessible areas is greater than the number of absolutely prohibited areas, the merchant is added to the accessible area. When the number of accessible areas is less than the number of absolutely prohibited areas, the merchant is added to the absolutely prohibited area. This is a more preferred solution suitable for most users. For special users, it can also be determined based on the user's specific actual needs. Through the above technical solution, the optimization of electronic fences is improved.

[0088] In one implementation, if the number of the accessible areas is less than the number of the absolutely prohibited areas, after the step of adding the merchant to the absolutely prohibited areas, the method further includes:

[0089] Determining whether the user has entered the absolutely prohibited area;

[0090] If the user enters the absolutely prohibited area, a red warning message is sent to the user's associated terminal;

[0091] If the user does not enter the absolutely prohibited area, a yellow warning message will be sent to the associated terminal of the user.

[0092] In this embodiment, the absolutely prohibited areas are businesses classified as absolutely prohibited areas after the business information is classified using the label model. The absolutely prohibited areas include businesses such as internet cafes and hotels that may have a certain negative impact on the user. The associated person may also designate businesses that pose safety risks or adverse effects to the user as inaccessible areas based on the user's specific situation. The label model is used to classify accessible and absolutely prohibited areas suitable for the user. When the route information locates the user entering the area, a strong reminder is sent to the associated person's terminal. The strong reminder may be a call reminder or a red warning message issued on the associated person's terminal. When the user is located and does not enter the area, a yellow warning message is sent to the user's associated person's terminal. The yellow warning message may be a silent reminder issued on the associated person's terminal and displayed when the user opens the terminal to inform the associated person of the user's specific situation. The warning information includes the name of the area the user entered, the duration of the stay, and video recordings. The user can set it as needed. By determining whether the user has entered the absolutely prohibited area, the corresponding reminder message is sent to the associated person's terminal, further ensuring the user's life safety.

[0093] Reference Figure 2 The present application provides an adaptive fence construction device for a wearable student card, the device comprising:

[0094] The region acquisition module 10 is used to acquire the starting region and the ending region;

[0095] A distance acquisition module 20 is used to acquire distance information from the starting area to the end area;

[0096] A preliminary electronic fence module 30 constructs and generates a preliminary electronic fence based on the starting area, the ending area, and the distance information from the starting area to the ending area;

[0097] The electronic fence optimization module 40 adaptively constructs an optimized electronic fence based on the distance information of the preliminary electronic fence.

[0098] As described above, it can be understood that the various components of the adaptive fence construction device for the wearable student card proposed in this application can realize the functions of any of the adaptive fence construction methods for the wearable student card described above.

[0099] In one embodiment, the electronic fence optimization module 40 further includes executing:

[0100] Acquire tag information according to the distance information, wherein the tag information is information of businesses on both sides of the distance from the starting area to the end area;

[0101] Inputting the label information into a label classification model to obtain a label classification result;

[0102] Obtaining an accessible area according to the label classification result;

[0103] The accessible area is added to the preliminary electronic fence to construct the optimized electronic fence.

[0104] In one embodiment, the electronic fence optimization module 40 further includes executing:

[0105] Obtaining the label information from the big data, and annotating the label information with the label classification result to obtain an annotation result;

[0106] Storing the labeling results in a database to obtain a label information database;

[0107] Establishing the label classification model, copying the data in the label information database into training data and test data;

[0108] Inputting the training data into the label classification model for training until a training accuracy threshold or a preset number of training times is reached;

[0109] Inputting the test data into the trained label classification model for testing until a test accuracy threshold or a preset number of tests is reached;

[0110] When the test accuracy threshold or the preset number of tests is reached, the label classification model is obtained.

[0111] In one embodiment, the electronic fence optimization module 40 further includes executing:

[0112] The area of ​​the optimized electronic fence where no tag information is obtained is set as an unknown area;

[0113] Based on the unknown area, an optimized electronic fence is constructed.

[0114] In one embodiment, the electronic fence optimization module 40 further includes executing:

[0115] Obtaining business information in the unknown area;

[0116] According to the merchant information, obtaining the area division information of the merchant for the wearable student card of the same type, wherein the area division information includes an accessible area and an absolutely prohibited area;

[0117] Counting the number of the accessible areas and the number of the absolutely prohibited areas respectively;

[0118] Determining, according to the number of the accessible areas and the absolutely prohibited areas, whether to add the merchant information to the accessible areas or to add the merchant to the absolutely prohibited areas;

[0119] If the merchant information is added to the accessible area, the merchant information is added to the re-optimized electronic fence to construct the re-optimized electronic fence.

[0120] In one embodiment, the electronic fence optimization module 40 further includes executing:

[0121] If the number of the accessible areas is greater than the number of the absolutely prohibited areas, the merchant is added to the accessible areas;

[0122] If the number of the accessible areas is less than the number of the absolutely prohibited areas, the merchant is added to the absolutely prohibited areas.

[0123] In one embodiment, the electronic fence optimization module 40 further includes executing:

[0124] Determining whether the user has entered the absolutely prohibited area;

[0125] If the user enters the absolutely prohibited area, a red warning message is sent to the user's associated terminal;

[0126] If the user does not enter the absolutely prohibited area, a yellow warning message will be sent to the associated terminal of the user.

[0127] Reference Figure 3 In the embodiment of the present application, a device is also provided, the internal structure of the computer device can be as follows Figure 3 As shown. The device includes a processor, a memory, a network interface, a display device, and an input device connected via a system bus. The network interface of the device is used to communicate with an external terminal via a network connection. The display device of the device is used to display an interactive page. The input device of the device is used to receive user input. The processor designed for the device is used to provide computing and control capabilities. The memory of the device includes a non-volatile storage medium. The non-volatile storage medium stores an operating system, a computer program, and a database. The database of the device is used to store raw data. When the computer program is executed by the processor, it implements a method for constructing an adaptive fence for a wearable student card. The above-mentioned processor executes the above-mentioned method for constructing an adaptive fence for a wearable student card, including: obtaining a starting area and obtaining an end area; obtaining the distance from the starting area to the end area; constructing and generating a preliminary electronic fence based on the starting area, the end area, and the distance information from the starting area to the end area; and adaptively constructing an optimized electronic fence based on the distance information of the preliminary electronic fence.

[0128] The present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by the processor, implements a method for constructing an adaptive fence for a wearable student card, comprising the following steps: The present application relates to a method, apparatus, device, and medium for constructing an adaptive fence for a wearable student card, and relates to the field of intelligent wearable technology, wherein the method comprises obtaining a starting area and an ending area; obtaining the distance from the starting area to the ending area; constructing a preliminary electronic fence based on the starting area, the ending area, and the distance from the starting area to the ending area; and adaptively constructing an optimized electronic fence based on the distance information of the preliminary electronic fence. Setting the electronic fence area according to the student's activity trajectory or actual needs not only protects the student's activity range, but also improves the effectiveness of guardianship.

[0129] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct RAM bus dynamic RAM (DRDRAM), and RAM bus dynamic RAM (RDRAM), etc.

[0130] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0131] The above description is only a preferred embodiment of the present application and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for constructing an adaptive fence for a wearable student card, characterized in that: The method comprises: Get the starting area and the ending area; Obtaining distance information from the starting area to the end area; Constructing and generating a preliminary electronic fence based on the starting area, the ending area, and the distance information from the starting area to the ending area; Adaptively constructing an optimized electronic fence based on the distance information of the preliminary electronic fence; The adaptively constructing and optimizing the electronic fence based on the distance information of the preliminary electronic fence includes: Acquire tag information according to the distance information, wherein the tag information is information of businesses on both sides of the distance from the starting area to the end area; Inputting the label information into a label classification model to obtain a label classification result; Obtaining an accessible area according to the label classification result; Adding the accessible area to the preliminary electronic fence to construct the optimized electronic fence; Wherein, after the step of adding the accessible area to the preliminary electronic fence and constructing the optimized electronic fence, the following steps are included: The area of ​​the optimized electronic fence where no tag information is obtained is set as an unknown area; Based on the unknown area, construct and optimize the electronic fence again; The step of constructing a re-optimized electronic fence based on the unknown area includes: Obtaining business information in the unknown area; According to the merchant information, obtaining the area division information of the merchant for the wearable student card of the same type, wherein the area division information includes an accessible area and an absolutely prohibited area; Counting the number of the accessible areas and the number of the absolutely prohibited areas respectively; Determining, according to the number of the accessible areas and the absolutely prohibited areas, whether to add the merchant information to the accessible areas or to add the merchant to the absolutely prohibited areas; If the merchant information is added to the accessible area, the merchant information is added to the re-optimized electronic fence to construct the re-optimized electronic fence.

2. The method for constructing an adaptive fence for a wearable student card according to claim 1, characterized in that: Before the step of inputting the tag information into the tag classification model to obtain the tag classification result, the method includes: Obtaining the label information from the big data, and annotating the label information with the label classification result to obtain an annotation result; Storing the labeling results in a database to obtain a label information database; Establishing the label classification model to divide the data in the label information database into training data and test data; Inputting the training data into the label classification model for training until a training accuracy threshold or a preset number of training times is reached; Inputting the test data into the trained label classification model for testing until a test accuracy threshold or a preset number of tests is reached; When the test accuracy threshold or the preset number of tests is reached, the label classification model is obtained.

3. The method for constructing an adaptive fence for a wearable student card according to claim 2, characterized in that: The determining, based on the number of the accessible areas and the absolutely prohibited areas, whether to add the merchant information to the accessible areas or to add the merchant to the absolutely prohibited areas includes: If the number of the accessible areas is greater than the number of the absolutely prohibited areas, the merchant is added to the accessible areas; If the number of the accessible areas is less than the number of the absolutely prohibited areas, the merchant is added to the absolutely prohibited areas.

4. The method for constructing an adaptive fence for a wearable student card according to claim 3, characterized in that: After the step of adding the merchant to the absolutely prohibited area if the number of the accessible areas is less than the number of the absolutely prohibited areas, the method further includes: Determining whether the user has entered the absolutely prohibited area; If the user enters the absolutely prohibited area, a red warning message is sent to the user's associated terminal; If the user does not enter the absolutely prohibited area, a yellow warning message is sent to the user's associated terminal.

5. An adaptive fence construction device for a wearable student card, characterized in that: The device comprises: The area acquisition module is used to obtain the starting area and the end area; A distance acquisition module is used to obtain distance information from the starting area to the end area; A preliminary electronic fence module is configured to construct and generate a preliminary electronic fence based on the starting area, the ending area, and the distance information from the starting area to the ending area; An optimized electronic fence module is configured to adaptively construct an optimized electronic fence based on the distance information of the preliminary electronic fence; The step of adaptively constructing an optimized electronic fence based on the distance information of the preliminary electronic fence includes: Acquire tag information according to the distance information, wherein the tag information is information of businesses on both sides of the distance from the starting area to the end area; Inputting the label information into a label classification model to obtain a label classification result; Obtaining an accessible area according to the label classification result; Adding the accessible area to the preliminary electronic fence to construct the optimized electronic fence; Wherein, after the step of adding the accessible area to the preliminary electronic fence and constructing the optimized electronic fence, the following steps are included: The area of ​​the optimized electronic fence where no tag information is obtained is set as an unknown area; Based on the unknown area, construct and optimize the electronic fence again; The step of constructing a re-optimized electronic fence based on the unknown area includes: Obtaining business information in the unknown area; According to the merchant information, obtaining the area division information of the merchant for the wearable student card of the same type, wherein the area division information includes an accessible area and an absolutely prohibited area; Counting the number of the accessible areas and the number of the absolutely prohibited areas respectively; Determining, according to the number of the accessible areas and the absolutely prohibited areas, whether to add the merchant information to the accessible areas or to add the merchant to the absolutely prohibited areas; If the merchant information is added to the accessible area, the merchant information is added to the re-optimized electronic fence to construct the re-optimized electronic fence.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the processor implements the steps of the adaptive fence construction method of the wearable student card according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for constructing an adaptive fence for a wearable student card according to any one of claims 1 to 4 are implemented.

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