Network construction scheme generation method and device, program product and electronic equipment

Abstract

The invention relates to the technical field of computers, and provides a network construction scheme generation method and device, a program product and electronic equipment. The method comprises the following steps: predicting a first new user number of each residence according to area data of a target area and network user data of each residence; determining a second new user number of each machine room in the target area based on the first new user number of each residence; based on the second newly-added user number, the existing user number and the first number of the existing passive optical network equipment interfaces, determining the distributed user number of the single existing passive optical network equipment interface of each machine room; for each machine room, obtaining the number of passive optical network equipment interfaces needing to be newly built in the machine room based on the relationship between the number of the distributed users in the machine room and a preset threshold value of the machine room; and generating a network construction scheme according to the number of the newly-added users of each machine room and the number of the newly-built passive optical network equipment interfaces of each machine room. The accuracy of the generated network construction scheme can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to a network construction plan generating method, a network construction plan generating device, a computer program product, and an electronic device. Background Art

[0002] When telecommunications operators make network investments, they can predict the scale of network investment based on user development to avoid irrational allocation of network resources.

[0003] In the related art, the number of new users in the previous month is used as the future user increment to determine the network construction plan. However, since the situation is different every month, using the number of new users in the previous month as the future user increment may lead to a large deviation between the predicted user increment and the actual user development situation, which in turn leads to an inaccurate network construction plan.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The purpose of the present disclosure is to provide a method and device for generating a network construction plan, a computer program product and an electronic device, thereby improving the accuracy of the generated network construction plan at least to a certain extent.

[0006] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0007] According to a first aspect of the present disclosure, a method for generating a network construction plan is provided, comprising: predicting a first number of new users in each residence based on regional data of a target area and network user data of each residence in the target area; determining a second number of new users in each computer room in the target area based on the first number of new users in each residence; determining a number of allocated users of a single existing passive optical network device interface in each computer room based on the second number of new users in each computer room, the number of existing users in each computer room, and a first number of existing passive optical network device interfaces in each computer room; for each computer room, obtaining a number of new passive optical network device interfaces that need to be built in the computer room based on a relationship between the number of allocated users of the single existing passive optical network device interface in the computer room and a preset threshold value of the computer room; and generating a network construction plan for the target area based on the second number of new users in each computer room and the number of new passive optical network device interfaces in each computer room.

[0008] According to a second aspect of the present disclosure, a network construction plan generating device is provided, comprising: a prediction module, configured to predict a first number of new users in each residential area according to regional data of the target area and network user data of each residence in the target area; a second number of new users determining module, configured to determine a second number of new users in each computer room of the target area based on the first number of new users in each residence; a single interface allocated user number determination module, configured to determine the number of allocated users of a single existing passive optical network device interface of each computer room based on the second number of new users in each computer room, the number of existing users in each computer room, and the first number of existing passive optical network device interfaces in each computer room; a newly created interface number determination module, configured to obtain, for each computer room, the number of newly created passive optical network device interfaces of the computer room based on the relationship between the number of allocated users of the single existing passive optical network device interface of the computer room and a preset threshold value of the computer room; a network construction plan generating module, configured to generate a network construction plan for the target area according to the number of new users in each computer room and the number of newly created passive optical network device interfaces in each computer room.

[0009] According to a third aspect of the present disclosure, a computer program product comprising instructions is provided, which, when executed on a computer, enables the computer to execute the steps of the network construction plan generating method as described in the first aspect.

[0010] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the network construction plan generating method as described in the first aspect of the above embodiment is implemented.

[0011] According to the fifth aspect of an embodiment of the present disclosure, there is provided an electronic device, comprising: a processor; and a storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the network construction plan generating method as described in the first aspect of the above embodiment.

[0012] It can be seen from the above technical solutions that the network construction plan generation method, network construction plan generation device, and computer program product and electronic device implementing the network construction plan generation method in the exemplary embodiment of the present disclosure have at least the following advantages and positive effects:

[0013] In the technical solutions provided by some embodiments of the present disclosure, the first number of new users of each residence can be predicted based on the regional data of the target area and the network user data of each residence, and the first number of new users of each residence can be summarized to obtain the second number of new users of the computer room, and then the number of users of a single passive optical network device interface of the computer room can be determined based on the second number of new users of the computer room, and the number of new passive optical network device interfaces required for the computer room can be obtained based on the relationship between the number of users of the single passive optical network device interface and the preset threshold, thereby generating a network construction plan for the target area. Compared with the prior art, on the one hand, the present disclosure can predict user increments based on multi-dimensional and multi-source data through regional data and network user data, thereby improving the accuracy of user increment predictions and further assisting in improving the accuracy of the generated network construction plan; on the other hand, the present disclosure can obtain the number of new passive optical network device interfaces required based on the relationship between the number of users of a single passive optical network device interface of the computer room and the threshold value, thereby providing a reasonable number of new passive optical network devices while ensuring the quality of network services, thereby avoiding the waste of passive optical network device resources.

[0014] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0016] Figure 1 A schematic diagram showing an exemplary system architecture to which embodiments of the present disclosure may be applied;

[0017] Figure 2 A schematic diagram showing a flow chart of a method for generating a network construction plan in an exemplary embodiment of the present disclosure;

[0018] Figure 3 A schematic flow chart showing a method for determining the number of second newly added users in an exemplary embodiment of the present disclosure;

[0019] Figure 4 A flow chart showing a method for obtaining the number of new passive optical network device interfaces required to be built in each computer room in an exemplary embodiment of the present disclosure;

[0020] Figure 5 A schematic diagram showing the structure of a system for generating a gigabit network construction plan in an exemplary embodiment of the present disclosure is shown;

[0021] Figure 6 A schematic flow chart showing a method for determining the number of newly built passive optical network devices in an exemplary embodiment of the present disclosure;

[0022] Figure 7 A schematic diagram showing a graphical user interface in an exemplary embodiment of the present disclosure;

[0023] Figure 8 A schematic diagram showing another graphical user interface in an exemplary embodiment of the present disclosure;

[0024] Fig. 9 A schematic diagram showing the composition of a network construction plan generating device in an exemplary embodiment of the present disclosure;

[0025] Fig.10 A schematic structural diagram of an electronic device in an exemplary embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0026] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as being limited to the examples set forth herein; on the contrary, these embodiments are provided so that the present disclosure will be more comprehensive and complete, and the concepts of the example embodiments are fully conveyed to those skilled in the art. The described features, structures, or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0027] The terms "a", "an", "the" and "said" are used in this specification to indicate the presence of one or more elements / components / etc.; the terms "including" and "having" are used to express an open-ended inclusion and mean that additional elements / components / etc. may exist in addition to the listed elements / components / etc.; the terms "first" and "second" etc. are used only as labels and are not intended to limit the quantity of their objects.

[0028] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0029] When telecommunications operators invest in and build networks, they can calculate the scale of investment based on user development before formal construction to avoid irrational allocation of network resources.

[0030] In the related technologies, when calculating the scale of network investment, the user growth data of the previous month is used instead of the predicted number of user growth. Since the speed of user growth in different periods is not exactly the same, there is a large deviation between the number of users predicted by this method and the actual number of user growth, which leads to the low accuracy of the generated network investment and construction plan. At the same time, in the related technologies, the investment and expansion equipment need to be manually reported by the lower-level units, and there are many breakpoints and manual participation in each link from planning, design, feasibility study, plan generation, etc., with a low degree of intelligence, resulting in low efficiency of network construction.

[0031] In order to solve the above problems, the present invention provides a method and device for generating a network construction plan, which can be applied to Figure 1 In the system architecture of the exemplary application environment shown.

[0032] like Figure 1 As shown, the system architecture 100 may include a terminal device 110 and a server 120. The terminal device 110 may be a terminal device such as a smart phone, a tablet computer, a desktop computer, a laptop computer, a smart wearable device, etc. The server 120 generally refers to a background system that provides related services of the network construction solution generation method in this exemplary embodiment, and may be a server or a cluster formed by multiple servers. The terminal 110 and the server 120 may be connected via a wired or wireless communication link to exchange data.

[0033] In an exemplary embodiment, the above-mentioned network construction plan generation method can be executed by the terminal device 110. Accordingly, the network construction plan generation device can be set in the terminal 110 to realize the corresponding module functions. For example, the user can input the area identifier of the target area where the network construction is required in the terminal device 110, and the terminal device 110 can obtain the area data and the network user data of the area according to the area identifier, and then predict the first number of new users in each residence, and then obtain the second number of new users in each computer room according to the first number of new users in each residence, and then obtain the number of users of the single passive optical network device interface of each computer room according to the second number of new users, so as to obtain the number of new passive optical network devices that need to be built according to the relationship between the number of users of the single passive optical network device interface and the threshold value, so as to generate the network construction plan for the area.

[0034] In an exemplary embodiment, the above-mentioned network construction plan generation method can be executed by the server 120. Accordingly, the network construction plan generation device can be set in the server 120 to realize the corresponding module functions. For example, the user can input the area identifier of the target area where the network construction is required in the terminal device 110, and the terminal device 110 can send the area identifier to the server 120. The server 120 obtains the area data and the network user data of the area according to the area identifier, and then predicts the first number of new users in each residence, and then obtains the second number of new users in each computer room according to the first number of new users in each residence, and then obtains the number of users of the single passive optical network device interface of each computer room according to the second number of new users, so as to obtain the number of new passive optical network devices that need to be built according to the relationship between the number of users of the single passive optical network device interface and the threshold value, so as to generate the network construction plan for the area, and return the generated network construction plan to the terminal device 110, so as to display the generated network construction plan in the terminal device 110.

[0035] It should be understood that Figure 1 The number of terminal devices and servers in the example is only illustrative. Any number of terminal devices and servers may be provided as required. For example, the server 120 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.

[0036] Figure 2 A schematic diagram showing a flow chart of a method for generating a network construction plan in an exemplary embodiment of the present disclosure is shown. Figure 2 , the method comprising:

[0037] Step S210, predicting the number of first newly added users in each residence based on the regional data of the target area and the network user data of each residence in the target area;

[0038] Step S220, determining the second number of new users in each computer room in the target area based on the first number of new users in each residence;

[0039] Step S230, determining the number of allocated users of a single existing passive optical network device interface of each computer room based on the second number of newly added users of each computer room, the number of existing users of each computer room, and the first number of existing passive optical network device interfaces of each computer room;

[0040] Step S240, for each computer room, based on the relationship between the number of allocated users of the single existing passive optical network device interface of the computer room and the preset threshold of the computer room, obtaining the number of new passive optical network device interfaces that need to be built in the computer room;

[0041] Step S250: generating a network construction plan for the target area according to the number of the second newly added users in each computer room and the number of newly built passive optical network device interfaces in each computer room.

[0042] exist Figure 2 In the technical solution provided by the illustrated embodiment, the first number of newly added users of each residence can be predicted based on the regional data of the target area and the network user data of each residence, and the first number of newly added users of each residence can be summarized to obtain the second number of newly added users of the computer room, and then the number of users of a single passive optical network device interface of the computer room can be determined based on the second number of newly added users of the computer room, and the number of newly built passive optical network device interfaces of the computer room can be obtained based on the relationship between the number of users of the single passive optical network device interface and the preset threshold, thereby generating a network construction plan for the target area. Compared with the prior art, on the one hand, the present disclosure can predict user increments based on multi-dimensional and multi-source data through regional data and network user data, thereby improving the accuracy of user increment predictions and further assisting in improving the accuracy of the generated network construction plan; on the other hand, the present disclosure can obtain the number of newly built passive optical network device interfaces based on the relationship between the number of users of a single passive optical network device interface of the computer room and the threshold value, thereby providing a reasonable number of newly built passive optical network devices while ensuring the quality of network services, thereby avoiding waste of passive optical network device resources.

[0043] The following Figure 2 The specific implementation methods of each step in the embodiment shown are described in detail:

[0044] Next, the specific implementation method of "step S210, predicting the first number of newly added users in each residence according to the regional data of the target area and the network user data of each residence in the target area" will be described in detail.

[0045] In an exemplary embodiment, the target area can be determined according to demand, and the target area can be a specific geographical area, such as city A or county B of city A, etc. The residences in the target area can include residential communities that have been built in the target area and / or residential communities that are under construction in the target area. In other words, the present disclosure can predict the number of new users in a cell with the cell as the granularity. Taking a gigabit network as an example, the first number of new users can be understood as the predicted number of new gigabit network users in a certain cell.

[0046] In an exemplary embodiment, the regional data of the target area includes one or more of the network coverage data of the target area and the development potential score of the target area; the network user data of each residence includes one or more of the number of households in the residence, whether the residence is a newly built residence, the historical number of new users of the residence, the number of users who have migrated into the network in the residence, the number of users who have migrated out of the network in the residence, the number of users who have dismantled network signal receiving equipment in the residence, and the network consumption capacity of residential users.

[0047] Taking the target area of ​​District B in City A as an example, the operator's information platform can be used to obtain data on the number of households in each community in District B, the proportion of different operator networks in the community, the number of users in the community who use the operator's high-value network products, the number of new operator network users in the community last month or in the past N months, the number of users who have removed their phones in the community, the number of community user migrations (i.e., the number of people moving in and out of the network), and community user satisfaction with the network. The developer's marketing platform can be used to obtain whether the community is a newly built community, and a label can be added to the community to determine whether it is a newly built community. The community label can also be used as one of the network user data. The development potential evaluation score of District B, the 5G network coverage of District B, and other regional data can be determined based on the data in the operator's information platform, thereby obtaining multi-source and multi-dimensional data to predict the future population growth in the community.

[0048] In an exemplary embodiment, the network of the present disclosure may include a gigabit network, that is, when performing gigabit network construction estimation, the incremental number of gigabit network users may be predicted based on regional data of the region and multi-source, multi-dimensional data related to users. In other words, the first number of newly added users in step S210 may include the first number of newly added gigabit network users, that is, the number of newly added users using the gigabit network in each residential area.

[0049] Exemplarily, a specific implementation of step S210 may include: predicting the first number of new users in each residence based on the regional data of the target area and the network user data of each residence in the target area based on a pre-trained multi-dimensional time series neural network prediction model.

[0050] For example, regional data of different areas at different times and network user data of each residence in each area during that time can be obtained as training data, the number of users of each residence during that time can be used as a label, and a multi-dimensional time series neural network prediction model can be trained to perform residential user increment prediction, thereby obtaining a pre-trained multi-dimensional time series neural network prediction model.

[0051] In an exemplary embodiment, the multidimensional time series neural network model in the present disclosure may include a sarima (Seasonal Autoregressive Integrated Moving Average) model. The process of building a sarima model may include a data exploration stage, a parameter selection stage, a model fitting stage, and a model diagnosis stage. The data exploration stage mainly checks whether the time series shows a trend and / or seasonal pattern. The parameter selection stage determines the values ​​of p, d, q and P, D, Q in the model based on the autocorrelation function (ACF) and partial autocorrelation function (PACF) diagrams of the time series. The model fitting stage can use the selected parameters to fit the sarima model. The model diagnosis stage can verify the validity of the model through residual analysis and other means. After the validity of the model is verified, a pre-trained multidimensional time series neural network model can be obtained, and the model can be used to predict future time points.

[0052] In some time series, there are obvious periodic changes. This period is caused by seasonal changes such as quarterly, monthly, weekly changes or some other inherent factors such as weather, holidays, etc. Such series can be called seasonal series. The sarima model can support time series data with seasonal components. The parameters p in the model represent the order of non-seasonal autoregression, q represents the order of non-seasonal moving average, d represents the number of one-step differences, P represents the order of seasonal autoregression, Q represents the order of seasonal moving average, and D represents the number of seasonal differences.

[0053] Taking the construction of a gigabit network as an example, after obtaining a pre-trained sarima model through training data, for any residence in any target area, the regional data of the target area and the gigabit network user data of the residence can be input into the sarima model, and the number of new gigabit network users in the residence in the future can be predicted based on the output of the sarima model, that is, the number of new gigabit network users that can be added by the above-mentioned first number of new users.

[0054] Currently, the network in the present disclosure may also be other types of networks, such as a 100M network, a 5G network, etc. This exemplary embodiment does not specifically limit this. When the network is a 100M network, the first number of newly added users is the number of newly added 100M network users.

[0055] It should be noted that in the present disclosure, the number of new users can also be predicted by other multi-dimensional time series neural network models, such as the autoregressive integrated moving average model, etc., and this exemplary embodiment does not make any special limitation on this.

[0056] The specific implementation of "step S220, determining the second number of new users in each computer room in the target area based on the first number of new users in each residence" is described in detail below.

[0057] For example, after obtaining the first number of new users in each residence in the target area, the second number of new users in each computer room in the target area can be obtained based on the relationship between each residence and the computer room. The second number of new users can be understood as the total number of users who need to provide network services in each computer room in the future.

[0058] For example, Figure 3 A flow chart showing a method for determining the second number of newly added users in an exemplary embodiment of the present disclosure is shown. Figure 3 , the method may include steps S310 to S340.

[0059] in:

[0060] In step S310, when a residence is associated with a computer room, the computer room associated with the residence is determined as a target computer room of the residence.

[0061] Taking Gigabit Network as an example, when a computer room is connected to a residential community in a passive optical network, the computer room is the target computer room of the community.

[0062] In step S320, when a residence is associated with multiple computer rooms, the computer room with the largest number of idle passive optical network device interfaces among the multiple computer rooms is determined as the target computer room of the residence.

[0063] Continuing with the example of Gigabit network, when a residential community with a passive optical network is connected to multiple computer rooms, the computer room with the most idle Gigabit users can be selected from its multiple upstream OLT (Optical Line Terminal, a terminal device used to connect to the optical fiber trunk line) computer rooms as the target computer room for the residential community.

[0064] In step S320, the target associated residence of each computer room is determined according to the target computer room of each residence.

[0065] For example, the residences of the same target computer room may be aggregated according to the target computer room of each residence, and the residences associated with each computer room may be determined, that is, the target associated residences of each computer room may be obtained.

[0066] In step S330, the second number of new users in each computer room is determined according to the sum of the first number of new users in the target associated residences of each computer room.

[0067] For example, after obtaining the target associated residences of each computer room, the number of newly added gigabit network users of the target associated residences corresponding to each computer room can be summarized to obtain the number of newly added gigabit network users of each computer room, that is, the second number of newly added users.

[0068] Below, the specific implementation method of "step S230, determining the number of allocated users of a single existing passive optical network device interface in each computer room based on the second number of new users in each computer room, the number of existing users in each computer room, and the first number of existing passive optical network device interfaces in each computer room" is described in detail.

[0069] In an exemplary embodiment, the passive optical network device interface can be understood as a PON (Passive Optical Network) interface. The PON interface can include one or more of a GPON (Gigabit Passive Optical Network) and an EPON (Ethernet Passive Optical Network) interface.

[0070] Exemplarily, a specific implementation of step S230 may include: determining the number of users to be allocated in each computer room based on the sum of the second number of new users in each computer room and the number of existing users in each computer room; determining the number of allocated users of a single existing passive optical network device interface in each computer room based on the quotient of the number of users to be allocated in each computer room and the first number of existing passive optical network device interfaces in each computer room.

[0071] In an exemplary embodiment, the existing passive optical network device interfaces of the computer room can be understood as the existing passive optical network device interfaces of each computer room, which can include the passive optical network device interfaces in use in the computer room and the idle passive optical network device interfaces in the computer room. That is, the first number of existing passive optical network interfaces of the computer room is the sum of the number of passive optical network device interfaces in use in the computer room and the number of idle passive optical network device interfaces in the computer room.

[0072] For example, the total number of users that each computer room needs to serve in the future, that is, the number of users to be allocated, can be obtained based on the sum of the second number of newly added users in each computer room and the original number of existing users in each computer room. Then, the number of users allocated to each PON port in the existing PON interface of the computer room is obtained by dividing the total number of users that each computer room needs to serve in the future by the first number of existing passive optical network device interfaces in each computer room, that is, the number of users carried by a single PON interface in the computer room.

[0073] Below, the specific implementation method of "step S240, for each computer room, based on the relationship between the number of allocated users of the single existing passive optical network device interface of the computer room and the preset threshold of the computer room, obtaining the number of new passive optical network device interfaces that need to be built in the computer room" is described in detail.

[0074] In an exemplary implementation, the preset threshold is used to characterize the maximum number of users that can be connected to a single passive optical network device interface of the computer room. That is, taking a gigabit network as an example, the preset threshold can be understood as the threshold value of the number of gigabit users of a single PON port.

[0075] For example, preset thresholds for computer rooms in different areas can be pre-configured, and the preset thresholds can be determined based on demand and experience. For example, the preset threshold for densely populated areas can be set larger, and the preset threshold for sparsely populated areas can be set smaller. For example, the preset threshold for computer rooms in the main urban area can be 3.5, the preset threshold for computer rooms in the suburbs can be 2, and the preset threshold for computer rooms in the county town can be 1.5. For another example, taking the example of each computer room being managed by different branches, the single PON port gigabit user number threshold value of the computer room of the branch in the main urban area is 3.5, the single PON port gigabit user number threshold value of the computer room of the first type of branch is 2.8, the single PON port gigabit user number threshold value of the second type of branch is 2, the single PON port gigabit user number threshold value of the third type of branch is 1.5, etc. The specific division levels and values ​​can be determined based on demand and experience, and this exemplary embodiment does not make any special restrictions on this.

[0076] Based on this, for example, Figure 4 A flow chart showing a method for obtaining the number of new passive optical network device interfaces required for each computer room in an exemplary embodiment of the present disclosure is shown. Figure 4 , the method may include steps S410 to S430. Wherein:

[0077] In step S410, based on the relationship between the number of allocated users of the single existing passive optical network device interface of the computer room and a preset threshold of the computer room, the number of newly added passive optical network device interfaces of the computer room is obtained.

[0078] Exemplarily, a specific implementation method of step S410 may include: when the number of allocated users of the single existing passive optical network device interface in the computer room is greater than the preset threshold, obtaining the number of new passive optical network device interfaces in the computer room according to the quotient between the second number of new users in the computer room and the preset threshold of the computer room.

[0079] For example, when the number of users of a single PON interface in a computer room is greater than a preset threshold, it indicates that the number of new users in the computer room will cause the number of users carried by the existing passive optical network device interface in the computer room to exceed the threshold value, that is, the existing passive optical network device cannot support the use of new users and original users at the same time, so it is necessary to add a new PON port in the computer room. The number of new PON ports required in the computer room can be obtained according to the following formula (1):

[0080]

[0081] Exemplarily, another specific implementation of step S410 may include: when the number of allocated users of a single existing passive optical network device interface in the computer room is less than the preset threshold, determining that the computer room does not need to add a new passive optical network device interface.

[0082] For example, when the number of allocated users of a single existing passive optical network device interface in the computer room is less than the preset threshold, it means that the existing passive optical network equipment in the computer room can support new users and existing users, so there is no need to add new passive optical network equipment in the computer room, that is, the number of new passive optical network device interfaces in the computer room is 0.

[0083] In an exemplary implementation, the number of PON ports that each computer room theoretically needs to add is calculated in step S410. Considering the reuse of old ports, the number of currently idle PON ports in each computer room can be subtracted from this value to obtain the actual demand for PON ports in each computer room.

[0084] In step S420, the idle passive optical network devices in the target area are acquired, and the idle passive optical network devices in the target area are allocated to each computer room in the target area according to a preset rule.

[0085] For example, the number of idle passive optical network devices in the PON board management unit of the target area can be obtained, and then the idle passive optical network devices can be allocated to each computer room in the target area according to preset rules to achieve the reuse of passive optical network devices and avoid the waste of passive optical network device resources.

[0086] In an exemplary embodiment, the preset rules may include sorting each computer room in ascending order according to the actual demand for the PON ports of each computer room, and then, based on the sorting results, allocating idle passive optical network equipment to computer rooms with less actual demand for passive optical network equipment according to the actual demand for the PON ports of each computer room. In this way, some computer rooms can be expanded completely based on the old PON boards, which is convenient for subsequent troubleshooting and positioning, and also makes the demand sources of boards in each computer room more concentrated, which is convenient for subsequent construction when expanding the computer room.

[0087] In another exemplary embodiment, the preset rules may also include sorting each computer room in descending order according to the actual demand for PON ports of each computer room, and then allocating idle PON boards to computer rooms with large actual demand for PON ports based on the sorting results. In this way, the timeliness of the supply of PON boards to computer rooms with large demand can be guaranteed, thereby giving priority to meeting the needs of more users.

[0088] In another exemplary embodiment, the preset rules may include allocating idle PON boards based on the distance between each computer room and the unit that manages the PON boards in the target area, such as allocating idle PON boards preferentially to computer rooms that are close to the unit that manages the PON boards, thereby saving transportation costs during subsequent network construction.

[0089] In step S430, the number of passive optical network device interfaces required to be newly built in the computer room is obtained according to the difference between the number of newly added passive optical network device interfaces in the computer room and the number of passive optical network device interfaces that can be provided by the idle passive optical network devices allocated to the computer room.

[0090] For example, for each computer room, the number of new PON interfaces actually needed in the computer room can be obtained by subtracting the number of old PON interfaces that can be provided by the idle PON boards allocated to the computer room from the management unit from the actual demand for PON ports in the computer room calculated in step S410.

[0091] That is to say, since there may be idle PON devices in the passive optical device management units in the target area (such as branches in various regions), the number of PON devices that each computer room actually needs to build can be obtained based on the actual demand for PON ports in each computer room and the number of idle PON devices that can be allocated. For example, if the actual demand for PON ports in the computer room is 100, the number of idle PON devices allocated is 5, and each PON device can provide 8 PON ports, then 60 new PON ports need to be built. If each newly built PON device can provide 8 PON ports, the number of new PON devices that need to be built is 60 divided by 8, rounded up, which is 8.

[0092] Through the above steps S410 to S430, idle PON boards can be allocated to each computer room according to the number of PON interfaces actually needed in each computer room, and the number of new PON boards actually needed in each computer room can be obtained, thereby achieving reuse of PON boards and avoiding waste of PON boards.

[0093] The following is a detailed description of the implementation of "step S250, generating a network construction plan for the target area according to the second number of newly added users in each computer room and the number of newly built passive optical network device interfaces in each computer room".

[0094] Exemplarily, a specific implementation of step S250 may include: inputting the number of new users in each computer room, the number of newly built passive optical network equipment interfaces in each computer room, and the number of idle passive optical network equipment allocated to each computer room into the large language model, and generating a network construction plan for the target area based on the output of the large language model.

[0095] For example, information such as the area name of the target area, the predicted number of new users in the target area (i.e., the sum of the first number of new users in each residence), the number of new users in each computer room, the total number of allocated idle PON boards, the number of idle PON boards allocated to each computer room, the number of new PON boards needed in the computer room, and the type of new PON boards needed in each computer room can be input into the large language model. The large language model can integrate this information according to the preset content generation version to generate a network construction plan for the target area.

[0096] For example, Figure 5 A schematic diagram of the structure of a system for generating a gigabit network construction plan in an exemplary embodiment of the present disclosure is shown. Figure 5 The system may include a user growth prediction module 51, a PON board calculation module 52 and a large model generation construction plan module 53.

[0097] For example, the user growth prediction module 51 can combine the basic data of the residential community in the target area, the community data label (whether it is a newly built community label), the relationship between the community and the computer room, the relationship between the computer room and the target area, etc., to screen, organize and eliminate outliers, train the user growth time series prediction model, and predict the number of all gigabit network users of the network operator in a single computer room in the target area based on the multi-dimensional time series neural network prediction algorithm. For example, the community population of the residential community in the target area, the number of new gigabit network users last month, whether the community is newly built, the proportion of users consuming high-value network products in the community, the broadband penetration rate of the community (i.e., the proportion of network users of different operators), the number of users who disassembled the machine, the regional development potential score and other multi-dimensional data can be input into the time series prediction model. The time series prediction model can obtain the predicted gigabit user increment of each residential community through model reasoning, algorithm verification and other links, and the gigabit user increment of each residential community is summarized at the computer room level to obtain the predicted gigabit user increment of each computer room.

[0098] The PON board calculation module 52 can predict the number of PON boards that should be newly built and can be reused in each computer room based on information such as the board type, idle boards, idle ports, preset thresholds, the predicted gigabit user increase in each computer room, and the original number of gigabit users in each computer room.

[0099] The data calculated by the user growth prediction module 51 and the PON board calculation module 52 can be integrated according to a certain output template to obtain prompt words corresponding to disagreement questions, and the prompt words can be input into the large model generation construction plan 53 to obtain the network construction plan through the output of the large language model.

[0100] Below, reference Figure 6 The implementation method of the above-mentioned PON card calculation module 52 predicting the number of PON cards that should be newly built and the number of PON cards that can be reused in each computer room is further described. For example, Figure 6 A flow chart showing a method for determining the number of newly built passive optical network devices in an exemplary embodiment of the present disclosure is shown, with reference to Figure 6 The method may include steps S601 to S615. Wherein:

[0101] In step S601, the predicted number of gigabit users in residential areas of the target area is obtained; in step S602, the number of gigabit users in residential areas is aggregated into the number of gigabit users in a single computer room; in step S603, the number of newly added PON interfaces in the computer room dimension is generated; in step S604, the number of idle PON interfaces in the computer room is obtained, and the actual PON port demand of the computer room is obtained according to the difference between the number of newly added PON interfaces and the number of idle PON interfaces in the computer room. If the actual PON port demand is less than or equal to If it is equal to 0, go to step S605, otherwise go to step S606; in step S605, determine that the computer room will not be expanded; in step S606, expand the computer room according to the idle PON boards. When the number of idle PON boards in the same branch is greater than or equal to the actual required number of PON boards, go to step S607, otherwise go to step 608; in step S607, call the idle PON boards to expand the computer room; in step S608, create new PON boards for expansion.

[0102] For example, the number of newly added PON interfaces in the computer room can be obtained based on the quotient of the number of newly added users and the threshold value of the number of users with a single PON port in the computer room, and then the number of newly added PON ports in the computer room minus the number of idle PON ports in the computer room can be obtained to obtain the number of PON ports that actually need to be added in the computer room, that is, the actual PON port demand. If the actual PON port demand is less than or equal to 0, the computer room does not need to be expanded, that is, the number of new PON cards required in the computer room is 0. If the actual PON port demand is greater than 0, the actual number of PON boards required can be obtained based on the actual PON port demand and the number of PON ports that a single PON board can provide. For example, if the actual PON port demand is 50 and a single PON board can provide 8 PON ports, 50 can be divided by 8 and rounded up to obtain the actual number of PON boards required, which is 7. If the number of idle PON boards in the same branch is greater than or equal to the actual demand for PON boards, the idle PON boards in the same branch can be directly called for capacity expansion. If the number of idle PON boards in the same branch is less than the actual demand for PON boards, the actual demand for PON boards minus the number of idle PON boards in the same branch will be used to obtain the number of PON boards that need to be newly built, so as to expand the capacity of the computer room according to the number of newly built PON boards that need to be newly built. Among them, the number of PON boards that need to be newly built is the number of PON boards that need to be purchased.

[0103] Among them, when the optical network cell is connected to multiple computer rooms, the computer room with the most idle gigabit users in the upstream OLT computer room can be selected as the associated computer room of the optical network cell to aggregate user incremental data.

[0104] Below, reference Figure 7 and Figure 8An exemplary application scenario of the present disclosure is described. For example, Figure 7 In the interface shown, controls corresponding to various network construction scheme calculation scenarios are configured, such as Figure 7 The newly added Gigabit user prediction scenario control, user Gigabit plan scenario control, and investment Gigabit plan scenario control shown in 71 in the figure are used to implement the plan prediction for different scenarios. For example, in response to the user triggering the control of the investment Gigabit plan scenario, the following may be displayed: Figure 7 In the interface shown in 72, the user can enter relevant data, such as the name of the branch company, whether to consider reuse, investment amount, name of the computer room, etc. At the same time, the parameter configuration control can be used to configure the Gigabit user threshold value of a single 10G PON port per unit time. After entering the relevant information, you can click Figure 7 The generated controls in response to Figure 7 The trigger operation of the generate control in the command can calculate the required number of 10G PON boards and the number of idle 10G PON boards in the computer room according to the information such as the computer room of the branch selected by the user, whether to consider the reuse of idle boards, the input investment amount, and the threshold value of the number of gigabit users under the configured single 10G PON port, and input this information into the large language model, which integrates this information and outputs it through a fixed template such as Figure 8 The construction plan shown. Figure 8 As shown, the large language model can generate detailed content related to the construction plan, including branch name, number of gigabit users, computer room, required 10G PON type, number of PONs, number of idle ports, number of new ports required, etc.

[0105] The present invention predicts network user growth based on multi-source and multi-dimensional data according to a time series model, thereby improving the accuracy of user growth prediction and thus assisting in improving the accuracy of the generated construction plan. The present invention calculates the newly built and reused PON boards through the threshold value of a single PON port in a computer room, and inputs the calculation results into a large model to automatically generate a network construction plan, thereby improving the efficiency of generating the construction plan.

[0106] In addition, it should be noted that the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0107] Furthermore, the exemplary embodiment of the present disclosure also provides a network construction plan generating device. Fig. 9As shown, the network construction plan generating device 900 includes the following program modules: a prediction module 910, configured to predict the first number of new users in each residential area according to the regional data of the target area and the network user data of each residence in the target area; a second number of new users determining module 920, configured to determine the second number of new users in each computer room of the target area based on the first number of new users in each residence; a single interface allocated user number determination module 930, configured to determine the number of allocated users of a single existing passive optical network device interface of each computer room based on the second number of new users in each computer room, the number of existing users in each computer room, and the first number of existing passive optical network device interfaces in each computer room; a newly created interface number determination module 940, configured to obtain the number of newly created passive optical network device interfaces of the computer room for each computer room based on the relationship between the number of allocated users of the single existing passive optical network device interface of the computer room and a preset threshold value of the computer room; a network construction plan generating module 950, configured to generate a network construction plan for the target area according to the number of new users in each computer room and the number of newly created passive optical network device interfaces in each computer room.

[0108] In an exemplary embodiment, the prediction module 910 can be specifically configured as follows: according to the regional data of the target area and the network user data of each residence in the target area, based on a pre-trained multi-dimensional time series neural network prediction model, predict the first number of new users in each residence; wherein the regional data of the target area includes one or more of the network coverage data of the target area and the development potential score of the target area; the network user data of each residence includes one or more of the number of households in the residence, whether the residence is a newly built residence, the historical number of new network users of the residence, the number of users who have migrated into the network in the residence, the number of users who have migrated out of the network in the residence, the number of users who have dismantled network signal receiving equipment in the residence, and the network consumption capacity of residential users.

[0109] In an exemplary embodiment, the second new user quantity determination module 920 can be specifically configured as follows: when the residence is associated with one computer room, the computer room associated with the residence is determined as the target computer room of the residence; when the residence is associated with multiple computer rooms, the computer room with the largest number of idle passive optical network device interfaces among the multiple computer rooms is determined as the target computer room of the residence; based on the target computer room of each residence, the target associated residence of each computer room is determined; and based on the sum of the first number of new users of the target associated residence of each computer room, the second number of new users of each computer room is determined.

[0110] In an exemplary embodiment, the single-interface allocated user number determination module 930 can be specifically configured as: based on the sum of the second number of new users in each computer room and the number of existing users in each computer room, determine the number of users to be allocated in each computer room; according to the quotient of the number of users to be allocated in each computer room and the first number of existing passive optical network device interfaces in each computer room, determine the number of allocated users of a single existing passive optical network device interface in each computer room.

[0111] In an exemplary embodiment, the above-mentioned preset threshold is used to characterize the maximum number of users that can be accessed by a single passive optical network device interface of the computer room. Based on this, the above-mentioned new interface quantity determination module 940 can be specifically configured as follows: based on the relationship between the number of allocated users of the single existing passive optical network device interface of the computer room and the preset threshold of the computer room, the newly added number of passive optical network device interfaces of the computer room is obtained; the idle passive optical network devices in the target area are obtained, and the idle passive optical network devices in the target area are allocated to each computer room in the target area according to preset rules; according to the difference between the newly added number of passive optical network device interfaces of the computer room and the number of passive optical network device interfaces that can be provided by the idle passive optical network devices allocated to the computer room, the number of newly built passive optical network device interfaces that need to be built in the computer room is obtained.

[0112] In an exemplary embodiment, the method of obtaining the newly added number of passive optical network device interfaces in the computer room based on the relationship between the number of allocated users of the single existing passive optical network device interface in the computer room and the preset threshold of the computer room includes: when the number of allocated users of the single existing passive optical network device interface in the computer room is greater than the preset threshold, obtaining the newly added number of passive optical network device interfaces in the computer room according to the quotient between the second newly added number of users in the computer room and the preset threshold of the computer room; and obtaining the newly added number of passive optical network device interfaces in the computer room according to the quotient between the second newly added number of users in the computer room and the preset threshold of the computer room. When the number is less than the preset threshold, the newly added number of passive optical network device interfaces of the computer room is determined in the following manner: the number of users to be allocated in the computer room is determined according to the sum of the second number of newly added users in the computer room and the number of existing users in the computer room; the demand for passive optical network device interfaces of the computer room is determined based on the quotient of the number of users to be allocated and the preset threshold of the computer room; the newly added number of passive optical network device interfaces of the computer room is determined according to the difference between the demand for passive optical network device interfaces of the computer room and the number of occupied passive optical network device interfaces of the computer room.

[0113] In an exemplary embodiment, the network construction plan generation module 950 can be configured to: input the second number of new users in each computer room, the number of newly built passive optical network equipment interfaces in each computer room, and the number of idle passive optical network equipment allocated to each computer room into the large language model, and generate a network construction plan for the target area based on the output of the large language model.

[0114] The specific details of each part of the above-mentioned device have been described in detail in the implementation method of the method part. The undisclosed details can be found in the implementation method of the method part, so they will not be repeated here.

[0115] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the exemplary embodiments of the present 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 into multiple modules or units to be embodied.

[0116] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0117] The exemplary embodiments of the present disclosure also provide a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the above-mentioned network construction plan generation method is implemented.

[0118] In one embodiment, the computer program product may be a tangible product containing a computer program, such as a computer-readable storage medium storing a computer program. The readable storage medium may be a storage medium based on electrical, magnetic, optical, electromagnetic, infrared, or other signals, including but not limited to: random access memory (RAM), read-only memory (ROM), magnetic tape, floppy disk, flash memory (Flash), mechanical hard disk (HDD), solid-state drive (SSD), and the like. Exemplarily, the computer program product may be implemented as a non-volatile storage medium storing a computer program, such as a read-only memory, a NAND flash memory, and the like.

[0119] In one embodiment, the computer program product may be an intangible product including a computer program. Exemplarily, the computer program product may be implemented as a virtual digital product, such as a digital file storing an executable file, an installation package, etc. of the computer program.

[0120] The code of the computer program can be written in one or more programming languages. Programming languages ​​such as C language, Java, C++, Python, etc. The program code can be executed entirely on the user computing device, or partially on the user computing device, or as a separate software package, or partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, such as a local area network (LAN), a wide area network (WAN), etc., or can be connected to an external computing device (e.g., an Internet connection provided by an operator).

[0121] Computer programs can be carried or transmitted through electrical, magnetic, optical, electromagnetic, infrared, etc. Electronic devices can convert signals carrying computer programs into digital signals and then run the computer programs. When the computer program is executed on an electronic device, its code is used to enable the electronic device to execute (more specifically, to enable the processor of the electronic device to execute) the method steps of various exemplary embodiments of the present disclosure, such as the above-mentioned network construction plan generation method, which includes the following steps: predicting the first number of new users of each residence based on the regional data of the target area and the network user data of each residence in the target area; determining the second number of new users of each computer room in the target area based on the first number of new users of each residence; determining the number of allocated users of a single existing passive optical network device interface of each computer room based on the second number of new users of each computer room, the number of existing users of each computer room, and the first number of existing passive optical network device interfaces of each computer room; for each computer room, obtaining the number of new passive optical network device interfaces that need to be built in the computer room based on the relationship between the number of allocated users of the single existing passive optical network device interface of the computer room and a preset threshold of the computer room; generating a network construction plan for the target area based on the second number of new users of each computer room and the number of new passive optical network device interfaces of each computer room.

[0122] By executing the above method steps through a computer program, on the one hand, through regional data and network user data, user increment prediction can be performed based on multi-dimensional and multi-source data, thereby improving the accuracy of user increment prediction, and further assisting in improving the accuracy of the generated network construction plan; on the other hand, based on the relationship between the number of users and the threshold value of a single passive optical network device interface in the computer room, the number of new passive optical network device interfaces that need to be built is obtained, and a reasonable number of new passive optical network devices can be given while ensuring the quality of network services, thereby avoiding waste of passive optical network device resources.

[0123] The exemplary embodiments of the present disclosure also provide an electronic device, such as the terminal device 110 or the server 120 described above. The electronic device may include a processor and a memory. The memory stores executable instructions of the processor, such as a computer program. The processor executes the method steps of various exemplary embodiments of the present disclosure by executing the executable instructions. In addition, the electronic device may also include a display for displaying a graphical user interface.

[0124] Reference below Fig.10 , the electronic device is exemplarily described in the form of a general-purpose computing device. It should be understood that Fig.10 The electronic device 1000 shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0125] like Fig.10 As shown, the electronic device 1000 may include: a processor 1010 , a memory 1020 , a bus 1030 , an I / O (input / output) interface 1040 , a network adapter 1050 , and a display 1060 .

[0126] The memory 1020 may include a volatile memory, such as a RAM 1021, a cache unit 1022, and may also include a non-volatile memory, such as a ROM 1023. The memory 1020 may also include one or more program modules 1024, such program modules 1024 include but are not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or a combination thereof may include the implementation of a network environment. For example, the program module 1024 may include each module in the above-mentioned device.

[0127] The processor 1010 may include one or more processing units. For example, the processor 1010 may include an AP (Application Processor), a modem processor, a GPU (Graphics Processing Unit), an ISP (Image Signal Processor), a controller, an encoder, a decoder, a DSP (Digital Signal Processor), a baseband processor and / or an NPU (Neural-Network Processing Unit), etc.

[0128] The processor 1010 can be used to execute executable instructions stored in the memory 1020, such as the above-mentioned network construction plan generation method, which includes the following steps: predicting the first number of new users in each residence based on the regional data of the target area and the network user data of each residence in the target area; determining the second number of new users in each computer room in the target area based on the first number of new users in each residence; determining the number of allocated users of a single existing passive optical network device interface in each computer room based on the second number of new users in each computer room, the number of existing users in each computer room, and the first number of existing passive optical network device interfaces in each computer room; for each computer room, obtaining the number of new passive optical network device interfaces that need to be built in the computer room based on the relationship between the number of allocated users of the single existing passive optical network device interface in the computer room and a preset threshold value of the computer room; generating a network construction plan for the target area based on the second number of new users in each computer room and the number of new passive optical network device interfaces in each computer room.

[0129] The above method is implemented through a computer program. On the one hand, through regional data and network user data, user increment prediction can be carried out according to multi-dimensional and multi-source data, thereby improving the accuracy of user increment prediction, and further assisting in improving the accuracy of the generated network construction plan; on the other hand, according to the relationship between the number of users and the threshold value of a single passive optical network device interface in the computer room, the number of new passive optical network device interfaces that need to be built is obtained, and a reasonable number of new passive optical network devices can be given while ensuring the quality of network services, thereby avoiding the waste of passive optical network device resources.

[0130] The bus 1030 is used to realize the connection between different components of the electronic device 1000, and may include a data bus, an address bus, and a control bus.

[0131] The electronic device 1000 can communicate with one or more external devices 1100 (eg, a keyboard, a mouse, an external controller, etc.) through the I / O interface 1040 .

[0132] The electronic device 1000 can communicate with one or more networks through the network adapter 1050. For example, the network adapter 1050 can provide mobile communication solutions such as 3G / 4G / 5G, or provide wireless communication solutions such as wireless LAN, Bluetooth, near field communication, etc. The network adapter 1050 can communicate with other modules of the electronic device 1000 through the bus 1030.

[0133] The electronic device 1000 can display a graphical user interface through the display 1060, such as displaying the above Figure 7 and Figure 8 The interface shown, etc.

[0134] although Fig.10Not shown, other hardware and / or software modules may also be provided in the electronic device 1000, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0135] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0136] As can be seen from the above, the technical solution of the present disclosure can be implemented as a method, an apparatus, a system, a computer program product, a storage medium, an electronic device, etc. Those skilled in the art can understand that various aspects of the present disclosure can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software, such as being respectively referred to as a "circuit", "module" or "system".

[0137] It should be understood that the present disclosure is not limited to the specific method steps or structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from the scope thereof. Those skilled in the art will easily think of other embodiments based on the specific embodiments provided by the present disclosure. Therefore, the specific embodiments provided by the present disclosure are only exemplary, and the scope and spirit of the present disclosure are indicated by the claims, and any variations, uses or adaptive changes of the present disclosure should be covered, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the technical field that are not disclosed in the present disclosure.

Claims

1. A method for generating a network construction plan, characterized in that: include: Predicting the number of first newly added users in each residence based on the regional data of the target area and the network user data of each residence in the target area; Determine the second number of new users in each computer room in the target area based on the first number of new users in each residence; Determine the number of users allocated to a single existing passive optical network device interface in each computer room based on the second number of newly added users in each computer room, the number of existing users in each computer room, and the first number of existing passive optical network device interfaces in each computer room; For each computer room, based on the relationship between the number of allocated users of the single existing passive optical network device interface of the computer room and the preset threshold of the computer room, the number of new passive optical network device interfaces that need to be built in the computer room is obtained; A network construction plan for the target area is generated according to the number of the second newly added users in each computer room and the number of newly built passive optical network equipment interfaces in each computer room.

2. The method according to claim 1, characterized in that The predicting the first number of new users in each residence according to the regional data of the target area and the network user data of each residence in the target area comprises: According to the regional data of the target area and the network user data of each residence in the target area, based on a pre-trained multi-dimensional time series neural network prediction model, predicting the first number of newly added users in each residence; Among them, the regional data of the target area includes one or more of the network coverage data of the target area and the development potential score of the target area; the network user data of each residence includes one or more of the number of households in the residence, whether the residence is a newly built residence, the historical number of new network users of the residence, the number of users who have migrated into the network in the residence, the number of users who have migrated out of the network in the residence, the number of users who have dismantled network signal receiving equipment in the residence, and the network consumption capacity of residential users.

3. The method according to claim 1, characterized in that The determining the second number of new users in each computer room in the target area based on the first number of new users in each residence comprises: In the case where the residence is associated with a computer room, determining the computer room associated with the residence as the target computer room of the residence; In the case where the residence is associated with multiple computer rooms, determining the computer room with the largest number of idle passive optical network device interfaces among the multiple computer rooms as the target computer room of the residence; According to the target computer room of each residence, the target associated residence of each computer room is determined; The second number of new users in each computer room is determined according to the sum of the first number of new users in the target associated residences of each computer room.

4. The method according to claim 1, characterized in that Determining the number of allocated users of a single existing passive optical network device interface in each computer room based on the second number of newly added users in each computer room, the number of existing users in each computer room, and the first number of existing passive optical network device interfaces in each computer room includes: Determine the number of users to be allocated in each computer room based on the sum of the second number of newly added users in each computer room and the number of existing users in each computer room; The number of allocated users of a single existing passive optical network device interface in each computer room is determined according to the quotient of the number of users to be allocated in each computer room and the first number of existing passive optical network device interfaces in each computer room.

5. The method according to claim 1, characterized in that The preset threshold is used to characterize the maximum number of users that can be accessed by a single passive optical network device interface of the computer room. For each computer room, based on the relationship between the number of allocated users of the single existing passive optical network device interface of the computer room and the preset threshold of the computer room, obtaining the number of new passive optical network device interfaces that need to be built in the computer room includes: Based on the relationship between the number of allocated users of the single existing passive optical network device interface of the computer room and a preset threshold of the computer room, obtaining the number of newly added passive optical network device interfaces of the computer room; Acquire idle passive optical network devices in the target area, and allocate the idle passive optical network devices in the target area to each computer room in the target area according to a preset rule; The number of passive optical network device interfaces required to be newly built in the computer room is obtained according to the difference between the number of newly added passive optical network device interfaces in the computer room and the number of passive optical network device interfaces that can be provided by the idle passive optical network devices allocated to the computer room.

6. The method according to claim 5, characterized in that The method of obtaining the newly added number of passive optical network device interfaces of the computer room based on the relationship between the number of allocated users of the single existing passive optical network device interface of the computer room and a preset threshold of the computer room includes: When the number of allocated users of a single existing passive optical network device interface in the computer room is greater than the preset threshold, obtaining the number of newly added passive optical network device interfaces in the computer room according to the quotient between the second number of newly added users in the computer room and the preset threshold of the computer room; When the number of allocated users of a single existing passive optical network device interface in the computer room is less than the preset threshold, it is determined that no new passive optical network device interface is needed in the computer room.

7. The method according to claim 5, characterized in that According to the number of the second newly added users in each computer room and the number of newly built passive optical network equipment interfaces in each computer room, generating a network construction plan for the target area includes: The number of the second newly added users in each computer room, the number of newly built passive optical network equipment interfaces in each computer room, and the number of idle passive optical network equipment allocated to each computer room are input into the large language model, and a network construction plan for the target area is generated according to the output of the large language model.

8. A network construction plan generating device, characterized in that: include: A prediction module is configured to predict the first number of newly added users in each residential area based on the area data of the target area and the network user data of each residence in the target area; A second new user number determination module is configured to determine a second new user number of each computer room in the target area based on the first new user number of each residence; The single-interface allocated user quantity determination module is configured to determine the number of allocated users of a single existing passive optical network device interface of each computer room based on the second number of newly added users of each computer room, the number of existing users of each computer room, and the first number of existing passive optical network device interfaces of each computer room; A newly-built interface quantity determination module is configured to obtain, for each computer room, the quantity of newly-built passive optical network device interfaces of the computer room based on the relationship between the quantity of allocated users of the single existing passive optical network device interface of the computer room and a preset threshold of the computer room; The network construction plan generating module is configured to generate a network construction plan for the target area according to the number of newly added users in each computer room and the number of newly built passive optical network equipment interfaces in each computer room.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the method according to any one of claims 1 to 7.

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