A method and system for coverage planning of wireless signals

Abstract

The present disclosure provides a wireless signal coverage planning method and system, relating to the technical field of wireless communication network. The method comprises: based on the environmental attribute information, matching network system, working frequency band and core radio frequency parameters for different regions in the pumped storage power station construction area respectively, obtaining the dynamic matching result corresponding to each region, based on the dynamic matching result corresponding to each region and the spatial structure information, constructing a multi-factor coupled link loss calculation model, and based on the link loss calculation model, calculating the wireless signal coverage performance of each region to obtain the coverage prediction result corresponding to each region, associating and displaying each coverage prediction result with the building information model to obtain the association display result, and adjusting the equipment layout and wireless parameters based on the association display result to obtain the target coverage planning scheme. Through the above method, the wireless signal coverage planning has reliability and guidance, and the visual adjustment of the wireless signal coverage is realized.

Description

Technical Field

[0001] This disclosure relates to the field of wireless communication network technology, and in particular to a method and system for wireless signal coverage planning. Background Technology

[0002] As the scale of pumped storage power stations continues to expand, underground powerhouses are increasingly characterized by complex cave-like structures, narrow and enclosed spaces, and highly dense electromechanical equipment. This places higher demands on the coverage, link stability, and multi-mode coordination of wireless communication systems. Therefore, to assess the propagation characteristics of wireless signals in underground powerhouses during the planning stage, a three-dimensional scene-based wireless signal simulation technology is introduced. This technology is used to predict the coverage and performance of wireless signals such as 4G, 5G, and WiFi within underground powerhouses.

[0003] Currently, Building Information Modeling (BIM) models of underground powerhouses are typically used as the geometric basis. The 3D structure of the underground powerhouse's caverns and large electromechanical equipment is imported into ray-tracing simulation tools such as electromagnetic simulation software. Different wireless signal standards are then independently simulated and analyzed to obtain results such as field strength distribution and link loss. However, these BIM models primarily focus on geometric and structural information. The electromagnetic parameters required for wireless signal simulation need to be manually supplemented, resulting in redundancy and low efficiency in the modeling process. Furthermore, independent simulation analysis of different wireless signal standards makes it difficult to assess the interference effects, switching stability, and electromagnetic compatibility of multiple coexisting signals in a unified scenario. Simultaneously, the complex environment of multiple caverns and equipment obstruction in underground powerhouses makes it difficult to balance simulation accuracy and computational efficiency in setting relevant simulation parameters and simplifying BIM models. This leads to significant deviations between simulation results and reality in complex scenarios, failing to accurately reflect the actual coverage effect after equipment deployment.

[0004] In summary, the deployment of wireless signal equipment mainly relies on experience and local simulation results, making it difficult to accurately predict and effectively optimize the overall wireless signal coverage of underground powerhouses during the planning stage. This results in a lack of reliability and guidance in wireless signal coverage planning. Summary of the Invention

[0005] To overcome the problems existing in related technologies, this disclosure provides a method and system for wireless signal coverage planning.

[0006] According to a first aspect of the present disclosure, a method for wireless signal coverage planning is provided, the method comprising:

[0007] Obtain spatial structure and environmental attribute information corresponding to the construction area of ​​the pumped storage power station;

[0008] Based on the environmental attribute information, network type, operating frequency band and core radio frequency parameters are matched to different areas in the construction area of ​​the pumped storage power station to obtain the dynamic matching result for each area.

[0009] Based on the dynamic matching results corresponding to each region and the spatial structure information, a multi-factor coupled link loss calculation model is constructed, and the wireless signal coverage performance of each region is calculated based on the link loss calculation model to obtain the coverage prediction results corresponding to each region.

[0010] Each coverage prediction result is associated with the building information model to obtain an associated display result. Based on the associated display result, the equipment layout and wireless parameters are adjusted to obtain the target coverage planning scheme. The wireless parameters include at least the core radio frequency parameters.

[0011] In one possible embodiment, the step of matching network standards, operating frequency bands, and core radio frequency parameters for different areas within the pumped storage power station construction area based on the environmental attribute information to obtain dynamic matching results for each area includes:

[0012] From the environmental attribute information, spatial enclosure degree, interference intensity index and equipment distribution characteristics are extracted for different spatial locations within the construction area of ​​the pumped storage power station, and corresponding scene label information is generated.

[0013] Based on the scene label information, multiple spatial locations with the same scene label information are merged to obtain multiple regions;

[0014] The scenario type and coverage requirements for each region are determined, and the network standard and operating frequency band for each region are automatically matched based on the scenario type; the scenario type represents the wireless propagation characteristics of the corresponding region.

[0015] Based on the coverage requirements, the core radio frequency parameters corresponding to each region are automatically configured. The core radio frequency parameters include: transmit power, antenna gain, and receive threshold.

[0016] Based on the network standard, operating frequency band, and core radio frequency parameters corresponding to each region, the dynamic matching result corresponding to each region is generated.

[0017] In one possible embodiment, the step of constructing a multi-factor coupled link loss calculation model based on the dynamic matching results corresponding to each region and the spatial structure information includes:

[0018] Based on the spatial structure information and the dynamic matching results corresponding to each region, the installation method and mounting height of the wireless device in each region are determined;

[0019] Based on the dynamic matching results corresponding to each region, the installation method, and the installation height, a multi-factor coupled link loss calculation model is constructed.

[0020] In one possible embodiment, the step of calculating the wireless signal coverage performance of each region based on the link loss calculation model to obtain the coverage prediction result corresponding to each region includes:

[0021] Multiple link measurement points are determined in each region; the link measurement points represent the wireless signal coverage at different spatial locations within the corresponding region.

[0022] Determine the link measurement rules and multi-dimensional loss parameters corresponding to each link measurement point;

[0023] The link measurement rules and the multi-dimensional loss parameters are input into the link loss calculation model, and the link corresponding to each link measurement point is calculated to obtain the link calculation result corresponding to each link measurement point.

[0024] Based on the link calculation results of multiple link measurement points within the same area, the coverage prediction result corresponding to each area is generated.

[0025] In one possible embodiment, determining the link measurement rules corresponding to each link measurement point includes:

[0026] Based on the propagation complexity and coverage distance requirements corresponding to the link measurement points, the corresponding link measurement rules are selected.

[0027] In one possible embodiment, selecting the corresponding link measurement rule based on the propagation complexity and coverage distance requirements corresponding to the link measurement point includes:

[0028] If the link measurement point is located in a densely populated area, then the complex point link measurement rule shall be adopted;

[0029] If the link measurement point is located at the area boundary or the farthest coverage area, the farthest point link measurement rule shall be adopted.

[0030] In one possible embodiment, determining the multi-dimensional loss parameters corresponding to each link measurement point includes:

[0031] Structural obstruction loss is calculated for the wireless propagation path corresponding to the link measurement point to obtain the structural obstruction loss value.

[0032] Electromagnetic interference loss of equipment is calculated in the area where the link measurement point is located, and the electromagnetic interference loss value of the corresponding area is obtained. The electromagnetic interference loss value includes electromagnetic interference loss generated by generator set, electrical equipment or metal components.

[0033] Environmental attenuation loss is calculated for the link measurement point to obtain the environmental attenuation loss value corresponding to the link measurement point. The environmental attenuation loss value includes: multipath attenuation and human body occlusion attenuation.

[0034] The structural shading loss value, the equipment electromagnetic interference loss value, and the environmental attenuation loss value are determined as the multi-dimensional loss parameters.

[0035] In one possible embodiment, the step of calculating the structural obstruction loss of the wireless propagation path corresponding to the link measurement point to obtain the structural obstruction loss value includes:

[0036] Determine the wireless propagation path corresponding to the link measurement point;

[0037] The occlusion loss is accumulated for the wall type or structural component type through which the wireless propagation path passes, and the structural occlusion loss value corresponding to the link measurement point is obtained.

[0038] In one possible embodiment, the step of associating each of the coverage prediction results with the building information model to obtain the associated display result includes:

[0039] Determine the wireless device parameters corresponding to each region and the coverage prediction results;

[0040] The wireless device parameters and the coverage prediction results are associated with the structural component attributes in the building information model, and then visualized in the building information model in the form of coverage color levels or link parameter annotations to obtain the associated display results.

[0041] In one possible embodiment, adjusting the device layout and wireless parameters based on the associated display results to obtain the target coverage planning scheme includes:

[0042] Adjust the location of wireless devices or the wireless parameters in the building information model;

[0043] Based on the adjusted location of the wireless device or the wireless parameters, the coverage prediction results for each area are updated in real time to obtain the target coverage planning scheme.

[0044] According to a second aspect of the present disclosure, a wireless signal coverage planning system is provided, comprising:

[0045] The data acquisition module is used to acquire spatial structure information and environmental attribute information corresponding to the construction area of ​​the pumped storage power station;

[0046] The adaptive matching module is used to match network type, operating frequency band and core radio frequency parameters for different areas in the construction area of ​​the pumped storage power station based on the environmental attribute information, so as to obtain the dynamic matching result for each area.

[0047] The link measurement module is used to construct a multi-factor coupled link loss calculation model based on the dynamic matching results corresponding to each region and the spatial structure information, and to measure the wireless signal coverage performance of each region based on the link loss calculation model to obtain the coverage prediction result corresponding to each region.

[0048] The scheme determination module is used to associate and display each coverage prediction result with the building information model to obtain the associated display result, and adjust the equipment layout and wireless parameters based on the associated display result to obtain the target coverage planning scheme; the wireless parameters include at least the core radio frequency parameters.

[0049] In one possible embodiment, the adaptive matching module is specifically used to extract spatial enclosure degree, interference intensity index, and equipment distribution characteristics from the environmental attribute information for different spatial locations within the construction area of ​​the pumped storage power station, generate corresponding scene label information, merge multiple spatial locations with the same scene label information based on the scene label information to obtain multiple regions, determine the scene type and coverage requirements corresponding to each region, and automatically match the network standard and operating frequency band corresponding to each region based on the scene type; the scene type characterizes the wireless propagation characteristics of the corresponding region, and automatically configure the core radio frequency parameters corresponding to each region based on the coverage requirements, the core radio frequency parameters including: transmit power, antenna gain, and receive threshold, and generate the dynamic matching result corresponding to each region based on the network standard, the operating frequency band, and the core radio frequency parameters corresponding to each region.

[0050] In one possible embodiment, the link measurement module is specifically used to determine the installation method and mounting height of the wireless device in each region based on the spatial structure information and the dynamic matching result corresponding to each region, and to construct a multi-factor coupled link loss calculation model based on the dynamic matching result corresponding to each region, the installation method and the mounting height.

[0051] In one possible embodiment, the link calculation module is further configured to determine multiple link calculation points in each area; the link calculation points represent the wireless signal coverage at different spatial locations within the corresponding area, determine the link calculation rules and multi-dimensional loss parameters corresponding to each link calculation point, input the link calculation rules and the multi-dimensional loss parameters into the link loss calculation model, calculate the link corresponding to each link calculation point, obtain the link calculation result corresponding to each link calculation point, and generate the coverage prediction result corresponding to each area based on the link calculation results of multiple link calculation points in the same area.

[0052] In one possible embodiment, the link calculation module is further configured to select the corresponding link calculation rule based on the propagation complexity and coverage distance requirements corresponding to the link calculation point.

[0053] In one possible embodiment, the link calculation module is further configured to use complex point link calculation rules if the link calculation point is located in a densely populated area, and use the farthest point link calculation rules if the link calculation point is located at the area boundary or the farthest coverage area.

[0054] In one possible embodiment, the link calculation module is further configured to perform structural obstruction loss calculation on the wireless propagation path corresponding to the link calculation point to obtain a structural obstruction loss value; perform equipment electromagnetic interference loss calculation on the area where the link calculation point is located to obtain an equipment electromagnetic interference loss value for the corresponding area, wherein the electromagnetic interference loss value includes electromagnetic interference loss generated by generator sets, electrical equipment, or metal components; and perform environmental attenuation loss calculation on the link calculation point to obtain an environmental attenuation loss value corresponding to the link calculation point, wherein the environmental attenuation loss value includes multipath attenuation and human body obstruction attenuation; and determine the structural obstruction loss value, the equipment electromagnetic interference loss value, and the environmental attenuation loss value as the multidimensional loss parameter.

[0055] In one possible embodiment, the link measurement module is further configured to determine the wireless propagation path corresponding to the link measurement point, perform cumulative calculation of the occlusion loss of the wall type or structural component type traversed by the wireless propagation path, and obtain the structural occlusion loss value corresponding to the link measurement point.

[0056] In one possible embodiment, the scheme determination module is specifically used to determine the wireless device parameters corresponding to each region and the coverage prediction result, associate the wireless device parameters and the coverage prediction result with the structural component attributes in the building information model, and visualize them in the building information model in the form of coverage color level or link parameter annotation to obtain the association display result.

[0057] In one possible embodiment, the scheme determination module is further configured to adjust the location of wireless devices or the wireless parameters in the building information model, and based on the adjusted location of wireless devices or the wireless parameters, update the coverage prediction results corresponding to each area in real time to obtain the target coverage planning scheme.

[0058] According to a third aspect of the present disclosure, a computer device is provided, comprising:

[0059] A memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the method of the first or second aspect described above.

[0060] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, wherein a computer program is stored in the storage medium, and when the computer program is executed by a processor, the method of the first aspect described above is implemented.

[0061] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0062] In this embodiment, automatic identification and scene division of different areas within the construction area of ​​a pumped storage power station are achieved. Based on the scene label information corresponding to each area, the corresponding network standard, operating frequency band, and core radio frequency parameters are matched for each area, ensuring the determinism of wireless signal coverage. Based on a multi-factor coupled link loss calculation model, the coverage prediction result corresponding to each area is determined, improving the accuracy of wireless signal coverage prediction. The coverage prediction result is deeply correlated with the building information model, realizing the visualization of the coverage prediction result. Iterative updates of the target coverage planning scheme are also realized, significantly improving the accuracy, interpretability, and engineering implementation efficiency of wireless signal coverage planning.

[0063] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0064] The accompanying drawings, which are incorporated in and form part of this disclosure, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0065] Figure 1 This disclosure is a schematic diagram of the structure of a deep integration visualization system based on building information modeling and simulation results, according to an exemplary embodiment.

[0066] Figure 2 This is a flowchart illustrating a wireless signal coverage planning method according to an exemplary embodiment of the present disclosure;

[0067] Figure 3 This is a schematic diagram of the process for determining the dynamic matching result corresponding to each region according to an exemplary embodiment of the present disclosure;

[0068] Figure 4 This is a schematic flowchart illustrating the process of determining the coverage prediction result for each region according to an exemplary embodiment of the present disclosure;

[0069] Figure 5 This is a schematic diagram of the structure of a wireless signal coverage planning system according to an exemplary embodiment of the present disclosure;

[0070] Figure 6 This is a schematic diagram of the structure of a computer device according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0071] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0072] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The singular forms “a,” “the,” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0073] It should be understood that although the terms first, second, third, etc., may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are used only to distinguish information of the same type from one another. For example, without departing from the scope of this disclosure, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0074] In related technologies, wireless signal simulation technology based on 3D scenes for coverage prediction and performance analysis of 4G, 5G, and WiFi wireless signals in underground powerhouses has the following drawbacks: BIM models mainly focus on geometric and structural information, and the electromagnetic parameters required for wireless signal simulation need to be manually supplemented, resulting in redundancy and low efficiency in the modeling process; independent simulation analysis of different wireless signal standards makes it difficult to assess the interference impact, switching stability, and electromagnetic compatibility when multiple signals coexist in a unified scenario; simultaneously, the complex environment of multiple interconnected chambers and equipment obstruction in underground powerhouses makes it difficult to balance simulation accuracy and computational efficiency in setting relevant simulation parameters and simplifying BIM models, leading to significant deviations between simulation results and reality in complex scenarios. This fails to accurately reflect the actual coverage effect after equipment deployment, making it difficult to accurately predict and effectively optimize the overall wireless signal coverage of underground powerhouses during the planning stage. Therefore, solving the lack of reliability and guidance in wireless signal coverage planning has become a major technical problem.

[0075] The wireless signal coverage planning method in this example embodiment will now be described in detail.

[0076] The wireless signal coverage planning method in this embodiment is implemented based on a deep integration visualization system of building information modeling and simulation results. A schematic diagram of the structure of the deep integration visualization system of building information modeling and simulation results is provided below. Figure 1 As shown, in Figure 1 In this system, data-driven, functionally collaborative, visually interactive, and platform-supported architecture is the core architecture. It integrates BIM model, wireless signal link calculation, simulation analysis, and platform management capabilities, taking into account the closed nature of the pumped storage power station construction area, the density of equipment, and the requirements of multiple signal standards. It is divided into four layers: data layer, core function layer, visualization layer, and platform support layer. The functions of each layer are interconnected to form a complete wireless signal simulation closed loop.

[0077] The aforementioned data layer supports importing the Building Information Model (BIM) corresponding to the construction area of ​​the pumped storage power station, and automatically extracts the geometric structure data, material attribute data, and topological relationship data corresponding to the BIM model. It also imports the indicated wireless parameters, underground powerhouse scene data, and measured data. The underground powerhouse scene data can include: cavern environment temperature, humidity, equipment interference source parameters, etc., while the measured data can include: historical received signal level, blind spot location records, etc. This can solve the problems of data dispersion and large errors in manual parameter assignment in related technologies.

[0078] The aforementioned core functional layer revolves around three core requirements: wireless equipment selection, link calculation, and simulation collaboration. It automatically recommends wireless equipment configurations for various areas within the pumped storage power station construction area, integrates factors such as structural obstruction, interference, and multipath, calculates the link calculation results corresponding to the link measurement points, supports simultaneous simulation of 4G / 5G / WiFi, calculates the superposition effect of wireless signals, and corrects the BIM model based on measured data. It can solve the problems of poor multi-signal adaptation, inaccurate underground scene simulation, and disconnect between BIM model and simulation in related technologies.

[0079] The aforementioned visualization layer is based on the corresponding 3D simulation models of the BIM model and the Geographic Information System (GIS) model, enabling intuitive, interactive, and interconnected display of simulation results. This solves the problems of the disconnect between coverage prediction results and the BIM model, and the inability to locate the causes of blind spots in related technologies.

[0080] The aforementioned platform support layer provides the basic guarantee for the entire system in terms of access control, data interaction, and computing power scheduling, ensuring that the system adapts to the module positioning of the intelligent construction platform to meet the needs of multi-role collaboration.

[0081] like Figure 2 As shown, Figure 2 This disclosure is a flowchart illustrating a wireless signal coverage planning method according to an exemplary embodiment, comprising the following steps:

[0082] In step S210, spatial structure information and environmental attribute information corresponding to the construction area of ​​the pumped storage power station are obtained.

[0083] In the construction area of ​​pumped storage power stations, especially in underground powerhouses and their ancillary caverns, the wireless propagation environment varies significantly across different areas due to the large spatial scale, complex structure, and dense equipment. If the differences in spatial structure and environment are not differentiated during the coverage planning process, and a uniform network standard and wireless parameter configuration are adopted instead, it will result in insufficient coverage in some areas, signal redundancy in others, and even mutual interference, thereby affecting the overall performance and stability of the wireless communication system. Therefore, this application embodiment needs to obtain the BIM model corresponding to the pumped storage power station construction area. The BIM model includes geometric structure data, material attribute data, and topological relationship data corresponding to the pumped storage power station construction area, and then extracts spatial structure information and environmental attribute information from the BIM model corresponding to the pumped storage power station construction area.

[0084] The aforementioned spatial structure information includes, but is not limited to: geometric structure data, material property data, and topological relationship data; the aforementioned environmental attribute information includes, but is not limited to: spatial enclosure degree, interference intensity index, equipment density, and electromagnetic interference source information.

[0085] Specifically, the aforementioned geometric structure data includes: cavity dimensions, equipment locations, and wall distribution. Cavity dimensions can be the diameter of the main plant and the length of the busbar tunnel. Equipment locations include the spatial location and dimensions of large electrical equipment such as generator sets and transformers. Wall distribution can include the locations of 30 cm concrete load-bearing walls, 20 cm brick walls, etc. The aforementioned material attribute data includes: material types such as concrete, metal, and glass read from the structural component labels of the BIM model, as well as electromagnetic parameters associated with the material types. The aforementioned topological relationship data includes the cavity topological relationship of "main plant - auxiliary plant - tailrace tunnel" to ensure that spatial topological basis is provided for ray tracing.

[0086] The aforementioned spatial enclosure levels can be categorized as fully enclosed, semi-enclosed, and open. Interference intensity indicators can be the number of concrete walls and the distribution of metal components. Based on equipment density, each area can be divided into equipment-dense areas, the furthest coverage area, etc.

[0087] By using the above methods, we can obtain spatial structure information and environmental attribute information corresponding to the construction area of ​​pumped storage power stations. This enables subsequent wireless signal coverage planning to no longer be based solely on planar layouts or simplified models, but to truly reflect the complex three-dimensional structure and environmental characteristics within the construction area of ​​pumped storage power stations, providing a reliable data foundation for regional division and parameter matching.

[0088] In step S220, based on environmental attribute information, network type, operating frequency band and core radio frequency parameters are matched for different areas in the construction area of ​​the pumped storage power station to obtain the dynamic matching result for each area.

[0089] To address the issues of blind selection of wireless devices and poor parameter compatibility, this application embodiment uses environmental attribute information of the pumped storage power station construction area to match network standards, operating frequency bands, and core radio frequency parameters to different areas of the pumped storage power station construction area, thereby obtaining dynamic matching results for each area.

[0090] The flowchart illustrating the process of determining the dynamic matching result for each region in this embodiment is shown below. Figure 3 As shown, the specific process is as follows:

[0091] Step S310: Extract spatial enclosure degree, interference intensity index and equipment distribution characteristics from the environmental attribute information for different spatial locations within the pumped storage power station construction area, and generate corresponding scene label information.

[0092] To ensure that wireless signals can cover all areas within the construction area of ​​the pumped storage power station, this application embodiment needs to extract the spatial enclosure degree, interference intensity index, and equipment distribution characteristics of different spatial locations from environmental attribute information. The interference intensity index can represent the comprehensive impact of electromagnetic interference sources on the target wireless signal reception quality within the target spatial location, and the equipment distribution characteristics can represent the density of equipment. Corresponding scene label information is generated for each spatial location.

[0093] The degree of spatial enclosure is determined based on three dimensions: the shape of the chamber, the distribution of walls, and the opening ratio. The formula for calculating the degree of spatial enclosure is as follows:

[0094] C= × (length-to-diameter ratio of the chamber / 5) + × Wall area ratio + × (1 / opening ratio)

[0095] Where C is the spatial closure parameter and the weighting coefficient is... =0.4、 =0.3、 =0.3. The aspect ratio of a cavity represents the ratio of the effective length of the cavity corresponding to the target spatial location along the main axis to the equivalent width of the cavity in the direction perpendicular to the main axis, used to characterize the elongation of the cavity. The wall area ratio represents the proportion of the total area of ​​the enclosing walls of the cavity corresponding to the target spatial location to the total area of ​​the cavity's enclosing interface, used to characterize the degree of enclosure of the space by the enclosing structure. The opening ratio represents the proportion of the total projected area of ​​the openings of the cavity corresponding to the target spatial location to the projected area of ​​the cavity's enclosing interface. The weighting coefficients can be determined based on the Analytic Hierarchy Process (AHP). Since AHP is a method well-known to those skilled in the art, it will not be elaborated upon here.

[0096] If the spatial closure parameter is greater than the maximum spatial closure parameter threshold, the corresponding spatial location is determined to be a high-closure region; if the spatial closure parameter is between the minimum and maximum spatial closure parameter thresholds, the corresponding spatial location is determined to be a medium-closure region; if the spatial closure parameter is less than the minimum spatial closure parameter threshold, the corresponding spatial location is determined to be an open region.

[0097] For example, if C > 0.70, the corresponding spatial location is defined as a highly enclosed area, which includes: underground cavern groups such as the main plant, auxiliary plant, busbar tunnel, and tailrace tunnel, as well as caverns with a length-to-diameter ratio > 5, a wall area ratio > 70%, and an opening ratio < 10%; if 0.40 ≤ C ≤ 0.70, the corresponding spatial location is defined as a medium-enclosed area, which includes: cavern connection areas, entrance and exit passages, underground substations, as well as caverns with a length-to-diameter ratio of 3 to 5, a wall area ratio of 50% to 70%, and an opening ratio of 10% to 30%; if C < 0.40, the corresponding spatial location is defined as an open area, which includes: the upper reservoir, lower reservoir, surface plant area, outdoor roads, as well as areas without obvious enclosed structures and with an opening ratio > 50%.

[0098] Scene tag information can be: underground enclosed area + dense equipment; cavern connection area + long-distance passage; upper and lower warehouse open area + long-distance coverage.

[0099] The above-mentioned equipment density refers to the spatial distribution density of large electromechanical equipment within a given location. The formula for calculating equipment density is as follows:

[0100] D = Σ(projected area of ​​a single device) / total planar area of ​​the space

[0101] Where D represents the density of equipment, and Σ (projected area of ​​a single device) is the sum of the projected areas of all devices within the spatial location.

[0102] Different equipment density levels correspond to different equipment density ranges, and equipment density can be classified based on these different equipment density ranges.

[0103] If the equipment density is greater than the maximum equipment density threshold, the corresponding spatial location is determined to be a high-density area; if the equipment density is between the minimum equipment density threshold and the maximum equipment density threshold, the corresponding spatial location is determined to be a medium-density area; if the equipment density is less than the minimum equipment density threshold, the corresponding spatial location is determined to be a low-density area.

[0104] For example, if D>0.30, the corresponding spatial location is designated as a high-density area, which includes: unit platform, transformer room, GIS equipment area, and ≥3 large equipment units per 100 square meters; if 0.15≤D≤0.30, the corresponding spatial location is designated as a medium-density area, which includes: maintenance passage, cable tray, auxiliary equipment area, and 1~3 equipment units per 100 square meters; if D<0.15, the corresponding spatial location is designated as a low-density area, which includes: upper and lower storage areas, open caverns, and pipe gallery areas.

[0105] The aforementioned interference intensity index is used to quantify the impact of electromagnetic interference sources on wireless signals within a spatial location. The calculation formula for the interference intensity index is as follows:

[0106] I= ×Power frequency interference power+ × (Number of wireless devices on the same frequency × 2) + × (Reflectance coefficient of metal equipment × 10)

[0107] Where I is the interference intensity index, and the weighting coefficient is... =0.5、 =0.3、 =0.2, the weighting coefficient can be adjusted based on the actual engineering scenario. Power frequency interference power represents the equivalent power value of interference components generated by power frequency electrical equipment within the target spatial location and falling into the target's receiving frequency band. Number of co-frequency wireless devices represents the number of wireless devices operating simultaneously on the same operating frequency band as the target network within the target spatial location. Metal equipment reflection coefficient represents the equivalent coefficient of the electromagnetic wave reflection capability of major metal components / equipment within the target spatial location, used to characterize the multipath and interference intensity caused by metal reflection.

[0108] If the interference intensity index is greater than the maximum interference intensity index threshold, the corresponding spatial location is determined to be a high interference zone; if the interference intensity index is between the minimum interference intensity index threshold and the maximum interference intensity index threshold, the corresponding spatial location is determined to be a medium interference zone; if the interference intensity index is less than the minimum interference intensity index threshold, the corresponding spatial location is determined to be a low interference zone.

[0109] For example, if I > 8 dB, the corresponding spatial location is defined as a high interference zone. High interference zones include: generator operating area, main transformer room, GIS switch room, power frequency interference power of 50 Hz, 50 Hz > 10 dBm, causing the 4G signal-to-noise ratio (SNR) to decrease from the normal 20 dB to below 12 dB; if 4 dB ≤ I ≤ 8 dB, the corresponding spatial location is defined as a medium interference zone. Medium interference zones include: cable interlayer, auxiliary equipment area, maintenance area, instantaneous interference of 5-7 dBm during switch operation; if I < 4 dB, the corresponding spatial location is defined as a low interference zone. Low interference zones include: open areas of upper and lower warehouses, open caverns, and areas far from equipment that generates electromagnetic interference and areas without obvious electromagnetic interference sources.

[0110] Using the methods described above, different spatial locations within the pumped storage power station construction area are associated with corresponding scene tag information, enabling accurate matching between the BIM model and wireless signal simulation, which is beneficial for the management and maintenance of the pumped storage power station construction area.

[0111] Step S320: Based on scene label information, multiple spatial locations with the same scene label information are merged to obtain multiple regions.

[0112] After determining the corresponding scene label information for each spatial location, multiple spatial locations with the same scene label information are merged to form multiple regions.

[0113] In one possible design, a multi-index weighted evaluation method is adopted. Based on the spatial enclosure degree C, equipment density D, and interference intensity index I, a comprehensive score corresponding to the spatial location is calculated, and the formula for calculating the comprehensive score is as follows:

[0114] S= r 1×C+ r 2×D+ r 3×I

[0115] Where S is the overall score for spatial location, with a weighting coefficient: r 1 = 0.40 r 2 = 0.35 r 3 = 0.25 r 1. r 2. r 3. Determined using the Analytic Hierarchy Process (AHP), and all can be adjusted based on actual engineering scenarios. For example, when determining using the AHP... r 1. r 2. r At point 3, a hierarchical structure can be constructed with the comprehensive spatial location score S as the target layer and "spatial enclosure degree C, equipment density degree D, and interference intensity index I" as the criterion layers. Based on historical engineering data, pairwise importance comparisons are performed on C, D, and I to form a judgment matrix. The eigenvectors of the judgment matrix are then solved and normalized to obtain... r 1. r 2. r 3. Perform a consistency check on the judgment matrix. If the consistency does not meet the preset threshold, adjust the judgment matrix and recalculate until the consistency check is passed.

[0116] It should be noted that the spatial enclosure degree C, equipment density degree D, and interference intensity index I need to be normalized to convert the dimensional expressions into dimensionless expressions before calculating the comprehensive score. The normalization method can be the standard deviation normalization (Z-score normalization) method, etc. Since the normalization method is known to those skilled in the art, it will not be elaborated on here.

[0117] This application embodiment can classify spatial locations based on the comprehensive score range to which the comprehensive score belongs, thereby obtaining multiple areas. If the comprehensive score is greater than the maximum comprehensive score threshold, the corresponding spatial location is determined to be an underground closed area; if the comprehensive score is between the minimum comprehensive score threshold and the maximum comprehensive score threshold, the corresponding spatial location is determined to be a cavern connection area; if the comprehensive score is less than the minimum comprehensive score threshold, the corresponding spatial location is determined to be an upper and lower storage open area.

[0118] For example, if S>0.65, the corresponding spatial location is determined as an underground enclosed area, which is a complex scenario with high enclosure, high density, and high interference, such as the core areas of the main plant and auxiliary plant, where signal attenuation is severe and multipath effects are significant; if 0.40≤S≤0.65, the corresponding spatial location is determined as a cavern connection area, which is a transitional scenario with medium enclosure, medium density, and medium interference, such as cavern connection passages and entrance / exit areas, where signal abrupt changes are obvious; if S<0.40, the corresponding spatial location is determined as an open area between the upper and lower reservoirs, which is an open scenario with low enclosure, low density, and low interference, such as the upper and lower reservoirs and the ground plant area, where signal propagation is mainly due to free space loss.

[0119] By using the above method, multiple spatial locations with the same scene label information are merged to achieve automatic identification and scene division of different underground space areas in the construction area of ​​pumped storage power stations. This ensures that each area has high consistency in wireless propagation characteristics, thereby improving the efficiency of wireless signal planning.

[0120] Step S330: Determine the scene type and coverage requirements for each region, and automatically match the network standard and operating frequency band for each region based on the scene type.

[0121] Due to the significant differences in building structures among the underground powerhouse, water conveyance system, and booster station within the pumped storage power station construction area, and the varying signal requirements of equipment during operation, signal propagation is susceptible to obstruction and multipath effects. Therefore, it is necessary to first clarify the scenario type and coverage requirements of each area, as this scenario type characterizes the wireless propagation characteristics of the corresponding area. Then, based on the scenario type, the corresponding network standard and operating frequency band for each area should be matched to adapt to the signal propagation characteristics of different scenarios, ensuring the overall communication quality and equipment linkage efficiency of the pumped storage power station construction area.

[0122] For example, in enclosed areas within underground workshops: WiFi 5.0 GHz, 5G 2.6 GHz / 3.5 GHz, and 4G 1.8 GHz / 2.1 GHz bands with strong anti-interference capabilities are preferred to reduce interference from multiple devices; in connecting areas between caverns: 4G 900 MHz and 5G 700 MHz / 2.6 GHz bands with a balance between penetration and coverage are preferred to adapt to scenarios with sudden signal changes; in open areas between upper and lower warehouses: 4G 700 MHz / 800 MHz and 5G 700 MHz / 900 MHz bands with low attenuation are preferred to achieve long-distance coverage.

[0123] Using the above method, network standards and operating frequency bands are dynamically matched for each region based on the scene type, ensuring that the network configuration of each region meets the requirements. This avoids repeated trial and error in manual network configuration, ensuring the quality of wireless signal coverage, reducing the waste of frequency band resources, and realizing the adjustment of network standards and operating frequency bands based on scene type. This also prevents signal disconnection when the operating status of the pumped storage power station construction area changes.

[0124] Step S340: Based on coverage requirements, automatically configure the core radio frequency parameters corresponding to each area.

[0125] To achieve a precise match between wireless signal coverage and service requirements in the construction area of ​​pumped storage power stations, and to address issues such as signal power waste, severe interference, or insufficient coverage caused by differences in functional positioning and equipment layout in different areas, after matching scenario types, network standards, and operating frequency bands, it is necessary to further configure corresponding core radio frequency parameters for each area based on its specific coverage requirements, such as data transmission rate, latency requirements, and number of device connections. These core radio frequency parameters include transmit power, antenna gain, and receive threshold, to ensure that the wireless signal coverage quality meets service requirements while optimizing radio frequency resource utilization and reducing signal interference between areas.

[0126] The antenna gain mentioned above needs to be determined based on coverage requirements and interference control requirements. The antenna gain can be calculated based on the antenna gain formula, combined with the effective area of ​​the antenna and the operating wavelength. Since the antenna gain formula is a well-known formula to those skilled in the art, it will not be explained in detail here.

[0127] Specifically, a reasonable range of transmission power is set based on the spatial scale and degree of obstruction of each area, and the direction and range of wireless signal coverage are controlled in combination with the antenna type. At the same time, a reception threshold that matches the network standard is set to ensure that the wireless signal can achieve the expected coverage effect in different areas, and to effectively reduce the risk of interference while ensuring communication quality.

[0128] For example, the transmit power is set to 20-24 dBm in the underground factory area; the antenna gain is set to 3-8 dBi dual-polarized antennas in the underground factory area; the receive threshold is set according to the operating frequency band and in combination with the receiver sensitivity, channel bandwidth and target service quality threshold of the selected wireless device. For example, under the conditions of preset bandwidth and target packet error rate, the receive threshold of WiFi can be set to no less than -105 dBm, the receive threshold of 4G can be set to no less than -102 dBm, and the receive threshold of 5G can be set to no less than -100 dBm, so as to constrain the coverage verification and parameter optimization of the corresponding area.

[0129] By using the above method, core radio frequency parameters are automatically matched for each area based on coverage requirements, ensuring the stability of wireless signal coverage and improving the utilization rate of wireless signal resources.

[0130] Step S350: Based on the network standard, operating frequency band and core radio frequency parameters corresponding to each region, generate the dynamic matching result corresponding to each region.

[0131] After matching the corresponding network standard, operating frequency band, and core RF parameters for each region, a dynamic matching result for each region is generated based on the corresponding network standard, operating frequency band, and core RF parameters for each region.

[0132] By using the above method, a dynamic matching mechanism of standard, frequency band, and parameters is constructed to generate dynamic matching results for each region. This achieves accurate adaptation of network standards, operating frequency bands, and core radio frequency parameters in different regions, avoiding problems such as insufficient wireless signal coverage or excessive interference caused by unified configuration. It also solves the problems of blind selection of wireless equipment and poor parameter adaptability.

[0133] In step S230, based on the dynamic matching results and spatial structure information corresponding to each region, a multi-factor coupled link loss calculation model is constructed, and the wireless signal coverage performance of each region is calculated based on the link loss calculation model to obtain the coverage prediction results corresponding to each region.

[0134] Since the propagation of wireless signals in various spatial areas is affected by the spatial structure, installation method, and environmental factors, this application embodiment will determine the installation method and mounting height of the wireless device in each area based on the spatial structure information and the dynamic matching results corresponding to each area.

[0135] The above installation methods refer to the installation form adopted by wireless devices in actual engineering, such as wall-mounted, ceiling-mounted, or bracket-mounted; the above installation height refers to the vertical height of the wireless device antenna relative to the ground, which will affect the propagation path and coverage of the wireless signal.

[0136] Furthermore, the formula for calculating the minimum installation height is as follows:

[0137] Hmin = Hobstacle + k × R1

[0138] Wherein, Hmin is the minimum installation height, Hobstacle is the maximum obstruction height, k is the Fresnel clearance coefficient, usually taken as 0.6, and R1 is the radius of the first Fresnel zone, which is determined based on the Fresnel zone radius formula. Since the Fresnel zone radius formula is a formula known to those skilled in the art, it will not be elaborated here.

[0139] The aforementioned minimum installation height can reduce the additional losses caused by obstacles during signal propagation, laying the foundation for the accurate calculation of the total link loss value in each area.

[0140] Installation methods and scenario-based design examples for mounting height: Underground plant interior area: wall-mounted pole installation, mounting height 3-8 meters, avoiding unit obstruction; Cavern top area: extension frame installation, mounting height 8-15 meters, covering the bottom of the cavern; Upper and lower warehouse areas: three-tube tower or angle steel tower installation, mounting height 15-30 meters, to adapt to long-distance coverage.

[0141] Based on the above method, the installation method and mounting height of wireless devices in each area are determined. In actual engineering, the location of wireless devices in the corresponding area can be accurately located directly, avoiding repeated adjustments to the position of wireless devices and improving the efficiency of wireless device layout.

[0142] Because signal calculations based on a single empirical model or a simplified free-space model cannot accurately reflect the real and complex wireless propagation environment of each area within the pumped-storage power station construction zone, there is a significant discrepancy between the wireless signal planning results and the actual operational performance. Furthermore, wireless signals are affected by multiple factors during propagation. Therefore, this application's embodiments construct a multi-factor coupled link loss calculation model based on the dynamic matching results, installation method, and installation height for each area, thereby distinguishing and quantifying the factors interfering with wireless signal propagation. The link loss calculation model comprehensively reflects the calculation system of distance loss, structural obstruction, and environmental influences, and is used to describe the power attenuation of the wireless signal throughout its journey from the transmitter to the receiver.

[0143] The link loss calculation model described above can be constructed based on the Ray Tracing Optimization Algorithm (RTOA) and the Multi-Signal Cooperative Simulation Algorithm (MSCSA). The Ray Tracing Optimization Algorithm is used to simulate the propagation path of electromagnetic waves in different media, including reflection and refraction, making the link calculation results more consistent with the actual physical process. The Multi-Signal Cooperative Simulation Algorithm is used to simultaneously simulate the superimposed interference of 4G / 5G / WiFi and output channel avoidance suggestions to fill the gaps in multi-signal coordination. For example, if 2.4GHz WiFi co-channel interference causes a 40% bandwidth attenuation, the output channel avoidance suggestion is to switch to 5.8GHz.

[0144] It should be noted that the number of ray reflections corresponding to the ray tracing optimization algorithm can be adjusted based on the actual engineering scenario. For example, it can be increased from the default 3-5 times to 8-10 times to cover multi-cavity intersecting scenarios and solve the problem of large blind zone prediction deviation in the cavity connection area, which leads to inaccurate positioning of wireless devices.

[0145] The link loss calculation model described above can be implemented using the Free Space Propagation Model (FSPM) at the basic calculation level, which will not be elaborated on here.

[0146] Based on the above method, the installation method and mounting height are introduced in the process of constructing the link loss calculation model, so that the link loss calculation model is more in line with the deployment scenario of real wireless devices, thereby improving the reliability and applicability of the wireless signal coverage prediction results from the source.

[0147] To ensure that the link loss calculation model can distinguish and quantify factors interfering with wireless signal propagation, this embodiment of the application requires calculating the wireless signal coverage performance of each area based on the link loss calculation model to obtain the coverage prediction result for each area. A flowchart illustrating the process of determining the coverage prediction result for each area is provided below. Figure 4 The specific process is as follows:

[0148] Step S410: Determine multiple link measurement points in each region.

[0149] To improve the efficiency of wireless signal link loss calculation, this application embodiment needs to determine multiple link measurement points in each area. These link measurement points are representative points of wireless signal coverage at different spatial locations within the corresponding area. The location and number of link measurement points can be adjusted and set based on the actual engineering scenario, which will not be elaborated here.

[0150] By using the above method, link measurement points can be determined in each area, which can accurately reflect the coverage boundaries and weak points of wireless signals in the area with limited computing power.

[0151] Step S420: Determine the link measurement rules and multi-dimensional loss parameters corresponding to each link measurement point.

[0152] To achieve accurate assessment of wireless signal coverage in various areas within the construction zone of the pumped storage power station and ensure stable communication for wireless devices in different locations, it is necessary to determine the link calculation rules and multi-dimensional loss parameters corresponding to each link calculation point in order to accurately reflect the actual coverage situation of each link calculation point. This is because the environments of different areas vary greatly. For example, some areas have dense equipment and complex signal propagation, while others have long distances and severe signal attenuation.

[0153] Specifically, since the process of determining the link measurement rules and multi-dimensional loss parameters corresponding to each link measurement point is the same, this application embodiment will describe the process of determining the link measurement rules and multi-dimensional loss parameters corresponding to a single link measurement point. The specific process is as follows:

[0154] Determine the propagation complexity and coverage distance requirements corresponding to the link measurement point. Then, based on the propagation complexity and coverage distance requirements corresponding to the link measurement point, select the corresponding link measurement rule. If the link measurement point is located in a densely populated area, the complex point link measurement rule is adopted; if the link measurement point is located at the area boundary or the farthest coverage area, the farthest point link measurement rule is adopted.

[0155] Based on the above method, a corresponding link measurement rule is matched for each link measurement point, so that the coverage analysis can focus on the most unfavorable or most critical spatial location, thereby avoiding the problem of average calculation masking local coverage risks.

[0156] To accurately reflect the impact of the complex structure and environment within the pumped storage power station construction area on wireless signals, this application embodiment needs to determine the multi-dimensional loss parameters corresponding to each link measurement point. The specific determination process is as follows:

[0157] The aforementioned multi-dimensional loss parameters are used to describe the various attenuation factors experienced by wireless signals during actual propagation. The multi-dimensional loss parameters include at least: structural obstruction loss value, equipment electromagnetic interference loss value, and environmental attenuation loss value.

[0158] It should be noted that the total link loss value at a single link measurement point = path propagation loss value + loss values ​​corresponding to multi-dimensional loss parameters. The path propagation loss value is the loss value of the wireless signal during propagation caused only by spatial distance and operating frequency band. The path propagation loss value can be calculated based on the basic link budget formula, which is as follows:

[0159] PRx=PTx+GTx-LTx-Lpath-Lstructure-Linterference-Lfading+GRx-LRx-Lmargin

[0160] Wherein, PRx is the received power, PTx is the transmitted power, GTx is the transmitter antenna gain, LTx is the transmitter feeder loss, Lpath is the path propagation loss, Lstructure is the structural obstruction loss, including walls, equipment, floors, etc., Linterference is the equivalent loss introduced by interference factors, Lfading is the fading margin, including shadow fading or multipath fading, GRx is the receiver antenna gain, LRx is the receiver feeder loss, and Lmargin is the system margin power, which is the reserved engineering margin of the system.

[0161] In one possible design, different link loss calculation models are used for different areas. This application uses three areas—the underground enclosed area, the cavern connection area, and the upper and lower open areas—as examples for illustration. The specific process is as follows:

[0162] The link loss calculation model for underground enclosed areas is constructed based on an empirical model and a ray tracing correction method. This model is used to calculate the path propagation loss value within the underground enclosed areas. The calculation formula is as follows:

[0163]

[0164] in, For transmission distance, The operating frequency is a specific value within the operating frequency band. This is the distance attenuation factor. If the link measurement point is located in a densely populated area, then... Take 2.5; if the link measurement point is located at the area boundary or the farthest coverage area, then Take 3.1.

[0165] The link loss calculation model for the cavern connection area is a logarithmic distance model. This model is used to calculate the path propagation loss value in the cavern connection area. The calculation formula is as follows:

[0166]

[0167] in, For reference distance Path loss at the location, usually It is 1 meter. This is the path loss index. Between 2.8 and 3.5 The loss value of shadow fading follows a normal distribution with a standard deviation of . It belongs to the range [6dB, 8dB].

[0168] The link loss calculation model for the open areas of the upper and lower warehouses is based on a free-space model and a ground feature correction method. The ground feature correction method adds a correction term to the free-space model based on the influence of different ground features on the signal. The link loss calculation model is used to calculate the path propagation loss values ​​for the open areas of the upper and lower warehouses. The calculation formula is as follows:

[0169]

[0170] in, For transmission distance, For operating frequency, This represents the loss value due to terrain features, for example: 5dB for plains, 10dB for hilly areas, and 3~6dB for vegetated areas.

[0171] Based on the path propagation loss values ​​of the above regions, the transmit power is further calculated accurately. The minimum transmit power satisfies the condition that the receive power ≥ receive sensitivity + system margin power. Receive sensitivity is an inherent performance parameter of the wireless device, representing the minimum power threshold for normal demodulation of the signal. It can be obtained from the technical specifications of the wireless device. The system margin power can be adjusted based on the actual engineering scenario, which will not be discussed in detail here.

[0172] Furthermore, structural obstruction loss is calculated for the wireless propagation path corresponding to the link measurement point to obtain the structural obstruction loss value. The specific process is as follows:

[0173] Each link measurement point corresponds to a wireless propagation path, which is the path that the wireless signal travels from the transmitter to the receiver of the wireless device. The wireless propagation path includes the influence of factors such as spatial distance, structural obstruction, and distribution of wireless devices on the wireless signal transmission. Therefore, in order to accurately reflect the propagation characteristics of wireless signals in the actual environment, it is necessary to determine the wireless propagation path corresponding to the link measurement point.

[0174] Subsequently, the obstruction loss of the wireless propagation path through the wall type or structural component type is cumulatively calculated to obtain the structural obstruction loss value corresponding to the link measurement point. The structural obstruction loss value is the attenuation caused by the wireless signal passing through or around the wall, floor or structural component along the propagation path.

[0175] The structural shading loss values ​​corresponding to the aforementioned structural components or walls can be determined based on publicly available material loss databases or actual test data, which will not be elaborated upon here.

[0176] Based on the above method, determining the wireless propagation path corresponding to each link measurement point can more accurately reflect the signal attenuation in the actual environment, thereby improving the accuracy and reliability of coverage prediction results.

[0177] The electromagnetic interference loss of equipment in the area where the link measurement point is located is calculated to obtain the corresponding equipment electromagnetic interference loss value. The specific process is as follows:

[0178] The electromagnetic interference loss value of equipment refers to the equivalent attenuation of wireless signal reception quality caused by the complex electromagnetic environment formed by the operation of generator sets, electrical equipment or metal components in the area. In this application embodiment, the electromagnetic interference loss value is uniformly quantified at the regional level in combination with the equipment distribution characteristics. That is, the electromagnetic interference loss of equipment is calculated in the area where the link measurement point is located to obtain the electromagnetic interference loss value of the corresponding area. The electromagnetic interference loss value of equipment includes electromagnetic interference loss generated by generator sets, electrical equipment or metal components.

[0179] The electromagnetic interference loss of the above-mentioned equipment can be calculated based on an electromagnetic interference measuring instrument. The electromagnetic interference measuring instrument can determine the strength of the interference signal and, in combination with the operating frequency band of the wireless equipment, calculate the corresponding electromagnetic interference loss value of the equipment.

[0180] Based on the above method, determining the electromagnetic interference loss value of the equipment in each region is beneficial for optimizing equipment with severe electromagnetic interference and provides data support for subsequent local adjustments of equipment, thus avoiding mutual interference between different devices.

[0181] Environmental attenuation loss is calculated for the link measurement point. The environmental attenuation loss calculation is used to evaluate the impact of dynamic environmental factors such as multipath attenuation and human body blockage on the wireless signal. The intensity of the incident signal and the outgoing signal corresponding to the link measurement point is determined, and then the difference between the intensity of the incident signal and the intensity of the outgoing signal is calculated. This difference is determined as the environmental attenuation loss value corresponding to the link measurement point. The environmental attenuation loss value includes multipath attenuation and human body blockage attenuation.

[0182] Based on the above method, the environmental attenuation loss value can accurately determine the signal attenuation of different links in the actual environment, and can quickly locate the area with the most severe environmental interference by comparing the environmental attenuation loss values ​​corresponding to the measurement points of different links, which is conducive to achieving local optimization of wireless signal coverage.

[0183] The structural shielding loss value, the equipment electromagnetic interference loss value, and the environmental attenuation loss value are determined as multi-dimensional loss parameters.

[0184] For example, if the link measurement point is located in a densely populated area, the spatial distance between the transmitting point and the receiving point is determined. The calculation is performed with a spatial distance of ≤10 meters and an attenuation factor of 2.5. For example, the basic loss of the 1800MHz band is 62.5dB, the wireless signal will attenuate by 20dB when passing through a brick wall, the multipath attenuation is 3dB, and the human body blockage attenuation is 3dB. The total link loss value = 62.5 + 20 + 3 + 3 = 88.5dB.

[0185] If the link measurement point is located at the area boundary or the farthest coverage area, the calculation is based on a spatial distance of ≤22 meters and an attenuation factor of 3.1. For example, the basic loss of the 1800MHz band is 79.1dB, the multipath attenuation is 3dB, the human body blockage attenuation is 3dB, and the total link loss value is 79.1+3+3=85.1dB.

[0186] Using the methods described above, multi-dimensional loss parameters can comprehensively reflect the loss status of wireless devices and links from multiple dimensions, and the link measurement rules can ensure the consistency and accuracy of the measurement process, which is conducive to establishing a more accurate link loss calculation model.

[0187] Step S430: Input the link measurement rules and multi-dimensional loss parameters into the link loss calculation model, calculate the link corresponding to each link measurement point, and obtain the link calculation result corresponding to each link measurement point.

[0188] To ensure the accuracy of the link calculation results, the link measurement rules and multi-dimensional loss parameters corresponding to each region need to be input into the link loss calculation model. Based on the link loss calculation model, the link corresponding to each link measurement point is calculated, thereby outputting the link calculation result corresponding to each link measurement point. The link calculation result can reflect the received signal strength or received power of the link measurement point.

[0189] By using the above method, the link calculation result corresponding to each link calculation point can be determined, which can accurately determine the received signal strength or received power corresponding to each link calculation point. This ensures the accuracy of wireless signal coverage, avoids blind spots in wireless signal coverage, and enables reasonable adjustment of equipment layout, transmission power, and antenna angle based on the link calculation results, thereby ensuring the efficient utilization of communication resources.

[0190] Step S440: Based on the link calculation results of multiple link measurement points in the same area, generate the coverage prediction results for each area.

[0191] To ensure the overall coverage of the pumped storage power station construction area by wireless signals, this application embodiment determines the link calculation results corresponding to all link calculation points in the same area, and then maps the link calculation results corresponding to each area to the space of the corresponding entire area, thereby obtaining the coverage prediction results corresponding to each area. The coverage prediction results characterize the overall spatial distribution of the wireless signal propagation capability of the corresponding area under the current wireless device configuration and wireless parameters.

[0192] This application's embodiments map the link calculation results corresponding to a single link measurement point to the space of the corresponding entire area, including but not limited to interpolation methods or regional averaging methods, transforming discrete point data into a continuous coverage map. Therefore, the coverage prediction results can reflect the distribution of wireless signal strength at different locations within the entire area, or reflect which areas the wireless signal can reach or exceed the set reception threshold.

[0193] The above interpolation methods can be implemented based on inverse distance weighted interpolation (IDW), Kriging (KR), etc., while the area averaging method can be implemented based on Voronoi Diagram (VD), Grid Average (GA), etc., which will not be elaborated here.

[0194] This application embodiment can generate coverage prediction results for the entire pumped storage power station construction area based on the coverage prediction results for each area, thereby determining which areas have good wireless signal coverage and which areas have insufficient wireless signal coverage.

[0195] By using the above methods, the coverage prediction results for each area can be determined, which can comprehensively reflect the wireless signal distribution of the entire area. Based on the coverage prediction results of multiple areas, the control of wireless equipment resources and the configuration of wireless parameters can be achieved more accurately, thereby improving the overall network performance of the pumped storage power station construction area.

[0196] In step S240, each coverage prediction result is associated with the building information model to obtain the associated display result. Based on the associated display result, the equipment layout and wireless parameters are adjusted to obtain the target coverage planning scheme.

[0197] Since the coverage prediction results exist in the form of tables, two-dimensional layers, or independent simulation interfaces, it is impossible to determine whether the insufficient signal is caused by building structure obstruction or unreasonable equipment location, and it is difficult to spatially correspond with the actual engineering structure. Therefore, the embodiments of this application need to associate each coverage prediction result with the building information model to obtain the associated display result.

[0198] Specifically, since the Building Information Model (BIM) accurately describes the true three-dimensional positional relationships of various structural components, spatial forms, and equipment carriers within the construction area of ​​a pumped storage power station, this embodiment of the application needs to determine the wireless device parameters and coverage prediction results for each area. The wireless device parameters are the hardware parameters of a single device, such as network standard, operating frequency band, transmit power, antenna gain, and receive threshold. The wireless device parameters and coverage prediction results are then associated with the structural component attributes in the BIM, and visualized in the BIM in the form of coverage color gradation or link parameter annotations. The coverage color gradation represents the signal strength distribution, and the link parameter annotations include core radio frequency parameters or wireless parameters. This results in an associated display, making the wireless coverage status a visual attribute of the BIM.

[0199] At this point, the current wireless device layout and wireless parameter configuration can be determined as the coverage planning scheme.

[0200] Based on the above correlation display results, the spatial alignment between the coverage prediction results and the actual building structure is achieved, thereby enabling an intuitive determination of the wireless signal coverage under different regions, heights, and structural obstruction conditions. This allows for the rapid identification of wireless signal coverage blind spots, over-coverage areas, or structural attenuation problems.

[0201] For example, in the BIM model, structural components such as wall-mounted poles and base stations are automatically associated with an installation height of 5 meters and an antenna gain of 3dBi; electromagnetic parameters such as concrete dielectric constant and metal reflectivity are automatically matched to wall and equipment components.

[0202] A multi-dimensional visualization example shows how red represents wireless signal coverage blind spots, yellow represents weak wireless signal coverage areas, and green represents good wireless signal coverage areas in the BIM model. The corresponding coverage areas are marked by color and overlaid on the corresponding areas. Alternatively, link parameters can be marked by clicking on any location in the BIM model to display detailed data on wireless parameters, such as: 1800MHz, wall-mounted, installation height of 5 meters, link loss of 88.5dB, received signal level of -102.3dBm, interference value of 5dB, and "received signal level is substandard due to obstruction by two brick walls, with a total attenuation of 40dB".

[0203] In this embodiment, color levels can be divided according to the receiving level of the wireless device. If the receiving level is <-105dBm, the area covered by the wireless device is determined as a blind zone; if the receiving level is in the range of [-105dBm, -100dBm), the area covered by the wireless device is determined as a weak coverage area; if the receiving level is ≥-100dBm, the area covered by the wireless device is determined as a good coverage area.

[0204] Based on the above-mentioned correlation display results, the cause of blind spots can also be located, and the associated obstruction component is "2 brick walls". At the same time, the installation height adjustment suggestion is output, such as increasing it from 5 meters to 7 meters.

[0205] To obtain a feasible and implementable target coverage plan while meeting the wireless signal coverage requirements, it is necessary to adjust the location or parameters of wireless devices. Wireless parameters include hardware parameters, network-level parameters, and wireless device performance parameters, such as core radio frequency parameters, frequency bandwidth, environmental parameters, number of channels, and feeder loss. This adjustment process directly affects the device entities and their parameter attributes in the building information model.

[0206] When the location of a wireless device or wireless parameters change, the link loss calculation model is automatically triggered to recalculate the link calculation results corresponding to the link measurement points in each area, and the coverage prediction results corresponding to each area are updated in real time to obtain the updated coverage prediction results.

[0207] Once the updated coverage prediction results meet the coverage requirements of the corresponding area, the current wireless device layout and wireless parameter configuration can be determined as the target coverage planning scheme.

[0208] It should be noted that the system can display comparison charts or curves of coverage trends before and after wireless parameter adjustments. For example, a bar chart showing that after the base station power increased from 20dBm to 24dBm, the blind area decreased from 200 square meters to 50 square meters; a curve comparing the coverage effects of 5G and WiFi, which shows that 5G 3.5GHz has better coverage than WiFi within 10 meters, while WiFi is more stable beyond 10 meters.

[0209] For example, equipment deployment simulation: drag and drop equipment to candidate locations in the BIM model and output coverage prediction results in real time; parameter adjustment feedback: update the coverage range changes after modifying wireless parameters such as transmission power; linkage between BIM model and GIS model: different spatial levels of areas can use different 3D simulation models to carry coverage prediction results, and the spatial coordinate systems corresponding to different spatial models are consistent. For example, underground areas are displayed through BIM models to achieve fine coverage, while upper and lower warehouse areas are displayed through GIS models to achieve large-scale coverage.

[0210] Specifically, the GIS map in the GIS model can display the wireless signal coverage of the upper and lower warehouse areas and generate distance-received level decrease curves. For example, the 4G 700MHz band has a received level of -90dBm at 1km and -100dBm at 2km. It can also mark the location of base stations and use radius circles to show the coverage area, such as a green circle with a radius of 1.5km.

[0211] The linkage between the BIM model and the GIS model also supports seamless switching between the "underground plant BIM model" and the "upper and lower warehouse area GIS map". Specifically, clicking on the "main plant entrance" in the GIS map will automatically redirect to the plant's internal coverage map in the BIM model; and the blind spot data of the underground plant will be synchronized to the GIS model, and the optimization direction of "extending the signal from the base station in the upper and lower warehouse area to the plant entrance" will be marked.

[0212] Based on the above method, by introducing the correlation between coverage prediction results and building information model, and on this basis, the dynamic adjustment of wireless device layout and wireless parameters is realized. This achieves the spatial consistency between wireless signal coverage prediction results and actual building structure, improves the intuitiveness and operability of wireless signal coverage analysis and optimization process, reduces the dependence of wireless signal coverage planning on human experience, improves the efficiency of wireless signal coverage planning, and supports rapid iteration and accurate optimization of wireless signal coverage planning schemes. Ultimately, a feasible and verifiable target coverage planning scheme is formed.

[0213] In addition, to adapt to the full life cycle management needs of power plants and solve the problems of non-standard calculations and inefficient collaboration, this application embodiment constructs a standardized process and permission system. The standardized process is based on standardized calculation templates, which include: open scene templates and complex scene templates. The open scene template has built-in upper and lower database area parameters, including the maximum base station transmit power of 46dBm, shadow fading margin of 14.6dB, and ground object loss of 10dB. The complex scene template has built-in underground powerhouse parameters, such as: multipath attenuation of 3dB, human body loss of 3dB, attenuation factor of 2.5, etc., and supports custom occlusion conditions.

[0214] The aforementioned permission system includes: role-based permission control, namely: the device management role can modify wireless parameters and trigger link calculations corresponding to link measurement points; the viewer role can only browse results; and the administrator role has permissions to create user accounts, assign permissions, and configure the system.

[0215] The aforementioned building information model can achieve dynamic updates throughout the entire lifecycle. When a new underground plant component is added, regional simulation is automatically triggered to update the target coverage planning scheme. During operation, when the received signal level is -105dBm, the BIM model parameters are corrected based on the received signal level, for example, the brick wall attenuation is increased from 20dB to 22dB. When the wireless device fails, the blind zone change is pushed to the BIM model in real time, prompting the adjustment of the backup wireless device parameters.

[0216] Based on the above method, different underground powerhouses or different projects can directly use standardized calculation templates without having to redevelop them each time. This enables people from different departments to access and modify data according to the system's permission system, avoiding information silos. After the construction of the pumped storage power station construction area is completed, actual changes in wireless equipment and structural components can be fed back into the building information model, thereby achieving real-time updates of wireless signal link calculation results and ensuring the effectiveness of the system.

[0217] The above methods enable automatic identification and scene segmentation of different areas within the construction area of ​​pumped storage power stations. Based on the scene label information corresponding to each area, the corresponding network standard, operating frequency band, and core radio frequency parameters are matched for each area, ensuring the determinism of wireless signal coverage. Based on a multi-factor coupled link loss calculation model, the coverage prediction results for each area are determined, improving the accuracy of wireless signal coverage prediction. The coverage prediction results are deeply correlated with the building information model, enabling the visualization of the coverage prediction results and the iterative updating of the target coverage planning scheme. This significantly improves the accuracy, interpretability, and engineering implementation efficiency of wireless signal coverage planning.

[0218] like Figure 5 The diagram shown is a schematic representation of a wireless signal coverage planning system according to an exemplary embodiment, comprising the following modules:

[0219] Data acquisition module 501 is used to acquire spatial structure information and environmental attribute information corresponding to the construction area of ​​pumped storage power station;

[0220] The adaptive matching module 502 is used to match the network standard, operating frequency band and core radio frequency parameters for different areas in the construction area of ​​the pumped storage power station based on environmental attribute information, so as to obtain the dynamic matching result for each area.

[0221] The link measurement module 503 is used to construct a multi-factor coupled link loss calculation model based on the dynamic matching results and spatial structure information corresponding to each area, and to measure the wireless signal coverage performance of each area based on the link loss calculation model to obtain the coverage prediction results corresponding to each area.

[0222] The scheme determination module 504 is used to associate and display each coverage prediction result with the building information model to obtain the associated display result, and adjust the equipment layout and wireless parameters based on the associated display result to obtain the target coverage planning scheme; the wireless parameters include at least the core radio frequency parameters.

[0223] In one possible design, the adaptive matching module 502 is specifically used to extract spatial enclosure degree, interference intensity index, and equipment distribution characteristics from environmental attribute information for different spatial locations within the pumped storage power station construction area, generate corresponding scene label information, merge multiple spatial locations with the same scene label information to obtain multiple regions, determine the scene type and coverage requirements corresponding to each region, and automatically match the network standard and operating frequency band corresponding to each region based on the scene type. The scene type represents the wireless propagation characteristics of the corresponding region. Based on the coverage requirements, the core radio frequency parameters corresponding to each region are automatically configured. The core radio frequency parameters include: transmit power, antenna gain, and receive threshold. Based on the network standard, operating frequency band, and core radio frequency parameters corresponding to each region, a dynamic matching result corresponding to each region is generated.

[0224] In one possible design, the link measurement module 503 is specifically used to determine the installation method and mounting height of the wireless device in each area based on the spatial structure information and the dynamic matching results corresponding to each area, and to construct a multi-factor coupled link loss calculation model based on the dynamic matching results, installation method and mounting height corresponding to each area.

[0225] In one possible design, the link calculation module 503 is also used to determine multiple link calculation points in each area; the link calculation points represent the wireless signal coverage at different spatial locations in the corresponding area, determine the link calculation rules and multi-dimensional loss parameters corresponding to each link calculation point, input the link calculation rules and multi-dimensional loss parameters into the link loss calculation model, calculate the link corresponding to each link calculation point, obtain the link calculation result corresponding to each link calculation point, and generate the coverage prediction result corresponding to each area based on the link calculation results of multiple link calculation points in the same area.

[0226] In one possible design, the link calculation module 503 is also used to select the corresponding link calculation rule based on the propagation complexity and coverage distance requirements corresponding to the link calculation point.

[0227] In one possible design, the link calculation module 503 is also used to adopt the complex point link calculation rule if the link calculation point is located in a densely populated area of ​​devices, and to adopt the farthest point link calculation rule if the link calculation point is located at the area boundary or the farthest coverage area.

[0228] In one possible design, the link calculation module 503 is also used to calculate the structural obstruction loss of the wireless propagation path corresponding to the link calculation point, obtain the structural obstruction loss value, calculate the equipment electromagnetic interference loss of the area where the link calculation point is located, obtain the equipment electromagnetic interference loss value of the corresponding area, the electromagnetic interference loss value includes: electromagnetic interference loss generated by generator sets, electrical equipment or metal components, and calculate the environmental attenuation loss of the link calculation point, obtain the environmental attenuation loss value corresponding to the link calculation point, the environmental attenuation loss value includes: multipath attenuation and human body obstruction attenuation, and determine the structural obstruction loss value, the equipment electromagnetic interference loss value and the environmental attenuation loss value as multi-dimensional loss parameters.

[0229] In one possible design, the link measurement module 503 is also used to determine the wireless propagation path corresponding to the link measurement point, and to perform cumulative calculation of the occlusion loss of the wall type or structural component type through which the wireless propagation path passes, so as to obtain the structural occlusion loss value corresponding to the link measurement point.

[0230] In one possible design, the scheme determination module 504 is specifically used to determine the wireless device parameters and coverage prediction results for each area, associate the wireless device parameters and coverage prediction results with the structural component attributes in the building information model, and visualize them in the building information model in the form of coverage color levels or link parameter annotations to obtain the association display results.

[0231] In one possible design, the scheme determination module 504 is also used to adjust the location or wireless parameters of wireless devices in the building information model, and based on the adjusted location or wireless parameters of wireless devices, to update the coverage prediction results for each area in real time to obtain the target coverage planning scheme.

[0232] This disclosure provides a computer device, including:

[0233] A memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the method of any of the above embodiments.

[0234] This specification describes an embodiment of a wireless signal coverage planning method that can be applied to computer devices, such as servers or terminal devices. The device embodiment can be implemented in software, hardware, or a combination of both. Taking software implementation as an example, as a logical device, it is formed by a processor that processes the file, loading the corresponding computer program instructions from non-volatile memory into memory for execution. From a hardware perspective, such as... Figure 6The diagram shown illustrates the structure of a computer device used in a wireless signal coverage planning method according to an embodiment of this specification. The computer device includes a processor 610, a memory 630, a network interface 620, a non-volatile memory 640, and a file processing device 631. The file processing device 631 can be used to execute computer instructions in the wireless signal coverage planning method. Depending on the actual function, the computer device may also include other hardware, which will not be described in detail here.

[0235] This disclosure provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can implement the methods of any of the above embodiments. The execution method and beneficial effects are similar, and will not be described again here.

[0236] The aforementioned computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0237] The computer program described above can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this disclosure. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer device, partially on the user's device, as a standalone software package, partially on the user's computer device and partially on a remote computer device, or entirely on a remote computer device or server.

[0238] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0239] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0240] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for wireless signal coverage planning, characterized in that, include: Obtain spatial structure and environmental attribute information corresponding to the construction area of ​​the pumped storage power station; Based on the environmental attribute information, network type, operating frequency band and core radio frequency parameters are matched to different areas in the construction area of ​​the pumped storage power station to obtain the dynamic matching result for each area. Based on the dynamic matching results corresponding to each region and the spatial structure information, a multi-factor coupled link loss calculation model is constructed, and the wireless signal coverage performance of each region is calculated based on the link loss calculation model to obtain the coverage prediction results corresponding to each region. Each coverage prediction result is associated with the building information model to obtain an associated display result. Based on the associated display result, the device layout and wireless parameters are adjusted to obtain the target coverage planning scheme. The wireless parameters include at least the core radio frequency parameters; wherein: The process of matching network standards, operating frequency bands, and core radio frequency parameters for different areas within the pumped storage power station construction area based on the environmental attribute information to obtain dynamic matching results for each area includes: extracting spatial enclosure degree, interference intensity index, and equipment distribution characteristics from the environmental attribute information for different spatial locations within the pumped storage power station construction area, generating corresponding scene label information; merging multiple spatial locations with the same scene label information to obtain multiple areas; determining the scene type and coverage requirements for each area; automatically matching the network standard and operating frequency band for each area based on the scene type; the scene type characterizes the wireless propagation characteristics of the corresponding area; automatically configuring the core radio frequency parameters for each area based on the coverage requirements, the core radio frequency parameters including: transmit power, antenna gain, and receive threshold; and generating the dynamic matching results for each area based on the network standard, operating frequency band, and core radio frequency parameters for each area. The step of constructing a multi-factor coupled link loss calculation model based on the dynamic matching results corresponding to each region and the spatial structure information includes: determining the installation method and installation height of the wireless device in each region according to the spatial structure information and the dynamic matching results corresponding to each region; and constructing the multi-factor coupled link loss calculation model based on the dynamic matching results corresponding to each region, the installation method, and the installation height.

2. The method of claim 1, wherein, The calculation of wireless signal coverage performance for each region based on the link loss calculation model, to obtain coverage prediction results for each region, includes: Multiple link measurement points are determined in each region; the link measurement points represent the wireless signal coverage at different spatial locations within the corresponding region. Determine the link measurement rules and multi-dimensional loss parameters corresponding to each link measurement point; The link measurement rules and the multi-dimensional loss parameters are input into the link loss calculation model, and the link corresponding to each link measurement point is calculated to obtain the link calculation result corresponding to each link measurement point. Based on the link calculation results of multiple link measurement points within the same area, the coverage prediction result corresponding to each area is generated.

3. The method of claim 2, wherein, The link measurement rules for each link measurement point include: Based on the propagation complexity and coverage distance requirements corresponding to the link measurement points, the corresponding link measurement rules are selected.

4. The method of claim 3, wherein, The selection of corresponding link measurement rules based on the propagation complexity and coverage distance requirements corresponding to the link measurement points includes: If the link measurement point is located in a densely populated area, then the complex point link measurement rule shall be adopted; If the link measurement point is located at the area boundary or the farthest coverage area, the farthest point link measurement rule shall be adopted.

5. The method of claim 2, wherein, The determination of the multi-dimensional loss parameters corresponding to each link measurement point includes: Structural obstruction loss is calculated for the wireless propagation path corresponding to the link measurement point to obtain the structural obstruction loss value. Electromagnetic interference loss of equipment is calculated in the area where the link measurement point is located, and the electromagnetic interference loss value of the corresponding area is obtained. The electromagnetic interference loss value includes electromagnetic interference loss generated by generator set, electrical equipment or metal components. Environmental attenuation loss is calculated for the link measurement point to obtain the environmental attenuation loss value corresponding to the link measurement point. The environmental attenuation loss value includes: multipath attenuation and human body occlusion attenuation. The structural shading loss value, the equipment electromagnetic interference loss value, and the environmental attenuation loss value are determined as the multi-dimensional loss parameters.

6. The method of claim 5, wherein, The step of calculating the structural obstruction loss of the wireless propagation path corresponding to the link measurement point to obtain the structural obstruction loss value includes: Determine the wireless propagation path corresponding to the link measurement point; The occlusion loss is accumulated for the wall type or structural component type through which the wireless propagation path passes, and the structural occlusion loss value corresponding to the link measurement point is obtained.

7. The method of claim 1, wherein, The step of associating each coverage prediction result with the building information model to obtain the associated display result includes: Determine the wireless device parameters corresponding to each region and the coverage prediction results; The wireless device parameters and the coverage prediction results are associated with the structural component attributes in the building information model, and then visualized in the building information model in the form of coverage color levels or link parameter annotations to obtain the associated display results.

8. The method of claim 1, wherein, The step of adjusting the device layout and wireless parameters based on the associated display results to obtain the target coverage planning scheme includes: Adjust the location of wireless devices or the wireless parameters in the building information model; Based on the adjusted location of the wireless device or the wireless parameters, the coverage prediction results for each area are updated in real time to obtain the target coverage planning scheme.

9. A system for coverage planning of wireless signals for implementing the method for coverage planning of wireless signals according to any one of claims 1 to 8, characterized in that, The system includes: The data acquisition module is used to acquire spatial structure information and environmental attribute information corresponding to the construction area of ​​the pumped storage power station; The adaptive matching module is used to match network type, operating frequency band and core radio frequency parameters for different areas in the construction area of ​​the pumped storage power station based on the environmental attribute information, so as to obtain the dynamic matching result for each area. The link measurement module is used to construct a multi-factor coupled link loss calculation model based on the dynamic matching results corresponding to each region and the spatial structure information, and to measure the wireless signal coverage performance of each region based on the link loss calculation model to obtain the coverage prediction result corresponding to each region. The scheme determination module is used to associate and display each coverage prediction result with the building information model to obtain the associated display result, and adjust the equipment layout and wireless parameters based on the associated display result to obtain the target coverage planning scheme; the wireless parameters include at least the core radio frequency parameters.

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