High-precision positioning anti-loss intelligent shoe system

Abstract

The invention relates to the technical field of high-precision positioning, in particular to a high-precision positioning anti-lost intelligent shoe system. The device comprises an extrusion coordinate unit, a direction analysis unit, a high-precision positioning unit and a change judgment unit. The coordinate path module calculates the central pressure coordinates of the intelligent shoes according to the positions of the pressure sensors, the measured pressure and the number of the pressure sensors, the central pressure coordinates of the intelligent shoes at all moments are connected to form a moving curve, and the moving curve is the moving path of the child wearing the intelligent shoes. By continuously recording the center pressure coordinates, the moving path of the child can be tracked in real time, parents can check the real-time position and moving track of the child through intelligent equipment and can set a safety area at the same time, when the child exceeds the safety area, the system can automatically give an alarm and feed back, and the safety guarantee of the child is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of high-precision positioning, and in particular to a high-precision positioning and anti-loss intelligent shoe system. Background Art

[0002] With the continuous development of intelligent technology, the application scope of intelligent wearable devices in daily life is expanding. As a new type of wearable device, smart shoes show broad application prospects in the fields of sports tracking, health monitoring and anti-loss. At present, the main functions of traditional smart shoes are concentrated on sports tracking and health monitoring, and they can also provide users with positioning functions to help manage and use footwear products.

[0003] However, in actual applications, the positioning function of existing smart shoes is usually in a continuous working state, regardless of whether positioning is actually needed. This mode may have a certain impact on the battery life and service life of smart shoes. In addition, when the sensor type of smart shoes is relatively single, the accuracy of data processing may be limited, especially in the face of complex walking conditions, false alarms may occur, thereby affecting the accuracy of positioning and anomaly detection. For example, when the sensor cannot comprehensively consider abnormal fluctuations in multi-dimensional data such as acceleration, angular velocity and pressure, the identification of stolen or abnormal use of smart shoes may not be accurate enough, which in turn affects the user experience and economic security.

[0004] To address the above challenges, we provide a high-precision positioning and anti-loss smart shoe system to further optimize the anti-loss and service life of smart shoes. Summary of the invention

[0005] The purpose of the present invention is to provide a high-precision positioning and anti-loss intelligent shoe system to solve the problems raised in the above background technology.

[0006] To achieve the above-mentioned purpose, the present invention provides a high-precision positioning and anti-loss intelligent shoe system, comprising an extrusion coordinate unit, a direction analysis unit, a high-precision positioning unit and a change judgment unit;

[0007] The squeezing coordinate unit measures the pressure between the foot and the smart shoe through a pressure sensor, obtains the acceleration and angular velocity of the smart shoe, records the position of the pressure sensor, measures the strain force generated by the foot and the smart insole through a strain sensor, records the area of ​​the strain sensor, and uses the squeezing force algorithm to calculate the squeezing force borne by the smart shoe and the center pressure coordinates of the smart shoe according to the number of pressure sensors, the measured pressure, the strain force, and the area of ​​the strain sensor. The squeezing force borne by the smart shoe is used to determine whether to trigger the positioning algorithm, and the center pressure coordinates of the smart shoe are connected to form a moving curve, which is the moving path taken by the child wearing the smart shoe;

[0008] The direction analysis unit is used to receive the center pressure coordinates of the smart shoe in the squeeze coordinate unit, calculate the distance and direction of the moving path, and use the distance of the moving path to determine whether the child wearing the smart shoe moves within a set range. When the child moves beyond the set range, the sound sensor in the smart shoe is used to obtain sound data around the center pressure coordinates of the smart shoe, generate a spectrum diagram and a resonance peak and a fundamental tone analysis based on the acquired sound data, and use the generated spectrum diagram, resonance peak and fundamental tone to determine whether the acquired sound data is the voice of the child's family;

[0009] Specific judgment situations include:

[0010] Case ①: When the similarity value is within the set similarity range, it means that the acquired voice data is the voice of the child's family member;

[0011] Case ②: When the similarity value is not within the set similarity range, it means that the acquired sound data is not the voice of the child's family. If the smart shoes show the child running out of the set distance range, and there is no family member around the child, it is determined that the child is at risk of being lost.

[0012] The high-precision positioning unit is used to receive a command from the analysis unit that determines that the child is at risk of being lost, and the high-precision positioning unit obtains the positioning point and sound data of the smart shoe from the direction analysis unit, and performs high-precision positioning based on the positioning point of the smart shoe and the obtained sound data;

[0013] The change judgment unit is used to receive the acceleration, angular velocity and measured pressure in the extrusion coordinate unit, calculate the acceleration amplitude, angular velocity amplitude, pressure change and calculate the average acceleration amplitude, angular velocity amplitude and pressure amplitude, calculate the abnormal acceleration, angular velocity and pressure amplitude thresholds according to the average acceleration, angular velocity and pressure amplitude, and use the acceleration amplitude, angular velocity amplitude and pressure change and the abnormal acceleration, angular velocity and pressure amplitude thresholds to judge whether the smart shoes are stolen.

[0014] As a further improvement of the technical solution, the extrusion coordinate unit includes an extrusion trigger module and a coordinate path module;

[0015] The squeezing trigger module measures the pressure between the foot and the smart shoe through the pressure sensor in the smart shoe, obtains the acceleration and angular velocity of the child walking in the smart shoe, records the position of the pressure sensor at time t, and the number of pressure sensors, and measures the strain force generated by the foot and the smart insole through the strain sensor, records the area and number of strain sensors, and the Young's modulus of the smart insole material, and then uses the squeezing force algorithm to calculate the squeezing force borne by the smart shoe according to the number of pressure sensors, the measured pressure, the strain force, the area and number of strain sensors, and the Young's modulus of the smart insole material, and uses the squeezing force algorithm to determine whether to trigger the positioning algorithm based on the squeezing force borne by the smart shoe. When the squeezing force borne by the smart shoe is greater than the set squeezing force, the squeezing force borne by the smart shoe calculated last time is updated to the squeezing force borne by the smart shoe calculated most recently, and the positioning algorithm is triggered;

[0016] The coordinate path module is used to receive the command of triggering the positioning algorithm in the squeeze trigger module. The coordinate path module obtains the position of the pressure sensor at time t, the pressure measured at time t and the number of pressure sensors from the squeeze trigger module, calculates the center pressure coordinates of the smart shoe, and connects the center pressure coordinates of the smart shoe at each moment to form a moving curve. The moving curve is the moving path taken by the child wearing the smart shoe.

[0017] As a further improvement of the technical solution, the direction analysis unit includes a distance direction module and an analysis and judgment module;

[0018] The distance and direction module is used to receive the center pressure coordinates of the smart shoe in the coordinate path module and the number of pressure sensors in the squeeze trigger module, calculate the distance and direction of the moving path according to the center pressure coordinates of the smart shoe, and use the distance of the moving path to determine whether the child wearing the smart shoe is moving within a set range. When the distance of the moving path is greater than the set distance range, it means that the child wearing the smart shoe has run out of the set distance range;

[0019] The analysis and judgment module is used to receive a command greater than a set distance range in the distance direction module. The analysis and judgment module obtains the center pressure coordinates of the smart shoe from the coordinate path module, and uses the sound sensor in the smart shoe to obtain sound data around the center pressure coordinates of the smart shoe, extracts the audio signal from the obtained sound data, generates a spectrogram according to the extracted audio signal using short-time Fourier transform, and then uses cepstrum analysis to extract resonance peaks and fundamental tones from the generated spectrogram, and extracts the spectrograms, resonance peaks and fundamental tones of different family members from a database, and calculates a similarity value using the generated spectrogram, the extracted resonance peaks and fundamental tones and the spectrograms, resonance peaks and fundamental tones of different family members extracted from the database, and then uses the similarity value to determine whether the acquired sound data is the voice of the child's family member.

[0020] As a further improvement of the technical solution, the specific judgment situations in the analysis and judgment module include:

[0021] Case ①: When the similarity value is within the set similarity range, it means that the acquired sound data is the voice of the child's family, and the child is accompanied by the child's family. The smart shoes will feedback the presence of the child's family to the parent's smart device;

[0022] Case ②: When the similarity value is not within the set similarity range, it means that the acquired sound data is not the voice of the child's family. If the smart shoes show that the child is wearing the smart shoes and runs out of the set distance range, and there is no family member around the child, it is determined that the child is at risk of being lost. The smart shoes will feedback to the parent's smart device. At this time, the smart shoes do not need to trigger the positioning algorithm through the condition of a squeezing force greater than the set pressure. The pressure is directly measured through the pressure sensor in the smart shoe, and the position of the pressure sensor is recorded. The pressure center algorithm is used to calculate the positioning point of the smart shoe based on the position of the pressure sensor, the measured pressure and the number of pressure sensors.

[0023] As a further improvement of the present technical solution, the high-precision positioning unit is used to receive a command in the analysis and judgment module to determine that a child is at risk of being lost. The high-precision positioning unit obtains the positioning point of the smart shoes, sound data and the moving path from the coordinate path module in the analysis and judgment module. The smart shoes use a camera to capture environmental image data around the positioning point of the smart shoes, and then perform feature analysis on the acquired surrounding environment image data. The analyzed features include vehicles, pedestrians, and trees. High-precision positioning is performed based on the positioning point of the smart shoes, the acquired sound data and the captured environmental image data, and then the lost child is searched according to the moving path based on the high-precision positioning.

[0024] As a further improvement of the technical solution, the change judgment unit includes a data change module and a misappropriation judgment module;

[0025] The data change module is used to receive the acceleration, angular velocity and measured pressure in the squeeze trigger module, obtain the maximum acceleration, minimum acceleration, maximum angular velocity and minimum angular velocity from the acceleration and angular velocity, and then obtain the maximum pressure and minimum pressure from the measured pressure, calculate the acceleration amplitude change according to the maximum acceleration and the minimum acceleration, and then calculate the angular velocity amplitude change according to the maximum angular velocity and the minimum angular velocity, and calculate the pressure change according to the maximum pressure and the minimum pressure;

[0026] The misappropriation judgment module is used to receive the number of time points in the squeeze trigger module and the acceleration amplitude change, angular velocity amplitude change, and pressure change in the data change module, calculate the average acceleration amplitude according to the acceleration and the number of time points, then calculate the average angular velocity amplitude according to the angular velocity and the number of time points, calculate the average pressure amplitude using the measured pressure and the number of time points, then calculate the acceleration standard deviation using the acceleration amplitude change, the number of time points, and the average acceleration amplitude, calculate the angular velocity standard deviation according to the angular velocity amplitude change, the number of time points, and the average angular velocity amplitude, and then calculate the pressure standard deviation according to the pressure change, the number of time points, and the average pressure amplitude , the abnormal acceleration amplitude threshold, abnormal angular velocity amplitude threshold and abnormal pressure amplitude threshold are calculated according to the average acceleration amplitude, average angular velocity amplitude, average pressure amplitude, acceleration standard deviation, angular velocity standard deviation and pressure standard deviation, and the acceleration amplitude change, angular velocity amplitude change, pressure change and abnormal acceleration amplitude threshold, abnormal angular velocity amplitude threshold and abnormal pressure amplitude threshold are used to judge whether the smart shoes are stolen. When the acceleration amplitude change is greater than the abnormal acceleration amplitude threshold, the angular velocity amplitude change is greater than the abnormal angular velocity amplitude threshold, and the pressure change is greater than the abnormal pressure amplitude threshold, it means that the smart shoes are stolen.

[0027] As a further improvement of the technical solution, the steps for calculating the abnormal acceleration, angular velocity and pressure amplitude thresholds are as follows:

[0028] Step ①: Collect the average acceleration amplitude a avg and the standard deviation of acceleration a bz Calculate the abnormal acceleration amplitude threshold Td a , the specific algorithm formula is:

[0029] Td a =a avg +k×a bz ;

[0030] Among them, k refers to an adjustment coefficient;

[0031] Step 2: Collect the average angular velocity amplitude ω avg and the standard deviation of angular velocity ω bz Calculate the abnormal angular velocity amplitude threshold Td ω , the specific algorithm formula is:

[0032] Td ω =ω avg +k×ω bz ;

[0033] Among them, k refers to an adjustment coefficient;

[0034] Step ③: Collect the average pressure amplitude P avg and pressure standard deviation P bzCalculate the abnormal pressure amplitude threshold Td P , the specific algorithm formula is:

[0035] Td P =P avg +k×P bz ;

[0036] Here, k refers to an adjustment factor.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] 1. In this high-precision positioning and anti-loss smart shoe system, the coordinate path module calculates the center pressure coordinates of the smart shoe according to the position of the pressure sensor, the measured pressure and the number of pressure sensors, and connects the center pressure coordinates of the smart shoe at each moment to form a moving curve. The moving curve is the moving path taken by the child wearing the smart shoe. By continuously recording the center pressure coordinates, the child's moving path can be tracked in real time. Parents can view the child's real-time position and movement trajectory through smart devices. At the same time, parents can set a safe area. When the child exceeds the safety range, the system will automatically alarm and feedback to improve the safety of the child.

[0039] 2. In the high-precision positioning and anti-loss smart shoe system, the theft judgment module calculates the average acceleration, angular velocity and pressure amplitude according to the acceleration, angular velocity, measured pressure and the number of time points, and then calculates the acceleration, angular velocity and pressure standard deviations. The abnormal acceleration, angular velocity and pressure amplitude thresholds are calculated according to the average acceleration amplitude, the average angular velocity amplitude, the average pressure amplitude, the acceleration standard deviation, the angular velocity standard deviation and the pressure standard deviation. By combining the abnormal thresholds of multiple sensors, the possibility of false alarm of a single sensor can be reduced, thereby improving the accuracy of detection. At the same time, when the smart shoes are stolen, abnormal fluctuations in acceleration, angular velocity and pressure data will occur. By calculating reasonable thresholds, these anomalies can be detected at the first time, thereby enhancing the security of the smart shoes. Timely detection of theft can reduce potential economic losses and security risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is an overall block diagram of the present invention;

[0041] Figure 2 It is a block diagram of the real-time detection unit of the present invention.

[0042] The meaning of each number in the figure is:

[0043] 1. Extrusion coordinate unit; 11. Extrusion trigger module; 12. Coordinate path module;

[0044] 2. Direction analysis unit; 21. Distance direction module; 22. Analysis and judgment module; 3. High-precision positioning unit;

[0045] 4. Change judgment unit; 41. Data change module; 42. Theft judgment module. DETAILED DESCRIPTION

[0046] The following will be combined with the accompanying drawings in 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.

[0047] Example 1

[0048] The present invention provides a high-precision positioning and anti-loss smart shoe system. Figure 1-Figure 2 , including an extrusion coordinate unit 1, a direction analysis unit 2, a high-precision positioning unit 3 and a change judgment unit 4;

[0049] The squeezing coordinate unit 1 measures the pressure between the foot and the smart shoe through the pressure sensor, obtains the acceleration and angular velocity of the smart shoe, records the position of the pressure sensor, measures the strain force generated by the foot and the smart insole through the strain sensor, records the area of ​​the strain sensor, and then uses the squeezing force algorithm to calculate the squeezing force and the central pressure coordinate of the smart shoe according to the number of pressure sensors, the measured pressure, the strain force, and the area of ​​the strain sensor. The squeezing force borne by the smart shoe is then used to determine whether to trigger the positioning algorithm, and the central pressure coordinates of the smart shoe are connected to form a moving curve, which is the moving path taken by the child wearing the smart shoe; the direction analysis unit 2 is used to receive the central pressure coordinates of the smart shoe in the squeezing coordinate unit 1, calculate the distance and moving direction of the moving path, and use the distance of the moving path to determine whether the child wearing the smart shoe is moving within the set range. When the child moves beyond the set range, the sound sensor in the smart shoe is used to obtain the sound data around the central pressure coordinates of the smart shoe, and generate a spectrum diagram and resonance peak and fundamental tone analysis based on the acquired sound data, and use the generated spectrum diagram, resonance peak and fundamental tone to determine whether the acquired sound data is the sound of the child's family. The specific judgment situations include: situation ①, when the similarity value is within the set similarity range value, it means that the acquired sound data is the voice of the child's family; situation ②, when the similarity value is not within the set similarity range value, it means that the acquired sound data is not the voice of the child's family. If the smart shoes run the child out of the set distance range while wearing the smart shoes, and there is no child's family member around the child, it is determined that the child is at risk of being lost; the high-precision positioning unit 3 is used to receive the command of the analysis unit 2 to determine that the child is at risk of being lost, the high-precision positioning unit 3 obtains the positioning point and sound data of the smart shoes from the direction analysis unit 2, and performs high-precision positioning based on the positioning point of the smart shoes and the acquired sound data; the change judgment unit 4 is used to receive the acceleration, angular velocity and measured pressure in the extrusion coordinate unit 1, calculate the acceleration amplitude, angular velocity amplitude, pressure change and calculate the average acceleration amplitude, angular velocity amplitude and pressure amplitude, calculate the abnormal acceleration, angular velocity and pressure amplitude threshold according to the average acceleration, angular velocity and pressure amplitude, and use the acceleration amplitude, angular velocity amplitude and pressure change and the abnormal acceleration, angular velocity and pressure amplitude threshold to determine whether the smart shoes are stolen.

[0050] The following is a refinement of the above units, see Figure 1-Figure 2 ;

[0051] The extrusion coordinate unit 1 includes an extrusion trigger module 11 and a coordinate path module 12;

[0052] When a child puts on the smart shoes, the squeeze trigger module 11 measures the pressure between the foot and the smart shoes through the pressure sensor in the smart shoes, obtains the measured pressure P, and then obtains the acceleration and angular velocity of the child walking in the smart shoes, and records the position of the pressure sensor at time t (xα (t),y α (t)), and the number n of pressure sensors, where α refers to the serial number of the pressure sensor, and then the strain force ε generated by the foot and the smart insole is measured by the strain sensor, and the area A and number m of the strain sensors and the Young's modulus E of the smart insole material are recorded. Then, the extrusion force F borne by the smart shoe is calculated by using the extrusion force algorithm according to the number n of pressure sensors, the measured pressure P, the strain force ε, the area A and number m of the strain sensors, and the Young's modulus E of the smart insole material. By calculating the extrusion force, the pressure distribution of each area of ​​the foot can be accurately analyzed, and the high-pressure area and the low-pressure area can be identified to help users understand the force situation of the foot, thereby improving the accuracy of analyzing the pressure distribution. The extrusion force F borne by the smart shoe and the set extrusion force are used to determine whether to trigger the positioning algorithm. When the extrusion force F borne by the smart shoe is greater than the set extrusion force, the extrusion force F borne by the smart shoe calculated last time is updated to the extrusion force F borne by the smart shoe calculated most recently, and the positioning algorithm is triggered;

[0053] The acceleration includes the maximum acceleration of each stage max(a phase ) and minimum acceleration min(a phase );

[0054] The angular velocity includes the maximum angular velocity max(ω phase ) and minimum angular velocity min(ω phase );

[0055] Each stage refers to t 0 The time period to time t, and the number of time points N;

[0056] The principle of calculating the squeezing force on smart shoes:

[0057] The number of pressure sensors n, the measured pressure P, the strain force ε, the area A and number m of the strain sensors, and the Young's modulus E of the smart insole material are collected and standardized, and then the extrusion force F borne by the smart shoe is calculated. The specific algorithm formula is:

[0058]

[0059] Among them, P i refers to the i-th pressure measured, ε i The value is the jth strain force generated. This formula is used to calculate the extrusion force that the smart shoe withstands.

[0060] The coordinate path module 12 is used to receive the command of triggering the positioning algorithm in the extrusion trigger module 11, and the coordinate path module 12 obtains the position (x) of the pressure sensor at time t from the extrusion trigger module 11. α (t),y α(t)), the pressure P measured at time t and the number of pressure sensors n, and the pressure center algorithm is used according to the position of the pressure sensor at time t (x α (t),y α (t)), the pressure P measured at time t and the number of pressure sensors n are used to calculate the pressure coordinates (x COP (t),y COP (t)), the center pressure coordinates of the smart shoes at each moment are connected to form a movement curve. The movement curve is the movement path of the child wearing the smart shoes. By continuously recording the center pressure coordinates, the movement path of the child can be tracked in real time. Parents can view the real-time position and movement trajectory of their children through smart devices. At the same time, parents can set a safe area. When the child exceeds the safe range, the system will automatically alarm and feedback to improve the safety of the child;

[0061] Principle of calculating the center pressure coordinates of smart shoes:

[0062]

[0063] Among them, x COP (t) refers to the horizontal axis coordinate of the center pressure of the smart shoe at time t, P i (t) refers to the i-th pressure measured at time t. This formula is used to calculate the horizontal axis coordinate in the center pressure coordinate of the smart shoe. By obtaining the horizontal axis coordinate in the center pressure coordinate, it is convenient to accurately calculate the center pressure coordinate of the smart shoe;

[0064]

[0065] Among them, y COP (t) refers to the vertical axis coordinate of the center pressure of the smart shoe at time t, P i (t) refers to the i-th pressure measured at time t. This formula is used to calculate the vertical axis coordinate in the center pressure coordinate of the smart shoe. By obtaining the vertical axis coordinate in the center pressure coordinate, it is convenient to accurately calculate the center pressure coordinate of the smart shoe;

[0066] The direction analysis unit 2 includes a distance direction module 21 and an analysis and judgment module 22;

[0067] The distance direction module 21 is used to receive the pressure coordinates (x COP (t),y COP (t)) and the number n of pressure sensors in the squeeze trigger module 11, according to the pressure coordinates (x COP (t),y COP (t)) to calculate the distance d of the moving path, and then according to the pressure coordinates of the center of the shoe (x COP (t),y COP(t)) calculate the moving direction θ, and prevent the child from getting lost by calculating the moving direction of the child. The direction of search can be determined according to the moving direction of the child, and the lost child can be found accurately and quickly to improve the safety of the child. The distance d of the moving path and the set range are used to determine whether the child wearing the smart shoes is moving within the set range. When the distance d of the moving path is greater than the set distance range, it means that the child wearing the smart shoes has run out of the set distance range;

[0068] Calculate the distance of the moving path to achieve the principle:

[0069]

[0070] Among them, t 1 and t 2 Refers to two consecutive points in time;

[0071] Collect the pressure coordinates of the center of the smart shoe (x COP (t),y COP (t)) calculates the moving direction θ, the specific algorithm formula is:

[0072]

[0073] Among them, t 1 and t 2 Refers to two consecutive time points. The formula is used to calculate the direction of movement. By calculating the direction of children's movement, we can prevent children from getting lost. According to the direction of children's movement, we can determine the direction of search, find lost children accurately and quickly, and improve children's safety.

[0074] The analysis and judgment module 22 is used to receive the distance range command greater than the set distance range from the distance direction module 21, and the analysis and judgment module 22 obtains the pressure coordinates (x COP (t),y COP (t)), and use the sound sensor in the smart shoe to obtain the pressure coordinates of the center of the smart shoe (x COP (t),y COP (t)) sound data around the child, extract an audio signal from the acquired sound data, generate a spectrogram based on the extracted audio signal using short-time Fourier transform (the spectrogram shows the energy distribution of the audio signal at different frequencies), and then use cepstrum analysis to extract resonance peaks (resonance peaks are the resonance frequencies formed in the vocal tract by the sound generated by the vibration of the vocal cords) and pitches (pitch is the basic frequency generated by the vibration of the vocal cords) from the generated spectrogram, and extract the spectrograms, resonance peaks and pitches of different family members from a database, calculate a similarity value using the generated spectrogram, the extracted resonance peaks and pitches and the spectrograms, resonance peaks and pitches of different family members extracted from the database, and then use the similarity value and a set similarity range value to determine whether the acquired sound data is the voice of the child's family member;

[0075] The specific judgment situations in the analysis and judgment module 22 include:

[0076] Case ①: When the similarity value is within the set similarity range, it means that the acquired sound data is the voice of the child's family, and the child is accompanied by the child's family. The smart shoes will feedback the presence of the child's family to the parent's smart device;

[0077] Case ②: When the similarity value is not within the set similarity range, it means that the acquired sound data is not the voice of the child's family. If the smart shoe shows that the child is wearing the smart shoe and runs out of the set distance range, and there is no family member around the child, it is determined that the child is at risk of being lost. The smart shoe will feedback to the parent's smart device. At this time, the smart shoe does not need to trigger the positioning algorithm through the condition of a squeezing force greater than the set pressure, but directly measures the pressure through the pressure sensor in the smart shoe and records the position of the pressure sensor (x α ,y α ), using the pressure center algorithm (the pressure center algorithm here has the same calculation principle as the pressure center algorithm mentioned above) according to the position of the pressure sensor (x α ,y α ), measure the pressure and the number of pressure sensors n to calculate the positioning point of the smart shoe (x COP ,y COP ), the positioning point of the children's smart shoes is calculated by the pressure center algorithm, and the position information of the children can be obtained in real time;

[0078] The principle of generating spectrum using short-time Fourier transform:

[0079] Divide the audio signal into multiple short-time windows, apply a window function (such as the Hamming window, a commonly used signal processing window function, mainly used to reduce spectrum leakage and improve the accuracy of spectrum analysis) to each window, and then perform Fourier transform on each window to generate a spectrogram;

[0080] The implementation principle of extracting resonance peaks using spectrum analysis:

[0081] Obtain the spectrum from the generated spectrogram, and take the logarithm of the spectrum (in spectrum analysis, taking the logarithm of the spectrum is a common processing method, usually used to convert the spectrum on a linear scale to the spectrum on a logarithmic scale), perform inverse Fourier transform on the logarithm to obtain the cepstral coefficients, and then analyze the cepstral coefficients and extract the resonance peak frequency;

[0082] The principle of extracting fundamental tone using spectrum analysis:

[0083] Obtain a spectrum from the generated spectrogram, then take the logarithm of the spectrum (in spectrum analysis, taking the logarithm of the spectrum is a common processing method, usually used to convert a linear scale spectrum to a logarithmic scale spectrum), perform an inverse Fourier transform on the logarithm to obtain the cepstral coefficients, and then analyze the cepstral coefficients and extract the fundamental frequency;

[0084] The high-precision positioning unit 3 is used to receive the command from the analysis and judgment module 22 that the child is at risk of being lost, and the high-precision positioning unit 3 obtains the positioning point (x COP ,y COP ), the sound data and the coordinate path module 12 obtain the moving path, and the smart shoe takes a picture of the positioning point (x COP ,y COP ) surrounding environment image data, and then analyze the features of the acquired surrounding environment image data, the analyzed features include vehicles, pedestrians, and trees; using the positioning point (x COP ,y COP ), obtain the sound data and the captured environmental image data for high-precision positioning, and then find the lost child according to the moving path based on the high-precision positioning;

[0085] The change judgment unit 4 includes a data change module 41 and a misappropriation judgment module 42;

[0086] The data change module 41 is used to receive the acceleration, angular velocity and measured pressure P from the squeeze trigger module 11, and obtain the maximum acceleration max (a phase )、minimum acceleration min(a phase ), maximum angular velocity max(ω phase ) and minimum angular velocity min(ω phase ), and then obtain the maximum pressure max(P phase ) and minimum pressure min(P phase ), according to the maximum acceleration max(a phase ) and minimum acceleration min(a phase ) Calculate the acceleration amplitude change Δa amp =max(a phase )-min(a phase ), and then according to the maximum angular velocity max(ω phase ) and minimum angular velocity min(ω phase ) Calculate the angular velocity amplitude change Δω amp =max(ω phase )-min(ω phase ), according to the maximum pressure max(P phase ) and minimum pressure min(P phase) Calculate the pressure change ΔP amp =max(P phase )-min(P phase );

[0087] The misappropriation judgment module 42 is used to receive the number of time points N from the squeeze trigger module 11 and the acceleration amplitude change Δa from the data change module 41 amp , angular velocity amplitude change Δω amp , pressure change ΔP amp , calculate the average acceleration amplitude a based on the acceleration and the number of time points N avg , and then calculate the average angular velocity amplitude ω according to the angular velocity and the number of time points N avg , using the measured pressure P and the number of time points N to calculate the average pressure amplitude P avg , and then use the acceleration amplitude change Δa amp , the number of time points N and the average acceleration amplitude a avg Calculate the standard deviation of acceleration a bz , according to the angular velocity amplitude change Δω amp , the number of time points N and the average angular velocity amplitude ω avg Calculate the standard deviation of angular velocity ω bz , and then according to the pressure change ΔP amp , the number of time points N and the average pressure amplitude P avg Calculate the pressure standard deviation P bz , according to the average acceleration amplitude a avg , average angular velocity amplitude ω avg , average pressure amplitude P avg , acceleration standard deviation a bz , angular velocity standard deviation ω bz and pressure standard deviation P bz Calculate the abnormal acceleration amplitude threshold, abnormal angular velocity amplitude threshold and abnormal pressure amplitude threshold. By integrating the abnormal thresholds of multiple sensors, the possibility of false alarms from a single sensor can be reduced, thereby improving the accuracy of detection. At the same time, when smart shoes are stolen, abnormal fluctuations in acceleration, angular velocity and pressure data can be caused. By calculating reasonable thresholds, these anomalies can be detected at the first time, thereby enhancing the security of smart shoes. Timely detection of theft can reduce potential economic losses and safety risks. Using the acceleration amplitude change Δa amp , angular velocity amplitude change Δω amp , pressure change ΔP amp The abnormal acceleration amplitude threshold, abnormal angular velocity amplitude threshold and abnormal pressure amplitude threshold are used to judge whether the smart shoes are stolen. When the acceleration amplitude changes by Δa amp Greater than the abnormal acceleration amplitude threshold, and the angular velocity amplitude changes by Δω ampis greater than the abnormal angular velocity amplitude threshold, and the pressure change ΔP amp When the pressure is greater than the abnormal pressure amplitude threshold, it indicates that the smart shoes have been stolen.

[0088] Steps to calculate abnormal acceleration, angular velocity and pressure amplitude thresholds:

[0089] Step ①: Collect the average acceleration amplitude a avg and the standard deviation of acceleration a bz Calculate the abnormal acceleration amplitude threshold Td a , the specific algorithm formula is:

[0090] Td a =a avg +k×a bz ;

[0091] Among them, k refers to an adjustment coefficient, which can be regarded as a "tolerance" parameter, which controls the sensitivity of the system to acceleration changes. This formula is used to calculate the abnormal acceleration amplitude threshold, which serves as the basis for subsequent judgment of whether the smart shoes are stolen;

[0092] Step 2: Collect the average angular velocity amplitude ω avg and the standard deviation of angular velocity ω bz Calculate the abnormal angular velocity amplitude threshold Td ω , the specific algorithm formula is:

[0093] Td ω =ω avg +k×ω bz ;

[0094] Among them, k refers to an adjustment coefficient, which can be regarded as a "tolerance" parameter, which controls the sensitivity of the system to acceleration changes. This formula is used to calculate the abnormal angular velocity amplitude threshold, which is the basis for subsequent judgment of whether the smart shoes are stolen;

[0095] Step ③: Collect the average pressure amplitude P avg and pressure standard deviation P bz Calculate the abnormal pressure amplitude threshold Td P , the specific algorithm formula is:

[0096] Td P =P avg +k×P bz ;

[0097] Among them, k refers to an adjustment coefficient, which can be regarded as a "tolerance" parameter, which controls the system's sensitivity to acceleration changes. This formula is used to calculate the abnormal pressure amplitude threshold, which serves as the basis for subsequent judgment on whether the smart shoes have been stolen.

[0098] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A high-precision positioning and anti-loss smart shoe system, characterized by: It comprises an extrusion coordinate unit (1), a direction analysis unit (2), a high-precision positioning unit (3) and a change judgment unit (4); The squeezing coordinate unit (1) measures the pressure between the foot and the smart shoe through a pressure sensor, obtains the acceleration and angular velocity of the smart shoe, records the position of the pressure sensor, measures the strain force generated by the foot and the smart insole through a strain sensor, records the area of ​​the strain sensor, and uses a squeezing force algorithm to calculate the squeezing force borne by the smart shoe and the center pressure coordinates of the smart shoe according to the number of pressure sensors, the measured pressure, the strain force, and the area of ​​the strain sensor. The squeezing force borne by the smart shoe is used to determine whether to trigger a positioning algorithm, and the center pressure coordinates of the smart shoe are connected to form a moving curve, which is the moving path taken by the child wearing the smart shoe; The direction analysis unit (2) is used to receive the central pressure coordinates of the smart shoe in the squeezing coordinate unit (1), calculate the distance and direction of the moving path, and use the distance of the moving path to determine whether the child wearing the smart shoe is moving within a set range. When the child is moving beyond the set range, the sound sensor in the smart shoe is used to obtain sound data around the central pressure coordinates of the smart shoe, generate a spectrum diagram and a resonance peak and a fundamental tone analysis based on the obtained sound data, and use the generated spectrum diagram, resonance peak and fundamental tone to determine whether the obtained sound data is the voice of the child's family; Specific judgment situations include: Case ①: When the similarity value is within the set similarity range, it means that the acquired voice data is the voice of the child's family member; Case ②: When the similarity value is not within the set similarity range, it means that the acquired sound data is not the voice of the child's family. If the smart shoes show the child running out of the set distance range, and there is no family member around the child, it is determined that the child is at risk of being lost. The high-precision positioning unit (3) is used to receive a command from the analysis unit (2) that the child is at risk of being lost, and the high-precision positioning unit (3) obtains the positioning point and sound data of the smart shoe from the direction analysis unit (2), and performs high-precision positioning based on the positioning point of the smart shoe and the obtained sound data; The change judgment unit (4) is used to receive the acceleration, angular velocity and measured pressure in the extrusion coordinate unit (1), calculate the acceleration amplitude, angular velocity amplitude, pressure change and calculate the average acceleration amplitude, angular velocity amplitude and pressure amplitude, calculate the abnormal acceleration, angular velocity and pressure amplitude threshold according to the average acceleration, angular velocity and pressure amplitude, and use the acceleration amplitude, angular velocity amplitude and pressure change and the abnormal acceleration, angular velocity and pressure amplitude threshold to judge whether the smart shoes are stolen.

2. The high-precision positioning and anti-loss smart shoe system according to claim 1, characterized in that: The extrusion coordinate unit (1) comprises an extrusion trigger module (11) and a coordinate path module (12); The squeezing trigger module (11) measures the pressure between the foot and the smart shoe through the pressure sensor in the smart shoe, obtains the acceleration and angular velocity of the child walking in the smart shoe, records the position of the pressure sensor at time t and the number of pressure sensors, measures the strain force generated by the foot and the smart insole through the strain sensor, records the area and number of strain sensors and the Young's modulus of the smart insole material, and calculates the squeezing force borne by the smart shoe using the squeezing force algorithm according to the number of pressure sensors, the measured pressure, the strain force, the area and number of strain sensors and the Young's modulus of the smart insole material, determines whether to trigger the positioning algorithm using the squeezing force borne by the smart shoe, and when the squeezing force borne by the smart shoe is greater than the set squeezing force, updates the squeezing force borne by the smart shoe calculated last time to the squeezing force borne by the smart shoe calculated most recently, and triggers the positioning algorithm; The coordinate path module (12) is used to receive a command from the squeeze trigger module (11) to trigger a positioning algorithm. The coordinate path module (12) obtains the position of the pressure sensor at time t, the pressure measured at time t, and the number of pressure sensors from the squeeze trigger module (11), calculates the central pressure coordinates of the smart shoe, and connects the central pressure coordinates of the smart shoe at each time to form a movement curve. The movement curve is the movement path taken by the child wearing the smart shoe.

3. The high-precision positioning and anti-loss smart shoe system according to claim 2, characterized in that: The direction analysis unit (2) comprises a distance direction module (21) and an analysis and judgment module (22); The distance and direction module (21) is used to receive the central pressure coordinates of the smart shoe in the coordinate path module (12) and the number of pressure sensors in the squeeze trigger module (11), calculate the distance and direction of the moving path according to the central pressure coordinates of the smart shoe, and use the distance of the moving path to determine whether the child wearing the smart shoe is moving within a set range; when the distance of the moving path is greater than the set distance range, it means that the child wearing the smart shoe has run out of the set distance range; The analysis and judgment module (22) is used to receive a command greater than a set distance range in the distance direction module (21), the analysis and judgment module (22) obtains the central pressure coordinates of the smart shoe from the coordinate path module (12), and uses the sound sensor in the smart shoe to obtain sound data around the central pressure coordinates of the smart shoe, extracts an audio signal from the obtained sound data, generates a spectrogram based on the extracted audio signal using short-time Fourier transform, extracts resonance peaks and fundamental tones from the generated spectrogram using cepstrum analysis, extracts the spectrograms, resonance peaks and fundamental tones of different family members from a database, calculates a similarity value using the generated spectrogram, the extracted resonance peaks and fundamental tones and the spectrograms, resonance peaks and fundamental tones of different family members extracted from the database, and uses the similarity value to judge whether the obtained sound data is the voice of a child's family member.

4. The high-precision positioning and anti-loss smart shoe system according to claim 3 is characterized by: The specific judgment situations in the analysis and judgment module (22) include: Case ①: When the similarity value is within the set similarity range, it means that the acquired sound data is the voice of the child's family, and the child is accompanied by the child's family. The smart shoes will feedback the presence of the child's family to the parent's smart device; Case ②: When the similarity value is not within the set similarity range, it means that the acquired sound data is not the voice of the child's family. If the smart shoes show that the child is wearing the smart shoes and runs out of the set distance range, and there is no family member around the child, it is determined that the child is at risk of being lost. The smart shoes will feedback to the parent's smart device. At this time, the smart shoes do not need to trigger the positioning algorithm through the condition of a squeezing force greater than the set pressure. The pressure is directly measured through the pressure sensor in the smart shoe, and the position of the pressure sensor is recorded. The pressure center algorithm is used to calculate the positioning point of the smart shoe based on the position of the pressure sensor, the measured pressure and the number of pressure sensors.

5. The high-precision positioning and anti-loss smart shoe system according to claim 3, characterized in that: The high-precision positioning unit (3) is used to receive a command from the analysis and judgment module (22) that a child is at risk of being lost. The high-precision positioning unit (3) obtains the positioning point of the smart shoe from the analysis and judgment module (22), the sound data and the moving path from the coordinate path module (12). The smart shoe uses a camera to capture environmental image data around the positioning point of the smart shoe, and then performs feature analysis on the acquired surrounding environmental image data. The analyzed features include vehicles, pedestrians, and trees. High-precision positioning is performed based on the positioning point of the smart shoe, the acquired sound data and the captured environmental image data, and then the lost child is searched for along the moving path based on the high-precision positioning.

6. The high-precision positioning and anti-loss smart shoe system according to claim 2, characterized in that: The change judgment unit (4) comprises a data change module (41) and a theft judgment module (42); The data change module (41) is used to receive the acceleration, angular velocity and measured pressure in the squeeze trigger module (11), obtain the maximum acceleration, minimum acceleration, maximum angular velocity and minimum angular velocity from the acceleration and angular velocity, then obtain the maximum pressure and minimum pressure from the measured pressure, calculate the acceleration amplitude change according to the maximum acceleration and minimum acceleration, then calculate the angular velocity amplitude change according to the maximum angular velocity and minimum angular velocity, and calculate the pressure change according to the maximum pressure and minimum pressure; The misappropriation judgment module (42) is used to receive the number of time points in the squeeze trigger module (11) and the acceleration amplitude change, angular velocity amplitude change, and pressure change in the data change module (41), calculate the average acceleration amplitude according to the acceleration and the number of time points, then calculate the average angular velocity amplitude according to the angular velocity and the number of time points, calculate the average pressure amplitude using the measured pressure and the number of time points, then calculate the acceleration standard deviation using the acceleration amplitude change, the number of time points, and the average acceleration amplitude, calculate the angular velocity standard deviation according to the angular velocity amplitude change, the number of time points, and the average angular velocity amplitude, and then calculate the pressure change, the number of time points, and the average pressure amplitude. The pressure standard deviation is calculated, and the abnormal acceleration amplitude threshold, abnormal angular velocity amplitude threshold and abnormal pressure amplitude threshold are calculated according to the average acceleration amplitude, the average angular velocity amplitude, the average pressure amplitude, the acceleration standard deviation, the angular velocity standard deviation and the pressure standard deviation. The acceleration amplitude change, the angular velocity amplitude change, the pressure change and the abnormal acceleration amplitude threshold, the abnormal angular velocity amplitude threshold and the abnormal pressure amplitude threshold are used to determine whether the smart shoes are stolen. When the acceleration amplitude change is greater than the abnormal acceleration amplitude threshold, the angular velocity amplitude change is greater than the abnormal angular velocity amplitude threshold, and the pressure change is greater than the abnormal pressure amplitude threshold, it means that the smart shoes are stolen.

7. The high-precision positioning and anti-loss smart shoe system according to claim 6, characterized in that: Steps to calculate abnormal acceleration, angular velocity and pressure amplitude thresholds: Step ①: Collect the average acceleration amplitude a avg and the standard deviation of acceleration a bz Calculate the abnormal acceleration amplitude threshold Td a , the specific algorithm formula is: Td a =a avg +k×a bz ; Among them, k refers to an adjustment coefficient; Step 2: Collect the average angular velocity amplitude ω avg and the standard deviation of angular velocity ω bz Calculate the abnormal angular velocity amplitude threshold Td ω , the specific algorithm formula is: Td ω =ω avg +k×ω bz ; Among them, k refers to an adjustment coefficient; Step ③: Collect the average pressure amplitude P avg and pressure standard deviation P bz Calculate the abnormal pressure amplitude threshold Td P , the specific algorithm formula is: Td P =P avg +k×P bz ; Here, k refers to an adjustment factor.

Images