Signal detection methods, devices, equipment, media and products

Abstract

This application discloses a signal detection method, apparatus, device, medium, and product. The method includes: acquiring first measurement data from at least one observation cell, wherein the at least one observation cell is a cell within a preset area centered on a first station address, and the first station address is the station address to which the serving cell signal belongs; the first measurement data includes multiple first signal strengths of the serving cell signal and multiple second signal strengths of a signal to be detected uploaded by a terminal located in each observation cell at multiple times; determining a correlation index between the serving cell signal and the signal to be detected based on the multiple first signal strengths and multiple second signal strengths corresponding to each observation cell; and determining that the signal to be detected originates from the first station address if the correlation index meets preset conditions. Using the signal detection method, apparatus, device, medium, and product provided in this application can avoid the problem of inaccurate detection caused by insufficient location information data and errors in engineering parameter data.

Description

Technical Field

[0001] This application belongs to the field of data processing technology, and in particular relates to a signal detection method, apparatus, equipment, medium and product. Background Technology

[0002] Currently, in order to ensure the accuracy of the site rental fees collected by the tower site management agency and to detect whether other operators' signals are affecting their own signals, operators usually check whether other operators' signals are sharing site locations with their own signals.

[0003] In existing technologies, when detecting whether a signal shares a site with the operator's signal, it is usually necessary to rely on the location information in the Minimization of Drive-tests (MDT) data and the latitude, longitude, azimuth, downtilt, and station elevation information in the engineering parameter data.

[0004] However, since the location information in MDT data can only be obtained when the network where the user terminal is located meets the requirements and the user terminal has enabled the global positioning function, the amount of location information in MDT data is relatively small. Moreover, the latitude, longitude, azimuth, downtilt, and station elevation information in the engineering parameter data have large errors. Therefore, the existing technology relies on the location information in MDT data and the latitude, longitude, azimuth, downtilt, and station elevation information in the engineering parameter data to detect whether a signal shares a site with the signal of the same operator, which has poor accuracy. Summary of the Invention

[0005] This application provides a signal detection method, apparatus, device, medium, and product, which can at least solve the problem that the existing technology relies on location information in MDT data and latitude, longitude, azimuth, downtilt angle, station height, and other information in engineering parameter data to detect whether a signal shares a site with the operator's signal, resulting in poor accuracy.

[0006] In a first aspect, embodiments of this application provide a signal detection method, the method comprising:

[0007] Acquire first measurement data of at least one observation cell, wherein at least one observation cell is a cell within a preset area centered on a first site, the first site is the site to which the serving cell signal belongs, and the first measurement data includes multiple first signal strengths of the serving cell signal and multiple second signal strengths of the signal to be detected uploaded by the terminal located in each observation cell at multiple times, wherein the serving cell signal is the signal of the first operator, and the signal to be detected is the signal of another operator besides the first operator;

[0008] Based on the multiple first signal strengths and multiple second signal strengths corresponding to each observation cell, the correlation index between the serving cell signal and the signal to be detected is determined;

[0009] If the correlation index meets the preset conditions, the signal to be detected is determined to come from the first station site.

[0010] Secondly, embodiments of this application provide a signal detection device, which includes:

[0011] The first acquisition module is used to acquire first measurement data of at least one observation cell. The at least one observation cell is a cell in a preset area centered on a first station address. The first station address is the station address to which the serving cell signal belongs. The first measurement data includes multiple first signal strengths of the serving cell signal and multiple second signal strengths of the signal to be detected uploaded by the terminal located in each observation cell at multiple times. The serving cell signal is the signal of the first operator, and the signal to be detected is the signal of another operator besides the first operator.

[0012] The first determining module is used to determine the correlation index between the serving cell signal and the signal to be detected based on multiple first signal strengths and multiple second signal strengths corresponding to each observation cell;

[0013] The second determination module is used to determine that the signal to be detected comes from the first station address when the correlation index meets the preset conditions.

[0014] Thirdly, embodiments of this application provide an electronic device, the device comprising: a processor and a memory storing computer program instructions;

[0015] When the processor executes the computer program instructions, it implements the signal detection method as shown in any embodiment of the first aspect.

[0016] Fourthly, embodiments of this application provide a computer storage medium storing computer program instructions, which, when executed by a processor, implement the signal detection method shown in any embodiment of the first aspect.

[0017] Fifthly, embodiments of this application provide a computer program product in which instructions, when executed by a processor of an electronic device, cause the electronic device to perform the signal detection method shown in any embodiment of the first aspect.

[0018] The signal detection method, apparatus, device, medium, and product of this application embodiment can acquire first measurement data of at least one observation cell, and determine the correlation index between the serving cell signal and the signal to be detected based on multiple first signal strengths of the serving cell signal uploaded by the terminal located in each observation cell at multiple times and multiple second signal strengths of the signal to be detected, which are included in the first measurement data. Then, when the correlation index meets preset conditions, it is determined that the signal to be detected comes from a first site. That is, both the signal to be detected and the serving cell signal come from the first site, i.e., they share the first site. In this way, the signal of another operator can be detected by using the first measurement data of at least one observation cell around the first site to detect whether the signal of another operator shares the site with the signal of the first operator, without relying on the location information in the MDT data and the latitude, longitude, azimuth, downtilt angle, station height, and other information in the engineering parameter data. This avoids the problem of inaccurate detection caused by the small amount of location information data and the error of the engineering parameter data. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of a signal detection method provided in one embodiment of this application;

[0021] Figure 2 This is a schematic diagram of the structure of a signal detection device provided in one embodiment of this application;

[0022] Figure 3 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0023] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0024] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, 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. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0025] In wireless network signal measurement data, MDT (Multi-Targeting Technology) is a wireless network signal measurement technique that can include location information. Operators collect wireless measurement information from ordinary commercial network terminals by enabling base station-related functions, replacing the method of obtaining wireless measurement data through drive testing with professional testing terminals. The signal strength measurement target can be set to the operating frequency of other operators, and the measurement results can include Global Navigation Satellite System (GNSS) location information to indicate the location where the measurement sample points were generated.

[0026] In MDT-based technology applications, MDT data is typically used to study the correlation of wireless signals between serving cells and neighboring cells, neighboring cells and neighboring cells, serving base stations and neighboring base stations, and is applied to application scenarios such as shared site detection and verification, site location estimation and verification.

[0027] For example, most tower sites are operated and managed by tower companies. In the management of tower service fees, the accuracy of payment depends on the accuracy of the basic information on the leased tower sites. The more operators sharing a site, the greater the discount they receive. Timely detection and updating of the number of operators sharing a site is crucial for reducing tower service fees. Currently, the update of information on the number of shared users for leased tower sites mainly relies on proactive updates by the tower company. However, untimely or non-updated shared site information can lead to numerous errors, resulting in inaccurate billing by the tower company. Furthermore, an excessive number of operators sharing a site can degrade signal quality. Therefore, timely detection of other operators using a particular site's signal is critical to protecting the interests of the operators.

[0028] Existing technical solutions generally use MDT measurement samples directly from a specific cell site, or extract MDT measurements from surrounding cells and combine them with the MDT measurement samples from the cell site. Existing technologies employ signal positioning, which is highly dependent on the accuracy of the base station's technical parameters, including latitude, longitude, azimuth, downtilt angle, and elevation.

[0029] Existing technical solutions heavily rely on MDT location information. However, in complex geographical environments, it is difficult to obtain accurate location information. Moreover, the proportion of MDT measurement samples containing location information is very low. MDT measurement samples only contain location information when the terminal is outdoors, the terminal supports reporting location information, and the user allows the terminal to report. Therefore, the amount of MDT location information is relatively small. Abandoning or discarding the MDT data samples generated by surrounding sites and the location-related unique attributes cannot achieve good application results.

[0030] Furthermore, the signal strength received by the terminal is greatly affected by the azimuth and downtilt angle of the base station's transmitting antenna. Therefore, when conducting signal correlation assessment, it may be impossible to obtain accurate information such as antenna position, azimuth, downtilt angle, and antenna parameters. Even if this information is obtained, it may not match the actual parameters, and the parameters may also become invalid due to antenna aging. If MDT samples within the site are used to calculate and assess wireless signal correlation, it is difficult to eliminate the influence of the above factors, resulting in inaccurate correlation assessment.

[0031] Based on this, this application proposes a signal detection method, namely, acquiring first measurement data of at least one observation cell, wherein the at least one observation cell is a cell within a preset area centered on a first site, the first site is the site to which the serving cell signal belongs, the first measurement data includes multiple first signal strengths of the serving cell signal and multiple second signal strengths of the signal to be detected uploaded by a terminal located in each observation cell at multiple times, the serving cell signal is the signal of a first operator, and the signal to be detected is the signal of another operator besides the first operator; based on the multiple first signal strengths and the multiple second signal strengths corresponding to each observation cell, a correlation index between the serving cell signal and the signal to be detected is determined; if the correlation index meets preset conditions, it is determined that the signal to be detected comes from the first site.

[0032] In this way, it is possible to detect whether a signal from another operator shares a site with the signal from the first operator using the first measurement data from at least one observation cell surrounding the first site, without relying on location information in the MDT data or latitude, longitude, azimuth, downtilt angle, and station elevation information in the engineering parameter data. This avoids the problem of inaccurate detection caused by insufficient location information data and errors in the engineering parameter data. At the same time, it greatly shortens the data collection time, saves bandwidth resources, and significantly shortens the result output cycle.

[0033] In addition, this application can also collect MDT data containing other sites through specific base station configurations; the MDT measurement data samples contain information such as the signal strength of the serving cell, as well as information such as the signal strength of different cells on other frequency points of specific configurations, and the samples may also contain GNSS location information when the measurement data samples were generated.

[0034] Currently, the distance between mobile wireless network stations generally ranges from 500 meters to several kilometers. When the serving cell signal and the signal to be detected are obtained from the MDT sample points of surrounding sites, the difference in the azimuth angle and downtilt angle of the two signals received at these MDT sample points will be within a relatively small range. When the azimuth angle and downtilt angle are similar, the antenna radiation gain is similar. In other words, the signal difference caused by antenna radiation can be ignored between the two signals obtained from these MDT sample points. No additional antenna radiation gain compensation algorithm is needed, which can greatly improve the accuracy and reliability of correlation measurement and improve practicality.

[0035] In this way, the location relationship between the surrounding cells and the first site can be utilized to overcome the influence of signal radiation factors such as antenna position, azimuth angle, downtilt angle, and antenna aging of the serving cell signal and the transmitting antenna of the signal to be detected, thus providing a strong supplementary and enhancing effect. Therefore, while using the signal detection method of this application, the above methods can be combined to further improve the accuracy of signal detection.

[0036] Figure 1 The diagram illustrates a flowchart of a signal detection method according to an embodiment of this application. It should be noted that this signal detection method can be applied to signal detection devices, such as... Figure 1 As shown, the signal detection method may include the following steps:

[0037] S110, acquire first measurement data of at least one observation cell;

[0038] S120, Based on the multiple first signal strengths and multiple second signal strengths corresponding to each observation cell, determine the correlation index between the serving cell signal and the signal to be detected;

[0039] S130, if the correlation index meets the preset conditions, determine that the signal to be detected comes from the first station site.

[0040] Therefore, it is possible to acquire first measurement data from at least one observation cell, and based on the first measurement data including multiple first signal strengths of the serving cell signal uploaded by terminals located in each observation cell at multiple times and multiple second signal strengths of the signal to be detected, the correlation index between the serving cell signal and the signal to be detected is determined. Then, if the correlation index meets preset conditions, it is determined that the signal to be detected comes from the first site. That is, both the signal to be detected and the serving cell signal come from the first site, i.e., they share the first site. In this way, it is possible to detect whether the signal of another operator shares the site with the signal of the first operator by using the first measurement data from at least one observation cell around the first site, without relying on the location information in the MDT data and the latitude, longitude, azimuth, downtilt angle, station height and other information in the engineering parameter data. This avoids the problem of inaccurate detection caused by the small amount of location information data and the error of the engineering parameter data.

[0041] Regarding S110, at least one observation cell can be a cell within a preset area centered on a first site. For example, a circular area with a radius of 600 to 1800 meters centered on the first site can be defined as the preset area, and cells within this circular area can be selected as observation cells. The first site can be a site used by a first operator, and the first site can be the site to which the serving cell signal belongs. The serving cell signal can be the signal of the first operator. The first measurement data can include multiple first signal strengths of the serving cell signal and multiple second signal strengths of the signal to be detected uploaded by the terminal located in each observation cell at multiple times. The signal to be detected can be the signal of another operator besides the first operator. Here, the serving cell signal and the signal to be detected can be considered as a neighboring cell pair, and the signal strength of the neighboring cell pair at multiple times in each observation cell can be obtained, that is, multiple first signal strengths and multiple second signal strengths corresponding to each observation cell.

[0042] In some examples, each data point in the first measurement data may include: base station identifier, sampling point identifier, serving cell frequency, serving cell physical cell identifier (PCI), neighboring cell frequency, neighboring cell serving cell identifier, first signal strength, and second signal strength. Each sampling data point corresponds to a sampling point identifier. For example, the sampling data at a certain moment included in the first measurement data may be as shown in Table 1.

[0043] Table 1 - Sampling Data Table

[0044]

[0045] In some implementations, when the signal detection device operates automatically, it first needs to determine the site address of a first operator, and then determine whether the site address is a shared site address by detecting whether the signal to be monitored may originate from that site address. Here, a shared site address refers to different operators using the same site address. In order for the signal detection device to automatically determine whether the site address to be detected as a shared site address during automatic operation, before S110, the method may further include:

[0046] Obtain the second measurement data;

[0047] The second measurement data is matched with the pre-stored engineering parameter data to determine the first station location.

[0048] Here, second measurement data can be acquired periodically, for example, automatically downloading the most recent day's MDT data and / or MRO (Maintenance, Repair & Operations) data weekly. This second measurement data may include the base station identifier, serving cell frequency, and serving cell physical cell identifier. The frequency and physical cell identifier determine a signal; the serving cell signal can be determined based on the serving cell frequency and serving cell physical cell identifier. Pre-stored engineering parameter data may include at least one data entry, each containing the base station identifier, frequency, physical cell identifier, and site address. Matching the base station identifier, serving cell frequency, and serving cell physical cell identifier included in the second measurement data with those included in the engineering parameter data determines the first site address corresponding to that base station identifier, serving cell frequency, and serving cell physical cell identifier. This first site address is the site that needs to be checked to determine if it is a shared site.

[0049] In addition, the pre-stored engineering parameter data may also include the cell name, and the first cell name can be determined after matching the second measurement data with the pre-stored engineering parameter data.

[0050] In some examples, the second measurement data can be "base station identifier: S1, serving cell frequency E1, serving cell physical cell identifier: P1". The pre-stored engineering parameter data includes multiple data items, namely: A, B, and C. The second measurement data is matched with A, B, and C. It is determined that the "base station identifier: S1, serving cell frequency E1, serving cell physical cell identifier: P1" included in B is the same as the data included in the second measurement data. Therefore, B is the engineering parameter data that matches the second measurement data. Thus, it can be determined that "site address: 0001" included in B is the first site address and "cell name: C1" is the first cell name.

[0051] In this way, by matching the second measurement data with the pre-stored engineering parameter data, the first site that needs to be detected as a shared site can be automatically determined.

[0052] In some implementations, whether a first cell address is shared is determined by detecting whether a signal originates from the first cell address. To enable the signal detection device to automatically determine the signal to be detected, the second measurement data may also include signals from multiple neighboring cells. Before S110, the method may further include:

[0053] The signals of the first operator are filtered out from the signals of multiple neighboring cells to obtain at least one first signal to be detected;

[0054] One of the at least one first signal to be detected is randomly selected as the signal to be detected.

[0055] Here, since shared site refers to different operators using the same site, it is meaningless to detect the signal of the first operator. Therefore, when determining the signal to be detected, the signal of the first operator can be filtered out first.

[0056] Specifically, a neighboring cell can be a cell that has a handover relationship with the serving cell. The neighboring cell signal can be determined by the neighboring cell's frequency and physical cell identifier. By associating the aforementioned second measurement data with pre-stored engineering parameter data, multiple sampling data can be obtained. Each sampling data may include a first site address, a first cell name, a base station identifier, a serving cell frequency, a serving cell's physical cell identifier, a neighboring cell's frequency, and a neighboring cell's physical cell identifier. It may also include a sampling point identifier, and each sampling data includes a unique sampling point identifier. Therefore, deduplication can be performed based on the first site address, the first cell name, the base station identifier, the serving cell frequency, and the serving cell's physical cell identifier. That is, sampling data that is not the first site address, the first cell name, the base station identifier, the serving cell frequency, and the serving cell's physical cell identifier can be filtered out. The neighboring cell frequency and physical cell identifier included in the remaining sampling data can then be determined, thereby determining the neighboring cell signal based on the neighboring cell frequency and physical cell identifier.

[0057] Since the remaining sampled data can be at least one, at least one first signal to be detected can be determined, and then one of the at least one first signal to be detected can be randomly selected as the final signal to be detected.

[0058] In this way, by filtering out the signals of the primary operator from multiple neighboring cell signals, it is possible to avoid detecting the signals of the local operator and performing meaningless operations, thereby improving work efficiency.

[0059] In some implementations, to avoid reduced efficiency due to repeated detection, the method may further include the following steps before filtering out the first operator's signal from multiple neighboring cell signals to obtain at least one first signal to be detected:

[0060] Filter out signals that have been identified as originating from the first site from multiple neighboring cell signals.

[0061] Here, some signals from neighboring cells may have already been identified as originating from the first site during previous detection processes, and therefore do not need to be detected again. When determining the signal to be detected, signals already identified as originating from the first site can be filtered out to avoid repeatedly detecting signals that have already been detected.

[0062] Specifically, after each signal detection is completed, the detected signal and its corresponding station address can be recorded in the station address database so that when determining the signal to be detected, the signal corresponding to the first station address recorded in the station address database can be filtered out.

[0063] Therefore, by filtering out signals already identified from the first station before determining the signal to be detected, we can avoid repeatedly detecting signals that have already been detected, thereby improving work efficiency.

[0064] In connection with S120, based on the multiple signal strengths corresponding to the serving cell signal and the signal to be detected in each observation cell, namely multiple first signal strengths and multiple second signal strengths, the correlation index between the serving cell signal and the signal to be detected can be determined. This correlation index can characterize the correlation between the signal strength fluctuations of the serving cell signal and the signal to be detected.

[0065] In some implementations, in order to determine the correlation between the serving cell signal and the signal to be detected, S120 may specifically include:

[0066] The first signal intensity is sorted according to the first order of time, from early to late or from late to early, to obtain a first signal intensity sequence; and the second signal intensity is sorted according to the first order to obtain a second signal intensity sequence.

[0067] The correlation between the first signal intensity sequence and the second signal intensity sequence is calculated to determine the first correlation index;

[0068] Based on the weight corresponding to each observation cell, the first correlation index corresponding to each observation cell is weighted and summed to determine the correlation index.

[0069] Here, the first correlation index characterizes the temporal correlation between the signal strength fluctuations of the serving cell signal and the signal under test. To determine the temporal correlation between the signal strength fluctuations of the serving cell signal and the signal under test, multiple first signal strengths and multiple second signal strengths can be sorted in chronological order from early to late or from late to early. It is important to note that the sorting rules for the multiple first signal strengths and multiple second signal strengths must be the same, i.e., both must be either from early to late or both from late to early. Specifically, sorting the multiple first signal strengths yields a first signal strength sequence, and sorting the multiple second signal strengths yields a second signal strength sequence.

[0070] Then, the correlation between the first signal intensity sequence and the second signal intensity sequence can be calculated. For example, the correlation can be calculated using the CORREL function, as shown in the following formula:

[0071] M1 i =CORREL(NcRsrpList1 i ,NcRsrpList2 i )

[0072] Among them, M1 i This can be the first correlation index corresponding to the i-th observation cell, NcRsrpList1 i This can be the first signal intensity sequence corresponding to the i-th observation cell, NcRsrpList2 i It can be the second signal intensity sequence corresponding to the i-th observation cell.

[0073] The specific formula for calculating the CORREL function is as follows:

[0074]

[0075] Here, x = NcRsrpList1 i ,y=NcRsrpList2 i Substituting into the specific calculation formula of the CORREL function above, CORREL(NcRsrpList1) is calculated. i ,NcRsrpList2 i ), that is, M1 i .

[0076] After calculating the first correlation index for each observation cell using the above method, the correlation index between the serving cell signal and the signal to be detected can be finally determined by weighted summation of the first correlation indices for each observation cell.

[0077] Before performing the weighted calculation, it is necessary to determine the weight corresponding to each observation cell. Specifically, the weight of each observation cell can be determined based on the number of sample points in each observation cell, that is, the number of data points sampled. The formula for calculating the weight of each observation cell can be:

[0078]

[0079] Among them, W i SampleCount can be the weight corresponding to the i-th observation cell. i This can be the number of sample points in the i-th observation cell.

[0080] Thus, through the above process, the correlation index between the serving cell signal and the signal to be detected can be determined, thereby determining the temporal correlation between the signal strength fluctuations of the serving cell signal and the signal to be detected.

[0081] In other implementations, in order to determine the correlation between the serving cell signal and the signal to be detected, S120 may specifically include:

[0082] The first frequency of each third signal strength with the same intensity value among multiple first signal strengths, and the second frequency of each fourth signal strength with the same intensity value among multiple second signal strengths;

[0083] According to the second order of signal strength values ​​from largest to smallest or smallest to largest, multiple first frequencies are sorted to determine the first frequency sequence, and according to the second order, multiple second frequencies are sorted to determine the second frequency sequence;

[0084] The correlation between the first frequency sequence and the second frequency sequence is calculated to determine the second correlation index;

[0085] Based on the weight corresponding to each observation cell, the second correlation index corresponding to each observation cell is weighted and summed to determine the correlation index.

[0086] Here, the second correlation index characterizes the correlation between the signal strength fluctuations of the serving cell signal and the signal under test in terms of signal strength values. To determine the correlation between the signal strength fluctuations of the serving cell signal and the signal under test in terms of signal strength values, the first frequency of each third signal strength with the same strength value among multiple first signal strengths, and the second frequency of each fourth signal strength with the same strength value among multiple second signal strengths, can be counted. For example, if multiple first signal strengths can be "45, 43, 45, 46, 46", then multiple third signal strengths and their corresponding first frequencies can be "45, 2, 43, 1, 46, 2". If multiple second signal strengths can be "42, 45, 46, 43, 45", then multiple fourth signal strengths and their corresponding second frequencies can be "42, 1, 45, 2, 46, 1, 43, 1". Then, the multiple first frequencies and multiple second frequencies are sorted in descending or ascending order of signal strength values. It should be noted that the sorting rules for the multiple first frequencies and multiple second frequencies must be the same, that is, both must be from descending or both must be from ascending. If a signal strength value that is not present in any of the multiple third signal strengths is present in any of the multiple fourth signal strengths, or vice versa, then it is padded with 0. For example, if the multiple third signal strengths and their corresponding first frequencies can be “45, 2, 43, 1, 46, 2”, and the multiple fourth signal strengths and their corresponding second frequencies can be “42, 1, 45, 2, 46, 1, 43, 1”, then after padding with 0, the multiple third signal strengths and their corresponding first frequencies can be “42, 0, 43, 1, 45, 2, 46, 2”, and the multiple fourth signal strengths and their corresponding second frequencies can be “42, 1, 43, 1, 45, 2, 46, 1”. The resulting first frequency sequence can be (0, 1, 2, 2), and the second frequency sequence can be (1, 1, 2, 1).

[0087] In some examples, the correlation between the first and second frequency sequences can be calculated using the CORREL function, as shown in the following formula:

[0088] M2 i =CORREL(NcSampleCountList1) i ,NcSampleCountList2 i )

[0089] Among them, M2 i This can be the second correlation index corresponding to the i-th observation cell, NcSampleCountList1 i This can be the first frequency sequence corresponding to the i-th observation cell, NcSampleCountList2 iIt can be the second frequency sequence corresponding to the i-th observation cell.

[0090] The specific formula for calculating the CORREL function is as follows:

[0091]

[0092] Here, x = NcSampleCountList1 i y = NcSampleCountList2 i Substituting these values ​​into the specific calculation formula of the CORREL function above, we obtain CORREL(NcSampleCountList1) i ,NcSampleCountList2 i ), that is, M2 i .

[0093] After calculating the second correlation index for each observation cell using the method described above, the correlation index between the serving cell signal and the signal to be detected can be finally determined by weighted summation of the second correlation indices for each observation cell. The calculation method for the weights of each observation cell is the same as above and will not be repeated here.

[0094] Thus, through the above process, the correlation index between the serving cell signal and the signal to be detected can be determined, thereby determining the correlation between the signal strength fluctuations of the serving cell signal and the signal to be detected in terms of signal strength values.

[0095] In some implementations, the intensity values ​​of the plurality of first signal strengths and the plurality of second signal strengths may differ significantly, causing the calculated second correlation index to be 0. To avoid the second correlation index being worthless due to a value of 0, the method may further include the following before calculating the first frequency of each third signal strength with the same intensity value among the plurality of first signal strengths, and the second frequency of each fourth signal strength with the same intensity value among the plurality of second signal strengths:

[0096] The mean difference between the multiple first signal strengths and the multiple second signal strengths is determined based on the median of the multiple first signal strengths and the median of the multiple second signal strengths;

[0097] Compensation is performed on multiple first signal strengths or multiple second signal strengths based on the mean difference.

[0098] Here, if the intensity values ​​of multiple first signal strengths and multiple second signal strengths differ significantly, the calculated second correlation index may be zero, making it impossible to determine the correlation between the signal strength fluctuations of the serving cell signal and the signal under test in terms of signal strength values. To avoid the second correlation index being zero due to significant differences in the intensity values ​​of multiple first signal strengths and multiple second signal strengths, compensation can be applied to the multiple first signal strengths or multiple second signal strengths.

[0099] Specifically, we can first determine the median of multiple first signal strengths and the median of multiple second signal strengths. The difference between the two medians is the mean difference between the multiple first signal strengths and the multiple second signal strengths. By adding the mean difference to each signal strength among the multiple first signal strengths or multiple second signal strengths, and then calculating the first frequency and the second frequency, we can avoid the situation where the second correlation index is 0 due to large differences in the intensity values ​​of the multiple first signal strengths and multiple second signal strengths.

[0100] Thus, by determining the mean difference and compensating for multiple first signal strengths or multiple second signal strengths based on the mean difference, the situation where the second correlation index is 0 due to the large difference in the strength values ​​of multiple first signal strengths and multiple second signal strengths can be avoided. This avoids the situation where the second correlation index cannot characterize the correlation between the signal strength fluctuations of the serving cell signal and the signal under test in terms of signal strength values.

[0101] In some implementations, to more accurately determine the correlation between the serving cell signal and the signal to be detected, S120 may specifically include:

[0102] Based on the weight corresponding to each observation cell, the average values ​​of the first and second correlation indices corresponding to each observation cell are weighted and summed to determine the correlation index.

[0103] Here, in order to more accurately determine the correlation between the serving cell signal and the signal to be detected, the final correlation index can be determined by combining the first correlation index and the second correlation index.

[0104] Specifically, after calculating the first and second correlation indices for each observation cell using the methods described above, the correlation index between the serving cell signal and the signal to be detected can be finally determined by weighted summation of the average values ​​of the first and second correlation indices for each observation cell. The calculation method for the weights corresponding to each observation cell is the same as above and will not be repeated here.

[0105] In some examples, the formula for calculating the correlation index can be:

[0106]

[0107] Among them, M final It can be a correlation indicator, W i M1i can be the weight corresponding to the i-th observation cell, M2i can be the first correlation index corresponding to the i-th observation cell, and M2i can be the second correlation index corresponding to the i-th observation cell.

[0108] Thus, by combining the first and second correlation indicators to determine the final correlation indicator, the correlation between the serving cell signal and the signal to be detected can be determined more accurately.

[0109] Regarding S130, the preset condition can be greater than a preset correlation index threshold, for example, greater than 0.8.

[0110] If the correlation index meets the preset conditions, such as being greater than 0.8, it can be determined that the signal to be detected comes from the first site. Since the first site is the site used by the first operator, and the signal to be detected is the signal of another operator, the first site is a shared site of the first operator and other operators.

[0111] If the correlation index does not meet the preset conditions, such as being less than or equal to 0.8, it can be determined that the signal to be detected does not come from the first station.

[0112] If signals from many other operators originate from the first site, it may affect the signal quality of the first operator. Furthermore, if other operators share the first site with the first operator, the cost of leasing the first site for the first operator can be reduced.

[0113] In addition, after determining that the signal to be detected comes from the first station address, the station address database can be updated to record that the signal to be detected comes from the first station address.

[0114] Based on the same inventive concept, embodiments of this application also provide a signal detection device. The following, in conjunction with… Figure 2 The signal detection device provided in the embodiments of this application will be described in detail.

[0115] Figure 2 A schematic diagram of the structure of a signal detection device provided in one embodiment of this application is shown.

[0116] like Figure 2 As shown, the signal detection device may include:

[0117] The first acquisition module 201 is used to acquire first measurement data of at least one observation cell. The at least one observation cell is a cell in a preset area centered on a first station address. The first station address is the station address to which the serving cell signal belongs. The first measurement data includes multiple first signal strengths of the serving cell signal and multiple second signal strengths of the signal to be detected uploaded by the terminal located in each observation cell at multiple times. The serving cell signal is the signal of the first operator, and the signal to be detected is the signal of another operator besides the first operator.

[0118] The first determining module 202 is used to determine the correlation index between the serving cell signal and the signal to be detected based on the multiple first signal strengths and multiple second signal strengths corresponding to each observation cell;

[0119] The second determining module 203 is used to determine that the signal to be detected comes from the first station address when the correlation index meets the preset conditions.

[0120] Therefore, it is possible to acquire first measurement data from at least one observation cell, and based on the first measurement data including multiple first signal strengths of the serving cell signal uploaded by terminals located in each observation cell at multiple times and multiple second signal strengths of the signal to be detected, the correlation index between the serving cell signal and the signal to be detected is determined. Then, if the correlation index meets preset conditions, it is determined that the signal to be detected comes from the first site. That is, both the signal to be detected and the serving cell signal come from the first site, i.e., they share the first site. In this way, it is possible to detect whether the signal of another operator shares the site with the signal of the first operator by using the first measurement data from at least one observation cell around the first site, without relying on the location information in the MDT data and the latitude, longitude, azimuth, downtilt angle, station height and other information in the engineering parameter data. This avoids the problem of inaccurate detection caused by the small amount of location information data and the error of the engineering parameter data.

[0121] In some implementations, to determine the correlation between the serving cell signal and the signal to be detected, the first determining module 202 may specifically include:

[0122] The first sorting submodule is used to sort multiple first signal strengths in a first order from early to late or from late to early to obtain a first signal strength sequence, and to sort multiple second signal strengths in a first order to obtain a second signal strength sequence.

[0123] The first calculation submodule is used to perform correlation calculation on the first signal strength sequence and the second signal strength sequence to determine the first correlation index. The first correlation index characterizes the temporal correlation between the signal strength fluctuations of the serving cell signal and the signal to be detected.

[0124] The first determination submodule is used to perform a weighted summation of the first correlation index corresponding to each observation cell based on the weight corresponding to each observation cell, and to determine the correlation index.

[0125] In some implementations, to determine the correlation between the serving cell signal and the signal to be detected, the first determining module 202 may specifically include:

[0126] The statistics submodule is used to count the first frequency of each third signal strength with the same intensity value among multiple first signal strengths, and the second frequency of each fourth signal strength with the same intensity value among multiple second signal strengths;

[0127] The second sorting submodule is used to sort multiple first frequencies according to a second order of signal strength values ​​from largest to smallest or smallest to largest to determine a first frequency sequence, and to sort multiple second frequencies according to a second order to determine a second frequency sequence.

[0128] The second calculation submodule is used to perform correlation calculation on the first frequency sequence and the second frequency sequence to determine the second correlation index. The second correlation index characterizes the correlation between the signal strength fluctuations of the serving cell signal and the signal to be detected on the signal strength value.

[0129] The second determination submodule is used to perform a weighted summation of the second correlation indicators corresponding to each observation cell based on the weight corresponding to each observation cell, and to determine the correlation indicators.

[0130] In some implementations, to more accurately determine the correlation between the serving cell signal and the signal to be detected, the first determining module 202 may specifically include:

[0131] The third determination submodule is used to determine the correlation index by weighted summation of the average values ​​of the first and second correlation indices corresponding to each observation cell, based on the weight corresponding to each observation cell.

[0132] In some implementations, the intensity values ​​of the multiple first signal strengths and the multiple second signal strengths may differ significantly, causing the calculated second correlation index to be 0. To avoid the second correlation index being worthless due to a value of 0, the signal detection device may further include:

[0133] The third determining module is used to determine the mean difference between the multiple first signal strengths and the multiple second signal strengths based on the median of the multiple first signal strengths and the median of the multiple second signal strengths before calculating the first frequency of each third signal strength with the same intensity value among the multiple first signal strengths and the second frequency of each fourth signal strength with the same intensity value among the multiple second signal strengths.

[0134] The compensation module is used to compensate for multiple first signal strengths or multiple second signal strengths based on the mean difference.

[0135] In some implementations, when the signal detection device operates automatically, it first needs to determine the site address of a first operator, and then determine whether the site address is a shared site address by detecting whether the signal to be monitored may originate from that site address. Here, a shared site address refers to different operators using the same site address. In order for the signal detection device to automatically determine whether the site address to be detected as a shared site address when operating automatically, the signal detection device may further include:

[0136] The second acquisition module is used to acquire second measurement data before acquiring first measurement data of at least one observation cell. The second measurement data includes a base station identifier, a serving cell frequency, and a physical cell identifier of the serving cell. The serving cell signal is determined based on the serving cell frequency and the physical cell identifier.

[0137] The fourth determination module is used to match the second measurement data with the pre-stored engineering parameter data to determine the first station location.

[0138] In some implementations, whether a first cell address is shared is determined by detecting whether a signal originates from that first cell address. To enable the signal detection device to automatically identify the signal to be detected, the second measurement data also includes signals from multiple neighboring cells. The signal detection device may further include:

[0139] The first filtering module is used to filter out the first operator's signal from multiple neighboring cell signals before acquiring the first measurement data of at least one observation cell, so as to obtain at least one first signal to be detected.

[0140] The fifth determining module is used to randomly determine one of the at least one first detectable signal as the detectable signal.

[0141] In some embodiments, to avoid reduced efficiency due to repeated detection, the signal detection device may further include:

[0142] The second filtering module is used to filter out signals determined to originate from the first site from the signals of multiple neighboring cells before obtaining at least one first signal to be detected by filtering out the signals of the first operator from the signals of multiple neighboring cells.

[0143] Figure 3 A schematic diagram of the structure of an electronic device provided in one embodiment of this application is shown.

[0144] like Figure 3As shown, the electronic device 3 is a structural diagram of an exemplary hardware architecture of an electronic device that can implement the signal detection method and signal detection device according to the embodiments of this application. This electronic device may refer to the electronic device in the embodiments of this application.

[0145] The electronic device 3 may include a processor 301 and a memory 302 storing computer program instructions.

[0146] Specifically, the processor 301 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0147] Memory 302 may include mass storage for data or instructions. For example, and not limitingly, memory 302 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 302 may include removable or non-removable (or fixed) media. Where appropriate, memory 302 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 302 is non-volatile solid-state memory. In a particular embodiment, memory 302 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Thus, generally, memory 302 includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of this application.

[0148] The processor 301 implements any of the signal detection methods described in the above embodiments by reading and executing computer program instructions stored in the memory 302.

[0149] In one example, the electronic device may also include a communication interface 303 and a bus 304. Wherein, for example... Figure 3 As shown, the processor 301, memory 302, and communication interface 303 are connected through bus 304 and complete communication with each other.

[0150] The communication interface 303 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0151] Bus 304 includes hardware, software, or both, that couples components of an electronic device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 304 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0152] The electronic device can execute the signal detection method in the embodiments of this application, thereby achieving the combination Figures 1 to 2 The described signal detection method and apparatus.

[0153] Furthermore, in conjunction with the signal detection methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the signal detection methods in the above embodiments.

[0154] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0155] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0156] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0157] The aspects of this application have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0158] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A signal detection method, characterized in that, include: Acquire first measurement data of at least one observation cell, wherein the at least one observation cell is a cell within a preset area centered on a first site, the first site is the site to which the serving cell signal belongs, and the first measurement data includes multiple first signal strengths of the serving cell signal and multiple second signal strengths of a signal to be detected uploaded by a terminal located in each of the observation cells at multiple times, wherein the serving cell signal is a signal of a first operator, and the signal to be detected is a signal of another operator besides the first operator; Based on the plurality of first signal strengths and the plurality of second signal strengths corresponding to each of the observed cells, a correlation index between the serving cell signal and the signal to be detected is determined; If the correlation index meets the preset conditions, it is determined that the signal to be detected comes from the first station address; The step of determining the correlation index between the serving cell signal and the signal to be detected based on the plurality of first signal strengths and the plurality of second signal strengths corresponding to each of the observed cells includes: The plurality of first signal intensities are sorted in a first order from early to late or from late to early to obtain a first signal intensity sequence, and the plurality of second signal intensities are sorted in the first order to obtain a second signal intensity sequence. The correlation between the first signal strength sequence and the second signal strength sequence is calculated to determine a first correlation index, which characterizes the temporal correlation between the signal strength fluctuations of the serving cell signal and the signal to be detected. Based on the weight corresponding to each observation cell, the first correlation index corresponding to each observation cell is weighted and summed to determine the correlation index.

2. The method according to claim 1, characterized in that, The step of determining the correlation index between the serving cell signal and the signal to be detected based on the plurality of first signal strengths and the plurality of second signal strengths corresponding to each of the observed cells includes: The first frequency of each third signal strength with the same intensity value among the plurality of first signal strengths, and the second frequency of each fourth signal strength with the same intensity value among the plurality of second signal strengths; According to the second order of signal strength values ​​from largest to smallest or smallest to largest, multiple first frequencies are sorted to determine a first frequency sequence, and multiple second frequencies are sorted according to the second order to determine a second frequency sequence; The correlation between the first frequency sequence and the second frequency sequence is calculated to determine a second correlation index, which characterizes the correlation between the signal strength fluctuations of the serving cell signal and the signal to be detected in terms of signal strength values. Based on the weight corresponding to each observation cell, the second correlation index corresponding to each observation cell is weighted and summed to determine the correlation index.

3. The method according to claim 1 or 2, characterized in that, The step of determining the correlation index between the serving cell signal and the signal to be detected based on the plurality of first signal strengths and the plurality of second signal strengths corresponding to each of the observed cells includes: Based on the weight corresponding to each observation cell, the average values ​​of the first correlation index and the second correlation index corresponding to each observation cell are weighted and summed to determine the correlation index.

4. The method according to claim 2, characterized in that, Before counting the first frequency of each third signal intensity with the same intensity value among the plurality of first signal intensities, and the second frequency of each fourth signal intensity with the same intensity value among the plurality of second signal intensities, the method further includes: The mean difference between the plurality of first signal strengths and the plurality of second signal strengths is determined based on the median of the plurality of first signal strengths and the median of the plurality of second signal strengths; The plurality of first signal strengths or the plurality of second signal strengths are compensated based on the mean difference.

5. The method according to claim 1, characterized in that, Before acquiring the first measurement data of at least one observation cell, the method further includes: Acquire second measurement data, which includes a base station identifier, a serving cell frequency, and a serving cell physical cell identifier. The serving cell signal is determined based on the serving cell frequency and the physical cell identifier. The second measurement data is matched with pre-stored engineering parameter data to determine the first station location.

6. The method according to claim 5, characterized in that, The second measurement data also includes signals from multiple neighboring cells; Before acquiring the first measurement data of at least one observation cell, the method further includes: The first operator's signal is filtered out from the signals of the plurality of neighboring cells to obtain at least one first signal to be detected; One of the at least one first signal to be detected is randomly selected as the signal to be detected.

7. The method according to claim 6, characterized in that, Before filtering out the first operator's signal from the plurality of neighboring cell signals to obtain at least one first signal to be detected, the method further includes: Filter out signals that have been determined to originate from the first site from the signals of the plurality of neighboring cells.

8. A signal detection device, characterized in that, The device includes: The first acquisition module is used to acquire first measurement data of at least one observation cell, wherein the at least one observation cell is a cell within a preset area centered on a first site, the first site is the site to which the serving cell signal belongs, and the first measurement data includes multiple first signal strengths of the serving cell signal and multiple second signal strengths of a signal to be detected uploaded by a terminal located in each of the observation cells at multiple times, wherein the serving cell signal is the signal of a first operator, and the signal to be detected is the signal of another operator besides the first operator; The first determining module is used to determine the correlation index between the serving cell signal and the signal to be detected based on the plurality of first signal strengths and the plurality of second signal strengths corresponding to each of the observed cells; The second determining module is used to determine that the signal to be detected comes from the first station address when the correlation index meets the preset conditions; The first determining module is specifically used for: The plurality of first signal intensities are sorted in a first order from early to late or from late to early to obtain a first signal intensity sequence, and the plurality of second signal intensities are sorted in the first order to obtain a second signal intensity sequence. The correlation between the first signal strength sequence and the second signal strength sequence is calculated to determine a first correlation index, which characterizes the temporal correlation between the signal strength fluctuations of the serving cell signal and the signal to be detected. Based on the weight corresponding to each observation cell, the first correlation index corresponding to each observation cell is weighted and summed to determine the correlation index.

9. An electronic device, characterized in that, The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the signal detection method as described in any one of claims 1-7.

10. A computer storage medium, characterized in that, The computer storage medium stores computer program instructions, which, when executed by a processor, implement the signal detection method as described in any one of claims 1-7.

11. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device performs the signal detection method as described in any one of claims 1-7.

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