Magnetic resonance data processing method, device, equipment and storage medium

Abstract

The application discloses an MR data processing method, device and equipment and a storage medium. The method comprises the following steps: collecting MR original data reported by a terminal corresponding to a first cell, and extracting key fields involved in the MR original data; determining a relationship table to be backfilled based on the key fields; determining network frequency point data under different networks based on the MR original data; and performing backfilling of MR data under different networks based on the network frequency point data and the relationship table to be backfilled. In the application, MR data under a (2G / 4G / 5G) network is adopted, MR data is backfilled completely and accurately, a data basis is provided for network optimization, and the perceptual experience of users is further improved.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to an MR data processing method, apparatus, device and storage medium. Background Technology

[0002] In a multi-network converged environment, it is necessary to collect MR (Measurement Report) data and backfill the data to provide guidance for cell positioning, cell overlap coverage analysis, cell over-coverage analysis, and cell weak coverage analysis, so as to optimize the network and further improve the user's perception experience.

[0003] However, in the existing technology, when performing MR data backfilling, it is only considered that the user (UE) occupies MR data under only one network. That is, at this stage, only the MR data of LTE under one network is obtained and processed.

[0004] However, processing MR data for LTE on only one network often misses data reported from other networks, leading to MR data backfilling failures or inaccurate backfilling, resulting in poor network optimization. Summary of the Invention

[0005] The main objective of this application is to provide an MR data processing method, apparatus, device, and storage medium, which aims to solve the technical problem of poor network optimization results caused by existing MR data backfilling failures or inaccurate backfilling.

[0006] To achieve the above objectives, this application provides an MR data processing method, the MR data processing method comprising:

[0007] Collect the raw MR data reported by the terminal corresponding to the first cell, and extract the key fields involved in the raw MR data;

[0008] Based on the aforementioned key fields, determine the relationship tables to be filled back;

[0009] Based on the raw MR data, determine the network frequency data under different networks;

[0010] Based on the different network frequency data and the relationship table to be backfilled, MR data under different networks is backfilled.

[0011] Optionally, the MR data includes MR neighbor cell data. After the step of backfilling MR data under different networks based on the different network frequency data and the relationship table to be backfilled, the method includes:

[0012] Determine whether the MR neighbor cell data has been backfilled in different networks;

[0013] If the backfilling is not completed, determine the second cell that is closest to the first cell and whose frequency and PCI are the same as those of the first cell;

[0014] Based on the data from the second cell, the MR neighbor cell data under the different networks will continue to be backfilled.

[0015] Optionally, before the step of determining network frequency data under different networks based on the original MR data, the method includes:

[0016] Based on the common fields in the original MR data, the base station ID is extracted and hashed to obtain different partition queues;

[0017] The step of determining network frequency point data under different networks based on the original MR data includes:

[0018] The raw MR data is pushed to the different partition queues to obtain fragment resource information;

[0019] Based on the partitioned queue and the frequency range under different networks, the fragmented resource information is identified under different networks to obtain network frequency data under different networks.

[0020] Optionally, the different network frequency data includes 2G frequency data, 4G frequency data, and 5G frequency data. The step of backfilling MR data under different networks based on the different network frequency data and the relationship table to be backfilled includes:

[0021] The first preset service process corresponding to 2G frequency point data, the second preset service process corresponding to 4G frequency point data, and the third preset service process corresponding to 5G frequency point data are determined respectively.

[0022] Based on the first preset service process, the 2G frequency point data, and the 2G relationship table to be filled in the relationship table to be filled, the MR data under the 2G network is filled in.

[0023] Based on the second preset service process, the 4G frequency point data, and the 4G relationship table to be filled in the relationship table to be filled, the MR data under the 4G network is filled in.

[0024] Based on the third preset service process, the 5G frequency point data, and the 5G relationship table to be filled in the relationship table to be filled, the MR data under the 5G network is filled in.

[0025] Optionally, the step of backfilling MR data under the 2G network based on the first preset service process, the 2G frequency point data, and the 2G relationship table to be backfilled in the relationship table to be backfilled includes:

[0026] The BCCH and BSIC of the neighboring cells of the first cell are extracted from the 2G frequency data.

[0027] Based on the preset OMC, determine the first neighbor cell configuration table of the first cell under the 2G network;

[0028] Match the BCCH configuration in the first neighbor cell configuration table with the BCCH of the neighbor cell, and the BSIC configuration with the BSIC of the neighbor cell respectively;

[0029] If all matches are successful, the neighbor cell LAC, neighbor cell CI, and first neighbor cell distance in the first neighbor cell configuration table are extracted, and the MR neighbor cell data under the 2G network is backfilled based on the 2G relationship table to be backfilled.

[0030] The LAC and CI of the first cell are extracted from the 2G frequency point data. Based on the 2G relationship table to be filled, the MR non-neighbor cell data under the 2G network is filled.

[0031] Optionally, the step of backfilling MR data under the 4G network based on the second preset service process, the 4G frequency point data, and the 4G relationship table to be backfilled in the relationship table to be backfilled includes:

[0032] The PCI of the neighboring cells and the frequency points of the neighboring cells of the first cell are extracted from the 4G frequency point data.

[0033] Based on the preset OMC, neighboring cell PCI, and neighboring cell frequency, the second neighboring cell configuration table of the first cell is determined;

[0034] Match the configured frequency points in the second neighbor cell configuration table with the neighbor cell frequency points, and match the configured PCI with the neighbor cell PCI respectively;

[0035] If all matches are successful, the neighboring cell CGI and the second neighboring cell distance in the second neighboring cell configuration table are extracted, and the MR neighboring cell data under the 4G network is backfilled based on the 4G relationship table to be backfilled.

[0036] The CGI of the first cell is extracted from the 4G frequency point data, and the MR non-neighbor cell data under the 4G network is backfilled based on the 4G relationship table to be backfilled.

[0037] Optionally, the step of backfilling MR data under the 5G network based on the third preset service process, the 5G frequency point data, and the 5G relationship table to be backfilled in the relationship table to be backfilled includes:

[0038] The GNB ID number and neighboring cell PCI field are extracted from the 5G frequency point data;

[0039] Based on the preset OMC, determine the 5G cell operating parameter table associated with the first cell;

[0040] The configuration ID number in the 5G cell parameter table is matched with the ID number of the GNB, and the configuration PCI field is matched with the PCI field of the neighboring cell.

[0041] If all matches are successful, the NCGI information, the city information, and the distance to the third neighboring cell are extracted. Based on the 5G relationship table to be filled, the MR data under the 5G network is then filled.

[0042] This application also provides an MR data processing apparatus, the MR data processing apparatus comprising:

[0043] The acquisition module is used to acquire the raw MR data reported by the terminal corresponding to the first cell and extract the key fields involved in the raw MR data.

[0044] The first determining module is used to determine the relationship table to be filled based on the key fields;

[0045] The second determining module is used to determine network frequency point data under different networks based on the original MR data;

[0046] The backfilling module is used to backfill MR data under different networks based on the different network frequency point data and the relationship table to be backfilled.

[0047] This application also provides an MR data processing device, which is a physical node device. The MR data processing device includes: a memory, a processor, and a program of the MR data processing method stored in the memory and executable on the processor. When the program of the MR data processing method is executed by the processor, it can implement the steps of the MR data processing method as described above.

[0048] This application also provides a storage medium storing a program that implements the above-described MR data processing method. When the program is executed by a processor, it implements the steps of the MR data processing method as described above.

[0049] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the MR data processing method described above.

[0050] This application provides a method, apparatus, device, and storage medium for MR data processing. Compared with existing technologies that only process MR data from a single LTE network, resulting in the omission of data reported from other networks and leading to MR data backfilling failure or inaccuracy, this application collects raw MR data reported by the terminal corresponding to the first cell and extracts key fields involved in the raw MR data; based on the key fields, it determines the relationship table to be backfilled; based on the raw MR data, it determines the network frequency data under different networks; and based on the different network frequency data and the relationship table to be backfilled, it performs MR data backfilling under different networks. In this application, after collecting the raw MR data reported by the terminal corresponding to the first cell, it does not only process MR data from a single network, but also adopts network frequency data from different networks to backfill the MR data in the corresponding relationship table to be backfilled under different networks. It can be understood that in this application, MR data from (2G / 4G / 5G) networks is adopted to completely and accurately backfill the MR data, providing a data foundation for network optimization, improving network optimization effects, and further enhancing the user's perceived experience. Attached Figure Description

[0051] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0052] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a flowchart illustrating the first embodiment of the MR data processing method of this application;

[0054] Figure 2 This is a flowchart illustrating the process after step S40 in the MR data processing method of this application.

[0055] Figure 3 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of this application;

[0056] Figure 4 This is a schematic diagram of the overall business processing logic involved in the MR data processing business method of this application;

[0057] Figure 5 This is a flowchart illustrating the data partitioning-based business processing method involved in the MR data processing method of this application.

[0058] Figure 6 This is a flowchart of the MR data processing method for 2 / 4 / 5G involved in this application.

[0059] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0060] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0061] This application provides an MR data processing method. In the first embodiment of the MR data processing method of this application, refer to... Figure 1 The MR data processing method includes:

[0062] Step S10: Collect the raw MR data reported by the terminal corresponding to the first cell, and extract the key fields involved in the raw MR data;

[0063] Step S20: Based on the key fields, determine the relationship table to be filled back;

[0064] Step S30: Based on the original MR data, determine the network frequency point data under different networks;

[0065] Step S40: Based on the different network frequency data and the relationship table to be backfilled, backfill the MR data under different networks.

[0066] The specific steps are as follows:

[0067] Step S10: Collect the raw MR data reported by the terminal corresponding to the first cell, and extract the key fields involved in the raw MR data;

[0068] In this embodiment, it should be noted that the MR data processing method can be applied to an MR data processing device, which belongs to an MR data processing server, which belongs to an MR data processing system, and which belongs to an MR data processing equipment.

[0069] In this embodiment, the application background is:

[0070] After years of evolution and development, mobile networks have formed a convergence of multiple networks such as 2G, 4G, and 5G. When user terminals communicate with the network in service mode, they frequently report Measurement Reports (MR) to the network to inform the network of the current wireless environment of the user terminal. This allows the base station to schedule and control the user terminal's time and frequency resources, power resources, code resources, etc., according to mobility management algorithms, so as to ensure the normal continuity of user services.

[0071] In the case of multiple networks coexisting, user terminals or the terminals they carry are quite complex (most of them are currently 2 / 3 / 4 / 5G multi-mode terminals). As the strength of different network wireless signals varies, user terminals and networks need to frequently reselect and switch. If the reselection and switching are inaccurate or untimely, it will lead to a decline in the user's experience of voice calls and Internet access, resulting in user complaints.

[0072] In this context, overall:

[0073] First, in this embodiment, the application scenario can be:

[0074] Currently, MR measurement reports contain not only data from a single network, but also data from multiple networks at different frequencies. However, most existing MR data processing methods only consider the MR data processing situation under one network, often omitting data reported from other networks. This leads to problems such as incompleteness, backfilling failure, and inaccurate backfilling during MR data processing.

[0075] At present, only MR data of LTE under one network is acquired and processed. For example, after the raw MR data is reported, the MR data processing server parses the raw MR data to find that the primary cell is 4G LTE data and the neighboring cell data is also 4G LTE data, referred to as 4-4 relationship data. Relationship data 4-2, 4-3 and 4-5 are missing.

[0076] Because it only analyzes 4-4 relationship data and lacks 4-2, 4-3, and 4-5 data, it is difficult to provide comprehensive guidance for cell positioning, cell overlap coverage analysis, cell over-coverage analysis, and cell weak coverage analysis, making it difficult to accurately optimize the network and thus difficult to improve the user's perception experience.

[0077] In this embodiment, MR data is not only processed from a single network, but also adopted from (2G / 4G / 5G) networks to completely and accurately backfill the MR data, providing a data foundation for network optimization, improving network optimization effects, and further enhancing the user's perceived experience.

[0078] Second, in this embodiment, the application scenario can also be:

[0079] Currently, when performing MR data backfilling, only some business operations are considered, meaning that the business level is not fully taken into account. Furthermore, when performing data backfilling, if the neighbor cell table cannot be associated with the corresponding neighbor cell, the backfilling of distance factors is not considered (i.e., the frequency points and PCI carried in the MR are not considered, and there are cells with the same frequency and PCI in the engineering parameter table). This results in incomplete, missing, and inaccurate MR data backfilling.

[0080] In this embodiment, when the neighbor cell table cannot be associated with a neighbor cell, the distance factor is supplemented by backfilling (considering the frequency and PCI carried in the MR, and the existence of cells with the same frequency and PCI in the engineering parameter table), so as to avoid incomplete, missing or inaccurate MR data backfilling.

[0081] Third, in this embodiment, the application scenario can also be:

[0082] The lack of consideration for real-time processing efficiency during MR data processing resulted in slow MR data analysis, which affected network optimization performance.

[0083] In this embodiment, under the background of multi-network convergence, HASH partitioning technology is combined to partition the real-time data of MR data, thereby improving the real-time processing of massive MR data and improving data processing efficiency.

[0084] In this embodiment, it should be noted that, compared with traditional road test data, MR data has advantages such as low acquisition cost and short data collection cycle.

[0085] In this embodiment, the MR data processing server collects the raw MR data reported by the terminal corresponding to the first cell and extracts the key fields involved in the raw MR data.

[0086] In this embodiment, the raw MR data is the full-service MR data of all carriers at the cell level.

[0087] In this embodiment, specifically, the OMC (Operation and Maintenance Center) in the MR data processing server collects the raw MR data reported by the terminal corresponding to the first cell and extracts the key fields involved in the raw MR data.

[0088] In this embodiment, it should be noted that the OMC can trigger the collection of raw MR data through settings or configuration. Furthermore, the specific content configured or set in the OMC can be: determining the collection scope (first cell), such as raw MR data within a city, or raw MR data within a base station cluster. The specific content configured or set in the OMC can also be determining the timing of the collection, such as in this embodiment, collecting recent 7x24 periodic MR data.

[0089] In this embodiment, after the collection range (first cell) and collection timing are determined, the terminals within the collection range (first cell) will actively report the corresponding MR raw data to the OMC in the MR data processing server within the corresponding time.

[0090] In this embodiment, the MR data used as the basis for network optimization analysis should be the raw MR data within the current 30 minutes to ensure data real-time performance.

[0091] In this embodiment, the raw MR data specifically includes data from various operators on different networks and frequency points, such as MR data corresponding to China Mobile, China Telecom, and China Unicom, as well as MR data of China Mobile on (2G / 4G / 5G) networks, MR data of China Telecom on (2G / 4G / 5G) networks, and MR data of China Unicom on (2G / 4G / 5G) networks.

[0092] In this embodiment, it should be noted that the OMC in the MR data processing server previously stored 2 / 4 / 5G related information tables. That is, in this embodiment, the OMC pre-stored or collected the corresponding table data, which may be the prior measurement statistics table and prior configuration table reported during previous measurements. It should also be noted that only some of the neighboring cells reported by MR may have been measured previously; that is, the previously collected data may be incomplete.

[0093] In this embodiment, after collecting the raw MR data reported by the terminal corresponding to the first cell, the key fields involved in the raw MR data are extracted. These key fields can be added, deleted, or configured.

[0094] Specifically, in this embodiment, the first cell is the primary cell, so the key fields involved in the original MR data may be: the frequency of the first cell, the PCI of the first cell, the frequency of the neighboring cell, the PCI of the neighboring cell, etc.

[0095] Step S20: Based on the key fields, determine the relationship table to be filled back;

[0096] In this embodiment, after obtaining the key fields, the relationship table to be backfilled is determined. Specifically, the key fields are input into the preset measurement statistics table template and the preset configuration table template to obtain the measurement statistics table to be backfilled and the configuration table to be backfilled. The measurement statistics table to be backfilled and the configuration table to be backfilled are used for backfilling MR data.

[0097] In this embodiment, it should be noted that the OMC has pre-stored or collected the previous measurement statistics table and previous configuration table reported during the measurement process. The reason why the MR data needs to be re-filled is as follows:

[0098] First, during the construction and operation of the network, due to network operations such as engineering construction and cutover, a large number of network parameters are adjusted and frequently changed, resulting in data loss.

[0099] Second, the data collection process was not standardized, which led to inaccurate data.

[0100] Step S30: Based on the original MR data, determine the network frequency point data under different networks;

[0101] In this embodiment, based on the raw MR data, network frequency data under different networks are determined. Specifically, based on the raw MR data, network frequency data under different networks, such as 3G, 4G, and 5G networks, are determined.

[0102] It should be noted that (900 and 1800MHz bands, frequency points: 0-124) belong to the 2G network frequency points; (all frequency points included in D, E, and F bands) belong to the 4G cell network frequency points; and (the frequency points belonging to N41 and N79 bands) belong to the 5G cell network frequency points.

[0103] Before the step of determining network frequency point data under different networks based on the original MR data, the method includes:

[0104] Step S01: Based on the common fields in the original MR data, extract the base station ID and perform hash partitioning to obtain different partition queues;

[0105] In this embodiment, under the background of multi-network convergence, HASH partitioning technology is combined to partition the real-time data of MR data, thereby improving the real-time processing of massive MR data and improving data processing efficiency.

[0106] Specifically, in this embodiment, the MR data processing server or OMC is configured or set with a partitioning strategy, such as partitioning based on the base station. Therefore, after obtaining the MR raw data, the base station ID is extracted based on the common field (carrying base station information) in the MR raw data to perform Hash partitioning and obtain different partition queues.

[0107] Specifically, for example, the base station ID is extracted and hashed to create 10 different partition queues.

[0108] The step of determining network frequency point data under different networks based on the original MR data includes:

[0109] Step S31: Push the raw MR data to the different partition queues respectively to obtain shard resource information;

[0110] In this embodiment, the MR data processing server pushes the raw MR data to the different partition queues to obtain sharded resource information, specifically, as follows: Figure 5 As shown, the original MR data is divided into 10 resource information segments or 10 data files according to the common field base station, and uploaded to different processing clusters corresponding to the partition queue via FTP (File Transfer Protocol), such as 10 processing clusters.

[0111] In this embodiment, specifically, the MR data processing server maintains a long connection and a near real-time file scanning and download mechanism for all OMCs. After downloading the raw MR data, it extracts the base station ID, performs hash partitioning, and pushes the data to 10 different partition queues.

[0112] Step S32: Based on the partitioned queue and the frequency range under different networks, perform data identification on the fragmented resource information under different networks to obtain network frequency data under different networks.

[0113] In this embodiment, based on the partitioned queue and the frequency range under different networks, the fragmented resource information is identified for data under different networks. Specifically, as follows: Figure 5 As shown, the resource information or data file of the fragment includes 2 / 4 / 5G neighbor cell configuration table data, 2 / 4 / 5G cell operating parameter table data, etc. Based on the partition queue, the 2 / 4 / 5G neighbor cell configuration table data and the 2 / 4 / 5G cell operating parameter table data are loaded into MapDB (MapDB is a key-value structure component based on disk files). Since the partition queue loads the resource information or data file of the fragment into MapDB, the data in the backfilling process is more readable, thus improving the data processing efficiency.

[0114] In this embodiment, after the partition queue loads the fragmented resource information or fragmented data files into MapDB, the MR data processing server obtains MR measurement data (network frequency data) under 2G, MR measurement data (network frequency data) under 4G, and MR measurement data (network frequency data) under 5G through cell frequency point identification.

[0115] Step S40: Based on the different network frequency data and the relationship table to be backfilled, backfill the MR data under different networks.

[0116] In this embodiment, based on the different network frequency data and the relationship table to be filled, MR data under different networks is filled. Specifically, based on the different network frequency data and the relationship table to be filled, data association and filling are performed through a preset "2 / 4 / 5G MR data classification processing business process". The resource information records of 2 / 4 / 5G that are successfully associated will be temporarily stored in memory to improve the time efficiency of association.

[0117] This application provides a method, apparatus, device, and storage medium for MR data processing. Compared with existing technologies that only process MR data from a single LTE network, resulting in the omission of data reported from other networks and leading to MR data backfilling failure or inaccuracy, this application collects raw MR data reported by the terminal corresponding to the first cell and extracts key fields involved in the raw MR data; based on the key fields, it determines the relationship table to be backfilled; based on the raw MR data, it determines the network frequency data under different networks; and based on the different network frequency data and the relationship table to be backfilled, it performs MR data backfilling under different networks. In this application, after collecting the raw MR data reported by the terminal corresponding to the first cell, it does not only process MR data from a single network, but also adopts network frequency data from different networks to backfill the MR data in the corresponding relationship table to be backfilled under different networks. It can be understood that in this application, MR data from (2G / 4G / 5G) networks is adopted to completely and accurately backfill the MR data, providing a data foundation for network optimization and further improving the user's perceived experience.

[0118] Furthermore, based on the first embodiment of this application, another embodiment of this application is provided. In this embodiment, the different network frequency data includes 2G frequency data, 4G frequency data, and 5G frequency data. The step of backfilling MR data under different networks based on the different network frequency data and the relationship table to be backfilled includes:

[0119] Step S41: Determine the first preset service process corresponding to 2G frequency data, the second preset service process corresponding to 4G frequency data, and the third preset service process corresponding to 5G frequency data.

[0120] In this embodiment, before determining the backfill, it is necessary to obtain or collect the first preset service process (2GMR data classification and processing service process) corresponding to the 2G frequency point data, the second preset service process (4GMR data classification and processing service process) corresponding to the 4G frequency point data, and the third preset service process (5GMR data classification and processing service process) corresponding to the 5G frequency point data from the MR data processing server.

[0121] Step S42: Based on the first preset service process, the 2G frequency point data, and the 2G relationship table to be filled in the relationship table to be filled, fill in the MR data under the 2G network.

[0122] Step S43: Based on the second preset service process, the 4G frequency point data, and the 4G relationship table to be filled in the relationship table to be filled, fill in the MR data under the 4G network.

[0123] Step S44: Based on the third preset service process, the 5G frequency point data, and the 5G relationship table to be filled in the relationship table to be filled, the MR data under the 5G network is filled.

[0124] In this embodiment, as Figure 6 As shown, under multi-network convergence, 2G frequency data, 4G frequency data, and 5G frequency data are parsed from MR data. Based on the corresponding preset service process and the relationship table to be filled, MR neighbor cell data is filled in by combining the neighbor cell configuration data of each network.

[0125] Specifically, the step of backfilling MR data under the 2G network based on the first preset service process, the 2G frequency point data, and the 2G relationship table to be backfilled in the relationship table to be backfilled includes:

[0126] Step A1: Extract the neighboring cell BCCH and neighboring cell BSIC of the first cell from the 2G frequency point data;

[0127] In this embodiment, the LAC (Location Area Code), CI (Cell Identifier), BCCH (Broadcast Control Channel), and BSIC (Base Station Identifier) ​​of the first cell are extracted from the 2G frequency point data.

[0128] Step A2: Based on the preset OMC, determine the first neighbor cell configuration table of the first cell under the 2G network;

[0129] In this embodiment, the first neighbor cell configuration table of the first cell under the 2G network is determined by matching the LAC (Location Area Code) of the first cell, and / or the CI (Cell Identifier) ​​of the first cell, and / or the BCCH (Broadcast Control Channel) of the neighboring cells, and / or the BSIC (Base Station Identifier).

[0130] Step A3: Match the configuration BCCH in the first neighbor cell configuration table with the BCCH of the neighbor cell, and the configuration BSIC with the BSIC of the neighbor cell respectively;

[0131] In this embodiment, after the first neighbor cell configuration table is determined, the configuration data of each item in the first neighbor cell configuration table is obtained. Then, the configuration BCCH in the first neighbor cell configuration table is matched with the BCCH of the neighbor cell, and the configuration BSIC is matched with the BSIC of the neighbor cell.

[0132] Step A4: If all matches are successful, extract the neighbor cell LAC, neighbor cell CI, and first neighbor cell distance from the first neighbor cell configuration table, and backfill the MR neighbor cell data under the 2G network based on the 2G relationship table to be backfilled.

[0133] In this embodiment, if the configured BCCH in the first neighbor cell configuration table matches the BCCH of the neighbor cell, and if the configured BSIC matches the BSIC of the neighbor cell, then the neighbor cell LAC, neighbor cell CI, and first neighbor cell distance in the first neighbor cell configuration table are extracted and filled into the 2G relationship table to be backfilled, and the MR neighbor cell data under the 2G network is backfilled.

[0134] Step A5: Extract the LAC and CI of the first cell from the 2G frequency point data, and backfill the MR non-neighbor cell data under the 2G network based on the 2G relationship table to be backfilled.

[0135] In this embodiment, the LAC and CI of the first cell are extracted from the 2G frequency point data and used to fill the non-neighbor cell data in the 2G relationship table to be filled back. Specifically, based on the CGI of the first cell, the 2G cell parameter table in the OMC is associated with the first cell, and the city and base station information of the first cell are extracted and filled back into the non-neighbor cell data.

[0136] In this embodiment, if the matching fails, the second cell that is closest to the first cell and whose frequency and PCI are the same as the first cell is determined. That is, the 2G neighbor cell distance table stored in the OMC is obtained, and the nearest neighbor cell that is consistent with the BCCH and BSIC of the first cell is obtained from the 2G neighbor cell distance table. The data corresponding to the neighbor cell is then filled back into the MRO data.

[0137] In this embodiment, a first preset service process corresponding to 2G frequency point data, a second preset service process corresponding to 4G frequency point data, and a third preset service process corresponding to 5G frequency point data are determined respectively. Based on the first preset service process, the 2G frequency point data, and the 2G relationship table to be filled in the relationship table to be filled, MR data under the 2G network is filled. Based on the second preset service process, the 4G frequency point data, and the 4G relationship table to be filled in the relationship table to be filled, MR data under the 4G network is filled. Based on the third preset service process, the 5G frequency point data, and the 5G relationship table to be filled in the relationship table to be filled, MR data under the 5G network is filled. In this embodiment, frequency point data under different networks is classified and processed, improving processing efficiency.

[0138] Furthermore, based on the first and second embodiments of this application, another embodiment of this application is provided. In this embodiment, the step of backfilling MR data under the 4G network based on the second preset service process, the 4G frequency point data, and the 4G relationship table to be backfilled in the relationship table to be backfilled includes:

[0139] Step B1: Extract the neighboring cell PCI and neighboring cell frequency points of the first cell from the 4G frequency point data;

[0140] Step B2: Based on the preset OMC, neighboring cell PCI, and neighboring cell frequency, determine the second neighboring cell configuration table of the first cell;

[0141] In this embodiment, the neighboring cell PCI and neighboring cell frequency points of the first cell are extracted from the 4G frequency point data. Based on the neighboring cell PCI and / or neighboring cell frequency points of the first cell, the 4G neighboring cell configuration tables pre-stored by the OMC are matched to obtain the second neighboring cell configuration table of the first cell.

[0142] Step B3: Match the configured frequency points in the second neighbor cell configuration table with the neighbor cell frequency points, and match the configured PCI with the neighbor cell PCI respectively;

[0143] In this embodiment, the configured frequency points in the second neighbor cell configuration table are matched with the frequency points of the neighbor cells, and the configured PCI in the second neighbor cell configuration table is matched with the PCI of the neighbor cells.

[0144] Step B4: If all matches are successful, extract the neighbor cell CGI and the second neighbor cell distance from the second neighbor cell configuration table, and backfill the MR neighbor cell data under the 4G network based on the 4G relationship table to be backfilled.

[0145] In this embodiment, if all matches are successful, the neighboring cell CGI and the second neighboring cell distance in the second neighboring cell configuration table are extracted, and the MR neighboring cell data in the 4G relationship table to be backfilled under the 4G network is backfilled.

[0146] Step B5: Extract the CGI of the first cell from the 4G frequency point data, and backfill the MR non-neighbor cell data under the 4G network based on the 4G relationship table to be backfilled.

[0147] In this embodiment, based on the CGI of the first cell, the corresponding 4G cell parameter table in the OMC is associated, and the city and base station information of the primary serving cell are extracted based on the 4G cell parameter and backfilled into the MR non-neighbor cell data in the 4G relationship table to be backfilled.

[0148] In this embodiment, if the matching fails, the 4G neighbor cell distance table is obtained from the OMC. Based on the 4G neighbor cell distance table, the nearest neighbor cell with the same frequency and PCI as the first cell is determined, and the neighbor cell data is obtained and backfilled into the MR neighbor cell data.

[0149] In this embodiment, the neighboring cell PCI and neighboring cell frequency points of the first cell are extracted from the 4G frequency point data. Based on the preset OMC, neighboring cell PCI, and neighboring cell frequency points, a second neighboring cell configuration table for the first cell is determined. The configured frequency points in the second neighboring cell configuration table are matched with the neighboring cell frequency points, and the configured PCI is matched with the neighboring cell PCI. If all matches are successful, the neighboring cell CGI and second neighboring cell distance in the second neighboring cell configuration table are extracted. Based on the 4G relationship table to be filled, MR neighboring cell data under the 4G network is filled. The CGI of the first cell is extracted from the 4G frequency point data, and based on the 4G relationship table to be filled, MR non-neighboring cell data under the 4G network is filled. In this embodiment, frequency point data under the 4G network is filled, improving processing efficiency.

[0150] Furthermore, based on the first, second, and third embodiments of this application, another embodiment of this application is provided. In this embodiment, the step of backfilling MR data under the 5G network based on the third preset service process, the 5G frequency point data, and the 5G relationship table to be backfilled in the relationship table to be backfilled includes:

[0151] Step C1: Extract the GNB ID number and neighboring cell PCI field from the 5G frequency point data;

[0152] Step C2: Based on the preset OMC, determine the 5G cell operating parameter table associated with the first cell;

[0153] In this embodiment, the ID number and PCI field of the GNB (the next generation NodeB) are extracted from the 5G frequency point data, and the 5G cell operating parameter table is obtained by associating the ID number of the GNB with the PCI field of the neighboring cell from the various operating parameter tables in the OMC.

[0154] Step C3: Match the configuration ID number in the 5G cell parameter table with the ID number of the GNB, and match the configuration PCI field with the PCI field of the neighboring cell respectively.

[0155] In step C4, if all matches are successful, extract the NCGI information, the city information, and the distance to the third neighboring cell, and backfill the MR data under the 5G network based on the 5G relationship table to be backfilled.

[0156] The configuration ID number in the 5G cell parameter table is matched with the GNB ID number, and the configuration PCI field in the 5G cell parameter table is matched with the neighboring cell PCI field. If both matches are successful, the NCGI information, the city information, and the distance to the third neighboring cell are extracted to backfill the MR data in the 5G relationship table to be backfilled.

[0157] In this embodiment, if the match fails, the GNB ID number and ObjectId (object identifier or resource identifier) ​​are used to obtain the calculated value using the formula GNBID*4096+ObjectID, and this calculated value is used as the NCGI.

[0158] Furthermore, in this embodiment, according to the 460-00-Gnbid-PCI general format, the formatted first cell NCGI is generated by rounding down GNBID = NCGI / 4096 and taking the remainder of PCI = NCGI%4096.

[0159] Furthermore, in this embodiment, 5G neighbor cell resource data is obtained from the OMC. After calculation using the formula Nci = GNBID * 4096 + CELLID, the NCGI of the neighbor cell is obtained. By associating the cell table, the NCGI of the first cell, the latitude and longitude information of the first cell, and the latitude and longitude information of the neighbor cell are obtained. The distance between the two is calculated, and the final territorial configuration table that can be backfilled and queried is generated.

[0160] Furthermore, the NCGI calculated using MRO is first associated with the neighboring cell frequency points and PCI, and then with the 5G neighbor cell configuration table on the OMC.

[0161] In this embodiment, the GNB ID and neighboring cell PCI field are extracted from the 5G frequency point data; based on a preset OMC, the 5G cell parameter table associated with the first cell is determined; the configuration ID in the 5G cell parameter table is matched with the GNB ID, and the configuration PCI field is matched with the neighboring cell PCI field; if all matches are successful, the NCGI information, the city information, and the distance to the third neighboring cell are extracted, and the MR data under the 5G network is backfilled based on the 5G relationship table to be backfilled. In this embodiment, frequency point data under the 5G network is filled, improving processing efficiency.

[0162] Furthermore, based on the first, second, third, and fourth embodiments of this application, another embodiment of this application is provided, in which, as... Figure 2 As shown, the MR data includes MR neighbor cell data. After the step of backfilling MR data under different networks based on the different network frequency data and the relationship table to be backfilled, the method includes:

[0163] Step S50: Determine whether the MR neighbor cell data has been backfilled under different networks;

[0164] Step S60: If backfilling is not completed, determine the second cell that is closest to the first cell and whose frequency and PCI are the same as those of the first cell.

[0165] Step S70: Based on the data from the second cell, continue to backfill the MR neighbor cell data under the different networks.

[0166] like Figure 4 As shown, in this embodiment, it is determined whether the MR neighbor cell data under different networks has been backfilled. If the backfilling has been completed, it is not necessary to determine the second cell. If the backfilling has not been completed, the second cell that is closest to the first cell and whose frequency and PCI are the same as those of the first cell is determined. The purpose of determining the second cell is that, generally, MR frequency and PCI data in the engineering parameter table may have the same frequency and PCI. Therefore, based on the data of the second cell, the MR neighbor cell data under different networks can be backfilled.

[0167] Based on the data from the second cell, the MR neighbor cell data under the different networks will continue to be backfilled.

[0168] In this embodiment, it is determined whether the MR neighbor cell data under different networks has been backfilled. If the backfilling is not complete, a second cell that is closest to the first cell and whose frequency and PCI correspond to the first cell is identified. Based on the data of the second cell, the MR neighbor cell data under different networks is backfilled. In this embodiment, the completeness of neighbor cell backfilling is further improved and the MR backfilling rate is increased.

[0169] Reference Figure 3 , Figure 3 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of this application.

[0170] like Figure 3 As shown, the MR data processing device may include: a processor 1001, such as a CPU, a memory 1005, and a communication bus 1002. The communication bus 1002 is used to establish communication between the processor 1001 and the memory 1005. The memory 1005 may be a high-speed RAM or a stable, non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0171] Optionally, the MR data processing device may also include a rectangular user interface, a network interface, a camera, RF (Radio Frequency) circuitry, sensors, audio circuitry, a WiFi module, etc. The rectangular user interface may include a display screen and an input submodule such as a keyboard. Optionally, the rectangular user interface may also include a standard wired interface or a wireless interface. The network interface may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0172] Those skilled in the art will understand that Figure 3 The MR data processing device structure shown does not constitute a limitation on the MR data processing device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0173] like Figure 3 As shown, the memory 1005, serving as a storage medium, may include an operating system, a network communication module, and an MR data processing program. The operating system is a program that manages and controls the hardware and software resources of the MR data processing device, supporting the operation of the MR data processing program and other software and / or programs. The network communication module is used to enable communication between the various components within the memory 1005, as well as communication with other hardware and software in the MR data processing system.

[0174] exist Figure 3In the MR data processing device shown, the processor 1001 is used to execute the MR data processing program stored in the memory 1005 to implement the steps of the MR data processing method described in any of the above claims.

[0175] The specific implementation of the MR data processing device in this application is basically the same as the embodiments of the MR data processing method described above, and will not be repeated here.

[0176] This application also provides an MR data processing apparatus, the MR data processing apparatus comprising:

[0177] The acquisition module is used to acquire the raw MR data reported by the terminal corresponding to the first cell and extract the key fields involved in the raw MR data.

[0178] The first determining module is used to determine the relationship table to be filled based on the key fields;

[0179] The second determining module is used to determine network frequency point data under different networks based on the original MR data;

[0180] The backfilling module is used to backfill MR data under different networks based on the different network frequency point data and the relationship table to be backfilled.

[0181] Optionally, the MR data processing apparatus further includes:

[0182] The third determination module is used to determine whether the MR neighbor cell data has been backfilled in different networks;

[0183] The fourth determination module is used to determine the second cell that is closest to the first cell and whose frequency and PCI are the same as those of the first cell if backfilling is not completed.

[0184] The backfill module is used to continue backfilling the MR neighbor cell data under different networks based on the data of the second cell.

[0185] Optionally, the MR data processing apparatus further includes:

[0186] The partitioning module is used to extract the base station ID based on the common fields in the original MR data, perform hash partitioning, and obtain different partition queues.

[0187] The second determining module includes:

[0188] The first acquisition unit is used to push the original MR data to the different partition queues respectively to obtain the fragment resource information;

[0189] The second acquisition unit is used to identify the data of the fragmented resource information under different networks based on the partitioned queue and the frequency range under different networks, so as to obtain the network frequency data under different networks.

[0190] Optionally, the different network frequency data includes 2G frequency data, 4G frequency data, and 5G frequency data, and the backfilling module includes:

[0191] The determining unit is used to determine the first preset service process corresponding to 2G frequency point data, the second preset service process corresponding to 4G frequency point data, and the third preset service process corresponding to 5G frequency point data respectively.

[0192] The first backfilling unit is used to backfill MR data under the 2G network based on the first preset service process, the 2G frequency point data, and the 2G relationship table to be backfilled in the relationship table to be backfilled.

[0193] The second backfilling unit is used to backfill MR data under the 4G network based on the second preset service process, the 4G frequency point data, and the 4G relationship table to be backfilled in the relationship table to be backfilled.

[0194] The third backfilling unit is used to backfill MR data under the 5G network based on the third preset service process, the 5G frequency point data, and the 5G relationship table to be backfilled in the relationship table to be backfilled.

[0195] Optionally, the first backfill unit is used to implement:

[0196] The BCCH and BSIC of the neighboring cells of the first cell are extracted from the 2G frequency data.

[0197] Based on the preset OMC, determine the first neighbor cell configuration table of the first cell under the 2G network;

[0198] Match the BCCH configuration in the first neighbor cell configuration table with the BCCH of the neighbor cell, and the BSIC configuration with the BSIC of the neighbor cell respectively;

[0199] If all matches are successful, the neighbor cell LAC, neighbor cell CI, and first neighbor cell distance in the first neighbor cell configuration table are extracted, and the MR neighbor cell data under the 2G network is backfilled based on the 2G relationship table to be backfilled.

[0200] The LAC and CI of the first cell are extracted from the 2G frequency point data. Based on the 2G relationship table to be filled, the MR non-neighbor cell data under the 2G network is filled.

[0201] Optionally, the second backfill unit is used to achieve:

[0202] The PCI of the neighboring cells and the frequency points of the neighboring cells of the first cell are extracted from the 4G frequency point data.

[0203] Based on the preset OMC, neighboring cell PCI, and neighboring cell frequency, the second neighboring cell configuration table of the first cell is determined;

[0204] Match the configured frequency points in the second neighbor cell configuration table with the neighbor cell frequency points, and match the configured PCI with the neighbor cell PCI respectively;

[0205] If all matches are successful, the neighboring cell CGI and the second neighboring cell distance in the second neighboring cell configuration table are extracted, and the MR neighboring cell data under the 4G network is backfilled based on the 4G relationship table to be backfilled.

[0206] The CGI of the first cell is extracted from the 4G frequency point data, and the MR non-neighbor cell data under the 4G network is backfilled based on the 4G relationship table to be backfilled.

[0207] Optionally, the third backfill unit is used to achieve:

[0208] The GNB ID number and neighboring cell PCI field are extracted from the 5G frequency point data;

[0209] Based on the preset OMC, determine the 5G cell operating parameter table associated with the first cell;

[0210] The configuration ID number in the 5G cell parameter table is matched with the ID number of the GNB, and the configuration PCI field is matched with the PCI field of the neighboring cell.

[0211] If all matches are successful, the NCGI information, the city information, and the distance to the third neighboring cell are extracted. Based on the 5G relationship table to be filled, the MR data under the 5G network is then filled.

[0212] The specific implementation of the MR data processing device in this application is basically the same as the embodiments of the MR data processing method described above, and will not be repeated here.

[0213] This application provides a storage medium that stores one or more programs, which can be executed by one or more processors to implement the steps of the MR data processing method described above.

[0214] The specific implementation of the storage medium in this application is basically the same as the embodiments of the MR data processing method described above, and will not be repeated here.

[0215] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the MR data processing method described above.

[0216] The specific implementation of the computer program product of this application is basically the same as the embodiments of the MR data processing method described above, and will not be repeated here.

[0217] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0218] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0219] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0220] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. An MR data processing method, characterized in that, The MR data processing method includes: Collect the raw MR data reported by the terminal corresponding to the first cell, and extract the key fields involved in the raw MR data; Based on the aforementioned key fields, determine the relationship tables to be filled back; Based on the raw MR data, determine the network frequency data under different networks; Based on the different network frequency data and the relationship table to be filled, MR data under different networks is filled. The MR data filling includes the filling of MR data under the convergence of 2G, 4G and 5G networks. When filling, if the adjacent area cannot be associated, the distance factor is also considered. In the context of multi-network convergence, the MR data is also partitioned for filling. The different network frequency data includes 2G frequency data, 4G frequency data, and 5G frequency data. The step of backfilling MR data under different networks based on the different network frequency data and the relationship table to be backfilled includes: The first preset service process corresponding to 2G frequency point data, the second preset service process corresponding to 4G frequency point data, and the third preset service process corresponding to 5G frequency point data are determined respectively. Based on the first preset service process, the 2G frequency point data, and the 2G relationship table to be filled in the relationship table to be filled, the MR data under the 2G network is filled in. Based on the second preset service process, the 4G frequency point data, and the 4G relationship table to be filled in the relationship table to be filled, the MR data under the 4G network is filled in. Based on the third preset service process, the 5G frequency point data, and the 5G relationship table to be filled in the relationship table to be filled, the MR data under the 5G network is filled in.

2. The MR data processing method according to claim 1, characterized in that, The MR data includes MR neighbor cell data. After the step of backfilling MR data under different networks based on the different network frequency point data and the relationship table to be backfilled, the method includes: Determine whether the MR neighbor cell data has been backfilled in different networks; If the backfilling is not completed, determine the second cell that is closest to the first cell and whose frequency and PCI are the same as those of the first cell; Based on the data from the second cell, the MR neighbor cell data under the different networks will continue to be backfilled.

3. The MR data processing method as described in claim 1, characterized in that, Before the step of determining network frequency point data under different networks based on the original MR data, the method includes: Based on the common fields in the original MR data, the base station ID is extracted and hashed to obtain different partition queues; The step of determining network frequency point data under different networks based on the original MR data includes: The raw MR data is pushed to the different partition queues to obtain fragment resource information; Based on the partitioned queue and the frequency range under different networks, the fragmented resource information is identified under different networks to obtain network frequency data under different networks.

4. The MR data processing method as described in claim 1, characterized in that, The step of backfilling MR data under the 2G network based on the first preset service process, the 2G frequency point data, and the 2G relationship table to be backfilled in the relationship table to be backfilled includes: The BCCH and BSIC of the neighboring cells of the first cell are extracted from the 2G frequency data. Based on the preset OMC, determine the first neighbor cell configuration table of the first cell under the 2G network; Match the BCCH configuration in the first neighbor cell configuration table with the BCCH of the neighbor cell, and the BSIC configuration with the BSIC of the neighbor cell respectively; If all matches are successful, the neighbor cell LAC, neighbor cell CI, and first neighbor cell distance in the first neighbor cell configuration table are extracted, and the MR neighbor cell data under the 2G network is backfilled based on the 2G relationship table to be backfilled. The LAC and CI of the first cell are extracted from the 2G frequency point data. Based on the 2G relationship table to be filled, the MR non-neighbor cell data under the 2G network is filled.

5. The MR data processing method as described in claim 1, characterized in that, The step of backfilling MR data under the 4G network based on the second preset service process, the 4G frequency point data, and the 4G relationship table to be backfilled in the relationship table to be backfilled includes: The PCI of the neighboring cells and the frequency points of the neighboring cells of the first cell are extracted from the 4G frequency point data. Based on the preset OMC, neighboring cell PCI, and neighboring cell frequency, the second neighboring cell configuration table of the first cell is determined; Match the configured frequency points in the second neighbor cell configuration table with the neighbor cell frequency points, and match the configured PCI with the neighbor cell PCI respectively; If all matches are successful, the neighboring cell CGI and the second neighboring cell distance in the second neighboring cell configuration table are extracted, and the MR neighboring cell data under the 4G network is backfilled based on the 4G relationship table to be backfilled. The CGI of the first cell is extracted from the 4G frequency point data, and the MR non-neighbor cell data under the 4G network is backfilled based on the 4G relationship table to be backfilled.

6. The MR data processing method as described in claim 1, characterized in that, The step of backfilling MR data under the 5G network based on the third preset service process, the 5G frequency point data, and the 5G relationship table to be backfilled in the relationship table to be backfilled includes: The GNB ID number and neighboring cell PCI field are extracted from the 5G frequency point data; Based on the preset OMC, determine the 5G cell operating parameter table associated with the first cell; The configuration ID number in the 5G cell parameter table is matched with the ID number of the GNB, and the configuration PCI field is matched with the PCI field of the neighboring cell. If all matches are successful, the NCGI information, the city information, and the distance to the third neighboring cell are extracted. Based on the 5G relationship table to be filled, the MR data under the 5G network is then filled.

7. An MR data processing apparatus, characterized in that, The MR data processing device includes: The acquisition module is used to acquire the raw MR data reported by the terminal corresponding to the first cell and extract the key fields involved in the raw MR data. The first determining module is used to determine the relationship table to be filled based on the key fields; The second determining module is used to determine network frequency point data under different networks based on the original MR data; The backfilling module is used to backfill MR data under different networks based on the different network frequency data and the relationship table to be backfilled. The backfilling of MR data includes the backfilling of MR data under the convergence of 2G, 4G and 5G networks. When backfilling, if the adjacent area cannot be associated, the distance factor is also considered. In the context of multi-network convergence, the MR data is also partitioned for backfilling. The different network frequency point data includes 2G frequency point data, 4G frequency point data, and 5G frequency point data. The MR data processing device is used to achieve: The first preset service process corresponding to 2G frequency point data, the second preset service process corresponding to 4G frequency point data, and the third preset service process corresponding to 5G frequency point data are determined respectively. Based on the first preset service process, the 2G frequency point data, and the 2G relationship table to be filled in the relationship table to be filled, the MR data under the 2G network is filled in. Based on the second preset service process, the 4G frequency point data, and the 4G relationship table to be filled in the relationship table to be filled, the MR data under the 4G network is filled in. Based on the third preset service process, the 5G frequency point data, and the 5G relationship table to be filled in the relationship table to be filled, the MR data under the 5G network is filled in.

8. An MR data processing device, characterized in that, The MR data processing device includes: a memory, a processor, and a program stored in the memory for implementing the MR data processing method. The memory is used to store programs that implement MR data processing methods; The processor is configured to execute a program that implements the MR data processing method to carry out the steps of the MR data processing method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium stores a program for implementing the MR data processing method, which is executed by a processor to implement the steps of the MR data processing method as described in any one of claims 1 to 6.

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