Cellular network building envelope construction method based on dynamic adaptive clustering

Abstract

The invention discloses a cellular network building envelope construction method based on dynamic adaptive clustering, which relates to the technical field of mobile communication network optimization, and comprises the following steps: S1, collecting and processing multi-dimensional data of buildings and base stations; s2, enveloping is divided according to base station coverage and building distribution; s3, for the building which is only included by one envelope, the envelope which the building belongs to is the representative envelope of the building; for the buildings contained by the multiple envelopes, performing quantitative comparison through the number of the buildings contained by the envelopes and the construction states of the base stations to which the envelopes belong, and selecting representative envelopes; s4, calculating an envelope competitiveness value index through weighted summation of scores of three dimensions of coverage, capacity and complaint to evaluate an envelope coverage value and formulate a priority strategy; and S5, based on the construction of the base station and the change of the building, executing the steps S1 to S4 according to a set period. According to the invention, the coverage value of base station construction can be maximized, and the resource release accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mobile communication network optimization, and in particular to a method for constructing a cellular network building envelope based on dynamic adaptive clustering. Background Art

[0002] Currently, operators are gradually expanding the coverage of their base stations, building base stations to provide as comprehensive coverage as possible for buildings in different residential areas. However, as the number of buildings increases, the speed of base station construction gradually lags behind the pace of building construction, resulting in some buildings experiencing no signal coverage or substandard coverage. Furthermore, due to factors such as the capital and land required for base stations, it is not possible to build an unlimited number of base stations to achieve full building coverage. The optimal solution is to maximize coverage value. Therefore, operators need to divide buildings into clusters covered by different base stations and assess the value of the base stations (already built or under construction) belonging to these clusters to prioritize base station maintenance or construction.

[0003] The following technical bottlenecks exist in the current optimization of indoor cellular network coverage:

[0004] 1. Dynamic Tracking Failure: Traditional building envelopes use a single-plane clustering algorithm, which is unable to track the evolution of building issues over time. When the same building cluster is repeatedly identified as a problem in different cycles, the system generates independent envelopes, resulting in a break in historical associations and a lack of automated management of the envelope lifecycle.

[0005] 2. Lagging planning decisions: The association of envelopes with existing sites and ongoing plans relies on manual matching, without a dynamic coupling mechanism. For example, even after a pre-planned base station site corresponding to an envelope enters the ongoing plan or becomes an existing site, the system continues to generate new redundant envelopes, resulting in wasted resources and failing to address the issue of cross-cycle data fusion. Summary of the Invention

[0006] The present invention addresses the core pain point that traditional buildings are unable to dynamically track the evolution of base station network problems. It provides a cellular network building envelope construction method based on dynamic adaptive clustering. Through the dynamic envelope competitiveness value index, it maximizes the coverage value of base station construction, improves the accuracy of resource allocation, and shortens the average problem-solving cycle.

[0007] The present invention provides a method for constructing a building envelope of a cellular network based on dynamic adaptive clustering, which solves the above-mentioned technical problems by adopting the following technical solutions:

[0008] A method for constructing a building envelope of a cellular network based on dynamic adaptive clustering comprises the following steps:

[0009] S1. Collect and process multi-dimensional data of buildings and base stations;

[0010] S2, divide the envelope according to base station coverage and building distribution;

[0011] S3. For buildings included in only one envelope, the envelope to which they belong is the representative envelope of the building; for buildings included in multiple envelopes, the representative envelope is selected by quantitatively comparing the number of buildings included in the envelope and the construction status of the base stations to which the envelopes belong;

[0012] S4. Calculate the envelope competitiveness value index by weighted summing the scores of coverage, capacity, and complaints to assess the envelope coverage value and formulate a priority strategy;

[0013] S5. Based on the construction of base stations and changes in buildings, execute steps S1-S4 according to the set period.

[0014] Optionally, step S1 specifically includes:

[0015] S1.1. Extract building-level indicator data, including the building's spatial location, coverage indicators, capacity indicators, and complaint data, through the network optimization platform and user complaint system; synchronize base station archive data from the resource management system, including the base station's spatial location, coverage radius, and construction status;

[0016] S1.2. Eliminate missing values ​​and outliers from the acquired data and correct logical errors;

[0017] S1.3. Unify the coordinate system and normalize the numerical indicators;

[0018] S1.4. Match buildings and base stations using a spatial distance algorithm and establish an initial association table between buildings and base stations.

[0019] Optionally, step S2 specifically includes:

[0020] S2.1. For existing or en route base stations, calculate all buildings within their theoretical coverage area. Group the buildings within the theoretical coverage area by base station. Each base station corresponds to a proximity envelope. The attributes of each proximity envelope include the base station ID / name, a list of covered buildings and their spatial distribution, and comprehensive indicators.

[0021] S2.2. Extract buildings that are farther than the theoretical coverage radius from all existing / on-the-go base stations. Use the DBSCAN or K-means clustering algorithm to group uncovered buildings by spatial location. Each cluster represents a far-station envelope, with the cluster center serving as the recommended location for the pre-planned base station. The attributes of each far-station envelope include the number of buildings in the cluster, the mean of core indicators, the recommended site type, and a priority label.

[0022] Optionally, step S3 is performed to select a representative envelope for buildings included in multiple envelopes by performing quantitative comparison based on the number of buildings included in the envelopes and the construction status of the base stations to which the envelopes belong. This process specifically includes:

[0023] S3.1. Clarify the values ​​assigned to different base station construction statuses: assign a value of 1.5 to existing base stations, 1.2 to base stations in transit, and 1.0 to pre-planned base stations;

[0024] S3.2. For a building included in multiple envelopes, the score of the i-th envelope to which the building belongs is calculated based on the following formula:

[0025]

[0026] Where build_num is the number of original buildings included in the envelope, and α / β / γ are 1 if the base station corresponding to the envelope is an existing base station / on-the-way base station / pre-planned base station, and 0 otherwise;

[0027] S3.3. Compare the scores of the envelopes of the building and select the envelope with the highest score as the representative envelope of the building.

[0028] Optionally, step S4 is performed. For a building within the envelope, the calculation process of its coverage score is as follows:

[0029] S4.1.1. Determine the presence and corresponding coverage of the building's labels in the MDT weak coverage and CQT weak coverage data:

[0030] S4.1.2. If the building has an MDT weak coverage tag or a CQT weak coverage tag, the single tag score is calculated according to the following rules:

[0031] a) If the single tag coverage rate of the building is less than or equal to 60%, the corresponding tag score is 15 points;

[0032] b) If the single tag coverage rate of the building is 100%, the corresponding tag score is 0;

[0033] c) If the coverage of the building is between 60% and 100%, excluding the endpoints, the label score is calculated using linear interpolation, as follows:

[0034]

[0035] Where, X represents MDT or CQT;

[0036] S4.1.3. Calculate the average comprehensive score of the building's label dimensions;

[0037] S4.1.4. Select buildings in the envelope that have at least one of the MDT weak coverage label and the CQT weak coverage label, calculate the coverage score of each building, and finally average the scores of all eligible buildings in the envelope to obtain the final score of the envelope coverage dimension.

[0038] Preferably, step S4.1.3 is executed to calculate the average score of each tag dimension of the building, and the formula is as follows:

[0039] cover_score j =(core MDT +core CQT ) / (tag MDT +tag CQT ),

[0040] Where, cover_score j The average comprehensive score of the label dimension of building j; core MDT Score for MDT weak coverage label; core CQT is the score of CQT weak coverage tag; when building j has MDT weak coverage tag, tag MDT 1, otherwise 0; when building j has a CQT weak coverage tag tag CQT is 1 if yes, otherwise it is 0.

[0041] Optionally, step S4 is performed to calculate the capacity score of a building within the envelope as follows:

[0042] S4.2.1. For each building within the envelope, first determine whether it has any of the four capacity problem labels: high load, high flow, high backflow, and high terminal. Each existing label is assigned a value of 1, otherwise 0. The scores for each existing label are accumulated and multiplied by the preset capacity factor of 3.75 to obtain the building's capacity score.

[0043] S4.2.2. Select buildings in the envelope that have at least one capacity-related problem label and aggregate their capacity scores. Sum the capacity scores of all eligible buildings and divide by the number of eligible buildings to obtain the final capacity score for the envelope.

[0044] Optionally, step S4 is performed to calculate the complaint score of a building within the envelope based on the number of 4G concentrated complaints for the building within the envelope. The specific process is as follows:

[0045] S4.3.1. For each building within the envelope, count the total number of 4G concentrated complaint points, recorded as sum(complain), and determine the score of each building based on the complaint number interval:

[0046] a) If the total number of complaints for a single building is greater than or equal to 10 within a set period, the score is 15 points;

[0047] b) If the total number of complaints for a single building is equal to 0 within the set period, the score will be 0;

[0048] c) If the total number of complaints for a single building is between 1 and 10 within a set period, the score will be calculated based on a linear relationship;

[0049] S4.3.2. Select the buildings with complaint labels in the envelope, add up the complaint scores of all selected buildings, divide by the number of selected buildings, and obtain the average score. This average score is used as the final score of the complaint dimension of the envelope.

[0050] Optionally, weights corresponding to the three dimensions of coverage, capacity, and complaints are set, and the product of the set weights and the scores of the corresponding dimensions is calculated and summed to obtain the envelope competitive value index;

[0051] The higher the envelope competition value index, the greater the combined impact of coverage issues, capacity pressure, and complaint concentration on buildings within the envelope, and the higher the priority for optimization or construction of the corresponding base station;

[0052] The lower the envelope competitive value index, the better the overall network performance is, and resource investment can be temporarily postponed.

[0053] The method for constructing a cellular network building envelope based on dynamic adaptive clustering of the present invention has the following beneficial effects compared with the prior art:

[0054] This invention solves the core pain point that traditional buildings cannot dynamically track the evolution of base station network problems. At the same time, by calculating the dynamically changing envelope competitive value index, it maximizes the coverage value of base station construction, improves the accuracy of resource allocation, and shortens the average problem-solving cycle. It is particularly suitable for indoor and outdoor collaborative network optimization scenarios in high-density urban areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Attachment Figure 1 It is a flow chart of a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the technical solution, the technical problems solved and the technical effects of the present invention more clear, the technical solution of the present invention is clearly and completely described below in conjunction with specific embodiments.

[0057] Example:

[0058] Combined with attachment Figure 1 This embodiment proposes a method for constructing a building envelope of a cellular network based on dynamic adaptive clustering, which includes the following steps:

[0059] S1. Collect and process multi-dimensional data from buildings and base stations, including:

[0060] S1.1. Extract building-level indicator data, including the building's spatial location, coverage indicators, capacity indicators, and complaint data, through the network optimization platform and user complaint system; synchronize base station archive data from the resource management system, including the base station's spatial location, coverage radius, and construction status;

[0061] S1.2. Eliminate missing values ​​and outliers from the acquired data and correct logical errors;

[0062] S1.3. Unify the coordinate system and normalize the numerical indicators;

[0063] S1.4. Match buildings and base stations using a spatial distance algorithm and establish an initial association table between buildings and base stations.

[0064] S2. Divide the envelope based on base station coverage and building distribution, including:

[0065] S2.1. For existing or en route base stations, calculate all buildings within their theoretical coverage area. Group the buildings within the theoretical coverage area by base station. Each base station corresponds to a proximity envelope. The attributes of each proximity envelope include the base station ID / name, a list of covered buildings and their spatial distribution, and comprehensive indicators.

[0066] S2.2. Extract buildings that are farther than the theoretical coverage radius from all existing / on-the-go base stations. Use the DBSCAN or K-means clustering algorithm to group uncovered buildings by spatial location. Each cluster represents a far-station envelope, with the cluster center serving as the recommended location for the pre-planned base station. The attributes of each far-station envelope include the number of buildings in the cluster, the mean of core indicators, the recommended site type, and a priority label.

[0067] This step creates an initial association table between buildings and envelopes.

[0068] S3. For a building that is included in only one envelope, the envelope to which it belongs is the representative envelope of the building; for a building that is included in multiple envelopes, the representative envelope is selected by quantitatively comparing the number of buildings included in the envelope and the construction status of the base stations to which the envelope belongs.

[0069] For buildings included in multiple envelopes, a representative envelope is selected by quantitatively comparing the number of buildings included in the envelope and the construction status of the base stations to which the envelope belongs. This process specifically includes:

[0070] S3.1. Clarify the values ​​assigned to different base station construction statuses: assign a value of 1.5 to existing base stations, 1.2 to base stations in transit, and 1.0 to pre-planned base stations;

[0071] S3.2. For a building included in multiple envelopes, the score of the i-th envelope to which the building belongs is calculated based on the following formula:

[0072]

[0073] Where build_num is the number of original buildings included in the envelope, and α / β / γ are 1 if the base station corresponding to the envelope is an existing base station / on-the-way base station / pre-planned base station, and 0 otherwise;

[0074] S3.3. Compare the scores of the envelopes of the building and select the envelope with the highest score as the representative envelope of the building.

[0075] This step can further establish the final association table between buildings and envelopes.

[0076] S4. Calculate the envelope competitiveness value index by weighted summing the scores of coverage, capacity, and complaints to evaluate the envelope coverage value and formulate a priority strategy.

[0077] S4.1. For a building within the envelope, the calculation process for its coverage score is as follows:

[0078] S4.1.1. Determine the presence and corresponding coverage of the building's labels in the MDT weak coverage and CQT weak coverage data:

[0079] S4.1.2. If the building has an MDT weak coverage tag or a CQT weak coverage tag, the single tag score is calculated according to the following rules:

[0080] a) If the single tag coverage rate of the building is less than or equal to 60%, the corresponding tag score is 15 points;

[0081] b) If the single tag coverage rate of the building is 100%, the corresponding tag score is 0;

[0082] c) If the coverage of the building is between 60% and 100%, excluding the endpoints, the label score is calculated using linear interpolation, as follows:

[0083]

[0084] Where, X represents MDT or CQT;

[0085] S4.1.3. Calculate the average comprehensive score of the building's label dimensions using the following formula:

[0086] cover_score j =(core MDT +core CQT ) / (tag MDT +tag CQT ),

[0087] Where, cover_score j The average comprehensive score of the label dimension of building j; core MDT Score for MDT weak coverage label; core CQT is the score of CQT weak coverage tag; when building j has MDT weak coverage tag, tag MDT 1, otherwise 0; when building j has a CQT weak coverage tag tag CQT If yes, it is 1, otherwise it is 0;

[0088] That is to say, if the building has both MDT weak coverage and CQT weak coverage labels, its coverage score is the arithmetic mean of the corresponding scores of the two labels; if the building only has MDT weak coverage labels or CQT weak coverage labels, its coverage score is directly taken as the corresponding score of the existing label, and the score of the non-existent label is regarded as 0 and is not included in the average calculation.

[0089] S4.1.4. Select buildings in the envelope that have at least one of the MDT weak coverage label and the CQT weak coverage label, calculate the coverage score of each building, and finally average the scores of all eligible buildings in the envelope to obtain the final score of the envelope coverage dimension.

[0090] S4.2. For a building within the envelope, the capacity score is calculated as follows:

[0091] S4.2.1. For each building within the envelope, first determine whether it has any of the four capacity problem labels: high load, high flow, high backflow, and high terminal. Each existing label is assigned a value of 1, otherwise 0. The scores for each existing label are accumulated and multiplied by the preset capacity factor of 3.75 to obtain the building's capacity score.

[0092] S4.2.2. Select buildings in the envelope that have at least one capacity-related problem label and aggregate their capacity scores. Sum the capacity scores of all eligible buildings and divide by the number of eligible buildings to obtain the final capacity score for the envelope.

[0093] S4.3. For a building within the envelope, the complaint score is calculated based on the number of 4G concentrated complaints received by the building within the envelope. The specific process is as follows:

[0094] S4.3.1. For each building within the envelope, count the total number of 4G concentrated complaint points, recorded as sum(complain), and determine the score of each building based on the complaint number interval:

[0095] a) If the total number of complaints for a single building is greater than or equal to 10 within a set period, the score is 15 points;

[0096] b) If the total number of complaints for a single building is equal to 0 within the set period, the score will be 0;

[0097] c) If the total number of complaints for a single building is between 1 and 10 within a set period, the score will be calculated based on a linear relationship;

[0098] S4.3.2. Select buildings with complaint labels in the envelope, add up the complaint scores of all selected buildings, and divide by the number of selected buildings to obtain the average score. This average score is used as the final score of the complaint dimension of the envelope. The formula is as follows:

[0099]

[0100] In the formula, A, B, and C represent the weights of the scores of the three dimensions of coverage, capacity, and complaints, respectively, and A+B+C=1. complain_score j They represent the scores of building j in coverage, capacity and complaints respectively;

[0101] The formula indicates that for a building j within the envelope, after calculating its scores in the three dimensions of coverage, capacity, and complaints, the product of the set weights and the scores of the corresponding dimensions is calculated based on the pre-set weights of the three dimensions, and the sum is added to obtain the envelope competitive value index to evaluate the envelope coverage value and formulate a priority strategy.

[0102] Among them, the higher the envelope competition value index, the greater the combined impact of coverage problems, capacity pressure and complaint concentration of buildings within the envelope, and the higher the optimization or construction priority of the corresponding base station.

[0103] S5. Based on the construction of base stations and changes in buildings, execute steps S1-S4 according to the set period.

[0104] It should be added that if the pre-planned base station data is already included in step S1 (such as historical planning data), then after determining the relationship between the building and the envelope in step S3, the association table of the three building-envelope-pre-planned base station can be directly established through the base station planning data; if the pre-planned base station is dynamically updated (such as the new plan in step S5), the association table of the three building-envelope-pre-planned base station needs to be updated during the cycle execution.

[0105] In summary, the cellular network building envelope construction method based on dynamic adaptive clustering adopted in the present invention solves the core pain point that traditional buildings cannot dynamically track the evolution of base station network problems. At the same time, by calculating the dynamically changing envelope competitive value index, the coverage value of base station construction is maximized, the accuracy of resource allocation is improved, and the average problem-solving cycle is shortened. It is particularly suitable for indoor and outdoor collaborative network optimization scenarios in high-density urban areas.

[0106] The above specific examples are used to illustrate the principles and implementation methods of the present invention in detail. These examples are only used to help understand the core technical content of the present invention. Based on the above specific embodiments of the present invention, any improvements and modifications made by those skilled in the art without departing from the principles of the present invention should fall within the scope of patent protection of the present invention.

Claims

1. A method for constructing a building envelope of a cellular network based on dynamic adaptive clustering, characterized in that: The steps include: S1. Collect and process multi-dimensional data of buildings and base stations; S2, divide the envelope according to base station coverage and building distribution; S3. For buildings included in only one envelope, the envelope to which they belong is the representative envelope of the building; for buildings included in multiple envelopes, the representative envelope is selected by quantitatively comparing the number of buildings included in the envelope and the construction status of the base stations to which the envelopes belong; S4. Calculate the envelope competitiveness value index by weighted summing the scores of coverage, capacity, and complaints to assess the envelope coverage value and formulate a priority strategy; S5. Based on the construction of base stations and changes in buildings, execute steps S1-S4 according to the set period.

2. The method for constructing a cellular network building envelope based on dynamic adaptive clustering according to claim 1, characterized in that: The step S1 specifically includes: S1.

1. Extract building-level indicator data, including the building's spatial location, coverage indicators, capacity indicators, and complaint data, through the network optimization platform and user complaint system; synchronize base station archive data from the resource management system, including the base station's spatial location, coverage radius, and construction status; S1.

2. Eliminate missing values ​​and outliers from the acquired data and correct logical errors; S1.

3. Unify the coordinate system and normalize the numerical indicators; S1.

4. Match buildings and base stations using a spatial distance algorithm and establish an initial association table between buildings and base stations.

3. The method for constructing a cellular network building envelope based on dynamic adaptive clustering according to claim 1, characterized in that: The step S2 specifically includes: S2.

1. For existing or en route base stations, calculate all buildings within their theoretical coverage area. Group the buildings within the theoretical coverage area by base station. Each base station corresponds to a proximity envelope. The attributes of each proximity envelope include the base station ID / name, a list of covered buildings and their spatial distribution, and comprehensive indicators. S2.

2. Extract buildings that are farther than the theoretical coverage radius from all existing / on-the-go base stations. Use the DBSCAN or K-means clustering algorithm to group uncovered buildings by spatial location. Each cluster represents a far-station envelope, with the cluster center serving as the recommended location for the pre-planned base station. The attributes of each far-station envelope include the number of buildings in the cluster, the mean of core indicators, the recommended site type, and a priority label.

4. The method for constructing a cellular network building envelope based on dynamic adaptive clustering according to claim 1, characterized in that: Execute step S3. For buildings included in multiple envelopes, select a representative envelope by performing quantitative comparison based on the number of buildings included in the envelope and the construction status of the base stations to which the envelopes belong. This process specifically includes: S3.

1. Clarify the values ​​assigned to different base station construction statuses: assign a value of 1.5 to existing base stations, 1.2 to base stations in transit, and 1.0 to pre-planned base stations; S3.

2. For a building included in multiple envelopes, the score of the i-th envelope to which the building belongs is calculated based on the following formula: Where build_num is the number of original buildings included in the envelope, and α / β / γ are 1 if the base station corresponding to the envelope is an existing base station / on-the-way base station / pre-planned base station, and 0 otherwise; S3.

3. Compare the scores of the envelopes of the building and select the envelope with the highest score as the representative envelope of the building.

5. The method for constructing a cellular network building envelope based on dynamic adaptive clustering according to claim 1, characterized in that: Execute step S4, and for a building within the envelope, the calculation process of its coverage score is as follows: S4.1.

1. Determine the presence and corresponding coverage of the building's labels in the MDT weak coverage and CQT weak coverage data: S4.1.

2. If the building has an MDT weak coverage tag or a CQT weak coverage tag, the single tag score is calculated according to the following rules: a) If the single tag coverage rate of the building is less than or equal to 60%, the corresponding tag score is 15 points; b) If the single tag coverage rate of the building is 100%, the corresponding tag score is 0; c) If the coverage of the building is between 60% and 100%, excluding the endpoints, the label score is calculated using linear interpolation, as follows: Where, X represents MDT or CQT; S4.1.

3. Calculate the average comprehensive score of the building's label dimensions; S4.1.

4. Select buildings in the envelope that have at least one of the MDT weak coverage label and the CQT weak coverage label, calculate the coverage score of each building, and finally average the scores of all eligible buildings in the envelope to obtain the final score of the envelope coverage dimension.

6. The method for constructing a cellular network building envelope based on dynamic adaptive clustering according to claim 5, characterized in that: Execute step S4.1.3 to calculate the average score of each tag dimension of the building. The formula is as follows: cover_score j =(core MDT +core CQT ) / (tag MDT +tag CQT ), Where, cover_score j The average comprehensive score of the label dimension of building j; core MDT Score for MDT weak coverage label; core CQT is the score of CQT weak coverage tag; when building j has MDT weak coverage tag, tag MDT 1, otherwise 0; when building j has a CQT weak coverage tag tag CQT is 1 if yes, otherwise it is 0.

7. The method for constructing a cellular network building envelope based on dynamic adaptive clustering according to claim 5, characterized in that: Execute step S4, and for a building within the envelope, the capacity score is calculated as follows: S4.2.

1. For each building within the envelope, first determine whether it has any of the four capacity problem labels: high load, high flow, high backflow, and high terminal. Each existing label is assigned a value of 1, otherwise 0. The scores for each existing label are accumulated and multiplied by the preset capacity factor of 3.75 to obtain the building's capacity score. S4.2.

2. Select buildings in the envelope that have at least one capacity-related problem label and aggregate their capacity scores. Sum the capacity scores of all eligible buildings and divide by the number of eligible buildings to obtain the final capacity score for the envelope.

8. The method for constructing a cellular network building envelope based on dynamic adaptive clustering according to claim 7, characterized in that: Execute step S4. For a building within the envelope, the complaint score is calculated based on the number of 4G concentrated complaints for the building within the envelope. The specific process is as follows: S4.3.

1. For each building within the envelope, count the total number of 4G concentrated complaint points, recorded as sum(complain), and determine the score of each building based on the complaint number interval: a) If the total number of complaints for a single building is greater than or equal to 10 within a set period, the score is 15 points; b) If the total number of complaints for a single building is equal to 0 within the set period, the score will be 0; c) If the total number of complaints for a single building is between 1 and 10 within a set period, the score will be calculated based on a linear relationship; S4.3.

2. Select the buildings with complaint labels in the envelope, add up the complaint scores of all selected buildings, divide by the number of selected buildings, and obtain the average score. This average score is used as the final score of the complaint dimension of the envelope.

9. The method for constructing a cellular network building envelope based on dynamic adaptive clustering according to claim 8, characterized in that: Set the corresponding weights for the three dimensions of coverage, capacity, and complaints, calculate the product of the set weights and the corresponding dimension scores, and add them up to obtain the envelope competitive value index; The higher the envelope competition value index, the greater the combined impact of coverage issues, capacity pressure, and complaint concentration on buildings within the envelope, and the higher the priority for optimization or construction of the corresponding base station; The lower the envelope competitive value index, the better the overall network performance is, and resource investment can be temporarily postponed.

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