Measurement data correction method, device and electronic equipment

Abstract

Embodiments of the present invention provide a measurement data correction method, apparatus, and electronic device. The method includes: obtaining a first measurement report reported by a user terminal via a network; and correcting the measurement data in the first measurement report based on a preset correction model; wherein the correction model is pre-trained using the first measurement training report as a sample and a second measurement report corresponding to the first measurement training report as a sample label, and the second measurement report is reported by an application installed on the user terminal. Through embodiments of the present invention, measurement data can be quickly and accurately corrected, resulting in more realistic measurement data.

Description

Technical Field

[0001] The present invention relates to the field of mobile communication technology, and in particular to a measurement data correction method, device and electronic equipment. Background Art

[0002] Wireless network measurement reports are collected and reported regularly by user terminals. These data reflect the network quality during user service periods. The Operations & Maintenance Center (OMC) / Maintenance, Repair, Overhaul (MRO) services aggregate and process the data, which can then be evaluated by the network optimization platform to assess wireless network coverage and quality. Therefore, the authenticity of measurement report data is crucial for network planning and optimization.

[0003] In actual applications, measurement reports may be distorted during transmission due to technical reasons or human factors. For example, since measurement report data is also used as evaluation and assessment indicators for network operation and maintenance, operation and maintenance personnel may tamper with the data. Therefore, to ensure the authenticity and reliability of the measurement report data obtained, the measurement report data can be corrected based on personal experience.

[0004] It can be seen that the existing measurement data correction method is inefficient and cannot accurately judge and correct the measurement data in a large number of measurement reports. Summary of the Invention

[0005] The purpose of the embodiments of the present invention is to provide a measurement data correction method, device, and electronic device to solve the problem of being unable to accurately judge and correct the measurement data in a measurement report.

[0006] In order to solve the above technical problems, the embodiment of the present invention is implemented as follows:

[0007] In a first aspect, an embodiment of the present invention provides a measurement data correction method, comprising:

[0008] Obtaining a first measurement report reported by a user terminal through a network;

[0009] The measurement data contained in the first measurement report is corrected based on a preset correction model; wherein the correction model is pre-trained using the first measurement training report as a sample and the second measurement report corresponding to the first measurement training report as a sample label, and the second measurement report is reported by an application installed on the user terminal.

[0010] In a second aspect, an embodiment of the present invention provides a measurement data correction device, comprising:

[0011] A collection module, configured to obtain a first measurement report reported by a user terminal through a network;

[0012] A correction module is used to correct the measurement data contained in the first measurement report based on a preset correction model; wherein the correction model is pre-trained using the first measurement training report as a sample and the second measurement report corresponding to the first measurement training report as a sample label, and the second measurement report is reported by an application installed on the user terminal.

[0013] In a third aspect, an embodiment of the present invention provides an electronic device comprising a processor, a communication interface, a memory and a communication bus; wherein the processor, the communication interface and the memory communicate with each other through a bus; the memory is used to store computer programs; and the processor is used to execute the programs stored in the memory to implement the steps of the measurement data correction method as described in the first aspect.

[0014] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the measurement data correction method as described in the first aspect are implemented.

[0015] As can be seen from the technical solutions provided by the above embodiments of the present invention, the embodiments of the present invention obtain a first measurement report reported by a user terminal via a network; and correct the measurement data of the first measurement report based on a preset correction model; wherein the correction model is pre-trained using the first measurement training report as a sample and a second measurement report corresponding to the first measurement training report as a sample label, and the second measurement report is reported by an application installed on the user terminal. Through the embodiments of the present invention, the measurement data can be corrected quickly and accurately, obtaining more realistic measurement data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 A schematic diagram of a flow chart of a measurement data correction method provided by an embodiment of the present invention;

[0018] Figure 2Another flowchart of the measurement data correction method provided by an embodiment of the present invention;

[0019] Figure 3 A schematic diagram of the module composition of the measurement data correction device provided by an embodiment of the present invention;

[0020] Figure 4 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] Embodiments of the present invention provide a measurement data correction method, device, and electronic equipment.

[0022] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0023] like Figure 1 As shown, an embodiment of the present invention provides a measurement data correction method. The execution subject of the method can be a server or a network platform, such as a network optimization platform, wherein the server can be an independent server or a server cluster composed of multiple servers. Moreover, the server can be a server capable of performing network operation processing, such as a server for configuring network resources. The method can specifically include the following steps:

[0024] Step S110: Obtain a first measurement report reported by the user terminal through the network.

[0025] After the user terminal generates the first measurement report, it will report it to the server through the network. During the reporting process, it will pass through multiple network nodes, including base stations, OMC / MRO servers, other collection and storage nodes, etc.

[0026] The first measurement report includes identification information and measurement data. The identification information may include: user identification, serving cell identification, region identification, equipment manufacturer identification, MRO server identification, measurement time identification, etc. The measurement data may include: Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), neighboring cell reference signal received power, etc.

[0027] Since some network nodes passed through during the reporting process of the first measurement report are untrustworthy nodes, it is necessary to correct the abnormal measurement data in the first measurement report.

[0028] Step S120: Correct the measurement data contained in the first measurement report based on a preset correction model; wherein the correction model is pre-trained using the first measurement training report as a sample and the second measurement report corresponding to the first measurement training report as a sample label, and the second measurement report is reported by an application installed on the user terminal.

[0029] To determine and correct the accuracy of the first measurement report, an application is pre-installed on the user terminal or some user terminals. The application collects a second measurement report directly from the user terminal through collection. The second measurement report may also include identification information and measurement data. The application reports the collected second measurement report to the server via a trusted network node, such as a managed collection and storage node, thereby ensuring the authenticity of the first measurement report.

[0030] After obtaining the first measurement report and the second measurement report, the server may associate the first measurement report and the second measurement report with the user big data, and uniformly organize them into the same format, for example, as shown in Table 1 below:

[0031]

[0032] Table 1

[0033] The server pre-builds a correction model for correcting the measurement data. The correction model can be in various forms. In one embodiment, the correction model can be represented by the following correction function:

[0034]

[0035] Wherein, the y is the measurement data that needs to be corrected, is a vector value obtained based on the identification information and measurement data in the first measurement report.

[0036] A first measurement training report and a second measurement report corresponding to the first measurement training data are selected from the acquired first measurement report and the second measurement report, and the correction model is trained to obtain a trained correction model.

[0037] The measurement data in the acquired first measurement report is corrected according to the trained correction model.

[0038] It should be understood that the correction model may be for a specific measurement data, for example, a correction model for RSRP, or a correction model for RSRQ, etc. The correction model may also be a correction model for simultaneously correcting all the measurement data in Table 1, which is not specifically limited here. However, for simplicity, the following embodiments are illustrated by taking only RSRP as an example.

[0039] As can be seen from the technical solutions provided by the above embodiments of the present invention, the embodiments of the present invention obtain a first measurement report reported by a user terminal via a network; and correct the measurement data of the first measurement report based on a preset correction model; wherein the correction model is pre-trained using the first measurement training report as a sample and a second measurement report corresponding to the first measurement training report as a sample label, and the second measurement report is reported by an application installed on the user terminal. Through the embodiments of the present invention, the measurement data can be corrected quickly and accurately, obtaining more realistic measurement data.

[0040] Based on the above embodiment, further, step S120 includes:

[0041] Step S121: Determine a grouping set of the first measurement report according to the identification information of the first measurement report.

[0042] In order to more accurately locate measurement data that may be abnormal, the first measurement report can be divided into multiple group sets based on the identification information. There are various specific division methods. For example, the first measurement report can be divided according to different MRO server identifiers, or according to different regional identifiers, equipment manufacturer identifiers, and MRO server identifiers. The first measurement report can also be divided in combination with different measurement time identifiers. In one embodiment, the value range or value set of the identification information corresponding to each group set can be pre-determined based on the value corresponding to each identification information.

[0043] After obtaining the first measurement report, the server may determine the group set to which the first measurement report belongs based on the identification information of the first measurement report. Similarly, after obtaining the second measurement report, the server may also determine the group set to which the second measurement report belongs based on the identification information of the second measurement report.

[0044] Step S122: Correct the measurement data in the first measurement report based on the correction model corresponding to the grouping set.

[0045] For different group sets, the server selects a first measurement training report and a corresponding second measurement report from the acquired first measurement report and second measurement report corresponding to the group set, for training the correction model to obtain a correction model corresponding to the group set.

[0046] According to the acquired group set corresponding to the first measurement report, the measurement data in the first measurement report is corrected by using the trained correction model of the group set.

[0047] As can be seen from the technical solutions provided by the above embodiments of the present invention, the embodiments of the present invention determine a grouping set for the first measurement report based on the identification information of the first measurement report; and correct the measurement data of the first measurement report based on a correction model corresponding to the grouping set. By dividing the first measurement report, the measurement data of the first measurement report can be corrected more accurately.

[0048] Based on the above embodiment, the training method of the correction model can be various. The embodiment of the present application only provides one implementation method. The method further includes:

[0049] The server associates the first measurement report and the second measurement report obtained according to the identification information, that is, associates the first measurement report and the second measurement report collected from the same user terminal and with the same measurement time, and records them as sample data in the sample data set.

[0050] In one embodiment, a first measurement report and a second measurement report determined to have the same identification information are recorded in a sample data set. The determination that they have the same identification information may mean that all or part of the identification information of the first measurement report and the second measurement report is the same. For example, the first measurement report and the second measurement report having the same user identifier, serving cell identifier, and measurement time identifier may be associated to form the sample data.

[0051] Whether the measurement data in the first measurement report has been modified is determined by analyzing the measurement data in the first measurement report and the measurement data in the second measurement report in the sample data set. Various analysis methods can be used, and cluster analysis can be used to classify the first measurement reports into two categories: one for indicating that the measurement data has been "modified" and the other for indicating that the measurement data is "original."

[0052] In one embodiment, the second measurement report may be used to classify the first measurement report through machine learning classification training. The feature data of the classification training may include the following Table 2:

[0053]

[0054]

[0055] The first measurement report determined to be modified after the above classification is used as the first measurement training report, and the correction model is obtained through a preset training algorithm.

[0056] In one embodiment, the preset training algorithm is a regression algorithm.

[0057] Taking RSRP as the measurement data as an example, since there is a possibility that the measurement time of the second measurement report collected by the user terminal application and the measurement time reported by the network may deviate, there is a distribution relationship between the RSRP value of the second measurement report and the RSRP value of the first measurement report reported by the network.

[0058] The function of the constructed correction model can be defined as follows:

[0059] y=f(X)+ε,X=ω T *X,X=(x1,x2,…x n ) (1)

[0060] Where y represents the RSRP value of the first measurement report, X represents the neighboring cell reference signal received power, ω represents the weight of each neighboring cell reference signal received power, and ε is the residual. The weight ω of each neighboring cell reference signal received power is calculated using the conditional probability method as follows:

[0061]

[0062] in, It is completely determined by the correction model. Taking the residual of the correction model as an example, which obeys the normal distribution with a mean of 0, the calculation method is as follows:

[0063]

[0064] For the calculation of p(ω), the posterior distribution of any form is iteratively approximated and finally calculated. By applying the collected reference signal received power, the estimated value of the reference signal received power weight is calculated using the above method. Then, for the “modified” RSRP, use formula (1) to calculate the RSRP close to the real one and make corrections to it.

[0065] As can be seen from the technical solutions provided by the above embodiments of the present invention, the embodiments of the present invention record the first measurement report and the second measurement report determined to have the same identification information in a sample data set; analyze the measurement data of the first measurement report and the second measurement report in the sample data set to determine whether the measurement data of the first measurement report has been modified; use the first measurement report determined to be modified as the first measurement training report, and obtain the correction model through a preset training algorithm. Through the embodiments of the present invention, the correction model can be quickly trained, and the measurement data can be quickly and accurately corrected to obtain more realistic measurement data.

[0066] Corresponding to the measurement data correction method provided in the above embodiment, based on the same technical concept, an embodiment of the present invention further provides a measurement data correction device. Figure 3 A schematic diagram of the module composition of a measurement data correction device provided by an embodiment of the present invention, wherein the measurement data correction device is used to perform Figures 1 to 2 The measurement data correction method described in Figure 3 As shown, the measurement data correction device includes: an acquisition module 301 and a correction module 302.

[0067] The acquisition module 301 is used to obtain a first measurement report reported by a user terminal through a network; the correction module 302 is used to correct the measurement data contained in the first measurement report based on a preset correction model; wherein the correction model is pre-trained using the first measurement training report as a sample and the second measurement report corresponding to the first measurement training report as a sample label, and the second measurement report is reported by an application installed on the user terminal.

[0068] As can be seen from the technical solutions provided by the above embodiments of the present invention, the embodiments of the present invention obtain a first measurement report reported by a user terminal via a network; and correct the measurement data of the first measurement report based on a preset correction model; wherein the correction model is pre-trained using the first measurement training report as a sample and a second measurement report corresponding to the first measurement training report as a sample label, and the second measurement report is reported by an application installed on the user terminal. Through the embodiments of the present invention, the measurement data can be corrected quickly and accurately, obtaining more realistic measurement data.

[0069] Furthermore, the correction module is used to:

[0070] determining a grouping set of the first measurement report according to the identification information of the first measurement report;

[0071] The measurement data of the first measurement report is corrected based on a correction model corresponding to the group set.

[0072] As can be seen from the technical solutions provided by the above embodiments of the present invention, the embodiments of the present invention determine a grouping set for the first measurement report based on the identification information of the first measurement report; and correct the measurement data of the first measurement report based on a correction model corresponding to the grouping set. By dividing the first measurement report, the measurement data of the first measurement report can be corrected more accurately.

[0073] Furthermore, the correction module is further configured to:

[0074] Recording the first measurement report and the second measurement report determined to have the same identification information into the sample data set;

[0075] determining, by analyzing measurement data of a first measurement report and measurement data of a second measurement report in the sample data set, whether the measurement data of the first measurement report is modified;

[0076] The first measurement report determined to be modified is used as the first measurement training report, and the correction model is obtained through a preset training algorithm.

[0077] Furthermore, the preset training algorithm is a regression algorithm.

[0078] As can be seen from the technical solutions provided by the above embodiments of the present invention, the embodiments of the present invention record the first measurement report and the second measurement report determined to have the same identification information in a sample data set; analyze the measurement data of the first measurement report and the second measurement report in the sample data set to determine whether the measurement data of the first measurement report has been modified; use the first measurement report determined to be modified as the first measurement training report, and obtain the correction model through a preset training algorithm. Through the embodiments of the present invention, the correction model can be quickly trained, and the measurement data can be quickly and accurately corrected to obtain more realistic measurement data.

[0079] The measurement data correction device provided in the embodiment of the present invention can implement each process in the embodiment corresponding to the above-mentioned measurement data correction method, and will not be described again here to avoid repetition.

[0080] It should be noted that the measurement data correction device provided in the embodiment of the present invention and the measurement data correction method provided in the embodiment of the present invention are based on the same inventive concept, so the specific implementation of this embodiment can refer to the implementation of the aforementioned measurement data correction method, and the repeated parts will be omitted.

[0081] Corresponding to the measurement data correction method provided in the above embodiment, based on the same technical concept, an embodiment of the present invention further provides an electronic device, which is used to execute the above measurement data correction method. Figure 4 A schematic diagram of the structure of an electronic device for implementing various embodiments of the present invention is shown in FIG. Figure 4 As shown. Electronic devices may have relatively large differences due to different configurations or performances, and may include one or more processors 401 and memory 402, and the memory 402 may store one or more storage applications or data. Among them, the memory 402 may be a temporary storage or a persistent storage. The application stored in the memory 402 may include one or more modules (not shown in the figure), and each module may include a series of computer-executable instructions in the electronic device. Furthermore, the processor 401 may be configured to communicate with the memory 402 to execute a series of computer-executable instructions in the memory 402 on the electronic device. The electronic device may also include one or more power supplies 403, one or more wired or wireless network interfaces 404, one or more input and output interfaces 405, and one or more keyboards 406.

[0082] Specifically in this embodiment, the electronic device includes a processor, a communication interface, a memory, and a communication bus; wherein the processor, the communication interface, and the memory communicate with each other via the bus; the memory is used to store a computer program; and the processor is used to execute the program stored in the memory to implement the following method steps:

[0083] Obtaining a first measurement report reported by a user terminal through a network;

[0084] The measurement data contained in the first measurement report is corrected based on a preset correction model; wherein the correction model is pre-trained using the first measurement training report as a sample and the second measurement report corresponding to the first measurement training report as a sample label, and the second measurement report is reported by an application installed on the user terminal.

[0085] The present application also provides a computer-readable storage medium, wherein the storage medium stores a computer program. When the computer program is executed by a processor, the following method steps are implemented:

[0086] Obtaining a first measurement report reported by a user terminal through a network;

[0087] The measurement data contained in the first measurement report is corrected based on a preset correction model; wherein the correction model is pre-trained using the first measurement training report as a sample and the second measurement report corresponding to the first measurement training report as a sample label, and the second measurement report is reported by an application installed on the user terminal.

[0088] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, apparatus, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0089] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0090] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0091] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0092] In a typical configuration, an electronic device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0093] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0094] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0095] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0096] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, devices, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0097] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A measurement data correction method, characterized in that: The method comprises: Obtaining a first measurement report reported by a user terminal through a network, where there are untrusted network nodes passed through by the first measurement report in a process of being reported through the network; The measurement data contained in the first measurement report is corrected based on a preset correction model; wherein the correction model is pre-trained using the first measurement training report as a sample and the second measurement report corresponding to the first measurement training report as a sample label, and the second measurement report is reported by an application installed on the user terminal, and the application reports the collected second measurement report through a trusted network node.

2. The method according to claim 1, characterized in that The correcting the measurement data included in the first measurement report based on a preset correction model includes: determining a grouping set of the first measurement report according to the identification information of the first measurement report; The measurement data of the first measurement report is corrected based on a correction model corresponding to the group set.

3. The method according to claim 1 or 2, characterized in that The method further comprises: Recording the first measurement report and the second measurement report determined to have the same identification information into the sample data set; determining, by analyzing measurement data of a first measurement report and measurement data of a second measurement report in the sample data set, whether the measurement data of the first measurement report is modified; The first measurement report determined to be modified is used as the first measurement training report, and the correction model is obtained through a preset training algorithm.

4. The method according to claim 3, characterized in that The preset training algorithm is a regression algorithm.

5. A measurement data correction device, characterized in that: The device comprises: a collection module, configured to obtain a first measurement report reported by a user terminal through a network, wherein a network node passed through by the first measurement report in a process of being reported through the network includes an untrusted node; A correction module is used to correct the measurement data contained in the first measurement report based on a preset correction model; wherein the correction model is pre-trained using the first measurement training report as a sample and the second measurement report corresponding to the first measurement training report as a sample label, and the second measurement report is reported by an application installed on the user terminal, and the application reports the collected second measurement report through a trusted network node.

6. The device according to claim 5, characterized in that The correction module is used to: determining a grouping set of the first measurement report according to the identification information of the first measurement report; The measurement data of the first measurement report is corrected based on a correction model corresponding to the group set.

7. The device according to claim 5 or 6, characterized in that The correction module is also used for: Recording the first measurement report and the second measurement report determined to have the same identification information into the sample data set; determining, by analyzing measurement data of a first measurement report and measurement data of a second measurement report in the sample data set, whether the measurement data of the first measurement report is modified; The first measurement report determined to be modified is used as the first measurement training report, and the correction model is obtained through a preset training algorithm.

8. The device according to claim 7, characterized in that The preset training algorithm is a regression algorithm.

9. An electronic device, characterized in that: The method comprises a processor, a communication interface, a memory and a communication bus; wherein the processor, the communication interface and the memory communicate with each other via the bus; the memory is used to store computer programs; and the processor is used to execute the programs stored in the memory to implement the steps of the measurement data correction method according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the measurement data correction method according to any one of claims 1 to 4 are implemented.

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