Network parameter derivation method, electronic device, and storage medium

Abstract

The application discloses a network parameter derivation method, an electronic device and a storage medium, and relates to the technical field of network optimization. The network parameter derivation method comprises the following steps: acquiring a first network parameter of a first test point position; acquiring a second network parameter of a second test point position; and calculating a network parameter of a first derived point position according to the first network parameter and the second network parameter, wherein the first derived point position is determined according to the first test point position. According to the application, the network parameter of a position that cannot be tested around a road test point is derived from the network parameter of the road test point, the road test data is enriched, the road test analysis is more comprehensive, and the road test result is closer to the actual road test network quality.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of network optimization technology, and in particular to a method for deriving network parameters, an electronic device, and a storage medium. Background Technology

[0002] In network optimization, drive testing has always been an important means of collecting wireless network data. Traditional drive testing is a targeted, localized test, with a relatively small testing area, mostly focused on the road itself. It cannot perceive the signal quality around the road, making drive test analysis somewhat one-sided. Summary of the Invention

[0003] The following is an overview of the subject matter described in detail herein. This overview is not intended to limit the scope of the claims.

[0004] This invention provides a network parameter derivation method, electronic device, and storage medium, which can derive network parameters for locations around the road test point that cannot be tested based on the network parameters of the road test point, making the road test analysis more comprehensive.

[0005] In a first aspect, embodiments of the present invention provide a method for deriving network parameters, including:

[0006] Obtain the first network parameters at the location of the first test point;

[0007] Obtain the second network parameters at the location of the second test point;

[0008] The network parameters for the location of the first derived point are calculated based on the first network parameters and the second network parameters, wherein the location of the first derived point is determined based on the location of the first test point.

[0009] In a second aspect, embodiments of the present invention provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the network parameter derivation method as described above.

[0010] Thirdly, embodiments of the present invention provide a computer-readable storage medium storing computer-executable instructions, the computer-executable instructions being used to execute the network parameter derivation method described above.

[0011] The network parameter derivation method, electronic device, and storage medium proposed in this invention obtain first network parameters at a first test point location, second network parameters at a second test point location, and calculate network parameters at a first derived point location based on the first and second network parameters. The first derived point location is determined based on the first test point location. This invention derives network parameters for locations around the road test point that cannot be tested, enriching the road test data, making road test analysis more comprehensive, and resulting in road test results that more closely reflect actual road test network quality.

[0012] It is understood that the beneficial effects of the second and third aspects mentioned above compared with the related technologies are the same as the beneficial effects of the first aspect mentioned above compared with the related technologies. Please refer to the relevant description in the first aspect mentioned above, which will not be repeated here. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a flowchart illustrating a network parameter derivation method provided in one embodiment of the present invention;

[0015] Figure 2 This is a structural schematic diagram of the test point location provided in one embodiment of the present invention;

[0016] Figure 3 This is a flowchart illustrating a network parameter derivation method provided in another embodiment of the present invention;

[0017] Figure 4 This is a structural schematic diagram of the test point location provided in another embodiment of the present invention;

[0018] Figure 5 This is a schematic diagram illustrating the specific process of calculating the network parameters for the position of the first derived point in a network parameter derivation method provided in another embodiment of the present invention;

[0019] Figure 6 This is a schematic diagram illustrating the specific process of updating the network parameters at the position of the first derived point in the network parameter derivation method provided in another embodiment of the present invention;

[0020] Figure 7 This is a schematic diagram illustrating the specific process of updating the network parameters at the position of the first derived point in the network parameter derivation method provided in another embodiment of the present invention;

[0021] Figure 8 This is a flowchart illustrating a network parameter derivation method provided in another embodiment of the present invention;

[0022] Figure 9 This is a structural schematic diagram of the test point location provided in another embodiment of the present invention. Detailed Implementation

[0023] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the present invention. However, those skilled in the art will understand that the embodiments of the present invention may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the embodiments of the present invention with unnecessary detail.

[0024] It should be noted that although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0025] It should also be understood that references to "one embodiment" or "some embodiments" in the specification of embodiments of the present invention mean that one or more embodiments of the present invention include the specific features, structures, or characteristics described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0026] Currently, operators are working to build a robust 5G network. Network performance is becoming increasingly important in future optimization efforts. A prerequisite for effective network optimization is obtaining as much network test data as possible to ensure the comprehensiveness and objectivity of network quality analysis.

[0027] In network optimization, drive testing has always been an important means of collecting wireless network data. Traditional drive testing is a targeted, localized test, with a relatively small testing area, mostly focused on the road itself. It cannot perceive the signal quality around the road, making drive test analysis somewhat one-sided.

[0028] To address these issues, comprehensive testing is commonly employed in related technologies. The purpose of comprehensive testing is to collect sufficient drive test data within the optimized area, covering as many roads as possible. However, during real-time testing, it's still impossible to obtain the current network coverage quality for non-road areas or lines that haven't been tested. Furthermore, at problem points on the drive test lines, such as weak coverage areas, only the poor signal quality at that specific location can be identified; the signal quality in surrounding areas not covered by the drive test cannot be determined. Therefore, drive test analysis still has a certain degree of limitation.

[0029] Based on the above, embodiments of the present invention provide a network parameter derivation method, an electronic device, and a storage medium. The method derives network parameters for locations around the road test points that cannot be tested based on the network parameters of the road test points, thereby enriching the road test data, making the road test analysis more comprehensive, and making the road test results closer to the actual road test network quality.

[0030] The technical solution of the present invention will be described below with reference to specific embodiments.

[0031] Firstly, embodiments of the present invention provide a network parameter derivation method, which is applied to drive test equipment. For example... Figure 1 As shown, the method includes, but is not limited to, steps S110, S120 and S130.

[0032] Step S110: Obtain the first network parameters at the location of the first test point;

[0033] Step S120: Obtain the second network parameters at the location of the second test point;

[0034] Step S130: Calculate the network parameters of the first derived point position based on the first network parameters and the second network parameters, wherein the position of the first derived point is determined based on the position of the first test point.

[0035] Steps S110 to S130 are described in detail below:

[0036] Step S110: Obtain the first network parameters at the location of the first test point;

[0037] In some embodiments, the first test point location is a test point location on the actual road test route. The test point location can be the location of a test grid or a specific test point location. A test grid refers to dividing the road test route into multiple intervals, with each interval serving as a grid. The location of a test grid can be the center of that grid. The first network parameters of the first test point location can be obtained through road testing.

[0038] Step S120: Obtain the second network parameters at the location of the second test point;

[0039] In some embodiments, the second network parameter at the second test point location can be a simulated network parameter value obtained by simulating road test data (e.g., the first network parameter at the first test point location), or a network parameter value obtained based on MR (Measurement Report). Alternatively, it can be a network parameter value generated in this embodiment; that is, if actual road test data already exists at the second test point location, the actual road test data can be directly used as the second network parameter.

[0040] Step S130: Calculate the network parameters of the first derived point position based on the first network parameters and the second network parameters, wherein the position of the first derived point is determined based on the position of the first test point.

[0041] In some embodiments, the location of the first derived point is a location that cannot be tested around the test point, such as a location blocked by houses or other buildings, or a location that is inaccessible. The location of the first derived point is determined based on the location of the first test point; that is, based on the test point locations on the actual road test route, the location closest to the test point location (or a location within a preset distance) is selected as the location to be calculated as the first derived point. Then, the network parameters of the first derived point location are calculated based on the first network parameters and the second network parameters. The calculation method can be to calculate the average of the first network parameters and the second network parameters, or to take a weighted average of the first network parameters and the second network parameters, or to calculate using the network parameters of the first test point location, the second test point location, and other test point locations around them, or other reasonable calculation methods; this embodiment does not limit this to any particular method.

[0042] Combining steps S110 to S130, such as Figure 2 As shown, the Y-axis represents the road test route, and the direction perpendicular to the Y-axis is used as the X-axis. T i At the current test time of the road test, point A is the first test point, A' is the second test point, and A”, which is closest to point A, is the first derived point.

[0043] In some embodiments, the first network parameter, the second network parameter, and the network parameter of the first derived point location may be parameters such as RSRP (Reference Signal Receiving Power), uplink and downlink traffic, uplink and downlink interference indicators, or other parameters used to measure the quality of wireless networks. This embodiment does not specifically limit these parameters.

[0044] In this embodiment, by adopting the network parameter derivation method including the above steps S110 to S130, the network parameters of locations around the road test points that cannot be tested can be derived based on the network parameters of the actual road test points, which enriches the road test data, makes the road test analysis more comprehensive, and makes the road test results closer to the actual road test network quality.

[0045] In some embodiments, such as Figure 3 As shown, after performing step S130, the network parameter derivation method may also include, but is not limited to, the following steps:

[0046] Step S140: Determine at least one third test point location based on the first test point location, wherein the first test point location and the third test point location are on the same test route;

[0047] Step S150: Determine at least one fourth test point location based on the second test point location. The second test point location and the fourth test point location are on the same test route, and the third test point location corresponds one-to-one with the fourth test point location.

[0048] Step S160: Obtain the third network parameters at the location of the third test point;

[0049] Step S170: Obtain the fourth network parameters at the location of the fourth test point;

[0050] Step S180: Calculate the network parameters for the position of the second derived point based on the third network parameters and the fourth network parameters. The position of the second derived point is determined based on the position of the third test point, and the positions of the third test point and the second derived point correspond one-to-one.

[0051] Step S190: Update the network parameters of the first derived point position based on the network parameters of the first derived point position and the network parameters of at least one second derived point position.

[0052] Steps S140 to S190 are described in detail below:

[0053] Step S140: Determine at least one third test point location based on the first test point location, wherein the first test point location and the third test point location are on the same test route;

[0054] In some embodiments, such as Figure 4 As shown, at least one third test point location B is determined based on the first test point location A, and the first test point location A and the third test point location B are on the same test route. That is, based on the test point locations on the actual road test route, at least one other test point location is determined on the same road test route.

[0055] Because the test signal exhibits a certain degree of continuity and correlation during drive testing, to avoid jitter or abrupt changes in the test signal due to uncertainties at the current test moment and to reflect a more objective wireless signal performance, network parameter values ​​for the derived point location are calculated using network parameters at continuous time intervals. In other words, preferably, to ensure the temporal continuity of the drive test data, the location B of the third test point should be selected as the current test moment T. i Previous test time T i-1 The corresponding position.

[0056] Understandably, to improve calculation accuracy, multiple third test point locations B can be selected. For example, the current test time T can be selected. i Previous test time T i-1 The corresponding position and the next test time T i+1 The corresponding locations are all designated as the third test point locations.

[0057] Step S150: Determine at least one fourth test point location based on the second test point location. The second test point location and the fourth test point location are on the same test route, and the third test point location corresponds one-to-one with the fourth test point location.

[0058] In some embodiments, such as Figure 4 As shown, at least one fourth test point location B' is determined based on the second test point location A'. The second test point location A' and the fourth test point location B' are on the same test route, and the third test point location B' corresponds one-to-one with the fourth test point location B'. That is, based on the test point locations corresponding to the simulated network parameter values, at least one other test point location is determined on the same road test route. The effect is the same as step S140, and will not be repeated here.

[0059] Understandably, to improve calculation accuracy, multiple fourth test point positions B' can be selected. These multiple third test point positions B must correspond one-to-one with the multiple fourth test point positions B'.

[0060] Step S160: Obtain the third network parameters at the location of the third test point;

[0061] In some embodiments, obtaining the third network parameters at the third test point location B is the same as in step S110, and will not be repeated here.

[0062] Step S170: Obtain the fourth network parameters at the location of the fourth test point;

[0063] In some embodiments, obtaining the fourth network parameters at the fourth test point location B' is the same as in step S120, and will not be repeated here.

[0064] Step S180: Calculate the network parameters for the position of the second derived point based on the third network parameters and the fourth network parameters. The position of the second derived point is determined based on the position of the third test point, and the positions of the third test point and the second derived point correspond one-to-one.

[0065] In some embodiments, such as Figure 4 As shown, the second derived point location B” is a location that cannot be tested around the road test point, such as a location blocked by houses or other buildings, or a location that is inaccessible. The second derived point location B” is determined based on the third test point location B, that is, based on the test point location on the actual road test route, the location closest to this test point location (or a location less than a preset distance) is selected as the second derived point location to be calculated. The third test point location B and the second derived point location B” correspond one-to-one, and since the third test point location B and the fourth test point location B’ also correspond one-to-one, the third test point location B, the fourth test point location B’, and the second derived point location B” are all corresponding. Then, the network parameters of the second derived point location are calculated based on the third network parameters and the fourth network parameters.

[0066] Step S190: Update the network parameters of the first derived point position based on the network parameters of the first derived point position and the network parameters of at least one second derived point position.

[0067] In some embodiments, the network parameters of the first derived point position A” are updated based on the network parameters of the first derived point position A” and at least one second derived point position B”. That is, based on the initially calculated current test time T. i The network parameters corresponding to the first derived point position A” and the previous test time T i-1 The network parameters of the corresponding second derived point position B” are used to jointly calculate the network parameters of the first derived point position A”, so as to update the network parameters of the first derived point position A”, thereby reducing the calculation result error caused by signal jitter or sudden change.

[0068] In some embodiments, after performing step S190, the network parameter derivation method may further include, but is not limited to, the following steps:

[0069] Update the second network parameters based on the network parameters of the updated first derived point location.

[0070] In some embodiments, since the second network parameters of the second test point location A' are simulated network parameter values ​​obtained from road test data simulation, or network parameter values ​​obtained from MR (Measurement Report), the simulation results may not be accurate. Therefore, updating the second network parameters of the second test point location A' according to the updated network parameters of the first derived point location A' can achieve the purpose of correcting the second network parameters of the second test point location A'.

[0071] In some embodiments, the first derived point position A” is located between the first test point position A and the second test point position A'.

[0072] In some embodiments, such as Figure 2 and Figure 4 As shown, the Y-axis represents the road test route, and the direction perpendicular to the Y-axis is used as the X-axis. T i "At the current test moment of the road test, the location closest to the first test point A (or a location less than a preset distance) is selected as the first derived point location A to be calculated." The reason is that the farther away a location is from the first test point A, the lower its reliability in correcting the second network parameters of the second test point A'. The network parameters of the location closest to the first test point A are relatively close to the network parameters of the first test point A. Therefore, the location closest to the first test point A (or a location less than a preset distance) is selected as the first derived point location A.

[0073] In some embodiments, such as Figure 5 As shown, step S130 may include, but is not limited to, the following steps:

[0074] Step S131: Obtain the first distance value between the location of the first test point and the location of the first derived point;

[0075] Step S132: Obtain the second distance value between the position of the first derived point and the position of the second test point;

[0076] Step S133: Calculate the network parameters of the first derived point position based on the first distance value, the second distance value, the first network parameter, and the second network parameter.

[0077] In some embodiments, the specific method for calculating the network parameters of the first derived point position A” is as follows: Obtain the first distance value between the first test point position A and the first derived point position A”, denoted as d1; obtain the second distance value between the first derived point position A” and the second test point position A’, denoted as d2. Calculate the network parameters of the first derived point position A” based on the first distance value d1, the second distance value d2, the first network parameters, and the second network parameters. The calculation formula is as follows:

[0078] RA”=RA*(1-d1 / (d1+d2))+RA’*(1-d2 / (d1+d2)) (1)

[0079] Wherein, RA is the first network parameter, RA' is the second network parameter, and RA” is the network parameter of the first derived point position A”.

[0080] Similarly, the formula for calculating the network parameters at the second derived point position B” is as follows:

[0081] RB”=RB*(1-d1 / (d1+d2))+RB’*(1-d2 / (d1+d2)) (2)

[0082] Wherein, RB is the first network parameter, RB' is the second network parameter, and RB” is the network parameter of the second derived point position B”.

[0083] In some embodiments, such as Figure 6 As shown, step S190 may include, but is not limited to, the following steps:

[0084] Step S191: Update the network parameters of the first derived point position based on the average value of the network parameters of the first derived point position and the network parameters of at least one second derived point position.

[0085] In some embodiments, combined with Figure 4 The network parameters of the first derived point position A” are updated based on the average value of the network parameters RA” of the first derived point position A” and the network parameters RB” of at least one second derived point position B”, that is:

[0086] RA”*=(RA”+RB”) / 2 (3)

[0087] Where RA”* represents the network parameters of the updated first derived point position A”.

[0088] In some embodiments, such as Figure 7 As shown, step S190 may include, but is not limited to, the following steps:

[0089] Step S192: Update the network parameters of the first derived point position based on the weighted average of the network parameters of the first derived point position and the network parameters of at least one second derived point position.

[0090] In some embodiments, combined with Figure 4 The network parameters of the first derived point position are updated based on the weighted average of the network parameters RA” of the first derived point position A” and the network parameters RB” of at least one second derived point position B”, i.e.:

[0091] RA”*=x*RA”+y*RB” (4)

[0092] Where RA”* represents the network parameter of the updated first derived point position A”, x represents the weight of the network parameter RA” of the first derived point position A”, y represents the weight of the network parameter RB” of the second derived point position B”, and x+y=1.

[0093] In some embodiments, in order to increase the authenticity of the signal, it is preferable to set the weight x of the network parameter RA” at the first derived point position A” to be greater than 0.5, and correspondingly, set the weight y of the network parameter RB” at the second derived point position B” to be less than 0.5.

[0094] It is worth noting that steps S191 and S192 in this embodiment are parallel technical solutions.

[0095] In some embodiments, the weights of the network parameter RA” at the first derived point location A” and the network parameter RB” at the second derived point location B” can be defined based on the number of sampling points at the corresponding locations. The more sampling points there are, the higher the reliability of the network parameters RA” and RB” representing the average network parameters at the corresponding locations, and the greater their weights. It should be noted that the network parameters RA” and RB” can be calculated using the average value of the network parameters of all sampling points at the corresponding locations, or they can be calculated using other methods. This embodiment of the invention does not limit this method.

[0096] In some embodiments, the above steps are used to iteratively calculate the network parameters for each second derivative point location, thereby allowing the network parameters for all corresponding derivative point locations to be calculated along the entire road test route.

[0097] In some embodiments, the network parameter derivation method further includes the following steps:

[0098] The updated network parameters of the first derived point location are sent to the cloud server so that the cloud server updates the status value of the first derived point location. The status value is used to indicate whether the network parameters of the first derived point location have been updated.

[0099] In some embodiments, the drive test device also sends the updated network parameters RA"* of the first derived point location A" to the cloud server for storage. Furthermore, the cloud server can update the status value of the first derived point location A" based on the updated network parameters RA"*. The status value indicates whether the network parameters of the first derived point location A" have been updated. Specifically, the status value can be 0, 1, or 2, where 1 represents that the first derived point location A" has been updated but has not converged, 0 represents that it has not been updated, and 2 represents that it has been updated and converged.

[0100] In some embodiments, for example: if the network parameters of the first derived point position A” are updated three times in succession according to step S191 or step S192, and the updated values ​​are -80, -82, and -81 respectively, since the difference between the two consecutive updates is 1, or the difference between the two consecutive updates is less than a certain threshold value, the state value of the first derived point position A” is 2, which means that it has been updated and converged.

[0101] In some embodiments, if the second test point location A' already has actual road test data, that is, there is no need to use the first test point location A and the first derived point location A” to correct it, then the state value of the first derived point location A” is also 2.

[0102] It is understood that remote communication between the road test equipment and the cloud server includes, but is not limited to: internet communication based on wired connections, internet communication based on wireless connections, and peer-to-peer (P2P) communication. This embodiment does not specifically limit the particular method of remote communication between the road test equipment and the cloud server.

[0103] In some embodiments, such as Figure 8 As shown, the network parameter derivation method also includes the following steps:

[0104] Step S1100: Obtain the status value from the cloud server;

[0105] Step S1110: When the network parameters representing the position of the first derived point are updated, the calculation of the network parameters of the first derived point position is terminated.

[0106] In some embodiments, the road test device can also obtain the status value of the first derived point location A” from the cloud server. When the status value indicates that the network parameters of the first derived point location A” have been updated, the calculation of the network parameters of the first derived point location A” is terminated to reduce the computational load of the road test device.

[0107] The network parameter derivation method of this invention will be described below with a specific application example.

[0108] like Figure 9 As shown, in Figure 4 Based on this, continue to select the current test time T. i The next test time T i+1 The corresponding position is designated as the fifth test point position C, and C' is the sixth test point position corresponding to the fifth test point position C. The third derived point position C is determined based on the fifth test point position C.

[0109] Similarly, the third derivative point position C” is also the closest position to the fifth test point position C (or a position less than the preset distance).

[0110] Similarly, obtain the fifth network parameter RC at the fifth test point location C and the sixth network parameter RC' at the sixth test point location C', and calculate the network parameters at the third derived point location C” based on the fifth network parameter RC and the sixth network parameter RC'. The calculation formula is as follows:

[0111] RC”=RC*(1-d1 / (d1+d2))+RC’*(1-d2 / (d1+d2)) (5)

[0112] Wherein, RC” represents the network parameter of the third derived point position C”.

[0113] Correspondingly, formula (3) becomes:

[0114] RA”*=(RA”+RB”+RC”) / 3 (6)

[0115] Correspondingly, formula (4) becomes:

[0116] RA”*=x*RA”+y*RB”+z*RC” (7)

[0117] Where z is the weight of the network parameter RC” at the third derived point position C”, and x+y+z=1.

[0118] In some embodiments, if x = c, where c is a constant, then Similarly, to increase the realism of the signal, the constant c is preferably set to be greater than 0.5.

[0119] Where, N i-1 For the previous test time T i-1 The number of sampling points, N i+1 For the next test time T i+1 The number of sampling points.

[0120] By weighted averaging the network parameters of the first derived point position A” at the current test time, the previous test time, and the next test time, the final network parameter RA”* of the first derived point position A” at the current test time is obtained. This network parameter RA”* is considered to be the network parameter of the first derived point position A”, and it is uploaded to the cloud server for storage and updating of its state value.

[0121] In addition, one embodiment of the present invention provides an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0122] The processor and memory can be connected via a bus or other means.

[0123] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0124] It should be noted that the electronic device in this embodiment can be applied as the road test device as described in the first aspect. The electronic device in this embodiment and the road test device as described in the first aspect have the same inventive concept. Therefore, these embodiments have the same implementation principle and technical effect, which will not be described in detail here.

[0125] The non-transient software program and instructions required to implement the network parameter derivation method of the above embodiments are stored in memory. When executed by a processor, the network parameter derivation method of the above embodiments is executed, for example, the method described above is executed. Figure 1 Method steps S110 to S130 in the text Figure 3 Method steps S140 to S190 in the text Figure 5 Method steps S131 to S133 in the text Figure 6 Method steps S191 Figure 7 Method step S192.

[0126] Furthermore, one embodiment of the present invention also provides a computer-readable storage medium storing computer-executable instructions that are executed by a processor or controller, for example, by a processor in the above-described electronic device embodiment, causing the processor to perform the network parameter derivation method described above, for example, performing the above-described... Figure 1 Method steps S110 to S130 in the text Figure 3 Method steps S140 to S190 in the text Figure 5 Method steps S131 to S133 in the text Figure 6 Method steps S191 Figure 7 Method step S192.

[0127] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0128] The above is a detailed description of the preferred embodiments of the present invention. However, the embodiments of the present invention are not limited to the above-described implementation methods. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the embodiments of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the embodiments of the present invention.

Claims

1. A method for deriving network parameters, comprising: obtaining a first network parameter of a first test point; obtaining a second network parameter of a second test point; calculating a network parameter of a first derived point according to the first network parameter and the second network parameter, wherein the first derived point is determined according to the first test point; determining at least one third test point according to the first test point, wherein the first test point and the third test point are on a same test route; determining at least one fourth test point according to the second test point, wherein the second test point and the fourth test point are on the same test route, and the third test point corresponds to the fourth test point one by one; obtaining a third network parameter of the third test point; obtaining a fourth network parameter of the fourth test point; calculating a network parameter of a second derived point according to the third network parameter and the fourth network parameter, wherein the second derived point is determined according to the third test point, and the third test point corresponds to the second derived point one by one; updating the network parameter of the first derived point according to the network parameter of the first derived point and the network parameter of at least one of the second derived points.

2. The network parameter derivation method of claim 1, wherein, Further comprising the following steps: updating the second network parameter according to the updated network parameter of the first derived point.

3. The network parameter derivation method of claim 2, wherein, The first derived point is located between the first test point and the second test point.

4. The network parameter derivation method of claim 3, wherein, The calculating a network parameter of a first derived point according to the first network parameter and the second network parameter comprises: obtaining a first distance value between the first test point and the first derived point; obtaining a second distance value between the first derived point and the second test point; calculating the network parameter of the first derived point according to the first distance value, the second distance value, the first network parameter and the second network parameter.

5. The network parameter derivation method of claim 1, wherein, The updating the network parameter of the first derived point according to the network parameter of the first derived point and the network parameter of at least one of the second derived points comprises: updating the network parameter of the first derived point according to an average value of the network parameter of the first derived point and the network parameter of at least one of the second derived points; or updating the network parameter of the first derived point according to a weighted average value of the network parameter of the first derived point and the network parameter of at least one of the second derived points. Further comprising the following steps:

6. The network parameter derivation method of claim 1, wherein, sending the updated network parameter of the first derived point to a cloud server, so that the cloud server updates a state value of the first derived point, wherein the state value is used to represent whether the network parameter of the first derived point is completed. Further comprising the following steps:

7. The network parameter derivation method of claim 6, wherein, obtaining the state value from the cloud server; terminating the calculation of the network parameter of the first derived point when the state value represents that the network parameter of the first derived point is completed. ​ 8. An electronic device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the network parameter derivation method of any one of claims 1 to 7 when executing the computer program.

9. A computer readable storage medium storing computer executable instructions for performing the network parameter derivation method of any one of claims 1 to 7.

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