Combined positioning method and system based on variational Bayesian theory

Abstract

The invention relates to the technical field of intelligent positioning, in particular to a combined positioning method and system based on the variational Bayesian theory, and the method comprises the steps: respectively obtaining a detection acceleration set and a detection angular velocity set through a detection frequency, a target carrier and an inertial positioning unit, determining a target acceleration signal and a target angular velocity signal based on the detection acceleration set and the detection angular velocity set, obtaining a satellite positioning signal, and constructing a measurement equation by using the monitoring frequency, the satellite positioning signal, the target acceleration signal and the target angular velocity signal; acquiring a fitting noise signal set and a fitting filtering algorithm set based on the data fusion instruction, acquiring a positioning pose based on the fitting noise signal set, the fitting filtering algorithm set and a measurement equation, and performing encryption operation on the positioning pose by using a public key to obtain encrypted data, and sending the encrypted data and the private key to an initiating end of a positioning instruction to realize positioning of the target carrier. According to the invention, the accuracy and safety of positioning the target carrier can be improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent positioning technology, and in particular to a combined positioning method and system based on variational Bayesian theory. Background Art

[0002] At present, with the completion of the global network of BeiDou satellite navigation system, the BeiDou system has been widely used in many industries such as transportation, maritime, electricity, civil affairs, meteorology, fishery, etc. However, in a highly dynamic and strong interference environment, a single satellite navigation system has problems such as signal susceptibility to obstruction and poor dynamic performance, and cannot guarantee the security of positioning data.

[0003] At present, most of the existing integrated navigation systems adopt loosely coupled or tightly coupled models, and apply classic Kalman filtering, extended Kalman filtering and unscented Kalman filtering to perform data fusion, and realize the positioning of the target vehicle based on the fused data.

[0004] Although the use of loosely coupled or tightly coupled models can improve the accuracy of positioning the target vehicle, when there is strong nonlinear noise and time-varying noise in the Beidou satellite signal, the positioning is still inaccurate. Therefore, improving the accuracy and safety of positioning the target vehicle has become an urgent problem to be solved. Summary of the invention

[0005] The present invention provides a combined positioning method based on variational Bayesian theory and a computer-readable storage medium, the main purpose of which is to improve the accuracy and safety of positioning a target vehicle.

[0006] To achieve the above object, the present invention provides a combined positioning method based on variational Bayesian theory, comprising:

[0007] receiving a positioning instruction, and confirming a positioning system and a target vehicle for positioning based on the positioning instruction, wherein the positioning system includes a data fusion unit and a data encryption unit, and the target vehicle includes a satellite positioning unit and an inertial positioning unit, wherein the inertial positioning unit includes a plurality of acceleration detection units and a plurality of angular velocity detection units;

[0008] A detection acceleration set and a detection angular velocity set are respectively obtained by using a preset detection frequency, a target vehicle and an inertial positioning unit, wherein the detection acceleration set includes a plurality of detection acceleration time sequences, and the detection acceleration time sequences correspond to the acceleration detection units one-to-one, the detection angular velocity set includes a plurality of detection angular velocity time sequences, and the detection angular velocity time sequences correspond to the angular velocity detection units one-to-one, and the detection acceleration time sequences include a plurality of detection accelerations, and the detection angular velocity time sequences include a plurality of detection angular velocities, and a target acceleration signal and a target angular velocity signal are confirmed based on the detection acceleration set and the detection angular velocity set;

[0009] Confirming receipt of a positioning instruction from a satellite positioning unit, acquiring a satellite positioning signal based on the positioning instruction, and constructing a measurement equation using a preset monitoring frequency, a satellite positioning signal, a target acceleration signal, and a target angular velocity signal;

[0010] Confirming receipt of a data fusion instruction from a data fusion unit, acquiring a fitting noise signal set and a fitting filter algorithm set based on the data fusion instruction, and acquiring a positioning posture based on the fitting noise signal set, the fitting filter algorithm set and a measurement equation;

[0011] Confirm receipt of the data encryption instruction from the data encryption unit, obtain the public key and the private key based on the data encryption instruction and the positioning posture, use the public key to perform an encryption operation on the positioning posture to obtain encrypted data, and send the encrypted data and the private key to the initiator of the positioning instruction to achieve the positioning of the target vehicle.

[0012] Optionally, the determining a target acceleration signal and a target angular velocity signal based on the detected acceleration set and the detected angular velocity set includes:

[0013] Performing identification operations on a plurality of acceleration detection units and a plurality of angular velocity detection units in the inertial positioning unit respectively to obtain a plurality of identified acceleration units and a plurality of identified angular velocity units, wherein the identified acceleration unit includes an acceleration detection unit identified with a serial number;

[0014] Using a preset sliding window, a plurality of local acceleration group sets are retrieved from a plurality of detection acceleration time sequences corresponding to the detection acceleration set, wherein the local acceleration group set includes a plurality of local acceleration groups, wherein the local acceleration groups correspond to the detection acceleration time sequences one by one, and the local acceleration groups include a plurality of local accelerations;

[0015] The following operations are performed on each of the multiple local acceleration group sets:

[0016] The following operations are performed on each of the multiple local acceleration groups corresponding to the local acceleration group set:

[0017] Using the local acceleration group to obtain a local acceleration mean and a local acceleration variance, wherein the local acceleration mean and the local acceleration variance are respectively the mean and variance of multiple local accelerations in the local acceleration group;

[0018] Obtaining a screening evaluation value using the local acceleration mean and the local acceleration variance;

[0019] comparing the screening evaluation value with a preset screening threshold;

[0020] If the screening evaluation value is greater than or equal to the screening threshold, the detection acceleration time sequence corresponding to the screening evaluation value is eliminated from the detection acceleration set, and the identification acceleration unit corresponding to the screening evaluation value is eliminated from the plurality of identification acceleration units;

[0021] Otherwise, respectively summarize the detection acceleration time series corresponding to the screening evaluation value and the identification acceleration unit corresponding to the screening evaluation value to obtain a first acceleration set and a first acceleration unit set;

[0022] A target acceleration signal and a target angular velocity signal are confirmed by using the first acceleration set, the first acceleration unit set, the plurality of identification angular velocity units and the detection angular velocity set.

[0023] Optionally, the obtaining a screening evaluation value by using the local acceleration mean and the local acceleration variance includes:

[0024] The local evaluation value is calculated based on the pre-built local evaluation relationship, the local acceleration mean, and the local acceleration variance, where the local evaluation relationship is as follows:

[0025] J=αμσ

[0026] Where J represents the local evaluation value, α is the preset coefficient, μ represents the local acceleration mean, and σ represents the local acceleration variance;

[0027] Summarize the local evaluation values ​​to obtain a local evaluation value set, and perform the following operations on the local evaluation values ​​in the local evaluation value set:

[0028] The screening evaluation value is calculated based on the pre-built initial screening formula, local evaluation value and local evaluation value set, where the initial screening formula is as follows:

[0029]

[0030] Among them, X i represents the screening evaluation value corresponding to the i-th local evaluation value in the local evaluation value set, J i represents the i-th local evaluation value in the local evaluation value set, J j represents the jth local evaluation value in the local evaluation value set, and n represents that there are n local evaluation values ​​in the local evaluation value set.

[0031] Optionally, the step of using the first acceleration set, the first acceleration unit set, the plurality of identification angular velocity units and the detection angular velocity set to identify the target acceleration signal and the target angular velocity signal includes:

[0032] The following operations are performed for each detected acceleration sequence in the first acceleration set:

[0033] An overall evaluation value is obtained based on the detected acceleration time sequence, and the first acceleration unit is retrieved from the first acceleration unit set using the overall evaluation value, and the overall evaluation value is associated with the serial number corresponding to the first acceleration unit to obtain an evaluation point group, and the evaluation point group is mapped to a pre-constructed evaluation coordinate system to obtain an overall point set, and an evaluation straight line is fitted using the overall point set;

[0034] Using the serial number corresponding to the evaluation point group, a target point group is retrieved in the evaluation line, wherein the target point group includes a target serial number and a target evaluation value, and the target serial number corresponding to the target point group is the same as the serial number corresponding to the evaluation point group;

[0035] Based on the target point group and the evaluation point group, the evaluation differences are obtained, and the evaluation differences are summarized to obtain an evaluation difference set. The evaluation differences in the evaluation difference set are sorted in descending order to obtain an evaluation difference sequence, wherein the evaluation difference is the absolute difference between the target evaluation value corresponding to the target point group and the overall evaluation value corresponding to the evaluation point group;

[0036] Obtaining a target evaluation sequence using the evaluation difference sequence, wherein the target evaluation sequence includes one or more target evaluation values, and the target evaluation value is less than or equal to a preset evaluation threshold;

[0037] Based on the target evaluation sequence, a second acceleration unit set is identified in the first acceleration unit set, wherein the second acceleration unit set includes one or more second acceleration units, and the second acceleration units correspond one-to-one to the target evaluation values ​​in the target evaluation sequence;

[0038] Acquire an acceleration signal set using the second acceleration unit set, wherein the acceleration signal set includes one or more acceleration signals, and acquire a target acceleration signal based on the acceleration signal set, wherein the target acceleration signal is an average value of the one or more acceleration signals in the acceleration signal set;

[0039] A target angular velocity signal is acquired according to a plurality of identification angular velocity units and a detection angular velocity set.

[0040] Optionally, acquiring the positioning posture based on the fitted noise signal set, the fitted filtering algorithm set and the measurement equation includes:

[0041] The fitting noise signal set includes: a first noise signal, a second noise signal and a third noise signal, and the fitting filtering algorithm set includes a first filtering algorithm, a second filtering algorithm and a third filtering algorithm;

[0042] Respectively associating the first noise signal with the first filtering algorithm, the second noise signal with the second filtering algorithm, and the third noise signal with the third filtering algorithm to obtain three position prediction combinations;

[0043] The following operations are performed for each of the three position prediction combinations:

[0044] Acquire initial positioning information by using a preset iteration number threshold, a preset evaluation threshold, a measurement equation and a position prediction combination, wherein the initial positioning information includes: initial positioning posture, initial positioning time and positioning evaluation threshold;

[0045] The initial positioning information is aggregated to obtain an initial positioning information set, and a positioning posture is acquired based on the initial positioning information set.

[0046] Optionally, acquiring a positioning posture based on the initial positioning information set includes:

[0047] If there is one or more initial positioning information in the initial positioning information set whose corresponding positioning evaluation threshold is less than or equal to the evaluation threshold, then obtaining the positioning pose based on the one or more initial positioning information, wherein the positioning pose is the initial positioning pose with the smallest positioning evaluation threshold among the one or more initial positioning information;

[0048] If the positioning evaluation thresholds corresponding to the initial positioning information in the initial positioning information set are all greater than the evaluation threshold, the positioning posture is obtained based on the initial positioning information set, wherein the positioning posture is the initial positioning posture corresponding to the minimum initial positioning time in the initial positioning information set.

[0049] Optionally, obtaining a public key and a private key based on the data encryption instruction and the positioning posture includes:

[0050] Parsing the data encryption instruction to obtain an initial storage unit, storing the positioning posture in the initial storage unit, obtaining a target storage unit, and using the target storage unit to obtain a storage memory value;

[0051] An encryption parameter relationship is constructed based on the stored memory value, a public key parameter is calculated using the encryption parameter relationship, a private key parameter is calculated based on the public key parameter, and a public key and a private key are obtained based on the public key parameter and the private key parameter.

[0052] Optionally, the encryption parameter relationship is as follows:

[0053]

[0054] Where G represents the public key parameter, random represents the random function, {} represents the prime number, [] represents the integer, ρ and ω are the preset coefficients, N represents the storage memory value, h(t) represents the time processing function, t represents the initial positioning time corresponding to the positioning posture, z krepresents the serial number of the kth acceleration detection unit among one or more acceleration detection units corresponding to the target acceleration signal, m represents that there are m acceleration detection units corresponding to the target acceleration signal, and d l represents the serial number of the lth angular velocity detection unit in one or more angular velocity detection units corresponding to the target angular velocity signal, and p represents that there are p one or more angular velocity detection units corresponding to the target angular velocity signal.

[0055] Optionally, calculating the private key parameters according to the public key parameters includes:

[0056] The private key solving equation is constructed using the public key parameters, and the private key solving equation is used to calculate the private key parameters, wherein the private key solving equation is as follows:

[0057]

[0058] Among them, S represents the private key parameter, is the preset coefficient.

[0059] To achieve the above object, the present invention also provides a combined positioning system based on variational Bayesian theory, comprising:

[0060] A positioning instruction receiving module, used to receive a positioning instruction, and to identify a positioning system and a target vehicle used for positioning based on the positioning instruction, wherein the positioning system includes a data fusion unit and a data encryption unit, and the target vehicle includes a satellite positioning unit and an inertial positioning unit, wherein the inertial positioning unit includes a plurality of acceleration detection units and a plurality of angular velocity detection units;

[0061] A positioning preparation module, used to respectively obtain a detection acceleration set and a detection angular velocity set by using a preset detection frequency, a target vehicle and an inertial positioning unit, wherein the detection acceleration set includes a plurality of detection acceleration time sequences, and the detection acceleration time sequences correspond to the acceleration detection units one-to-one, the detection angular velocity set includes a plurality of detection angular velocity time sequences, and the detection angular velocity time sequences correspond to the angular velocity detection units one-to-one, and the detection acceleration time sequences include a plurality of detection accelerations, and the detection angular velocity time sequences include a plurality of detection angular velocities, and a target acceleration signal and a target angular velocity signal are confirmed based on the detection acceleration set and the detection angular velocity set;

[0062] A positioning data confirmation module, used to confirm the reception of a positioning instruction from a satellite positioning unit, obtain a satellite positioning signal based on the positioning instruction, and construct a measurement equation using a preset monitoring frequency, a satellite positioning signal, a target acceleration signal, and a target angular velocity signal;

[0063] Confirming receipt of a data fusion instruction from a data fusion unit, acquiring a fitting noise signal set and a fitting filter algorithm set based on the data fusion instruction, and acquiring a positioning posture based on the fitting noise signal set, the fitting filter algorithm set and a measurement equation;

[0064] The positioning data encryption module is used to confirm the receipt of the data encryption instruction from the data encryption unit, obtain the public key and the private key based on the data encryption instruction and the positioning posture, use the public key to perform encryption operations on the positioning posture to obtain encrypted data, and send the encrypted data and the private key to the initiator of the positioning instruction to realize the positioning of the target vehicle.

[0065] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0066] a memory storing at least one instruction; and

[0067] The processor executes the instructions stored in the memory to implement the above-mentioned combined positioning method based on variational Bayesian theory.

[0068] In order to solve the above problem, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned combined positioning method based on variational Bayesian theory.

[0069] The present invention is to solve the problems described in the background technology. The present invention receives a positioning instruction, and confirms a positioning system and a target vehicle for positioning based on the positioning instruction, wherein the positioning system includes a data fusion unit and a data encryption unit, and the target vehicle includes a satellite positioning unit and an inertial positioning unit, wherein the inertial positioning unit includes a plurality of acceleration detection units and a plurality of angular velocity detection units. It can be seen that the present invention considers using a plurality of acceleration detection units and a plurality of angular velocity detection units to realize the positioning of the target vehicle, thereby avoiding the problem of inaccurate positioning of the target vehicle due to damage or failure of the acceleration detection unit and the angular velocity detection unit when using a single acceleration detection unit and an angular velocity detection unit. The present invention utilizes a preset detection frequency, a target vehicle and an inertial detection unit. The positioning unit obtains a detection acceleration set and a detection angular velocity set respectively, wherein the detection acceleration set includes multiple detection acceleration timings, and the detection acceleration timings correspond one-to-one to the acceleration detection units; the detection angular velocity set includes multiple detection angular velocity timings, and the detection angular velocity timings correspond one-to-one to the angular velocity detection units, and the detection acceleration timings include multiple detection accelerations, and the detection angular velocity timings include multiple detection angular velocities. The target acceleration signal and the target angular velocity signal are confirmed based on the detection acceleration set and the detection angular velocity set. It can be seen that the present invention obtains the first acceleration unit set by screening multiple acceleration detection units, so that the target acceleration signal obtained by using the second acceleration unit set is more accurate, thereby improving the accuracy of positioning the target vehicle. The present invention confirms receiving a data fusion instruction from a data fusion unit, obtains a fitting noise signal set and a fitting filter algorithm set based on the data fusion instruction, and obtains a positioning posture based on the fitting noise signal set, the fitting filter algorithm set and the measurement equation. It can be seen that the present invention takes into account the possible existence of noise signals when obtaining satellite positioning signals, target acceleration signals and target angular velocity signals. Therefore, a relatively accurate and random positioning posture is obtained by fitting the noise signal set and the fitting filter algorithm set, thereby improving the accuracy of positioning the target vehicle. The present invention confirms receiving a data encryption instruction from a data encryption unit, obtains a public key and a private key based on the data encryption instruction and the positioning posture, uses the public key to perform an encryption operation on the positioning posture, obtains encrypted data, and sends the encrypted data and the private key to the initiator of the positioning instruction to achieve the positioning of the target vehicle. It can be seen that the present invention combines the information of the positioning posture, the serial number of the acceleration detection unit and the angular velocity detection unit to obtain the public key, and uses the public key to encrypt the positioning posture, thereby improving the security of the positioning posture of the target vehicle. Therefore, the present invention can improve the accuracy and security of positioning the target vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A schematic diagram of a flow chart of a combined positioning method based on variational Bayesian theory provided in one embodiment of the present invention;

[0071] Figure 2 A functional module diagram of a combined positioning system based on variational Bayesian theory provided by an embodiment of the present invention;

[0072] Figure 3 A schematic diagram of the structure of an electronic device for implementing the combined positioning method based on variational Bayesian theory provided by an embodiment of the present invention.

[0073] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0074] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0075] The embodiment of the present application provides a combined positioning method based on variational Bayesian theory. The execution subject of the combined positioning method based on variational Bayesian theory includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the combined positioning method based on variational Bayesian theory can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0076] Reference Figure 1 FIG. 1 is a flow chart of a combined positioning method based on variational Bayesian theory provided by an embodiment of the present invention. In this embodiment, the combined positioning method based on variational Bayesian theory includes:

[0077] S1. Receive a positioning instruction, and confirm a positioning system and a target vehicle used for positioning based on the positioning instruction, wherein the positioning system includes a data fusion unit and a data encryption unit, and the target vehicle includes a satellite positioning unit and an inertial positioning unit, wherein the inertial positioning unit includes multiple acceleration detection units and multiple angular velocity detection units.

[0078] It should be explained that the positioning instruction is an instruction issued by a positioning detection personnel, and the purpose of issuing the positioning instruction is to realize the positioning of the target vehicle. The positioning system is a software or app used to realize the positioning of the target vehicle, and the positioning system includes: a data fusion unit and a data encryption unit. For the specific application of the units, please refer to the subsequent embodiments. The target vehicle refers to a carrier equipped with a satellite positioning unit and an inertial positioning unit. Optionally, the target vehicle is a vehicle equipped with a satellite positioning unit and an inertial positioning unit. Optionally, a three-axis accelerometer is used as the acceleration detection unit, and a three-axis gyroscope is used as the angular velocity detection unit. The embodiments of the present invention can improve the accuracy and safety of positioning the target vehicle.

[0079] For example, Xiao Zhang wants to locate a vehicle equipped with a satellite positioning unit and an inertial positioning unit. Here, the vehicle equipped with a satellite positioning unit and an inertial positioning unit is the target vehicle. Therefore, Xiao Zhang issues a positioning command, and uses the positioning command to identify the positioning system and the target vehicle used to locate the target vehicle. After connecting the positioning system to the on-board computer and dynamically monitoring the data of the target vehicle, the data detected by the target vehicle is processed by the positioning system, thereby realizing the positioning of the target vehicle.

[0080] S2. Utilize the preset detection frequency, target vehicle and inertial positioning unit to respectively obtain the detection acceleration set and the detection angular velocity set, wherein the detection acceleration set includes multiple detection acceleration timings, and the detection acceleration timings correspond one-to-one to the acceleration detection units; the detection angular velocity set includes multiple detection angular velocity timings, and the detection angular velocity timings correspond one-to-one to the angular velocity detection units, and the detection acceleration timings include multiple detection accelerations, and the detection angular velocity timings include multiple detection angular velocities; and confirm the target acceleration signal and the target angular velocity signal based on the detection acceleration set and the detection angular velocity set.

[0081] It should be explained that the acceleration detection timing and the angular velocity detection timing are respectively the acceleration timing and angular velocity timing obtained when the acceleration detection unit and the angular velocity detection unit in the inertial positioning unit detect the target vehicle at the detection frequency. For example: using a three-axis accelerometer as an acceleration detection unit, the target vehicle is detected at the detection frequency to obtain multiple detection accelerations, and the multiple detection accelerations are sorted according to the order from first to last of the time corresponding to each of the multiple detection accelerations to obtain the detection acceleration timing. The method for obtaining the detection angular velocity timing is the same as the method for obtaining the detection acceleration timing, which will not be repeated here.

[0082] It should be understood that the step of determining the target acceleration signal and the target angular velocity signal based on the detected acceleration set and the detected angular velocity set includes:

[0083] Performing identification operations on a plurality of acceleration detection units and a plurality of angular velocity detection units in the inertial positioning unit respectively to obtain a plurality of identified acceleration units and a plurality of identified angular velocity units, wherein the identified acceleration unit includes an acceleration detection unit identified with a serial number;

[0084] Using a preset sliding window, a plurality of local acceleration group sets are retrieved from a plurality of detection acceleration time sequences corresponding to the detection acceleration set, wherein the local acceleration group set includes a plurality of local acceleration groups, wherein the local acceleration groups correspond to the detection acceleration time sequences one by one, and the local acceleration groups include a plurality of local accelerations;

[0085] The following operations are performed on each of the multiple local acceleration group sets:

[0086] The following operations are performed on each of the multiple local acceleration groups corresponding to the local acceleration group set:

[0087] Using the local acceleration group to obtain a local acceleration mean and a local acceleration variance, wherein the local acceleration mean and the local acceleration variance are respectively the mean and variance of multiple local accelerations in the local acceleration group;

[0088] Obtaining a screening evaluation value using the local acceleration mean and the local acceleration variance;

[0089] comparing the screening evaluation value with a preset screening threshold;

[0090] If the screening evaluation value is greater than or equal to the screening threshold, the detection acceleration time sequence corresponding to the screening evaluation value is eliminated from the detection acceleration set, and the identification acceleration unit corresponding to the screening evaluation value is eliminated from the plurality of identification acceleration units;

[0091] Otherwise, respectively summarize the detection acceleration time series corresponding to the screening evaluation value and the identification acceleration unit corresponding to the screening evaluation value to obtain a first acceleration set and a first acceleration unit set;

[0092] A target acceleration signal and a target angular velocity signal are confirmed by using the first acceleration set, the first acceleration unit set, the plurality of identification angular velocity units and the detection angular velocity set.

[0093] It is understandable that the purpose of marking multiple acceleration detection units is to distinguish the detection acceleration timings detected by different acceleration detection units, and by screening the acceleration detection units, the screened acceleration detection units can more truly reflect the data of the target vehicle, avoid the distortion of the acceleration detection unit detection data due to damage or failure of the acceleration detection unit, and thus improve the accuracy of positioning the target vehicle. For example: if there are three acceleration detection units in total, then after marking each of the three acceleration detection units, three marked acceleration units are obtained, wherein the three marked acceleration units are: 1-acceleration detection unit, 2-acceleration detection unit and 3-acceleration detection unit, and 1 in 1-acceleration detection unit is the serial number of the marked acceleration unit. The method of performing the marking operation on the angular velocity detection unit is the same as the method of performing the marking operation on the acceleration detection unit, and can achieve the same effect, which will not be repeated here. Optionally, a fixed sliding window is used as the sliding window.

[0094] Exemplarily, the detected acceleration set includes 2 detected acceleration time sequences, wherein the first detected acceleration time sequence is: 1, 2, 3, 4 and 5, and the second detected acceleration time sequence is: 2, 3, 4, 5 and 7. Then the sliding window can extract two local acceleration groups in the first detected acceleration time sequence, wherein the two local acceleration groups are: 1, 2, 3 and 4 and 2, 3, 4 and 5, respectively. Among the local acceleration groups, 1, 2, 3, 4 and 5 are all the local accelerations. In the second detected acceleration time sequence, two local acceleration groups can be extracted, wherein the two local acceleration groups are: 2, 3, 4 and 5 and 3, 4, 5 and 7, respectively. Then two local acceleration group sets can be retrieved in the two detected acceleration time sequences using the sliding window, wherein the first local acceleration group set is: 1, 2, 3 and 4 and 2, 3, 4 and 5, and the second local acceleration group set is: 2, 3, 4 and 5 and 3, 4, 5 and 7.

[0095] It is understandable that when the screening evaluation value is greater than or equal to the screening threshold, it indicates that a large error exists in the detection acceleration timing corresponding to the screening evaluation value and the identification acceleration unit may be damaged.

[0096] Furthermore, the method of obtaining a screening evaluation value by using the local acceleration mean and the local acceleration variance includes:

[0097] The local evaluation value is calculated based on the pre-built local evaluation relationship, the local acceleration mean, and the local acceleration variance, where the local evaluation relationship is as follows:

[0098] J=αμσ

[0099] Where J represents the local evaluation value, α is the preset coefficient, μ represents the local acceleration mean, and σ represents the local acceleration variance;

[0100] Summarize the local evaluation values ​​to obtain a local evaluation value set, and perform the following operations on the local evaluation values ​​in the local evaluation value set:

[0101] The screening evaluation value is calculated based on the pre-built initial screening formula, local evaluation value and local evaluation value set, where the initial screening formula is as follows:

[0102]

[0103] Among them, X i represents the screening evaluation value corresponding to the i-th local evaluation value in the local evaluation value set, J i represents the i-th local evaluation value in the local evaluation value set, J j represents the jth local evaluation value in the local evaluation value set, and n represents that there are n local evaluation values ​​in the local evaluation value set.

[0104] The key point is that, ideally, each local acceleration group in the same local acceleration group set should be consistent. The preset coefficients in the local evaluation relationship can increase the difference between the local evaluation values. Then, combined with the initial screening formula, the obtained screening evaluation values ​​can be made more accurate, thereby improving the accuracy of eliminating damaged or faulty identification acceleration units.

[0105] It is understandable that the use of the first acceleration set, the first acceleration unit set, the plurality of identification angular velocity units and the detection angular velocity set to identify the target acceleration signal and the target angular velocity signal includes:

[0106] The following operations are performed for each detected acceleration sequence in the first acceleration set:

[0107] An overall evaluation value is obtained based on the detected acceleration time sequence, and the first acceleration unit is retrieved from the first acceleration unit set using the overall evaluation value, and the overall evaluation value is associated with the serial number corresponding to the first acceleration unit to obtain an evaluation point group, and the evaluation point group is mapped to a pre-constructed evaluation coordinate system to obtain an overall point set, and an evaluation straight line is fitted using the overall point set;

[0108] Using the serial number corresponding to the evaluation point group, a target point group is retrieved in the evaluation line, wherein the target point group includes a target serial number and a target evaluation value, and the target serial number corresponding to the target point group is the same as the serial number corresponding to the evaluation point group;

[0109] Based on the target point group and the evaluation point group, the evaluation differences are obtained, and the evaluation differences are summarized to obtain an evaluation difference set. The evaluation differences in the evaluation difference set are sorted in descending order to obtain an evaluation difference sequence, wherein the evaluation difference is the absolute difference between the target evaluation value corresponding to the target point group and the overall evaluation value corresponding to the evaluation point group;

[0110] Obtaining a target evaluation sequence using the evaluation difference sequence, wherein the target evaluation sequence includes one or more target evaluation values, and the target evaluation value is less than or equal to a preset evaluation threshold;

[0111] Based on the target evaluation sequence, a second acceleration unit set is identified in the first acceleration unit set, wherein the second acceleration unit set includes one or more second acceleration units, and the second acceleration units correspond one-to-one to the target evaluation values ​​in the target evaluation sequence;

[0112] Acquire an acceleration signal set using the second acceleration unit set, wherein the acceleration signal set includes one or more acceleration signals, and acquire a target acceleration signal based on the acceleration signal set, wherein the target acceleration signal is an average value of the one or more acceleration signals in the acceleration signal set;

[0113] A target angular velocity signal is acquired according to a plurality of identification angular velocity units and a detection angular velocity set.

[0114] It should be understood that the method for obtaining the overall evaluation value is the same as the method for obtaining the local evaluation value, and can achieve the same effect, which will not be repeated here. Optionally, the horizontal axis of the evaluation coordinate system is the serial number, and the vertical axis is the overall evaluation value. Optionally, the method for fitting the evaluation line using the overall point set is the least squares method. The method for obtaining the target angular velocity signal is the same as the method for obtaining the target acceleration signal, and can achieve the same effect, which will not be repeated here. The target point group refers to the point extracted from the evaluation line with the same serial number as the evaluation point group.

[0115] It is understandable that the difference between the points in the evaluation line and the evaluation point group is that the points in the evaluation line are close to the ideal value, while the evaluation point group is the real value, and the larger the evaluation difference, the worse the signal collected by the first acceleration unit. The second acceleration unit set is identified in the first acceleration unit set, the acceleration signal corresponding to each second acceleration unit in the second acceleration unit set is summarized to obtain the acceleration signal set, and the target acceleration signal is obtained according to the acceleration signal set. This can improve the accuracy of the target acceleration signal acquisition, and further improve the accuracy of positioning the target vehicle.

[0116] Exemplarily, there are three first acceleration units in the first acceleration unit set, and the overall evaluation values ​​corresponding to the three first acceleration units are 11, 12 and 9, and the serial numbers corresponding to the three first acceleration units are 1, 2 and 3, respectively. After associating the overall evaluation values ​​with the serial numbers corresponding to the first acceleration units, three evaluation point groups can be obtained, wherein the three evaluation point groups are (1, 11), (2, 12) and (3, 9), respectively. The three evaluation point groups are mapped to the evaluation coordinate system to obtain the overall point set.

[0117] S3. Confirm receipt of a positioning instruction from a satellite positioning unit, obtain a satellite positioning signal based on the positioning instruction, and construct a measurement equation using a preset monitoring frequency, satellite positioning signal, target acceleration signal, and target angular velocity signal.

[0118] It is understandable that the satellite positioning unit refers to a unit capable of receiving Beidou satellite signals. Optionally, a Beidou satellite navigation system receiver is used as the satellite positioning unit. The satellite positioning signal refers to a signal sent by a Beidou satellite.

[0119] Furthermore, before constructing the measurement equation, it is also necessary to obtain the initial state estimate and covariance matrix based on prior knowledge, and use the monitoring frequency, target acceleration signal, target angular velocity signal and satellite positioning signal to respectively obtain the position information and the position measurement equation from the satellite positioning signal, the speed information and the speed measurement equation from the satellite positioning signal, and fuse the position information, the position measurement equation, the speed information and the speed measurement equation to obtain the measurement equation. The technology of constructing the measurement equation using the position information, the position measurement equation, the speed information and the speed measurement equation is a prior art and will not be repeated here.

[0120] S4. Confirm receipt of a data fusion instruction from a data fusion unit, obtain a fitting noise signal set and a fitting filter algorithm set based on the data fusion instruction, and obtain a positioning posture based on the fitting noise signal set, the fitting filter algorithm set and the measurement equation.

[0121] It should be explained that the obtaining of positioning posture based on the fitting noise signal set, the fitting filter algorithm set and the measurement equation includes:

[0122] The fitting noise signal set includes: a first noise signal, a second noise signal and a third noise signal, and the fitting filtering algorithm set includes a first filtering algorithm, a second filtering algorithm and a third filtering algorithm;

[0123] Respectively associating the first noise signal with the first filtering algorithm, the second noise signal with the second filtering algorithm, and the third noise signal with the third filtering algorithm to obtain three position prediction combinations;

[0124] The following operations are performed for each of the three position prediction combinations:

[0125] Acquire initial positioning information by using a preset iteration number threshold, a preset evaluation threshold, a measurement equation and a position prediction combination, wherein the initial positioning information includes: initial positioning posture, initial positioning time and positioning evaluation threshold;

[0126] The initial positioning information is aggregated to obtain an initial positioning information set, and a positioning posture is acquired based on the initial positioning information set.

[0127] It is understandable that the first noise signal is a random noise signal, optionally, a Gaussian noise signal is used as the first noise signal, the second noise signal is a bounded noise signal, optionally, a uniform noise signal is used as the second noise signal, and the third noise signal includes both a random noise signal and a bounded noise signal, optionally, a quantized noise signal is used as the third noise signal. The first filtering algorithm is a cubature Kalman filtering algorithm based on variational Bayesian theory, the second filtering algorithm is: set membership filtering algorithm, and the third filtering algorithm is: variational Bayesian expectation maximization filtering algorithm.

[0128] Furthermore, the technology of obtaining initial positioning information by combining a preset number of iterations, a preset evaluation threshold, a measurement equation and a position prediction is prior art and will not be described in detail here. The initial positioning pose includes but is not limited to: the position information of the target vehicle, the speed of the target vehicle, the attitude information of the target vehicle, etc. The initial positioning pose can be set according to actual needs. Optionally, the position information includes longitude, latitude and altitude. The positioning evaluation threshold is the difference between the updated state estimate and the initial state estimate calculated by the target filtering algorithm using the measurement equation, wherein the target filtering algorithm is: the first filtering algorithm, the second filtering algorithm or the third filtering algorithm, and the technology of calculating the difference between the updated state estimate and the initial state estimate is prior art and will not be described in detail here. Optionally, the updated state estimate is obtained by minimizing the KL divergence between the predicted distribution and the observed distribution.

[0129] Generally speaking, there are two judgment conditions that can make the target filtering algorithm output a positioning evaluation threshold: one is: the number of iterations in the target filtering algorithm reaches a preset value, and the other is: the positioning evaluation threshold is less than or equal to the evaluation threshold. The purpose of incorporating noise signals into the position prediction combination is to simulate the noise signals that the target vehicle may be subjected to at a certain stage. The noise signals include but are not limited to: thermal noise signals, atmospheric noise, receiver noise signals, etc. Therefore, incorporating different types of noise signals and combining different filtering algorithms can improve the accuracy of obtaining positioning postures.

[0130] It should be understood that the obtaining of the positioning posture based on the initial positioning information set includes:

[0131] If there is one or more initial positioning information in the initial positioning information set whose corresponding positioning evaluation threshold is less than or equal to the evaluation threshold, then obtaining the positioning pose based on the one or more initial positioning information, wherein the positioning pose is the initial positioning pose with the smallest positioning evaluation threshold among the one or more initial positioning information;

[0132] If the positioning evaluation thresholds corresponding to the initial positioning information in the initial positioning information set are all greater than the evaluation threshold, the positioning posture is obtained based on the initial positioning information set, wherein the positioning posture is the initial positioning posture corresponding to the minimum initial positioning time in the initial positioning information set.

[0133] It can be understood that when the positioning evaluation threshold is less than or equal to the evaluation threshold, it indicates that the target filtering algorithm is more accurate in predicting the target vehicle's posture. Therefore, the initial positioning posture with the smallest positioning evaluation threshold in one or more initial positioning information is output as the positioning posture. When the positioning evaluation thresholds are all greater than the evaluation threshold, it indicates that the prediction of the target vehicle posture still needs to be updated, that is, the prediction result using the target filtering algorithm is not accurate enough. The purpose of using the initial positioning posture corresponding to the smallest initial positioning time in the initial positioning information set as the positioning posture is to improve the randomness of the parameters required to encrypt the positioning posture, and thereby improve the security of encrypting the positioning posture.

[0134] S5. Confirm receipt of the data encryption instruction from the data encryption unit, and obtain the public key and the private key based on the data encryption instruction and the positioning posture.

[0135] It should be understood that the acquisition of the public key and the private key based on the data encryption instruction and the positioning posture includes:

[0136] Parsing the data encryption instruction to obtain an initial storage unit, storing the positioning posture in the initial storage unit, obtaining a target storage unit, and using the target storage unit to obtain a storage memory value;

[0137] An encryption parameter relationship is constructed based on the stored memory value, a public key parameter is calculated using the encryption parameter relationship, a private key parameter is calculated based on the public key parameter, and a public key and a private key are obtained based on the public key parameter and the private key parameter.

[0138] Furthermore, storing the memory value refers to storing the memory value corresponding to the positioning posture in the target storage unit. The technology of obtaining the public key and the private key using the public key parameter and the private key parameter is prior art and will not be described in detail here.

[0139] It is understandable that the encryption parameter relationship is as follows:

[0140]

[0141] Where G represents the public key parameter, random represents the random function, {} represents the prime number, [] represents the integer, ρ and ω are the preset coefficients, N represents the storage memory value, h(t) represents the time processing function, t represents the initial positioning time corresponding to the positioning posture, z k represents the serial number of the kth acceleration detection unit among one or more acceleration detection units corresponding to the target acceleration signal, m represents that there are m acceleration detection units corresponding to the target acceleration signal, and d l represents the serial number of the lth angular velocity detection unit in one or more angular velocity detection units corresponding to the target angular velocity signal, and p represents that there are p one or more angular velocity detection units corresponding to the target angular velocity signal.

[0142] It should be understood that, optionally, the Eratosthenes sieve method is used as the method for extracting prime numbers. Other methods can achieve the same effect, which will not be repeated here. The time processing function refers to a function that can convert time into seconds. For example, if the initial positioning time is: 1 a.m., the time processing function can be used to convert the initial positioning time into: 3600 seconds.

[0143] Further, calculating the private key parameters according to the public key parameters includes:

[0144] The private key solving equation is constructed using the public key parameters, and the private key solving equation is used to calculate the private key parameters, wherein the private key solving equation is as follows:

[0145]

[0146] Among them, S represents the private key parameter, is the preset coefficient.

[0147] S6. Using the public key, perform an encryption operation on the positioning posture to obtain encrypted data.

[0148] It is understandable that using a public key to encrypt the positioning posture can ensure the security of the encrypted data, and the technology of encrypting data using a public key is an existing technology and will not be repeated here.

[0149] S7. Send the encrypted data and the private key to the initiator of the positioning instruction to locate the target vehicle.

[0150] It should be understood that the encrypted data and private key are sent to the initiator of the positioning command. When the initiator of the positioning command successfully receives the encrypted data and the private key, the encrypted data is decrypted using the private key, and the positioning posture of the target vehicle can be obtained, thereby realizing the positioning of the target vehicle. The technology of decrypting the encrypted data using the key is an existing technology and will not be repeated here.

[0151] The present invention is to solve the problems described in the background technology. The present invention receives a positioning instruction, and confirms a positioning system and a target vehicle for positioning based on the positioning instruction, wherein the positioning system includes a data fusion unit and a data encryption unit, and the target vehicle includes a satellite positioning unit and an inertial positioning unit, wherein the inertial positioning unit includes a plurality of acceleration detection units and a plurality of angular velocity detection units. It can be seen that the present invention considers using a plurality of acceleration detection units and a plurality of angular velocity detection units to realize the positioning of the target vehicle, thereby avoiding the problem of inaccurate positioning of the target vehicle due to damage or failure of the acceleration detection unit and the angular velocity detection unit when using a single acceleration detection unit and an angular velocity detection unit. The present invention utilizes a preset detection frequency, a target vehicle and an inertial detection unit. The positioning unit obtains a detection acceleration set and a detection angular velocity set respectively, wherein the detection acceleration set includes multiple detection acceleration timings, and the detection acceleration timings correspond one-to-one to the acceleration detection units; the detection angular velocity set includes multiple detection angular velocity timings, and the detection angular velocity timings correspond one-to-one to the angular velocity detection units, and the detection acceleration timings include multiple detection accelerations, and the detection angular velocity timings include multiple detection angular velocities. The target acceleration signal and the target angular velocity signal are confirmed based on the detection acceleration set and the detection angular velocity set. It can be seen that the present invention obtains the first acceleration unit set by screening multiple acceleration detection units, so that the target acceleration signal obtained by using the second acceleration unit set is more accurate, thereby improving the accuracy of positioning the target vehicle. The present invention confirms receiving a data fusion instruction from a data fusion unit, obtains a fitting noise signal set and a fitting filter algorithm set based on the data fusion instruction, and obtains a positioning posture based on the fitting noise signal set, the fitting filter algorithm set and the measurement equation. It can be seen that the present invention takes into account the possible existence of noise signals when obtaining satellite positioning signals, target acceleration signals and target angular velocity signals. Therefore, a relatively accurate and random positioning posture is obtained by fitting the noise signal set and the fitting filter algorithm set, thereby improving the accuracy of positioning the target vehicle. The present invention confirms receiving a data encryption instruction from a data encryption unit, obtains a public key and a private key based on the data encryption instruction and the positioning posture, uses the public key to perform an encryption operation on the positioning posture, obtains encrypted data, and sends the encrypted data and the private key to the initiator of the positioning instruction to achieve the positioning of the target vehicle. It can be seen that the present invention combines the information of the positioning posture, the serial number of the acceleration detection unit and the angular velocity detection unit to obtain the public key, and uses the public key to encrypt the positioning posture, thereby improving the security of the positioning posture of the target vehicle. Therefore, the present invention can improve the accuracy and security of positioning the target vehicle.

[0152] like Figure 2 , which is a functional module diagram of a combined positioning system based on variational Bayesian theory provided by an embodiment of the present invention.

[0153] The combined positioning system 100 based on variational Bayesian theory of the present invention can be installed in an electronic device. According to the functions to be implemented, the combined positioning system 100 based on variational Bayesian theory can include a positioning instruction receiving module 101, a positioning preparation module 102, a positioning data confirmation module 103 and a positioning data encryption module 104. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0154] The positioning instruction receiving module 101 is used to receive a positioning instruction, and to identify a positioning system and a target vehicle for positioning based on the positioning instruction, wherein the positioning system includes a data fusion unit and a data encryption unit, and the target vehicle includes a satellite positioning unit and an inertial positioning unit, wherein the inertial positioning unit includes a plurality of acceleration detection units and a plurality of angular velocity detection units;

[0155] The positioning preparation module 102 is used to respectively obtain a detection acceleration set and a detection angular velocity set by using a preset detection frequency, a target vehicle and an inertial positioning unit, wherein the detection acceleration set includes a plurality of detection acceleration time sequences, and the detection acceleration time sequences correspond to the acceleration detection units one-to-one, the detection angular velocity set includes a plurality of detection angular velocity time sequences, and the detection angular velocity time sequences correspond to the angular velocity detection units one-to-one, and the detection acceleration time sequences include a plurality of detection accelerations, and the detection angular velocity time sequences include a plurality of detection angular velocities, and a target acceleration signal and a target angular velocity signal are confirmed based on the detection acceleration set and the detection angular velocity set;

[0156] The positioning data confirmation module 103 is used to confirm the reception of the positioning instruction from the satellite positioning unit, obtain the satellite positioning signal based on the positioning instruction, and construct the measurement equation using the preset monitoring frequency, the satellite positioning signal, the target acceleration signal and the target angular velocity signal;

[0157] Confirming receipt of a data fusion instruction from a data fusion unit, acquiring a fitting noise signal set and a fitting filter algorithm set based on the data fusion instruction, and acquiring a positioning posture based on the fitting noise signal set, the fitting filter algorithm set and a measurement equation;

[0158] The positioning data encryption module 104 is used to confirm the receipt of the data encryption instruction from the data encryption unit, obtain the public key and the private key based on the data encryption instruction and the positioning posture, use the public key to perform encryption operations on the positioning posture to obtain encrypted data, and send the encrypted data and the private key to the initiator of the positioning instruction to realize the positioning of the target vehicle.

[0159] In detail, each module in the combined positioning system 100 based on variational Bayesian theory in the embodiment of the present invention is used in the same manner as above. Figure 1 The combined positioning method based on variational Bayesian theory described in the present invention has the same technical means and can produce the same technical effects, so it will not be repeated here.

[0160] like Figure 3 , which is a schematic diagram of the structure of an electronic device for implementing a combined positioning method based on variational Bayesian theory provided by an embodiment of the present invention.

[0161] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a combined positioning method program based on variational Bayesian theory.

[0162] Wherein, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. The memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, such as a mobile hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the combined positioning method program based on the variational Bayesian theory, but also can be used to temporarily store data that has been output or is to be output.

[0163] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (such as a combined positioning method program based on variational Bayesian theory, etc.), and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0164] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0165] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0166] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management system, so that the power management system can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0167] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0168] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0169] The combined positioning method program based on variational Bayesian theory stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0170] receiving a positioning instruction, and confirming a positioning system and a target vehicle for positioning based on the positioning instruction, wherein the positioning system includes a data fusion unit and a data encryption unit, and the target vehicle includes a satellite positioning unit and an inertial positioning unit, wherein the inertial positioning unit includes a plurality of acceleration detection units and a plurality of angular velocity detection units;

[0171] A detection acceleration set and a detection angular velocity set are respectively obtained by using a preset detection frequency, a target vehicle and an inertial positioning unit, wherein the detection acceleration set includes a plurality of detection acceleration time sequences, and the detection acceleration time sequences correspond to the acceleration detection units one-to-one, the detection angular velocity set includes a plurality of detection angular velocity time sequences, and the detection angular velocity time sequences correspond to the angular velocity detection units one-to-one, and the detection acceleration time sequences include a plurality of detection accelerations, and the detection angular velocity time sequences include a plurality of detection angular velocities, and a target acceleration signal and a target angular velocity signal are confirmed based on the detection acceleration set and the detection angular velocity set;

[0172] Confirming receipt of a positioning instruction from a satellite positioning unit, acquiring a satellite positioning signal based on the positioning instruction, and constructing a measurement equation using a preset monitoring frequency, a satellite positioning signal, a target acceleration signal, and a target angular velocity signal;

[0173] Confirming receipt of a data fusion instruction from a data fusion unit, acquiring a fitting noise signal set and a fitting filter algorithm set based on the data fusion instruction, and acquiring a positioning posture based on the fitting noise signal set, the fitting filter algorithm set and a measurement equation;

[0174] Confirm receipt of the data encryption instruction from the data encryption unit, obtain the public key and the private key based on the data encryption instruction and the positioning posture, use the public key to perform an encryption operation on the positioning posture to obtain encrypted data, and send the encrypted data and the private key to the initiator of the positioning instruction to achieve the positioning of the target vehicle.

[0175] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0176] Furthermore, if the module / unit integrated in the electronic device 1 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. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or system that can carry the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0177] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0178] receiving a positioning instruction, and confirming a positioning system and a target vehicle for positioning based on the positioning instruction, wherein the positioning system includes a data fusion unit and a data encryption unit, and the target vehicle includes a satellite positioning unit and an inertial positioning unit, wherein the inertial positioning unit includes a plurality of acceleration detection units and a plurality of angular velocity detection units;

[0179] A detection acceleration set and a detection angular velocity set are respectively obtained by using a preset detection frequency, a target vehicle and an inertial positioning unit, wherein the detection acceleration set includes a plurality of detection acceleration time sequences, and the detection acceleration time sequences correspond to the acceleration detection units one-to-one, the detection angular velocity set includes a plurality of detection angular velocity time sequences, and the detection angular velocity time sequences correspond to the angular velocity detection units one-to-one, and the detection acceleration time sequences include a plurality of detection accelerations, and the detection angular velocity time sequences include a plurality of detection angular velocities, and a target acceleration signal and a target angular velocity signal are confirmed based on the detection acceleration set and the detection angular velocity set;

[0180] Confirming receipt of a positioning instruction from a satellite positioning unit, acquiring a satellite positioning signal based on the positioning instruction, and constructing a measurement equation using a preset monitoring frequency, a satellite positioning signal, a target acceleration signal, and a target angular velocity signal;

[0181] Confirming receipt of a data fusion instruction from a data fusion unit, acquiring a fitting noise signal set and a fitting filter algorithm set based on the data fusion instruction, and acquiring a positioning posture based on the fitting noise signal set, the fitting filter algorithm set and a measurement equation;

[0182] Confirm receipt of the data encryption instruction from the data encryption unit, obtain the public key and the private key based on the data encryption instruction and the positioning posture, use the public key to perform an encryption operation on the positioning posture to obtain encrypted data, and send the encrypted data and the private key to the initiator of the positioning instruction to achieve the positioning of the target vehicle.

[0183] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and actual implementation may have other division methods.

[0184] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0185] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0186] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0187] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A combined positioning method based on variational Bayesian theory, characterized in that: The method comprises: receiving a positioning instruction, and confirming a positioning system and a target vehicle for positioning based on the positioning instruction, wherein the positioning system includes a data fusion unit and a data encryption unit, and the target vehicle includes a satellite positioning unit and an inertial positioning unit, wherein the inertial positioning unit includes a plurality of acceleration detection units and a plurality of angular velocity detection units; A detection acceleration set and a detection angular velocity set are respectively obtained by using a preset detection frequency, a target vehicle and an inertial positioning unit, wherein the detection acceleration set includes a plurality of detection acceleration time sequences, and the detection acceleration time sequences correspond to the acceleration detection units one-to-one, the detection angular velocity set includes a plurality of detection angular velocity time sequences, and the detection angular velocity time sequences correspond to the angular velocity detection units one-to-one, and the detection acceleration time sequences include a plurality of detection accelerations, and the detection angular velocity time sequences include a plurality of detection angular velocities, and a target acceleration signal and a target angular velocity signal are confirmed based on the detection acceleration set and the detection angular velocity set; Confirming receipt of a positioning instruction from a satellite positioning unit, acquiring a satellite positioning signal based on the positioning instruction, and constructing a measurement equation using a preset monitoring frequency, a satellite positioning signal, a target acceleration signal, and a target angular velocity signal; Confirming receipt of a data fusion instruction from a data fusion unit, acquiring a fitting noise signal set and a fitting filter algorithm set based on the data fusion instruction, and acquiring a positioning posture based on the fitting noise signal set, the fitting filter algorithm set and a measurement equation; Confirm receipt of the data encryption instruction from the data encryption unit, obtain the public key and the private key based on the data encryption instruction and the positioning posture, use the public key to perform an encryption operation on the positioning posture to obtain encrypted data, and send the encrypted data and the private key to the initiator of the positioning instruction to achieve the positioning of the target vehicle.

2. The combined positioning method based on variational Bayesian theory as claimed in claim 1, characterized in that: The step of determining a target acceleration signal and a target angular velocity signal based on the detected acceleration set and the detected angular velocity set includes: Performing identification operations on a plurality of acceleration detection units and a plurality of angular velocity detection units in the inertial positioning unit respectively to obtain a plurality of identified acceleration units and a plurality of identified angular velocity units, wherein the identified acceleration unit includes an acceleration detection unit identified with a serial number; Using a preset sliding window, a plurality of local acceleration group sets are retrieved from a plurality of detection acceleration time sequences corresponding to the detection acceleration set, wherein the local acceleration group set includes a plurality of local acceleration groups, wherein the local acceleration groups correspond to the detection acceleration time sequences one by one, and the local acceleration groups include a plurality of local accelerations; The following operations are performed on each of the multiple local acceleration group sets: The following operations are performed on each of the multiple local acceleration groups corresponding to the local acceleration group set: Using the local acceleration group to obtain a local acceleration mean and a local acceleration variance, wherein the local acceleration mean and the local acceleration variance are respectively the mean and variance of multiple local accelerations in the local acceleration group; Obtaining a screening evaluation value using the local acceleration mean and the local acceleration variance; comparing the screening evaluation value with a preset screening threshold; If the screening evaluation value is greater than or equal to the screening threshold, the detection acceleration time sequence corresponding to the screening evaluation value is eliminated from the detection acceleration set, and the identification acceleration unit corresponding to the screening evaluation value is eliminated from the plurality of identification acceleration units; Otherwise, respectively summarize the detection acceleration time series corresponding to the screening evaluation value and the identification acceleration unit corresponding to the screening evaluation value to obtain a first acceleration set and a first acceleration unit set; A target acceleration signal and a target angular velocity signal are confirmed by using the first acceleration set, the first acceleration unit set, the plurality of identification angular velocity units and the detection angular velocity set.

3. The combined positioning method based on variational Bayesian theory as claimed in claim 2, characterized in that: The method of obtaining a screening evaluation value by using the local acceleration mean and the local acceleration variance includes: The local evaluation value is calculated based on the pre-built local evaluation relationship, the local acceleration mean, and the local acceleration variance, where the local evaluation relationship is as follows: J=αμσ Where J represents the local evaluation value, α is the preset coefficient, μ represents the local acceleration mean, and σ represents the local acceleration variance; Summarize the local evaluation values ​​to obtain a local evaluation value set, and perform the following operations on the local evaluation values ​​in the local evaluation value set: The screening evaluation value is calculated based on the pre-built initial screening formula, local evaluation value and local evaluation value set, where the initial screening formula is as follows: Among them, X i represents the screening evaluation value corresponding to the i-th local evaluation value in the local evaluation value set, J i represents the i-th local evaluation value in the local evaluation value set, J j represents the jth local evaluation value in the local evaluation value set, and n represents that there are n local evaluation values ​​in the local evaluation value set.

4. The combined positioning method based on variational Bayesian theory as claimed in claim 3, characterized in that: The method of using the first acceleration set, the first acceleration unit set, the plurality of identification angular velocity units and the detection angular velocity set to identify the target acceleration signal and the target angular velocity signal includes: The following operations are performed for each detected acceleration sequence in the first acceleration set: An overall evaluation value is obtained based on the detected acceleration time sequence, and the first acceleration unit is retrieved from the first acceleration unit set using the overall evaluation value, and the overall evaluation value is associated with the serial number corresponding to the first acceleration unit to obtain an evaluation point group, and the evaluation point group is mapped to a pre-constructed evaluation coordinate system to obtain an overall point set, and an evaluation straight line is fitted using the overall point set; Using the serial number corresponding to the evaluation point group, a target point group is retrieved in the evaluation line, wherein the target point group includes a target serial number and a target evaluation value, and the target serial number corresponding to the target point group is the same as the serial number corresponding to the evaluation point group; Based on the target point group and the evaluation point group, the evaluation differences are obtained, and the evaluation differences are summarized to obtain an evaluation difference set. The evaluation differences in the evaluation difference set are sorted in descending order to obtain an evaluation difference sequence, wherein the evaluation difference is the absolute difference between the target evaluation value corresponding to the target point group and the overall evaluation value corresponding to the evaluation point group; Obtaining a target evaluation sequence using the evaluation difference sequence, wherein the target evaluation sequence includes one or more target evaluation values, and the target evaluation value is less than or equal to a preset evaluation threshold; Based on the target evaluation sequence, a second acceleration unit set is identified in the first acceleration unit set, wherein the second acceleration unit set includes one or more second acceleration units, and the second acceleration units correspond one-to-one to the target evaluation values ​​in the target evaluation sequence; Acquire an acceleration signal set using the second acceleration unit set, wherein the acceleration signal set includes one or more acceleration signals, and acquire a target acceleration signal based on the acceleration signal set, wherein the target acceleration signal is an average value of the one or more acceleration signals in the acceleration signal set; A target angular velocity signal is acquired according to a plurality of identification angular velocity units and a detection angular velocity set.

5. The combined positioning method based on variational Bayesian theory as claimed in claim 4, characterized in that: The positioning posture is obtained based on the fitting noise signal set, the fitting filter algorithm set and the measurement equation, include: The fitting noise signal set includes: a first noise signal, a second noise signal and a third noise signal, and the fitting filtering algorithm set includes a first filtering algorithm, a second filtering algorithm and a third filtering algorithm; Respectively associating the first noise signal with the first filtering algorithm, the second noise signal with the second filtering algorithm, and the third noise signal with the third filtering algorithm to obtain three position prediction combinations; The following operations are performed for each of the three position prediction combinations: Acquire initial positioning information by using a preset iteration number threshold, a preset evaluation threshold, a measurement equation and a position prediction combination, wherein the initial positioning information includes: initial positioning posture, initial positioning time and positioning evaluation threshold; The initial positioning information is aggregated to obtain an initial positioning information set, and a positioning posture is acquired based on the initial positioning information set.

6. The combined positioning method based on variational Bayesian theory as claimed in claim 5, characterized in that: The acquiring of the positioning posture based on the initial positioning information set includes: If there is one or more initial positioning information in the initial positioning information set whose corresponding positioning evaluation threshold is less than or equal to the evaluation threshold, then obtaining the positioning pose based on the one or more initial positioning information, wherein the positioning pose is the initial positioning pose with the smallest positioning evaluation threshold in the one or more initial positioning information; If the positioning evaluation thresholds corresponding to the initial positioning information in the initial positioning information set are all greater than the evaluation threshold, the positioning posture is obtained based on the initial positioning information set, wherein the positioning posture is the initial positioning posture corresponding to the minimum initial positioning time in the initial positioning information set.

7. The combined positioning method based on variational Bayesian theory as claimed in claim 6, characterized in that: The obtaining of a public key and a private key based on the data encryption instruction and the positioning posture includes: Parsing the data encryption instruction to obtain an initial storage unit, storing the positioning posture in the initial storage unit, obtaining a target storage unit, and using the target storage unit to obtain a storage memory value; An encryption parameter relationship is constructed based on the stored memory value, a public key parameter is calculated using the encryption parameter relationship, a private key parameter is calculated based on the public key parameter, and a public key and a private key are obtained based on the public key parameter and the private key parameter.

8. The combined positioning method based on variational Bayesian theory as claimed in claim 7, characterized in that: The encryption parameter relationship is as follows: Where G represents the public key parameter, random represents the random function, {} represents the prime number, [ ] represents the integer, ρ and ω are the preset coefficients, N represents the storage memory value, h(t) represents the time processing function, t represents the initial positioning time corresponding to the positioning posture, z k represents the serial number of the kth acceleration detection unit among one or more acceleration detection units corresponding to the target acceleration signal, m represents that there are m acceleration detection units corresponding to the target acceleration signal, and d l represents the serial number of the lth angular velocity detection unit in one or more angular velocity detection units corresponding to the target angular velocity signal, and p represents that there are p one or more angular velocity detection units corresponding to the target angular velocity signal.

9. The combined positioning method based on variational Bayesian theory as claimed in claim 8, characterized in that: The step of calculating the private key parameters according to the public key parameters includes: The private key solving equation is constructed using the public key parameters, and the private key solving equation is used to calculate the private key parameters, wherein the private key solving equation is as follows: Among them, S represents the private key parameter, is the preset coefficient.

10. A combined positioning system based on variational Bayesian theory, characterized in that: The system comprises: A positioning instruction receiving module, used to receive a positioning instruction, and to identify a positioning system and a target vehicle used for positioning based on the positioning instruction, wherein the positioning system includes a data fusion unit and a data encryption unit, and the target vehicle includes a satellite positioning unit and an inertial positioning unit, wherein the inertial positioning unit includes a plurality of acceleration detection units and a plurality of angular velocity detection units; A positioning preparation module, used to respectively obtain a detection acceleration set and a detection angular velocity set by using a preset detection frequency, a target vehicle and an inertial positioning unit, wherein the detection acceleration set includes a plurality of detection acceleration time sequences, and the detection acceleration time sequences correspond to the acceleration detection units one-to-one, the detection angular velocity set includes a plurality of detection angular velocity time sequences, and the detection angular velocity time sequences correspond to the angular velocity detection units one-to-one, and the detection acceleration time sequences include a plurality of detection accelerations, and the detection angular velocity time sequences include a plurality of detection angular velocities, and a target acceleration signal and a target angular velocity signal are confirmed based on the detection acceleration set and the detection angular velocity set; A positioning data confirmation module, used to confirm the reception of a positioning instruction from a satellite positioning unit, obtain a satellite positioning signal based on the positioning instruction, and construct a measurement equation using a preset monitoring frequency, a satellite positioning signal, a target acceleration signal, and a target angular velocity signal; Confirming receipt of a data fusion instruction from a data fusion unit, acquiring a fitting noise signal set and a fitting filter algorithm set based on the data fusion instruction, and acquiring a positioning posture based on the fitting noise signal set, the fitting filter algorithm set and a measurement equation; The positioning data encryption module is used to confirm the receipt of the data encryption instruction from the data encryption unit, obtain the public key and the private key based on the data encryption instruction and the positioning posture, use the public key to perform encryption operations on the positioning posture to obtain encrypted data, and send the encrypted data and the private key to the initiator of the positioning instruction to realize the positioning of the target vehicle.

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