Wave position planning method and device, equipment, storage medium and computer program product

Abstract

The invention discloses a beam position planning method and device, equipment, a storage medium and a computer program product, which are used for solving the problem that beam position planning cannot be accurately carried out according to user service requirements and user aggregation areas in the existing scheme. The method comprises the following steps: clustering users in a to-be-planned area according to obtained space behavior data of each user in the to-be-planned area to obtain user clusters; determining an initial wave position according to the user cluster; according to the initial wave position, carrying out isolation verification on each wave position center in the initial wave position, and carrying out wave position combination according to a verification result to obtain a final wave position plan; and according to the final beam position plan, determining a service duration corresponding to each beam position, the service duration including a first service duration determined according to a common channel period and a second service duration determined according to a service volume corresponding to the beam position.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a waveform planning method, apparatus, device, storage medium, and computer program product. Background Technology

[0002] In emergency communication scenarios, such as natural disasters or emergencies, terrestrial communication facilities are often damaged. In such cases, satellite communication becomes a crucial means of maintaining communication lifelines. Satellite position planning technology can precisely allocate scarce satellite resources to ensure uninterrupted communication in disaster-stricken areas, which is essential for the timeliness and effectiveness of rescue operations.

[0003] Because disasters or emergencies typically result in highly uneven user distribution, severely limited network resources, and spatiotemporally abrupt communication demands, traditional uniform coverage beam planning methods are insufficient to effectively meet the high-capacity concurrent communication needs of critical areas. Therefore, in emergency scenarios, satellite beams need to possess rapid response capabilities for user groups in emergency areas and the ability to dynamically adapt to complex environmental changes.

[0004] Currently, several dynamic beam planning technologies exist in the industry. For example, hopping beam technology divides time into micro-slots and dynamically allocates the dwell time of beams in different areas to achieve resource allocation. Another example is intelligent beam planning algorithms that use artificial intelligence to predict user hotspot distribution and service demands, thereby dynamically optimizing beam dwell time, frequency band, and power allocation.

[0005] However, these existing technologies still have limitations in practical applications. For example, hopping beam technology, which frequently switches beam patterns within microseconds, is prone to synchronization errors and latency jitter in the context of high-speed movement of low Earth orbit satellites. Intelligent beam planning methods, relying on complex AI models and algorithms, require extremely high computing power for satellite-ground collaboration, and their accuracy is very low in emergency scenarios lacking sufficient training data.

[0006] Therefore, in emergency communication scenarios, how to quickly and accurately determine the target user group and dynamically and efficiently plan wave positions according to actual business needs has become an urgent technical problem to be solved. Summary of the Invention

[0007] This application provides a wave position planning method to solve the problem that existing solutions cannot accurately plan wave positions based on user business needs and user gathering areas.

[0008] This application also provides a wave position planning device to solve the problem that existing solutions cannot accurately plan wave positions according to user business needs and user gathering areas.

[0009] This application also provides a wave position planning device to solve the problem that existing solutions cannot accurately plan wave positions according to user business needs and user gathering areas.

[0010] This application also provides a computer-readable storage medium to solve the problem that existing solutions cannot accurately plan wave positions based on user business needs and user gathering areas.

[0011] A computer program product designed to address the problem that existing solutions cannot accurately plan wave positions based on user business needs and user gathering areas.

[0012] The embodiments of this application adopt the following technical solutions: A wavelength positioning planning method includes: clustering users within a region to be planned based on spatial behavior data of each user in the region to be planned to obtain user clusters; determining initial wavelength positions based on the user clusters; verifying the isolation of the centers of each wavelength position in the initial wavelength positions based on the initial wavelength positions, and merging wavelength positions based on the verification results to obtain a final wavelength positioning plan; and determining the service duration corresponding to each wavelength position based on the final wavelength positioning plan, wherein the service duration includes a first service duration determined based on the common channel period and a second service duration determined based on the traffic volume corresponding to the wavelength position.

[0013] A wavelength planning device includes: a user clustering determination unit, configured to cluster users within a planned area based on acquired spatial behavior data of each user, to obtain user clusters; an initial wavelength determination unit, configured to determine initial wavelengths based on the user clusters; a wavelength planning unit, configured to perform isolation verification on the centers of each wavelength in the initial wavelengths based on the initial wavelengths, and to merge wavelengths based on the verification results, to obtain a final wavelength planning; and a service duration planning unit, configured to determine the service duration corresponding to each wavelength based on the final wavelength planning, wherein the service duration includes a first service duration determined based on the common channel period and a second service duration determined based on the traffic volume corresponding to the wavelength.

[0014] A wave position planning device, comprising: The processor; and a memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform the following operations: clustering users within the planned area based on spatial behavior data of each user within the planned area to obtain user clusters; determining initial wave positions based on the user clusters; performing isolation checks on the center of each wave position in the initial wave positions based on the initial wave positions, and merging wave positions based on the check results to obtain a final wave position plan; determining the service duration corresponding to each wave position based on the final wave position plan, wherein the service duration includes a first service duration determined based on the common channel period and a second service duration determined based on the traffic volume corresponding to the wave position.

[0015] A computer-readable storage medium stores one or more programs that, when executed by an electronic device including multiple applications, cause the electronic device to perform the following operations: clustering users within a planned area based on acquired spatial behavior data of users within the planned area to obtain user clusters; determining initial wave positions based on the user clusters; performing isolation checks on the center of each wave position in the initial wave positions based on the initial wave positions, and merging wave positions based on the check results to obtain a final wave position plan; and determining the service duration corresponding to each wave position based on the final wave position plan, wherein the service duration includes a first service duration determined based on the common channel period and a second service duration determined based on the traffic volume corresponding to the wave position.

[0016] A computer program product includes a computer program that, when executed by a processor, performs the following: clustering of users within a planned area based on acquired spatial behavior data of each user; determining initial wave positions based on the user clusters; performing isolation verification on the center of each wave position based on the initial wave positions, and merging wave positions based on the verification results to obtain a final wave position plan; and determining the service duration corresponding to each wave position based on the final wave position plan, wherein the service duration includes a first service duration determined based on the common channel period and a second service duration determined based on the traffic volume corresponding to the wave position.

[0017] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects: The beam planning method provided in this application allows for beam planning based on the spatial behavior data of users within the planned area. Users are clustered to obtain user clusters, and initial beams are determined based on these clusters. Isolation is then verified for each beam center within the initial beams, and beams are merged based on the verification results to obtain the final beam plan. Finally, the service duration corresponding to each beam is determined based on the final beam plan to complete the beam planning. This beam planning method, on the one hand, determines initial beams by clustering users based on user spatial behavior data. It can identify potential user groups with similar geographical distributions and behavioral patterns based on the historical and habitual behaviors of users within the planned area (e.g., disaster-affected users). This allows for initial beam planning based on these user groups, making beam planning more than just simple geometric coverage; it closely matches the actual distribution and activity hotspots of users, thus achieving precise delivery of satellite beam energy and communication services. On the other hand, by introducing a beam center isolation verification and merging mechanism… During the waveband planning phase, the spatial isolation between wavebands is proactively ensured, effectively avoiding co-channel interference that may occur due to wavebands being too close together. This ensures the smooth and stable transmission of rescue commands and critical data in emergency scenarios. Finally, the waveband planning method provided in this application can allocate service time for each waveband by combining fixed base duration and dynamic service duration. Based on the real-time service needs of each waveband, its service time can be dynamically adjusted. In the event of sudden and uneven service traffic in emergency communications, it can maximize the utilization of satellite resources and effectively balance the contradiction between limited resources and urgent needs. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A schematic diagram illustrating a specific flow of a wave position planning method provided in an embodiment of this application; Figure 2 This is a schematic diagram illustrating the service duration allocated to each wavelength by the wavelength planning system provided in this application embodiment. Figure 3 A schematic diagram of the specific structure of a wave position planning device provided in an embodiment of this application; Figure 4 This is a schematic diagram of the specific structure of a wave position planning device provided in an embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] This application provides a wave position planning method to solve the problem that existing solutions cannot accurately plan wave positions based on user business needs and user gathering areas.

[0021] The execution subject of the beam planning method provided in this application embodiment may be, but is not limited to, at least one of an emergency communication server, a beam planning system, and a satellite beam scheduling system; in addition, the execution subject of the method may also be the system or application itself running on these servers.

[0022] For ease of description, the following description uses a wave position planning system as the execution subject to illustrate the implementation of this method. It should be understood that using a wave position planning system as the execution subject is merely an illustrative example and should not be construed as a limitation of the method.

[0023] The schematic diagram illustrating the specific implementation process of the position planning method provided in this application is shown below. Figure 1 As shown, the main steps include the following: Step 11: Based on the spatial behavior data of each user in the area to be planned, cluster the users in the area to be planned to obtain user clusters; In this embodiment, the area to be planned can be a disaster-stricken area where emergency communication is needed, and the acquired spatial behavior data can be data used to characterize the spatial distribution characteristics of users. In one embodiment, the spatial behavior data includes at least one of the following: 1. User registration location data; The user registration location data refers to the latitude and longitude coordinates corresponding to the user's fixed address, such as home or office, registered in the operator's network system. In this embodiment, the user registration location data can be represented by the following formula [1]: [1] 2. User's frequently visited location data; The user's permanent location data refers to the set of locations where the user frequently appears within a historical period (such as the week before a disaster), which can be obtained through base station handover signaling or location data.

[0024] In this embodiment of the application, the user's permanent location data can be represented by the following formula [2]: [2] 3. User movement trajectory data.

[0025] User movement trajectory data can be represented by a continuous sequence of timestamped locations of a user over a period of time. In this embodiment, the user movement trajectory data can be represented by the following formula [3]: [3] It should be noted that, after acquiring the aforementioned spatial behavior data, in order to quantify the user's spatial behavior so that wave position planning can be performed based on this spatial behavior data, in this embodiment of the application, the wave position planning system can extract features from the aforementioned acquired raw spatial behavior data to obtain key features that can characterize the distribution of user spatial behavior.

[0026] Specifically, in the embodiments of this application, the extracted spatial features may include, but are not limited to, the following: 1. Location of User Activity Center: For a user, the location of their activity center can be obtained by calculating the geometric center of their frequently visited location dataset. In one implementation, the mean latitude and longitude of all the user's frequently visited locations can be determined by the following formula [4], thereby determining the user's activity center location: [4] 2. Density of user movement trajectories The trajectory density is used to characterize the frequency of a user's activity within a certain area. In this embodiment, the user's movement trajectory points and the corresponding movement areas are determined based on the user's movement trajectory data, and the user's movement trajectory density is determined based on the ratio of the user's movement trajectory points to the area of ​​the movement area.

[0027] In one implementation, the wave position planning system can determine the density of user movement trajectories by following sub-steps, including: Sub-step 1101: Traverse all of the user's movement trajectory data and determine the maximum latitude value lat. max minimum latitude lat min Maximum longitude (lon) max Minimum longitude (lon) min .

[0028] Sub-step 1102: Calculate the range of latitude and longitude based on the obtained latitude and longitude values; Specifically, the latitude range and longitude range can be calculated according to the following formulas [5] and [6] respectively: [5] [6] Sub-step 1103: Use the sum of the latitude and longitude ranges multiplied by a unit conversion factor K to determine the area of ​​the trajectory coverage region.

[0029] Specifically, the area covered by the trajectory can be determined according to the following formula [7]: [7] Sub-step 1104, according to the following formula [8], finally determines the user movement trajectory density: [8] Where N is the number of all trajectory points in the user's movement trajectory data.

[0030] Finally, based on the spatial behavior data and extracted spatial features obtained above, feature vectors are constructed for each user in the area to be planned. Taking user i as an example, the constructed feature vector is shown in the following formula [9]: [9] It should be noted that the feature vector constructed above is only an example, and the spatial features included therein are not limited to this, and may also include other possible spatial features.

[0031] Based on the feature vectors corresponding to each user obtained by performing the above sub-steps, user clusters are obtained through clustering. Specifically, in this embodiment, user clustering can be performed according to the following sub-steps: Sub-step 11-a: Calculate the spatial distribution similarity between any two users within the area to be planned. In this embodiment of the application, it can be based on the user's registration location (lat ireg lon ireg ) and activity center (lat icenter lon icenter The Haversine distance between ) geo The calculation is performed, and the normalized distance is calculated according to the following formula

[10] : :

[10] in, is a preset maximum distance used for normalization.

[0032] Sub-step 11-b: Calculate the trajectory similarity between any two users within the area to be planned; Specifically, the similarity of the original trajectory sequences can be calculated based on the trajectory density in the feature vectors of both parties, or by using algorithms such as Dynamic Time Warping (DTW).

[0033] In one implementation, trajectory similarity can be calculated according to the following formula

[11] :

[11] Sub-step 11-c: Based on the spatial distribution similarity and trajectory similarity obtained by executing the above sub-steps 11-a to 11-b, determine the comprehensive similarity between any two users; In this embodiment of the application, the overall similarity can be calculated according to the following formula

[12] :

[12] Where w1 + w2 = 1.

[0034] A similarity matrix is ​​constructed based on the comprehensive similarity of all user pairs. The users are then clustered using a clustering algorithm (such as K-means clustering). Each cluster corresponds to a potential group of service users. It should be noted that those skilled in the art can also use other clustering algorithms such as hierarchical clustering and DBSCAN. This application does not limit the specific clustering algorithm used.

[0035] Step 12: Determine the initial wave position based on the user clustering obtained by performing Step 11; In one implementation, each user cluster obtained in step 11 can be initialized as an independent waveform.

[0036] The center of each initial wave position can be determined by calculating the average latitude and longitude of the registration location and activity center location of all users within that cluster.

[0037] Step 13: Based on the initial wave positions determined by executing Step 12, perform isolation verification on the center of each wave position in the initial wave positions, and merge the wave positions according to the verification results to obtain the final wave position plan. In order to avoid co-frequency interference between wave positions and ensure the anti-interference performance of the system, in this embodiment of the application, after determining the initial wave position, the wave position planning system can perform isolation verification on the center of each wave position in the initial wave position and merge the initial wave positions whose center interval is less than a preset threshold.

[0038] In one implementation, the wave position planning system can perform isolation verification and determine the final wave position plan by following the sub-steps: Sub-step 1301: Determine the interval distance between any two wave centers in the initial wave position based on the initial wave position; In the embodiments of this application, the geographical distance between any two wave positions in the initial wave position can be calculated. For example, the Haversine distance between any two wave position centers in the initial wave position can be calculated. It should be noted that calculating the Haversine distance is a common technical means in related fields, so the specific calculation method will not be described here.

[0039] Sub-step 1302: Determine whether the interval distance is less than a preset distance threshold; To determine whether the distance is less than a preset distance threshold, in this embodiment of the application, the distance threshold can be set to 30-100 kilometers according to the system's anti-interference requirements.

[0040] Sub-step 1303: If the judgment result obtained by executing sub-step 1302 is yes, then the two wave positions are merged.

[0041] In this embodiment of the application, when the wave position planning system determines that the interval between two initial wave positions is less than a preset distance threshold, the wave position planning system can merge the users covered by the two wave positions into a new user set, forming a new merged wave position, namely the second wave position.

[0042] At the same time, by calculating the average of the registration location and activity center location of the new user set, the wave center of the second wave obtained by the merger is recalculated, and the second wave center is obtained, thus completing a wave merge.

[0043] After one merge, the merged second wave position replaces the original two initial wave positions, and the isolation is checked again with the remaining initial wave positions. This process is repeated until the distance between the centers of all wave positions is greater than or equal to the distance threshold. The resulting wave position set is the final wave position plan, and the final wave position assignment for each user is determined.

[0044] Step 14: Based on the final wavelet planning determined by executing Step 12, determine the service duration corresponding to each wavelet, wherein the service duration includes a first service duration determined according to the common channel period and a second service duration determined according to the traffic volume corresponding to the wavelet.

[0045] After determining the wave positions, the wave position planning system can further plan the service duration for each wave position.

[0046] In this embodiment of the application, the service duration is composed of the following two parts: 1. First service duration D min : The first service duration is a fixed part determined by the common channel period. The user terminal needs at least this time period to complete system synchronization and information reception (e.g., receiving SSB, SIB1, and SIB19) before attempting to access the network. In one embodiment, the value of the first service duration is typically about 3 ms.

[0047] 2. Second service duration: Among them, the second service duration is a dynamic part and is dynamically determined according to the traffic volume Q corresponding to the wave position.

[0048] In one embodiment, the wave position planning system can determine the second service duration corresponding to each wave position according to the following sub-steps, including: Sub-step 1401: Perform traffic volume modeling for each wave position, and then determine the traffic volume corresponding to each wave position; In one embodiment, the wave position planning system can perform traffic volume modeling according to the following formula

[13] :

[13] Among them, S is the number of service request packets per unit time, P is the average size of a single packet (Bit / packet), and T is the packet arrival interval (s).

[0049] Sub-step 1402: Dynamically calculate the second service duration corresponding to each wave position according to the traffic volume corresponding to each wave position; Specifically, since the second service duration is related to the traffic volume Q of the wave position and the rate B (bit / s) that the system can provide for the wave position. Based on the queuing theory principle, in order to process the traffic load of the wave position and ensure the quality of service, the average total time of the service in the system can be used to determine the second service duration corresponding to the wave position. In one embodiment, the wave position planning system can calculate the second service duration according to the following formula

[14] :

[14] According to the above calculation method, the wave position planning system can ensure that the larger the traffic volume, the longer the second service duration. When the traffic volume Q approaches the system capacity B, the second service duration will increase significantly. At the same time, the wave position planning system ensures Q < B to avoid overload.

[0050] Sub-step 1403: Determine the service duration corresponding to the wave position according to the first service duration and the second service duration; In one embodiment, the wave position planning system can determine the service duration D corresponding to the wave position according to the following formula

[15] total :

[15] Finally, the service duration allocated by the wave position planning system for each wave position is as Figure 2 shown.

[0051] The beam planning method provided in this application allows for beam planning based on the spatial behavior data of users within the planned area. Users are clustered to obtain user clusters, and initial beams are determined based on these clusters. Isolation is then verified for each beam center within the initial beams, and beams are merged based on the verification results to obtain the final beam plan. Finally, the service duration corresponding to each beam is determined based on the final beam plan to complete the beam planning. This beam planning method, on the one hand, determines initial beams by clustering users based on user spatial behavior data. It can identify potential user groups with similar geographical distributions and behavioral patterns based on the historical and habitual behaviors of users within the planned area (e.g., disaster-affected users). This allows for initial beam planning based on these user groups, making beam planning more than just simple geometric coverage; it closely matches the actual distribution and activity hotspots of users, thus achieving precise delivery of satellite beam energy and communication services. On the other hand, by introducing a beam center isolation verification and merging mechanism… During the waveband planning phase, the spatial isolation between wavebands is proactively ensured, effectively avoiding co-channel interference that may occur due to wavebands being too close together. This ensures the smooth and stable transmission of rescue commands and critical data in emergency scenarios. Finally, the waveband planning method provided in this application can allocate service time for each waveband by combining fixed base duration and dynamic service duration. Based on the real-time service needs of each waveband, its service time can be dynamically adjusted. In the event of sudden and uneven service traffic in emergency communications, it can maximize the utilization of satellite resources and effectively balance the contradiction between limited resources and urgent needs.

[0052] In one embodiment, this application also provides a wave position planning device to solve the problem that existing solutions cannot accurately plan wave positions based on user service needs and user gathering areas. A schematic diagram of the specific structure of this wave position planning device is shown below. Figure 3 As shown, it includes: a user clustering determination unit 31, an initial wave position determination unit 32, a wave position planning unit 33, and a service duration planning unit 34.

[0053] The user clustering determination unit 31 is used to cluster the users in the area to be planned based on the spatial behavior data of each user in the area to be planned, and obtain user clusters. Initial wave position determination unit 32 is used to determine the initial wave position based on the user clustering; Wave position planning unit 33 is used to perform isolation verification on the center of each wave position in the initial wave position according to the initial wave position, and to merge the wave positions according to the verification result to obtain the final wave position plan; The service duration planning unit 34 is used to determine the service duration corresponding to each wavelength position according to the final wavelength position planning, wherein the service duration includes a first service duration determined according to the common channel period and a second service duration determined according to the traffic volume corresponding to the wavelength position.

[0054] In one implementation, spatial behavior data includes at least one of the following: user registration location data; and / or user's permanent location data; and / or user movement trajectory data.

[0055] In one embodiment, the user clustering determination unit 31 is specifically used for: determining at least one spatial feature corresponding to each user based on the spatial behavior data, wherein the spatial feature is used to represent the spatial distribution of the user; determining the spatial distribution similarity of each user in the area to be planned based on the spatial feature; and clustering each user in the area to be planned based on the spatial distribution similarity to obtain user clusters.

[0056] In one implementation, the spatial features include at least one of the following: the location of the user's activity center; and / or the density of the user's movement trajectory.

[0057] In one embodiment, the user clustering determination unit 31 is specifically used for: determining the user's activity center location based on the user's permanent location data; and / or determining the user's movement trajectory points and the corresponding movement area based on the user's movement trajectory data, and determining the user's movement trajectory density based on the ratio of the user's movement trajectory points to the area of ​​the movement area, wherein the movement area is determined based on the latitude and longitude coordinates corresponding to the user's movement trajectory data.

[0058] In one embodiment, the wave position planning unit 33 is specifically used to: determine the interval distance between any two wave position centers in the initial wave position according to the initial wave position; determine whether the interval distance is less than a preset distance threshold; and when the determination result is yes, merge the two wave positions.

[0059] In one embodiment, the wave position planning unit 33 is specifically used for: merging the two wave positions to obtain a second wave position; determining the second wave position center of the second wave position; and performing cyclic isolation checks on the remaining wave position centers in the initial wave position based on the second wave position center until the interval distance between all wave position centers is greater than or equal to the distance threshold.

[0060] Using the beam position planning device provided in this application embodiment, when beam position planning is required, users in the area to be planned can be clustered based on the spatial behavior data of each user in the area to be planned, resulting in user clusters. Initial beam positions are then determined based on these user clusters. Furthermore, the isolation of each beam position center in the initial beam positions is verified. Based on the verification results, beam positions are merged to obtain the final beam position plan. Finally, the service duration corresponding to each beam position is determined based on the final beam position plan to complete the beam position planning. The beam position planning method provided in this application embodiment, on the one hand, determines the initial beam positions by using user clusters obtained from user spatial behavior data clustering. This allows for the identification of potential user groups with similar geographical distribution and activity patterns based on the historical and habitual behaviors of users in the area to be planned (e.g., disaster-affected users in disaster areas). Initial beam position planning is then performed based on these user groups, making beam position planning no longer a simple geometric coverage but highly consistent with the actual distribution and activity hotspots of users, thereby achieving precise delivery of satellite beam energy and communication services. On the other hand, by introducing a beam position center isolation verification and merging mechanism… During the waveband planning phase, the spatial isolation between wavebands is proactively ensured, effectively avoiding co-channel interference that may occur due to wavebands being too close together. This ensures the smooth and stable transmission of rescue commands and critical data in emergency scenarios. Finally, the waveband planning method provided in this application can allocate service time for each waveband by combining fixed base duration and dynamic service duration. Based on the real-time service needs of each waveband, its service time can be dynamically adjusted. In the event of sudden and uneven service traffic in emergency communications, it can maximize the utilization of satellite resources and effectively balance the contradiction between limited resources and urgent needs.

[0061] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Please refer to it. Figure 4 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for other business operations.

[0062] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0063] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0064] The processor reads the corresponding computer program from non-volatile memory into main memory and then executes it, forming a wave planning device at the logical level. The processor executes the program stored in memory and specifically performs the following operations: Based on the spatial behavior data of each user in the area to be planned, the users in the area to be planned are clustered to obtain user clusters; initial wave positions are determined based on the user clusters; isolation is verified for the center of each wave position in the initial wave positions, and wave positions are merged based on the verification results to obtain the final wave position plan; the service duration corresponding to each wave position is determined based on the final wave position plan, wherein the service duration includes a first service duration determined based on the common channel period and a second service duration determined based on the traffic volume corresponding to the wave position.

[0065] The above is as stated in this application. Figure 4The wave position planning electronic device method disclosed in the illustrated embodiments can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0066] Of course, in addition to software implementation, the electronic device of this application does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. In other words, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0067] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by a portable electronic device including multiple applications, enable the portable electronic device to perform... Figure 1 The wave position planning method shown in the embodiment is specifically used to perform the following operations: Based on the spatial behavior data of each user in the area to be planned, the users in the area to be planned are clustered to obtain user clusters; initial wave positions are determined based on the user clusters; isolation is verified for the center of each wave position in the initial wave positions, and wave positions are merged based on the verification results to obtain the final wave position plan; the service duration corresponding to each wave position is determined based on the final wave position plan, wherein the service duration includes a first service duration determined based on the common channel period and a second service duration determined based on the traffic volume corresponding to the wave position.

[0068] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0069] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0070] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0071] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the function specified in one or more boxes.

[0072] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0073] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0074] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0075] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0076] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0077] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A wave position planning method, characterized in that, include: Based on the spatial behavior data of each user in the area to be planned, the users in the area to be planned are clustered to obtain user clusters; The initial wave position is determined based on the user clustering; Based on the initial wave positions, the isolation of each wave position center in the initial wave positions is checked, and wave positions are merged according to the check results to obtain the final wave position plan. Based on the final wavelet planning, the service duration corresponding to each wavelet is determined, wherein the service duration includes a first service duration determined based on the common channel period and a second service duration determined based on the traffic volume corresponding to the wavelet.

2. The method according to claim 1, characterized in that, The spatial behavior data includes at least one of the following: User registration location data; and / or User's persistent location data; and / or User movement trajectory data.

3. The method according to claim 1, characterized in that, The step of clustering users within the planned area based on the obtained spatial behavior data of each user in the planned area to obtain user clusters specifically includes: Based on the spatial behavior data, at least one spatial feature corresponding to a user within the area to be planned is determined, wherein the spatial feature is used to represent the spatial distribution of the user; Based on the spatial characteristics, determine the spatial distribution similarity of each user within the area to be planned; Based on the spatial distribution similarity, users within the area to be planned are clustered to obtain user clusters.

4. The method according to claim 3, characterized in that, At least one spatial feature corresponding to a user within the area to be planned includes at least one of the following: User activity center location; and / or User movement trajectory density.

5. The method according to claim 4, characterized in that, The step of determining at least one spatial feature corresponding to a user within the area to be planned based on the spatial behavior data specifically includes: Determine the user's activity center location based on the user's frequent location data; and / or Based on user movement trajectory data, user movement trajectory points and corresponding movement areas are determined. The user movement trajectory density is determined based on the ratio of the user movement trajectory points to the area of ​​the movement area. The movement area is determined based on the latitude and longitude coordinates corresponding to the user movement trajectory data.

6. The method according to claim 1, characterized in that, The step of verifying the isolation of the centers of each wave position in the initial wave position specifically includes: Based on the initial wave position, determine the interval distance between any two wave position centers in the initial wave position; Determine whether the interval distance is less than a preset distance threshold; If the judgment result is yes, then the two wave positions are merged.

7. A wave position planning device, characterized in that, include: The user clustering determination unit is used to cluster users in the area to be planned based on the spatial behavior data of each user in the area to be planned, and obtain user clusters. An initial wave position determination unit is used to determine the initial wave position based on the user clustering. The wave position planning unit is used to perform isolation verification on the center of each wave position in the initial wave position, and to merge the wave positions according to the verification results to obtain the final wave position plan. The service duration planning unit is used to determine the service duration corresponding to each wavelength position according to the final wavelength position planning, wherein the service duration includes a first service duration determined according to the common channel period and a second service duration determined according to the traffic volume corresponding to the wavelength position.

8. A wave position planning device, comprising: processor; as well as A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the following operations: Based on the spatial behavior data of each user in the area to be planned, the users in the area to be planned are clustered to obtain user clusters; The initial wave position is determined based on the user clustering; Based on the initial wave positions, the isolation of each wave position center in the initial wave positions is checked, and wave positions are merged according to the check results to obtain the final wave position plan. Based on the final wavelet planning, the service duration corresponding to each wavelet is determined, wherein the service duration includes a first service duration determined based on the common channel period and a second service duration determined based on the traffic volume corresponding to the wavelet.

9. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of applications, cause the electronic device to perform the wavelet planning method as described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the wave position planning method as described in any one of claims 1-6.

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