Magnetic resonance (MR) fingerprinting method and apparatus, electronic device, and storage medium

Abstract

The present disclosure discloses an MR fingerprint positioning method and device, electronic equipment and storage medium, relates to the technical field of wireless positioning, and improves the matching accuracy by correcting the received power, and uses target feature information to match in a preset tidal dynamic fingerprint library, so that the feature distribution of the matched reference signal features and online signal features is more similar, and the positioning accuracy is improved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of wireless positioning, and in particular to an MR fingerprint positioning method and device, an electronic device and a storage medium. BACKGROUND

[0002] In MR fingerprint positioning, the wireless signal received at the terminal side is taken as the input feature of fingerprint positioning, the Euclidean distance between the signal feature to be positioned and each signal feature in the position fingerprint database is calculated, and positioning is performed according to the calculation result.

[0003] Generally, each serving cell will construct an MR fingerprint library based on the AGPS MR data of the previous day, for positioning the position information of the wireless signal of the next day, but this method has certain limitations, resulting in low positioning accuracy, mainly in the following two aspects: first aspect: the MR fingerprint library is established based on the AGPS MR of the whole day, and the AGPS MR of the whole day can represent the space-time distribution of all users in the whole day, but the actual space-time distribution of users in each time period is not consistent with the space-time distribution of the whole day; second aspect: the beam weight adaptive technology dynamically adjusts the beam weight according to the actual distribution of users, thereby affecting the received beam signal power received at each location, at this time, the fingerprint library based on the statistics of the received power in the whole day can no longer represent the received power distribution at each time, thereby affecting the fingerprint positioning accuracy. SUMMARY

[0004] The present disclosure provides an MR fingerprint positioning method, device, electronic device and storage medium. Its main purpose is to solve the problem of low positioning accuracy.

[0005] According to a first aspect of the present disclosure, an MR fingerprint positioning method is provided, comprising:

[0006] Obtaining an online signal feature, and correcting the signal received power in the online signal feature based on a preset incremental value;

[0007] According to the target feature information of the online signal feature, determining each reference signal feature with the same target feature information in the preset tidal dynamic fingerprint library;

[0008] According to the corrected online signal feature and the determined each reference signal feature, calculating at least one signal space similarity; wherein one reference signal feature corresponds to one signal space similarity;

[0009] According to a preset similarity threshold, screening each signal space similarity, and retaining the reference signal feature corresponding to the signal space similarity;

[0010] The reference points corresponding to the reserved reference signal features are determined, and the average value between the reference points is determined as the target position of the online feature; wherein different reference signal features correspond to different reference points.

[0011] Optionally, before acquiring the online signal feature and correcting the signal receiving power of the signal feature based on the incremental value in the preset tidal dynamic fingerprint library, the method further comprises:

[0012] Based on historical signal data, the preset tidal dynamic fingerprint library is constructed.

[0013] Optionally, the historical signal feature includes a serving cell, a collection time, and collection reference point information, and the construction of the preset tidal dynamic fingerprint library based on historical signal data further comprises:

[0014] The probability of distribution of the historical signal data at different serving cells, different collection times, and different positions is calculated;

[0015] The first network management beam transmit power configuration and the first azimuth and downtilt angle configuration corresponding to the historical signal feature with the earliest collection time are determined as the reference network management beam transmit power configuration and the reference azimuth and downtilt angle configuration;

[0016] The first beam transmit power increment is calculated according to the network management beam transmit power configuration of each historical signal feature and the first reference network management beam transmit power configuration;

[0017] The first beam transmit power increment is calculated according to the azimuth and downtilt angle configuration of each historical signal feature and the first reference azimuth and downtilt angle configuration;

[0018] The signal receiving power is corrected according to the beam transmit power increment corresponding to each historical signal feature and the beam transmit power increment respectively;

[0019] The corrected historical signal feature and the distribution probability of each collection point are stored in the preset tidal dynamic fingerprint library.

[0020] Optionally, the correction of the signal receiving power in the online signal feature based on the preset incremental value comprises:

[0021] The second beam transmit power increment is calculated according to the second network management beam transmit power configuration of the online signal feature and the reference network management beam transmit power configuration;

[0022] The second beam transmit power increment is calculated according to the second azimuth and downtilt angle configuration of the online signal feature and the reference azimuth and downtilt angle configuration;

[0023] The second beam transmission power increment is used to correct the signal receiving power.

[0024] Optionally, the determining, according to the target feature information of the online signal feature, each reference signal feature with same target feature information in the preset tidal dynamic fingerprint library comprises:

[0025] According to the service cell and the collection time of the online signal feature, each reference signal feature with same service cell and same collection time is determined in the preset tidal dynamic fingerprint library.

[0026] According to a second aspect of the present disclosure, a MR fingerprint positioning device is provided, comprising:

[0027] The acquisition unit is configured to acquire an online signal feature, and correct a signal receiving power in the online signal feature based on a preset increment value;

[0028] The first determination unit is configured to determine, according to target feature information of the online signal feature, each reference signal feature with same target feature information in the preset tidal dynamic fingerprint library;

[0029] The calculation unit is configured to calculate, according to the corrected online signal feature and each determined reference signal feature, at least one signal space similarity; wherein one reference signal feature corresponds to one signal space similarity.

[0030] The screening unit is configured to screen each signal space similarity according to a preset similarity threshold, and retain a reference signal feature corresponding to a retained signal space similarity.

[0031] The second determination unit is configured to determine a reference point corresponding to the retained reference signal feature, and determine an average value of the reference points as a target position of the online feature; wherein different reference signal features correspond to different reference points.

[0032] Optionally, the device further comprises:

[0033] The construction unit is configured to, before the acquisition unit acquires an online signal feature and corrects a signal receiving power of the signal feature based on an increment value in a preset tidal dynamic fingerprint library, construct the preset tidal dynamic fingerprint library based on historical signal data.

[0034] Optionally, the historical signal feature comprises a service cell, a collection time and collection reference point information, and the construction unit is further configured to:

[0035] Calculate a distribution probability of the historical signal data at different positions in different service cells and at different collection times.

[0036] determine a first network management beam transmit power configuration and a first azimuth angle and downtilt angle configuration corresponding to the historical signal feature with the earliest acquisition time as a reference network management beam transmit power configuration and a reference azimuth angle and downtilt angle configuration;

[0037] calculate a first beam transmit power increment according to the network management beam transmit power configuration of each historical signal feature and the first reference network management beam transmit power configuration;

[0038] calculate a first beam transmit power increment according to the azimuth angle and downtilt angle configuration of each historical signal feature and the first reference azimuth angle and downtilt angle configuration;

[0039] correct the signal receiving power according to the beam transmit power increment corresponding to each historical signal feature and the beam transmit power increment respectively;

[0040] store the corrected historical signal features and the distribution probability of each acquisition point in the preset tidal dynamic fingerprint library.

[0041] Optionally, the acquisition unit is further configured to:

[0042] calculate a second beam transmit power increment according to the second network management beam transmit power configuration of the online signal feature and the reference network management beam transmit power configuration;

[0043] calculate a second beam transmit power increment according to the second azimuth angle and downtilt angle configuration of the online signal feature and the reference azimuth angle and downtilt angle configuration;

[0044] correct the signal receiving power according to the second beam transmit power increment and the second beam transmit power increment.

[0045] Optionally, the first determination unit comprises:

[0046] determine each reference signal feature with the same service cell and the same acquisition time in the preset tidal dynamic fingerprint library according to the service cell and the acquisition time of the online signal feature.

[0047] According to a third aspect of the present disclosure, an electronic device is provided, comprising:

[0048] at least one processor; and

[0049] a memory connected to the at least one processor in communication; wherein

[0050] the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of the first aspect.

[0051] According to a fourth aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method of the first aspect.

[0052] According to a fifth aspect of the present disclosure, there is provided a computer program product comprising a computer program configured to implement the method of the first aspect when executed by a processor.

[0053] The MR fingerprint positioning method, device, electronic device and storage medium provided by the present disclosure mainly include the following technical solutions: obtaining an online signal feature, correcting the signal receiving power in the online signal feature based on a preset incremental value; determining each reference signal feature with the same target feature information in the preset tidal dynamic fingerprint library according to the target feature information of the online signal feature; calculating at least one signal space similarity according to the corrected online signal feature and the determined each reference signal feature; wherein one reference signal feature corresponds to one signal space similarity; screening each signal space similarity according to a preset similarity threshold, and retaining the reference signal feature corresponding to the retained signal space similarity; determining the reference points corresponding to the retained reference signal features, and determining the average value of the reference points as the target position of the online feature; wherein different reference signal features correspond to different reference points. Compared with related technologies, the present application improves the matching accuracy by correcting the receiving power, and uses the target feature information to match in the preset tidal dynamic fingerprint library, so that the feature distribution of the matched reference signal feature and the online signal feature is more similar, and the positioning accuracy is improved.

[0054] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0055] The accompanying drawings are used to better understand the present application, and do not limit the present disclosure. Among them:

[0056] Figure 1 A flowchart of an MR fingerprint positioning method provided by an embodiment of the present disclosure is shown in the figure;

[0057] Figure 2 A flowchart of an MR fingerprint positioning method provided by an embodiment of the present disclosure is shown in the figure;

[0058] Figure 3 A structural diagram of an MR fingerprint positioning device provided by an embodiment of the present disclosure is shown in the figure;

[0059] Figure 4A structural schematic diagram of an MR fingerprint positioning device provided by an embodiment of the present disclosure is provided.

[0060] Figure 5 A schematic block diagram of an example electronic device provided by an embodiment of the present disclosure is provided. DETAILED DESCRIPTION

[0061] Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, which include various details of the embodiments of the present disclosure to assist in understanding, which should be considered in a descriptive sense only. Thus, it will be apparent to one of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Also, for the sake of brevity and clarity, descriptions of well-known functions and constructions are omitted from the following description.

[0062] An MR fingerprint positioning method, device, electronic device and storage medium of an embodiment of the present disclosure are described below with reference to the accompanying drawings.

[0063] Figure 1 A flowchart of an MR fingerprint positioning method provided by an embodiment of the present disclosure is provided.

[0064] As shown in the method includes the following steps: Figure 1

[0065] Step 101, obtaining online signal features, and correcting signal received power in the online signal features based on a preset incremental value.

[0066] In an implementable manner of an embodiment of the present application, the online signal features are signal features to be positioned, which include signal received power, signal strength, signal quality, etc., and these signal features can be used to evaluate the quality and stability of network connection.

[0067] The beam weight adaptive technology of 5G dynamically adjusts the beam weight according to the actual distribution of users, thereby affecting the signal received power of the beam signal received at each location, so that the signal received power of the online signal features is corrected using a preset incremental value, the difference in received power is eliminated, and the positioning accuracy is improved.

[0068] Step 102, determining, according to target feature information of the online signal features, each reference signal feature having the same target feature information in the preset tidal dynamic fingerprint library.

[0069] Before performing this step, a preset tidal dynamic fingerprint library is first established, and the reference signal features are included in the preset tidal dynamic fingerprint library, which can be online signal features from different devices or users, and the target feature information of which has been determined; in an implementable manner of an embodiment of the present application, the online signal features of the previous day are used to build the library.​

[0070] In the comparison according to the target feature information, it can be determined that the reference signal features with the same target feature information exist in the preset tidal dynamic fingerprint library, for example, the same acquisition time, the same serving cell, and the like. Specifically, embodiments of the present application do not limit this.

[0071] In step 103, at least one signal space similarity is obtained by calculating according to the modified online signal feature and the determined reference signal features. One reference signal feature corresponds to one signal space similarity.

[0072] For each reference signal feature, a suitable similarity calculation method is used to compare the similarity between it and the modified online signal feature. Commonly used similarity calculation methods include Euclidean distance, cosine similarity, correlation coefficient, and the like. According to the specific application requirements, the most suitable similarity calculation method can be selected, and embodiments of the present application do not limit the specific calculation method. For each reference signal feature, the similarity score between it and the modified online signal feature is calculated according to the similarity calculation method. This similarity score can represent the similarity degree or correlation between the online signal and the reference signal.

[0073] In step 104, the signal space similarities are screened according to a preset similarity threshold, and the reference signal features corresponding to the retained signal space similarities are reserved.

[0074] In one implementable manner of embodiments of the present application, a similarity threshold can be set according to the specific application requirements. This threshold can be determined according to the actual situation, and is used to judge whether the signal space similarity reaches a certain degree of similarity. For each calculated signal space similarity, the preset similarity threshold is compared. If a certain signal space similarity is greater than or equal to the preset similarity threshold, the reference signal feature corresponding to the signal space similarity is retained.

[0075] In step 105, the reference points corresponding to the retained reference signal features are determined, and the average value of the reference points is determined as the target position of the online feature. Different reference signal features correspond to different reference points.

[0076] Screening signal space similarity: The calculated signal space similarity is screened according to the preset similarity threshold. The reference signal features corresponding to the signal space similarities greater than or equal to the preset similarity threshold are retained.

[0077] For the reserved reference signal features, their corresponding reference points are determined; each reference signal corresponds to a reference point in the feature preset tidal dynamic fingerprint library; the coordinates of the reserved reference points are averaged to obtain the average coordinates of the reference points. The average coordinates can be used as the target position of the online signal features. The average coordinates can be calculated using a simple average algorithm, or a weighted average or other more complex algorithm can be used according to the actual situation; specifically, the embodiments of the present application do not limit this.

[0078] The MR fingerprint positioning method provided by the present disclosure mainly includes the following technical solutions: obtaining online signal features, correcting the signal received power in the online signal features based on a preset incremental value; determining each reference signal feature with the same target feature information in the preset tidal dynamic fingerprint library according to the target feature information of the online signal features; calculating at least one signal space similarity according to the corrected online signal features and the determined reference signal features; wherein one reference signal feature corresponds to one signal space similarity; filtering each signal space similarity according to a preset similarity threshold, and retaining the reference signal features corresponding to the signal space similarity; determining the reference points corresponding to the retained reference signal features, and determining the average value of the reference points as the target position of the online features; wherein different reference signal features correspond to different reference points. Compared with related technologies, the present application improves the matching accuracy by correcting the received power, and uses the target feature information in the preset tidal dynamic fingerprint library for matching, so that the feature distribution of the matched reference signal features and the online signal features is more similar, and the positioning accuracy is improved.

[0079] Before step 101 is performed, the preset tidal dynamic fingerprint library is first constructed based on historical signal data; the construction can be performed according to the following method; please refer to Figure 2 , Figure 2 A flowchart of an MR fingerprint positioning method provided by an embodiment of the present disclosure includes:

[0080] Optionally, the historical signal features include a serving cell, a collection time, and collection reference point information, and the construction of the preset tidal dynamic fingerprint library based on historical signal data further includes:

[0081] Step 201: calculating the distribution probability of the historical signal data at different serving cells, different collection times, and different positions.

[0082] The AGPS MR tidal distribution features are counted, and the number of AGPS MRs per serving cell, per hour, and per reference point position is counted, denoted as n(c, h, l), wherein c represents a serving cell, h represents an hour, and l represents a reference point position.

[0083] Calculate the AGPS MR distribution probability p(lc, h) per service cell per hour.

[0084]

[0085] Approximate the tidal position distribution probability of the AGPS MR as the tidal position distribution probability of all MRs.

[0086] Step 202, determine the first network management beam transmit power configuration and the first azimuth and tilt angle configuration corresponding to the earliest historical signal feature as the reference network management beam transmit power configuration and the reference azimuth and tilt angle configuration.

[0087] The beam weight adaptive technology of 5G will adjust the azimuth, tilt angle and power of beamforming in a macroscopic way.

[0088] The change of power will directly affect all directions, and the difference can be directly compensated on RSRP. Take the first AGPS of each day as the reference, query the network management beam transmit power configuration corresponding to this moment, and take it as the reference beam transmit power (denoted as w0).

[0089] The adjustment of azimuth and tilt angle will cause the change of beam gain in each direction, and then cause the change of user received RSRP. Similarly, take the first AGPS of each day as the reference, query the network management beam azimuth and tilt angle configuration (denoted as azimuth0, tilt0).

[0090] Step 203, calculate the first beam transmit power increment according to the network management beam transmit power configuration of each historical signal feature and the first reference network management beam transmit power configuration.

[0091] Then, query the network management beam transmit power configuration (denoted as w i ) corresponding to each AGPS i, and the beam transmit power increment of this moment compared with the reference is The RSRP of this AGPS is denoted as RSRP i , and the RSRP can be unified to the reference transmit power:

[0092]

[0093] Step 204, calculate the first beam transmit power increment according to the azimuth and tilt angle configuration of each historical signal feature and the first reference azimuth and tilt angle configuration.

[0094] Then, query the network management beam azimuth and tilt angle configuration (denoted as azimuth i , tilt i), respectively, in the beam azimuth map to query the beam gain (denoted as g0, g i ), respectively, in the beam azimuth map to query the beam gain (denoted as g0, g

[0095]

[0096] Step 205, respectively, according to the beam transmit power increment corresponding to each historical signal feature and the beam transmit power increment to the signal received power.

[0097] On the basis of the RSRP which has been unified to the reference power, continue to compensate the beam gain increment:

[0098]

[0099] Step 206, store the modified historical signal features and the distribution probability of each collection point in the preset tidal dynamic fingerprint library.

[0100] Save the modified historical signal features in the tidal dynamic fingerprint library. Each historical signal feature can contain multiple attributes, such as signal strength, frequency, direction, etc. A suitable data structure can be defined according to actual needs to store these attributes; in one implementable manner of the embodiment of the present application, the preset tidal dynamic fingerprint library contains the corresponding relationship between the signal features and the signal collection points.

[0101] In one implementable manner of the embodiment of the present application, after obtaining the online signal features, the reference network management beam transmit power and the reference azimuth angle, downtilt angle configuration in the preset tidal dynamic fingerprint library are used for calibration. The calibration can be performed according to the following steps, including:

[0102] According to the second network management beam transmit power configuration of the online signal features and the reference network management beam transmit power configuration, calculate the second beam transmit power increment;

[0103] According to the second azimuth angle, downtilt angle configuration of the online signal features and the reference azimuth angle, downtilt angle configuration, calculate the second beam transmit power increment;

[0104] According to the second beam transmit power increment and the second beam transmit power increment to the signal received power.

[0105] For detailed calibration steps, please refer to steps 203-205, which will not be described one by one in the embodiment of the present application.

[0106] In an implementation of the embodiment of the present application, when the target feature information of the online signal feature is determined to exist in each reference signal feature in the preset tidal dynamic fingerprint library in step 10, the following steps can be performed according to the target feature information of the online signal feature:

[0107] According to the service cell and the collection time of the online signal feature, each reference signal feature with the same service cell and the same collection time is determined in the preset tidal dynamic fingerprint library.

[0108] It should be noted that the embodiment of the present application can include multiple steps, and in order to facilitate description, these steps are numbered, but these numbers are not a limitation on the execution time slot and execution order between the steps; these steps can be implemented in any order, and the embodiment of the present application does not limit this.

[0109] Corresponding to the MR fingerprint positioning method described above, the present application also proposes an MR fingerprint positioning device. Since the device embodiment of the present application corresponds to the method embodiment described above, for details not disclosed in the device embodiment, reference can be made to the method embodiment described above, which will not be described in detail in the present application.

[0110] Figure 3 A structural schematic diagram of an MR fingerprint positioning device provided by the embodiment of the present application is shown in FIG. 1, which includes: Figure 3 An acquisition unit 31 is configured to acquire an online signal feature and correct the signal receiving power in the online signal feature based on a preset incremental value.

[0111] A first determination unit 32 is configured to determine, according to target feature information of the online signal feature, each reference signal feature with the same target feature information in the preset tidal dynamic fingerprint library.

[0112] A calculation unit 33 is configured to perform calculation according to the corrected online signal feature and each determined reference signal feature, to obtain at least one signal space similarity; wherein one reference signal feature corresponds to one signal space similarity.

[0113] A screening unit 34 is configured to screen each signal space similarity according to a preset similarity threshold, and retain the reference signal feature corresponding to the signal space similarity.

[0114] A second determination unit 35 is configured to determine the reference point corresponding to the retained reference signal feature, and determine the average value of the reference points as the target position of the online feature; wherein different reference signal features correspond to different reference points.

[0115]

[0116] ​The MR fingerprint positioning device provided by the present disclosure mainly comprises the following technical solutions: obtaining an online signal feature, correcting the signal receiving power in the online signal feature based on a preset increment value; determining each reference signal feature with the same target feature information in the preset tidal dynamic fingerprint library according to the target feature information of the online signal feature; calculating at least one signal space similarity according to the corrected online signal feature and the determined reference signal feature; wherein one reference signal feature corresponds to one signal space similarity; screening each signal space similarity according to a preset similarity threshold, and retaining the reference signal feature corresponding to the signal space similarity; determining the reference points corresponding to the retained reference signal features, and determining the average value of the reference points as the target position of the online feature; wherein different reference signal features correspond to different reference points. Compared with the related art, the present application improves the matching accuracy by correcting the receiving power, and uses the target feature information to match in the preset tidal dynamic fingerprint library, so that the feature distribution of the matched reference signal feature and the online signal feature is more similar, and the positioning accuracy is improved.

[0117] Further, in a possible implementation manner of the present embodiment, as shown in Figure 4 the device further comprises:

[0118] The construction unit 36 is configured to, before the acquisition unit 31 acquires the online signal feature and corrects the signal receiving power of the signal feature based on the increment value in the preset tidal dynamic fingerprint library, construct the preset tidal dynamic fingerprint library based on historical signal data.

[0119] Further, in a possible implementation manner of the present embodiment, as shown in Figure 4 the historical signal feature comprises a serving cell, a collection time, and collection reference point information, and the construction unit 36 is further configured to:

[0120] calculate the position distribution probability of the historical signal data at different serving cells and different collection times;

[0121] determine the first network management beam transmitting power configuration and the first azimuth and downtilt angle configuration corresponding to the historical signal feature with the earliest collection time as the reference network management beam transmitting power configuration and the reference azimuth and downtilt angle configuration;

[0122] calculate the first beam transmitting power increment according to the network management beam transmitting power configuration of each historical signal feature and the first reference network management beam transmitting power configuration;

[0123] calculate the first beam transmitting power increment according to the azimuth and downtilt angle configuration of each historical signal feature and the first reference azimuth and downtilt angle configuration;

[0124] respectively according to the beam transmit power increment corresponding to each historical signal feature and the beam transmit power increment correcting the signal receive power;

[0125] store the corrected historical signal features and the distribution probability of each collection point in the preset tidal dynamic fingerprint library.

[0126] Further, in a possible implementation manner of the embodiment, as shown in the figure, Figure 4 The acquisition unit 36 is further configured to:

[0127] calculate a second beam transmit power increment according to the second network management beam transmit power configuration of the online signal feature and the reference network management beam transmit power configuration;

[0128] calculate a second beam transmit power increment according to the second azimuth angle and downtilt angle configuration of the online signal feature and the reference azimuth angle and downtilt angle configuration;

[0129] correct the signal receive power according to the second beam transmit power increment and the second beam transmit power increment correcting the signal receive power.

[0130] Further, in a possible implementation manner of the embodiment, as shown in the figure, Figure 4 The first determination unit 32 includes:

[0131] determine each reference signal feature of the same service cell and the same collection time in the preset tidal dynamic fingerprint library according to the service cell and the collection time of the online signal feature.

[0132] It should be noted that the foregoing explanation and description of the method embodiment are also applicable to the device of the present embodiment, and the principle is the same, which is not limited in the present embodiment.

[0133] According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0134] Figure 5 A schematic block diagram of an example electronic device 400 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present disclosure described and / or claimed in this document.

[0135] like Figure 5 As shown, device 400 includes a computing unit 401, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 402 or a computer program loaded from storage unit 408 into RAM (Random Access Memory) 403. RAM 403 may also store various programs and data required for the operation of device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via bus 404. I / O (Input / Output) interface 405 is also connected to bus 404.

[0136] Multiple components in device 400 are connected to I / O interface 405, including: input unit 406, such as keyboard, mouse, etc.; output unit 407, such as various types of monitors, speakers, etc.; storage unit 408, such as disk, optical disk, etc.; and communication unit 409, such as network card, modem, wireless transceiver, etc. Communication unit 409 allows device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0137] The computing unit 401 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as the MR fingerprinting method. For example, in some embodiments, the MR fingerprinting method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed on device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of the methods described above can be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the aforementioned MR fingerprint localization method by any other suitable means (e.g., by means of firmware).

[0138] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a Field Programmable Gate Array (FPGA), an Application-Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a System on a Chip (SOC), a Complex Programmable Logic Device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0139] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general or special purpose computer, such that the program code, when executed by the processor or controller, causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0140] In the context of this disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include a linearly-programmed electrical connection, a portable computer diskette, a hard disk, RAM, ROM, EPROM (Electrically Programmable Read-Only-Memory), or flash memory, an optical fiber, a CD-ROM (Compact Disc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0141] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0142] The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a LAN (Local Area Network), a WAN (Wide Area Network), the Internet, and a blockchain network.

[0143] The computer system can include clients and servers. This relationship can be between a client and a server that are typically distant from each other and typically interact through a communication network. The relationship between the client and the server is generated by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, and solves the defects of large management difficulty and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or simply "VPS"). The server can also be a server of a distributed system, or a server combined with a blockchain.

[0144] It should be noted that artificial intelligence is a discipline that studies enabling computers to simulate some thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.) of people, and has both hardware and software technologies. Artificial intelligence hardware technology generally includes technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing, etc.; artificial intelligence software technology mainly includes computer vision technology, speech recognition technology, natural language processing technology, and machine learning / deep learning, big data processing technology, knowledge graph technology, etc. several major directions.

[0145] The first, second, and the like various numerical numbers involved in the present disclosure are only for the convenience of differentiation in the description, and do not limit the scope of the embodiments of the present disclosure, nor represent the order of precedence.

[0146] At least one of the present disclosure can also be described as one or more, and the plurality can be two, three, four or more, which is not limited by the present disclosure. In the embodiments of the present disclosure, for a technical feature, the technical features in the technical feature are distinguished by "first", "second", "third", "A", "B", "C" and "D". The technical features described by "first", "second", "third", "A", "B", "C" and "D" have no order or size order.

[0147] It should be understood that the steps shown above can be reordered, added or deleted. For example, the steps described in the present disclosure can be executed in parallel, sequentially or in different order, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, which is not limited herein.

[0148] The above detailed description does not limit the scope of the disclosure. Various modifications, combinations, sub-combinations and alternatives can be made to the detailed description. Any modification, equivalent replacement and improvement etc. made within the spirit and principle of the disclosure shall be included in the scope of the disclosure.

Claims

1. A method of MR fingerprinting, the method comprising: The method comprises the following steps: acquiring an online signal feature, and correcting signal receiving power in the online signal feature based on a preset increment value; determining, according to target feature information of the online signal feature, each reference signal feature with the same target feature information in a preset tidal dynamic fingerprint library; calculating at least one signal space similarity according to the corrected online signal feature and each determined reference signal feature; wherein one reference signal feature corresponds to one signal space similarity; screening each signal space similarity according to a preset similarity threshold, and retaining the reference signal feature corresponding to the retained signal space similarity; determining a reference point corresponding to the retained reference signal feature, and determining an average value of the reference points as a target position of the online signal feature; wherein different reference signal features correspond to different reference points.

2. The method of claim 1, wherein, Before the step of acquiring an online signal feature and correcting signal receiving power in the online signal feature based on an increment value in a preset tidal dynamic fingerprint library, the method further comprises: constructing the preset tidal dynamic fingerprint library based on historical signal data.

3. The method of claim 2, wherein, The historical signal feature comprises a serving cell, a collection time, and collection reference point information, and the step of constructing the preset tidal dynamic fingerprint library based on historical signal data further comprises: calculating different position distribution probabilities of the historical signal data in different serving cells and at different collection times; determining a first network management beam transmitting power configuration and a first azimuth and downtilt angle configuration corresponding to a historical signal feature with the earliest collection time as a reference network management beam transmitting power configuration and a reference azimuth and downtilt angle configuration; calculating a first beam transmitting power increment according to a network management beam transmitting power configuration of each historical signal feature and the reference network management beam transmitting power configuration; calculating a first beam transmitting power increment according to an azimuth and downtilt angle configuration of each historical signal feature and the reference azimuth and downtilt angle configuration; correcting signal receiving power according to the beam transmitting power increment corresponding to each historical signal feature and the beam transmitting power increment; storing the corrected historical signal feature and the distribution probability of each collection point in the preset tidal dynamic fingerprint library.

4. The method of claim 3, wherein, The step of correcting signal receiving power in the online signal feature based on a preset increment value comprises: calculating a second beam transmitting power increment according to a second network management beam transmitting power configuration of the online signal feature and the reference network management beam transmitting power configuration; calculating a second beam transmitting power increment according to a second azimuth and downtilt angle configuration of the online signal feature and the reference azimuth and downtilt angle configuration; correcting signal receiving power according to the second beam transmitting power increment and the second beam transmitting power increment.

5. The method of claim 3, wherein, The step of determining, according to target feature information of the online signal feature, each reference signal feature with the same target feature information in the preset tidal dynamic fingerprint library comprises: determining, according to a serving cell and a collection time of the online signal feature, each reference signal feature with the same serving cell and the same collection time in the preset tidal dynamic fingerprint library.

6. A MR fingerprinting apparatus, characterized in that, The method comprises the following steps: An acquisition unit is configured to acquire an online signal feature, and correct a signal receiving power in the online signal feature based on a preset incremental value; A first determination unit is configured to determine, in a preset tidal dynamic fingerprint library, each reference signal feature having the same target feature information as the online signal feature according to target feature information of the online signal feature; A calculation unit is configured to calculate at least one signal space similarity according to the corrected online signal feature and each determined reference signal feature; one reference signal feature corresponds to one signal space similarity; A screening unit is configured to screen each signal space similarity according to a preset similarity threshold, and retain a reference signal feature corresponding to a retained signal space similarity; A second determination unit is configured to determine reference points corresponding to the retained reference signal features, and determine an average value between the reference points as a target position of the online signal feature; different reference signal features correspond to different reference points.

7. The apparatus of claim 6, wherein, The device further comprises: A construction unit is configured to, before the acquisition unit acquires the online signal feature and corrects the signal receiving power in the online signal feature based on the incremental value in the preset tidal dynamic fingerprint library, construct the preset tidal dynamic fingerprint library based on historical signal data.

8. An electronic device, comprising: Comprise: At least one processor; And The memory is in communication connection with the at least one processor; wherein The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method of any one of claims 1-5.

9. A non-transitory computer-readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to enable the computer to execute the method according to any one of claims 1-5.

10. A computer program product, characterised in that, Comprise a computer program, the computer program is executed by the processor to realize the method according to any one of claims 1-5.

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