CN114204989B

CN114204989B - Evaluation method and device of spectroscope data, storage medium and electronic equipment

Info

Publication number: CN114204989B
Application number: CN202111504733.5A
Authority: CN
Inventors: 周江; 孙学斌
Original assignee: China Telecom Corp Ltd
Current assignee: China Telecom Corp Ltd
Priority date: 2021-12-10
Filing date: 2021-12-10
Publication date: 2023-06-16
Anticipated expiration: 2041-12-10
Also published as: CN114204989A

Abstract

The disclosure belongs to the technical field of computers, and relates to a method and a device for evaluating splitter data, a storage medium and electronic equipment. The method comprises the following steps: acquiring hanging measurement data corresponding to the beam splitter, and determining an area identifier corresponding to the hanging measurement data so as to divide the hanging measurement data according to the area identifier to obtain area data; calculating the repetition rate of the region data to obtain a calculation result, and determining a repetition rate threshold corresponding to the calculation result; if the calculated result is smaller than or equal to the repetition rate threshold value, the evaluation area data are real data; and if the calculated result is larger than the repetition rate threshold value, evaluating the regional data as false data. In the method, the computing result can be obtained by computing the repetition rate of the region data, so that the authenticity of the region data is automatically evaluated according to the relationship between the computing result and the repetition rate threshold, the labor cost consumed in evaluating the region data in the prior art is reduced, and the efficiency of evaluating the region data is improved.

Description

Evaluation method and device of spectroscope data, storage medium and electronic equipment

Technical Field

The present disclosure relates to the field of communications technologies, and in particular, to a method and apparatus for evaluating splitter data, a computer readable storage medium, and an electronic device.

Background

With the development of the communication technology field, the communication industry adopts an FTTH (Fiber To The Home) mode to construct an area broadband for a certain area so as to provide communication service for users in the area, and it is worth noting that after the construction of the area broadband is completed in the certain area, the two-stage optical splitter needs to be subjected to the meter hanging test, and after the test is qualified, the area broadband can be put into use.

In the prior art, a checking and accepting person is required to carry out the meter hanging test on the secondary beam splitters in a spot checking mode, on one hand, the spot checking mode can not cover all the secondary beam splitters, the probability of faults caused by the quality of the optical cable after the spot checking mode is added, on the other hand, the spot checking mode is completely dependent on manpower, the labor cost is increased, and the checking and accepting accuracy is reduced.

In view of the foregoing, there is a need in the art to develop a new method and apparatus for evaluating splitter data.

It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.

Disclosure of Invention

The disclosure aims to provide an evaluation method of optical splitter data, an evaluation device of the optical splitter data, a computer readable storage medium and an electronic device, so as to solve the problems of high labor cost, low evaluation accuracy and high probability of occurrence of faults due to the quality of an optical cable after being put into use caused by related technologies at least to a certain extent.

Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.

According to a first aspect of an embodiment of the present invention, there is provided a method for evaluating optical splitter data, the method including: acquiring hanging measurement data corresponding to the optical splitter, and determining an area identifier corresponding to the hanging measurement data so as to divide the hanging measurement data according to the area identifier to obtain area data; calculating the repetition rate of the region data to obtain a calculation result, and determining a repetition rate threshold corresponding to the calculation result; if the calculation result is smaller than or equal to the repetition rate threshold, evaluating the regional data as real data; and if the calculated result is larger than the repetition rate threshold, evaluating the regional data as false data.

In an exemplary embodiment of the present invention, the determining a repetition rate threshold corresponding to the calculation result includes: acquiring sample hanging measurement data, and calculating the repetition rate of the hanging measurement sample data to obtain a sample calculation result; the sample hanging measurement data comprise real hanging measurement sample data and false hanging measurement sample data; and clustering the sample calculation results according to the authenticity of the sample hanging data to obtain clustering results, and determining a repetition rate threshold value based on the clustering results.

In an exemplary embodiment of the present invention, the hooking data includes distance data of the secondary optical splitter to an optical line terminal, a region identifier corresponding to the secondary optical splitter, an optical splitter identifier corresponding to the secondary optical splitter, light attenuation data corresponding to the secondary optical splitter, and position data corresponding to the secondary optical splitter.

In an exemplary embodiment of the invention, the calculation result includes a first calculation result; the step of calculating the repetition rate of the region data to obtain a calculation result comprises the following steps: determining first distance data and second distance data in the distance data, and calculating a first data difference value between the first distance data and the second distance data; and if the first data difference value meets the repetition condition, determining that the first distance data and the second distance data are repetition data, and determining the first calculation result according to the number of the repetition data.

In an exemplary embodiment of the invention, the calculation result includes a second calculation result; the step of calculating the repetition rate of the region data to obtain a calculation result comprises the following steps: sorting the distance data to obtain a data sorting result, and determining third distance data and fourth distance data with adjacent sorting relations according to the data sorting result; calculating the difference between the third distance data and the fourth distance data to obtain a second data difference value, and calculating the difference between the second data difference values to obtain a difference value calculation result; and if the difference value calculation result meets the repetition condition, determining that the second data difference value corresponding to the difference value calculation result is repetition data, and determining the second calculation result according to the number of the repetition data.

In an exemplary embodiment of the present invention, the repetition rate threshold includes a first threshold corresponding to the first calculation result and a second threshold corresponding to the second calculation result; and if the calculation result is smaller than or equal to the repetition rate threshold, evaluating the region data as real data, including: and if the first calculation result is smaller than or equal to the first threshold value and the second calculation result is smaller than or equal to the second threshold value, evaluating the area data as real data.

In an exemplary embodiment of the present invention, if the calculation result is greater than or equal to the repetition rate threshold, evaluating the region data as false data includes: and if the first calculation result is greater than the first threshold value or the second calculation result is greater than the second threshold value, evaluating the area data as virtual data.

In an exemplary embodiment of the present invention, after the evaluating the area data as the real data if the calculation result is less than or equal to the repetition rate threshold, the method further includes: dividing the distance data contained in the region data based on the optical splitter identifier to obtain a first division result; acquiring final-stage beam splitter data corresponding to the region identifier, and dividing the final-stage beam splitter data based on the beam splitter identifier to obtain a second division result so as to determine the corresponding relation between the first division result and the second division result; wherein the final stage beam splitter data comprises network distance data and network connection relations; calculating a distance difference value obtained by the network distance data in the first division result and the second division result, and calculating a dimension loading material according to the distance difference value; the maintenance material is used for maintaining the optical fiber between the two-stage optical splitter and the final-stage optical splitter.

According to a second aspect of the embodiments of the present invention, there is provided an apparatus for evaluating splitter data, the apparatus comprising: the dividing module is configured to acquire hanging measurement data corresponding to the secondary beam splitter, determine a region identifier corresponding to the hanging measurement data, and divide the hanging measurement data according to the region identifier to obtain region data; the calculating module is configured to calculate the repetition rate of the area data to obtain a calculation result, and determine a repetition rate threshold corresponding to the calculation result; the first determining module is configured to determine that the region data is real data if the calculation result is smaller than or equal to the repetition rate threshold; and the second determining module is configured to determine that the area data is false data if the calculated result is larger than the repetition rate threshold.

According to a third aspect of an embodiment of the present invention, there is provided an electronic apparatus including: a processor and a memory; wherein the memory has stored thereon computer readable instructions which, when executed by the processor, implement the method of evaluating beam splitter data of any of the exemplary embodiments described above.

According to a fourth aspect of embodiments of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of evaluating splitter data in any of the above-described exemplary embodiments.

As can be seen from the above technical solutions, the method for evaluating optical splitter data, the device for evaluating optical splitter data, the computer storage medium, and the electronic device according to the exemplary embodiments of the present invention have at least the following advantages and positive effects:

in the method and the device provided by the exemplary embodiment of the disclosure, on one hand, the calculation result can be obtained by calculating the repetition rate of the region data, so that the authenticity of the region data can be automatically estimated according to the relation between the calculation result and the repetition rate threshold, the dependence on manpower in the estimation process is avoided, the labor cost of the evaluation of the optical splitter data is further reduced, and the efficiency of the evaluation of the optical splitter data is improved; on the other hand, the obtained hanging measurement data corresponding to the optical splitters are not the data on part of the optical splitters any more, so that the problem that the probability of faults occurring due to the quality of an optical cable after the optical cable is put into use only by sampling and detecting part of the two-stage optical splitters in the prior art is avoided.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.

FIG. 1 schematically illustrates a flow diagram of a method of evaluating splitter data in an embodiment of the disclosure;

fig. 2 schematically illustrates a topology structure diagram of an optical splitter in a cell in a unit of home in an evaluation method of optical splitter data in an embodiment of the present disclosure;

fig. 3 schematically illustrates a flowchart of determining a repetition rate threshold corresponding to a calculation result in the evaluation method of splitter data in the embodiment of the disclosure;

fig. 4 schematically illustrates a flowchart of calculating a repetition rate of region data to obtain a calculation result in the evaluation method of splitter data in the embodiment of the disclosure;

fig. 5 schematically illustrates a flowchart of calculating a repetition rate of region data to obtain a calculation result in the evaluation method of splitter data in the embodiment of the disclosure;

FIG. 6 schematically illustrates a flowchart of a dimension-filling material calculated in a method for evaluating beam splitter data in an embodiment of the disclosure;

FIG. 7 is a flow chart illustrating a method for evaluating beam splitter data in an application scenario;

fig. 8 schematically illustrates a structural diagram of an evaluation apparatus of splitter data in an embodiment of the present disclosure;

fig. 9 schematically illustrates an electronic device for an evaluation method of splitter data in an embodiment of the disclosure;

Fig. 10 schematically illustrates a computer-readable storage medium for an evaluation method of beam splitter data in an embodiment of the present disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the present disclosure. One skilled in the relevant art will recognize, however, that the aspects of the disclosure may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.

The terms "a," "an," "the," and "said" are used in this specification to denote the presence of one or more elements/components/etc.; the terms "comprising" and "having" are intended to be inclusive and mean that there may be additional elements/components/etc. in addition to the listed elements/components/etc.; the terms "first" and "second" and the like are used merely as labels, and are not intended to limit the number of their objects.

Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities.

In view of the problems in the related art, the present disclosure proposes a method for evaluating splitter data. Fig. 1 shows a flow chart of a method for evaluating splitter data, and as shown in fig. 1, the method for evaluating splitter data at least includes the following steps:

s110, acquiring hanging measurement data corresponding to the optical splitter, and determining an area identifier corresponding to the hanging measurement data so as to divide the hanging measurement data according to the area identifier to obtain area data.

And S120, calculating the repetition rate of the region data to obtain a calculation result, and determining a repetition rate threshold corresponding to the calculation result.

And S130, if the calculated result is smaller than the repetition rate threshold value, the data of the evaluation area are real data.

In step S140, if the calculation result is greater than or equal to the repetition rate threshold, the evaluation area data is false data.

The respective steps of the evaluation method of the spectroscope data are described in detail below.

In step S110, hanging measurement data corresponding to the beam splitter is obtained, and an area identifier corresponding to the hanging measurement data is determined, so that the hanging measurement data is divided according to the area identifier to obtain area data.

In exemplary embodiments of the present disclosure, the optical splitter refers to a passive device, also called an optical splitter, that must be used when constructing a regional broadband. Regional broadband refers to broadband coverage implemented in an area, where the area may be a home-based cell, a company, a office building, a hospital, or any area that requires broadband full coverage, and the exemplary embodiment is not limited in particular.

For example, fig. 2 schematically illustrates a topology structure of an optical splitter in a cell in a home unit, as shown in fig. 2, where the device 210 is an optical line terminal, a terminal device for connecting to an optical fiber trunk, the device 220 is a first-stage optical splitter, that is, an optical splitter directly connected to the device 210, the device 220 receives a signal in the device 210 by using an uplink optical interface carried by itself and transmits the signal out of a downlink optical interface carried by the device 220, the device 230 is a second-stage optical splitter, that is, an optical splitter connected to the first-stage optical splitter 220, which is typically installed in a corridor of the cell and receives a signal transmitted by the first-stage optical splitter 220 by using an uplink optical interface carried by itself, and the device 240 is a final-stage optical splitter, specifically, the final-stage optical splitter may be an optical cat, which is worth explaining that the final-stage optical splitter is connected to the second-stage optical splitter only when the optical splitter data is evaluated and the evaluation result shows that the optical splitter data is real data, so as to provide a communication service for a cell user.

The hanging data refers to data to be evaluated corresponding to the secondary beam splitter, and the hanging data is usually stored in a corresponding database, specifically, the hanging data can be stored in an access network server, and it is worth noting that the final beam splitter can be accessed to the secondary beam splitter only when the hanging data is real data, so as to provide communication service for users.

However, since the hanging data is collected by a constructor, no supervision exists in the collection process, and thus, in order for the constructor to pass the evaluation, the collected data may not be the required data of the second-stage beam splitter, may be the data of the first-stage beam splitter, may be the data of the optical line terminal, and may also be the data collected by the pigtail, so that the subsequent evaluation process needs to be performed to determine the authenticity of the hanging data.

The area identifier refers to an identifier corresponding to the hanging data, and it can be clearly known from which area the hanging data comes from through the area identifier, and the area identifier may be in a form of a number, a letter, or a character string, which is not particularly limited in this exemplary embodiment. Based on this, the area data is the hang-up data of the two-stage spectroscope in a certain area.

For example, the hanging data of the province is obtained from the access network server, and the hanging data to be evaluated at this time is the hanging data of the cell a, so the hanging data can be divided according to the area identifier corresponding to the hanging data, so as to obtain area data, wherein the area data includes the hanging data of the secondary beam splitter in the cell a.

In an alternative embodiment, the hanging measurement data includes distance data from the secondary beam splitter to the optical line terminal, a region identifier corresponding to the secondary beam splitter, a beam splitter identifier corresponding to the secondary beam splitter, light attenuation data corresponding to the secondary beam splitter, and position data corresponding to the secondary beam splitter.

The hanging data may include distance data, area identification, optical splitter identification, light attenuation data and position data, where the distance data refers to a distance value between the secondary optical splitter and the optical line terminal, for example, in fig. 2, the distance data refers to a distance value between the secondary optical splitter 220 and the optical line terminal 210, the area identification refers to an identification representing an area from which the hanging data is from, the optical splitter identification refers to an identification for distinguishing the estimated data from that secondary optical splitter, the light attenuation data refers to a loss of light in the optical cable under a unit length, and the position data may be a mounting position of the secondary optical splitter and may be a geographic position where the secondary optical splitter is located.

It should be noted that, in order to avoid the situation that the hanging data needs to be acquired from the access network server once in each evaluation, the hanging data may be stored in a new server according to the correspondence among the distance data, the area identifier, the optical splitter identifier, the light attenuation data and the position data after the hanging data is acquired from the access network server, so as to be used in the subsequent evaluation process, thereby avoiding unnecessary performance loss, improving convenience and efficiency of the subsequent evaluation, and in particular, may be stored in a form of a table and a new service.

Besides, the hanging data not only have distance data, but also have light attenuation data and position data, the data can be used for the subsequent expansion of evaluation business, for example, the follow-up verification of whether the light attenuation data accords with a normal value, if not, the potential risk of regional broadband constructed in the region is proved, and the constructor can also be verified according to the position data whether the constructor installs the beam splitter at the correct position according to the original designed drawing.

For example, the hanging measurement data may include 50 meters of distance data from the secondary optical splitter a to the optical line terminal, an area identifier X corresponding to the secondary optical splitter a, an optical splitter identifier 2 corresponding to the secondary optical splitter a, light attenuation data corresponding to the secondary optical splitter of-15 dB/km, and position data corresponding to the secondary optical splitter a (20, 65, 70).

In this exemplary embodiment, on the one hand, the hanging measurement data includes data required for subsequent evaluation, so that the hanging measurement data can be used in a subsequent evaluation process, thereby avoiding the occurrence of a phenomenon that the hanging measurement data needs to be acquired again in each evaluation, avoiding unnecessary performance loss, and increasing the efficiency of the subsequent evaluation; on the other hand, the hanging data also comprises light attenuation data and position data, which provides convenience for the expansion of subsequent evaluation service.

In step S120, the repetition rate calculation is performed on the area data to obtain a calculation result, and a repetition rate threshold corresponding to the calculation result is determined.

In an exemplary embodiment of the present disclosure, the repetition rate calculation is a process of calculating a probability that there is repetition data in the region data, and the repetition rate threshold is a threshold defining a calculation result to determine the authenticity of the region data according to a relationship between the calculation result and the repetition rate threshold.

For example, the repetition rate calculation is performed on the area data to obtain a calculation result of 14%, and the determined repetition rate threshold corresponding to the calculation result is 17%.

In an alternative embodiment, fig. 3 is a schematic flow chart of determining a repetition rate threshold corresponding to a calculation result in a method for evaluating splitter data, where, as shown in fig. 3, the method at least includes the following steps: in step S310, sample hanging measurement data is obtained, and repetition rate calculation is performed on the hanging measurement sample data to obtain a sample calculation result; the sample hanging data comprise real hanging sample data and false hanging sample data.

In practice, since the hanging measurement data is collected by a constructor, no supervision exists in the collection process, and thus, in order to pass the evaluation, the collected data of the constructor may not be the required data of the second-stage beam splitter, may be the data of the first-stage beam splitter, may also be the data of the optical line terminal, and may also be the data collected by the pigtail, so that in order to ensure the accuracy of the subsequent evaluation, the above-mentioned several actions are avoided to pass the evaluation.

Based on the above, the sample hanging data comprises real hanging sample data and false hanging sample data, wherein the real hanging sample data is the real data, the false hanging sample data comprises the collected data of the optical splitter in the primary optical splitter, the virtual hanging sample data also comprises the collected data in the optical line terminal, and the virtual hanging sample data also comprises the collected data by utilizing the tail fiber.

The sample calculation result comprises a result obtained by carrying out repetition rate calculation on the real hanging sample data, and the sample calculation result also comprises a result obtained by carrying out repetition rate calculation on the virtual hanging sample.

For example, region data in 400 cells are obtained, wherein 100 cells in 400 cells are qualified cells, that is, region data corresponding to the 100 cells are real hanging sample data a, in addition to the above, false hanging sample data B corresponding to an optical line terminal in the remaining 300 cells are collected, false hanging sample data C corresponding to a first-level optical splitter in the remaining 300 cells are collected, and then false hanging sample data D obtained by utilizing a tail fiber in the remaining 300 cells are collected, and based on the above, repetition rate calculation is performed on the real hanging sample data a, the false hanging sample data B, the false hanging sample data C and the false hanging sample data D respectively, so as to obtain corresponding sample calculation results.

In step S320, the sample calculation results are clustered according to the authenticity of the sample hanging data to obtain a clustered result, and the repetition rate threshold is determined based on the clustered result.

It is obvious that the sample calculation result corresponding to the real hanging sample data has authenticity, and the sample calculation result corresponding to the false hanging sample data does not have authenticity, so that the sample calculation result can be clustered to obtain a clustered result, specifically, the clustered result can be obtained by using a K-means clustering algorithm, can be obtained by using a mean shift clustering algorithm, can be obtained by using any clustering algorithm, and is not particularly limited.

Based on the clustering result, the method can be obviously determined, the sample calculation result of the false hanging measurement sample data is usually more than A%, and the sample calculation result of the true hanging measurement sample data is usually less than A%, so that the A% can be determined to be a repetition rate threshold.

For example, the repetition rate of the real hanging sample data a, the false hanging sample data B, the false hanging sample data C, and the false hanging sample data D is calculated to obtain corresponding sample calculation results 1, 2, 3, and 4, and the 4 sample calculation results are clustered to determine the repetition rate threshold based on the clustering result.

In the present exemplary embodiment, the sample calculation results are clustered according to the authenticity of the sample hanging data, and the repetition rate threshold is determined based on the clustering results, so that the repetition rate threshold is prevented from being obtained empirically, the accuracy of determining the repetition rate threshold is improved, and the accuracy of the evaluation results is further ensured.

In an alternative embodiment, fig. 4 shows a schematic flow chart of calculating a repetition rate of region data to obtain a calculation result in an evaluation method of beam splitter data, where the calculation result includes a first calculation result, and as shown in fig. 4, the method at least includes the following steps: in step S410, first distance data and second distance data are determined among the distance data, and a first data difference between the first distance data and the second distance data is calculated.

The calculation results include two kinds of calculation results, wherein the first calculation result is a first calculation result, specifically, in the process of obtaining the first calculation result, two pieces of data in the distance data are needed to be determined first, the two pieces of data are first distance data and second distance data respectively, and then a difference value between the first distance data and the second distance data is calculated to obtain a first data difference value.

For example, the determined distance data includes 10, specifically, the distance data is 50, 52, 70, 25, 60, 50, 25 and 33, respectively, based on which the first distance data may be 50, the second distance data may be 52, the first distance data may be 50, the second distance data may be 70, the first distance data may be 52, the second distance data may be 70, and so on, the first distance data may be any one of the 10 distance data, the second distance data may be any one of the distance data excluding the first distance data, and 90 first data differences may be obtained.

In step S420, if the first data difference value satisfies the repetition condition, the first distance data and the second distance data are determined as repetition data, so as to determine a first calculation result according to the number of repetition data.

The repetition condition refers to a condition for determining that the first distance data and the second distance data are repeated, and specifically, the repetition condition may be that the first data difference is 0, or that the first data difference is less than a certain threshold, which is not particularly limited in the present exemplary embodiment.

When the first data difference value meets the repetition condition, the first distance data and the second distance data can be determined to be repetition data, and after the repetition data are determined, a first calculation result can be obtained according to the number of the repetition data.

For example, the determined distance data includes 10, specifically, the distance data is 50, 52, 70, 25, 60, 50, 25, and 33, respectively, and it is obvious that 90 first data differences may be obtained, where it is obvious that when the first data in the 10 distance data is taken as the first distance data and the 6 th, 7 th, and 8 th data in the 10 distance data are taken as the second distance data, the first data differences are all 0, that is, the repetition condition is satisfied, based on which it may be determined that the 1 st, 6 th, 7 th, and 8 th data in the 10 data are the repetition data.

Similarly, the 4 th data and the 10 th data can be determined to be repeated data in the 10 data, and further it can be determined that 6 repeated data exist in total in the 10 data, and further it is determined that the first calculation result is 60%.

In the present exemplary embodiment, the first calculation result may be obtained according to the first data difference between the first distance data and the second distance data, and it is ensured that the hanging data may be evaluated from the perspective of the first calculation result in the following.

In an alternative embodiment, fig. 5 shows a schematic flow chart of calculating the repetition rate of the region data in the evaluation of the splitter data to obtain a calculation result, where the calculation result includes a second calculation result, and as shown in fig. 5, the method at least includes the following steps: in step S510, the distance data is ranked to obtain a data ranking result, and third distance data and fourth distance data having adjacent ranking relationships are determined according to the data ranking result.

The second calculation result is a second calculation result, specifically, in the process of determining the second calculation result, the distance data needs to be sequenced first to obtain a data sequencing result, specifically, the data sequencing result may be sequenced from top to bottom or from bottom to top, and the present exemplary embodiment does not limit this in particular.

Assuming that the data sorting result obtained by sorting the distance data is 100, 77, 63, 52, 20, the third distance data may be 100, the fourth distance data may be 77 having an adjacent sorting relationship with 100, the third distance data may be 77, and the fourth distance data may be 63 having an adjacent sorting relationship with 77, based on which there are 6 pairs of the third distance data and the fourth distance data.

For example, if the data sorting result obtained by sorting the distance data is 100, 77, 63, 52, 20, there are 6 pairs of third distance data and fourth distance data, specifically, the first pair of third distance data and fourth distance data is 100 and 77, the second pair of third distance data and fourth distance data is 77 and 63, the third pair of third distance data and fourth distance data is 63 and 63, the fourth pair of third distance data and fourth distance data is 63 and 52, the fifth pair of third distance data and fourth distance data is 52 and 52, and the sixth pair of third distance data and fourth distance data is 52 and 20.

In step S520, a difference between the third distance data and the fourth distance data is calculated to obtain a second data difference, and a difference between the second data differences is calculated to obtain a difference calculation result.

The second data difference value is a difference value between the third distance data and the second distance data, and after the difference value is calculated, the difference value calculation is continuously performed on the difference values to obtain a difference value calculation result.

For example, there are 6 pairs of third distance data and fourth distance data, specifically, the first pair of third distance data and fourth distance data are 100 and 77, the second pair of third distance data and fourth distance data are 77 and 63, the third pair of third distance data and fourth distance data are 63 and 63, the fourth pair of third distance data and fourth distance data are 63 and 52, the fifth pair of third distance data and fourth distance data are 52 and 52, and the sixth pair of third distance data and fourth distance data are 52 and 20.

Based on this, the second data difference between the first pair of third distance data and fourth distance data is 23, the second data difference between the second pair of third distance data and fourth distance data is 14, the second data difference between the third pair of third distance data and fourth distance data is 0, the second data difference between the fourth pair of third distance data and fourth distance data is 11, the second data difference between the fifth pair of third distance data and fourth distance data is 0, the second data difference between the sixth pair of third distance data and fourth distance data is 32, and the difference calculation result is obtained by performing the difference calculation on the 23, 14, 0, 11, 0 and 32.

In step S530, if the difference calculation result satisfies the repetition condition, it is determined that the second data difference corresponding to the difference calculation result is the repetition data, so as to determine the second calculation result according to the number of the repetition data.

If the difference value calculation result meets the repetition condition, determining a second data difference value corresponding to the difference value calculation result as repeated data, and finally determining the second calculation result according to the number of the repeated data.

For example, the obtained second data differences are 23, 14, 0, 11, 0 and 32, respectively, and the difference calculation result is calculated on the basis of the obtained second data differences, and the third data and the fourth data in the second data differences can be obtained as repeated data according to the difference calculation result, and on the basis of the repeated data, it can be determined that the second calculation result is 40%.

In the present exemplary embodiment, the second calculation result may be obtained according to the difference calculation result between the first data difference values, so that it is ensured that the hanging data may be evaluated from the angle of the second calculation result.

In step S130, if the calculation result is less than or equal to the repetition rate threshold, the evaluation area data is real data.

In an exemplary embodiment of the present disclosure, if the calculation result is less than or equal to the repetition rate threshold, the evaluation area data is qualified real data.

For example, the calculation results include a first calculation result and a second calculation result, and correspondingly, there is a first threshold corresponding to the first calculation result and a second threshold corresponding to the second calculation result in the repetition rate threshold.

Based on the above, if the first calculation result is smaller than or equal to the first threshold value and the second calculation result is also smaller than or equal to the second threshold value, the region data at this time is proved to be real data, that is, the evaluation result is qualified.

In an alternative embodiment, the repetition rate threshold includes a first threshold corresponding to the first calculation result and a second threshold corresponding to the second calculation result; if the calculation result is less than or equal to the repetition rate threshold, the evaluation area data is real data, including: if the first calculation result is smaller than or equal to the first threshold value and the second calculation result is smaller than or equal to the second threshold value, the evaluation area data is real data.

It should be noted that there are two calculation results, namely a first calculation result and a second calculation result, and according to the relationship between the two calculation results and the repetition rate threshold, the region data can be evaluated from different angles, specifically, according to the relationship between the first calculation result and the first threshold, the region data can be avoided when the constructor collects from the optical line terminal, and the region data is when the constructor collects from the first-level optical splitter, and according to the relationship between the second calculation result and the second threshold, the region data can be avoided when the constructor collects from the pigtail.

Furthermore, when the first calculation result is smaller than or equal to the first threshold value and the second calculation result is smaller than or equal to the second threshold value, the region data can be evaluated as the real data, that is, the evaluation result is qualified.

After determining that the region data is real data, the region data can also be stored in a database for subsequent calculation of the dimension material.

For example, the first calculation result is 16%, the second calculation result is 19%, the first threshold is 17%, and the second threshold is 23%, and it is obvious that the first calculation result is smaller than the first threshold and the second calculation result is smaller than the second threshold, the area data is estimated to be real data, and the estimated result is qualified.

In the present exemplary embodiment, only when the region data is evaluated as the real data in the case where the first calculation result is less than or equal to the first threshold value and the second calculation result is less than or equal to the second threshold value, the logic of evaluating the region data is perfected, and the accuracy of the evaluation result is ensured.

In step S140, if the calculation result is greater than the repetition rate threshold, the region data is evaluated as false data.

In an exemplary embodiment of the present disclosure, if the calculation result is greater than the repetition rate threshold, the evaluation area data is unqualified dummy data.

For example, the calculation results include a first calculation result and a second settlement result, and correspondingly, there are a first threshold corresponding to the first calculation result and a second threshold corresponding to the second calculation result in the repetition rate threshold.

Based on the above, if the first calculation result is greater than the first threshold, the region data at this time is proved to be false data, that is, the evaluation result is failed, if the second calculation result is greater than the second threshold, the region data at this time is proved to be false data, that is, the evaluation result is failed, and if the first calculation result is greater than the first threshold and the second calculation result is also greater than the second threshold, the region data at this time is proved to be false data, that is, the evaluation result is failed.

In an alternative embodiment, if the calculation result is greater than the repetition rate threshold, determining that the region data is false data includes: and if the first calculation result is greater than the first threshold value or the second calculation result is greater than the second threshold value, the evaluation area data is virtual data.

When the first calculation result is greater than the first threshold value or the second calculation result is greater than the second threshold value, the region data can be estimated to be false data, namely, the estimation result is unqualified.

For example, the first calculation result is 16%, the second calculation result is 24%, the first threshold is 17%, and the second threshold is 23%, and it is obvious that when the second calculation result is greater than the second threshold, the area data is estimated to be false data, and the estimated result is unqualified.

In the present exemplary embodiment, when the region data is evaluated as the dummy data only in the case where the first calculation result is greater than the first threshold value or the second calculation result is greater than the second threshold value, the logic of evaluating the region data is perfected, and the accuracy of the evaluation result is ensured.

In an alternative embodiment, fig. 6 shows a schematic flow chart of the dimension-finding material calculated in the evaluation of the splitter data, and as shown in fig. 6, the method at least includes the following steps: in step S610, the distance data included in the area data is divided based on the beam splitter identifier to obtain a first division result.

After the regional data are determined to be the real data, the assessment result is proved to be qualified, the regional data can be formally put into use, communication services are provided for users, and the final-stage beam splitter can be connected with the secondary beam splitter.

The splitter identifier, i.e. the identifier that distinguishes which two-stage splitter this is, for example, there are 5 two-stage splitters, where the splitter identifier of the first two-stage splitter may be a, the splitter identifier of the second two-stage splitter may be B, the splitter identifier of the third two-stage splitter may be C, and so on, and the splitter identifier of the last two-stage splitter may be E. The distance data is divided based on the optical splitter identifiers, and the distance data corresponding to each secondary optical splitter can be obtained.

For example, the area data includes distance data, and the distance data specifically includes data 1-1, data 1-2, and data 1-3, where data 1-1 is distance data of a secondary beam splitter identified as a, data 1-2 is distance data of a secondary beam splitter identified as B, and data 1-3 is distance data of a secondary beam splitter identified as C.

Based on the above, the distance data of the three secondary splitters can be obtained by dividing the distance data according to the splitter identification.

In step S620, obtaining final stage beam splitter data corresponding to the region identifier, and dividing the final stage beam splitter based on the beam splitter identifier to obtain a second division result, so as to determine a correspondence between the first division result and the second division result; the final-stage beam splitter data comprise final-stage distance data and network connection relations.

The final stage beam splitter data is typically stored in a server a corresponding to the optical line terminal, and as shown in fig. 2, after the area data is evaluated as real data, the final stage beam splitter may be connected to the secondary stage beam splitter, and the final stage beam splitter data of the same area as the area data belongs to may be acquired in the server a according to the area identifier.

Specifically, the final stage beam splitter data includes network distance data, that is, a distance value between the final stage beam splitter and an optical line terminal, and further includes a network connection relationship, that is, a connection relationship between the final stage beam splitter and the optical line terminal, based on which the final stage beam splitter data can be divided according to the beam splitter identifier in the region data.

After determining the final stage splitter data, the final stage splitter data may also be stored in a corresponding server for use in a subsequent calculation of the distance difference.

For example, there are 4 final stage splitter data, in which a final stage splitter corresponding to the first final stage splitter data is connected to the second stage splitter a, a final stage splitter corresponding to the second final stage splitter data is connected to the second stage splitter a, a final stage splitter corresponding to the third final stage splitter data is connected to the second stage splitter B, and a final stage splitter corresponding to the fourth final stage splitter data is connected to the second stage splitter C.

Based on this, it can be determined from the beam splitter identifier that the first final stage beam splitter data corresponds to the distance data of the second stage beam splitter a, the second final stage beam splitter data corresponds to the distance data of the second stage beam splitter a, the third final stage beam splitter data corresponds to the distance data of the second stage beam splitter B, and the fourth network final stage beam splitter data corresponds to the distance data of the second stage beam splitter C.

In step S630, calculating a distance difference value obtained from the network distance data in the first division result and the second division result, and calculating a dimension-filling material according to the distance difference value; wherein, the dress dimension material is used for maintaining the optic fibre between second grade beam splitter to last grade beam splitter.

The distance difference is the difference between the network distance data in the first division result and the second division result, and the dimension loading material is the material required in the maintenance process of the optical fiber.

For example, there is a correspondence between the distance data corresponding to the two-stage beam splitters a and the network distance data B, and the network distance data B is 100, and the distance data corresponding to the two-stage beam splitters a is 50, based on which the calculated distance difference is 50, and assuming that 100 two-stage beam splitters a are installed, the product result obtained by multiplying the dimension material by 50 by 100 can be determined at this time.

For example, there is a correspondence between the distance data corresponding to the secondary beam splitter A1 and the network distance data B1, there is a correspondence between the distance data corresponding to the secondary beam splitter A2 and the network distance data B2, there is a correspondence between the distance data corresponding to the secondary beam splitter A3 and the network distance data B3, and the distance data 20 corresponding to the secondary beam splitter A1, the distance data 25 corresponding to the secondary beam splitter A2, the distance data corresponding to the secondary beam splitter A3 is 38, the network distance data B1 is 33, the network distance data B2 is 30, and the network distance data B3 is 50, based on which it is possible to determine that 3 distance differences are 13, 5, and 12, respectively, calculate the average value of the three distance differences and the median of the three distance differences, and determine the smaller value of the average value and the median, so as to obtain the dimensional material based on the smaller value.

In this exemplary embodiment, the distance difference between the network distance data in the first division result and the second division result is calculated, so that reasonable dimension loading materials can be calculated, an accurate standard is provided for the dispensing of the dimension loading materials, the rationality of dispensing the dimension loading materials is improved, and the waste caused by excessive dispensed dimension loading materials in the prior art is avoided.

The method for evaluating the splitter data in the embodiments of the present disclosure is described in detail below with reference to an application scenario.

Fig. 7 schematically illustrates a flow chart of a method for evaluating splitter data in an application scenario, as shown in fig. 7, where data 710 is area data, and data 711 is network distance data in final splitter data, and it should be noted that, data 710 and data 711 belong to the same area, for example, belong to the same hospital, and database 720 may include an access network database storing data 710, and may also include an optical line device database storing data 711.

When the regional data of the hospital needs to be evaluated, only the data 710 needs to be acquired, and step S730 needs to be executed to obtain a first calculation result by using the first distance data and the second distance data in the regional data, and step S740 needs to be executed to obtain a second calculation result by using the third distance data and the fourth distance data in the data sorting result.

On this basis, step S750 and step S760 are executed, where in step S750, the first calculation result and the first threshold value are determined, and if the first calculation result is greater than the first threshold value, the result a is output, and if the first calculation result is less than or equal to the first threshold value, the result B is output, and in step S760, the second calculation result and the second threshold value are determined, and if the second calculation result is greater than the second threshold value, the result C is output, and if the second calculation result is greater than or equal to the second threshold value, the result D is output.

If the output result B and the output result C are obtained, the area data 710 is determined to be real data, if the output result a and the output result C are obtained, the area data 710 is determined to be false data, if the output result a and the deletion result D are obtained, the area data 710 is determined to be false data, and if the output result B and the output result C are obtained, the area data 710 is determined to be false data.

When the dimension material needs to be calculated, the area data 710 and the network distance data 720 need to be acquired at the same time, then step S770 is performed to determine a distance difference between the distance data in the area data and the network distance data, then step S780 is performed to compare an average value of the distance differences and a median of the distance differences, and the dimension material is determined according to the comparison result.

In the application scene, on one hand, the calculation result can be obtained by calculating the repetition rate of the regional data, so that the authenticity of the regional data can be automatically estimated according to the relation between the calculation result and the repetition rate threshold, the dependence on manpower in the estimation process is avoided, the labor cost of the evaluation of the optical splitter data is further reduced, and the efficiency of the evaluation of the optical splitter data is improved; on the other hand, the obtained hanging measurement data corresponding to the optical splitters are not the data on part of the optical splitters any more, so that the problem that the probability of faults occurring due to the quality of an optical cable after the optical cable is put into use only by sampling and detecting part of the two-stage optical splitters in the prior art is avoided.

In addition, in an exemplary embodiment of the present disclosure, an evaluation apparatus of splitter data is also provided. Fig. 8 shows a schematic structural diagram of evaluation of splitter data, and as shown in fig. 8, an evaluation apparatus 800 of splitter data may include: the partitioning module 810, the computing module 820, the first determining module 830, and the second determining module 840. Wherein:

the dividing module 810 is configured to acquire hanging measurement data corresponding to the secondary beam splitter, and determine an area identifier corresponding to the hanging measurement data, so as to divide the hanging measurement data according to the area identifier to obtain area data; a calculation module 820 configured to calculate a repetition rate of the region data to obtain a calculation result, and determine a repetition rate threshold corresponding to the calculation result; a first determining module 830 configured to evaluate the region data as real data if the calculation result is less than or equal to the repetition rate threshold; the second determining module 840 is configured to evaluate the region data as dummy data if the calculation result is greater than the repetition rate threshold.

The specific details of the above-mentioned evaluation device 800 for optical splitter data have been described in detail in the corresponding evaluation method for optical splitter data, and thus are not described here again.

It should be noted that although several modules or units of the evaluation device 800 of splitter data are mentioned in the above detailed description, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.

In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

An electronic device 900 according to such an embodiment of the invention is described below with reference to fig. 9. The electronic device 900 shown in fig. 9 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.

As shown in fig. 9, the electronic device 900 is embodied in the form of a general purpose computing device. Components of electronic device 900 may include, but are not limited to: the at least one processing unit 910, the at least one storage unit 920, a bus 930 connecting the different system components (including the storage unit 920 and the processing unit 910), and a display unit 940.

Wherein the storage unit stores program code that is executable by the processing unit 910 such that the processing unit 910 performs steps according to various exemplary embodiments of the present invention described in the above-described "exemplary methods" section of the present specification.

The storage unit 920 may include readable media in the form of volatile storage units, such as Random Access Memory (RAM) 921 and/or cache memory 922, and may further include Read Only Memory (ROM) 923.

The storage unit 920 may also include a program/usage tool 924 having a set (at least one) of program modules 925, such program modules 925 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which may include the reality of a network environment, or some combination thereof.

The bus 930 may be one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 900 may also communicate with one or more external devices 970 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 900, and/or any device (e.g., router, modem, etc.) that enables the electronic device 900 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 950. Also, electronic device 900 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 960. As shown, the network adapter 960 communicates with other modules of the electronic device 900 over the bus 930. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 900, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.

From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.

In an exemplary embodiment of the present disclosure, a computer-readable storage medium having stored thereon a program product capable of implementing the method described above in the present specification is also provided. In some possible embodiments, the various aspects of the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the invention as described in the "exemplary methods" section of this specification, when said program product is run on the terminal device.

Referring to fig. 10, a program product 1000 for implementing the above-described method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims

1. A method of evaluating splitter data, the method comprising:

acquiring hanging measurement data corresponding to a secondary beam splitter, and determining an area identifier corresponding to the hanging measurement data so as to divide the hanging measurement data according to the area identifier to obtain area data;

calculating the repetition rate of the region data to obtain a calculation result, obtaining sample hanging measurement data, and calculating the repetition rate of the sample hanging measurement data to obtain a sample calculation result; the sample hanging measurement data comprise real hanging measurement sample data and false hanging measurement sample data;

clustering the sample calculation results according to the authenticity of the sample hanging data to obtain clustering results, and determining a repetition rate threshold value based on the clustering results;

If the calculation result is smaller than or equal to the repetition rate threshold, evaluating the regional data as real data;

and if the calculated result is larger than the repetition rate threshold, evaluating the regional data as false data.

2. The method according to claim 1, wherein the hang-up data includes distance data of the secondary beam splitter to an optical line terminal, a region identifier corresponding to the secondary beam splitter, a beam splitter identifier corresponding to the secondary beam splitter, light attenuation data corresponding to the secondary beam splitter, and position data corresponding to the secondary beam splitter.

3. The method of evaluating splitter data of claim 2, wherein the calculation result comprises a first calculation result;

the step of calculating the repetition rate of the region data to obtain a calculation result comprises the following steps:

determining first distance data and second distance data in the distance data, and calculating a first data difference value between the first distance data and the second distance data;

and if the first data difference value meets the repetition condition, determining that the first distance data and the second distance data are repetition data, and determining the first calculation result according to the number of the repetition data.

4. A method of evaluating splitter data according to claim 3, wherein the calculation result comprises a second calculation result;

sorting the distance data to obtain a data sorting result, and determining third distance data and fourth distance data with adjacent sorting relations according to the data sorting result;

calculating the difference between the third distance data and the fourth distance data to obtain a second data difference value, and calculating the difference between the second data difference values to obtain a difference value calculation result;

and if the difference value calculation result meets the repetition condition, determining that the second data difference value corresponding to the difference value calculation result is repetition data, and determining the second calculation result according to the number of the repetition data.

5. The method of evaluating splitter data of claim 4, wherein the repetition rate threshold comprises a first threshold corresponding to the first calculation result and a second threshold corresponding to the second calculation result;

and if the calculation result is smaller than or equal to the repetition rate threshold, evaluating the region data as real data, including:

And if the first calculation result is smaller than or equal to the first threshold value and the second calculation result is smaller than or equal to the second threshold value, evaluating the area data as real data.

6. The method according to claim 5, wherein if the calculation result is greater than the repetition rate threshold, the step of evaluating the region data as false data includes:

and if the first calculation result is larger than the first threshold value or the second calculation result is larger than the second threshold value, evaluating the area data as false data.

7. The method for evaluating optical splitter data according to claim 2, wherein after determining that the area data is real data if the calculation result is less than or equal to the repetition rate threshold, the method further comprises:

dividing the distance data contained in the region data based on the optical splitter identifier to obtain a first division result;

acquiring final-stage beam splitter data corresponding to the region identifier, and dividing the final-stage beam splitter data based on the beam splitter identifier to obtain a second division result so as to determine the corresponding relation between the first division result and the second division result; wherein the final stage beam splitter data comprises network distance data and network connection relations;

Calculating a distance difference value obtained by the network distance data in the first division result and the second division result, and calculating a dimension loading material according to the distance difference value; the maintenance material is used for maintaining the optical fiber between the two-stage optical splitter and the final-stage optical splitter.

8. An evaluation device for splitter data, comprising:

the dividing module is configured to acquire hanging measurement data corresponding to the secondary beam splitter, determine a region identifier corresponding to the hanging measurement data, and divide the hanging measurement data according to the region identifier to obtain region data;

the calculating module is configured to calculate the repetition rate of the region data to obtain a calculation result, obtain sample hanging measurement data, and calculate the repetition rate of the sample hanging measurement data to obtain a sample calculation result; the sample hanging measurement data comprise real hanging measurement sample data and false hanging measurement sample data;

the first determining module is configured to cluster the sample calculation results according to the authenticity of the sample hanging data to obtain a clustering result, and determine a repetition rate threshold value based on the clustering result;

a second determining module configured to evaluate the region data as real data if the calculation result is less than or equal to the repetition rate threshold;

And thirdly, if the calculation result is larger than the repetition rate threshold value, evaluating the area data as false data.

9. An electronic device, comprising:

a processor;

a memory for storing executable instructions of the processor;

wherein the processor is configured to perform the method of evaluating the splitter data of any of claims 1-7 via execution of the executable instructions.

10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method of evaluating splitter data according to any one of claims 1-7.