Campus indoor and outdoor seamless positioning system and method based on multi-source data fusion

Abstract

The invention relates to the technical field of indoor and outdoor positioning, in particular to a campus indoor and outdoor seamless positioning system and method based on multi-source data fusion, and the system is provided with a data acquisition module, a data pre-analysis module, a feature recognition module and a positioning signal regulation and control module. A noise intensity curve of a mobile terminal is drawn in a feature extraction time window through a data pre-analysis module, a noise fluctuation parameter and a noise period trend parameter are determined through a feature recognition module to screen feature mobile terminals, and a feature period fluctuation curve is fitted through a positioning signal regulation and control module. And determining a signal sampling frequency adjustment mode of the positioning signal acquired by the characteristic mobile terminal. According to the invention, the mobile terminals located in the indoor and outdoor junction area are screened according to the positioning signals received by the mobile terminals and the environmental noise intensity, the signal sampling frequency of the positioning signals is adaptively adjusted according to the actual moving trend of the mobile terminals, and the reliability of the campus indoor and outdoor seamless positioning system is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of indoor and outdoor positioning technology, and in particular to a campus indoor and outdoor seamless positioning system and method based on multi-source data fusion. BACKGROUND

[0002] In today's digital age, the construction of smart campus is in full swing, and the campus indoor and outdoor seamless positioning technology as a key support, its importance is increasingly prominent, with the continuous expansion of the campus scale, the increasing complexity of functional areas, the demand for accurate position information of teachers, students and management personnel in the campus is growing exponentially, and the demand for high-precision positioning is gradually shifting from single scene to all scenes, and multi-source data fusion positioning technology emerges as the times require, becoming an inevitable choice to realize campus indoor and outdoor seamless, high-precision and high-reliability positioning. The indoor and outdoor interface area is a high-risk area for positioning deviation. The traditional positioning method for the indoor and outdoor interface area is to determine the boundary by the boundary between indoor and outdoor, and to determine the boundary of the area by manual setting, which is prone to large deviation. Therefore, it is an urgent technical problem to adaptively adjust the sampling frequency of the positioning signal according to the environmental characteristics of the mobile terminal and improve the adaptability and accuracy of the positioning system.

[0003] For example, Chinese patent No. CN114518587B discloses an indoor and outdoor seamless positioning system and method. In the system, the BDS antenna and the UWB antenna are arranged at different positions of the z-axis of the IMU coordinate system; the BDS antenna is connected with the BDS board card; the BDS board card is used for processing the ranging information broadcasted by the BDS satellite B1 I, B1C and B3I frequency and the real-time precise orbit / clock error correction number broadcasted by the BDS B2b frequency; the UWB antenna is connected with the UWB chip; the BDS board card, the UWB chip, the IMU, the magnetometer and the barometer are all connected with the ARM processor; the ARM processor is used for time synchronization and data fusion of the data collected by the BDS board card, the UWB chip, the IMU, the magnetometer and the barometer; the ARM processor is also connected with the communication module; and the power supply is connected with the BDS board card, the UWB chip, the IMU, the magnetometer, the barometer and the ARM processor respectively.

[0004] The existing technology also has the following problems:

[0005] The existing technology does not consider the existence of a large number of trees and buildings in the campus scene, which affects the accuracy of the positioning signal. The existing technology cannot filter the mobile terminal in the indoor and outdoor interface area according to the positioning signal received by the mobile terminal and the environmental noise intensity, and cannot adaptively adjust the signal sampling frequency of the positioning signal according to the actual moving trend of the mobile terminal, which affects the reliability of the campus indoor and outdoor seamless positioning system. SUMMARY

[0006] To this end, the present application provides a seamless positioning system and method for campus indoor and outdoor based on multi-source data fusion, to overcome the problem that the prior art cannot filter the mobile terminal located at the indoor and outdoor interface area according to the positioning signal received by the mobile terminal and the environmental noise intensity, and cannot adaptively adjust the signal sampling frequency of the positioning signal according to the actual moving trend of the mobile terminal, affecting the reliability of the seamless positioning system for campus indoor and outdoor.

[0007] To achieve the above-mentioned purpose, the present application provides a seamless positioning system for campus indoor and outdoor based on multi-source data fusion, comprising:

[0008] A data acquisition module is used to acquire the satellite positioning signal intensity, wireless positioning signal intensity received by each mobile terminal, and the noise intensity of the environment where the mobile terminal is located;

[0009] A data pre-analysis module is connected with the data acquisition module, used to determine the signal fluctuation parameter according to the characteristic positioning signal intensity within the preset acquisition period, to determine the feature extraction time window, and to draw the noise intensity curve of the mobile terminal in the feature extraction time window;

[0010] A feature recognition module is connected with the data acquisition module and the data pre-analysis module respectively, used to determine the noise fluctuation parameter and noise period trend parameter based on the noise intensity curve of the mobile terminal, to filter the feature mobile terminal;

[0011] A positioning signal regulation module is connected with the data pre-analysis module and the feature recognition module respectively, used to fit the characteristic period fluctuation curve based on the noise period characteristic value of the feature mobile terminal, to mark the characteristic curve segment according to the characteristic period fluctuation curve, to determine the characteristic aggregation curve segment and the characteristic time, and to determine the signal sampling frequency adjustment mode of the feature mobile terminal to acquire the positioning signal according to the comparison between the characteristic time of the feature mobile terminal and the feature extraction time window;

[0012] The positioning signal includes satellite positioning signal and wireless positioning signal.

[0013] Further, the data pre-analysis module is used to determine the signal intensity fluctuation parameter, wherein,

[0014] The data pre-analysis module is used to acquire the feature positioning signal intensity at several time points within each acquisition period of the preset acquisition period, and to determine the difference between the maximum value of the feature positioning signal intensity and the minimum value of the feature positioning signal intensity as the signal intensity fluctuation parameter of the acquisition period;

[0015] The feature positioning signal intensity is the maximum value of the satellite positioning signal intensity and the wireless positioning signal intensity at the first time point within the acquisition period.

[0016] Further, the data pre-analysis module is configured to determine a feature extraction time window, wherein,

[0017] The data pre-analysis module determines the acquisition time period as the feature extraction time window based on a determination result that the signal intensity fluctuation parameter of the acquisition time period meets the time window condition.

[0018] The time window condition is that the signal intensity fluctuation parameter exceeds a preset signal intensity fluctuation parameter threshold.

[0019] Further, the feature recognition module is configured to determine the noise fluctuation parameter and the noise period trend parameter, wherein,

[0020] The feature recognition module determines a time interval between adjacent wave crests in the noise intensity curve as a noise period representation value of a wave crest in a previous time among the adjacent wave crests.

[0021] The noise fluctuation parameter is a variance of noise fluctuation values, the noise fluctuation value is an absolute value of a noise intensity difference value between adjacent wave crests and wave troughs in the noise intensity curve, and the noise period trend parameter is a variance of noise period representation values, the noise intensity curve is plotted in a rectangular coordinate system with time as the horizontal axis and noise intensity as the vertical axis.

[0022] Further, the feature recognition module is configured to screen the feature mobile terminal, wherein,

[0023] The feature recognition module screens the mobile terminal as a suspected feature mobile terminal based on a determination result that the noise fluctuation parameter, the noise period trend parameter, and a dynamic change rate of the feature positioning signal intensity of the mobile terminal meet a suspected feature mobile terminal condition.

[0024] The suspected feature mobile terminal condition is that the noise fluctuation parameter exceeds a preset noise fluctuation parameter threshold, the noise period trend parameter exceeds a preset noise period trend parameter threshold, and an absolute value of the dynamic change rate exceeds a preset dynamic change rate threshold, the dynamic change rate is an average value of a ratio of a feature positioning signal intensity difference between adjacent time points in the feature extraction time window to a time interval of the adjacent time points.

[0025] Further, the positioning signal regulation module is configured to mark a feature curve segment, wherein,

[0026] The positioning signal regulation module is configured to divide the feature period fluctuation curve into a plurality of curve segments according to time points at which feature period fluctuation points are located, and calculate an average slope of each curve segment.

[0027] If the average slope of the curve segment exceeds zero, the positioning signal regulation module marks the curve segment as a first characteristic curve sub-segment;

[0028] If the average slope of the curve segment does not exceed zero, the positioning signal regulation module marks the curve segment as a second characteristic curve sub-segment;

[0029] The characteristic periodic fluctuation curve establishes a rectangular coordinate system with time as the horizontal axis and the numerical value of the noise period characteristic value as the vertical axis, and is fitted according to a plurality of characteristic periodic fluctuation points, which are determined according to the noise period characteristic value and the time corresponding to the noise period characteristic value.

[0030] Further, the positioning signal regulation module is used to determine a characteristic aggregated curve segment and a characteristic time point, wherein,

[0031] The characteristic aggregated curve segment does not include a first characteristic curve sub-segment, is composed of a plurality of second characteristic curve sub-segments, and the number of second characteristic curve sub-segments exceeds a preset number threshold;

[0032] The characteristic time point is the last time point in the time range of the characteristic aggregated curve segment.

[0033] Further, the positioning signal regulation module is used to determine a signal sampling frequency adjustment mode of the characteristic mobile terminal acquiring the positioning signal, wherein,

[0034] Based on the determination result that the comparison of the characteristic time point of the characteristic mobile terminal and the characteristic extraction time window meets the wireless positioning signal regulation condition, the positioning signal regulation module determines to increase the signal sampling frequency of the wireless positioning signal, and the increase amount of the signal sampling frequency is positively correlated with the environmental trend parameter;

[0035] Based on the determination result that the comparison of the characteristic time point of the characteristic mobile terminal and the characteristic extraction time window does not meet the wireless positioning signal regulation condition, the positioning signal regulation module determines to increase the signal sampling frequency of the satellite positioning signal, and the increase amount of the signal sampling frequency is negatively correlated with the environmental trend parameter.

[0036] Further, the wireless positioning signal regulation condition is that the interval time length between the characteristic time point and the last time point in the characteristic extraction time window does not exceed a preset interval time length threshold, and the environmental trend parameter is the time length of the characteristic aggregated curve segment.

[0037] The application also provides a campus indoor and outdoor seamless positioning method based on multi-source data fusion, comprising:

[0038] Obtaining the satellite positioning signal strength, the wireless positioning signal strength received by each mobile terminal, and the noise strength of the environment where the mobile terminal is located;

[0039] determine a signal fluctuation parameter according to the characteristic positioning signal strength in a preset acquisition period, to determine a characteristic extraction time window, and draw a noise intensity curve of the mobile terminal in the characteristic extraction time window;

[0040] determine a noise fluctuation parameter and a noise period trend parameter based on the noise intensity curve of the mobile terminal, to screen a characteristic mobile terminal;

[0041] fit a characteristic period fluctuation curve based on the noise period characteristic value of the characteristic mobile terminal, mark a characteristic curve segment according to the characteristic period fluctuation curve, to determine a characteristic aggregated curve segment and a characteristic time, and determine a signal sampling frequency adjustment mode of the characteristic mobile terminal to acquire positioning signals according to the comparison between the characteristic time of the characteristic mobile terminal and the characteristic extraction time window.

[0042] Compared with the prior art, the present application has the beneficial effects that the present application is provided with a data acquisition module, a data pre-analysis module, a characteristic identification module, and a positioning signal regulation module, the satellite positioning signal strength, the wireless positioning signal strength, and the noise intensity of the environment are acquired through the data acquisition module, the characteristic extraction time window is determined through the data pre-analysis module, the noise intensity curve of the mobile terminal is drawn in the characteristic extraction time window, the noise fluctuation parameter and the noise period trend parameter are determined through the characteristic identification module, to screen the characteristic mobile terminal, the characteristic period fluctuation curve is fitted through the positioning signal regulation module, the characteristic curve segment is marked, the characteristic aggregated curve segment and the characteristic time are determined, to determine the signal sampling frequency adjustment mode of the characteristic mobile terminal to acquire positioning signals, and then, the mobile terminal in the indoor-outdoor boundary area is screened according to the positioning signals received by the mobile terminal and the environmental noise intensity, the signal sampling frequency of the positioning signals is adaptively adjusted according to the actual moving trend of the mobile terminal, and the reliability of the seamless positioning system in the campus indoor and outdoor is improved.

[0043] Especially, the application determines the feature extraction time window according to the signal fluctuation parameters through the data pre-analysis module, draws the noise intensity curve of the mobile terminal in the feature extraction time window, and can be understood that the signal fluctuation parameters in the preset collection period can dynamically reflect the change trend of the signal intensity, and the change of the area where the mobile terminal is located is captured in real time, which provides a basis for determining the feature extraction time window. Analyzing the noise intensity curve in the feature extraction time window can accurately focus on positioning the key scene of unstable signal, avoid redundant processing of signal stable period, significantly improve the data processing efficiency, provide reliable basis for dynamic adjustment of subsequent positioning signal sampling frequency, and finally realize seamless switching and accurate adaptation of indoor and outdoor positioning in complex campus environment, balance the positioning performance and system resource consumption. The application determines the feature extraction time window according to the signal fluctuation parameters through the data pre-analysis module, draws the noise intensity curve of the mobile terminal in the feature extraction time window, and then realizes the determination of the feature extraction time window, and improves the reliability of the seamless positioning system in the campus indoor and outdoor.

[0044] Especially, the application determines the feature extraction time window according to the signal fluctuation parameters through the data pre-analysis module, draws the noise intensity curve of the mobile terminal in the feature extraction time window, and can be understood that the signal fluctuation parameters in the preset collection period can dynamically reflect the change trend of the signal intensity, and the change of the area where the mobile terminal is located is captured in real time, which provides a basis for determining the feature extraction time window. Analyzing the noise intensity curve in the feature extraction time window can accurately focus on positioning the key scene of unstable signal, avoid redundant processing of signal stable period, significantly improve the data processing efficiency, provide reliable basis for dynamic adjustment of subsequent positioning signal sampling frequency, and finally realize seamless switching and accurate adaptation of indoor and outdoor positioning in complex campus environment, balance the positioning performance and system resource consumption. The application determines the feature extraction time window according to the signal fluctuation parameters through the data pre-analysis module, draws the noise intensity curve of the mobile terminal in the feature extraction time window, and then realizes the determination of the feature extraction time window, and improves the reliability of the seamless positioning system in the campus indoor and outdoor.

[0045] Especially, the application determines the signal sampling frequency adjustment mode of the feature mobile terminal acquiring the positioning signal according to the comparison between the feature time of the feature mobile terminal and the feature extraction time window by the positioning signal regulation module, which can be understood that the regional movement trend of the mobile terminal is accurately identified by the correlation between the feature time and the signal fluctuation window, and the mobile terminal is distinguished to move indoors or outdoors, the accurate adaptation of the positioning signal is realized, the utilization efficiency of the positioning signal is improved, the sampling frequency of the wireless positioning signal increases with the demand of indoor environment, and the satellite positioning signal increases with the demand of outdoor, which not only ensures the positioning accuracy in complex environment, such as the anti-interference ability of indoor enhanced wireless signal, but also avoids the waste of resources caused by invalid sampling, such as the high-frequency wireless sampling is not needed outdoors, the regional movement trend of the mobile terminal is judged according to the environmental noise characteristics in the feature extraction time window, the sampling frequency setting strategy is adjusted in advance to ensure the uninterrupted positioning process of regional movement, the smooth transition of indoor and outdoor switching is realized, the sampling frequency adjustment is synchronized with the environmental change by comparing the feature time with the feature extraction time window, the positioning deviation caused by delay is reduced, and then, the signal sampling frequency of the positioning signal is adaptively adjusted according to the actual movement trend of the mobile terminal, and the reliability of the seamless positioning system in the campus indoor and outdoor is improved.

[0046] Especially, the application increases the signal sampling frequency of the wireless positioning signal when the comparison between the feature time of the feature mobile terminal and the feature extraction time window meets the wireless positioning signal regulation condition, and the increase of the signal sampling frequency is positively correlated with the environmental trend parameter, which can be understood that the feature time is the last time in the time range of the feature aggregation curve segment, which can represent the time when the mobile terminal is closest to the indoor, the shorter the interval between the last time in the feature extraction time window and the feature time, the more the feature mobile terminal moves indoors, and the more the signal sampling frequency of the wireless positioning signal needs to be increased, the longer the time length of the feature aggregation curve segment, the closer the mobile terminal is to the indoor, and the higher the signal sampling frequency of the wireless positioning signal needs to be, more positioning signal details are captured, and the accuracy of indoor positioning is improved, and then, the signal sampling frequency of the positioning signal is adaptively adjusted according to the actual movement trend of the mobile terminal, and the reliability of the seamless positioning system in the campus indoor and outdoor is improved.

[0047] Especially, the application increases the signal sampling frequency of the satellite positioning signal in the case that the comparison between the feature time and the feature extraction time window does not meet the wireless positioning signal regulation condition, and the increase amount of the signal sampling frequency is negatively correlated with the environmental trend parameter, it can be understood that the interval length between the feature time and the last time in the feature extraction time window exceeds the preset interval length threshold, which represents that the mobile terminal moves to the outdoor, and the satellite positioning signal is usually more stable and more accurate than the wireless positioning signal in the outdoor environment, increasing the sampling frequency of the satellite positioning signal can capture more signal details, thereby improving the positioning accuracy, the smaller the environmental trend parameter, the shorter the length of the mobile terminal close to the indoor, the closer to the outdoor, the higher the sampling frequency is needed to capture more signal details, thereby improving the outdoor positioning accuracy, and further, the signal sampling frequency of the positioning signal is adaptively adjusted according to the actual moving trend of the mobile terminal, and the reliability of the seamless positioning system in the campus indoor and outdoor is improved. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 The function block diagram of the seamless positioning system in the campus indoor and outdoor based on multi-source data fusion of the embodiment of the application is shown in the figure.

[0049] Figure 2 The logic flow chart for determining the feature extraction time window by the data pre-analysis module of the embodiment of the application is shown in the figure.

[0050] Figure 3 The schematic diagram of the feature cycle fluctuation curve of the embodiment of the application is shown in the figure.

[0051] Figure 4 The step diagram of the seamless positioning method in the campus indoor and outdoor based on multi-source data fusion of the embodiment of the application is shown in the figure.

[0052] In the figure: 1-first feature curve sub-section; 2-second feature curve sub-section; 3-feature aggregation curve section. DETAILED DESCRIPTION

[0053] In order to make the purpose and advantages of the application more clear and obvious, the application is further described below in combination with the embodiments; it should be understood that the specific embodiments described herein are only used to explain the application, and are not used to limit the protection scope of the application.

[0054] The preferred embodiments of the application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the application, and are not used to limit the protection scope of the application.

[0055] It should be noted that in the description of the present application, the terms indicating the direction or positional relationship of "upper", "lower", "inner", "outer" and the like are based on the direction or positional relationship shown in the drawings, which is only for the convenience of description, and does not indicate or imply that the device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application.

[0056] In addition, it should be noted that in the description of the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connection" should be understood broadly, for example, it can be fixed connection, or detachable connection, or integral connection; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through intermediate medium, or the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0057] Please refer to Figure 1 As shown in the figure, it is a function block diagram of a campus indoor and outdoor seamless positioning system based on multi-source data fusion according to an embodiment of the present application, and the campus indoor and outdoor seamless positioning system based on multi-source data fusion of the present application comprises:

[0058] A data acquisition module is used to acquire the satellite positioning signal strength, wireless positioning signal strength received by each mobile terminal, and the noise strength of the environment where the mobile terminal is located.

[0059] Specifically, the specific structure of the data acquisition module is not limited in the embodiment of the present application, and preferably, it can be a satellite positioning signal receiver, a wireless signal receiver and a microphone built in the mobile terminal, which is used to acquire the satellite positioning signal strength, wireless positioning signal strength and noise strength of the environment, the noise strength is the sound pressure level of the acoustic noise in the environment, and the unit is decibel, and the mobile terminal can be a mobile phone, which will not be repeated here.

[0060] Specifically, the campus has generally deployed a perfect Wi-Fi network, which can directly use the wireless signal of the existing router to realize positioning, without the need for additional large-scale equipment, low cost and wide coverage, suitable for large indoor areas such as teaching buildings and libraries, and the global satellite navigation system is the most commonly used technology for outdoor positioning of the campus, the satellite signal is determined through the receiving terminal (such as a mobile phone) to determine the position, which meets the demand of outdoor navigation of teachers and students, which will not be repeated here.

[0061] A data pre-analysis module is connected with the data acquisition module, used to determine the signal fluctuation parameter according to the characteristic positioning signal strength in the preset acquisition period, to determine the feature extraction time window, and to draw the noise strength curve of the mobile terminal in the feature extraction time window.

[0062] Specifically, the embodiment of the present application does not limit the specific structure of the data pre-analysis module, which can be preferably a microprocessor, used to determine signal fluctuation parameters, feature extraction time windows, and draw noise intensity curves of the mobile terminal, which will not be repeated here.

[0063] The feature recognition module is connected with the data acquisition module and the data pre-analysis module respectively, used to determine noise fluctuation parameters and noise period trend parameters based on the noise intensity curves of the mobile terminal, to screen feature mobile terminals.

[0064] Specifically, the embodiment of the present application does not limit the specific structure of the feature recognition module, which can be preferably a processor used in a computer, used to determine noise fluctuation parameters, noise period trend parameters, and screen feature mobile terminals, which will not be repeated here.

[0065] The positioning signal regulation module is connected with the data pre-analysis module and the feature recognition module respectively, used to fit a feature period fluctuation curve based on the noise period characteristic values of the feature mobile terminal, mark feature curve segments according to the feature period fluctuation curve, determine feature aggregated curve segments and feature time instants, and determine the signal sampling frequency adjustment mode of the feature mobile terminal to acquire positioning signals according to the comparison between the feature time instants of the feature mobile terminal and the feature extraction time windows.

[0066] The positioning signal includes satellite positioning signals and wireless positioning signals.

[0067] Specifically, the embodiment of the present application does not limit the specific structure of the feature recognition module, which can be preferably a microprocessor, used to fit a feature period fluctuation curve, mark feature curve segments, determine feature aggregated curve segments and feature time instants, and determine the signal sampling frequency adjustment mode of the feature mobile terminal to acquire positioning signals, which will not be repeated here.

[0068] Specifically, the data pre-analysis module is used to determine signal intensity fluctuation parameters, wherein,

[0069] The data pre-analysis module is used to acquire the feature positioning signal intensity at several time instants in each acquisition time period of a preset acquisition period, and determine the difference between the maximum value of the feature positioning signal intensity and the minimum value of the feature positioning signal intensity as the signal intensity fluctuation parameter of the acquisition time period.

[0070] The feature positioning signal intensity is the maximum value of the satellite positioning signal intensity and the wireless positioning signal intensity of the mobile terminal at the first time instant in the acquisition time period.

[0071] Specifically, the preset collection period can be set by a person skilled in the art according to the accuracy requirement of the seamless indoor and outdoor positioning system in the campus, the higher the accuracy requirement is, the shorter the preset collection period is, the value range of the preset collection period can be [5, 15] with the interval unit of min, preferably, the preset collection period can be 10 min, the collection time period can be set by a person skilled in the art according to the accuracy requirement of the seamless indoor and outdoor positioning system in the campus, the higher the accuracy requirement is, the shorter the collection time period is divided, the value range of the collection time period can be [1, 3] with the interval unit of min, preferably, the collection time period can be 2 min, the interval length between the several time points in the collection time period can be set by a person skilled in the art according to the accuracy requirement of the seamless indoor and outdoor positioning system in the campus, the higher the accuracy requirement is, the shorter the interval length is, the value range of the interval length can be [5, 10] with the interval unit of s, preferably, the interval length can be 6 s.

[0072] Referring to Figure 2 As shown in FIG. 1, which is a logic flow chart of determining a feature extraction time window by a data pre-analysis module of an embodiment of the present application, the data pre-analysis module is used to determine a feature extraction time window, wherein,

[0073] The data pre-analysis module determines the collection time period as the feature extraction time window based on the determination result of whether the signal strength fluctuation parameter of the collection time period meets the time window condition.

[0074] If the signal strength fluctuation parameter of the collection time period does not meet the time window condition, the data pre-analysis module does not screen the collection time period.

[0075] The time window condition is that the signal strength fluctuation parameter exceeds a preset signal strength fluctuation parameter threshold.

[0076] Specifically, the preset signal strength fluctuation parameter threshold is the product of a signal strength fluctuation parameter reference value and a signal strength fluctuation factor, the signal strength fluctuation parameter reference value is the average value of the signal strength fluctuation parameter in the historical data, and the signal strength fluctuation factor can be set by a person skilled in the art according to the accuracy requirement of the seamless indoor and outdoor positioning system in the campus, the higher the accuracy requirement is, the smaller the signal strength fluctuation factor is, and the value range of the signal strength fluctuation factor can be [1.1, 1.3], preferably, the signal strength fluctuation factor can be 1.2.

[0077] Specifically, the embodiment of the present application determines the feature extraction time window according to the signal fluctuation parameter through the data pre-analysis module, draws the noise intensity curve of the mobile terminal in the feature extraction time window, and can be understood that the signal fluctuation parameter in the preset collection period can dynamically reflect the change trend of the signal intensity, and the change of the area where the mobile terminal is located is captured in real time, which provides a basis for determining the feature extraction time window. Analyzing the noise intensity curve in the feature extraction time window can accurately focus on positioning the key scene of signal instability, avoid redundant processing of the signal stable period, significantly improve the data processing efficiency, provide a reliable basis for the dynamic adjustment of the subsequent positioning signal sampling frequency, and finally realize the seamless switching and accurate adaptation of indoor and outdoor positioning in the complex environment of the campus. The positioning performance and system resource consumption are balanced. The embodiment of the present application determines the feature extraction time window according to the signal fluctuation parameter through the data pre-analysis module, draws the noise intensity curve of the mobile terminal in the feature extraction time window, and further realizes the determination of the feature extraction time window, and improves the reliability of the seamless positioning system in the campus indoor and outdoor.

[0078] Specifically, it can be understood that in the campus environment, the stability of the positioning signal is closely related to the scene where the mobile terminal is located. The signal at the indoor and outdoor interface or the shielding area, such as tree shade and building, will fluctuate sharply due to environmental influence. The feature positioning signal strength selects the maximum value in the satellite and wireless signal, which can comprehensively reflect the optimal positioning capability of the mobile terminal in the current environment. The larger the signal fluctuation parameter is, the more complex the environment is, which may be indoor and outdoor switching or shading, building and other shielding. The feature extraction time window is determined by the signal fluctuation parameter, which locks the mobile terminal that may be at the indoor and outdoor interface, and needs to be analyzed in the key scene. At this time, the noise intensity curve is drawn synchronously, which can further distinguish the reason for the signal fluctuation through the noise characteristics, provide the environmental attribute basis for the subsequent positioning signal regulation, and further realize the determination of the feature extraction time window, and improve the reliability of the seamless positioning system in the campus indoor and outdoor.

[0079] Specifically, the feature recognition module is used to determine the noise fluctuation parameter and the noise period trend parameter.

[0080] The feature recognition module determines the time interval between adjacent wave crests in the noise intensity curve as the noise period characteristic value of the wave crest in the previous time among the adjacent wave crests.

[0081] The noise fluctuation parameter is the variance of the noise fluctuation value, the noise fluctuation value is the absolute value of the noise intensity difference between adjacent wave crests and wave troughs in the noise intensity curve, the noise period trend parameter is the variance of the noise period characteristic value, and the noise intensity curve is drawn in a rectangular coordinate system with time as the horizontal axis and noise intensity as the vertical axis.

[0082] Specifically, the feature identification module is used to screen the feature mobile terminal, wherein,

[0083] The feature identification module screens the mobile terminal as a suspected feature mobile terminal based on the determination result that the noise fluctuation parameter, the noise periodicity trend parameter, and the dynamic change rate of the feature positioning signal strength of the mobile terminal meet the suspected feature mobile terminal condition.

[0084] If the noise fluctuation parameter, the noise periodicity trend parameter, and the dynamic change rate of the feature positioning signal strength of the mobile terminal do not meet the suspected feature mobile terminal condition, the feature identification module does not screen the mobile terminal.

[0085] The suspected feature mobile terminal condition is that the noise fluctuation parameter exceeds a preset noise fluctuation parameter threshold, the noise periodicity trend parameter exceeds a preset noise periodicity trend parameter threshold, and the absolute value of the dynamic change rate exceeds a preset dynamic change rate threshold, the dynamic change rate being the average value of the ratio of the difference in feature positioning signal strength between adjacent time points in a feature extraction time window to the time interval between the adjacent time points.

[0086] Specifically, the preset noise fluctuation parameter threshold is the product of a noise fluctuation parameter reference value and a noise fluctuation factor, the preset noise periodicity trend parameter threshold is the product of a noise periodicity trend parameter reference value and a noise periodicity factor, and the preset dynamic change rate threshold is the product of an absolute value of a dynamic change rate reference value and a dynamic change rate factor, the noise fluctuation parameter reference value being the average value of the noise fluctuation parameter in historical data, the noise periodicity trend parameter reference value being the average value of the noise periodicity trend parameter in historical data, and the absolute value of the dynamic change rate reference value being the average value of the absolute value of the dynamic change rate in historical data, the noise fluctuation factor, the noise periodicity factor, and the dynamic change rate factor being set by a person skilled in the art according to the accuracy requirement of the campus indoor and outdoor seamless positioning system, the higher the accuracy requirement, the greater the noise fluctuation factor, the greater the noise periodicity factor, and the greater the dynamic change rate factor, the value range of the noise fluctuation factor being [1.1, 1.3], the value range of the noise periodicity factor being [1.1, 1.3], and the value range of the dynamic change rate factor being [1.2, 1.35], preferably, the noise fluctuation factor can be 1.2, the noise periodicity factor can be 1.2, and the dynamic change rate factor can be 1.25.

[0087] Specifically, the embodiment of the present application determines the noise fluctuation parameter and the noise period trend parameter based on the noise intensity curve of the mobile terminal through the feature recognition module to screen the feature mobile terminal. It can be understood that through the multiple screening of the noise fluctuation parameter, the noise period trend parameter, and the dynamic change rate of the feature positioning signal strength, the mobile terminals in pure indoor or pure outdoor environments are excluded, the mobile terminals in the indoor-outdoor switching area are focused, the invalid data interference is reduced, the positioning optimization pertinence is improved, the quantization results of the noise fluctuation parameter and the noise period trend parameter objectively reflect the complexity of the environment where the mobile terminal is located, and the limitation of relying only on the positioning signal fluctuation to judge the scene is avoided, such as the confusion of shielding and switching. The mobile terminal in the indoor-outdoor boundary area will cause the positioning signal strength to fluctuate sharply due to environmental switching, that is, the dynamic change rate is high. The noise burst and scene switching in the same scene can be effectively distinguished, the misjudgment is reduced, the root cause of signal fluctuation, that is, indoor-outdoor scene switching or simple shielding, can be more accurately distinguished, the misjudgment is reduced, the stability of positioning in a complex campus environment is enhanced, and then the mobile terminal in the indoor-outdoor boundary area is screened according to the positioning signal received by the mobile terminal and the environmental noise intensity, and the reliability of the seamless positioning system in the campus indoor-outdoor environment is improved.

[0088] Specifically, it can be understood that in the campus scene, the acoustic characteristics of the noise in the indoor-outdoor boundary area are significantly different. This difference can be quantified by the noise intensity curve. The noise fluctuation value, that is, the absolute value of the noise intensity difference between adjacent peaks and troughs in the noise intensity curve, can represent the short-term change degree of the noise intensity. This change is particularly significant in the indoor-outdoor boundary area due to the dual influence of indoor and outdoor environments. The stability of the noise intensity change is poorer when the mobile terminal is in the indoor-outdoor boundary area. The noise fluctuation parameter, that is, the variance of the noise fluctuation value, represents the stability of the noise intensity change. The larger the noise fluctuation parameter, the more unstable the noise intensity change. The noise period representation value, that is, the time interval between adjacent peaks in the noise intensity curve, reflects the periodicity of the noise. In the indoor-outdoor boundary area, the noise period will be affected by the indoor and outdoor environments, resulting in unstable period change. The larger the noise period trend parameter, that is, the variance of the noise period representation value, the more effectively the mobile terminal can be screened in the indoor-outdoor boundary area according to the positioning signal received by the mobile terminal and the environmental noise intensity, and the reliability of the seamless positioning system in the campus indoor-outdoor environment is improved.

[0089] Specifically, refer to Figure 3 As shown in the figure, which is a schematic diagram of the characteristic periodic fluctuation curve of the embodiment of the application, the positioning signal regulation module is used to mark the characteristic curve segments, wherein,

[0090] The positioning signal regulation module is used to divide the characteristic periodic fluctuation curve into several curve segments according to the time when the characteristic periodic fluctuation points are located, and calculate the average slope of each curve segment respectively;

[0091] If the average slope of the curve segment exceeds zero, the positioning signal regulation module marks the curve segment as the first characteristic curve sub-segment 1;

[0092] If the average slope of the curve segment does not exceed zero, the positioning signal regulation module marks the curve segment as the second characteristic curve sub-segment 2;

[0093] The characteristic periodic fluctuation curve establishes a rectangular coordinate system with time as the horizontal axis and the numerical value of the noise periodic characteristic value as the vertical axis, and is fitted according to a plurality of characteristic periodic fluctuation points, which are determined according to the noise periodic characteristic value and the time corresponding to the noise periodic characteristic value.

[0094] Specifically, the average slope is the ratio of the change amount of the noise periodic characteristic value to the time change amount between the two endpoints of any curve segment.

[0095] Specifically, the positioning signal regulation module is used to determine the characteristic aggregated curve segment and the characteristic time, wherein,

[0096] The first characteristic curve sub-segment 1 does not exist in the characteristic aggregated curve segment 3, which is composed of a plurality of second characteristic curve sub-segments 2, and the number of second characteristic curve sub-segments 2 exceeds the preset number threshold;

[0097] The characteristic time is the last time within the time range of the characteristic aggregated curve segment 3.

[0098] Specifically, the preset number threshold is the product of a number reference value and a number factor, the number reference value is the average of the number in the historical data, and the number factor can be set by a person skilled in the art according to the accuracy requirement of the campus indoor and outdoor seamless positioning system. The higher the accuracy requirement, the larger the number factor. The value range of the number factor can be [1.2, 1.3], and preferably, the number factor can be 1.25.

[0099] Specifically, the positioning signal regulation module is used to determine the signal sampling frequency adjustment mode of the characteristic mobile terminal for acquiring the positioning signal, wherein,

[0100] The positioning signal regulation module determines to increase the signal sampling frequency of the wireless positioning signal based on a determination result that the comparison between the feature time of the feature mobile terminal and the feature extraction time window meets the wireless positioning signal regulation condition, and the increase amount of the signal sampling frequency is positively correlated with the environment trend parameter.

[0101] Specifically, the increase amount of the signal sampling frequency is the current signal sampling frequency of the wireless positioning signal x the first environment trend factor x the environment trend parameter / environment trend parameter reference value, the environment trend parameter reference value is the average of the environment trend parameters collected by the mobile terminal in the indoor-outdoor interface area in the current campus scene in the historical data, the first environment trend factor can be set by the person skilled in the art according to the first environment trend factor in the historical data, the value range of the first environment trend factor can be [0.1, 0.4] to avoid excessive or insufficient adjustment of the signal sampling frequency, preferably, the first environment trend factor can be 0.2, and the total frequency after the signal sampling frequency adjustment does not exceed the maximum sampling frequency supported by the device hardware.

[0102] Based on a determination result that the comparison between the feature time of the feature mobile terminal and the feature extraction time window does not meet the wireless positioning signal regulation condition, the signal sampling frequency of the satellite positioning signal is determined to be increased, and the increase amount of the signal sampling frequency is negatively correlated with the environment trend parameter.

[0103] Specifically, the increase amount of the signal sampling frequency is the current signal sampling frequency of the wireless positioning signal x the second environment trend factor x the environment trend parameter reference value / environment trend parameter, the environment trend parameter reference value is the average of the environment trend parameters collected by the mobile terminal in the indoor-outdoor interface area in the current campus scene in the historical data, the second environment trend factor can be set by the person skilled in the art according to the second environment trend factor in the historical data, the value range of the second environment trend factor can be [0.1, 0.4] to avoid excessive or insufficient adjustment of the signal sampling frequency, preferably, the second environment trend factor can be 0.2, and the total frequency after the signal sampling frequency adjustment does not exceed the maximum sampling frequency supported by the device hardware.

[0104] Specifically, the wireless positioning signal regulation condition is that the interval duration between the feature time and the last time in the feature extraction time window does not exceed a preset interval duration threshold, and the environment trend parameter is the duration of the feature aggregation curve segment.

[0105] Specifically, the preset interval duration threshold is the product of the duration of the feature extraction time window and an interval duration factor, the interval duration factor can be set by a person skilled in the art according to the accuracy requirement of the campus indoor and outdoor seamless positioning system, the higher the accuracy requirement, the smaller the interval duration factor, the value range of the interval duration factor can be [0.2, 0.4], preferably, the interval duration factor can be 0.3.

[0106] Specifically, the positioning signal regulation module determines the signal sampling frequency adjustment mode of the feature mobile terminal according to the comparison of the feature time of the feature mobile terminal and the feature extraction time window, which can be understood as accurately identifying the regional movement trend of the mobile terminal by the correlation between the feature time and the signal fluctuation window, distinguishing whether the mobile terminal moves indoors or outdoors, realizing accurate adaptation of the positioning signal, improving the utilization efficiency of the positioning signal, and increasing the sampling frequency of the wireless positioning signal according to the indoor environment demand and the satellite positioning signal according to the outdoor demand, which not only ensures the positioning accuracy in complex environments, such as the anti-interference ability of indoor enhanced wireless signals, but also avoids resource waste caused by invalid sampling, such as unnecessary high-frequency wireless sampling outdoors, according to the environmental noise characteristics in the feature extraction time window, the regional movement trend of the mobile terminal is judged, the sampling frequency setting strategy is adjusted in advance, the positioning process of regional movement is ensured without interruption, the smooth transition of indoor and outdoor switching is realized, the sampling frequency adjustment is synchronized with the environmental change by comparing the feature time with the feature extraction time window, the positioning deviation caused by delay is reduced, and further, the signal sampling frequency of the positioning signal is adaptively adjusted according to the actual movement trend of the mobile terminal, and the reliability of the campus indoor and outdoor seamless positioning system is improved.

[0107] Specifically, it can be understood that by fitting the change curve of the noise period characteristic value over time, the change trend of the noise period can be intuitively obtained, so that the environment feature of the mobile terminal can be more accurately identified, the feature period fluctuation curve is divided into several curve segments according to the time when the noise period characteristic value is located, the average slope of each curve segment is calculated respectively, the average slope reflects the rising or falling trend of the curve segment, the first feature curve sub-segment is the curve segment that presents the rising trend, the second feature curve sub-segment is the curve segment that presents the falling trend or remains stable, the feature aggregation curve segment is the continuous second feature curve sub-segment, in the indoor environment, due to the space closure, the sound reflection is limited by the wall, the reverberation effect makes the noise energy attenuation law more stable, the interval time length between adjacent wave peaks will present the trend of first rapidly shortening and then tending to be stable with the stable environment, which is reflected on the feature period fluctuation curve, and is manifested as the continuous second feature curve sub-segment, in the outdoor environment, due to the space opening, the sound propagation has no obvious barrier, the noise source is multiple and random, the reverberation effect is weak, the interval time length between adjacent wave peaks presents irregular fluctuation, and the curve segment may alternately appear the first feature curve sub-segment and the second feature curve sub-segment, and it is difficult to form the continuous second feature curve sub-segment aggregation, and then, the actual moving trend of the mobile terminal is identified, and the reliability of the seamless positioning system in the campus indoor and outdoor is improved.

[0108] Specifically, in the case that the comparison between the feature time of the feature mobile terminal and the feature extraction time window meets the wireless positioning signal regulation condition, the signal sampling frequency of the wireless positioning signal is increased, and the increase amount of the signal sampling frequency is in a positive correlation with the environment trend parameter, it can be understood that the feature time is the last time in the time range of the feature aggregation curve segment, which can represent the time when the mobile terminal is closest to the indoor, the shorter the interval time length between the last time in the feature extraction time window and the feature time, the more the signal sampling frequency of the wireless positioning signal needs to be increased, the longer the time length of the feature aggregation curve segment, the more the signal sampling frequency of the wireless positioning signal needs to be increased, more positioning signal details can be captured, and the indoor positioning accuracy is improved, and then, the signal sampling frequency of the positioning signal is adaptively adjusted according to the actual moving trend of the mobile terminal, and the reliability of the seamless positioning system in the campus indoor and outdoor is improved.

[0109] Specifically, in the case that the comparison between the feature time of the feature mobile terminal and the feature extraction time window does not meet the wireless positioning signal regulation condition, the signal sampling frequency of the satellite positioning signal is increased, and the increase amount of the signal sampling frequency is negatively correlated with the environment trend parameter. It can be understood that the interval length between the feature time and the last time in the feature extraction time window exceeds the preset interval length threshold, which indicates that the mobile terminal is moving towards the outdoor. In the outdoor environment, the satellite positioning signal is usually more stable and more accurate than the wireless positioning signal. Increasing the sampling frequency of the satellite positioning signal can capture more signal details, thereby improving the positioning accuracy. The smaller the environment trend parameter is, the shorter the time when the mobile terminal is close to the indoor environment is, and the closer the mobile terminal is to the outdoor environment, the higher the sampling frequency is needed to capture more signal details, thereby improving the outdoor positioning accuracy. Further, the signal sampling frequency of the positioning signal is adaptively adjusted according to the actual moving trend of the mobile terminal, and the reliability of the seamless positioning system in the campus indoor and outdoor environment is improved.

[0110] Referring to Figure 4 As shown in the figure, it is a step diagram of a seamless positioning method in a campus indoor and outdoor environment based on multi-source data fusion according to an embodiment of the present application. A seamless positioning method in a campus indoor and outdoor environment based on multi-source data fusion comprises the following steps:

[0111] In step S100, the satellite positioning signal strength, the wireless positioning signal strength received by each mobile terminal, and the noise strength of the environment where the mobile terminal is located are obtained.

[0112] In step S200, the signal fluctuation parameter is determined according to the feature positioning signal strength in the preset acquisition period to determine the feature extraction time window, and the noise strength curve of the mobile terminal is drawn in the feature extraction time window.

[0113] In step S300, the noise fluctuation parameter and the noise cycle trend parameter are determined based on the noise strength curve of the mobile terminal to screen the feature mobile terminal.

[0114] In step S400, the feature cycle fluctuation curve is fitted based on the noise cycle characteristic value of the feature mobile terminal, the feature curve segment is marked according to the feature cycle fluctuation curve, the feature aggregation curve segment and the feature time are determined, and the signal sampling frequency adjustment mode of the feature mobile terminal to obtain the positioning signal is determined according to the comparison between the feature time of the feature mobile terminal and the feature extraction time window.

[0115] Specifically, the feature cycle fluctuation curve can be fitted by a polynomial to the feature cycle fluctuation point. The data is fitted by a polynomial function, and the coefficients are solved by the least square method to fit the feature cycle fluctuation curve, which will not be repeated here.

[0116] So far, the technical solutions of the present application have been described in combination with the preferred embodiments shown in the drawings, but it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical solutions after the changes or replacements will all fall within the protection scope of the present application.

[0117] The above only describes the preferred embodiments of the present application and is not intended to limit the present application; the present application can have various changes and variations for those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A seamless positioning system for indoor and outdoor campus based on multi-source data fusion, characterized in that, The method comprises the following steps: a data acquisition module is configured to acquire satellite positioning signal strength, wireless positioning signal strength received by each mobile terminal, and noise strength of an environment in which the mobile terminal is located; a data pre-analysis module is connected to the data acquisition module and is configured to determine signal fluctuation parameters according to characteristic positioning signal strength in a preset acquisition period, to determine a characteristic extraction time window, and to draw a noise strength curve of the mobile terminal in the characteristic extraction time window; a feature recognition module is connected to the data acquisition module and the data pre-analysis module and is configured to determine noise fluctuation parameters and noise period trend parameters based on the noise strength curve of the mobile terminal, to screen characteristic mobile terminals; a positioning signal regulation module is connected to the data pre-analysis module and the feature recognition module and is configured to fit a characteristic period fluctuation curve based on noise period characteristic values of the characteristic mobile terminals, to mark characteristic curve segments according to the characteristic period fluctuation curve, to determine characteristic aggregated curve segments and characteristic time instants, and to determine a signal sampling frequency adjustment mode in which the characteristic mobile terminals acquire positioning signals according to a comparison between the characteristic time instants of the characteristic mobile terminals and the characteristic extraction time window. The positioning signals comprise satellite positioning signals and wireless positioning signals. 2.The seamless indoor and outdoor positioning system based on multi-source data fusion of a campus according to claim 1, wherein, The data pre-analysis module is configured to determine signal strength fluctuation parameters, wherein the data pre-analysis module is configured to acquire the characteristic positioning signal strength at several time instants in each acquisition time segment of a preset acquisition period, and to determine the difference between the maximum value and the minimum value of the characteristic positioning signal strength as the signal strength fluctuation parameter of the acquisition time segment; the characteristic positioning signal strength is the maximum value of the satellite positioning signal strength and the wireless positioning signal strength of the mobile terminal at a first time instant in the acquisition time segment. 3.The seamless indoor-outdoor positioning system based on multi-source data fusion of a campus according to claim 2, characterized in that, The data pre-analysis module is configured to determine a characteristic extraction time window, wherein the data pre-analysis module determines the acquisition time segment as the characteristic extraction time window based on a determination result that the signal strength fluctuation parameter of the acquisition time segment meets a time window condition; the time window condition is that the signal strength fluctuation parameter exceeds a preset signal strength fluctuation parameter threshold value. 4.The seamless indoor-outdoor positioning system based on multi-source data fusion of a campus according to claim 3, characterized in that, The feature recognition module is configured to determine the noise fluctuation parameters and the noise period trend parameters, wherein the feature recognition module determines the time interval between adjacent wave crests in the noise strength curve as the noise period characteristic value of the wave crest at the previous time instant among the adjacent wave crests; the noise fluctuation parameter is the variance of noise fluctuation values, the noise fluctuation value is the absolute value of the noise strength difference between adjacent wave crests and wave troughs in the noise strength curve, and the noise period trend parameter is the variance of noise period characteristic values, the noise strength curve being drawn in a rectangular coordinate system with time as the horizontal axis and noise strength as the vertical axis. 5.The seamless indoor-outdoor positioning system based on multi-source data fusion of a campus according to claim 4, characterized in that, The feature recognition module is configured to screen the characteristic mobile terminals, wherein The feature recognition module screens the mobile terminal as a suspected feature mobile terminal based on a determination result that the noise fluctuation parameter, the noise period trend parameter, and a dynamic change rate of the feature positioning signal strength of the mobile terminal meet a suspected feature mobile terminal condition. The suspected feature mobile terminal condition is that the noise fluctuation parameter exceeds a preset noise fluctuation parameter threshold, the noise period trend parameter exceeds a preset noise period trend parameter threshold, and an absolute value of the dynamic change rate exceeds a preset dynamic change rate threshold, the dynamic change rate being an average value of a ratio of a difference in feature positioning signal strength between adjacent time instants in a feature extraction time window to a time interval between the adjacent time instants. 6.The seamless indoor-outdoor positioning system based on multi-source data fusion of a campus according to claim 5, characterized in that, The positioning signal regulation module is configured to mark a feature curve segment, wherein The positioning signal regulation module is configured to divide the feature period fluctuation curve into a plurality of curve segments according to time instants at which feature period fluctuation points are located, and to calculate an average slope of each curve segment respectively. If the average slope of a curve segment exceeds zero, the positioning signal regulation module marks the curve segment as a first feature curve sub-segment. If the average slope of a curve segment does not exceed zero, the positioning signal regulation module marks the curve segment as a second feature curve sub-segment. The feature period fluctuation curve is established in a rectangular coordinate system with time as the horizontal axis and the numerical value of the noise period characteristic value as the vertical axis, and is fitted according to a plurality of feature period fluctuation points, which are determined according to noise period characteristic values and time instants corresponding to the noise period characteristic values. 7.The seamless indoor-outdoor positioning system based on multi-source data fusion of a campus according to claim 6, characterized in that, The positioning signal regulation module is configured to determine a feature aggregated curve segment and a feature time instant, wherein The feature aggregated curve segment does not include a first feature curve sub-segment, is composed of a plurality of second feature curve sub-segments, and the number of the second feature curve sub-segments exceeds a preset number threshold. The feature time instant is the last time instant within a time range of the feature aggregated curve segment. 8.The seamless indoor-outdoor positioning system based on multi-source data fusion of a campus according to claim 7, characterized in that, The positioning signal regulation module is configured to determine a signal sampling frequency adjustment mode of the feature mobile terminal for acquiring a positioning signal, wherein The positioning signal regulation module determines to increase the signal sampling frequency of the wireless positioning signal based on a determination result that a comparison between the feature time instant of the feature mobile terminal and the feature extraction time window meets a wireless positioning signal regulation condition, and the increase in the signal sampling frequency is positively correlated with an environmental trend parameter. The positioning signal regulation module determines to increase the signal sampling frequency of the satellite positioning signal based on a determination result that the comparison between the feature time instant of the feature mobile terminal and the feature extraction time window does not meet the wireless positioning signal regulation condition, and the increase in the signal sampling frequency is negatively correlated with the environmental trend parameter. 9.The seamless indoor-outdoor positioning system based on multi-source data fusion of a campus according to claim 8, characterized in that, The wireless positioning signal regulation condition is that an interval length between the feature time instant and the last time instant in the feature extraction time window does not exceed a preset interval length threshold, and the environmental trend parameter is a length of the feature aggregated curve segment. 10.A seamless positioning method for campus indoor and outdoor based on multi-source data fusion, applied to the seamless positioning system for campus indoor and outdoor based on multi-source data fusion in any one of claims 1-9, characterized in that, The method comprises: acquiring satellite positioning signal strength, wireless positioning signal strength received by each mobile terminal, and noise strength of an environment in which the mobile terminal is located; According to the characteristic positioning signal strength in a preset acquisition period, a signal fluctuation parameter is determined to determine a characteristic extraction time window, and a noise intensity curve of the mobile terminal is drawn in the characteristic extraction time window; Based on the noise intensity curve of the mobile terminal, a noise fluctuation parameter and a noise period trend parameter are determined to screen a characteristic mobile terminal; Based on a noise period characteristic value of the characteristic mobile terminal, a characteristic period fluctuation curve is fitted, a characteristic curve segment is marked according to the characteristic period fluctuation curve, a characteristic aggregation curve segment and a characteristic time are determined, and according to the comparison between the characteristic time of the characteristic mobile terminal and the characteristic extraction time window, a signal sampling frequency adjustment mode of the characteristic mobile terminal for acquiring a positioning signal is determined.

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