Data processing method and system of intelligent anti-lost device

Abstract

The invention relates to the technical field of positioning, and discloses a data processing method and system for an intelligent anti-lost device, and the method comprises the steps: obtaining the position information of known equipment in a monitoring region, and the signal intensity data and timestamp of target equipment, and the signal intensity data comprises signal intensity and signal direction; estimating the distance from the known equipment to the target equipment according to the signal intensity data to obtain distance data; performing matrix analysis on the position information, the distance data and the signal intensity data to obtain estimated position information of the target equipment; calculating magnetic field intensity data according to the signal intensity data, and constructing a vector-based magnetic field model according to the magnetic field intensity data; and performing loss judgment according to the estimated position information, the magnetic field model and the timestamp, and determining predicted position information of the target equipment. According to the method, the positioning precision can be improved, and the problem of inaccurate anti-lost judgment is solved.

Description

Technical Field

[0001] The present invention relates to the field of positioning technology, and in particular to a data processing method and system for an intelligent anti-lost device. Background Art

[0002] With the rapid development of information technology, smart terminal devices, especially smartphones and tablets, have become an indispensable part of modern life. These devices not only greatly enrich our daily lives and provide a series of convenient services from communication, entertainment to office work, but they also carry a large amount of personal information and service functions. With the surge in the number of smart terminals, the probability of their loss or theft is also increasing. Once such a situation occurs and the lost device does not have sufficient security protection measures, the personal information in the device may be illegally obtained, thereby threatening the personal and property safety of individuals. Therefore, how to effectively prevent the loss of these devices or how to quickly locate, track and retrieve lost devices has become one of the technical problems that need to be solved urgently.

[0003] There is an existing anti-lost technology that combines multiple geographic positioning methods such as GPS satellite positioning, base station positioning and Wi-Fi signals to help users find lost devices. When a user realizes that the device is lost, the first thing to do is to ensure that the device has enabled the positioning service and is bound to the user's account. Once the device is confirmed to be lost, the user can log in to their account and access the corresponding device search platform online. Through these platforms, the system will use the device's built-in GPS module to receive signals from satellites to calculate the device's approximate location; at the same time, it will also use nearby mobile communication base stations and Wi-Fi hotspots to further refine the positioning information. GPS technology relies on a network of satellites in Earth orbit to provide accurate geographic location data, and can provide users with reliable positioning services even in open areas such as outdoors. However, in an urban environment with tall buildings or indoors, the GPS signal may be blocked or weakened. At this time, base station positioning estimates the relative position based on the signal strength and propagation time between the device and multiple surrounding base stations; while Wi-Fi signal positioning can detect the MAC address of nearby Wi-Fi hotspots and match the known location information in the database to achieve more accurate indoor positioning.

[0004] However, the existing technology ignores the signal strength during positioning through GPS, base stations and WIFI, which can easily cause positioning errors and inaccurate anti-lost judgment. Summary of the invention

[0005] The present invention provides a data processing method and system for an intelligent anti-loss device to achieve accurate anti-loss judgment.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a data processing method of an intelligent anti-lost device, comprising: Obtain location information of known devices in the monitoring area, signal strength data and timestamp of the target device, where the signal strength data includes signal strength and signal direction; estimating the distance from the known device to the target device according to the signal strength data to obtain distance data; Performing matrix analysis on the location information, the distance data, and the signal strength data to obtain estimated location information of the target device; Calculating magnetic field strength data according to the signal strength data, and constructing a vector-based magnetic field model according to the magnetic field strength data; A loss determination is performed based on the location information, the estimated location information, the magnetic field model and the timestamp, and the predicted location information of the target device is determined.

[0007] In an optional implementation, estimating the distance from the known device to the target device according to the signal strength data to obtain the distance data includes: Distance data is estimated using the following formula: in, Indicates The distance data from the known device to the target device, Indicates the target device The signal strength of known devices, Indicates the preset The reference signal strength of known devices, Indicates the preset environmental factors.

[0008] In an optional implementation, performing matrix analysis on the location information, the distance data, and the signal strength data to obtain estimated location information of the target device includes: Constructing a position matrix, a signal strength matrix and a distance matrix according to the position information, the signal strength data and the distance data; Calculate the signal strength mean and signal strength variance according to the signal strength matrix; Calculate the deviation between the elements in the signal strength matrix and the signal strength mean according to the signal strength mean to obtain mean deviation data; Calculate the deviation value between the elements in the signal strength matrix and the signal strength variance according to the signal strength variance to obtain variance deviation data; Selecting a minimum mean deviation and a minimum variance deviation according to the mean deviation data and the variance deviation data, and selecting corresponding elements in a signal strength matrix according to the minimum mean deviation and the minimum variance deviation to obtain a minimum mean deviation element and a minimum variance deviation element; Calculating signal strength deviation values ​​of adjacent elements in the signal strength matrix, constructing a signal strength deviation matrix according to the signal strength deviation values, and finding the corresponding element in the signal strength matrix according to the maximum deviation value in the signal strength deviation matrix to obtain the maximum deviation element; Calculating the distance mean of the distance matrix, and selecting the minimum difference element in the distance matrix whose absolute value of the difference with the distance mean is the smallest; Selecting the maximum distance element and the minimum distance element in the distance matrix; Selecting the corresponding minimum mean deviation position, minimum variance deviation position, maximum deviation position, minimum difference position, maximum distance position and minimum distance position in the position matrix according to the minimum mean deviation element, the minimum variance deviation element, the maximum deviation element, the minimum difference element, the maximum distance element and the minimum distance element; Based on the minimum mean deviation position, the minimum variance deviation position, the maximum deviation position, the minimum difference position, the maximum distance position, the minimum distance position and the corresponding distance data, estimated position information is calculated through multilateral positioning.

[0009] In an optional implementation, the calculating the estimated position information by multilateration based on the minimum mean deviation position, the minimum variance deviation position, the maximum deviation position, the minimum difference position, the maximum distance position, the minimum distance position and the corresponding distance data includes: Establishing a distance equation group and an error square sum function based on the minimum mean deviation position, the minimum variance deviation position, the maximum deviation position, the minimum difference position, the maximum distance position, the minimum distance position and the corresponding distance data; Based on the distance equation group and the error square sum function, the estimated position information is solved by the least square method.

[0010] The distance equations are as follows: The error sum of squares function is as follows: in, Indicates estimated location information. and Respectively represent the minimum mean deviation position and the corresponding distance data, and Respectively represent the minimum variance deviation position and the corresponding distance data, and Respectively represent the maximum deviation position and the corresponding distance data, and Respectively represent the minimum difference position and the corresponding distance data, and Respectively represent the maximum distance position and the corresponding distance data, and Respectively represent the minimum distance position and the corresponding distance data, and Respectively represent The deviation position and distance data.

[0011] In an optional implementation, the calculating magnetic field strength data according to the signal strength data, and constructing a vector-based magnetic field model according to the magnetic field strength data, includes: Define the target device to point to The direction of a known device is the reference direction of the magnetic field strength; According to the signal direction, the The cosine value of the magnetic field strength direction of the known device and the target device; Calculate the magnetic field strength of the device according to the signal strength and the cosine value; Constructing a magnetic field strength matrix according to the magnetic field strength, and constructing a magnetic field model in combination with the signal direction; Here, the cosine value is calculated according to the following formula: The magnetic field model is as follows: in, Indicates The cosine value of the magnetic field strength direction of the known device and the target device, Indicates The signal direction of a known device, represents the reference direction, Indicates the target device The magnetic field strength of a known device, Indicates The angle between the magnetic field strength direction of a known device and the target device, Represents the magnetic field model.

[0012] In an optional implementation, calculating the magnetic field strength of the device according to the signal strength and the cosine value includes: The magnetic field strength is calculated using the following formula: in, Indicates The cosine value of the magnetic field strength direction of the known device and the target device, Indicates the target device The signal strength of known devices, Indicates the preset scaling factor, Indicates the target device The magnetic field strength of a known device.

[0013] In an optional implementation, determining the predicted position information of the target device according to the position information, the estimated position information, the magnetic field model and the timestamp includes: Calculating an estimated distance between the estimated location information and the location information according to the timestamp; Subtract the estimated distance from the pre-stored historical estimated distance of the previous timestamp to obtain an absolute value to obtain a distance change, and update a pre-stored distance change matrix according to the distance change; Subtract the magnetic field strength data from the historical magnetic field strength of the previous timestamp stored in advance to obtain an absolute value to obtain a magnetic field change amount, and update a pre-stored magnetic field change matrix according to the magnetic field change amount; Performing a lost determination on the target device according to the distance change matrix, the magnetic field change matrix and a preset judgment threshold; If the target device is determined to be not lost, no warning is issued; If the target device is determined to be lost, an early warning is issued to the user, and predicted location information of the target device is obtained based on the distance change matrix, the magnetic field change matrix and the magnetic field model analysis.

[0014] In a second aspect, the present invention provides a data processing system for an intelligent anti-lost device, comprising: A data acquisition module, used to obtain the location information of known devices in the monitoring area, the signal strength data and timestamp of the target device, wherein the signal strength data includes the signal strength and the signal direction; A distance calculation module, used to estimate the distance from the known device to the target device according to the signal strength data to obtain distance data; A location estimation module, used to perform matrix analysis on the location information, the distance data and the signal strength data to obtain estimated location information of the target device; A magnetic field model building module, used to calculate magnetic field strength data according to the signal strength data, and to build a vector-based magnetic field model according to the magnetic field strength data; A determination output module is used to make a loss determination and determine the predicted location information of the target device based on the location information, the estimated location information, the magnetic field model and the timestamp. In a third aspect, the present invention also provides an electronic device, including a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the data processing method of the intelligent anti-lost device described in any one of the above when executing the computer program.

[0015] In a fourth aspect, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the data processing method of the intelligent anti-loss device described in any one of the above.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses a data processing method for an intelligent anti-lost device, including obtaining the location information of known devices in the monitoring area, the signal strength data and timestamp of the target device, wherein the signal strength data includes signal strength and signal direction; estimating the distance from the known device to the target device according to the signal strength data to obtain distance data; performing matrix analysis on the location information, the distance data and the signal strength data to obtain the estimated location information of the target device; calculating the magnetic field strength data according to the signal strength data, and constructing a vector-based magnetic field model according to the magnetic field strength data; performing loss judgment and determining the predicted location information of the target device according to the location information, the estimated location information, the magnetic field model and the timestamp. The present invention realizes effective prediction of the location of the target device by comprehensively analyzing the location of known devices in the monitoring area, the signal strength of the target device and its changing trend, time factors and the influence of the magnetic field environment, and judges the situation in which the target device may be lost or moved, thereby improving the positioning accuracy and solving the problem of inaccurate anti-lost judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of a data processing method of an intelligent anti-lost device provided by a first embodiment of the present invention; Figure 2 It is a structural diagram of a data processing system of an intelligent anti-loss device provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Reference Figure 1 The first embodiment of the present invention provides a data processing method for an intelligent anti-lost device, comprising the following steps: S11, obtaining location information of known devices in the monitoring area, signal strength data and timestamp of the target device, wherein the signal strength data includes signal strength and signal direction; S12, estimating the distance from the known device to the target device according to the signal strength data to obtain distance data; S13, performing matrix analysis on the location information, the distance data and the signal strength data to obtain estimated location information of the target device; S14, calculating magnetic field strength data according to the signal strength data, and constructing a vector-based magnetic field model according to the magnetic field strength data; S15, performing loss determination and determining predicted location information of the target device according to the location information, the estimated location information, the magnetic field model and the timestamp.

[0020] In step S11, the location information of known devices in the monitoring area, the signal strength data and timestamp of the target device are obtained, wherein the signal strength data includes signal strength and signal direction.

[0021] The location information of known devices comes from fixed nodes or reference points pre-deployed in the network. These nodes can be wireless access points (such as Wi-Fi routers), Bluetooth beacons or other forms of wireless communication devices installed at specific locations. The location information of each known device is usually manually entered during system initialization or determined through other high-precision positioning methods and stored in the system's database for subsequent use.

[0022] The signal strength data of the target device is collected by the target device itself or the sensors around it. Specifically, when the target device is in the monitoring area, it will periodically broadcast wireless signals to the surrounding area or respond to detection requests from known devices. After receiving the signal sent by the target device, the known device can measure the received signal strength indication (RSSI) of the signal, that is, the signal strength value. In addition, some advanced sensors can also capture the direction information of the signal, such as estimating the signal arrival angle through a multi-antenna array. Signal strength and direction together constitute complete signal strength data, which is of great significance for improving positioning accuracy.

[0023] Timestamps are time tags obtained from the system clock and are used to record the specific moment of each data collection. Timestamps ensure that all collected data can be processed in chronological order and help analyze the movement of the target device over time. For example, if multiple data points with different timestamps are obtained continuously over a period of time, it is possible to infer whether the target device has moved by comparing the change trends of these data points, thereby assisting in determining whether there is a risk of loss.

[0024] In step S12, the distance from the known device to the target device is estimated according to the signal strength data to obtain distance data.

[0025] In a specific implementation, estimating the distance from the known device to the target device according to the signal strength data to obtain the distance data includes: Distance data is estimated using the following formula: in, Indicates The distance data from the known device to the target device, Indicates the target device The signal strength of known devices, Indicates the preset The reference signal strength of known devices, Indicates the preset environmental factors.

[0026] Specifically, the system uses the following formula to estimate distance data: in, Indicates The distance data from the known device to the target device, Indicates the target device The signal strength of known devices, Indicates the preset The reference signal strength of known devices, Indicates the preset environmental factors.

[0027] The application of this formula is based on the free space propagation model and the logarithmic distance path loss model, which can convert the received wireless signal strength into the actual physical distance. Since the radio signal gradually weakens as the distance increases when it propagates in the air, the distance between the sending device and the receiving device can be inferred by measuring the received signal strength and combining it with the pre-set reference signal strength and environmental factors.

[0028] For example, suppose in a specific environment, the preset environmental factors is 2.7, the reference signal strength of the known device -40dBm, when the received signal strength from the target device When the value is -65 dBm, the The distance from the known device to the target device as follows: Based on the received signal strength, the system estimates that the target device is approximately The location of a known device is 8.43 meters.

[0029] In summary, in step S12, by analyzing the signal strength data and using the above formula, the system can accurately estimate the distance between each known device and the target device. Because accurate distance information is the basis for building a multi-lateration positioning algorithm, it helps to improve the accuracy of the final position prediction. In addition, considering the differences in signal propagation characteristics in different environments, the environmental factor is introduced. , which makes the distance estimation closer to the actual situation and enhances the adaptability and robustness of the system.

[0030] In step S13, matrix analysis is performed on the location information, the distance data and the signal strength data to obtain estimated location information of the target device.

[0031] In a specific implementation manner, performing matrix analysis on the location information, the distance data, and the signal strength data to obtain estimated location information of the target device includes: Constructing a position matrix, a signal strength matrix and a distance matrix according to the position information, the signal strength data and the distance data; Calculate the signal strength mean and signal strength variance according to the signal strength matrix; Calculate the deviation between the elements in the signal strength matrix and the signal strength mean according to the signal strength mean to obtain mean deviation data; Calculate the deviation value between the elements in the signal strength matrix and the signal strength variance according to the signal strength variance to obtain variance deviation data; Selecting a minimum mean deviation and a minimum variance deviation according to the mean deviation data and the variance deviation data, and selecting corresponding elements in a signal strength matrix according to the minimum mean deviation and the minimum variance deviation to obtain a minimum mean deviation element and a minimum variance deviation element; Calculating signal strength deviation values ​​of adjacent elements in the signal strength matrix, constructing a signal strength deviation matrix according to the signal strength deviation values, and finding the corresponding element in the signal strength matrix according to the maximum deviation value in the signal strength deviation matrix to obtain the maximum deviation element; Calculating the distance mean of the distance matrix, and selecting the minimum difference element in the distance matrix whose absolute value of the difference with the distance mean is the smallest; Selecting the maximum distance element and the minimum distance element in the distance matrix; Selecting the corresponding minimum mean deviation position, minimum variance deviation position, maximum deviation position, minimum difference position, maximum distance position and minimum distance position in the position matrix according to the minimum mean deviation element, the minimum variance deviation element, the maximum deviation element, the minimum difference element, the maximum distance element and the minimum distance element; Based on the minimum mean deviation position, the minimum variance deviation position, the maximum deviation position, the minimum difference position, the maximum distance position, the minimum distance position and the corresponding distance data, estimated position information is calculated through multilateral positioning.

[0032] In a specific implementation, the calculating the estimated position information by multilateration based on the minimum mean deviation position, the minimum variance deviation position, the maximum deviation position, the minimum difference position, the maximum distance position, the minimum distance position and the corresponding distance data includes: Establishing a distance equation group and an error square sum function based on the minimum mean deviation position, the minimum variance deviation position, the maximum deviation position, the minimum difference position, the maximum distance position, the minimum distance position and the corresponding distance data; Based on the distance equation group and the error square sum function, the estimated position information is solved by the least square method.

[0033] The distance equations are as follows: The error sum of squares function is as follows: in, Indicates estimated location information. and Respectively represent the minimum mean deviation position and the corresponding distance data, and Respectively represent the minimum variance deviation position and the corresponding distance data, and Respectively represent the maximum deviation position and the corresponding distance data, and Respectively represent the minimum difference position and the corresponding distance data, and Respectively represent the maximum distance position and the corresponding distance data, and Respectively represent the minimum distance position and the corresponding distance data, and Respectively represent The deviation position and distance data.

[0034] First, the system constructs three key matrices based on the location information of known devices, the received signal strength of target devices, and the estimated distance data: location matrix , signal strength matrix and the distance matrix . Position Matrix Contains the specific coordinates of each known device, signal strength matrix The received signal strength values ​​are recorded, and the distance matrix The distance estimate converted from the signal strength is saved.

[0035] Next, the system uses the signal strength matrix Elements in Calculate the mean signal strength and signal strength variance , these two statistics are used to describe the overall distribution of signal strength. The formula is as follows: Then, to evaluate changes in signal strength, the system calculates the signal strength matrix Each element in the signal strength The deviation value of ; At the same time, the variance of each element and signal strength is also calculated The deviation value forms the variance deviation data By comparing these deviation values, the signal strength value with the minimum mean deviation and the minimum variance deviation can be found and used as the corresponding minimum mean deviation element. and the minimum variance deviation element This helps identify those measurements that are closest to the overall trend, thereby improving the accuracy of the position estimate. The formula is as follows: In addition, the system calculates the signal strength matrix The signal strength deviation value between adjacent elements in , construct the signal strength deviation matrix From this matrix, find the element in the corresponding signal strength matrix according to the maximum deviation value, that is, the maximum deviation element This can highlight locations where there are significant differences in signal strength and is useful for troubleshooting outliers or sources of interference.

[0036] Next, for the distance matrix Elements , the system calculates the mean distance , and select the minimum difference element in the distance matrix with the smallest absolute difference from the mean At the same time, select the maximum distance element in the distance matrix and the minimum distance element By screening the distance data, we can ensure that the distance information used in subsequent calculations is as accurate and reliable as possible.

[0037] Finally, the corresponding minimum mean deviation position is selected in the position matrix according to the minimum mean deviation element, the minimum variance deviation element, the maximum deviation element, the minimum difference element, the maximum distance element and the minimum distance element. , minimum variance deviation position , Maximum deviation position , minimum difference position , Maximum distance position and minimum distance position , Combined with the corresponding distance data, the system uses multilateration to calculate the estimated location information of the target device. Specifically, a distance equation group consisting of six locations and their corresponding distances is established: In order to solve the above equations, the system introduces the error square sum function , the function is defined as follows: in, Indicates estimated location information. and Respectively represent the minimum mean deviation position and the corresponding distance data, and Respectively represent the minimum variance deviation position and the corresponding distance data, and Respectively represent the maximum deviation position and the corresponding distance data, and Respectively represent the minimum difference position and the corresponding distance data, and Respectively represent the maximum distance position and the corresponding distance data, and Respectively represent the minimum distance position and the corresponding distance data, and Respectively represent The deviation position and distance data.

[0038] To minimize , use the least squares method to solve the optimal estimated position information The least squares method is to minimize the sum of squares of the errors between the predicted value and the actual observed value by adjusting the parameters. The specific steps are as follows: Initialize estimated position .

[0039] For each iteration, we update the position estimate using gradient descent , until the convergence condition is met or the maximum number of iterations is reached, and the optimal estimated position information is obtained.

[0040] For example, in one iteration, the current estimated position is , then the new estimated position can be updated by the following formula: in, is the preset learning rate, is the error sum of squares function exist The gradient at . The gradient can be calculated by partial derivatives: In summary, in step S13, by performing a comprehensive and detailed matrix analysis of the location information, distance data, and signal strength data, the system can not only effectively remove the influence of noise and outliers, but also make full use of the advantages of multi-source data to accurately estimate the location of the target device. This method not only improves the positioning accuracy, but also enhances the robustness and adaptability of the system, and is suitable for various complex indoor environments.

[0041] In step S14, magnetic field strength data is calculated according to the signal strength data, and a vector-based magnetic field model is constructed according to the magnetic field strength data.

[0042] In a specific implementation, the calculating magnetic field strength data according to the signal strength data, and constructing a vector-based magnetic field model according to the magnetic field strength data, includes: Define the target device to point to The direction of a known device is the reference direction of the magnetic field strength; According to the signal direction, the The cosine value of the magnetic field strength direction of the known device and the target device; Calculate the magnetic field strength of the device according to the signal strength and the cosine value; Constructing a magnetic field strength matrix according to the magnetic field strength, and constructing a magnetic field model in combination with the signal direction; Here, the cosine value is calculated according to the following formula: The magnetic field model is as follows: in, Indicates The cosine value of the magnetic field strength direction of the known device and the target device, Indicates The signal direction of a known device, represents the reference direction, Indicates the target device The magnetic field strength of a known device, Indicates The angle between the magnetic field strength direction of a known device and the target device, Represents the magnetic field model.

[0043] In a specific implementation, calculating the magnetic field strength of the device according to the signal strength and the cosine value includes: The magnetic field strength is calculated using the following formula: in, Indicates The cosine value of the magnetic field strength direction of the known device and the target device, Indicates the target device The signal strength of known devices, Indicates the preset scaling factor, Indicates the target device The magnetic field strength of a known device.

[0044] First, to ensure the consistency and accuracy of the calculation, the system defines the target device to point to the The direction of the known device is used as the reference direction of the magnetic field strength. This choice provides a unified benchmark for all subsequent magnetic field-related calculations, helping to improve the reliability and comparability of the results.

[0045] Next, the system calculates the first The cosine value of the direction of the magnetic field strength between the known devices and the target device. Specifically, for each known device, the system uses the following formula to calculate the cosine value: in, Indicates The cosine value of the magnetic field strength direction of the known device and the target device, Indicates The signal direction of a known device, Indicates the reference direction.

[0046] The system then calculates the magnetic field strength of the device based on the received signal strength and the cosine value calculated above. Specifically, the following formula is used for calculation: in, Indicates The cosine value of the magnetic field strength direction of the known device and the target device, Indicates the target device The signal strength of known devices, Represents the preset proportionality factor, which is used to adjust the conversion ratio from signal strength to magnetic field strength. Indicates the target device The formula combines signal strength with direction information to more accurately reflect the magnetic field distribution in the actual physical environment.

[0047] The system then constructs a magnetic field strength matrix based on the calculated magnetic field strength. , and integrate the signal direction to form a complete magnetic field model. The magnetic field model is expressed as follows: in, Indicates the target device The magnetic field strength of a known device, Indicates The angle between the magnetic field strength direction of a known device and the target device, Represents the magnetic field model. This matrix not only contains the magnetic field intensity information of each location point, but also records the angle information of the corresponding direction, so as to fully describe the spatial distribution characteristics of the magnetic field.

[0048] In summary, in step S14, by deeply analyzing the signal strength data and building a magnetic field model in combination with the direction information, the system can not only effectively capture the changing characteristics of the electromagnetic field, but also provide richer and more accurate data support for subsequent position estimation. This method fully utilizes the advantages of multi-source data, enhances the robustness and adaptability of the system, and is suitable for precise positioning of target devices in complex electromagnetic environments.

[0049] In step S15, a loss determination is performed based on the location information, the estimated location information, the magnetic field model and the timestamp, and the predicted location information of the target device is determined.

[0050] In a specific implementation, determining the predicted location information of the target device according to the location information, the estimated location information, the magnetic field model, and the timestamp includes: Calculating an estimated distance between the estimated location information and the location information according to the timestamp; Subtract the estimated distance from the pre-stored historical estimated distance of the previous timestamp to obtain an absolute value to obtain a distance change, and update a pre-stored distance change matrix according to the distance change; Subtract the magnetic field strength data from the historical magnetic field strength of the previous timestamp stored in advance to obtain an absolute value to obtain a magnetic field change amount, and update a pre-stored magnetic field change matrix according to the magnetic field change amount; Performing a lost determination on the target device according to the distance change matrix, the magnetic field change matrix and a preset judgment threshold; If the target device is determined to be not lost, no warning is issued; If the target device is determined to be lost, an early warning is issued to the user, and predicted location information of the target device is obtained based on the distance change matrix, the magnetic field change matrix and the magnetic field model analysis.

[0051] First, the system calculates the estimated distance between the estimated location information of the current timestamp and the location information of the known device and records it in the memory.

[0052] Next, the system takes the absolute value of the difference between the estimated distance and the historical estimated distance of the previous timestamp, obtains the distance change, and updates the pre-stored distance change matrix based on the change. This process helps track the speed and direction of the target device over time.

[0053] Then, the system takes the absolute value of the difference between the current magnetic field strength data and the historical magnetic field strength of the previous timestamp stored, obtains the magnetic field change, and updates the pre-stored magnetic field change matrix based on this change. The magnetic field change reflects the changes in the electromagnetic environment and helps detect abnormal movement or interference sources.

[0054] Then, the system determines the target device is lost based on the distance change matrix, magnetic field change matrix and preset judgment threshold. If the distance change exceeds the set distance threshold, or the magnetic field change exceeds the set magnetic field threshold, it is considered that the target device may be lost.

[0055] If the target device is determined to be not lost, the system does not issue an early warning; conversely, if the target device is determined to be lost, the system issues an early warning to the user and further analyzes to determine the predicted location information of the target device.

[0056] In order to more accurately predict the location of the target device, the system performs a comprehensive analysis based on the distance change matrix, magnetic field change matrix and magnetic field model.

[0057] Specifically, the data distribution in the distance change matrix is ​​analyzed to identify whether there is a trend of continuous growth, which may be a signal that the target device is moving away from the known area.

[0058] Examine the data in the magnetic field variation matrix for significant magnetic field variations that may indicate the target device has entered a different electromagnetic environment, such as near metal objects or power lines.

[0059] Combined with the magnetic field model, the system can simulate the magnetic field distribution at different locations. By comparing the actual measured magnetic field strength and direction, the location that best matches the current magnetic field characteristics is found, thereby inferring the possible location of the target device.

[0060] In summary, in step S15, through in-depth analysis of the estimated position information, magnetic field model and timestamp, the system can not only effectively determine whether the target device is lost, but also accurately predict its possible location. This method combines multi-dimensional data processing and complex mathematical modeling to improve positioning accuracy and system robustness, and is suitable for precise positioning and tracking of target devices in complex environments. Through scientific and reasonable algorithm design, the intelligent anti-lost device has achieved efficient and accurate data processing capabilities.

[0061] The following describes the working process of the present invention using a relatively common scenario as an example. Figure 1 , a data processing method of an intelligent anti-lost device, comprising the following steps: In an indoor environment, there are three known devices (A, B, and C) and one target device (D), all of which are equipped with modules capable of sending and receiving wireless signals. The system first collects the location information of these known devices in the monitoring area, the signal strength data of the target device, and the timestamp.

[0062] In this scenario, it is known that device A is located at coordinates (1, 2), with a signal strength of -60 dBm and a signal direction pointing east; it is known that device B is located at coordinates (4, 5), with a signal strength of -70 dBm and a signal direction pointing south; it is known that device C is located at coordinates (7, 3), with a signal strength of -65 dBm and a signal direction pointing west. At the same time, the system records the timestamp of the target device D, indicating the data collection status at the current moment.

[0063] Next, the system estimates the distance from each known device to the target device based on the received signal strength. After analysis and processing, it is concluded that the distance from device A to target device D is about 3 meters, the distance from device B to target device D is about 5 meters, and the distance from device C to target device D is about 4 meters.

[0064] The system then performs a matrix analysis of the location information, distance data, and signal strength data to determine the estimated location information of the target device. Through a series of complex calculations and analyses, the system concludes that the target device D is likely located at the coordinates (3, 4).

[0065] Furthermore, the system calculates the magnetic field strength based on the signal strength data and constructs a magnetic field model. The analysis results show that the magnetic field strength between target device D and device A is about 0.0003 Tesla, with an angle of 45 degrees; the magnetic field strength between target device D and device B is about 0.0002 Tesla, with an angle of 90 degrees; and the magnetic field strength between target device D and device C is about 0.00025 Tesla, with an angle of 135 degrees. This step helps to more accurately describe the state of the target device in the electromagnetic environment.

[0066] At a later time, the system acquired new data again: the signal strength of device A changed to -75 dBm, but the signal direction remained unchanged; the signal strength of device B changed to -80 dBm, and the signal direction still pointed to the south; the signal strength of device C changed to -70 dBm, and the signal direction still pointed to the west. At the same time, the system recorded the new timestamp of target device D, indicating the data collection situation at this time.

[0067] After comparing the old and new data, the system found that the estimated distance changed by about 2 meters from the previous location to the new location, and the magnetic field strength also changed significantly, especially the magnetic field strength of devices A and B weakened. Based on the preset judgment threshold, the system determined that the target device D had moved significantly and may have left the safe range. Therefore, the system issued an early warning notification to the user, indicating that the target device may be lost.

[0068] In order to provide more accurate guidance, the system uses the latest magnetic field changes combined with the previous magnetic field model for in-depth analysis. Considering the changing trend and direction information of the magnetic field strength, the system predicts that the target device D may have moved north to the coordinates (5, 6).

[0069] In summary, in this specific example, the data processing method of the intelligent anti-loss device successfully detected the potential loss of the target device through comprehensive analysis of multi-source data and gave a reasonable predicted location.

[0070] Reference Figure 2The second embodiment of the present invention provides a data processing system for an intelligent anti-lost device, comprising: A data acquisition module, used to obtain the location information of known devices in the monitoring area, the signal strength data and timestamp of the target device, wherein the signal strength data includes the signal strength and the signal direction; A distance calculation module, used to estimate the distance from the known device to the target device according to the signal strength data to obtain distance data; A location estimation module, used to perform matrix analysis on the location information, the distance data and the signal strength data to obtain estimated location information of the target device; A magnetic field model building module, used to calculate magnetic field strength data according to the signal strength data, and to build a vector-based magnetic field model according to the magnetic field strength data; A determination output module is used to perform loss determination and determine the predicted location information of the target device based on the location information, the estimated location information, the magnetic field model and the timestamp.

[0071] It should be noted that the data processing device of an intelligent anti-loss device provided in an embodiment of the present invention is used to execute all the process steps of the data processing method of an intelligent anti-loss device in the above embodiment. The working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0072] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a data processing program for an intelligent anti-lost device. When the processor executes the computer program, the steps in the above-mentioned data processing method embodiments of the intelligent anti-lost device are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the data processing module of the intelligent anti-lost device.

[0073] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0074] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0075] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0076] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0077] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0078] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0079] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A data processing method for an intelligent anti-lost device, characterized in that: Executed by the controller, including: Obtain location information of known devices in the monitoring area, signal strength data and timestamp of the target device, where the signal strength data includes signal strength and signal direction; estimating the distance from the known device to the target device according to the signal strength data to obtain distance data; Performing matrix analysis on the location information, the distance data, and the signal strength data to obtain estimated location information of the target device; Calculating magnetic field strength data according to the signal strength data, and constructing a vector-based magnetic field model according to the magnetic field strength data; A loss determination is performed based on the location information, the estimated location information, the magnetic field model and the timestamp, and the predicted location information of the target device is determined.

2. The data processing method of the intelligent anti-lost device according to claim 1 is characterized in that: The estimating the distance from the known device to the target device according to the signal strength data to obtain the distance data includes: Distance data is estimated using the following formula: in, Indicates The distance data from the known device to the target device, Indicates the target device The signal strength of known devices, Indicates the preset The reference signal strength of known devices, Indicates the preset environmental factors.

3. The data processing method of the intelligent anti-lost device according to claim 1 is characterized in that: The performing matrix analysis on the location information, the distance data and the signal strength data to obtain estimated location information of the target device includes: Constructing a position matrix, a signal strength matrix and a distance matrix according to the position information, the signal strength data and the distance data; Calculate the signal strength mean and signal strength variance according to the signal strength matrix; Calculate the deviation between the elements in the signal strength matrix and the signal strength mean according to the signal strength mean to obtain mean deviation data; Calculate the deviation value between the elements in the signal strength matrix and the signal strength variance according to the signal strength variance to obtain variance deviation data; Selecting a minimum mean deviation and a minimum variance deviation according to the mean deviation data and the variance deviation data, and selecting corresponding elements in a signal strength matrix according to the minimum mean deviation and the minimum variance deviation to obtain a minimum mean deviation element and a minimum variance deviation element; Calculating signal strength deviation values ​​of adjacent elements in the signal strength matrix, constructing a signal strength deviation matrix according to the signal strength deviation values, and finding the corresponding element in the signal strength matrix according to the maximum deviation value in the signal strength deviation matrix to obtain the maximum deviation element; Calculating the distance mean of the distance matrix, and selecting the minimum difference element in the distance matrix whose absolute value of the difference with the distance mean is the smallest; Selecting the maximum distance element and the minimum distance element in the distance matrix; Selecting the corresponding minimum mean deviation position, minimum variance deviation position, maximum deviation position, minimum difference position, maximum distance position and minimum distance position in the position matrix according to the minimum mean deviation element, the minimum variance deviation element, the maximum deviation element, the minimum difference element, the maximum distance element and the minimum distance element; Based on the minimum mean deviation position, the minimum variance deviation position, the maximum deviation position, the minimum difference position, the maximum distance position, the minimum distance position and the corresponding distance data, estimated position information is calculated through multilateral positioning.

4. The data processing method of the intelligent anti-lost device according to claim 3 is characterized in that: The calculating the estimated position information by multilateration based on the minimum mean deviation position, the minimum variance deviation position, the maximum deviation position, the minimum difference position, the maximum distance position, the minimum distance position and the corresponding distance data comprises: Establishing a distance equation group and an error square sum function based on the minimum mean deviation position, the minimum variance deviation position, the maximum deviation position, the minimum difference position, the maximum distance position, the minimum distance position and the corresponding distance data; Based on the distance equation group and the error square sum function, solving the estimated position by the least square method; The distance equations are as follows: The error sum of squares function is as follows: in, Indicates estimated location information. and Respectively represent the minimum mean deviation position and the corresponding distance data, and Respectively represent the minimum variance deviation position and the corresponding distance data, and Respectively represent the maximum deviation position and the corresponding distance data, and Respectively represent the minimum difference position and the corresponding distance data, and Respectively represent the maximum distance position and the corresponding distance data, and Respectively represent the minimum distance position and the corresponding distance data, and Respectively represent The deviation position and distance data.

5. The data processing method of the intelligent anti-lost device according to claim 1 is characterized in that: The step of calculating magnetic field strength data according to the signal strength data and constructing a vector-based magnetic field model according to the magnetic field strength data includes: Define the target device to point to The direction of a known device is the reference direction of the magnetic field strength; According to the signal direction, the The cosine value of the magnetic field strength direction of the known device and the target device; Calculate the magnetic field strength of the device according to the signal strength and the cosine value; Constructing a magnetic field strength matrix according to the magnetic field strength, and constructing a magnetic field model in combination with the signal direction; Here, the cosine value is calculated according to the following formula: The magnetic field model is as follows: in, Indicates The cosine value of the magnetic field strength direction of the known device and the target device, Indicates The signal direction of a known device, represents the reference direction, Indicates the target device The magnetic field strength of a known device, Indicates The angle between the magnetic field strength direction of a known device and the target device, Represents the magnetic field model.

6. The data processing method of the intelligent anti-lost device according to claim 5 is characterized in that: The calculating the magnetic field strength of the device according to the signal strength and the cosine value comprises: The magnetic field strength is calculated using the following formula: in, Indicates The cosine value of the magnetic field strength direction of the known device and the target device, Indicates the target device The signal strength of known devices, Indicates the preset scaling factor, Indicates the target device The magnetic field strength of a known device.

7. The data processing method of the intelligent anti-lost device according to claim 1 is characterized in that: The determining the predicted position information of the target device according to the position information, the estimated position information, the magnetic field model and the timestamp includes: Calculating an estimated distance between the estimated location information and the location information according to the timestamp; Subtract the estimated distance from the pre-stored historical estimated distance of the previous timestamp to obtain an absolute value to obtain a distance change, and update a pre-stored distance change matrix according to the distance change; Subtract the magnetic field strength data from the historical magnetic field strength of the previous timestamp stored in advance to obtain an absolute value to obtain a magnetic field change amount, and update a pre-stored magnetic field change matrix according to the magnetic field change amount; Performing a lost determination on the target device according to the distance change matrix, the magnetic field change matrix and a preset judgment threshold; If the target device is determined to be not lost, no warning is issued; If the target device is determined to be lost, an early warning is issued to the user, and predicted location information of the target device is obtained based on the distance change matrix, the magnetic field change matrix and the magnetic field model analysis.

8. A data processing system for an intelligent anti-lost device, characterized in that: include: A data acquisition module, used to obtain the location information of known devices in the monitoring area, the signal strength data and timestamp of the target device, wherein the signal strength data includes the signal strength and the signal direction; A distance calculation module, used to estimate the distance from the known device to the target device according to the signal strength data to obtain distance data; A location estimation module, used to perform matrix analysis on the location information, the distance data and the signal strength data to obtain estimated location information of the target device; A magnetic field model building module, used to calculate magnetic field strength data according to the signal strength data, and to build a vector-based magnetic field model according to the magnetic field strength data; A determination output module is used to perform loss determination and determine the predicted location information of the target device based on the location information, the estimated location information, the magnetic field model and the timestamp.

9. An electronic device, characterized in that: It comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the data processing method of the intelligent anti-loss device as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the data processing method of the intelligent anti-loss device according to any one of claims 1 to 7.

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