CN113873532B - Intelligent park 5G network planning method - Google Patents
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Abstract
The invention provides a 5G network planning method for an intelligent park, which comprises the following steps: the method comprises the steps of obtaining mobile communication information, obtaining intelligent park planning requirements, wherein the requirements comprise automatic matching and on-site collection, the automatic matching is achieved through obtaining intelligent park environment images and generating three-dimensional image layers, recognizing environment scenes in MDT grids, and matching to obtain planning area 5G business requirements. And evaluating MDT data of the intelligent park to obtain the coverage and user distribution conditions of the intelligent park, estimating the coverage and service conditions of the intelligent park based on a link budget and a propagation model, generating a 5G coverage simulation layer, and formulating a 5G planning scheme.
Description
Technical Field
The invention belongs to the field of 5G communication network industry planning, and particularly relates to a 5G network planning method for an intelligent park.
Background
The wisdom garden is the important guarantee that promotes the digitalized transformation of enterprise, can digitalized, the wisdom with garden job scenario, improves garden operation, management, production efficiency and personnel work efficiency, realizes that the whole value of garden promotes, now coming the rapid development opportunity period. The maximum value of 5G is in ToB market, and the uplink that 5G network possessed is big bandwidth, low time delay, wide connection, high reliability characteristics, the demand of various business on meeting the garden that can be fine, and 5G and wisdom garden's integration can be realized from terminal, network, platform to 5G applied end-to-end ability, and 5G is including 5G+ high definition video monitoring to the application scenario on garden, 5G+ cloud AGV,5G+ inspection robot, 5G+AR auxiliary operation, aspects such as 5G+ cloud official working.
The 5G network planning scheme facing the public field is relatively mature, but for the network planning scheme of 5G in industrial application, the network planning of 5G+ intelligent park is concentrated on the idea and target level in the literature, or the 5G is analyzed from the 5G network slicing and edge computing technology level to be suitable for some application of the intelligent park, and the network planning scheme of 5G in the intelligent park is systematically described in the literature.
Disclosure of Invention
The invention aims to: in order to solve the technical problems in the background technology, the invention provides a 5G network planning method for an intelligent park.
The beneficial effects are that: the method combines the spatial layout of the park with the business requirements, establishes a spatial network demand model, combines the characteristics of a 5G network, evaluates by utilizing the existing resources, and provides 5G accurate planning.
Drawings
The foregoing and/or other advantages of the invention will become more apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings and detailed description.
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a diagram of LTE parameters and MDT information for the smart park.
Fig. 3 is a three-dimensional layer structure diagram of a planning area.
Fig. 4 is a 5G simulation layer and a planning scheme.
Figure 5 is a diagram of a smart campus compartment layout architecture.
Detailed Description
The invention provides a 5G network planning method for an intelligent park, which comprises the following steps:
Mobile communication information is acquired, wherein the mobile communication information comprises current network LTE industrial parameter information (LTE, long Term Evolution, long term evolution, commonly called 4G;) and MDT information (minimization of drive test of DRIVE TEST, abbreviated as MDT). The LTE industrial parameter information of the current network comprises the existing LTE planning site position, sector name, antenna azimuth angle, antenna height, antenna lower segment angle and frequency band of the intelligent park.
The current LTE network is in a mature stage, the construction scale meets the daily user demands, the coverage rate reaches more than 95%, and the distance between LTE macro stations in urban areas is 200-400 meters. The 5G network is in the initial stage of construction, the construction scale is far lower than that of the LTE network, and mainly because the 5G network has a plurality of limited factors including frequency resources, low terminal occupancy and high construction cost, wherein the current construction mode mainly comprises co-site construction with the LTE network, namely the same position of LTE and 5G, on site selection of a newly constructed 5G station, so that the LTE network is an important reference in the 5G planning construction process.
The industrial parameters of the base station are the main influencing factors of the coverage, wherein the position of the base station, the antenna heights, azimuth angles and downtilt angles of multiple sectors of the base station all influence the coverage of the sectors. In addition, the largest difference between LTE and 5G also comprises a frequency band, the current 5G mainly adopts a 2.6G frequency band, and compared with the D frequency, the F frequency and the FDD frequency adopted by LTE, the coverage capability of the LTE network is lower than that of the LTE network.
In order to reduce the cost and complexity of manual network drive test by mobile operators using dedicated equipment, the LTE system starts from Rel-10 version and introduces a series of Minimization of DRIVE TEST (MDT) functions. The MDT utilizes a common LTE terminal to automatically collect network related measurement results, reports the measurement results to the eNB through a control plane (ControlPlane) signaling, and further reports the measurement results to a tracking collection entity (Trace Collection Entity, abbreviated as TCE) of an operation AND MAINTENANCE (abbreviated as OAM) through an upstream ground interface of the eNB for network deployment and adjustment and optimization of operation parameters.
According to different attributes of the MDT measurement object, the measurement content of the current LTE MDT can be classified into 3 types or 3 layers: l1: for example, the statistical measurement of the strength RSRP and the quality RSRQ of the LTE downlink pilot signal (Common REFERENCE SIGNAL, CRS for short; CHANNEL STATE Information-REFERENCESIGNAL, CSI-RS for short), L2: for example, statistical measurement of packet delay/packet loss rate/packet loss amount of protocol layer such as LTE MAC/RLC/PDCP, L3: such as statistical measures of LTE specific data radio bearer (Data Radio Bearer, abbreviated DRB) data throughput rate/throughput and other mobility related performance (handover, dropped call, etc.) indicators. The accuracy of the above-mentioned 3-layer/3-class MDT measurement object results may be affected by local interference from the terminal in a certain period of time.
The MDT is based on measurement information of LTE network grid division, and the measurement information comprises measurement time, primary coverage cell name, average RSRP and measurement sampling point number.
Acquiring intelligent park planning requirements, wherein the requirements comprise automatic matching and field collection; the automatic matching is performed by acquiring an environment image of an intelligent park and generating a three-dimensional image layer, dividing the three-dimensional image layer into more than two areas, and overlapping the areas with an MDT grid; recognizing an environment scene in an MDT grid, and matching to obtain 5G service requirements of a planning area (the size of the MDT is fixed and is a square of 55 meters, and the size of the area after the three-dimensional layer is divided is consistent with that of the MDT); the on-site collection comprises collection of intelligent office, intelligent production and intelligent management.
When obtaining wisdom garden planning demand, distinguish automatic matching and on-the-spot collection, wherein automatic matching is through, earlier through shooting wisdom garden environment image, handles the image, produces three-dimensional image layer. The three-dimensional layer can intuitively reflect the environment of the whole park and provide convenience for subsequent path loss evaluation and service subdivision.
The method for generating the three-dimensional image layer by the image comprises the following steps:
Selecting a key position of a target object in a street view image; critical locations include edges or inflection points of a building. It can be understood that the street view image comprises objects such as buildings, parks, roads and the like;
Taking the key position as an image control point, and numbering the image control point; the numbering rules are key location name + number + control point + number. For example, selecting an edge of a building as a key location, numbering the key location as: a control point 1 for 1+ of a building, a control point 2 for 1+ of a building, etc. It will be appreciated that by numbering the control points, different objects and their characteristic points can be distinguished.
Manually selecting a position corresponding to the key position as a target control point based on a three-dimensional city vector target model;
numbering the target control points with the same numbers as the image control points;
Based on the control points with the same number, performing geometric correction on the street view image, and then projecting and overlapping the street view image on the three-dimensional city vector target model to complete the multi-model composite patch; the geometric correction and stereo mapping of images on the vector three-dimensional model are common techniques for graphic image processing and common functions of images and three-dimensional software. The basic principle comprises: 1. establishing a mapping relation between image point coordinates (row and column numbers) and corresponding point coordinates of a vector model, solving unknown parameters in the mapping relation, and correcting each pixel coordinate of the image according to the mapping relation; 2. for the uncovered portions outside the target control points, image interpolation is adopted. It can be understood that the street view picture where the image control point is located is projected and overlapped on the three-dimensional city vector target model based on the same numbered control point.
And transforming other angles (the images are limited by wide angles when being selected, only street view images can be processed from a certain angle, a plurality of angles are needed to be evaluated respectively in order to achieve 360-degree total coverage), other street view images with the same target and different angles are selected, and the steps are repeated until the three-dimensional city vector target model is completely covered by 360 degrees. Finally, a scene image is obtained.
The generated three-dimensional layer is divided into a plurality of regions, which overlap the MDT grid. The MDT grid is a square with a side length of 55 meters. The three-dimensional image layer in the MDT grid is obtained by identifying the corresponding three-dimensional image layer in the MDT grid, the three-dimensional image layer in the MDT grid belongs to an environmental scene, the main environmental scene comprises a road, a factory building, a residential area, an office building, a green belt, a parking lot and the like, the scene identification method for identifying the environmental scene in the MDT grid based on deep learning adopts a multi-scale convolutional neural network, the multi-scale convolutional neural network comprises a multi-scale layer for carrying out multi-scale processing on an input scene image, and the identification method comprises the following steps: setting a resolution level, wherein the resolution level correlates a resolution range with image processing parameters, the image processing parameters comprise scene image input, the frequency of the multi-scale convolutional neural network and a scale value of multi-scale processing; acquiring the image resolution of the scene image; and carrying out subsequent processing on the scene image according to the resolution range corresponding to the image resolution and the image processing parameters (the subsequent processing comprises the steps of inputting times, multi-scale processing scale values, intra-scale fusion, inter-scale fusion and the like, and the subsequent processing belongs to the prior art).
Obtaining 5G service requirements of a planning area according to environment scene matching, wherein the service requirements comprise three types: large bandwidth (eMBB), wide connection (mMTC), low latency (uRLLC).
The on-site collection includes collection of intelligent offices, intelligent production, intelligent management,
Evaluating MDT data of the intelligent park to obtain coverage and user distribution conditions of the intelligent park;
Based on a link budget and a propagation model, estimating coverage and business conditions of the intelligent park, and generating a 5G coverage simulation layer;
And 5G planning schemes are formulated, wherein the 5G planning schemes comprise construction types and individual service configurations, the construction types comprise new 5G of new sites and new 5G of co-site LTE, and the individual service configurations comprise eMBB, mMTC, uRLLC.
Further, on-site collection is concentrated wisdom office and is included show propaganda, security protection system, vehicle and personnel management, comprehensive control, wisdom production includes office, security protection system, production, storage commodity circulation, wisdom management includes that many platform systems are integrated, many service system are integrated, many terminal system are integrated. The specific requirements for field collection are shown in table 1:
TABLE 1
Further, the access device for the 5G service requirement comprises a 5G terminal and a non-5G terminal CPE (customer terminal equipment, full scale Customer Premise Equipment) data access, the 5G terminal comprises a 5G mobile phone, a PC and a PAD terminal, and the non-5G terminal CPE data access comprises a network camera, a VR/AR (VR, virtual reality technology, AR, augmented reality technology), an RFID terminal (radio frequency identification, full scale Radio Frequency Identification), a control box, an AGV (automatic guided vehicle, full scale Automated Guided Vehicle), a mechanical arm and a sensor. The sensor is mainly applied to the Internet of things on the 5G network, such as a sensor based on temperature, humidity, pH value and the like.
The 5G planning scheme comprises coverage demand assessment and business demand assessment;
the coverage demand assessment includes: judging whether LTE station addresses in the 5G coverage simulation layer meet 5G coverage requirements, and if the MDT grid coverage rate of the 5G coverage simulation layer is more than 95%, judging that the LTE station addresses meet the 5G coverage requirements;
The business requirement assessment includes: judging whether the service condition in the 5G coverage simulation layer meets the 5G service condition, wherein the 5G service requirement comprises a large bandwidth eMBB, a wide connection mMTC and a low delay uRLLC.
Table 2 below shows the 5G traffic situation for different terminal types in the park:
TABLE 2
If the conditions in Table 2 are satisfied, it is determined that the 5G traffic situation is satisfied.
Further, the on-site collection further comprises investigation of spatial layout of the park, investigation of geographic position, spatial size, building distribution, size and structure of the park, and decomposition of the park space from outdoor-park roads, outdoor-matched areas, indoor-workshops/workshops and indoor-office areas by combining differences of people and object focusing degrees and regional functions, wherein the outdoor-matched areas are living service areas and management areas except production and offices, and comprise parking lots, restaurants, movable rooms and rest areas.
Further, the link budget PL max is formulated as:
PLmax＝PTx-Lf+GTx-Mf-Ml+GRx-Lp-Lb-SRx
P Tx is the base station transmitting power, L f is the feeder loss, G Tx is the base station antenna gain, M f is the shadow fading and fast fading allowance, M l is the interference allowance, G Rx is the mobile phone antenna gain, L p is the building penetration loss, L b is the human body loss, and S Rx is the mobile phone receiving sensitivity;
the line-of-sight propagation model formula of the urban macro cell is as follows:
PL1＝32.4+20 lg d3D+20 lg fc
where PL 1 represents the path loss of the line-of-sight propagation model, f c is the operating frequency of the communication network (e.g., 2515MHz-2675MHz of 5G), and d 3D is the base station antenna to mobile station antenna linear distance (m).
Based on the MDT information and the 5G path loss of the intelligent park, estimating MDT data of the 5G of the intelligent park, and generating a 5G coverage simulation layer, wherein the 5G coverage simulation layer is an MDT grid coverage layer.
Example 1
A5G intelligent park macro station planning method comprises the following steps:
fig. 1 is a flow chart of a smart park 5G network planning method, which includes the following steps:
step 102, mobile communication information is acquired, wherein the mobile communication information comprises current network LTE industrial parameter information and MDT information. Fig. 2 is a diagram of LTE parameters and MDT information for the smart park, where the planning area includes LTE base station 1 and LTE base station 2, each having 3 sectors, as shown in table 3 below:
TABLE 3 Table 3
The MDT grid comprises sampling points and average RSRP numbers corresponding to the occupied cells, wherein the average RSRP in the green grid is greater than-85 dBm, the average RSRP in the blue grid is-85 to-100 dBm, the average RSRP in the yellow grid is-100 to-110 dBm, and the average RSRP in the red grid is less than-110 dBm. According to the LTE industrial parameter information and the MDT information, it can be found that a weak coverage grid exists in the south of the intelligent park, wherein the weak coverage grid is defined as that the average RSRP in the grid is smaller than-110 dBm, and the weak coverage is caused mainly by denser factory buildings in the area, and the periphery of the weak coverage has no main coverage base stations.
Step 104, obtaining intelligent park planning requirements, wherein the requirements comprise automatic matching and on-site collection; the automatic matching is performed by acquiring an environment image of an intelligent park and generating a three-dimensional image layer, dividing the three-dimensional image layer into a plurality of areas, and overlapping the areas with an MDT grid; recognizing an environment scene in the MDT grid, and matching to obtain 5G service requirements of a planning area; the on-site collection comprises collection of intelligent office, intelligent production and intelligent management.
Fig. 3 is a three-dimensional map layer structure diagram of a planning area, wherein an unmanned plane is used for carrying out image acquisition on a park, then carrying out image processing on the acquired image to generate a three-dimensional map image. And on the basis of a three-dimensional city vector target model, manually selecting a position corresponding to the key position as a target control point, numbering the target control point with the same number as the image control point, geometrically correcting a street view image on the basis of the control point with the same number, and then projecting and overlapping the street view image on the three-dimensional city vector target model to finish the multi-model composite patch. And transforming other angles, selecting other street view images of the same object and different angles, and repeating the steps until the three-dimensional city vector target model is completely covered by 360 degrees.
And then automatically matching corresponding scenes by an identification technology, wherein the scene identification method for identifying the environment scene in the MDT grid based on deep learning adopts a multi-scale convolutional neural network, the multi-scale convolutional neural network comprises a multi-scale layer for carrying out multi-scale processing on an input scene image, and the identification method comprises the following steps: setting a resolution level, wherein the resolution level correlates a resolution range with image processing parameters, the image processing parameters comprise scene image input, the frequency of the multi-scale convolutional neural network and a scale value of multi-scale processing; acquiring the image resolution of the scene image; and carrying out subsequent processing on the scene image according to the resolution range corresponding to the image resolution and the image processing parameters.
And 106, evaluating MDT data of the intelligent park to obtain coverage and user distribution conditions of the intelligent park. The coverage is based on the RSRP data of the MDT, and the user distribution is analyzed by the number of MDT sampling points in each grid.
Step 108, based on the link budget and the propagation model, estimating the coverage and service conditions of the intelligent park, and generating a 5G coverage simulation layer. The link budget PL max is formulated as:
PLmax＝PTx-Lf+GTx-Mf-Ml+GRx-Lp-Lb-SRx
P Tx is the base station transmitting power, L f is the feeder loss, G Tx is the base station antenna gain, M f is the shadow fading and fast fading allowance, M l is the interference allowance, G Rx is the mobile phone antenna gain, L p is the building penetration loss, L b is the human body loss, and S Rx is the mobile phone receiving sensitivity;
The line-of-sight propagation model for the urban macro cell is based on 3GPP protocol 38.901, which has the following formula:
PL1＝32.4+20 lg d3D+20 lg fc
PL2＝32.4+40 lg d3D+20 lg fc-10lg((d′BP)2+(hBS-hUT)2)
the above scenario requires that the following conditions be met: σ SF＝4,1.5m≤hUT≤22.5m,hBS =25m.
F c is the operating frequency (GHz), h BS is the base station antenna effective height (m), the base station height is 25m, h UT is the mobile station antenna effective height (m), d 2D is the base station-mobile station horizontal distance (m), d 3D is the base station antenna-mobile station antenna linear distance (m), σ SF pathloss standard value, and d' BP is the line of sight distance specified in the Uma model herein. By comparing different losses of LTE and 5G, the coverage condition of 5G in a planning area is estimated, wherein a main evaluation object is F c which is a working frequency (GHz), the LTE mainly adopts frequency bands such as D frequency band, F frequency band, FDD900 and FDD1800 at present, the 5G mainly samples a 2.6G frequency band, the FDD1800 frequency band is sampled according to the LTE, the 5G samples the 2.6G frequency band, and under the same other conditions, the coverage 5G with the same distance has about 6.34dB more loss than the LTE. In the actual process, the actual loss is more obvious compared with the LTE network due to the diversity of the environment.
Fig. 4 is a 5G simulation layer and a plan view, where the 5G simulation layer is generated based on the LTE grid through the loss estimation described above, where it can be seen that the coverage corresponding to 5G is worse at the location of the original LTE weak coverage grid, especially in the southern office building area, where the weak coverage is obvious.
Step 110, a 5G planning scheme is formulated, wherein the 5G planning scheme comprises a construction type and a personalized service configuration, the construction type comprises new 5G of a new site and new 5G of a co-site LTE, and the personalized service configuration comprises eMBB, mMTC, uRLLC. Through the above analysis, the 5G macro planning scheme of the intelligent park is as follows: in the roof of a southern office building, a 5G base station is newly built, 3 sectors are provided, the height of an antenna is 20 meters, the azimuth angles are respectively 70 degrees, 250 degrees and 310 degrees, and the downtilt angles are respectively 8 degrees, 8 degrees and 10 degrees. In terms of service configuration, the corresponding area eMBB is: dormitory area, office building area, mMTC corresponding area is: the areas corresponding to the research and development building, the intelligent factory area and the factory building uRLLC are as follows: building, intelligent factory, factory building and dormitory area are developed.
Example 2
A5G room division planning method for an intelligent park comprises the following steps:
Fig. 5 is a diagram of a smart campus compartment layout structure, which occupies 120 tens of thousands of square meters of the floor area, including a manufacturing plant, a scientific research test office building, and a supporting building. The 5G and the existing research and development design system, production control system, service management system and the like in the production of the park are combined in the 5G and intelligent park project, so that the deep innovation of the production flows of research and development design, production manufacturing, management service and the like of the 5G vertical industry can be comprehensively promoted, and the conversion of the manufacturing industry to intellectualization, service and high-end conversion is realized.
The specific steps of using the 5G intelligent park network planning scheme in the industrial park are as follows:
S1: and collecting park business requirements. The collection was performed from three aspects, intelligent office, intelligent production, intelligent management, as shown in table 4.
TABLE 4 Table 4
S2: converting park business requirements. And (3) classifying the terminals of the terminal access equipment for realizing the park business requirement in the step S1, counting the number of the terminals of each type, and giving the communication network requirement for reference of the terminals of each type.
S3: and (5) spatial layout of the investigation park. The total area of the park is 120 ten thousand square meters, the length of the north and south is 1.65 km, and the width of the park is 0.74 km. The garden is divided into a production-factory building, a production-office area, a matched area and a garden trunk according to different building functions of the garden.
S4: and establishing a park space network demand model. Combining the communication network requirements of each type of terminal in the step S2 with the four areas of the park in the step S3, determining the spatial distribution position of each type of terminal and the corresponding communication network requirements, as shown in table 5.
TABLE 5
S5: the garden 5G wireless network planning scheme has lower application capacity requirement on plants, production-scientific research buildings, production-office buildings and matched building planning room subsystem, and the indoor environment is open and less in partition, can adopt indoor 5G low-frequency coverage, and meets the coverage requirement by selecting AAU/RRU indoor installation. The scientific research building adopts RHIB+PRRU mode because of the multi-service type.
The present invention provides a method for planning a 5G network in an intelligent park, and the method and the way for implementing the technical scheme are numerous, the above is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention. The components not explicitly described in this embodiment can be implemented by using the prior art.
Claims (1)
1. The intelligent park 5G network planning method is characterized by comprising the following steps:
Step 1, obtaining mobile communication information;
Step 2, obtaining the intelligent park planning requirement through automatic matching and on-site collection,
Step 3, evaluating MDT data of the intelligent park to obtain coverage and user distribution conditions of the intelligent park;
Step 4, based on the link budget and the line-of-sight propagation model of the urban macro cell, estimating the coverage and service conditions of the intelligent park, and generating a 5G coverage simulation layer;
step 5, a 5G planning scheme is formulated;
in step 1, the mobile communication information comprises current network LTE industrial parameter information and MDT information;
the existing network LTE industrial parameter information comprises the existing LTE planning site position, sector name, antenna azimuth angle, antenna height, antenna lower segment angle and frequency band of the intelligent park;
The MDT information is based on measurement information of LTE network grid division, and the measurement information comprises measurement time, primary coverage cell name, average RSRP and measurement sampling point number;
In step2, the automatic matching includes: the method comprises the steps of obtaining an environment image of an intelligent park and generating a three-dimensional image layer, dividing the three-dimensional image layer into more than two areas, overlapping the areas with an MDT grid, identifying environment scenes in the MDT grid, and matching to obtain 5G business requirements of a planning area;
The on-site collection comprises collection of intelligent office, intelligent production and intelligent management;
in step 2, the method for generating the three-dimensional layer includes:
step a1, selecting a key position of a target object in a street view image;
step a2, taking the key position as an image control point, and numbering the image control point;
Step a3, selecting a position corresponding to the key position as a target control point based on a three-dimensional city vector target model;
step a4, numbering the target control points based on the same number as the image control points;
Step a5, based on the control points with the same number, performing geometric correction on the street view image, and then projecting and overlapping the street view image on the three-dimensional city vector target model to complete the multi-model composite patch;
Step a6, transforming other angles, selecting other street view images of the same object and different angles, repeating the steps a1 and a5 until the three-dimensional city vector object model is completely covered by 360 degrees, and finally obtaining a scene image;
In step2, the identifying an environment scene in the MDT grid includes: adopting a multi-scale convolutional neural network, and identifying an environment scene in the MDT grid based on a scene identification method of deep learning;
The multi-scale convolutional neural network comprises a multi-scale layer for performing multi-scale processing on an input scene image;
The scene recognition method based on the deep learning comprises the following steps: setting a resolution level, wherein the resolution level correlates a resolution range with image processing parameters, and the image processing parameters comprise scene image input, the frequency of a multi-scale convolutional neural network and a scale value of multi-scale processing; acquiring the image resolution of the scene image, and carrying out subsequent processing on the scene image according to the image processing parameters according to the resolution range corresponding to the image resolution;
In step 2, the access equipment for 5G service requirements includes a 5G terminal and a non-5G terminal CPE data access, where the 5G terminal includes a 5G mobile phone, a PC and a PAD terminal, and the non-5G terminal CPE data access includes a network camera, a VR/AR, an RFID terminal, a control box, an AGV, a mechanical arm and a sensor;
In the step 2, the intelligent office comprises a display propaganda system, a security system, vehicle and personnel management and comprehensive monitoring;
The intelligent production comprises an office, a security system, production and storage logistics;
the intelligent management comprises multi-platform system integration, multi-service system integration and multi-terminal system integration;
In step 2, the on-site collection further comprises investigation of spatial layout of the park, investigation of geographic position, spatial size, building distribution, size and structure of the park, and decomposition of the park space from outdoor to park road, outdoor to matched area, indoor to workshop or factory building, and indoor to office area by combining with differences of focusing degree and area functions of people and objects, wherein the outdoor to matched area is a living service area and a management area except production and office, and the living service area comprises a parking lot, a restaurant, a movable room and a rest area;
In step 4, the link budget PL max is calculated using the following formula:
PLmax＝PTx-Lf+GTx-Mf-Ml+GRx-Lp-Lb-SRx
Wherein, P Tx is the base station transmitting power, L f is the feeder loss, G Tx is the base station antenna gain, M f is the shadow fading and fast fading margin, M l is the interference margin, G Rx is the mobile phone antenna gain, L p is the building penetration loss, L b is the human body loss, S Rx is the mobile phone receiving sensitivity;
the line-of-sight propagation model formula of the urban macro cell is as follows:
PL1＝32.4+20lg d3D+20lg fc
Wherein PL 1 represents the path loss of the line-of-sight propagation model, f c is the operating frequency of the communication network, and d 3D is the linear distance between the base station antenna and the mobile station antenna;
Based on the MDT information and the 5G path loss of the intelligent park, estimating MDT data of the 5G of the intelligent park, and generating a 5G coverage simulation layer, wherein the 5G coverage simulation layer is an MDT grid coverage layer;
in step 5, the 5G planning scheme includes coverage demand assessment and business demand assessment;
the coverage demand assessment includes: judging whether LTE station addresses in the 5G coverage simulation layer meet 5G coverage requirements, and if the MDT grid coverage rate of the 5G coverage simulation layer is more than 95%, judging that the LTE station addresses meet the 5G coverage requirements;
The business requirement assessment includes: judging whether the service condition in the 5G coverage simulation layer meets the 5G service condition.
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