Dummy resource capacity expansion planning construction priority analysis method and device based on multivariate data and medium

Abstract

The invention discloses a dumb resource capacity expansion planning construction priority analysis method and device based on multivariate data and a medium, belongs to the technical field of dumb resource optimization in a communication network, and aims to solve the technical problem of how to realize reasonable planning and accurate decision of dumb resource capacity expansion. According to the technical scheme, the method comprises the following steps of: analyzing grid dimension high-load resources; building resource over-density analysis; service growth quantity analysis: for the tail end branch distributors in the grid region, judging the port incremental data level speed of the tail end branch distributors in the month, and evaluating the service growth condition in the region; an entropy weight method is adopted to calculate index weights of a high-load resource dimension, a construction resource over-dense dimension and a service increment dimension, and the larger the index weight is, the larger the effect of a corresponding index in comprehensive evaluation is, namely, the higher the priority is.

Description

Technical Field

[0001] The present invention relates to the technical field of optimization of dumb resources in communication networks, and specifically to a method, device, and medium for analyzing the priority of dumb resource expansion planning and construction based on multi-source data. Background Art

[0002] With the in-depth digital transformation of communication networks, higher requirements are put forward for the management of network resources, and the tentacles of the entire network management are extending to "planning, construction, optimization, and operation". In modern communication networks, dumb resources (such as optical cables, pipelines, optical splitters, optical cross-connects, manholes, utility poles, etc.) play a key role in connection and distribution. With the rapid development of communication services, the demand for dumb resources is increasing continuously. Traditional dumb resource expansion planning often relies on offline manual experience judgment or simple single-data index analysis, lacking comprehensiveness and accuracy, and easily leading to resource waste or untimely expansion, which can no longer meet the requirements of the complex and dynamic management and planning and construction of operators' networks, affecting the overall performance and business development of communication networks.

[0003] Therefore, how to achieve reasonable planning and accurate decision-making for dumb resource expansion, and improve the utilization efficiency of communication network resources and service carrying capacity is a technical problem to be solved urgently at present. Summary of the Invention

[0004] The technical task of the present invention is to provide a method, device, and medium for analyzing the priority of dumb resource expansion planning and construction based on multi-source data to solve the problems of how to achieve reasonable planning and accurate decision-making for dumb resource expansion, and improve the utilization efficiency of communication network resources and service carrying capacity.

[0005] The technical task of the present invention is realized in the following way. A method for analyzing the priority of dumb resource expansion planning and construction based on multi-source data is as follows:

[0006] Analysis of high-load resources in the grid dimension: For dumb resources such as optical cable lines, pipeline segments, pole segments, and optical cross-connects in the grid area, calculate whether the resources are in a high-load state from the dimensions of optical cable utilization rate, pipeline utilization rate, pole road utilization rate, and optical cross-connect utilization rate;

[0007] Analysis of over-dense construction resources: For resources such as manholes, utility poles, fiber distribution points, and DP boxes in the grid area, judge the over-dense situation of the resources from whether there are duplicate resource types within 5 meters of the resource construction specification;

[0008] Analysis of service growth volume: For the end distributors in the grid area, judge the monthly end distributor port increment data level and speed, and evaluate the service growth situation in the area;

[0009] The entropy weight method is used to calculate the index weights of the high-load resource dimension, the over-dense construction resource dimension, and the business growth volume dimension. The larger the index weight, the greater the role of the corresponding index in the comprehensive evaluation, that is, the higher the priority.

[0010] Preferably, the high-load resource analysis of the grid dimension is as follows:

[0011] The grid dimension calculates the quantity of high-load optical cable resources: Taking the grid area as the dimension, the optical cable segment data within the spatial boundary is calculated according to the grid spatial boundary. For a single optical cable segment data, the total number of fiber cores m in the corresponding optical cable segment is counted 总纤芯数 , and the number of fiber cores with the occupied usage status in the fiber cores of the corresponding optical cable segment is counted as n 纤芯数量 , and the fiber core occupancy rate of the corresponding optical cable segment is calculated If y≥80%, it is considered that the corresponding optical cable segment is a high-load optical cable resource; then, the quantity of high-load optical cable resources A = A1, A2,.....A under the grid area is calculated respectively n ;

[0012] The grid dimension calculates the quantity of high-load pipeline resources: Taking the grid area as the dimension, the pipeline segment data within the spatial boundary is calculated according to the grid spatial boundary. For a single pipeline segment data, according to the optical cable laying relationship data, the number of optical cable segments laid in the corresponding pipeline segment is obtained as m 光缆段数量 ; then, the number of pipe holes n in the pipeline segment is counted 管孔数量 ; then, the pipeline utilization rate is calculated according to the number of laid optical cable segments and the number of pipe holes If y≥80%, it is considered that the corresponding pipeline segment is a high-load pipeline resource; then, the quantity of high-load pipeline resources B = B1, B2,....., B under the grid area is calculated respectively n ;

[0013] The grid dimension calculates the quantity of high-load pole line resources: Taking the grid area as the dimension, the pole line segment data within the spatial boundary is calculated according to the grid spatial boundary. For a single pole line segment data, according to the optical cable laying relationship data, the number of optical cable segments laid in the corresponding pole line segment is obtained as m 光缆段数量 ; then, according to the number of laid optical cable segments and the reference number n of the optical cable carried by the pole line 杆路承载光缆基准数 (the default setting is 10, and the base number can be adjusted according to the actual production situation later) the pole line utilization rate is calculated If y≥80%, it is considered that the corresponding pole line segment is a high-load pole line resource; then, the quantity of high-load pole line resources C = C1, C2,....., C under the grid area is calculated respectively n ;

[0014] Grid dimension calculates the quantity of high-load optical cross-connect resources: Taking the grid area as the dimension, calculate the optical cross-connect data within the spatial boundary according to the grid spatial boundary. For each single optical cross-connect data, calculate the number of feeder optical fibers m associated with the corresponding optical cross-connect data based on the devices to which the terminals at both ends of the feeder optical fiber data belong. 局向光纤数量 ; Then, calculate the capacity n of the optical cross-connect according to the number of terminals in the optical cross-connect. 光交的容量 ; Then, calculate the pole road utilization rate according to the number of feeder optical fibers already associated with the optical cross-connect and the capacity of the optical cross-connect. y = m / n; If y ≥ 80%, then the corresponding pole road section is considered to be a high-load optical cross-connect resource; Then, calculate the quantity of high-load optical cross-connect resources D = D1, D2,....., D under the grid area respectively. n ;

[0015] Grid dimension calculates the total quantity of high-load resources.

[0016] More preferably, the grid dimension calculates the total quantity of high-load resources as follows:

[0017] Taking the grid area as the dimension, calculate the quantity of high-load resources S = the quantity of high-load optical cables A + the quantity of high-load pipelines B + the quantity of high-load pole roads C + the quantity of high-load optical cross-connects D under the corresponding area; Then, calculate the total quantity of high-load resources under each grid area respectively, and the formula is as follows:

[0018] S = S1, S2,....., S n =(A1 + B1 + C1 + D1), (A2 + B2 + C2 + D2),....., (A n + B n + C n + D n );

[0019] Among them, the greater the quantity of high-load resources within any grid area range, the greater the urgency of the corresponding area's planning and construction requirements.

[0020] Preferably, the analysis of over-dense construction resources is as follows:

[0021] Grid dimension calculates the quantity of over-dense manhole resources: Taking the grid area as the dimension, calculate the manhole data within the spatial boundary according to the grid spatial boundary. For each single manhole data, construct a range area M with a radius of 5 meters based on the manhole longitude and latitude data. 人井 , and determine whether there is any other manhole data in area M 人井 : If there is, the corresponding manhole is considered to be an over-dense manhole; Then, calculate the quantity of over-dense manhole resources E = E1, E2,.....E under each grid area respectively. n ;

[0022] Grid dimension calculates the number of over-dense resources of poles: Taking the grid area as the dimension, the pole data within the space boundary is calculated according to the grid space boundary. For each single pole data, a range area M with a radius of 5 meters is constructed based on the longitude and latitude data of the pole 电杆 , and judge the area M 电杆 to see if there is any other pole data: If there is, the corresponding pole is considered an over-dense pole; then calculate the number of over-dense resources of poles in each grid area respectively, denoted as F = F1, F2,.....F n ;

[0023] Grid dimension calculates the number of over-dense resources of DP boxes: Taking the grid area as the dimension, the DP box data within the space boundary is calculated according to the grid space boundary. For each single DP box data, a range area M with a radius of 5 meters is constructed based on the longitude and latitude data of the DP box DP盒 , and judge the area M DP盒 to see if there is any other DP box data: If there is, the corresponding DP box is considered an over-dense DP box; then calculate the number of over-dense resources of DP boxes in each grid area respectively, denoted as G = G1, G2,.....G n ;

[0024] Grid dimension calculates the number of over-dense resources of fiber splitting points: Taking the grid area as the dimension, the fiber splitting point data within the space boundary is calculated according to the grid space boundary. For each single fiber splitting point data, a range area M with a radius of 5 meters is constructed based on the longitude and latitude data of the fiber splitting point 分纤点 , and judge the area M 分纤点 to see if there is any other fiber splitting point data: If there is, the corresponding fiber splitting point is considered an over-dense fiber splitting point; then calculate the number of over-dense resources of fiber splitting points in each grid area respectively, denoted as H = H1, H2,.....H n ;

[0025] Grid dimension calculates the total number of over-dense resources.

[0026] More preferably, the grid dimension calculates the total number of over-dense resources as follows:

[0027] Taking the grid area as the dimension, calculate the total number of over-dense resources N in the corresponding area = the number of over-dense manholes E 人井 + the number of over-dense poles F 电杆 + the number of over-dense DP boxes G DP + the number of over-dense fiber splitting points F 分纤点 ; then calculate the total number of over-dense resources in each grid area respectively, and the formula is as follows:

[0028] N = N1, N2,.....N n =(E1 + F1 + G1 + H1),(E2 + F2 + G2 + H2),.....(E n + F n + Gn +H n )。

[0029] Preferably, the business growth analysis is as follows:

[0030] Taking the grid area as the dimension, calculate the data of the end splitter within the spatial boundary according to the grid space boundary;

[0031] Statistically analyze on a monthly granularity, and compare the usage of the end ports of the splitter with the growth rate of the previous month;

[0032] Calculate the growth of the end ports of the splitter corresponding to the grid area in the grid area dimension as the basis for judging the business growth, denoted as P = P1, P2,....., P n 。

[0033] Preferably, the entropy weight method is used to calculate the index weights of the high-load resource dimension, the over-dense construction resource dimension, and the business growth dimension as follows:

[0034] Construct a data matrix:

[0035] Among them, X nm represents the data value of the mth index of the nth object;

[0036] Perform a translation process on the data: X ij = X ij + 1; among them, X ij represents the data value of the mth index of the nth object;

[0037] Calculate the weight of the ith evaluation object under the jth index in the corresponding index, and the formula is as follows:

[0038]

[0039] Among them, i = 1, 2,... n; j = 1, 2,... m;

[0040] Calculate the entropy value of the jth index, and the formula is as follows:

[0041]

[0042] Among them, K > 0, ln is the natural logarithm, e j ≥ 0; the constant k is related to the number of samples m, generally let k = 1 / ln m, then 0 ≤ e ≤ 1;

[0043] Calculate the coefficient of variation of the jth index: For the jth index, the greater the difference in the index value X ij , the greater the role in the scheme evaluation, and the smaller the entropy value; g j = 1 - e j; among them, the difference coefficient g j The larger it is, the more important the indicator is;

[0044] Calculate the weight: where j = 1, 2, ···, n;

[0045] Calculate the comprehensive score corresponding to each evaluation: where i = 1, 2, ···, n.

[0046] A system for analyzing the priority of dumb resource expansion planning and construction based on multi-source data, which is used to implement the method for analyzing the priority of dumb resource expansion planning and construction based on multi-source data as described above; the system includes:

[0047] The high-load resource analysis module for grid dimension is used to calculate whether the resources are in a high-load state from the dimensions of optical cable utilization rate, pipeline utilization rate, pole road utilization rate, and optical cross-connection utilization rate for the dumb resources of optical cable lines, pipeline segments, pole road segments, and optical cross-connections within the grid area;

[0048] The over-dense construction resource analysis module is used to judge the over-dense situation of resources by determining whether there are duplicate resource types within 5 meters of the resource construction specification for manholes, utility poles, fiber distribution points, and DP boxes within the grid area;

[0049] The business growth analysis module is used to judge the monthly end distributor port increment data level speed for end distributors within the grid area and evaluate the business growth situation in the area;

[0050] The weight calculation module is used to calculate the index weights of the high-load resource dimension, the over-dense construction resource dimension, and the business growth dimension by using the entropy weight method. The larger the index weight, the greater the role played by the corresponding index in the comprehensive evaluation, that is, the higher the priority.

[0051] An electronic device includes: a memory and at least one processor;

[0052] wherein, a computer program is stored on the memory;

[0053] The at least one processor executes the computer program stored in the memory, so that the at least one processor executes the method for analyzing the priority of dumb resource expansion planning and construction based on multi-source data as described above.

[0054] A computer-readable storage medium stores a computer program, and the computer program can be executed by a processor to implement the method for analyzing the priority of dumb resource expansion planning and construction based on multi-source data as described above.

[0055] The method, device, and medium for analyzing the priority of dumb resource expansion planning and construction based on multi-source data of the present invention have the following advantages:

[0056] (1) Through comprehensively collecting diverse data and establishing a method for analyzing the priority of capacity expansion planning and construction of dormant resources, the present invention can help customers gain insights into the current network architecture status and guide customers in focusing on investment planning and construction areas. It has achieved a more than 10-fold improvement in the planning and evaluation efficiency of dormant resources, effectively enhancing the planning efficiency of network dormant resources, significantly improving the planning rationality, reducing labor costs, and remarkably enhancing investment benefits. At the same time, it reduces network hidden dangers, avoids economic losses, and gradually promotes the full-process digitalization of the overall planning of communication networks, thereby ultimately realizing the digital autopilot of communication networks;

[0057] (2) The present invention can efficiently utilize resources: By analyzing diverse data, it identifies dormant resources in the network that are not fully utilized and optimizes the resource utilization efficiency to avoid unnecessary resource expansion and save costs;

[0058] (3) The present invention can accurately predict capacity expansion: By combining historical data and real-time data, it establishes an analysis model to accurately predict future resource requirements and make advance capacity expansion plans to avoid over - or under - allocation of resources;

[0059] (4) The present invention optimizes decision - making: It provides an optimization decision - making model to help decision - makers make reasonable capacity expansion decisions under multiple constraints;

[0060] (5) The present invention establishes an intelligent analysis strategy that considers diverse data, including multiple dimensions such as high resource load conditions, resource construction density, and business development volume. It establishes an evaluation model through the entropy weight method to achieve intelligent evaluation of the planning and construction areas;

[0061] (6) During the planning analysis and evaluation process of the present invention, three - dimensional information is adopted. The risk of the area is jointly judged through the three dimensions, and through the entropy weight method, the weight analysis of the three planning analysis dimensions is realized. It is a scientific method for calculating impact factors, establishing an objective and targeted planning and construction analysis model to guide operators in precise planning and construction and precise investment;

[0062] (7) By combining multiple data sources (such as historical resource usage data, resource utilization rate, business growth volume, current network resource construction density, etc. within the regional scope), the present invention conducts optimization decisions on the capacity expansion planning of dormant resources in the grid area and focuses on the capacity expansion planning and construction of key areas to achieve reasonable planning and precise decision - making for the capacity expansion of dormant resources, improve the resource utilization efficiency and service - carrying capacity of communication networks, and at the same time can effectively utilize multiple data sources for resource capacity expansion planning, thereby improving resource utilization efficiency, reducing investment budgets, and achieving precise planning and investment construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The present invention will be further described below in conjunction with the drawings.

[0064] Attached Figure 1 It is a schematic diagram of the method for analyzing the priority of the expansion planning and construction of dumb resources based on multi-source data. Specific implementation manners

[0065] The method, device and medium for analyzing the priority of the expansion planning and construction of dumb resources based on multi-source data of the present invention are described in detail below with reference to the accompanying drawings of the specification and specific embodiments.

[0066] Embodiment 1

[0067] As attached Figure 1 As shown, this embodiment provides a method for analyzing the priority of the expansion planning and construction of dumb resources based on multi-source data, and the method is as follows:

[0068] S1. Analysis of high-load resources in the grid dimension: For the dumb resources of optical cable lines, pipeline segments, pole road segments and optical cable cross-connections in the grid area, calculate whether the resources are in a high-load state from the dimensions of optical cable utilization rate, pipeline utilization rate, pole road utilization rate and optical cable cross-connection utilization;

[0069] S2. Analysis of over-dense construction resources: For the resources of manholes, electric poles, fiber distribution points and DP boxes in the grid area, judge the over-dense situation of the resources from whether there are duplicate resource types within 5 meters of the resource construction specifications;

[0070] S3. Analysis of service growth volume: For the end distributors in the grid area, judge the monthly port increment data level speed of the end distributors and evaluate the service growth situation in the area;

[0071] S4. Use the entropy weight method to calculate the index weights of the high-load resource dimension, the over-dense construction resource dimension and the service growth volume dimension. The larger the index weight, the greater the role played by the corresponding index in the comprehensive evaluation, that is, the higher the priority.

[0072] The analysis of high-load resources in the grid dimension in step S1 of this embodiment is specifically as follows:

[0073] S101. Calculate the number of high-load optical cable resources in the grid dimension: Taking the grid area as the dimension, calculate the optical cable segment data within the spatial boundary according to the grid spatial boundary. For a single optical cable segment data, count the total number of fiber cores m 总纤芯数 in the corresponding optical cable segment, and count the number of fiber cores n 纤芯数量 with the occupied state in the fiber cores of the corresponding optical cable segment, and calculate the fiber core occupancy rate of the corresponding optical cable segment If y≥80%, it is considered that the corresponding optical cable segment is a high-load optical cable resource; then calculate the number of high-load optical cable resources A = A1, A2,.....A n under the grid area respectively;

[0074] S102. Calculate the number of high - load pipeline resources in terms of grid dimension: Taking the grid area as the dimension, calculate the pipeline segment data within the spatial boundary according to the grid spatial boundary. For each single - pipeline segment data, obtain the number of optical cable segments m laid in the corresponding pipeline segment according to the optical cable laying relationship data 光缆段数量 ; Then count the number of pipe holes n in the pipeline segment 管孔数量 ; Then calculate the pipeline utilization rate based on the number of laid optical cable segments and the number of pipe holes If y≥80%, it is considered that the corresponding pipeline segment is a high - load pipeline resource; then calculate the number of high - load pipeline resources B = B1, B2,....., B in the grid area respectively n ;

[0075] S103. Calculate the number of high - load pole - road resources in terms of grid dimension: Taking the grid area as the dimension, calculate the pole - road segment data within the spatial boundary according to the grid spatial boundary. For each single - pole - road segment data, obtain the number of optical cable segments m laid in the corresponding pole - road segment according to the optical cable laying relationship data 光缆段数量 ; Then calculate the pole - road utilization rate based on the number of laid optical cable segments and the reference number n of optical cables carried by the pole - road 杆路承载光缆基准数 (The default setting is 10, and the base number can be adjusted according to the actual production situation later) If y≥80%, it is considered that the corresponding pole - road segment is a high - load pole - road resource; then calculate the number of high - load pole - road resources C = C1, C2,....., C in the grid area respectively n ;

[0076] S104. Calculate the number of high - load optical cross - connect resources in terms of grid dimension: Taking the grid area as the dimension, calculate the optical cross - connect data within the spatial boundary according to the grid spatial boundary. For each single - optical cross - connect data, calculate the number of local - direction optical fibers m associated with the corresponding optical cross - connect data according to the devices to which the terminals at both ends of the local - direction optical fiber data belong 局向光纤数量 ; Then calculate the capacity n of the optical cross - connect according to the number of terminals in the optical cross - connect 光交的容量 ; Then calculate the pole - road utilization rate based on the number of local - direction optical fibers already associated with the optical cross - connect and the capacity of the optical cross - connect y = m / n; If y≥80%, it is considered that the corresponding pole - road segment is a high - load optical cross - connect resource; then calculate the number of high - load optical cross - connect resources D = D1, D2,....., D in the grid area respectively n ;

[0077] S105. Calculate the total number of high - load resources in terms of grid dimension; Taking the grid area as the dimension, calculate the number of high - load resources S in the corresponding area: S = the number of high - load optical cables A+the number of high - load pipelines B+the number of high - load pole - roads C+the number of high - load optical cross - connects D; Then calculate the total number of high - load resources in each grid area respectively. The formula is as follows:

[0078] S = S1, S2,....., Sn = (A1 + B1 + C1 + D1), (A2 + B2 + C2 + D2),....., (A n + B n + C n + D n );

[0079] Among them, the more the number of high-load resources within any grid area range, the greater the urgency of the corresponding area's planning and construction requirements.

[0080] The over-density analysis of construction resources in step S2 of this embodiment is specifically as follows:

[0081] S201. Calculate the over-density resource quantity of manholes in the grid dimension: Taking the grid area as the dimension, calculate the manhole data included within the spatial boundary according to the grid spatial boundary. For a single manhole data, construct a range area M with a radius of 5 meters using the manhole's longitude and latitude data 人井 , and determine whether there is other manhole data in area M 人井 : If there is, the corresponding manhole is considered an over-dense manhole; then calculate the over-density resource quantity of manholes in each grid area respectively, denoted as E = E1, E2,.....E n ;

[0082] S202. Calculate the over-density resource quantity of utility poles in the grid dimension: Taking the grid area as the dimension, calculate the utility pole data included within the spatial boundary according to the grid spatial boundary. For a single utility pole data, construct a range area M with a radius of 5 meters using the utility pole's longitude and latitude data 电杆 , and determine whether there is other utility pole data in area M 电杆 : If there is, the corresponding utility pole is considered an over-dense utility pole; then calculate the over-density resource quantity of utility poles in each grid area respectively, denoted as F = F1, F2,.....F n ;

[0083] S203. Calculate the over-density resource quantity of DP boxes in the grid dimension: Taking the grid area as the dimension, calculate the DP box data included within the spatial boundary according to the grid spatial boundary. For a single DP box data, construct a range area M with a radius of 5 meters using the DP box's longitude and latitude data DP盒 , and determine whether there is other DP box data in area M DP盒 : If there is, the corresponding DP box is considered an over-dense DP box; then calculate the over-density resource quantity of DP boxes in each grid area respectively, denoted as G = G1, G2,.....G n ;

[0084] S204. Calculate the number of over-dense resources of fiber splitting points in terms of grid dimension: Taking the grid area as the dimension, calculate the fiber splitting point data within the spatial boundary according to the grid spatial boundary. For each single fiber splitting point data, construct a range area M with a radius of 5 meters based on the longitude and latitude data of the fiber splitting point. 分纤点 , determine whether there is other fiber splitting point data in area M 分纤点 : If so, the corresponding fiber splitting point is considered an over-dense fiber splitting point; then calculate the number of over-dense resources of fiber splitting points in each grid area respectively, denoted as H = H1, H2,.....H n ;

[0085] S205. Calculate the total number of over-dense resources in terms of grid dimension:

[0086] Taking the grid area as the dimension, calculate the total number of over-dense resources N in the corresponding area = the number of over-dense manholes E 人井 + the number of over-dense utility poles F 电杆 + the number of over-dense DP boxes G DP + the number of over-dense fiber splitting points F 分纤点 ; then calculate the total number of over-dense resources in each grid area respectively, and the formula is as follows:

[0087] N = N1, N2,.....N n =(E1 + F1 + G1 + H1), (E2 + F2 + G2 + H2),.....(E n + F n + G n + H n ).

[0088] The analysis of business growth in step S3 of this embodiment is specifically as follows:

[0089] S301. Taking the grid area as the dimension, calculate the data of the end splitters within the spatial boundary according to the grid spatial boundary;

[0090] S302. Conduct monthly granularity statistical analysis on the growth of the usage of the end ports of the splitters compared with the previous month;

[0091] S303. Calculate the growth of the usage of the end ports of the splitters in the corresponding grid area in terms of grid dimension as the judgment basis for business growth, denoted as P = P1, P2,....., P n .

[0092] The specific method for calculating the index weights of the high-load resource dimension, the over-dense construction resource dimension, and the business growth dimension using the entropy weight method in step S4 of this embodiment is as follows:

[0093] S401. Construct a data matrix:

[0094] Among them, Xnm represents the data value of the m-th index of the n-th object;

[0095] S402. Since the entropy value method calculates using the ratio of a certain index of each object to the total value of the same index, there is no influence of dimension, and no standardization process is required. If there are negative numbers in the data, the data needs to be non-negativized. However, to avoid the meaningless logarithm calculation when the original data is 0 in calculating the entropy value, the data is shifted: X ij = X ij + 1; where X ij represents the data value of the m-th index of the n-th object;

[0096] S403. Calculate the weight of the i-th evaluation object under the j-th index, and the formula is as follows:

[0097]

[0098] where i = 1, 2,... n; j = 1, 2,... m;

[0099] S404. Calculate the entropy value of the j-th index, and the formula is as follows:

[0100]

[0101] where K > 0, ln is the natural logarithm, e j ≥ 0; the constant k is related to the number of samples m, generally let k = 1 / ln m, then 0 ≤ e ≤ 1;

[0102] S405. Calculate the coefficient of variation of the j-th index: For the j-th index, the greater the difference in the index value X ij , the greater the role in the scheme evaluation, and the smaller the entropy value; g j = 1 - e j ; where the coefficient of variation g j the greater it is, the more important the index;

[0103] S406. Calculate the weights: where j = 1, 2, ···, n;

[0104] S407. Calculate the comprehensive scores corresponding to each evaluation: where i = 1, 2, ···, n.

[0105] Among them, the calculation principle of the entropy weight method is specifically as follows: The analysis model has 3 dimension scores and m evaluation subjects. The entropy weight method is used to calculate the weights of each dimension. The original index score matrix is X = (X ij )m×n where m represents m evaluation subjects and n = 3 represents the scores of 4 dimension indicators. Among them, X ijRepresents the j-th indicator of the i-th evaluation subject. For a certain indicator X j , if X ij has a greater gap, then the greater the role of this indicator in the comprehensive evaluation, and the greater the weight calculated by the entropy weight method; if the indicator values of a certain indicator are all equal, then this indicator does not play a role in the comprehensive evaluation and the weight is 0.

[0106] Example 2:

[0107] This embodiment provides a system for analyzing the priority of dumb resource expansion planning and construction based on multi-source data, which is used to implement the method for analyzing the priority of dumb resource expansion planning and construction based on multi-source data in Example 1; the system includes:

[0108] A high-load resource analysis module for grid dimension, which is used to calculate whether the resources are in a high-load state from the dimensions of optical cable utilization rate, pipeline utilization rate, pole road utilization rate, and optical cross utilization rate for the dumb resources of optical cable lines, pipeline segments, pole road segments, and optical crosses within the grid area;

[0109] A construction resource over-density analysis module for grid dimension, which is used to judge the over-density of resources by whether there are repeated resource types within 5 meters of the resource construction specification for the manholes, electric poles, fiber distribution points, and DP boxes within the grid area;

[0110] A service growth analysis module for grid dimension, which is used to judge the monthly increment data level speed of the end distributor ports for the end distributors within the grid area and evaluate the service growth situation in the area;

[0111] A weight calculation module, which is used to calculate the indicator weights of the high-load resource dimension, construction resource over-density dimension, and service growth dimension by using the entropy weight method. The greater the indicator weight, the greater the role of the corresponding indicator in the comprehensive evaluation, that is, the higher the priority.

[0112] Example 3:

[0113] This embodiment also provides an electronic device, including: a memory and a processor;

[0114] Among them, the memory stores computer execution instructions;

[0115] The processor executes the computer execution instructions stored in the memory, so that the processor executes the method for analyzing the priority of dumb resource expansion planning and construction based on multi-source data in any embodiment of the present invention.

[0116] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0117] The memory can be used to store computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory, the processor realizes various functions of the electronic device. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc.; the data storage area can store data created according to the use of the terminal, etc. In addition, the memory can also include high-speed random access memory, and can also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one magnetic disk storage period, flash device, or other volatile solid-state storage devices.

[0118] Embodiment 4:

[0119] This embodiment also provides a computer-readable storage medium, in which multiple instructions are stored. The instructions are loaded by the processor, so that the processor executes the method for analyzing the priority of the dumb resource expansion plan construction based on multivariate data in any embodiment of the present invention. Specifically, a system or device equipped with a storage medium can be provided. On this storage medium, software program codes for implementing the functions of any one of the above embodiments are stored, and the computer (or CPU or MPU) of the system or device reads and executes the program codes stored in the storage medium.

[0120] In this case, the program code read from the storage medium itself can implement the functions of any one of the above embodiments. Therefore, the program code and the storage medium storing the program code constitute a part of the present invention.

[0121] Embodiments of the storage medium for providing program codes include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Optionally, the program code can be downloaded from a server computer via a communication network.

[0122] In addition, it should be clear that not only can part or all of the actual operations be completed by executing the program code read by a computer, but also by an operating system or the like operating on the computer based on the instructions of the program code, thereby implementing the functions of any one of the above embodiments.

[0123] In addition, it can be understood that the program code read from the storage medium is written into the memory provided in the expansion board inserted into the computer or into the memory provided in the expansion unit connected to the computer, and then part and all of the actual operations are executed by a CPU or the like installed on the expansion board or the expansion unit based on the instructions of the program code, thereby implementing the functions of any one of the above embodiments.

[0124] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for analyzing the priority of expanding and planning the construction of dumb resources based on multi-source data, characterized in that, The method is as follows: High-load resource analysis of grid dimension: For the dumb resources of optical cable lines, pipeline segments, pole segments and optical cross-connects in the grid area, calculate whether the resources are in a high-load state from the dimensions of optical cable utilization rate, pipeline utilization rate, pole road utilization rate and optical cross-connect utilization rate; Over-dense construction resource analysis: For the manholes, poles, fiber distribution points and DP box resources in the grid area, judge the over-dense situation of the resources from whether there are duplicate resource types within 5 meters of the resource construction specifications; Business growth volume analysis: For the end distributors in the grid area, judge the monthly end distributor port increment data level speed and evaluate the business growth situation in the area; The entropy weight method is used to calculate the index weights of the high-load resource dimension, the over-dense construction resource dimension and the business growth volume dimension. The larger the index weight, the greater the role played by the corresponding index in the comprehensive evaluation, that is, the higher the priority.

2. The method for analyzing the construction priority of expanding dumb resources based on multiple data according to claim 1, wherein The high-load resource analysis of grid dimension is as follows: Grid dimension calculation of the number of high-load optical cable resources: Taking the grid area as the dimension, calculate the optical cable segment data within the spatial boundary according to the grid spatial boundary. For a single optical cable segment data, count the total number of fiber cores m in the corresponding optical cable segment 总纤芯数 , and count the number of occupied fiber cores n among the fiber cores in the corresponding optical cable segment 纤芯数量 , calculate the fiber core occupancy rate of the corresponding optical cable segment If y ≥ 80%, then the corresponding optical cable segment is considered a high-load optical cable resource; then calculate the number of high-load optical cable resources A = A1, A2,.....A under the grid area respectively n ; Grid dimension calculation of the number of high-load pipeline resources: Taking the grid area as the dimension, calculate the pipeline segment data within the spatial boundary according to the grid spatial boundary, and for each single pipeline segment data, obtain the number m of optical cable segments laid for the corresponding pipeline segment according to the optical cable laying relationship data 光缆段数量 ; Then count the number n of pipe holes in the pipeline segment 管孔数量 ; Then calculate the pipeline utilization rate according to the number of laid optical cable segments and the number of pipe holes If y ≥ 80%, it is considered that the corresponding pipeline segment is a high-load pipeline resource; then calculate the number of high-load pipeline resources B = B1, B2,....., B under the grid area respectively n ; Grid dimension calculates the number of high-load pole line resources: Taking the grid area as the dimension, calculate the pole line segment data within the spatial boundary according to the grid spatial boundary, and for each single pole line segment data, obtain the number m of optical cable segments laid on the corresponding pole line segment according to the optical cable laying relationship data 光缆段数量 ; Then, according to the number of laid optical cable segments and the reference number n of the optical cable carried by the pole line 杆路承载光缆基准数 Calculate the utilization rate of the pole line If y≥80%, it is considered that the corresponding pole line segment is a high-load pole line resource; then calculate the number C of high-load pole line resources in the grid area respectively, C = C1, C2,....., C n ; Grid dimension calculation of the number of high-load optical cross-connect resources: Taking the grid area as the dimension, calculate the optical cross-connect data within the spatial boundary according to the grid spatial boundary. For a single optical cross-connect data, calculate the number of local-direction optical fibers m associated with the corresponding optical cross-connect data based on the devices to which the terminals at both ends of the local-direction optical fiber data belong. 局向光纤数量 ; Then, calculate the capacity n of the optical cross-connect according to the number of terminals in the optical cross-connect. 光交的容量 ; Then, calculate the pole line utilization rate according to the number of local-direction optical fibers already associated with the optical cross-connect and the capacity of the optical cross-connect. If y≥80%, it is considered that the corresponding pole line section is a high-load optical cross-connect resource; then calculate the number of high-load optical cross-connect resources D = D1, D2,....., D under the grid area respectively. n ; Calculate the total number of high-load resources in the grid dimension.

3. The method for analyzing the construction priority of dummy resource expansion based on multi-source data according to claim 2, wherein The calculation of the total number of high-load resources in the grid dimension is as follows: Taking the grid area as the dimension, calculate the number of high-load resources S = the number of high-load optical cables A + the number of high-load pipelines B + the number of high-load pole roads C + the number of high-load optical cross-connects D in the corresponding area; then calculate the total number of high-load resources in each grid area respectively. The formula is as follows: S = S1, S2,....., S n = (A1 + B1 + C1 + D1), (A2 + B2 + C2 + D2),....., (A n + B n + C n + D n ); Among them, the more the number of high-load resources within the scope of any grid area, the greater the urgency of the corresponding area's planning and construction requirements.

4. The method for analyzing the priority of the expansion planning and construction of dumb resources based on multi-source data according to claim 1, wherein The over-dense construction resource analysis is as follows: Grid dimension to calculate the over-dense resource quantity of manholes: Taking the grid area as the dimension, calculate the manhole data within the spatial boundary according to the grid spatial boundary. For each single manhole data, construct a range area M with a radius of 5 meters using the manhole longitude and latitude data 人井 , and determine whether there is other manhole data in area M 人井 : If there is, the corresponding manhole is considered an over-dense manhole; then calculate the over-dense resource quantity of manholes in each grid area respectively, denoted as E = E1, E2,.....E n ; Grid dimension calculates the number of over-dense pole resources: Taking the grid area as the dimension, the pole data within the spatial boundary is calculated according to the grid space boundary. For each single pole data, a range area M with a radius of 5 meters is constructed based on the pole's longitude and latitude data. 电杆 , determine whether there is other pole data in area M 电杆 : If there is, the corresponding pole is considered an over-dense pole. Then, calculate the number of over-dense pole resources in each grid area, denoted as F = F1, F2,.....F n ; Grid dimension calculation of the number of over-dense resources in the DP box: Taking the grid area as the dimension, calculate the DP box data within the spatial boundary according to the grid space boundary. For a single piece of DP box data, construct a range area M with a radius of 5 meters using the longitude and latitude data of the DP box DP盒 , and determine the area M DP盒 to check if there is any other DP box data: If there is, the corresponding DP box is considered an over-dense DP box; then calculate the number of over-dense resources of the DP box in each grid area respectively, denoted as G = G1, G2,.....G n ; Grid dimension calculation of the number of over-dense fiber splitting points: Taking the grid area as the dimension, calculate the fiber splitting point data within the spatial boundary according to the grid space boundary, and for each single fiber splitting point data, construct a range area M with a radius of 5 meters using the longitude and latitude data of the fiber splitting point 分纤点 , determine whether there is other fiber splitting point data in area M 分纤点 : If so, the corresponding fiber splitting point is considered an over-dense fiber splitting point; then calculate the number of over-dense fiber splitting point resources under each grid area respectively, denoted as H = H1, H2,.....H n ; Calculate the total number of over-dense resources in the grid dimension.

5. The method for analyzing the construction priority of expanding dumb resources based on multi-source data according to claim 1, wherein The calculation of the total number of over-dense resources in the grid dimension is as follows: Taking the grid area as the dimension, calculate the total number N of over-dense resources in the corresponding area = the number E of over-dense manholes 人井 + the number F of over-dense utility poles 电杆 + the number G of over-dense DP boxes DP + the number F of over-dense fiber distribution points 分纤点 ; Then calculate the total number of over-dense resources in each grid area respectively, and the formula is as follows: N = N1, N2,.....N n = (E1 + F1 + G1 + H1), (E2 + F2 + G2 + H2),.....(E n + F n + G n + H n )。 6. The method for analyzing the priority of the expansion planning and construction of dumb resources based on multi-source data according to claim 1, wherein The business growth volume analysis is as follows: Taking the grid area as the dimension, calculate the data of the end splitters included within the spatial boundary according to the grid space boundary; Statistical analysis is carried out on a monthly granularity, and the growth volume of the usage of the end ports of the splitters compared with the previous month is calculated; Calculate the growth of the end-port usage of the optical splitter in the corresponding grid area in terms of the grid area dimension as the basis for judging the service growth, denoted as P = P1, P2,....., P n .

7. The method for analyzing the construction priority of dumb resource expansion based on multi-source data according to claim 1, wherein The calculation of the index weights of the high-load resource dimension, the over-dense construction resource dimension and the business growth volume dimension by using the entropy weight method is as follows: Construct a data matrix: where X nm represents the data value of the m-th index of the n-th object; Perform a translation process on the data: X ij = X ij + 1; where X ij represents the data value of the m-th index of the n-th object; Calculate the weight of the i-th evaluation object under the j-th index in the corresponding index. The formula is as follows: Among them, i = 1, 2,... n; j = 1, 2,... m; Calculate the entropy value of the j-th index. The formula is as follows: where K > 0, ln is the natural logarithm, and e j ≥ 0; the constant k is related to the number of samples m. Generally, k = 1 / ln m, so 0 ≤ e ≤ 1; Calculate the coefficient of variation of the j-th indicator: For the j-th indicator, the indicator value X ij The greater the difference, the greater the effect on the scheme evaluation, and the smaller the entropy value; j =1-e j ; Among them, the coefficient of variation g j The larger the indicator, the more important it is; Calculation weight: where j = 1, 2, ···, n; Calculate the comprehensive score corresponding to each evaluation: where i = 1, 2, ···, n.

8. A priority analysis system for the expansion planning and construction of dumb resources based on multi-source data, characterized in that, This system is used to implement the method for analyzing the priority of dumb resource expansion planning and construction based on multi-source data as described in any one of claims 1 to 7; this system includes: A high-load resource analysis module for grid dimension, which is used to calculate whether the resources are in a high-load state from the dimensions of optical cable utilization rate, pipeline utilization rate, pole road utilization rate and optical cross-connect utilization rate for the dumb resources of optical cable lines, pipeline segments, pole segments and optical cross-connects in the grid area; An over-dense construction resource analysis module, which is used to judge the over-dense situation of the resources from whether there are duplicate resource types within 5 meters of the resource construction specifications for the manholes, poles, fiber distribution points and DP box resources in the grid area; A business growth volume analysis module, which is used to judge the monthly end distributor port increment data level speed and evaluate the business growth situation in the area for the end distributors in the grid area; A weight calculation module, which is used to calculate the index weights of high-load resource dimensions, over-dense construction resource dimensions, and business growth volume dimensions by using the entropy weight method. The larger the index weight, the greater the role played by the corresponding index in the comprehensive evaluation, that is, the higher the priority.

9. An electronic device, characterized in that, It includes: A memory and at least one processor; Wherein, a computer program is stored on the memory; The at least one processor executes the computer program stored in the memory, so that the at least one processor executes the method for analyzing the priority of the dumb resource expansion planning and construction based on multivariate data according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, A computer program is stored in the computer-readable storage medium, and the computer program can be executed by a processor to implement the method for analyzing the priority of the dumb resource expansion planning and construction based on multivariate data according to any one of claims 1 to 7.

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