Beidou navigation, Bluetooth and UWB cooperative work Internet of Things management method and device

Abstract

The invention is suitable for the technical field of Internet of Things equipment management, and particularly relates to an Internet of Things management method and equipment for cooperative work of Beidou navigation, Bluetooth and UWB, and the method comprises the steps: obtaining a cooperative positioning parameter set and a plurality of positioning data of target Internet of Things equipment in a cooperative positioning scene, comprehensively integrating the multi-source information of different positioning modules, and obtaining a cooperative positioning parameter set of the target Internet of Things equipment in a cooperative positioning scene; a data basis is provided for subsequent cooperative positioning; a cooperative positioning identifier of each piece of positioning data is generated based on the value of the positioning data under the cooperative positioning parameter set, a scientific and quantitative positioning identifier system is constructed, and an accurate positioning data identification framework is formed; according to the method, all the positioning data are fused according to the cooperative positioning identifier, the cooperative positioning management data of the target Internet of Things equipment is generated through a multi-technology fusion positioning mode, the position state of the equipment in a complex scene is accurately grasped, the positioning accuracy and reliability are improved, and the overall operation efficiency and the user experience are improved.

Description

Technical Field

[0001] This application relates to the technical field of Internet of Things device management, and particularly to an Internet of Things management method and device for collaborative work of Beidou navigation, Bluetooth, and UWB. Background Art

[0002] The management of Internet of Things (IoT) technology devices refers to a series of operations for monitoring, configuring, maintaining, optimizing, and securing various devices connected to the IoT through intelligent and automated technical means. The purpose is to ensure the efficient operation of IoT devices, the secure transmission of data, and the rational utilization of resources, thereby supporting the overall function and goal achievement of the IoT system.

[0003] Traditional IoT positioning management methods have significant limitations, such as poor adaptability to dynamic scenarios, inability to generate high-precision collaborative positioning management data in real time, and lack of an effective positioning integration mechanism, resulting in poor scene adaptability, low reliability, and reduced overall operation efficiency and user experience. Summary of the Invention

[0004] Embodiments of this application provide an Internet of Things management method and device for collaborative work of Beidou navigation, Bluetooth, and UWB, which can solve the problems of poor scene adaptability, low reliability, and reduced overall operation efficiency and user experience in the existing IoT positioning management process due to the lack of an effective positioning integration mechanism.

[0005] In a first aspect, embodiments of this application provide an Internet of Things management method for collaborative work of Beidou navigation, Bluetooth, and UWB, including: Obtaining a collaborative positioning parameter set and multiple positioning data of a target Internet of Things device in a collaborative positioning scenario; wherein, the collaborative positioning parameter set includes at least one collaborative positioning parameter; the positioning data is the positioning data independently collected by the target Internet of Things device through any one of a Beidou navigation module, a Bluetooth module, and a UWB module; Based on the values of the multiple positioning data of the target Internet of Things device in the collaborative positioning scenario under the collaborative positioning parameter set, obtaining a collaborative positioning identifier for each positioning data; According to the collaborative positioning identifier of each positioning data, fusing all the positioning data to obtain the collaborative positioning management data of the target Internet of Things device.

[0006] The above technical solutions in the embodiments of this application have at least the following technical effects: The Internet of Things management method for collaborative work of Beidou navigation, Bluetooth, and UWB provided by the embodiments of this application obtains the collaborative positioning parameter set and multiple positioning data of the target Internet of Things device in the collaborative positioning scenario, comprehensively integrates the multi-source information of different positioning modules, and provides a data basis for subsequent collaborative positioning. Based on the values of the positioning data under the collaborative positioning parameter set, the collaborative positioning identifier of each positioning data is generated, a scientific and quantitative positioning identifier system is constructed, and an accurate positioning data recognition framework is formed. All positioning data are fused according to the collaborative positioning identifier, and the collaborative positioning management data of the target Internet of Things device are generated through a positioning method that integrates multiple technologies, realizing in-depth analysis of the device's location information, accurately grasping the device's location status in complex scenarios, improving the accuracy and reliability of positioning, optimizing the management efficiency of Internet of Things devices, and enhancing the overall operation efficiency and user experience.

[0007] In a second aspect, the embodiments of this application provide an Internet of Things management system for collaborative work of Beidou navigation, Bluetooth, and UWB, including: An acquisition unit, configured to acquire the collaborative positioning parameter set and multiple positioning data of the target Internet of Things device in the collaborative positioning scenario; wherein, the collaborative positioning parameter set includes at least one collaborative positioning parameter; the positioning data is the positioning data independently collected by the target Internet of Things device through any one of the Beidou navigation module, the Bluetooth module, and the UWB module; A management identifier unit, configured to obtain the collaborative positioning identifier of each positioning data based on the values of the multiple positioning data of the target Internet of Things device in the collaborative positioning scenario under the collaborative positioning parameter set; A management unit, configured to fuse all the positioning data according to the collaborative positioning identifier of each positioning data to obtain the collaborative positioning management data of the target Internet of Things device.

[0008] In a third aspect, the embodiments of this application provide an Internet of Things management device for collaborative work of Beidou navigation, Bluetooth, and UWB, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method described in any one of the above aspects is implemented.

[0009] In a fourth aspect, the embodiments of this application provide a computer program product. When the computer program product runs on an Internet of Things management device for collaborative work of Beidou navigation, Bluetooth, and UWB, the Internet of Things management device for collaborative work of Beidou navigation, Bluetooth, and UWB is enabled to execute the method described in any one of the above aspects.

[0010] It can be understood that the beneficial effects of the second to fourth aspects above can be referred to the relevant descriptions in the above aspects and will not be elaborated here. Description of the Drawings

[0011] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for description in the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0012] Figure 1 It is a schematic flowchart of an Internet of Things management method for the collaborative work of Beidou navigation, Bluetooth, and UWB provided by an embodiment of the present application; Figure 2 It is an operating schematic diagram of an Internet of Things management method for the collaborative work of Beidou navigation, Bluetooth, and UWB provided by an embodiment of the present application; Figure 3 It is a schematic structural diagram of an Internet of Things management system for the collaborative work of Beidou navigation, Bluetooth, and UWB provided by an embodiment of the present application; Figure 4 It is a schematic structural diagram of an Internet of Things management device for the collaborative work of Beidou navigation, Bluetooth, and UWB provided by an embodiment of the present application. Detailed implementation manners

[0013] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0014] It should be understood that when used in the specification and appended claims of the present application, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0015] It should also be understood that the term " / and" as used in the specification and appended claims of the present application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0016] As used in the specification of this application and the appended claims, the term "if" may be construed contextually as "when", "once", "in response to determining", or "in response to detecting". Similarly, the phrases "if determined" or "if the described condition or event is detected" may be construed contextually to mean "once determined", "in response to determining", "once the described condition or event is detected", or "in response to detecting the described condition or event".

[0017] In addition, in the description of the specification of this application and the appended claims, the terms "first", "second", "third", etc. are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0018] The reference to "one embodiment" or "some embodiments" etc. described in the specification of this application means that a specific feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "comprising", "including", "having", and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0019] Traditional Internet of Things (IoT) positioning management methods have significant limitations, such as poor adaptability to dynamic scenarios, inability to generate high-precision collaborative positioning management data in real time, and lack of an effective positioning integration mechanism, resulting in poor scenario adaptability, low reliability, and reduced overall operational efficiency and user experience.

[0020] To solve the above problems, the embodiments of this application provide an IoT management method and device for collaborative work of Beidou navigation, Bluetooth, and UWB. In this method, by obtaining the collaborative positioning parameter set and multiple positioning data of the target IoT device in the collaborative positioning scenario, multi-source information of different positioning modules is comprehensively integrated to provide a data basis for subsequent collaborative positioning. Based on the values of the positioning data under the collaborative positioning parameter set, a collaborative positioning identifier for each positioning data is generated, constructing a scientifically quantified positioning identifier system and forming an accurate positioning data identification framework. All positioning data is fused according to the collaborative positioning identifier, and the collaborative positioning management data of the target IoT device is generated through a multi-technology fusion positioning method, realizing in-depth analysis of the device's location information, accurately grasping the device's location status in complex scenarios, improving the accuracy and reliability of positioning, optimizing the management efficiency of IoT devices, and enhancing the overall operational efficiency and user experience.

[0021] The Internet of Things management method provided by the embodiment of the present application for the collaborative work of Beidou navigation, Bluetooth, and UWB can be applied to the Internet of Things management device for the collaborative work of Beidou navigation, Bluetooth, and UWB. At this time, the Internet of Things management device for the collaborative work of Beidou navigation, Bluetooth, and UWB is the execution subject of the Internet of Things management method provided by the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of the Internet of Things management device for the collaborative work of Beidou navigation, Bluetooth, and UWB.

[0022] For example, the Internet of Things management device for the collaborative work of Beidou navigation, Bluetooth, and UWB can be a mobile phone, a tablet computer, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a desktop computer, a smart large screen, a smart TV, a handheld device with wireless communication function, a computing device or other processing devices connected to a wireless modem, a vehicle-mounted device, a vehicle Internet of Things terminal, a computer, a laptop computer, a communication device, a computing device, etc.

[0023] To better understand the Internet of Things management method provided by the embodiment of the present application for the collaborative work of Beidou navigation, Bluetooth, and UWB, the following provides an exemplary introduction to the specific implementation process of the Internet of Things management method provided by the embodiment of the present application for the collaborative work of Beidou navigation, Bluetooth, and UWB.

[0024] Figure 1 The schematic flowchart of the Internet of Things management method provided by the embodiment of the present application for the collaborative work of Beidou navigation, Bluetooth, and UWB is shown. Figure 2 The operation flowchart of the Internet of Things management method provided by the embodiment of the present application for the collaborative work of Beidou navigation, Bluetooth, and UWB is shown. The Internet of Things management method for the collaborative work of Beidou navigation, Bluetooth, and UWB includes: S100, obtaining a collaborative positioning parameter set and a plurality of positioning data of a target Internet of Things device in a collaborative positioning scenario; wherein, the collaborative positioning parameter set includes at least one collaborative positioning parameter; the positioning data is the positioning data independently collected by any one of the Beidou navigation module, the Bluetooth module, and the UWB module of the target Internet of Things device; It can be understood that the collaborative positioning parameter set refers to the core parameter set that needs to be dynamically adjusted when different positioning technologies (Beidou navigation, Bluetooth, UWB) work together. For example, the dilution of precision (DOP) of Beidou, the path loss coefficient of Bluetooth, the time synchronization deviation of UWB, etc. The collaborative positioning parameter set directly affects the positioning accuracy and the system collaborative efficiency. The positioning data is the original position information collected through a single module (such as the Beidou module independently outputting longitude and latitude, the Bluetooth module measuring the signal strength, and the UWB module calculating the time difference).

[0025] In an exemplary implementation, obtaining the collaborative positioning parameter set can be dynamically adjusted through a device configuration file or a real-time monitoring module. For example, the Beidou module calculates the DOP value in real time according to the satellite distribution, and the Bluetooth module calculates the propagation loss coefficient through RSSI (Received Signal Strength Indication). The positioning data is directly read through the hardware interfaces of each module (such as UART, SPI) and stored in a structured data format (such as JSON or binary stream) to provide input for subsequent multi-source data fusion.

[0026] S200. Based on the values of multiple positioning data of the target Internet of Things device in the collaborative positioning scenario under the collaborative positioning parameter set, obtain the collaborative positioning identifier for each positioning data.

[0027] It can be understood that each positioning data will have a corresponding specific numerical representation under the conditions defined by the collaborative positioning parameter set. For example, the distance value calculated for a certain positioning data under specific signal propagation model parameters, or the positioning coordinate value after weighted calculation under specific weight coefficients, etc. According to the value of the positioning data under the collaborative positioning parameter set, a unique collaborative positioning identifier can be assigned to each positioning data through a certain algorithm or rule. The collaborative positioning identifier can contain various information, such as the source module of the positioning data (Beidou, Bluetooth, or UWB), its reliability level in the current collaborative positioning scenario, and the association information with other positioning data.

[0028] In a possible implementation manner, S200. Based on the values of multiple positioning data of the target Internet of Things device in the collaborative positioning scenario under the collaborative positioning parameter set, obtain the collaborative positioning identifier for each positioning data, including: S210. Construct a multi-dimensional space model corresponding to the collaborative positioning parameter set; wherein, the multi-dimensional space model includes multiple data dimensions; each data dimension corresponds to a characteristic attribute of a positioning technology parameter.

[0029] It can be understood that the multi-dimensional space model is a mathematical model used to represent the relationships and interactions between different positioning technology parameters. Each data dimension corresponds to a characteristic attribute of a positioning technology parameter, such as the accuracy of Beidou navigation, the Bluetooth signal strength, or the time synchronization difference of UWB, etc. The multi-dimensional space model helps to analyze the performance of different positioning technologies and their impacts on the final positioning result. The boundaries of each dimension can be defined by the value range of the parameter set (such as the Beidou DOP value range is 1 - 10, mapped to 0 - 1), and can be implemented through matrix storage (such as covariance matrix) or graph structure (such as parameter association graph).

[0030] S220, extract the original observations of each positioning data under the corresponding positioning technical parameters; the corresponding positioning technical parameters are at least one of the positioning dilution of precision of Beidou navigation, the propagation loss coefficient of Bluetooth signal, or the time synchronization deviation parameter of UWB in the collaborative positioning parameter set.

[0031] It can be understood that the original observations are the unprocessed data read from each positioning module. For example, the original observations output by the Beidou module may include pseudorange and carrier phase; the Bluetooth module outputs the RSSI value; the UWB module outputs the time of flight (ToF). Data parsers can be designed for different modules: Beidou data parsing: Extract the longitude, latitude, and DOP value from the GPGGA sentence in the NMEA-0183 protocol.

[0032] Bluetooth data parsing: Obtain the RSSI from the HCI layer and convert it into a propagation loss coefficient (formula: PathLoss = TxPower - RSSI).

[0033] UWB data parsing: Calculate the timestamp deviation through two-way ranging (TWR).

[0034] S230, according to the data dimension mapping rules corresponding to the corresponding positioning technical parameters, convert the original observations of each positioning data under the corresponding positioning technical parameters into standardized dimension components.

[0035] It can be understood that the mapping of different data dimensions corresponding to the corresponding positioning technical parameters based on the preset mapping rules can convert the original observations of each positioning data under the corresponding positioning technical parameters into a standardized dimension component.

[0036] Processing of Beidou navigation positioning dilution of precision: When the corresponding positioning technical parameter is the positioning dilution of precision of Beidou navigation, first obtain the signal-to-noise ratio parameter and geometric dilution of precision of the current satellite signal, calculate the dynamic error threshold of Beidou navigation based on these two parameters, then compare the dynamic error threshold with the preset basic error threshold to determine the data truncation boundary of the original observations, perform data truncation processing on the original observations according to the boundary to generate compressed coordinate values, and finally convert the compressed coordinate values into binary floating-point codes with a fixed bit width to obtain the standardized dimension component.

[0037] Processing of Bluetooth signal propagation loss coefficient: When the corresponding positioning technical parameter is the propagation loss coefficient of Bluetooth signal, according to the preset radio frequency attenuation model, convert the original observations into the relative distance value between devices, then compare the maximum effective ranging range in the data dimension mapping rules with the relative distance value, and perform non-linear compression processing on the relative distance value that exceeds the maximum effective ranging range to obtain the standardized dimension component.

[0038] UWB Time Synchronization Deviation Parameter Processing: When the time synchronization deviation parameter corresponding to the positioning technology parameter is UWB, first calculate the first relative offset between the current timestamp and the preset reference clock source, then perform phase alignment processing on the offset through time window sliding to obtain the second relative offset after phase alignment, and finally convert the second relative offset into a cyclic redundancy check coding sequence to obtain the standardized dimension component.

[0039] Optionally, S230, according to the data dimension mapping rule corresponding to the positioning technology parameter, convert the original observation value of each positioning data under the corresponding positioning technology parameter into a standardized dimension component, including: S2311, according to the data dimension mapping rule corresponding to the positioning technology parameter, when the positioning accuracy factor corresponding to the positioning technology parameter is the Beidou Navigation, obtain the signal-to-noise ratio parameter and the geometric dilution of precision factor of the current satellite signal.

[0040] It can be understood that according to the data dimension mapping rule corresponding to the positioning technology parameter, when the positioning accuracy factor corresponding to the positioning technology parameter is the Beidou Navigation, the signal-to-noise ratio parameter of the current satellite signal can be obtained through spectrum analysis or correlation detection, and the geometric dilution of precision factor can be calculated through the GDOP matrix. The signal-to-noise ratio (Signal-to-Noise Ratio, SNR) parameter reflects the ratio of the effective signal power to the noise power in the satellite signal. The geometric dilution of precision (Geometric Dilution of Precision, GDOP) is an important index to measure the influence of satellite geometric distribution on positioning accuracy.

[0041] Exemplarily, the sampled satellite signal can be subjected to a fast Fourier transform (FFT) to convert the signal from the time domain to the frequency domain. In the frequency domain, determine the signal bandwidth and the noise bandwidth, and calculate the signal energy within the signal bandwidth and the noise energy within the noise bandwidth respectively. SNR = signal power / noise power, and the power can be obtained by dividing the energy by the corresponding bandwidth. Based on the position information of the satellite (which can be parsed from the ephemeris data) and the initial estimated position of the user, a geometric matrix G can be constructed. The matrix G reflects the geometric relationship between the satellite and the user. Calculate the inverse matrix of GTG, and obtain the GDOP value by taking the square root of the sum of the diagonal elements of the inverse matrix.

[0042] S2312, based on the signal-to-noise ratio parameter and the geometric dilution of precision factor of the satellite signal, calculate the dynamic error threshold of Beidou Navigation.

[0043] It can be understood that the signal-to-noise ratio parameter of the satellite signal reflects the proportional relationship between the effective signal power and the noise power in the satellite signal. The higher the signal-to-noise ratio, the better the signal quality, and the less the positioning data is affected by noise interference. The geometric dilution of precision measures the impact of the satellite geometric distribution on the positioning accuracy. The larger its value, the greater the impact of the non-ideal satellite distribution on the positioning accuracy. The dynamic error threshold of Beidou navigation is an error limit that changes dynamically with the satellite signal condition and the satellite geometric distribution, and is used to evaluate the error range of the current Beidou navigation positioning data.

[0044] Exemplarily, to calculate the dynamic error threshold of Beidou navigation based on the signal-to-noise ratio parameter and the geometric dilution of precision of the satellite signal, since the influence of the signal-to-noise ratio and the geometric dilution of precision on the positioning error is usually not a simple linear relationship, an empirical model can be established to describe their association with the positioning error. For example, based on a large amount of actual test data and theoretical analysis, a multivariate function relationship is obtained, assumed to be f(SNR, GDOP), where SNR is the signal-to-noise ratio parameter and GDOP is the geometric dilution of precision. The signal-to-noise ratio parameter and the geometric dilution of precision of the currently acquired satellite signal can be substituted into this function. If the function form is f(SNR, GDOP)=a×SNR + b×GDOP + c (where a, b, and c are coefficients obtained by fitting experimental data), the specific values of the signal-to-noise ratio parameter and the geometric dilution of precision are read, and then multiplication and addition operations are performed according to the function operation rules to obtain the dynamic error threshold of Beidou navigation.

[0045] S2313, compare the dynamic error threshold with the preset basic error threshold to determine the data truncation boundary of the original observation value.

[0046] It can be understood that the dynamic error threshold is an error limit calculated according to the current satellite signal condition and the satellite geometric distribution, and it will change with the changes in the satellite signal and the distribution; the preset basic error threshold is a fixed error limit preset for use as a reference standard. The data truncation boundary of the original observation value is the range limit for determining the truncation processing of the original observation value, and the data exceeding this boundary may be considered to have too large an error and need to be processed.

[0047] Exemplarily, the dynamic error threshold can be compared with a preset basic error threshold to determine the data truncation boundary of the original observation value, and the specific values of the dynamic error threshold and the preset basic error threshold are read. If the dynamic error threshold is less than the preset basic error threshold, it indicates that the current satellite signal and geometric distribution are good, and the positioning error is relatively small. At this time, the dynamic error threshold can be determined as the data truncation boundary of the original observation value; if the dynamic error threshold is not less than the preset basic error threshold, it means that the current positioning error is large. To ensure the reliability of the positioning data, the preset basic error threshold is determined as the data truncation boundary of the original observation value. For example, if the preset basic error threshold is 5 meters and the calculated dynamic error threshold is 3 meters, then 3 meters is determined as the data truncation boundary; if the dynamic error threshold is 6 meters, then 5 meters is determined as the data truncation boundary. The data truncation boundary determined in this way can be used for subsequent data truncation processing of the original observation value to reduce the influence of data with large errors on the final positioning result.

[0048] Exemplarily, S2313, characterized in that comparing the dynamic error threshold with a preset basic error threshold to determine the data truncation boundary of the original observation value includes: S23131, when the dynamic error threshold is less than the basic error threshold, determining the dynamic error threshold as the data truncation boundary of the original observation value.

[0049] It can be understood that the specific values of the dynamic error threshold and the basic error threshold can be obtained and then compared. If the dynamic error threshold is indeed less than the basic error threshold, using the dynamic error threshold as the standard and determining it as the data truncation boundary of the original observation value is beneficial to more accurately screen out the original observation values with smaller errors and improve the accuracy of the final positioning result. Because using a smaller error threshold as the truncation boundary can exclude those data with relatively large errors although they are within the basic error threshold range, making the quality of the positioning data participating in subsequent processing higher.

[0050] S23132, when the dynamic error threshold is not less than the basic error threshold, determining the basic error threshold as the data truncation boundary of the original observation value.

[0051] It can be understood that the specific values of the dynamic error threshold and the basic error threshold are obtained and compared. When it is found that the dynamic error threshold is greater than or equal to the basic error threshold, since the basic error threshold is a relatively reasonable error limit preset after comprehensively considering various factors, the basic error threshold can be used as the data truncation boundary for the original observation values. This is beneficial for screening the original observation values with a relatively reasonable standard even when the satellite signals and geometric distributions are not ideal. Although the actual error may be large, using the basic error threshold as the truncation boundary can avoid excluding too much data due to excessive actual errors, ensure that there is enough data to participate in the subsequent positioning calculation, and at the same time control the error range to a certain extent and maintain the reliability of the final positioning result.

[0052] S2314, perform data truncation processing on the original observation values according to the data truncation boundary to generate the compressed coordinate values corresponding to the original observation values.

[0053] It can be understood that the data truncation boundary is determined by comparing the dynamic error threshold and the basic error threshold. It is a boundary used to measure whether the original observation values are within the acceptable error range. The original observation values are the unprocessed positioning-related data directly obtained from the Beidou navigation module, such as pseudorange, carrier phase, etc. Data truncation processing is a data screening and correction operation, aiming to remove the data with excessive errors to make the data more reliable. The compressed coordinate values are generated after data truncation processing. They are more accurate coordinate representations obtained by screening and adjusting the original observation values.

[0054] Exemplarily, when performing data truncation processing, the specific value of the data truncation boundary can be read first, and then the original observation values are traversed. For each original observation value, calculate its error from the true value (which can be obtained through a high-precision reference device or theoretical calculation). If this error exceeds the data truncation boundary, it means that the error of this original observation value is too large and may have a greater impact on the final positioning result. This original observation value can be corrected or directly discarded. For example, if the original observation value is a distance value, when the calculated error exceeds the data truncation boundary, the average value of adjacent observation values or the estimated value obtained by predicting based on historical data can be used to replace this original observation value with excessive error. In this way, all the original observation values that meet the requirements are converted into compressed coordinate values, which is beneficial for reducing the interference of error data on the subsequent positioning calculation and improving the accuracy and reliability of positioning.

[0055] S2315, convert the compressed coordinate values into binary floating-point codes with a fixed bit width to obtain the standardized dimension components corresponding to the original observation values.

[0056] It can be understood that the compressed coordinate values are relatively accurate coordinate representations obtained after data truncation processing, and they are still in the form of ordinary numerical values. The fixed-bitwidth binary floating-point encoding is a specific data encoding format that converts numerical values into binary floating-point forms according to certain rules, and the length of this binary encoding is fixed. The standardized dimension components are the results obtained after encoding, and they are the standard forms used to uniformly represent and compare different positioning data in a multi-dimensional space model.

[0057] Exemplarily, when converting the compressed coordinate values into fixed-bitwidth binary floating-point encoding, the specific length of the fixed bitwidth is determined according to the preset encoding rules, such as 16 bits or 32 bits. The compressed coordinate values are converted according to the IEEE754 standard (a commonly used binary floating-point encoding standard). It can include determining the sign bit, exponent bit, and mantissa bit. The sign bit represents the positive or negative of the numerical value, the exponent bit represents the order of magnitude of the numerical value, and the mantissa bit represents the precision of the numerical value. Through a series of mathematical operations and shift operations, the compressed coordinate values are converted into the corresponding binary floating-point encoding.

[0058] Optionally, in S230, according to the data dimension mapping rules corresponding to the corresponding positioning technical parameters, the original observation values of each positioning data under the corresponding positioning technical parameters are converted into standardized dimension components, including: S2321, according to the data dimension mapping rules corresponding to the corresponding positioning technical parameters, when the corresponding positioning technical parameter is the propagation loss coefficient of the Bluetooth signal, based on the preset radio frequency attenuation model, the original observation value is converted into the relative distance value between devices.

[0059] It can be understood that the original observation values are the unprocessed data directly fetched from the Bluetooth module, usually the received signal strength indication (RSSI) values. The preset radio frequency attenuation model is a mathematical model that describes the relationship between the Bluetooth signal strength and the attenuation with distance. The relative distance value between devices is the numerical value representing the actual distance between two Bluetooth devices obtained by converting the original observation value through the radio frequency attenuation model.

[0060] Exemplarily, the corresponding data dimension mapping rules and the preset radio frequency attenuation model can be read. The radio frequency attenuation model is the logarithmic distance path loss model, and its formula is , where is the path loss when the distance is , is the reference distance at the path loss, is the path loss exponent. The original observation value (RSSI) can be converted into the path loss value because there is a relationship between the path loss and RSSI: , where TxPower is the transmit power. Then, according to the radio frequency attenuation model, substitute the path loss value into the formula and solve the equation to obtain the relative distance value between devices. . For example, given the reference distance , the path loss at the reference distance , the path loss exponent , and the calculated path loss , the value of can be solved through mathematical operations, which is conducive to converting the propagation loss information of Bluetooth signals into intuitive distance information between devices, facilitating subsequent analysis and processing in cooperative positioning.

[0061] S2322. Compare the maximum effective ranging range in the data dimension mapping rule with the relative distance value, and perform non-linear compression processing on the relative distance value that exceeds the maximum effective ranging range to obtain the standardized dimension component corresponding to the original observation value.

[0062] It can be understood that non-linear compression processing is a method for adjusting the relative distance value that exceeds the maximum effective ranging range. It is not a simple linear scaling, but a transformation according to a specific non-linear function, so that the processed value can be represented within a reasonable range. The specific value of the maximum effective ranging range in the data dimension mapping rule can be read, and then the relative distance value is compared with this maximum effective ranging range. If the relative distance value is less than or equal to the maximum effective ranging range, it means that the distance value is within the reliable measurement range and can be directly used as part of the standardized dimension component; if the relative distance value is greater than the maximum effective ranging range, perform non-linear compression processing on it. A common non-linear compression function can be the arctangent function, such as , where is the relative distance value, and is the maximum effective ranging range. The relative distance value that exceeds the range can be substituted into the non-linear compression function for calculation to obtain a compressed value, which is the standardized dimension component corresponding to the original observation value. This is conducive to avoiding deviations in the analysis results of the multi-dimensional space model caused by data beyond the effective ranging range while retaining the characteristics of Bluetooth positioning data, improving the accuracy and stability of cooperative positioning.

[0063] Optionally, in S230, according to the data dimension mapping rule corresponding to the corresponding positioning technical parameters, convert the original observation value of each positioning data under the corresponding positioning technical parameters into a standardized dimension component, including: S2331. According to the data dimension mapping rule corresponding to the corresponding positioning technical parameters, when the time synchronization deviation parameter of the corresponding positioning technical parameter is UWB, calculate the first relative offset between the current timestamp and the preset reference clock source.

[0064] It can be understood that the current timestamp is the time point currently recorded by the UWB device, and the preset reference clock source is a time reference source with high precision and stability, and its time is considered to be the accurate standard time. The first relative offset is the time difference between the current timestamp and the preset reference clock source, which reflects the deviation degree of the UWB device time from the standard time. It is possible to obtain the current timestamp read from the UWB device and at the same time obtain the time of the preset reference clock source. Through subtraction operation, subtract the time of the preset reference clock source from the current timestamp to get the difference between the two, and this difference is the first relative offset. For example, if the current timestamp of the UWB device is 1000 milliseconds and the time of the preset reference clock source is 990 milliseconds, then the first relative offset is 10 milliseconds. Calculating the first relative offset is beneficial to quantifying the time synchronization error of the UWB device, providing basic data for subsequent processing and analysis, and thus better solving the positioning error problem caused by inaccurate time synchronization in UWB positioning.

[0065] S2332, perform phase alignment processing on the first offset by sliding the time window to obtain the second relative offset after phase alignment.

[0066] It can be understood that "time window sliding" is a data processing method. It performs a sliding operation on time series data by setting a time window with a fixed length, and analyzes and processes the data within the window. Phase alignment processing is to adjust the first relative offset so that it is aligned with the preset phase standard in time to reduce the influence of time error. The second relative offset is the offset obtained after phase alignment processing, and it is more stable and accurate compared to the first relative offset.

[0067] A suitable time window length can be preset in advance, place the time window on the time series data of the first relative offset, starting from the beginning position of the sequence. Within each time window, analyze the offset data within the window, such as calculating statistical quantities such as the average value and median. According to these statistical quantities, adjust the offset within the window to align it with the preset phase standard. Slide the time window backward by one unit and repeat the above processing process until the entire time series of the first relative offset is processed. The finally obtained adjusted offset is the second relative offset after phase alignment, which is beneficial to eliminating the fluctuations and errors in the first relative offset, improving the accuracy of time synchronization, and providing more reliable data for subsequent generation of standardized dimension components.

[0068] S2333, convert the second relative offset after phase alignment into a cyclic redundancy check coding sequence to obtain the standardized dimension component corresponding to the original observation value.

[0069] It can be understood that the second relative offset after phase alignment is a relatively accurate time offset obtained through time window sliding and phase alignment processing. The cyclic redundancy check (CRC) coding sequence is a data checksum and coding method. It generates a check code by performing specific polynomial operations on the data, combines the check code with the original data to form a coding sequence, and is used to detect whether errors occur during data transmission or storage. The CRC coding polynomial adopted can be predetermined, such as CRC-16, CRC-32, etc. Convert the second relative offset after phase alignment into binary data. The remainder can be obtained by performing a division operation on this binary data using the selected CRC coding polynomial, and this remainder is the CRC check code. Add the CRC check code to the end of the binary data to form a cyclic redundancy check coding sequence, and this coding sequence is the standardized dimension component corresponding to the original observation value. For example, if CRC-16 coding is adopted, the binary data of the second relative offset is operated according to the polynomial rule of CRC-16 to obtain a 16-bit check code, and it is spliced with the original binary data, which is beneficial to improving the reliability and processability of UWB time synchronization deviation data. Errors in data transmission or processing can be detected through CRC coding, and at the same time, the standardized dimension component is convenient for fusion and analysis with other positioning data in a multi-dimensional space model.

[0070] S240, use the standardized dimension component as the coordinate representation value of the positioning data on the corresponding data dimension, and generate a collaborative positioning identifier for each positioning data based on the coordinate representation values of each data dimension; among them, a positioning identifier is formed by cross-bit splicing of the binary codes of each data dimension.

[0071] It can be understood that the standardized dimension component is a value obtained by converting the original observation values of different positioning technologies and is suitable for unified processing in a multi-dimensional space model. The coordinate representation value represents the specific position value of the positioning data on each data dimension in the multi-dimensional space model. Using the standardized dimension component as the coordinate representation value can accurately locate the positioning data in the multi-dimensional space. The collaborative positioning identifier is a code used to uniquely identify the characteristics and positions of each positioning data in a collaborative positioning scenario, and it integrates the information of each data dimension.

[0072] Exemplarily, each standardized dimension component can be associated with the data dimension of the corresponding multi-dimensional space model, and the standardized dimension component is used as the coordinate of the positioning data on this data dimension. Based on the coordinate representation values on each data dimension, a collaborative positioning identifier is generated using a specific coding method. Since the coordinates of each positioning data on different data dimensions are unique, the generated collaborative positioning identifier can also uniquely represent the position of the positioning data in the multi-dimensional space model. The binary codes of each data dimension can be spliced through cross bits to form a collaborative positioning identifier, fully integrating the information of each data dimension, making the identifier more compact and capable of reflecting the relationship between each dimension. For example, assume there are three data dimensions, and the binary codes of each dimension are 101, 011, and 110 respectively. Through cross-bit splicing, a collaborative positioning identifier such as 101011110 can be obtained, which is beneficial for quickly and accurately identifying and distinguishing different positioning data in the collaborative positioning scenario, and facilitating subsequent data fusion and management.

[0073] Optionally, in S240, the standardized dimension component is used as the coordinate representation value of the positioning data on the corresponding data dimension, and based on the coordinate representation values of each data dimension, a collaborative positioning identifier for each positioning data is generated, including: S241, converting the standardized dimension component of each data dimension into a binary code with a fixed length.

[0074] It can be understood that the binary code is to convert the standardized dimension component into a binary form with a fixed length. The length of this binary code is preset and unified, which is convenient for subsequent splicing and processing.

[0075] Exemplarily, the fixed length corresponding to each data dimension can be determined in advance, and the standardized dimension component is converted into a binary number. If the standardized dimension component is a decimal number, appropriate scaling and rounding operations can be performed first to make it an integer, and then it can be converted into a binary form by the method of dividing by 2 and taking the remainder. If the length of the converted binary number is less than the fixed length, 0s are filled in front to make it reach the fixed length. For example, if the binary number after converting the standardized dimension component is 101 and the fixed length is 8 bits, then 5 0s are filled in front to get 00000101. The advantage of this is to unify the standardized dimension components of different data dimensions into binary codes of the same format, which is convenient for subsequent splicing and processing according to unified rules, improving the efficiency and accuracy of data processing.

[0076] S242, performing cross-bit splicing on the binary codes according to the preset dimension order to form a collaborative positioning identifier for each positioning data; wherein, the length of the collaborative positioning identifier is the sum of the binary code lengths of each data dimension, and the collaborative positioning identifier is used to uniquely represent the position information of the positioning data in the multi-dimensional space model.

[0077] It can be understood that the preset dimension order is the arrangement order of each data dimension pre-determined in the system, for example, arranged in the order of Beidou navigation, Bluetooth, and UWB. Cross-bit splicing is a method of combining multiple binary codes together according to certain rules, and a new coding sequence is formed by alternately selecting the bits of each binary code. The collaborative positioning identifier is the finally generated code used to uniquely represent the position information of the positioning data in the multi-dimensional space model.

[0078] The preset dimension order can be read in advance, and the binary codes of each data dimension are sequentially selected according to the dimension order. Starting from the first bit of the first binary code, the corresponding bits of each binary code are sequentially selected for splicing. For example, if the binary codes of three data dimensions are 1010, 0110, and 1100 respectively, according to cross-bit splicing, first take the first bit 1 of the first code, then take the first bit 0 of the second code, and then take the first bit 1 of the third code, and so on, finally obtaining the spliced code 10101100. Since the length of the binary code of each data dimension is fixed, the length of the collaborative positioning identifier is the sum of the lengths of the binary codes of each data dimension. The generated collaborative positioning identifier can integrate the information of each data dimension and accurately represent the position of the positioning data in the multi-dimensional space model through a unique code, which is beneficial to quickly and accurately identify and manage different positioning data in the collaborative positioning system and provides a basis for subsequent data fusion and analysis.

[0079] S300. According to the collaborative positioning identifier of each positioning data, all the positioning data are fused to obtain the collaborative positioning management data of the target Internet of Things device.

[0080] It can be understood that in the collaborative positioning scenario, the target Internet of Things device will collect multiple positioning data through different positioning technologies (such as Beidou navigation, Bluetooth, and UWB), and each positioning data has a corresponding collaborative positioning identifier. The collaborative positioning identifier contains the characteristics and position information of the positioning data in the multi-dimensional space model. All these positioning data can be fused and processed to integrate the advantages of each positioning data and eliminate the possible errors and limitations of a single positioning technology, so as to obtain more accurate and reliable collaborative positioning management data of the target Internet of Things device, improve the accuracy and stability of positioning, and provide more effective position information for the positioning management and application of Internet of Things devices.

[0081] In a possible implementation manner, for S300, according to the collaborative positioning identifier of each positioning data, all the positioning data are fused to obtain the collaborative positioning management data of the target Internet of Things device, including: S310. According to the collaborative positioning identifier of each positioning data, determine the spatial position of each positioning data in the multi-dimensional space model.

[0082] It can be understood that the collaborative positioning identifier of each positioning data can be decoded to extract the coordinate components of each dimension in the multi-dimensional space model, including the floating-point encoding of the Beidou dimension, the fixed-point encoding of the Bluetooth dimension, and the cyclic redundancy check encoding of the UWB dimension. The decoded coordinate components of each data dimension can be mapped to the corresponding data dimensions in the multi-dimensional space model, including the geographical location dimension, the relative position dimension, and the time synchronization dimension. The specific spatial position of the positioning data in the multi-dimensional space model can be calculated based on the mapping results of the coordinate components of each data dimension, including determining the geographical location reference point through the Beidou coordinates, adjusting the relative position offset based on the Bluetooth distance value, and correcting the position update frequency using the UWB time offset.

[0083] S320, perform encoding feature recognition on the collaborative positioning identifier of each positioning data to obtain the reliability dynamic coefficient of each positioning data.

[0084] It can be understood that the collaborative positioning identifier not only contains the position information of the positioning data in the multi-dimensional space model but also implicitly contains some characteristic information of the positioning data. Encoding feature recognition is to analyze the binary encoding of the collaborative positioning identifier and extract the features related to the reliability of the positioning data. For example, the binary encoding of a certain data dimension may reflect the source module of the positioning data (Beidou, Bluetooth, or UWB), and the reliability of different source modules may be different; or some bits in the encoding may represent information such as the error range of the positioning data in the current collaborative positioning scenario. These features can be identified and analyzed through preset rules and algorithms, and the reliability dynamic coefficient of each positioning data can be calculated based on the recognition results. The reliability dynamic coefficient is a dynamic value that changes with the characteristics of the positioning data and the collaborative positioning scenario, and is used to measure the credibility of the positioning data in the data fusion process.

[0085] Exemplarily, the high-order significant bits of the floating-point encoding of the Beidou dimension can be extracted, the Beidou dimension encoding in the collaborative positioning identifier can be parsed to obtain its most significant bit (MSB) sequence, and the number of "1"s in the high-order significant bits can be counted, denoted as N_Beidou. According to historical data, the maximum number of significant bits L_Beidou of the Beidou dimension can be determined, and the reliability dynamic coefficient of the Beidou dimension can be calculated through the following formula: R1 = N / L, where R1 is the reliability dynamic coefficient; similarly, the Bluetooth dimension encoding in the collaborative positioning identifier can be parsed, its data bits and parity bits can be separated, and the matching degree between the parity bits and the data bits can be calculated, denoted as M, and the maximum matching degree M can be determined according to the Bluetooth signal stability model max through the formula R2 = M / M maxObtain the reliability dynamic coefficient R2 of the Bluetooth positioning data; the UWB dimension encoding in the collaborative positioning identifier can be parsed to obtain its cyclic redundancy check bit, calculate the error rate between the check bit and the data bit, denoted as E, and determine the maximum error rate E according to the preset UWB time synchronization accuracy model max , and obtain the reliability dynamic coefficient R3 of the UWB dimension through the formula R3 = 1 - E / E max .

[0086] S330. Based on the reliability dynamic coefficient of each positioning data and the spatial position in the multi-dimensional space model, obtain the collaborative positioning management data of the target Internet of Things device.

[0087] It can be understood that the collaborative positioning management data is a data set obtained after comprehensive processing and fusion of data collected by multiple positioning technologies in a multi-technology collaborative positioning scenario, and is used to comprehensively and accurately describe the position and related status information of the target Internet of Things device. For each positioning data, its coordinate position in the multi-dimensional space model is multiplied by the corresponding reliability dynamic coefficient for fusion to obtain the fused position information. For example, assume there are three positioning data, coming from Beidou, Bluetooth, and UWB respectively, and their coordinates in the multi-dimensional space model are , , , and the corresponding reliability dynamic coefficients are , , . Then the weighted position information is respectively , , . These position information can be summed up and then divided by the sum of all reliability dynamic coefficients , and the obtained result is the collaborative positioning management data of the target Internet of Things device. The positioning data with high reliability accounts for a larger proportion in the final result, thereby improving the accuracy and reliability of collaborative positioning.

[0088] The method further includes: S400. Based on the collaborative positioning management data of the target Internet of Things device, calculate the dynamic confidence interval of the collaborative positioning management data.

[0089] It can be understood that the collaborative positioning management data of the target Internet of Things device is obtained after fusing data from multiple positioning technologies and is used to determine the position information of the device. However, there are many uncertain factors in the positioning process, such as signal interference, measurement errors, etc., which make the collaborative positioning management data not absolutely accurate but have a certain error range. The dynamic confidence interval is a statistical concept used to measure the reliability and error range of the collaborative positioning management data, and it will be dynamically adjusted as the data changes.

[0090] It is possible to continuously sample the collaborative positioning management data of the target Internet of Things device to form a time series data set, and determine the statistical information of the time series data set, such as the historical error distribution of the data, the error characteristics of different positioning technologies, etc. Based on this statistical information, assuming that the collaborative positioning management data follows a certain probability distribution (usually a normal distribution), the dynamic confidence interval is calculated through corresponding mathematical formulas. For example, for data with a normal distribution, at a 95% confidence level, the dynamic confidence interval can be determined by adding and subtracting 1.96 times the standard deviation from the mean. The confidence interval indicates that in multiple repeated positioning processes, there is a 95% probability that the collaborative positioning management data falls within this range. By calculating the dynamic confidence interval, the uncertainty of the collaborative positioning management data can be quantified, providing an important basis for subsequent judgments and decisions.

[0091] S500, compare the dynamic confidence interval of the collaborative positioning management data with a preset confidence range. When the dynamic confidence interval exceeds the confidence range, trigger the relocalization mechanism.

[0092] It can be understood that the preset confidence range is a reasonable error range preset in advance according to the requirements for positioning accuracy in the actual application scenario. The confidence range represents the reliable interval within which the collaborative positioning management data should be under normal circumstances. The dynamic confidence interval of the collaborative positioning management data can be compared with the preset confidence range to determine whether the current positioning result is within the acceptable error range. If the dynamic confidence interval exceeds the preset confidence range, it indicates that the error of the current collaborative positioning management data may be too large and the reliability of the positioning result is low. The relocalization mechanism can be triggered, that is, restart the entire positioning process, including re-acquiring the positioning data of the target Internet of Things device, processing these data, generating collaborative positioning identifiers, fusing data, etc., to obtain more accurate collaborative positioning management data, which is beneficial to ensuring that the positioning result of the target Internet of Things device always has high accuracy and reliability, meeting the requirements of actual applications, improving scenario adaptability and reliability, and enhancing the overall operation efficiency and user experience.

[0093] Corresponding to the Internet of Things management method for the collaborative work of Beidou navigation, Bluetooth, and UWB in the above embodiment, the present application embodiment also provides an Internet of Things management system for the collaborative work of Beidou navigation, Bluetooth, and UWB. Each unit of this system can implement each step of the Internet of Things management method for the collaborative work of Beidou navigation, Bluetooth, and UWB. Figure 3 The structural block diagram of the Internet of Things management system for the collaborative work of Beidou navigation, Bluetooth, and UWB provided by the present application embodiment is shown. For the sake of convenience of description, only the parts related to the present application embodiment are shown.

[0094] Refer to Figure 3 , the Internet of Things management system for the collaborative work of Beidou navigation, Bluetooth, and UWB includes: An acquisition unit, configured to acquire a collaborative positioning parameter set and a plurality of positioning data of a target Internet of Things device in a collaborative positioning scenario; wherein, the collaborative positioning parameter set includes at least one collaborative positioning parameter; the positioning data is positioning data separately collected by the target Internet of Things device through any one of a Beidou navigation module, a Bluetooth module, and a UWB module; An identification unit, configured to obtain a collaborative positioning identifier of each piece of the positioning data based on the values of the plurality of pieces of the positioning data of the target Internet of Things device in the collaborative positioning parameter set; A management unit, configured to fuse all the positioning data according to the collaborative positioning identifier of each piece of the positioning data to obtain collaborative positioning management data of the target Internet of Things device.

[0095] It should be noted that the information interaction, execution process, etc. between the above systems / units, due to being based on the same concept as the method embodiment of the present application, for their specific functions and the technical effects brought, reference can be specifically made to the method embodiment part, and details will not be elaborated here.

[0096] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the above division of each functional unit and module is used as an example for illustration. In practical applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into a processing unit, or each unit module exists physically alone, or two or more unit modules are integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiment, and details will not be elaborated here.

[0097] The embodiment of the present application further provides an Internet of Things management device for collaborative work of Beidou navigation, Bluetooth, and UWB, Figure 4 which is a structural schematic diagram of an Internet of Things management device for collaborative work of Beidou navigation, Bluetooth, and UWB provided in an embodiment of the present application. As Figure 4 shown, the Internet of Things management device 6 for collaborative work of Beidou navigation, Bluetooth, and UWB in this embodiment includes: at least one processor 60 ( Figure 4 only one is shown herein), at least one memory 61 ( Figure 4(only one is shown in the figure), and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the Internet of Things management device 6 that enables Beidou navigation to cooperate with Bluetooth and UWB implements the steps in any of the above-mentioned embodiments of the Internet of Things management methods for Beidou navigation to cooperate with Bluetooth and UWB, or enables the Internet of Things management device 6 that enables Beidou navigation to cooperate with Bluetooth and UWB to implement the functions of each unit in the above-mentioned system embodiments.

[0098] Exemplarily, the computer program 62 can be divided into one or more units. The one or more units are stored in the memory 61 and executed by the processor 60 to complete this application. The one or more units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program 62 in the Internet of Things management device 6 that enables Beidou navigation to cooperate with Bluetooth and UWB.

[0099] The Internet of Things management device 6 that enables Beidou navigation to cooperate with Bluetooth and UWB can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server, etc. The Internet of Things management device that enables Beidou navigation to cooperate with Bluetooth and UWB may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art can understand that Figure 4 merely examples of the Internet of Things management device 6 that enables Beidou navigation to cooperate with Bluetooth and UWB, and do not constitute a limitation on the Internet of Things management device 6 that enables Beidou navigation to cooperate with Bluetooth and UWB. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, it may also include input / output devices, network access devices, buses, etc.

[0100] The processor 60 can be a central processing unit (CPU), and the processor 60 can also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0101] In some embodiments, the memory 61 may be an internal storage unit of the Internet of Things management device 6 that collaborates with Beidou navigation, Bluetooth, and UWB, such as the hard disk or memory of the Internet of Things management device 6 that collaborates with Beidou navigation, Bluetooth, and UWB. In other embodiments, the memory 61 may also be an external storage device of the Internet of Things management device 6 that collaborates with Beidou navigation, Bluetooth, and UWB, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc., equipped on the Internet of Things management device 6 that collaborates with Beidou navigation, Bluetooth, and UWB. Further, the memory 61 may also include both the internal storage unit of the Internet of Things management device 6 that collaborates with Beidou navigation, Bluetooth, and UWB and an external storage device. The memory 61 is used to store an operating system, application programs, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 61 may also be used to temporarily store data that has been output or will be output.

[0102] An embodiment of the present application also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0103] An embodiment of the present application provides a computer program product, and when the computer program product runs on the Internet of Things management device that collaborates with Beidou navigation, Bluetooth, and UWB, the Internet of Things management device that collaborates with Beidou navigation, Bluetooth, and UWB implements the steps in any of the above method embodiments.

[0104] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of the present application, a computer program can be used to instruct the relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can at least include: any entity or device capable of carrying the computer program code to the Internet of Things management device that collaborates with Beidou navigation, Bluetooth, and UWB, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0105] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0106] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0107] In the embodiments provided in the present application, it should be understood that the disclosed Internet of Things management system in which Beidou navigation works in cooperation with Bluetooth and UWB / the Internet of Things management device and method in which Beidou navigation works in cooperation with Bluetooth and UWB can be implemented in other ways. For example, the embodiments of the Internet of Things management system in which Beidou navigation works in cooperation with Bluetooth and UWB / the Internet of Things management device in which Beidou navigation works in cooperation with Bluetooth and UWB described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be in an electrical, mechanical or other form.

[0108] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0109] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for managing the Internet of Things in which Beidou navigation works in collaboration with Bluetooth and UWB, characterized in that: include: Acquire a collaborative positioning parameter set and multiple positioning data of a target IoT device in a collaborative positioning scenario; wherein the collaborative positioning parameter set includes at least one collaborative positioning parameter; and the positioning data is positioning data collected separately by the target IoT device through any one of a Beidou navigation module, a Bluetooth module, and a UWB module; Based on the values ​​of the plurality of positioning data of the target IoT device in the collaborative positioning scenario under the collaborative positioning parameter set, a collaborative positioning identifier of each positioning data is obtained; According to the collaborative positioning identifier of each positioning data, all the positioning data are fused to obtain the collaborative positioning management data of the target Internet of Things device.

2. The Internet of Things management method for Beidou navigation working in collaboration with Bluetooth and UWB as claimed in claim 1, characterized in that: The obtaining of the collaborative positioning identifier of each positioning data based on the values ​​of the multiple positioning data of the target IoT device in the collaborative positioning scenario under the collaborative positioning parameter set includes: Constructing a multidimensional space model corresponding to the collaborative positioning parameter set; wherein the multidimensional space model includes multiple data dimensions; each of the data dimensions corresponds to a characteristic attribute of a positioning technology parameter; Extracting the original observation value of each positioning data under the corresponding positioning technical parameter; the corresponding positioning technical parameter is at least one of the positioning precision factor of Beidou navigation, the propagation loss coefficient of the Bluetooth signal, or the time synchronization deviation parameter of UWB in the collaborative positioning parameter set; According to the data dimension mapping rule corresponding to the corresponding positioning technical parameter, the original observation value of each positioning data under the corresponding positioning technical parameter is converted into a standardized dimensional component; The standardized dimensional components are used as coordinate representation values ​​of the positioning data in the corresponding data dimensions, and based on the coordinate representation values ​​of each data dimension, a collaborative positioning identifier of each positioning data is generated; wherein each positioning identifier is formed by cross-bit splicing of the binary codes of each data dimension.

3. The Internet of Things management method for Beidou navigation working in collaboration with Bluetooth and UWB as claimed in claim 2, characterized in that: According to the data dimension mapping rule corresponding to the corresponding positioning technical parameter, the original observation value of each positioning data under the corresponding positioning technical parameter is converted into a standardized dimension component, including: According to the data dimension mapping rule corresponding to the corresponding positioning technical parameter, when the corresponding positioning technical parameter is the positioning precision factor of Beidou navigation, obtaining the signal-to-noise ratio parameter and the geometric precision reduction factor of the current satellite signal; Calculating a dynamic error threshold of Beidou navigation based on the signal-to-noise ratio parameter of the satellite signal and the geometric dilution of precision factor; Comparing the dynamic error threshold with a preset basic error threshold to determine a data truncation boundary of the original observation value; Performing data truncation processing on the original observation value according to the data truncation boundary to generate a compressed coordinate value corresponding to the original observation value; The compressed coordinate values ​​are converted into binary floating point codes of fixed bit width to obtain standardized dimensional components corresponding to the original observation values.

4. The Internet of Things management method for Beidou navigation working in collaboration with Bluetooth and UWB as claimed in claim 3, characterized in that: The step of comparing the dynamic error threshold with a preset basic error threshold to determine the data truncation boundary of the original observation value includes: When the dynamic error threshold is less than the basic error threshold, determining the dynamic error threshold as the data truncation boundary of the original observation value; When the dynamic error threshold is not less than the basic error threshold, the basic error threshold is determined as the data truncation boundary of the original observation value.

5. The Internet of Things management method for Beidou navigation working in collaboration with Bluetooth and UWB as claimed in claim 2, characterized in that: The converting the original observation value of each positioning data under the corresponding positioning technical parameter into a standardized dimensional component according to the data dimension mapping rule corresponding to the corresponding positioning technical parameter includes: According to the data dimension mapping rule corresponding to the corresponding positioning technical parameter, when the corresponding positioning technical parameter is a propagation loss coefficient of a Bluetooth signal, based on a preset radio frequency attenuation model, converting the original observation value into a relative distance value between devices; The maximum effective distance measurement range in the data dimension mapping rule is compared with the relative distance value, and nonlinear compression processing is performed on the relative distance value exceeding the maximum effective distance measurement range to obtain the standardized dimensional component corresponding to the original observation value.

6. The Internet of Things management method for Beidou navigation working in collaboration with Bluetooth and UWB as claimed in claim 2, characterized in that: The converting the original observation value of each positioning data under the corresponding positioning technical parameter into a standardized dimensional component according to the data dimension mapping rule corresponding to the corresponding positioning technical parameter includes: According to the data dimension mapping rule corresponding to the corresponding positioning technical parameter, when the corresponding positioning technical parameter is a time synchronization deviation parameter of UWB, calculating a first relative offset between a current timestamp and a preset reference clock source; Performing phase alignment processing on the first relative offset by sliding a time window to obtain a second relative offset after phase alignment; The second relative offset after phase alignment is converted into a cyclic redundancy check coding sequence to obtain a standardized dimensional component corresponding to the original observation value.

7. The Internet of Things management method for Beidou navigation working in collaboration with Bluetooth and UWB as claimed in claim 6, characterized in that: The step of using the standardized dimensional components as coordinate representation values ​​of the positioning data in corresponding data dimensions, and generating a collaborative positioning identifier for each positioning data based on the coordinate representation values ​​of each data dimension, includes: Converting the normalized dimension component of each of the data dimensions into a fixed-length binary code; The binary codes are cross-joined in a preset dimensional order to form a collaborative positioning identifier for each positioning data; wherein the length of the collaborative positioning identifier is the sum of the lengths of the binary codes of each data dimension, and the collaborative positioning identifier is used to uniquely represent the location information of the positioning data in a multidimensional space model.

8. The Internet of Things management method for Beidou navigation working in collaboration with Bluetooth and UWB as claimed in claim 2, characterized in that: The step of fusing all the positioning data according to the collaborative positioning identifier of each positioning data to obtain the collaborative positioning management data of the target IoT device includes: Determining the spatial position of each positioning data in the multidimensional space model according to the collaborative positioning identifier of each positioning data; Performing coding feature recognition on the collaborative positioning identifier of each positioning data to obtain a reliability dynamic coefficient of each positioning data; Based on the reliability dynamic coefficient of each positioning data and the spatial position in the multidimensional space model, the collaborative positioning management data of the target Internet of Things device is obtained.

9. The Internet of Things management method for Beidou navigation working in collaboration with Bluetooth and UWB as claimed in claim 1, characterized in that: The method further comprises: Based on the collaborative positioning management data of the target IoT device, calculating a dynamic confidence interval of the collaborative positioning management data; The dynamic confidence interval of the collaborative positioning management data is compared with a preset confidence range, and when the dynamic confidence interval exceeds the confidence range, a repositioning mechanism is triggered.

10. An Internet of Things management device that cooperates with Beidou navigation, Bluetooth and UWB, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 9 is implemented.

Images