Anti-lost data processing method and system and wrist strap

Abstract

The invention provides an anti-lost data processing method and system and a wrist strap, and relates to the technical field of data processing, and the method comprises the steps: obtaining the real-time positioning information of a target user, and carrying out the analysis according to the historical positioning information, and obtaining a positioning change coefficient; according to the real-time positioning information and a preset positioning coordinate, calculating to obtain a moving distance, and classifying to obtain a basic pushing frequency; acquiring a plurality of pieces of care positioning information of a plurality of care users, respectively calculating care distances, and calculating to obtain a plurality of distance coefficients; and performing compensation adjustment on the basic pushing frequency according to the plurality of distance coefficients and the positioning change coefficient to obtain a plurality of pushing frequencies, and pushing the real-time positioning information to the plurality of care users. According to the method and the device, the technical problem that the timeliness and the accuracy of anti-lost response are insufficient due to the fact that anti-lost generally depends on monitoring equipment, positioning is carried out only on the basis of a preset positioning area and the behavior pattern of the patient cannot be dynamically tracked or the lost risk cannot be evaluated in real time in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a data processing method, system and wristband for preventing loss. Background Art

[0002] With the aging of society and the increase in the number of patients with diseases, especially in hospitals, nursing homes, senior citizen apartments and other places, preventing patients or elderly people from getting lost has become a major problem that needs to be solved urgently. Lost incidents not only bring huge psychological burdens to patients and their families, but also increase the work pressure of caregivers, and may even endanger the lives of patients. Traditional anti-lost technologies generally rely on monitoring equipment, such as electronic fences and locators, which can sound an alarm when patients leave a designated area. However, these devices usually only locate based on preset positioning areas, and fail to dynamically track patients' behavior patterns or assess the risk of getting lost in real time, resulting in an inability to respond promptly and effectively to sudden behaviors. Summary of the invention

[0003] The present application provides a data processing method, system and wristband for preventing loss, aiming to solve the technical problem that the prior art of preventing loss generally relies on monitoring equipment, only performs positioning based on a preset positioning area, fails to dynamically track the patient's behavior pattern or assess the risk of loss in real time, resulting in insufficient timeliness and accuracy of the anti-lost response.

[0004] The first aspect disclosed in the present application provides a data processing method for preventing getting lost, the method comprising: obtaining real-time positioning information of a target user, and analyzing and obtaining a positioning change coefficient based on historical positioning information of the target user within a historical period; calculating and obtaining a moving distance based on the real-time positioning information and preset positioning coordinates corresponding to the target user, and classifying and obtaining a basic push frequency; obtaining multiple care positioning information of multiple care users, respectively calculating the care distances between the multiple care positioning information and the real-time positioning information, and calculating and obtaining multiple distance coefficients; compensating and adjusting the basic push frequency based on the multiple distance coefficients and the positioning change coefficient to obtain multiple push frequencies, and pushing the real-time positioning information of the target user to the multiple care users according to the multiple push frequencies.

[0005] The second aspect disclosed in the present application provides a data processing system for preventing getting lost, the system being used for the above-mentioned data processing method for preventing getting lost, the system comprising: a positioning change coefficient acquisition module, used to acquire the real-time positioning information of the target user, and to analyze and acquire the positioning change coefficient based on the historical positioning information of the target user in the historical time; a basic push frequency acquisition module, used to calculate and acquire the moving distance based on the real-time positioning information and the preset positioning coordinates corresponding to the target user, and to classify and acquire the basic push frequency; a distance coefficient acquisition module, used to acquire multiple care positioning information of multiple care users, respectively calculate the care distances between the multiple care positioning information and the real-time positioning information, and calculate and acquire multiple distance coefficients; a positioning information push module, used to compensate and adjust the basic push frequency based on the multiple distance coefficients and the positioning change coefficients, to acquire multiple push frequencies, and to push the real-time positioning information of the target user to the multiple care users according to the multiple push frequencies.

[0006] According to a third aspect disclosed in the present application, a wrist band is provided, wherein the wrist band includes a data processing system for preventing loss provided in the second aspect disclosed in the present application.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] By obtaining the historical positioning information of the target user, the positioning change coefficient is calculated. The positioning change coefficient reflects the activity pattern and risk of loss of the target user. The larger the coefficient, the more active the target user is and the higher the possibility of loss. By tracking and analyzing the user's behavior pattern, a quick response can be made when the risk of loss of the target user increases, thereby improving the accuracy and timeliness of preventing loss. By obtaining the real-time positioning information of the target user, the moving distance is calculated based on the real-time positioning information and the preset positioning coordinates of the target user. The larger the moving distance, the more the target user deviates from his or her predetermined position and the higher the risk of loss. At this time, the caregiver is reminded by increasing the push frequency. This mechanism of dynamically adjusting the push frequency improves the timeliness and effectiveness of preventing loss. By obtaining the care positioning information of multiple caregivers and calculating the distance between each caregiver and the target user The care distance is calculated to obtain the distance coefficient, which can dynamically evaluate the care difficulty of the caregiver. The smaller the distance coefficient, the closer the caregiver is to the target user and the easier it is to care. At this time, the push frequency is increased to ensure that these caregivers can quickly obtain the location information of the target user, making the information transmission more accurate and timely; the care distance coefficient and the positioning change coefficient are used to compensate and adjust the push frequency. When the caregiver is close and the target user is less active, the push frequency will be reduced to avoid unnecessary frequent push interference; when the target user deviates far from the predetermined location or has a high risk of getting lost, the push frequency will increase to ensure that the caregiver can respond quickly. Through this compensation adjustment mechanism, it can not only ensure that the caregiver obtains timely positioning information, but also avoids too frequent information interference, thereby improving the system availability and the work efficiency of the caregiver.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A flowchart of a data processing method for preventing loss provided in an embodiment of the present application.

[0011] Figure 2 A schematic diagram of the structure of a data processing system for preventing loss provided in an embodiment of the present application.

[0012] Description of the reference numerals: positioning variation coefficient acquisition module 10 , basic push frequency acquisition module 20 , distance coefficient acquisition module 30 , positioning information push module 40 . DETAILED DESCRIPTION

[0013] The embodiments of the present application provide a data processing method, system and wristband for preventing getting lost, thereby solving the technical problem that the prior art of preventing getting lost generally relies on monitoring equipment, only performs positioning based on a preset positioning area, fails to dynamically track the patient's behavior patterns or assess the risk of getting lost in real time, and results in insufficient timeliness and accuracy of the anti-get lost response.

[0014] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0015] Embodiment 1, as Figure 1 As shown, an embodiment of the present application provides a data processing method for preventing loss, the method comprising:

[0016] Step S100: acquiring the real-time positioning information of the target user, and analyzing and obtaining the positioning change coefficient according to the historical positioning information of the target user within a historical period.

[0017] Through positioning chips, such as Beidou positioning chips, the location of the target user is tracked in real time. The real-time positioning information obtained can be a longitude and latitude coordinate. Through historical records, the historical positioning information of the target user within a certain period of time in the past is obtained. The historical positioning information contains timestamp and location information, which represents the movement trajectory of the target user in the past period of time. According to the historical positioning information of the target user, the user's historical moving speed is calculated. The historical moving speed can be calculated by the time difference and position difference between two consecutive positioning points.

[0018] The ratio of the target user's historical moving speed to the preset moving speed is calculated to obtain the positioning variation coefficient. The preset moving speed is usually based on the normal behavior expectations of the user type. For example, for a patient, the preset moving speed may be slower. The larger the positioning variation coefficient is, the greater the difference between the target user's actual moving speed and the expected moving speed is. This usually means that the target user has abnormal movement behavior within a certain period of time and may be at risk of getting lost.

[0019] Step S200: Calculate the moving distance based on the real-time positioning information and the preset positioning coordinates corresponding to the target user, and classify and obtain the basic push frequency.

[0020] Get the preset location coordinates corresponding to the target user, which is usually the ward where the target user is located. The preset location coordinates are used to compare the target user's current location with the reference point. According to the real-time location information, calculate the moving distance between the target user's current location and the preset location coordinates. The distance is obtained by calculating the geographical distance between the two. The larger the distance, the farther the target user is from the preset location coordinates, and the greater the risk of getting lost. According to the calculated moving distance, the target user's behavior is divided into different categories, that is, the larger the moving distance, the greater the basic push frequency, so that frequent pushes are performed to avoid getting lost.

[0021] Step S300: Acquire multiple care location information of multiple care users, respectively calculate the care distances between the multiple care location information and the real-time location information, and calculate and obtain multiple distance coefficients.

[0022] Multiple caregivers, such as family members and nurses, obtain multiple caregiver positioning information of multiple caregivers through terminal devices of multiple caregivers. For each caregiver, the care distance between the caregiver and the target user is calculated based on the caregiver positioning information and the real-time positioning information. The caregiver distance reflects the spatial relationship between the caregiver and the target user.

[0023] Multiple distance coefficients are calculated based on the care distances between multiple care users and the target user. The distance coefficient represents the difficulty for the care user to care for the target user. That is, the greater the care distance, the greater the difficulty for the care user to care for the target user, and the smaller the distance coefficient; conversely, the smaller the care distance, the smaller the care difficulty, and the greater the distance coefficient.

[0024] Step S400: compensating and adjusting the basic push frequency according to the multiple distance coefficients and positioning change coefficients to obtain multiple push frequencies, and pushing the real-time positioning information of the target user to the multiple caring users according to the multiple push frequencies.

[0025] The compensation adjustment coefficient is calculated based on the distance coefficient of the attending user and the positioning change coefficient of the target user. Specifically, the distance coefficient and the positioning change coefficient can be multiplied to obtain a compensation coefficient. This compensation coefficient determines the adjustment range of the push frequency. The corrected push frequency is obtained by multiplying the basic push frequency by the compensation adjustment coefficient. This push frequency reflects the adjusted push frequency based on the changes in the attending distance between the attending user and the target user and the moving speed of the target user.

[0026] According to the corrected push frequency of each caregiver, the real-time positioning information of the target user is pushed to all caregivers. For caregivers with a larger distance coefficient (i.e., caregivers who are closer to the target user), the push frequency will be increased to ensure that these caregivers can obtain the positioning information of the target user as soon as possible and take timely measures; for caregivers with a smaller distance coefficient (i.e., caregivers who are farther away from the target user), although the push frequency is relatively low, it is still ensured that they can obtain the location information of the target user in time to avoid negligence.

[0027] Furthermore, obtaining the real-time positioning information of the target user and analyzing and obtaining the positioning change coefficient according to the historical positioning information of the target user in the historical time includes:

[0028] Step S110: obtaining the real-time positioning information of the target user through the positioning chip;

[0029] Step S120: Acquire multiple historical positioning information recorded by the target user in historical time to obtain a historical positioning information sequence;

[0030] Step S130: Calculate and obtain a positioning change coefficient based on the historical positioning information sequence.

[0031] Through positioning chips, such as Beidou positioning chips, by receiving signals transmitted from the Beidou positioning system, the current location of the target user is determined, usually displayed in the form of longitude and latitude coordinates. The real-time positioning information obtained includes not only the location of the target user, but also a timestamp, so that it can be compared with subsequent historical positioning information.

[0032] Obtain multiple historical positioning information of the target user in the past period of time. These historical positioning information are continuously recorded by the positioning chip to form a historical positioning information sequence. Each historical positioning information contains a timestamp and location coordinates.

[0033] According to the historical positioning information sequence, the moving speed of the target user between two consecutive positioning points is calculated. By calculating the moving speed of the target user between multiple time points in the historical positioning information sequence, multiple speed values ​​are obtained. The average of these speed values ​​is used as the average historical moving speed of the target user. The ratio of the average historical moving speed of the target user to the preset moving speed is calculated to obtain the positioning variation coefficient. The larger the positioning variation coefficient, the higher the moving speed of the target user than expected, which usually means that the target user's mobile behavior is more active and the risk of getting lost is greater.

[0034] Furthermore, calculating the positioning change coefficient according to the historical positioning information sequence includes:

[0035] Step S131: Calculate and obtain multiple historical moving speeds according to the historical positioning information sequence, and calculate the average to obtain an average historical moving speed;

[0036] Step S132: Calculate the ratio of the average historical moving speed to the preset moving speed to obtain a positioning variation coefficient.

[0037] The historical positioning information sequence is multiple positioning information of the target user within a period of time. Each positioning information contains a timestamp and the location information at that moment. By calculating the geographical distance between two consecutive positioning points, the actual moving distance of the target user between the two time points is obtained. The actual moving distance is divided by the time difference between the two time points to calculate the moving speed of the target user during this period. Similarly, by analyzing multiple consecutive time points in the historical positioning information sequence, multiple historical moving speeds are calculated, and all historical moving speeds are averaged to obtain the average historical moving speed of the target user.

[0038] The preset moving speed is a standard value set according to the expected behavior of the target user, and is usually estimated based on factors such as the target user's health status and behavioral habits. For example, for a patient with slower movements, the preset moving speed may be set to walk 100 meters per hour; while for a patient with more agile movements, the preset speed may be higher. The ratio of the average historical moving speed to the preset moving speed is calculated to obtain the positioning variation coefficient, which is used to represent the difference between the actual moving speed of the target user and the preset speed, reflecting the deviation between the target user's activity pattern and the expectation. Specifically, the larger the positioning variation coefficient, the more frequent the target user's activities and the higher the risk of getting lost; the smaller the positioning variation coefficient, the less active the target user is and the lower the risk of getting lost.

[0039] Furthermore, according to the real-time positioning information and the preset positioning coordinates corresponding to the target user, the moving distance is calculated and the basic push frequency is obtained by classification, including:

[0040] Step S210: Acquire the preset location coordinates of the target user;

[0041] Step S220: Calculate the distance between the real-time positioning information and the preset positioning coordinates to obtain a moving distance;

[0042] Step S230 classifies and obtains a basic push frequency according to the moving distance.

[0043] The preset positioning coordinates of the target user are obtained. The preset positioning coordinates refer to the reference position where the target user should be located. For example, the preset positioning coordinates of a patient may be the position of the ward where the patient is located. This position is usually set based on the expected activity range of the target user.

[0044] Calculate the distance between the target user's real-time location information and its preset location coordinates. This can be done by using a geographic distance calculation formula. By calculating, the moving distance of the target user from its preset location to the current location is obtained.

[0045] The activities of the target user are classified according to their moving distance. Different activity levels can be divided according to the moving distance of the target user, such as small moving distance, moderate moving distance, large moving distance, etc. According to the preset classification standards, different activity levels are converted into corresponding basic push frequencies. The push frequency is set to ensure that the caregiver can obtain the location of the target user in time through a higher push frequency when the risk of the target user getting lost is high.

[0046] Furthermore, according to the moving distance, the basic push frequency is obtained by classification, including:

[0047] Step S231: obtaining a set of sample moving distances, and respectively configuring different sample push frequencies to obtain a set of sample push frequencies, wherein the size of the sample push frequency is positively correlated with the size of the sample moving distance;

[0048] Step S232: constructing a push frequency classifier based on the mapping relationship between the sample moving distance set and the sample push frequency set;

[0049] Step S233: input the moving distance into the push frequency classifier, and classify to obtain the basic push frequency.

[0050] Collect historical sample data, which includes the user's moving distance information. In these samples, each data point represents the actual moving distance of a target user in a certain period of time. For each sample moving distance, configure a corresponding sample push frequency according to the distance. For example, if the user moves 100 meters, configure a push frequency of once every 10 minutes; if the user moves 500 meters, configure a push frequency of once every 5 minutes. In this step, the size of the sample push frequency is positively correlated with the moving distance, that is, the greater the user's moving distance, the higher the push frequency, because a longer moving distance means that the user may deviate from the normal position, the risk of getting lost increases, and the positioning information needs to be pushed more frequently.

[0051] By configuring a push frequency for each sample, a set of sample push frequencies corresponding to a set of sample moving distances is obtained, which represents the relationship between different moving distances and push frequencies.

[0052] Utilizing the mapping relationship between the sample moving distance set and the sample push frequency set, machine learning technology is used to build and train a recommendation frequency classifier. This classifier can output the corresponding push frequency based on the given input (moving distance of the target user), which provides a basis for dynamically adjusting the push frequency and can increase the push frequency in time when the risk of the target user getting lost increases.

[0053] The moving distance is input into the push frequency classifier. The classifier queries the mapping relationship and calculates the corresponding push frequency based on the input moving distance. For example, if the moving distance of the target user is 120 meters, the push frequency classifier may return a push frequency of once every 10 minutes. By using the classifier, the push frequency of the target user can be dynamically adjusted to ensure that the caregiver can receive the target user's positioning information in a timely manner.

[0054] Furthermore, obtaining multiple care location information of multiple care users, respectively calculating the care distances between the multiple care location information and the real-time location information, and calculating and obtaining multiple distance coefficients, includes:

[0055] Step S310: Acquire multiple care positioning information of multiple care users through terminal positioning of the multiple care users;

[0056] Step S320: respectively calculating the distances between the plurality of care positioning information and the real-time positioning information to obtain a plurality of care distances;

[0057] Step S330: Calculate the mean of the multiple care distances to obtain an average care distance;

[0058] Step S340: Calculate the ratio of the average care distance to the multiple care distances to obtain multiple distance coefficients.

[0059] Care users refer to those who are responsible for monitoring the target user, such as family members, nurses, caregivers, etc., and their care location information is obtained through the terminal devices of the care users. These terminal devices can be smart phones or wearable devices, etc., equipped with positioning functions. The multiple care location information obtained represents the current location of each care user, which can be expressed in latitude and longitude coordinates.

[0060] For each caring user, the distance between the two is calculated according to the caring location information of the caring user and the real-time location information of the target user. This process is repeated to calculate the distances between all caring users and the target user, and multiple caring distances are obtained.

[0061] The mean of the care distances between all care users and the target user is calculated. The mean represents the average care distance of all care users and can reflect the overall care difficulty level.

[0062] In order to evaluate the difficulty of caring for the target user, the caring distance between each caring user and the target user is compared with the average caring distance. By calculating the ratio of the average caring distance to multiple caring distances, multiple distance coefficients are calculated to reflect the caring difficulty of the corresponding caring user, so as to dynamically adjust the push frequency later. That is, the larger the caring distance, the more difficult it is for the caring user to care for the target user, and the smaller the distance coefficient; conversely, the smaller the caring distance, the smaller the difficulty of caring, and the larger the distance coefficient.

[0063] Furthermore, according to the multiple distance coefficients and the positioning change coefficients, the basic push frequency is compensated and adjusted to obtain multiple push frequencies, and the real-time positioning information of the target user is pushed to the multiple caring users according to the multiple push frequencies, including:

[0064] Step S410: multiplying each distance coefficient by the positioning change coefficient to obtain a plurality of compensation adjustment coefficients;

[0065] Step S420: multiplying the basic push frequency by the multiple compensation adjustment coefficients respectively to perform compensation adjustment to obtain multiple push frequencies;

[0066] Step S430: Pushing the real-time location information of the target user to the multiple caring users according to the multiple push frequencies.

[0067] By multiplying the distance coefficient of each caring user by the positioning change coefficient of the target user, a plurality of compensation adjustment coefficients are obtained. The compensation adjustment coefficients are used to reflect the combination of the caring difficulty and the activity intensity of the target user.

[0068] The basic push frequency represents the frequency at which the target user's location information is pushed to the care user without any adjustment. The compensation adjustment coefficient of each care user is multiplied by the corresponding basic push frequency to obtain the final push frequency of each care user. The role of the compensation adjustment coefficient is to dynamically adjust the push frequency according to the distance between the care user and the target user and the activity intensity of the target user.

[0069] According to the final push frequency of each caregiver user, the real-time positioning information of the target user is pushed to each caregiver user. Each caregiver user will receive the positioning information of the target user according to his or her corresponding push frequency. By dynamically adjusting the push frequency, it is ensured that when the risk of the target user getting lost increases, the caregiver user can obtain positioning information more frequently. For example, when the caregiver user is closer to the target user, the push frequency is increased to ensure that the caregiver user can take timely action; conversely, when the caregiver user is far away from the target user, the push frequency is reduced to avoid excessive pushes.

[0070] In summary, the data processing method for preventing loss provided by the embodiment of the present application has the following technical effects:

[0071] By obtaining the historical positioning information of the target user, the positioning change coefficient is calculated. The positioning change coefficient reflects the activity pattern and risk of loss of the target user. The larger the coefficient, the more active the target user is and the higher the possibility of loss. By tracking and analyzing the user's behavior pattern, a quick response can be made when the risk of loss of the target user increases, thereby improving the accuracy and timeliness of preventing loss. By obtaining the real-time positioning information of the target user, the moving distance is calculated based on the real-time positioning information and the preset positioning coordinates of the target user. The larger the moving distance, the more the target user deviates from his or her predetermined position and the higher the risk of loss. At this time, the caregiver is reminded by increasing the push frequency. This mechanism of dynamically adjusting the push frequency improves the timeliness and effectiveness of preventing loss. By obtaining the care positioning information of multiple caregivers and calculating the distance between each caregiver and the target user The care distance is calculated to obtain the distance coefficient, which can dynamically evaluate the care difficulty of the caregiver. The smaller the distance coefficient, the closer the caregiver is to the target user and the easier it is to care. At this time, the push frequency is increased to ensure that these caregivers can quickly obtain the location information of the target user, making the information transmission more accurate and timely; the care distance coefficient and the positioning change coefficient are used to compensate and adjust the push frequency. When the caregiver is close and the target user is less active, the push frequency will be reduced to avoid unnecessary frequent push interference; when the target user deviates far from the predetermined location or has a high risk of getting lost, the push frequency will increase to ensure that the caregiver can respond quickly. Through this compensation adjustment mechanism, it can not only ensure that the caregiver obtains timely positioning information, but also avoids too frequent information interference, thereby improving the system availability and the work efficiency of the caregiver.

[0072] Embodiment 2 is based on the same inventive concept as the data processing method for preventing loss in the above embodiment. Figure 2 As shown, an embodiment of the present application provides a data processing system for preventing loss, the system comprising:

[0073] The positioning change coefficient acquisition module 10 is used to acquire the real-time positioning information of the target user, and analyze the positioning change coefficient based on the historical positioning information of the target user in the historical time; the basic push frequency acquisition module 20 is used to calculate the moving distance based on the real-time positioning information and the preset positioning coordinates corresponding to the target user, and classify and obtain the basic push frequency; the distance coefficient acquisition module 30 is used to acquire multiple care positioning information of multiple care users, respectively calculate the care distances between the multiple care positioning information and the real-time positioning information, and calculate to obtain multiple distance coefficients; the positioning information push module 40 is used to compensate and adjust the basic push frequency according to the multiple distance coefficients and the positioning change coefficient, obtain multiple push frequencies, and push the real-time positioning information of the target user to the multiple care users according to the multiple push frequencies.

[0074] Furthermore, the positioning variation coefficient acquisition module 10 includes:

[0075] The real-time positioning information acquisition channel is used to obtain the real-time positioning information of the target user through the positioning chip; the historical positioning information sequence acquisition channel is used to obtain multiple historical positioning information recorded by the target user in the historical time to obtain the historical positioning information sequence; the positioning change coefficient calculation channel is used to calculate the positioning change coefficient according to the historical positioning information sequence.

[0076] Furthermore, the positioning variation coefficient calculation channel includes:

[0077] The average historical moving speed obtaining node is used to calculate and obtain multiple historical moving speeds according to the historical positioning information sequence, and calculate the average to obtain the average historical moving speed; the ratio calculation node is used to calculate the ratio of the average historical moving speed and the preset moving speed to obtain the positioning change coefficient.

[0078] Furthermore, the basic push frequency acquisition module 20 includes:

[0079] The preset positioning coordinate acquisition channel is used to obtain the preset positioning coordinates of the target user; the moving distance acquisition channel is used to calculate the distance between the real-time positioning information and the preset positioning coordinates to obtain the moving distance; the basic push frequency acquisition channel is used to classify and obtain the basic push frequency according to the moving distance.

[0080] Furthermore, the basic push frequency acquisition channel includes:

[0081] The sample push frequency set acquisition node is used to acquire the sample moving distance set, and configure different sample push frequencies respectively to obtain the sample push frequency set, wherein the size of the sample push frequency is positively correlated with the size of the sample moving distance; the push frequency classifier construction node is used to construct a push frequency classifier based on the mapping relationship between the sample moving distance set and the sample push frequency set; the basic push frequency acquisition node is used to input the moving distance into the push frequency classifier to classify and obtain the basic push frequency.

[0082] Furthermore, the distance coefficient acquisition module 30 includes:

[0083] The care positioning information acquisition channel is used to obtain multiple care positioning information of multiple care users through the terminal positioning of the multiple care users; the care distance acquisition channel is used to respectively calculate the distances between the multiple care positioning information and the real-time positioning information to obtain multiple care distances; the mean calculation channel is used to calculate the mean of the multiple care distances to obtain the average care distance; the ratio calculation channel is used to calculate the ratio of the average care distance to the multiple care distances to obtain multiple distance coefficients.

[0084] Furthermore, the positioning information push module 40 includes:

[0085] A compensation adjustment coefficient acquisition channel is used to obtain multiple compensation adjustment coefficients by multiplying each distance coefficient by the positioning change coefficient; a compensation adjustment channel is used to multiply the multiple compensation adjustment coefficients by the basic push frequency to perform compensation adjustment and obtain multiple push frequencies; a push channel is used to push the real-time positioning information of the target user to the multiple caring users according to the multiple push frequencies.

[0086] Through the above detailed description of a data processing method for preventing getting lost, those skilled in the art can clearly understand a data processing system for preventing getting lost in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0087] Embodiment 3, the embodiment of the present application provides a wristband, which includes a data processing system for preventing loss in embodiment 2.

[0088] The appearance structure of the wristband adopts a ring-shaped wristband design, and the shell is a detachable structure, which is convenient for the later repair and maintenance of the device and can realize timely and rapid replacement of the battery. The battery and chip are placed in the shell cavity. The shell surface is the same as the wristband for hospitalized patients, and can be personalized with ward information, hospitalization ID, patient name, gender, identifiable QR code information, etc.

[0089] In addition to the data processing system used to prevent getting lost, in the hardware design of the wristband, the solution used for real-time location acquisition uses a combination of satellite navigation chips, radio frequency circuits, and passive antennas for related designs. Another technology used in the wristband is narrowband Internet of Things technology. Narrowband Internet of Things mainly targets the characteristics of small data packets, and mainly enhances the functions of low data transmission rate, low power consumption, deep and wide coverage, and large connections. The wristband uses narrowband Internet of Things to remotely transmit information. Transmitting data to the telecommunications cloud allows medical staff and family members to remotely view the wearer's location information and grasp the wearer's dynamics. The battery uses a 200mAH lithium polymer battery to meet a battery life of more than one month and provide power for the operation of the data processing system used to prevent getting lost.

[0090] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A data processing method for preventing loss, characterized in that: The method comprises: Acquire the real-time positioning information of the target user, and analyze and obtain the positioning change coefficient based on the historical positioning information of the target user within a historical period; According to the real-time positioning information and the preset positioning coordinates corresponding to the target user, the moving distance is calculated and the basic push frequency is obtained by classification; Acquire multiple care location information of multiple care users, respectively calculate the care distances between the multiple care location information and the real-time location information, and calculate and obtain multiple distance coefficients; According to the multiple distance coefficients and positioning change coefficients, the basic push frequency is compensated and adjusted to obtain multiple push frequencies, and the real-time positioning information of the target user is pushed to the multiple caring users according to the multiple push frequencies.

2. The data processing method for preventing loss according to claim 1, characterized in that: Acquire the real-time positioning information of the target user, and analyze and obtain the positioning change coefficient based on the historical positioning information of the target user in the historical time, including: Obtain the real-time location information of the target user through the positioning chip; Acquire multiple historical positioning information of the target user recorded in historical time to obtain a historical positioning information sequence; A positioning change coefficient is calculated based on the historical positioning information sequence.

3. The data processing method for preventing loss according to claim 2, characterized in that: Calculating the positioning change coefficient according to the historical positioning information sequence includes: According to the historical positioning information sequence, a plurality of historical moving speeds are calculated, and an average is calculated to obtain an average historical moving speed; The ratio of the average historical moving speed to the preset moving speed is calculated to obtain the positioning change coefficient.

4. The data processing method for preventing loss according to claim 1, characterized in that: According to the real-time positioning information and the preset positioning coordinates corresponding to the target user, the moving distance is calculated and the basic push frequency is obtained by classification, including: Obtaining preset location coordinates of the target user; Calculating the distance between the real-time positioning information and the preset positioning coordinates to obtain the moving distance; According to the moving distance, the basic push frequency is obtained by classification.

5. The data processing method for preventing loss according to claim 4, characterized in that: According to the moving distance, the basic push frequency is obtained by classification, including: Obtain a set of sample moving distances, and respectively configure different sample push frequencies to obtain a set of sample push frequencies, wherein the size of the sample push frequency is positively correlated with the size of the sample moving distance; Based on the mapping relationship between the sample moving distance set and the sample pushing frequency set, construct a pushing frequency classifier; The moving distance is input into the push frequency classifier, and the basic push frequency is obtained by classification.

6. The data processing method for preventing loss according to claim 1, characterized in that: Acquiring multiple care location information of multiple care users, respectively calculating the care distances between the multiple care location information and the real-time location information, and calculating and obtaining multiple distance coefficients, including: Acquiring multiple care location information of multiple care users through terminal location of the multiple care users; Calculating the distances between the plurality of care location information and the real-time location information respectively to obtain a plurality of care distances; Calculating the average of the multiple care distances to obtain an average care distance; The ratio of the average care distance to the multiple care distances is calculated to obtain multiple distance coefficients.

7. The data processing method for preventing loss according to claim 1, characterized in that: According to the multiple distance coefficients and the positioning change coefficients, the basic push frequency is compensated and adjusted to obtain multiple push frequencies, and the real-time positioning information of the target user is pushed to the multiple caring users according to the multiple push frequencies, including: Multiplying each distance coefficient by the positioning change coefficient to obtain multiple compensation adjustment coefficients; The plurality of compensation adjustment coefficients are respectively multiplied by the basic push frequency to perform compensation adjustment to obtain a plurality of push frequencies; The real-time location information of the target user is pushed to the multiple caring users according to the multiple push frequencies.

8. A data processing system for preventing loss, characterized in that: A system for implementing a data processing method for preventing loss according to any one of claims 1 to 7, comprising: A positioning change coefficient acquisition module is used to acquire the real-time positioning information of the target user, and analyze and obtain the positioning change coefficient based on the historical positioning information of the target user within a historical period; A basic push frequency acquisition module is used to calculate the moving distance according to the real-time positioning information and the preset positioning coordinates corresponding to the target user, and classify and obtain the basic push frequency; A distance coefficient acquisition module is used to acquire multiple care location information of multiple care users, respectively calculate the care distances between the multiple care location information and the real-time location information, and calculate and obtain multiple distance coefficients; The positioning information push module is used to compensate and adjust the basic push frequency according to the multiple distance coefficients and positioning change coefficients, obtain multiple push frequencies, and push the real-time positioning information of the target user to the multiple caring users according to the multiple push frequencies.

9. A wristband, characterized in that: A data processing system for preventing loss as described in claim 8.

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