Network capacity planning method and apparatus, electronic device, and storage medium

Abstract

The application provides a network capacity planning method and device, electronic equipment and storage medium, and relates to the technical field of mobile communication. The method comprises the following steps: determining a single-user average demand rate based on the proportion of the traffic volume of various services in a target area and a target demand rate, wherein the target area is a signal coverage area of a mobile carrier corresponding to a passing base station; determining the number of concurrent users that can be carried by the base station based on the downlink capacity of a single sector of the base station and the single-user average demand rate; predicting the number of users in the target area based on the proportion of users in the network, the passenger capacity of the mobile carrier and the proportion of users of the mobile carrier; determining whether the network capacity of the base station meets the demand based on the predicted number of users in the network and the number of concurrent users that can be carried; if the demand is not met, determining a target capacity planning value based on the predicted number of users in the network and the single-user average demand rate, and performing network capacity optimization configuration on the passing base station. The application improves the accuracy of capacity planning and the service experience of users of the mobile carrier.

Description

Technical Field

[0001] This invention relates to the field of mobile communication technology, specifically to a network capacity planning method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the continuous development of mobile communication technology, high-bandwidth services such as high-definition video and cloud gaming are becoming increasingly popular. The demand for wireless network capacity in high-speed mobile scenarios such as high-speed rail and intercity trains is also constantly increasing. The unique communication characteristics of these scenarios have placed higher demands on the planning and optimization of mobile communication network capacity.

[0003] In related technologies, most mobile communication network capacity planning methods are based on static network indicators or multi-dimensional fixed standards for network capacity assessment. These methods cannot adapt to the characteristics of high-speed mobile scenarios, such as concentrated instantaneous access by users, large fluctuations in service traffic, and severe wireless signal loss. The assessment results are difficult to accurately reflect the actual capacity requirements of mobile lines, resulting in a lack of rationality in network capacity planning and an inability to effectively guarantee the mobile communication experience of users. Summary of the Invention

[0004] The present invention aims to provide a network capacity planning method, apparatus, electronic device and storage medium to at least solve the problem that the existing technology lacks rationality in network capacity planning and cannot effectively guarantee the mobile communication experience of users.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: This invention provides a network capacity planning method, comprising: Based on the traffic volume ratio of various services in the target area and the target demand rate, the average demand rate of a single user is determined. The target area is the signal coverage area of ​​the base station corresponding to the mobile carrier. The number of concurrent users that can be supported is determined based on the downlink capacity of a single sector of the passing base station and the average demand rate of a single user. Based on the proportion of registered users, the passenger volume of the mobile carrier, and the proportion of mobile carrier users, the predicted number of registered users in the target area is obtained. Based on the predicted number of users joining the network and the number of concurrent users that can be supported, determine whether the network capacity of the passing base station meets the requirements; If the determination result is that the demand is not met, the target capacity planning value is determined based on the predicted number of network users and the average demand rate per user, and the network capacity is optimized and configured for the passing base stations based on the target capacity planning value.

[0006] The technical solution provided by this invention brings at least the following beneficial effects: In the network capacity planning method provided by this invention, the average demand rate per user is determined based on the proportion of traffic of various services in the target area and the target demand rate. The obtained average demand rate per user closely matches the actual service usage, providing a reliable data foundation for subsequent network capacity calculation. Based on the downlink capacity of a single sector of a base station and the average demand rate per user, the number of concurrent users that the base station can support is determined, which clarifies the actual service carrying capacity limit of the base station and provides a basis for judging whether the base station capacity meets the service demand. Based on the proportion of network users, the passenger capacity of mobile carriers, and the proportion of mobile carrier users, the number of network users in the target area is predicted, which can adapt to the concentrated access characteristics of users in mobile scenarios, improve the accuracy of predicting the scale of user access, and further enhance the rationality of subsequent capacity planning. Based on the predicted number of network users and the number of concurrent users that can be supported, the network capacity is judged to meet the demand. When the demand is not met, the network capacity of the passing base stations is optimized based on the target capacity planning value, which can realize precise expansion and resource allocation on demand, effectively meet the communication needs of users in mobile carriers, and optimize the user's service experience.

[0007] Based on the above technical solution, the present invention can be further improved as follows.

[0008] Furthermore, determining the average demand rate per user based on the service volume ratio and target demand rate of various services within the target area includes: obtaining traffic statistics data of various services within the target area from the network-side big data platform, and determining the service volume ratio based on the traffic statistics data; determining the target demand rate corresponding to each service at a preset experience level through on-site drive testing and user perception experiments; and determining the average demand rate per user corresponding to the preset experience level based on the service volume ratio and target demand rate of each service.

[0009] The beneficial effects of this approach are as follows: By retrieving traffic statistics from the network-side big data platform and determining the proportion of various services based on the obtained traffic statistics, the actual distribution of service traffic in the target area can be accurately characterized; by combining on-site drive tests and user perception experiments to define the target service demand rates corresponding to different experience levels for various services, the determined target demand rates can be made to fit the actual communication environment and the real user experience; by integrating service proportions and hierarchical target demand rates to calculate the average demand rate per user, the rate demand can be accurately quantified, thereby providing reliable data support for subsequent network capacity planning.

[0010] Furthermore, determining the average demand rate per user for the corresponding service under the preset experience level based on the service volume ratio and the target demand rate includes: weighting and summing the service volume ratio and the target demand rate for each type of service to obtain the average demand rate per user under the corresponding preset experience level.

[0011] The beneficial effects of adopting this scheme are as follows: by weighting and summing the traffic volume of various services with the target demand rate to determine the average demand rate of a single user, it can quantitatively reflect the rate demand under the real business structure of the target area. It can take into account the bandwidth differences of different services and make the obtained target demand rate conform to the preset experience level requirements.

[0012] Furthermore, the step of predicting the predicted number of network users in the target area based on the proportion of network users, the passenger volume of the mobile carrier, and the proportion of mobile carrier users includes: calculating the mobile terminal penetration rate and network access rate, and calculating the product of the mobile terminal penetration rate and the network access rate, denoted as the proportion of network users; calculating the product of the proportion of network users and the passenger volume of the mobile carrier, denoted as the predicted number of mobile carrier users; determining the proportion of mobile carrier users based on the regional type and the number of non-mobile carrier users in the target area; and calculating the ratio of the predicted number of mobile carrier users to the proportion of mobile carrier users, denoted as the predicted number of network users.

[0013] The beneficial effects of this scheme are as follows: By statistically analyzing the mobile terminal penetration rate and network access rate and calculating their product, the proportion of network users is obtained. Then, combined with the mobile carrier passenger volume, regional type, and number of non-mobile carrier users, the proportion of mobile carrier users is determined. Finally, the predicted number of network users in the target area is accurately predicted through ratio calculation. This scheme adopts a multi-factor, scenario-based prediction method, which can adapt to the characteristics of high-speed mobile scenarios, such as instantaneous concentrated access by users and large differences between busy and idle periods. Compared with the traditional static prediction method, this scheme fully considers key factors such as terminal penetration, network access behavior, and regional attributes during the prediction process, which greatly improves the accuracy of predicting the number of network users. This provides reliable data support for subsequent base station capacity supply and demand judgment and configuration, thereby further enhancing the pertinence and rationality of network capacity planning.

[0014] Furthermore, determining the proportion of mobile carrier users based on the region type and the number of non-mobile carrier users of the target region includes: if the target region is a sparsely populated area, determining the value of the proportion of mobile carrier users to be a preset maximum value; if the target region is a densely populated area, counting the number of network users in the target region during the non-operation period of the mobile carrier, recording it as the number of non-mobile carrier users, and determining the proportion of mobile carrier users based on the number of non-mobile carrier users and the predicted number of mobile carrier users.

[0015] The beneficial effects of adopting this scheme are as follows: By differentiating between sparsely populated areas and densely populated areas to determine the proportion of mobile carrier users, it is possible to accurately match the user distribution characteristics of different road sections in high-speed mobile scenarios and improve the accuracy of the determined proportion of mobile carrier users.

[0016] Furthermore, the method also includes: obtaining the total number of network users accessing the passing base station and the number of service-activated users among them at multiple historical statistical times; calculating the historical average percentage of the number of service-activated users in the total number of network users, denoted as the activation ratio.

[0017] The beneficial effects of adopting this scheme are as follows: By obtaining the total number of users accessing the network and the number of users activating services through the downlink base station at multiple historical statistical moments, and calculating the historical average ratio of the two as the activation ratio, it can conform to the user behavior patterns in high-speed mobile scenarios, objectively reflect the actual service concurrency rate of users in the target area, and provide reliable data support for subsequent calculation of the number of concurrent users that the base station can support and capacity supply and demand determination, thereby improving the accuracy of network capacity measurement and planning configuration.

[0018] Furthermore, determining whether the network capacity of the passing base station meets the requirements based on the predicted number of network-connected users and the number of concurrent users that can be supported includes: calculating the product of the predicted number of network-connected users and the activation ratio, denoted as the predicted value of service-activated users; comparing the predicted value of service-activated users with the number of concurrent users that can be supported; if the number of concurrent users that can be supported is greater than or equal to the predicted value of service-activated users, determining that the network capacity of the passing base station meets the requirements; if the number of concurrent users that can be supported is less than the predicted value of service-activated users, determining that the network capacity of the passing base station does not meet the requirements.

[0019] The beneficial effects of this scheme are as follows: by multiplying the predicted number of users joining the network by the activation ratio to obtain the predicted value of service activation users, and then comparing it with the number of concurrent users that the base station can support to determine the capacity supply and demand, the concurrent behavior characteristics of users in high-speed mobile scenarios can be incorporated into the judgment logic, making the network capacity supply and demand judgment result more in line with the actual business needs, improving the accuracy of capacity assessment, and providing a reliable decision basis for subsequent capacity configuration.

[0020] Furthermore, determining the target capacity planning value based on the predicted number of new users and the average demand rate per user includes: calculating the product of the average demand rate per user, the predicted number of new users, and the activation ratio, and recording it as the target capacity planning value.

[0021] The beneficial effects of this scheme are as follows: By multiplying the average demand rate per user, the predicted number of users joining the network, and the activation ratio to calculate the target capacity planning value, the total bandwidth resource requirements of the base station in high-speed mobile scenarios can be quantified by combining the actual service rate, user scale, and concurrent behavior characteristics, providing a reliable configuration basis for subsequent capacity configuration optimization.

[0022] Furthermore, the step of optimizing the network capacity configuration of the passing base stations based on the target capacity planning value includes: dynamically adjusting the configuration parameters of the passing base stations so that the network capacity of the passing base stations reaches the target capacity planning value.

[0023] The beneficial effects of adopting this scheme are as follows: By dynamically adjusting the configuration parameters of the passing base stations according to the target capacity planning value, the network capacity of the base stations can be accurately matched with the actual concurrent service requirements, realize the on-demand optimization of network resources in high-speed mobile scenarios, avoid resource waste or insufficient capacity, effectively improve the utilization rate and carrying capacity of network resources, and ensure the quality of network services in high-speed mobile scenarios.

[0024] Correspondingly, the present invention also provides a network capacity planning device, comprising: The demand rate determination module is used to determine the average demand rate per user based on the service volume ratio of various services in the target area and the target demand rate. The target area is the signal coverage area of ​​the base station corresponding to the mobile carrier. The carrying capacity determination module is used to determine the number of concurrent users that can be carried based on the downlink capacity of a single sector of the passing base station and the average demand rate of a single user; The network access user prediction module is used to predict the number of network access users in the target area based on the proportion of network access users, the passenger volume of the mobile carrier, and the proportion of mobile carrier users. The optimized demand judgment module is used to determine whether the network capacity of the passing base station meets the demand based on the predicted number of network users and the number of concurrent users that can be supported. The capacity dynamic planning module is used to determine a target capacity planning value based on the predicted number of network users and the average demand rate per user if the judgment result is that the demand is not met, and to optimize the network capacity configuration of the passing base stations based on the target capacity planning value.

[0025] The present invention also provides an electronic device, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the instructions to implement the network capacity planning method described above.

[0026] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described network capacity planning method. Attached Figure Description

[0027] Figure 1 A flowchart illustrating a network capacity planning method provided by the present invention; Figure 2 A schematic block diagram of a network capacity planning device provided by the present invention; Figure 3 This is a schematic diagram of an electronic device provided by the present invention. Detailed Implementation

[0028] The principles and features of the present invention are described below. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0029] In related technologies, most mobile communication network capacity planning methods are based on static network indicators or multi-dimensional fixed standards for network capacity assessment. These methods cannot adapt to the characteristics of high-speed mobile scenarios, such as concentrated instantaneous access by users, large fluctuations in service traffic, and severe wireless signal loss. The assessment results are difficult to accurately reflect the actual capacity requirements of mobile lines, resulting in a lack of rationality in network capacity planning and an inability to effectively guarantee the mobile communication experience of users.

[0030] To address the aforementioned problems, this invention proposes a network capacity planning method, apparatus, electronic device, and computer-readable storage medium. The technical solutions of the embodiments of this disclosure are described in detail below: In one embodiment of the present invention, a network capacity planning method is provided. (See reference...) Figure 1 As shown, this network capacity planning method specifically includes the following steps: S110: Determine the average demand rate per user based on the traffic volume ratio of various services in the target area and the target demand rate. The target area is the signal coverage area of ​​the base station corresponding to the mobile carrier. S120: Determine the number of concurrent users that can be supported based on the downlink capacity of a single sector and the average demand rate per user of the passing base station; S130: Based on the proportion of registered users, the passenger volume of mobile carriers, and the proportion of mobile carrier users, the predicted number of registered users in the target area is obtained. S140: Based on the predicted number of users joining the network and the number of concurrent users that can be supported, determine whether the network capacity of the passing base stations meets the requirements; S150: If the judgment result is that the demand is not met, the target capacity planning value is determined based on the predicted number of users joining the network and the average demand rate per user, and the network capacity is optimized for the passing base stations based on the target capacity planning value.

[0031] In the network capacity planning method provided in the above embodiments, the average demand rate per user is determined based on the proportion of traffic of various services in the target area and the target demand rate. The obtained average demand rate per user closely matches the actual service usage, providing a reliable data foundation for subsequent network capacity calculation. Based on the downlink capacity of a single sector of the base station and the average demand rate per user, the number of concurrent users that the base station can support is determined, which can clarify the actual service carrying capacity limit of the base station and provide a basis for judging whether the base station capacity meets the service demand. Based on the proportion of network users, the passenger volume of mobile carriers, and the proportion of mobile carrier users, the number of network users in the target area is predicted, which can adapt to the concentrated access characteristics of users in mobile scenarios, improve the accuracy of predicting the scale of user access, and further improve the rationality of subsequent capacity planning. Based on the predicted number of network users and the number of concurrent users that can be supported, the network capacity is judged to meet the demand. When the demand is not met, the network capacity of the passing base stations is optimized based on the target capacity planning value, which can realize precise expansion and resource allocation on demand, effectively meet the communication needs of users in mobile carriers, and optimize the user's service experience.

[0032] The above steps will now be described in more detail in another embodiment.

[0033] In S110, the average demand rate per user is determined based on the proportion of traffic of various services in the target area and the target demand rate. The target area is the signal coverage area of ​​the base station corresponding to the mobile carrier.

[0034] The aforementioned mobile carrier is a passenger transport vehicle that carries a large number of mobile communication users and travels at high speed along a fixed route. It has the characteristics of high operating speed, high concentration of users, and strong suddenness of call traffic. For example, the aforementioned mobile carrier can be a passenger transport vehicle such as a high-speed rail, intercity train, or subway.

[0035] The aforementioned transit base stations are mobile communication base stations that a mobile vehicle passes through while traveling along its fixed route, and are used to provide mobile communication services to the target area.

[0036] The target area mentioned above is the signal coverage area of ​​the base stations that the mobile carrier is currently passing through.

[0037] The aforementioned services refer to various data services provided by mobile communication networks to users; for example, the aforementioned services may include high-definition video, cloud gaming, web browsing, instant messaging, voice calls, video calls, file transfer, navigation, social applications, online music, and other services.

[0038] The above-mentioned business volume percentage refers to the proportion of the corresponding business traffic in the total business traffic of the target area.

[0039] The target required rate is the minimum network speed required to ensure the normal operation of the corresponding service under the preset experience level.

[0040] The aforementioned preset experience level refers to the network service quality level preset to ensure different user experiences. For example, the aforementioned preset experience level includes at least two levels: basic experience level and premium experience level. The basic experience level is the minimum network service standard to ensure that various services can be used normally and smoothly, which can meet the user's basic communication needs. The premium experience level is a high-level network service standard to ensure that various services are high-definition, lag-free, and low-latency, which can meet the user's high-quality communication needs.

[0041] The above average demand rate per user is used to characterize the average bandwidth demand of a single user under a preset experience level.

[0042] Since different types of services have different requirements for network service quality (e.g., high-definition video requires higher bandwidth than web browsing), in order to improve the accuracy of the quantified average demand rate per user, this embodiment combines the proportion of various service volumes with the target demand rate for joint calculation when calculating the average demand rate per user, so as to improve the calculation accuracy.

[0043] For example, the above-mentioned determination of the average demand rate per user based on the traffic volume ratio and target demand rate of various services in the target area can be achieved as follows: obtain traffic statistics of various services in the target area from the network-side big data platform, and determine the traffic volume ratio based on the traffic statistics; determine the target demand rate of various services under the preset experience level through on-site drive testing and user perception experiments; and determine the average demand rate per user corresponding to the preset experience level based on the traffic volume ratio and target demand rate of various services.

[0044] Specifically, the above-mentioned acquisition of traffic statistics data for various services within the target area from the network-side big data platform, and determination of service volume proportion based on the traffic statistics data, can be implemented as follows: Select the signal coverage area of ​​each base station along the path of the mobile carrier as the target area for network capacity planning; collect service traffic data of all users within the target area from the network-side big data platform, and classify them into different types of services such as video, games, web pages, and instant messaging; for each type of service, calculate the ratio of the traffic of that type of service to the total service traffic of the target area, and record it as the service volume proportion of that type of service.

[0045] The specific implementation of determining the target required rate for various services under the preset experience level through on-site road tests and user perception experiments can be as follows: Select real network environments as test scenarios in typical road sections (such as urban areas, suburbs, and rural areas) traversed by mobile carriers. The selected test scenarios need to cover different network frequency bands and base station configurations to ensure that the test results are representative of the scenarios; For each type of service, use professional test terminals to conduct multiple tests under different signal strength, vehicle speed, and load conditions, and record the service operation status at different rates; Calculate the minimum rate that allows the service to run smoothly without lag and play / interact normally, as a candidate value for the target required rate under the basic experience level; Calculate the minimum rate that allows the service to run in high definition, smoothly, without buffering, and with low latency, as a candidate value for the target required rate under the premium experience level; Recruit a certain number of real users, let the recruited users use various services in the same scenario, and let the users subjectively evaluate the smoothness, clarity, and satisfaction based on the preset experience level; Combine the objective data from road tests and the subjective evaluation of user perception to determine the target required rate for each type of service under the basic experience level and the premium experience level.

[0046] The above-mentioned determination of the average demand rate per user corresponding to the preset experience level based on the business volume ratio and target demand rate of various services can be implemented as follows: The average demand rate per user under the corresponding preset experience level is obtained by weighted summation of the business volume ratio and target demand rate of various services, using the following formula: in, The average demand rate per user under the preset experience level; This represents the proportion of traffic of type i in the total traffic of the target area. Let be the target demand rate for the i-th type of service traffic under the preset experience level.

[0047] The process of determining the average demand rate per user will be described in detail below in a specific application scenario of this embodiment: S1: Determine the business volume proportion of each type of business within the target area.

[0048] The network-side big data platform retrieves traffic data for all users within the target area. The three main types of services are streaming media, web browsing, and instant messaging (the average demand rate per user is approximated using these three types of services; other niche services have a low traffic share and minimal impact on the overall average rate, so they are not included in the statistics below for ease of calculation). The proportion of the above three types of services in the total traffic of the target area is calculated under both 4G and 5G networks.

[0049] Specifically, in this embodiment, the traffic share of the three types of services in the application scenario is calculated as follows using the above-mentioned method for calculating traffic share: 4G network streaming media traffic share is 49.61%, web browsing traffic share is 28.46%, and instant messaging traffic share is 6.20%; 5G network streaming media traffic share is 47.70%, web browsing traffic share is 30.04%, and instant messaging traffic share is 5.50%.

[0050] S2: Determine the target demand rate for each type of service under the preset experience level.

[0051] Specifically, this embodiment uses the above-mentioned on-site road tests and user perception experiments to determine the target demand rates for various services under the preset experience level in this application scenario as follows: For streaming media services, the basic experience level that meets 720P resolution and smooth playback requires 2Mbps on 4G networks and 5Mbps on 5G networks; the premium experience level that meets 1080P resolution and smooth playback requires 4Mbps on 4G networks and 8Mbps on 5G networks.

[0052] Web browsing services are categorized into two types: basic web browsing and web media browsing. Basic web browsing includes basic text browsing, checking emails, and accessing static social content, typically requiring 1-5 Mbps for a smooth experience. Web media browsing involves numerous high-definition images, autoplaying videos, or interactive scripts, requiring at least 5-10 Mbps for a smooth experience. Specifically, in this application scenario, to meet the basic experience level of web browsing, a 4G network requires 1 Mbps and a 5G network requires 2 Mbps; to meet the premium experience level of web browsing, a 4G network requires 5 Mbps and a 5G network requires 10 Mbps.

[0053] Instant messaging services include two categories: voice calls and video calls. For voice calls, a smooth experience is achieved with 0.1-0.5 Mbps. For video calls, the speed varies depending on the resolution: standard definition (480P) requires 0.5-1 Mbps, high definition (720P) requires 2-3 Mbps, and full high definition (1080P) requires at least 3-5 Mbps. Specifically, in this application scenario, a basic instant messaging experience requires 0.1 Mbps on a 4G network and 0.2 Mbps on a 5G network; a premium instant messaging experience requires 5 Mbps on a 4G network and 10 Mbps on a 5G network.

[0054] S3: Based on the business volume ratio and target demand rate of various services, determine the average demand rate per user corresponding to the preset experience level.

[0055] Specifically, under the Basic Experience Level and the Premium Experience Level, the traffic volume proportion and target demand rate of various services determined in S1 and S2 above are substituted into the formula for calculating the average demand rate per user to obtain the average demand rate per user corresponding to the Basic Experience Level and Premium Experience Level under 4G and 5G networks, as shown in Table 1 below: Table 1: As shown in the table above, this embodiment uses the proportion of traffic for various services within the target area as weights, and combines the perceived rate of each service under different experience levels to obtain the average demand rate per user through weighted summation. Specifically, the average demand rate per user meeting the basic experience level is 1.28 Mbps under 4G networks, and the average demand rate per user meeting the premium experience level is 3.72 Mbps; under 5G networks, the average demand rate per user meeting the basic experience level is 2.52 Mbps, and the average demand rate per user meeting the premium experience level is 7.37 Mbps.

[0056] In S120, the number of concurrent users that can be supported is determined based on the downlink capacity of a single sector of the passing base station and the average demand rate of a single user.

[0057] The aforementioned single-sector downlink capacity refers to the maximum effective data traffic that a single sector of the corresponding passing base station can stably transmit per unit time in the downlink transmission direction. This single-sector downlink capacity is determined by the equipment model and configuration parameters of the passing base station, as shown in Table 2 below. Table 2: The aforementioned concurrent user capacity refers to the maximum number of online users that a single base station sector can stably access and normally use mobile communication services at the same time, under the premise of meeting the preset experience level network service standards. It is used to characterize the user access capacity of a base station sector in high-speed mobile scenarios.

[0058] For example, the determination of the number of concurrent users that can be supported based on the downlink capacity of a single sector of the passing base station and the average demand rate of a single user can be achieved as follows: Calculate the ratio of the downlink capacity of a single sector of the passing base station to the average demand rate of a single user, denoted as the number of concurrent users that can be supported, using the following formula: in, The number of concurrent users it can support; This refers to the downlink capacity of a single sector of the base station. This represents the average demand rate per user under the preset experience level.

[0059] In S130, based on the proportion of new users, the passenger volume of mobile carriers, and the proportion of mobile carrier users, the predicted number of new users in the target area is obtained.

[0060] The aforementioned percentage of new users refers to the percentage of users who own a mobile communication terminal (such as a 5G communication terminal) and use that mobile communication terminal to access and use the mobile communication network. For example, this percentage of new users can be determined based on historical data and industry trend statistics.

[0061] The passenger capacity of the aforementioned mobile carriers refers to the total capacity of personnel that the mobile carrier (such as high-speed rail, intercity trains, and subways) can accommodate.

[0062] The aforementioned mobile carrier user percentage refers to the percentage of passengers in the target area who possess a mobile communication terminal within the mobile carrier and use that mobile communication terminal to access and use the mobile communication network.

[0063] The aforementioned predicted number of network users represents the number of effective online users (including mobile carrier online users and regular online users within the target area) expected to access the mobile communication network and generate service traffic when the mobile carrier passes through the target area. This number is used to characterize the actual scale of network users in the target area.

[0064] For example, the above-mentioned prediction of the number of network users in a target area based on the proportion of network users, the passenger volume of mobile carriers, and the proportion of mobile carrier users can be achieved as follows: Statistically calculate the mobile terminal penetration rate and network access rate, and multiply the mobile terminal penetration rate and network access rate, denoted as the proportion of network users; calculate the product of the proportion of network users and the passenger volume of mobile carriers, denoted as the predicted number of mobile carrier users; determine the proportion of mobile carrier users based on the regional type of the target area and the number of non-mobile carrier users; calculate the ratio of the predicted number of mobile carrier users to the proportion of mobile carrier users, denoted as the predicted number of network users.

[0065] The mobile terminal penetration rate refers to the proportion of users holding the mobile communication terminals in the statistical population, representing the degree of mobile communication terminal penetration. For example, the 5G terminal penetration rate can be determined by calculating the proportion of people holding 5G terminals in the statistical population.

[0066] The aforementioned network access rate refers to the proportion of users among those who own mobile communication terminals who actually access and successfully remain on the mobile communication network and can use network services normally, representing the network access activity level of terminal users.

[0067] Because the number of non-mobile users (regular users within the target area) varies significantly in different types of target areas, such as dense regular users in urban areas and sparse regular users in rural / suburban areas, the proportion of mobile users will be completely different under the same train passenger capacity. Therefore, this embodiment needs to fully consider this factor when calculating the proportion of mobile users so that the calculation of the proportion of mobile users is in line with the actual scenario.

[0068] For example, the determination of the proportion of mobile carrier users based on the regional type of the target area and the number of non-mobile carrier users can be achieved as follows: if the target area is a sparsely populated area (such as sparsely populated farmland), the value of the proportion of mobile carrier users is set to a preset maximum value; if the target area is a densely populated area (such as urban sections, rural densely populated road sections, and other mixed public and private network sections), the number of network users in the target area during the non-operational period of mobile carriers is counted and recorded as the number of non-mobile carrier users, and the proportion of mobile carrier users is determined based on the number of non-mobile carrier users and the predicted number of mobile carrier users.

[0069] In one specific implementation of this embodiment, the aforementioned preset maximum value can be taken as 1, that is, when the mobile carrier passes through sparsely populated farmland or other areas, the value of the mobile carrier user ratio is approximately taken as 1; in addition, the specific implementation of determining the mobile carrier user ratio based on the number of non-mobile carrier users and the predicted number of mobile carrier users is as follows: calculate the sum of the number of non-mobile carrier users and the predicted number of mobile carrier users, and record it as the total predicted number of users, and calculate the ratio of the predicted number of mobile carrier users to the total predicted number of users, and record it as the mobile carrier user ratio.

[0070] In summary, in one specific implementation of this embodiment, the predicted number of new users can be determined using the following formula: Where N is the predicted number of network users in the target area; U is the passenger capacity of the mobile carrier; P1 is the mobile terminal penetration rate; and P2 is the network access rate. This represents the percentage of mobile carrier users in the aforementioned target areas.

[0071] Preferably, in the actual prediction process of the number of network users in the target area, this embodiment can substitute the relevant parameters of the corresponding time period into the above calculation process for different time periods of mobile carrier passenger flow (such as peak, off-peak, and low-peak periods) to obtain the number of network users in the target area. This achieves differentiated prediction of the number of network users under different travel periods, providing reliable data support for dynamic capacity scheduling and load balancing of base stations. In addition, if this embodiment is used for network capacity planning of a certain operator's base station, when calculating the proportion of network users, it is also necessary to multiply the corresponding operator's market share proportion on the basis of P1 (mobile terminal penetration rate) × P2 (network access rate).

[0072] In S140, based on the predicted number of users joining the network and the number of concurrent users that can be supported, it is determined whether the network capacity of the passing base station meets the requirements.

[0073] After determining the predicted number of network users in the target area and the corresponding number of concurrent users that the passing base stations can support through the above steps, it is possible to further determine whether the network capacity of the passing base stations meets the current network service requirements by comparing the predicted number of network users and the number of concurrent users that can be supported.

[0074] Preferably, since a large number of users who are registered on the network but do not generate any business (such as those in standby or running in the background), these users consume very little base station resources. Therefore, in order to improve the accuracy of business traffic prediction, this embodiment can also introduce an activation ratio before making comparisons. The above-mentioned predicted number of users who are registered on the network can be corrected to the predicted value of the actual number of users who are activated for business through the activation ratio, and invalid online users can be eliminated to avoid wasting resources in the network capacity planning stage due to overestimation of business volume.

[0075] For example, the activation ratio can be determined by the following method: obtaining the total number of network users accessing the base station and the number of service-activated users among them at multiple historical statistical times; calculating the historical average of the proportion of service-activated users in the total number of network users, and recording it as the activation ratio.

[0076] Furthermore, the above-mentioned determination of whether the network capacity of the passing base station meets the requirements based on the predicted number of new users and the number of concurrent users it can support can be achieved as follows: Calculate the product of the predicted number of new users and the activation ratio, and record it as the predicted value of service activation users; compare the predicted value of service activation users with the number of concurrent users it can support; if the number of concurrent users it can support is greater than or equal to the predicted value of service activation users, it is determined that the network capacity of the passing base station meets the requirements; if the number of concurrent users it can support is less than the predicted value of service activation users, it is determined that the network capacity of the passing base station does not meet the requirements.

[0077] Specifically, if the following conditions are met, the network capacity of the passing base station is determined to meet the demand: in, The number of concurrent users that the target area determined by S120 can support through the corresponding base stations; The predicted number of network users in the target area obtained from S130 prediction; The activation ratio is as described above; Activate the predicted user value for the above services.

[0078] In S150, if the judgment result is that the demand is not met, the target capacity planning value is determined based on the predicted number of users joining the network and the average demand rate per user, and the network capacity is optimized and configured for the passing base stations based on the target capacity planning value.

[0079] If it is determined through S140 that the current network capacity of the passing base station does not meet the network service requirements, the network capacity of the passing base station needs to be optimized to meet the requirements.

[0080] The aforementioned target capacity planning value is the minimum downlink capacity value that the base station needs to be configured with, determined based on the predicted number of users joining the network and the average demand rate per user, when the existing capacity of the base station does not meet user demand. It is used to guide the base station capacity expansion and optimization.

[0081] For example, the above method of determining the target capacity planning value based on the predicted number of new users and the average demand rate per user can be achieved as follows: Calculate the product of the average demand rate per user, the predicted number of new users, and the activation ratio, and denote it as the target capacity planning value. The specific formula is as follows: in, This is the target capacity planning value (i.e., the minimum downlink capacity that the base station needs to be configured with). Average demand rate per user under the preset experience level determined for S120; The predicted number of network users in the target area obtained from S130 prediction; This refers to the activation ratio; The predicted value of service activation users determined by S140.

[0082] After determining the target capacity planning value, this embodiment can dynamically adjust the configuration parameters of the passing base stations to ensure that the network capacity of the passing base stations reaches the target capacity planning value. Specifically, this can be achieved by dynamically adjusting parameters such as bandwidth, frequency band, antenna beam and downtilt angle, physical resource block (PRB) allocation, modulation and coding, and multiple input multiple output (MIMO) stream count of the passing base stations. Operations such as enabling or disabling carrier aggregation as needed, performing cell splitting or merging, deploying temporary base stations in high-load areas, and expanding capacity by adding macro and micro sites can be performed to ensure that the downlink network capacity of the base stations reaches the target capacity planning value, thus achieving real-time matching and optimized configuration of capacity and load.

[0083] The following section uses a high-speed railway traffic scenario as an example to illustrate the practical application of the network capacity planning method proposed in this application: Data collected from the network-side big data platform showed that 5G streaming media services accounted for 47.71% and web browsing services accounted for 30.04% of the high-speed rail line's traffic. Based on these two mainstream services, and using the formula for calculating the average demand rate per user, the average demand rate per user to meet the premium experience level under the 5G network was approximately calculated to be 7.37 Mbps.

[0084] Furthermore, the number of online users on this high-speed rail line (i.e., the predicted number of new users in the target area) can be predicted through the following calculations: The high-speed train has a passenger capacity of 1200 people (16 carriages × 75 people). The operator accounts for 40% of the high-speed train user market share, the 5G terminal penetration rate is 83%, and the network access rate is 80%. Therefore, the predicted number of 5G users on the high-speed train is: 1200 × 40% × 66% = 317 people; the number of 5G users online simultaneously on the dedicated network in the urban area (γ=0.7, β=36%) is: 317 × 36% ÷ 0.7 = 163 people; the number of 5G users online simultaneously on the dedicated network in the suburban area (γ=0.9, β=31%) is: 317 × 31% ÷ 0.9 = 109 people.

[0085] The target capacity planning value is calculated as follows: The target capacity planning value corresponding to the excellent experience level in the urban area is 163 × 7.37 Mbps = 1201.31 Mbps, and the target capacity planning value corresponding to the basic experience level is 163 × 2.52 Mbps = 410.76 Mbps; The target capacity planning value corresponding to the excellent experience level in the suburban area is 109 × 7.37 Mbps = 803.33 Mbps, and the target capacity planning value corresponding to the basic experience level is 109 × 2.52 Mbps = 274.68 Mbps.

[0086] If it is estimated that the value of (5G terminal penetration rate × network access rate) will increase from 66% to 80% annually within 3 years, base station expansion and optimization can be achieved through the following process: For the public network segment in urban areas, using the NR3.5G 200M base station model (single sector downlink capacity 1150Mbps), based on the average user demand rate of 7.37Mbps corresponding to the premium experience level, it can support a maximum of 156 users, which is lower than the 163 active users in the urban segment, reaching the current limit of the base station model and unable to guarantee a premium experience for all users. If it is the NR3.5G 100MHz base station model, it can only support 78 users, and expansion is required as the number of users increases. For the private network segment, using the NR3.5G 100MHz base station model (single sector downlink capacity 575Mbps), it can support 78 users at the premium experience level, which is lower than the 109 active users in the private network, and does not meet the demand. The existing NR2.1G 40M narrow bandwidth base station model has even lower performance and needs to be upgraded to at least NR3.5G 100MHz.

[0087] Current network statistics for this high-speed rail station show that its average number of users, total traffic, and PRB utilization are low, failing to reach traditional expansion thresholds. The average user speed during off-peak hours is 27.24 Mbps, significantly higher than 7.37 Mbps. However, the maximum concurrent user count surges when trains pass through. The high-speed rail scenario's characteristic of low speeds during off-peak hours and high speeds during peak hours leads to serious misjudgments by traditional expansion models based on average metrics. Actual test results on peak-hour sections of the high-speed rail network show that although the average user speed is 13.27 Mbps, 15.27% of users have speeds below 7 Mbps, and the success rate of video software playback within 5 seconds is only 89.1%, indicating that many users experience buffering and slow loading issues. Traditional backend key performance indicators (KPIs) are artificially inflated during off-peak hours, severely overestimating network quality and underestimating peak risks, which is significantly inconsistent with users' actual perceptions. Therefore, capacity planning must be based on peak active user counts.

[0088] Specifically, in this particular scenario, capacity expansion is achieved by upgrading some of the old 2.1GHz / 40MHz narrow-bandwidth base stations along the high-speed rail line to 3.5GHz high-bandwidth base stations.

[0089] After the expansion, user experience improved significantly with a slight increase in users, traffic increased by 25%, pent-up demand for network traffic was released, and market revenue increased.

[0090] Correspondingly, the present invention also provides a network capacity planning device, with reference to Figure 2As shown, the network capacity planning device 200 may include a demand rate determination module 210, a capacity determination module 220, a user access prediction module 230, an optimized demand judgment module 240, and a capacity dynamic planning module 250. Wherein: The demand rate determination module 210 is used to determine the average demand rate per user based on the service volume ratio of various services in the target area and the target demand rate. The target area is the signal coverage area of ​​the base station corresponding to the mobile carrier. The capacity determination module 220 is used to determine the number of concurrent users that can be supported based on the downlink capacity of a single sector and the average demand rate of a single user of the passing base station. The network user prediction module 230 is used to predict the number of network users in the target area based on the proportion of network users, the passenger volume of mobile carriers, and the proportion of mobile carrier users. The demand judgment module 240 is optimized to determine whether the network capacity of the passing base station meets the demand based on the predicted number of users entering the network and the number of concurrent users that can be supported. The capacity dynamic planning module 250 is used to determine the target capacity planning value based on the predicted number of network users and the average demand rate per user if the judgment result is that the demand is not met, and to optimize the network capacity configuration of the passing base stations based on the target capacity planning value.

[0091] In one implementation of this embodiment, the aforementioned demand rate determination module is specifically used to: obtain traffic statistics data of various services within the target area from the network-side big data platform, and determine the service volume ratio based on the traffic statistics data; determine the target demand rate corresponding to various services under the preset experience level through on-site drive testing and user perception experiments; and determine the average demand rate per user corresponding to the preset experience level based on the service volume ratio and target demand rate of various services.

[0092] In one implementation of this embodiment, the demand rate determination module is specifically used to: perform a weighted summation of the business volume proportion of various services and the target demand rate to obtain the average demand rate per user under the corresponding preset experience level.

[0093] In one implementation of this embodiment, the aforementioned network access user prediction module is specifically used for: calculating the mobile terminal penetration rate and network access rate, and calculating the product of the mobile terminal penetration rate and network access rate, denoted as the network access user ratio; calculating the product of the network access user ratio and the passenger capacity of the mobile carrier, denoted as the predicted number of mobile carrier users; determining the mobile carrier user ratio based on the regional type of the target area and the number of non-mobile carrier users; and calculating the ratio of the predicted number of mobile carrier users to the mobile carrier user ratio, denoted as the predicted number of network access users.

[0094] In one implementation of this embodiment, the above-mentioned network access user prediction module is specifically used for: if the target area is a sparsely populated area, determining the value of the mobile carrier user ratio as a preset maximum value; if the target area is a densely populated area, counting the number of network access users in the target area during the non-operation period of the mobile carrier, recording them as the number of non-mobile carrier users, and determining the mobile carrier user ratio based on the number of non-mobile carrier users and the predicted number of mobile carrier users.

[0095] In one implementation of this embodiment, the above-mentioned device further includes an activation ratio determination module, which is specifically used to: obtain the total number of network users accessing the passing base station and the number of service-activated users among them at multiple historical statistical times; calculate the historical average value of the proportion of service-activated users in the total number of network users, and record it as the activation ratio.

[0096] In one implementation of this embodiment, the above-mentioned optimization requirement judgment module is specifically used to: calculate the product of the predicted number of network users and the activation ratio, and record it as the predicted value of service activation users; compare the predicted value of service activation users with the number of concurrent users that can be supported; if the number of concurrent users that can be supported is greater than or equal to the predicted value of service activation users, determine that the network capacity of the passing base station meets the requirements; if the number of concurrent users that can be supported is less than the predicted value of service activation users, determine that the network capacity of the passing base station does not meet the requirements.

[0097] In one implementation of this embodiment, the above-mentioned capacity dynamic planning module is specifically used to: calculate the product of the average demand rate per user, the predicted number of new users, and the activation ratio, and record it as the target capacity planning value.

[0098] In one implementation of this embodiment, the above-mentioned capacity dynamic planning module is specifically used to: dynamically adjust the configuration parameters of the passing base stations so that the network capacity of the passing base stations reaches the target capacity planning value.

[0099] It should be noted that the specific implementation details of the aforementioned network capacity planning device have been explained in detail in the corresponding section of the aforementioned network capacity planning method, so they will not be repeated here.

[0100] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0101] An electronic device according to the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned network capacity planning methods. That is, an electronic device according to the present invention may include, but is not limited to: a processor and a memory; the memory is used to store the computer program; the processor is used to execute the network capacity planning method shown in any embodiment of the present invention by calling the computer program.

[0102] In one alternative embodiment, an electronic device is provided, such as Figure 3 As shown, Figure 3 The illustrated electronic device 3000 includes a processor 3001 and a memory 3003. The processor 3001 and the memory 3003 are connected, for example, via a bus 3002. Optionally, the electronic device 3000 may further include a transceiver 3004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 3004 is not limited to one type, and the structure of the electronic device 3000 does not constitute a limitation on the present invention.

[0103] Processor 3001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. Processor 3001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0104] Bus 3002 may include a path for transmitting information between the aforementioned components. Bus 3002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 3002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The bus 3002 is represented by only one thick line, but this does not mean that there is only one bus or one type of bus.

[0105] The memory 3003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0106] The memory 3003 stores application code (computer program) for executing the present invention, and its execution is controlled by the processor 3001. The processor 3001 executes the application code stored in the memory 3003 to implement the content shown in the foregoing method embodiments.

[0107] Among them, electronic devices can also be terminal devices, which can be any device that can install applications, including at least one of smartphones, tablets, laptops, desktop computers, smart speakers, smartwatches, smart TVs, and smart in-vehicle devices.

[0108] It should be noted that, Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the invention.

[0109] The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-described network capacity planning methods.

[0110] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device, etc.

[0111] In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the network capacity planning method described above.

[0112] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0113] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0114] The computer-readable storage medium provided by this invention can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EEPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0115] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the method shown in the above embodiments.

[0116] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0117] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0118] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this invention can be specifically implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0119] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A network capacity planning method, characterized in that, The method includes: Based on the traffic volume ratio of various services in the target area and the target demand rate, the average demand rate of a single user is determined. The target area is the signal coverage area of ​​the base station corresponding to the mobile carrier. The number of concurrent users that can be supported is determined based on the downlink capacity of a single sector of the passing base station and the average demand rate of a single user. Based on the proportion of registered users, the passenger volume of the mobile carrier, and the proportion of mobile carrier users, the predicted number of registered users in the target area is obtained. Based on the predicted number of users joining the network and the number of concurrent users that can be supported, determine whether the network capacity of the passing base station meets the requirements; If the determination result is that the demand is not met, the target capacity planning value is determined based on the predicted number of network users and the average demand rate per user, and the network capacity is optimized and configured for the passing base stations based on the target capacity planning value.

2. The network capacity planning method according to claim 1, characterized in that, The determination of the average demand rate per user based on the proportion of business volume of various services within the target area and the target demand rate includes: Obtain traffic statistics data for various services within the target area from the network-side big data platform, and determine the service volume percentage based on the traffic statistics data; The target demand rate for various services at the preset experience level was determined through on-site road tests and user perception experiments. Based on the business volume proportion of each type of business and the target demand rate, the average demand rate per user corresponding to the preset experience level is determined.

3. The network capacity planning method according to claim 2, characterized in that, The step of determining the average demand rate per user for the corresponding service under the preset experience level based on the business volume ratio and the target demand rate includes: The average demand rate per user under the corresponding preset experience level is obtained by weighted summation of the business volume proportion of each type of business and the target demand rate.

4. The network capacity planning method according to claim 1, characterized in that, The method of predicting the predicted number of new users in the target area based on the proportion of new users, the passenger volume of the mobile carrier, and the proportion of mobile carrier users includes: The mobile terminal penetration rate and network access rate are statistically analyzed, and the product of the mobile terminal penetration rate and the network access rate is calculated and denoted as the proportion of network users. Calculate the product of the percentage of users joining the network and the passenger capacity of the mobile carrier, and record it as the predicted number of mobile carrier users; The proportion of mobile carrier users is determined based on the region type and the number of non-mobile carrier users in the target region. The ratio of the predicted number of mobile carrier users to the proportion of mobile carrier users is calculated and denoted as the predicted number of network users.

5. The network capacity planning method according to claim 4, characterized in that, The determination of the proportion of mobile carrier users based on the region type and the number of non-mobile carrier users in the target region includes: If the target area is a sparsely populated area, the value of the proportion of mobile carrier users is determined to be a preset maximum value; If the target area is a densely populated area, the number of users connected to the network in the target area during the non-operation period of the mobile carrier is counted and recorded as the number of non-mobile carrier users. The proportion of mobile carrier users is determined based on the number of non-mobile carrier users and the predicted number of mobile carrier users.

6. The network capacity planning method according to claim 1, characterized in that, The method further includes: Obtain the total number of network users accessing the passed base station and the number of service-activated users among them at multiple historical statistical times; The historical average percentage of the number of activated users in the total number of new users is calculated and denoted as the activation ratio.

7. The network capacity planning method according to claim 6, characterized in that, The step of determining whether the network capacity of the passing base station meets the demand based on the predicted number of users joining the network and the number of concurrent users that can be supported includes: Calculate the product of the predicted number of new users and the activation ratio, and record it as the predicted value of activated users. Compare the predicted number of users activated by the service with the number of concurrent users that can be supported; If the number of concurrent users that can be supported is greater than or equal to the predicted value of the service-activated users, it is determined that the network capacity of the passing base station meets the requirements. If the number of concurrent users that can be supported is less than the predicted number of users activated by the service, it is determined that the network capacity of the passing base station does not meet the requirements.

8. The network capacity planning method according to claim 6, characterized in that, The process of determining the target capacity planning value based on the predicted number of network users and the average demand rate per user includes: The product of the average demand rate per user, the predicted number of new users, and the activation ratio is calculated and denoted as the target capacity planning value.

9. The network capacity planning method according to claim 1, characterized in that, The process of optimizing network capacity configuration for the passing base stations based on the target capacity planning value includes: The configuration parameters of the passing base stations are dynamically adjusted so that the network capacity of the passing base stations reaches the target capacity planning value.

10. A network capacity planning device, characterized in that, The device includes: The demand rate determination module is used to determine the average demand rate per user based on the service volume ratio of various services in the target area and the target demand rate. The target area is the signal coverage area of ​​the base station corresponding to the mobile carrier. The carrying capacity determination module is used to determine the number of concurrent users that can be carried based on the downlink capacity of a single sector of the passing base station and the average demand rate of a single user; The network access user prediction module is used to predict the number of network access users in the target area based on the proportion of network access users, the passenger volume of the mobile carrier, and the proportion of mobile carrier users. The optimized demand judgment module is used to determine whether the network capacity of the passing base station meets the demand based on the predicted number of network users and the number of concurrent users that can be supported. The capacity dynamic planning module is used to determine a target capacity planning value based on the predicted number of network users and the average demand rate per user if the judgment result is that the demand is not met, and to optimize the network capacity configuration of the passing base stations based on the target capacity planning value.

11. An electronic device, characterized in that, include: processor; Memory for storing the executable instructions of the processor; The processor is configured to execute the instructions to implement the network capacity planning method as described in any one of claims 1 to 9.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the network capacity planning method according to any one of claims 1 to 9.

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