A park high-density monitoring and WiFi coverage integrated system

Abstract

The present application relates to the technical field of wireless communication, especially to a kind of garden high-density monitoring WiFi coverage integration system, by comprehensively collecting the characteristics of monitoring service flow and WiFi user data service flow in garden, and synchronously scanning channel interference, accurate data basis is provided, so as to optimize resource allocation;Through interference compatibility analysis algorithm, the service quality of service level and channel compatibility mapping table are obtained, which provides basis for resource allocation, ensures the service quality of different business types;According to the mapping table, the available spectrum is stereoscopic sliced from the three dimensions of frequency, space and time, virtual resource blocks are formed, and a constraint optimization algorithm is used to pre-allocate resources to generate an initial three-dimensional resource slicing scheme, improving resource utilization efficiency;Based on the initial scheme and service-channel state matrix, real-time monitoring of load and dynamic scheduling optimization are carried out to ensure efficient and stable operation of the system, and improve overall network performance and user experience.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to an integrated system for high-density WiFi coverage for campus surveillance. Background Technology

[0002] In the context of the rapid development of intelligent and digital campuses, wireless networks, as the infrastructure connecting various intelligent devices and systems, are becoming increasingly important. However, traditional campus wireless networks still face multiple technical bottlenecks: resource allocation often lacks a dynamic adjustment mechanism, making it impossible to flexibly allocate resources according to the actual needs of different service types and channel conditions; in campus environments where high-density monitoring and WiFi users coexist, different service types are prone to severe mutual interference when operating on the same or adjacent channels; some channels may become polluted channels due to continuous interference from non-WiFi signals, rendering these channels unusable; simultaneously, the overall channel utilization rate may be low due to unreasonable resource allocation, failing to meet the ever-increasing data transmission demands within the campus; and traditional campus wireless networks struggle to provide differentiated network services based on service priority and quality of service requirements. Therefore, this invention proposes an integrated system for high-density monitoring and WiFi coverage in campuses. Summary of the Invention

[0003] The purpose of this invention is to solve the problems in the background art by proposing an integrated system for high-density surveillance WiFi coverage in a campus.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] A high-density monitoring WiFi coverage integrated system for a campus includes: a service channel feature acquisition module, a service compatibility mapping analysis module, a resource block pre-allocation slicing module, and a resource block scheduling optimization module;

[0006] Service channel feature acquisition module: Collects features of all service flows within the campus, including features of monitoring service flows and WiFi user data service flows, and simultaneously scans the interference spectrum of each channel to generate a service-channel state matrix;

[0007] Service compatibility mapping analysis module: Based on the service-channel state matrix, it determines various services; through the interference compatibility analysis algorithm, it calculates the probability of interference collisions when different service types coexist in the same or adjacent channels, and then obtains the service quality level-channel compatibility mapping table.

[0008] Resource block pre-allocation slicing module: Based on the service quality level-channel compatibility mapping table, virtual resource blocks are formed by three-dimensional slicing of available spectrum from the frequency, spatial and temporal dimensions, and a constraint optimization algorithm is used to pre-allocate resources to generate an initial three-dimensional resource slicing scheme;

[0009] Resource block scheduling optimization module: Based on the initial three-dimensional resource slicing scheme and service-channel state matrix, it performs load monitoring and dynamic scheduling optimization on virtual resource blocks and outputs the optimized resource scheduling instruction set.

[0010] Furthermore, the service channel feature acquisition module collects features of all service flows within the campus, including features of monitoring service flows and WiFi user data service flows, and simultaneously scans the interference spectrum of each channel. The process of generating the service-channel state matrix includes:

[0011] For monitoring service streams, APs deployed in various areas of the park establish a collaborative mechanism with the monitoring streaming media server: real-time stream information of the monitoring service stream is obtained through the API interface of the streaming media server, the SDP protocol description field is parsed, and the encoding resolution and target frame rate of each video stream are extracted. The I / P frame ratio is obtained by calculating the I frame and P frame interval, thereby obtaining the characteristics of all monitoring services in the park.

[0012] For WiFi user data service flows, the uplink data packets sent from WiFi user terminals are received through the network access function of the AP; the DPI engine is used to perform load depth analysis on the data packets at the AP uplink, and the WiFi user data service flows are classified into different sub-service types by combining the transport layer port number, application layer protocol fingerprint and load feature pattern matching.

[0013] The AP with spectrum analysis capability performs a synchronous scanning task of the interference spectrum of each channel, measures and generates a channel interference profile, which includes the busy / idle ratio of the Wi-Fi network and the interference power spectral density of non-Wi-Fi signals.

[0014] Finally, the central controller generates an initial service-channel state matrix: the row index of the matrix is ​​the physical channel number, and the column index is the service type, which includes monitoring services and WiFi user data services; each matrix cell uniquely corresponds to a channel-service type pair, and each cell is filled with three data items: a) the number of currently active sessions belonging to the service type corresponding to the cell on the channel corresponding to the cell; b) the real-time interference profile of the channel corresponding to the cell; c) the dynamic service weight: for monitoring services, this weight is a bitstream strength factor calculated based on the monitoring service characteristics; for WiFi user data services, this weight is a priority factor preset according to the WiFi user data sub-service type.

[0015] Furthermore, the service compatibility mapping analysis module determines various services based on the service-channel state matrix; through interference compatibility analysis algorithms, it calculates the probability of interference collisions between different service types coexisting in the same or adjacent channels, and thus obtains the service quality level-channel compatibility mapping table. This process includes:

[0016] The central controller reads the service-channel state matrix and analyzes the dynamic service weight of each cell in the matrix: for monitoring services, when its bitstream strength factor is higher than the preset bitstream strength threshold, its service quality level is defined as high service quality level; for WiFi user data services, the service quality level is divided according to the preset priority factor corresponding to its sub-service type: high priority services correspond to high service quality level; standard priority services correspond to medium service quality level; low priority services correspond to basic service quality level.

[0017] The real-time interference profile of each channel in the integrated service-channel state matrix is ​​analyzed, and the dynamic weights of each service defined in the matrix are used for judgment: services with dynamic weights higher than a preset weight threshold are identified as high-weight services, and vice versa. For co-channel compatibility, the mutual interference is analyzed: the ratio of the signal power of the high-weight service to the interference power of the low-weight service is calculated, and it is determined whether the ratio is lower than a preset dynamic adjustment threshold. The probability of occurrence is calculated, i.e., the co-channel interference collision probability p1. Then, the compatibility coefficient in the co-channel scenario = 1 - p1. For adjacent channel compatibility, the out-of-band interference is analyzed: the out-of-band leakage power of the high-weight service in the adjacent channel is calculated, and it is determined whether the power exceeds the sensitivity threshold of the receiver of the low-weight service. The probability of occurrence is calculated, i.e., the adjacent channel leakage interference probability p2. Then, the compatibility coefficient in the adjacent channel scenario = 1 - p2. Finally, the minimum value of the compatibility coefficients of the two scenarios is taken as the compatibility coefficient of the service-channel pair.

[0018] Generate a service quality level-channel compatibility mapping table; where the row dimension of the table is set to service type, the column dimension is set to physical channel number, and each data item in the table corresponds to a service-channel pair.

[0019] Furthermore, the process by which the resource block pre-allocation slicing module forms virtual resource blocks from the available spectrum in three dimensions—frequency, spatial, and temporal—based on the service quality level-channel compatibility mapping table includes:

[0020] The central controller acquires the service quality level-channel compatibility mapping table and extracts the defined service quality levels and compatibility coefficients with each channel. Based on the mapping table, the total available spectrum is divided in the frequency dimension: based on the full-channel interference profile obtained from spectrum scanning, and according to a preset interference power spectral density threshold, contaminated channels whose interference power spectral density continuously exceeds the threshold are removed. Based on the compatibility coefficient requirements between adjacent sub-bands, the total available spectrum is divided into several sub-bands. In the spatial dimension, the beam coverage of each AP is calculated based on its deployment location and antenna configuration parameters, and the campus is divided into multiple virtual cells. Resources are independently allocated within each virtual cell, and the division is based on the service type and corresponding compatibility constraints within each virtual cell. In the temporal dimension, the wireless channel transmission time is divided into dynamic time slots, and the time slot length is dynamically adjusted according to the service quality level in the mapping table. Through the coordinated division of spectrum resources in the frequency, spatial, and temporal dimensions, the basic framework of the campus network virtual resource block is constructed.

[0021] Furthermore, the resource block pre-allocation slicing module uses a constrained optimization algorithm to pre-allocate resources, and the process of generating the initial three-dimensional resource slicing scheme includes:

[0022] Based on the constructed virtual resource block framework of the campus network, a constrained optimization algorithm is used for resource pre-allocation: the compatibility coefficient and service quality level of each service-channel pair in the input mapping table are used; when allocating resources for monitoring services, the service quality level of the service and the compatibility coefficient with each channel in the mapping table are read, and channels with high compatibility coefficients are selected to allocate exclusive resource blocks with short time slot periods; the bit rate strength factor of the monitoring service is calculated during allocation; when allocating resources for WiFi user data services, the shared resource block combination is flexibly allocated according to the service quality level corresponding to its sub-service type in the mapping table and the compatibility coefficient with each channel.

[0023] The final generated 3D resource slicing scheme is presented in the form of a structured data table, which records the allocation results of each virtual resource block. Each resource block is uniquely identified by three coordinates: frequency band index, cell identifier, and time slot number. The scheme also records the pre-allocated service type and its corresponding compatibility coefficient for each resource block.

[0024] Furthermore, the resource block scheduling optimization module, based on the initial three-dimensional resource slicing scheme and the service-channel state matrix, performs load monitoring and dynamic scheduling optimization on virtual resource blocks, and outputs the optimized resource scheduling instruction set. The process includes:

[0025] The central controller performs real-time monitoring of the service load rate of each virtual resource block. It collects dynamic service weights and channel interference profiles within each resource block in real time using a service-channel state matrix. Load rate calculation is performed independently for each virtual resource block: D1. Obtain all dynamic service weights and calculate the current total weight requirement based on these weights: for monitoring services, this is the sum of the bitrate strength factors of all video streams within the resource block; for WiFi user data services, it is the sum of the preset priority factors of all sessions. D2. Evaluate the current actual available capacity by combining the real-time interference profile of the channel where the virtual resource block is located. D3. Calculate the real-time load rate of the resource block based on the current total weight requirement and the current actual available capacity.

[0026] When the resource allocation status is detected to deviate from the preset optimal operating range, the dynamic scheduling engine is triggered;

[0027] Once the dynamic scheduling engine is triggered, it uses the service quality level-channel compatibility mapping table as a constraint. At the same time, the dynamic scheduling engine uses a heuristic search algorithm to obtain the resource scheduling instruction set.

[0028] Furthermore, the dynamic scheduling engine employs a heuristic search algorithm, and the process of obtaining the resource scheduling instruction set includes:

[0029] E1. Using the current 3D resource slicing scheme as the initial state, load the service-channel state matrix and the service quality level-channel compatibility mapping table;

[0030] E2. Generate candidate resource adjustment operations according to predefined resource optimization rules;

[0031] E3. Perform compatibility verification and load impact assessment for each candidate operation: In each operation assessment, call the compatibility coefficient calculation method defined by the service compatibility mapping analysis module to recalculate the compatibility coefficient of the new service-channel pair, verify whether it still meets the mapping table constraints, and estimate the load rate change of the relevant resource blocks after the operation.

[0032] E4. Based on the preset optimization goal, select the current optimal operation from the candidate operations and update the resource allocation status;

[0033] E5. Repeat steps E2 to E4 until the load distribution tends to be balanced or the preset maximum number of iterations is reached;

[0034] E6. The final optimized resource block allocation scheme is transformed into an operation sequence, which serves as the optimized resource scheduling instruction set.

[0035] Compared with existing technologies, the beneficial effects of this invention are as follows: By using deep packet inspection and streaming media protocol parsing, the traffic characteristics of monitoring services and WiFi user services are identified and quantified. Combined with real-time spectrum scanning to obtain channel interference profiles, the complex network environment is transformed into a structured service-channel state matrix, providing an accurate and comprehensive data foundation for subsequent intelligent decision-making, and realizing the digitization and perceptibility of network status. Based on dynamic service weights and channel interference information, the probability of interference conflicts between different services coexisting in the same or adjacent channels is quantitatively analyzed, generating a service quality level-channel compatibility mapping table. This enables compatibility prediction between service requirements and channel conditions, transforming abstract service quality requirements into specific and measurable channel compatibility coefficients, providing a key basis for resource allocation, and reducing interference risks from the source. Based on the generated... The Service Quality of Service (SQSS) level-channel compatibility mapping table slices spectrum resources in a three-dimensional manner across frequency, space, and time, forming virtual resource blocks. Initial pre-allocation is performed using a constrained optimization algorithm, breaking away from the traditional single-dimensional resource allocation model. This enables the collaborative integration and segmentation of multi-dimensional resources, pre-matching the most suitable resource containers for different service levels, improving spectrum utilization efficiency and the rationality of resource allocation. By monitoring resource block load in real time and triggering a dynamic scheduling engine based on a heuristic search algorithm when there is uneven load or performance degradation, the system possesses continuous load balancing and interference avoidance capabilities. It can dynamically adjust resource allocation strategies according to real-time network conditions, effectively responding to traffic fluctuations and channel environment changes, ensuring the experience of high-priority services, and ultimately improving the throughput and stability of the entire campus network. Attached Figure Description

[0036] Figure 1 This is a block diagram of an integrated high-density surveillance WiFi coverage system for a park proposed in this invention. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] Reference Figure 1 A high-density monitoring WiFi coverage integrated system for a park, comprising a service channel feature acquisition module, a service compatibility mapping analysis module, a resource block pre-allocation slicing module, and a resource block scheduling optimization module;

[0039] Service channel feature acquisition module: Collects features of all service flows within the campus, including features of monitoring service flows and WiFi user data service flows, and simultaneously scans the interference spectrum of each channel to generate a service-channel state matrix;

[0040] Service compatibility mapping analysis module: Based on the service-channel state matrix, it determines various services (including monitoring services and WiFi user data services); through the interference compatibility analysis algorithm, it calculates the probability of interference collisions when different service types coexist in the same or adjacent channels, and then obtains the service quality level-channel compatibility mapping table;

[0041] Resource block pre-allocation slicing module: Based on the service quality level-channel compatibility mapping table, virtual resource blocks are formed by three-dimensional slicing of available spectrum from the frequency, spatial and temporal dimensions, and a constraint optimization algorithm is used to pre-allocate resources to generate an initial three-dimensional resource slicing scheme;

[0042] Resource block scheduling optimization module: Based on the initial three-dimensional resource slicing scheme and service-channel state matrix, it performs load monitoring and dynamic scheduling optimization on virtual resource blocks and outputs the optimized resource scheduling instruction set.

[0043] It should be further explained that, in the specific implementation process, the service channel feature acquisition module collects the features of all service flows within the campus, including the features of monitoring service flows and WiFi user data service flows, and simultaneously scans the interference spectrum of each channel to generate the service-channel state matrix. The process includes:

[0044] For monitoring service flows, APs (access points) deployed in various areas of the park establish a collaborative mechanism with the monitoring streaming media server: real-time stream information of the monitoring service flows is obtained through the API interface of the streaming media server, the SDP protocol description field is parsed, and the encoding resolution and target frame rate of each video stream are extracted. The I / P frame ratio is obtained by calculating the I frame and P frame interval, thereby obtaining the characteristics of all monitoring services in the park.

[0045] For WiFi user data service flows, the network access function of the AP receives uplink data packets sent from WiFi user terminals (such as mobile phones and laptops); using the DPI engine (Deep Packet Inspection), the AP performs load depth analysis on the data packets at the uplink, and combines the transport layer port number, application layer protocol fingerprint, and load feature pattern matching to classify the WiFi user data service flow into different sub-service types, including high bit rate and low latency services (such as video conferencing services), bursty interactive services (such as web browsing services), and background high bandwidth services (such as file download services).

[0046] Understandably, this invention integrates a DPI (Deep Packet Inspection) engine at the uplink of the AP. The specific workflow of this engine is as follows: The AP's network interface card (NIC) operates in promiscuous mode, capturing all data packets passing through it. The captured raw data packets are preprocessed, including: checksum verification, protocol anomaly packet filtering, and preliminary terminal type identification based on MAC addresses (e.g., distinguishing between mobile phones, laptops, etc.). The DPI engine parses the preprocessed data packets layer by layer, extracting key features for classification. These key features include: transport layer features: parsing the TCP / UDP header, obtaining the source / destination port numbers, and establishing and maintaining a port number-common application mapping table (e.g., port number 443 is often associated with HTTPS (HTTP)). (Secure) protocol association, port numbers 5060 and 5061 are associated with the SIP protocol) as a preliminary classification clue; Application layer protocol fingerprinting: pattern matching is performed on the first few bytes (e.g., the first 100-200 bytes) of the data packet payload, and compared with the built-in application layer protocol feature library, which includes handshake patterns, keywords or fixed fields of various application layer protocols; Deep payload feature pattern matching: for data streams that have already identified application layer protocols, their payload content is further analyzed in depth, achieved through regular expressions or specific byte sequence pattern matching; Business classification decision is based on the correlation analysis of multiple data packets in the initial stage of the data stream: the DPI engine extracts features The system combines port, protocol fingerprint, and initial payload pattern with dynamic behavior characteristics of the data flow (e.g., packet size distribution, sending interval, and traffic persistence) to make a comprehensive judgment using a built-in rule set: if signaling protocols such as SIP are identified and accompanied by small, timed audio / video streams (RTP / RTCP), it is determined to be a high-bitrate, low-latency service (such as video conferencing); if HTTP / HTTPS protocols are identified and the traffic pattern shows short-term, bursty multiple connections, it is determined to be a bursty interactive service (such as web browsing); if continuous, high-speed data transmission is identified (such as FTP or HTTP large file downloads), it is determined to be a background high-bandwidth service (such as file downloads).

[0047] The AP with spectrum analysis capability performs a synchronous scanning task of the interference spectrum of each channel. During the scanning process, the AP silently listens on each channel for a predetermined duration, measures and generates a channel interference profile, which includes the busy-idle ratio of the CCA (Channel Busy Indicator) of the Wi-Fi network and the interference power spectral density of non-Wi-Fi signals (such as Bluetooth and ZigBee).

[0048] Finally, the central controller generates an initial service-channel state matrix: the row index of the matrix is ​​the physical channel number (e.g., CH1, CH6, CH11), and the column index is the service type, which includes monitoring services and WiFi user data services; each matrix cell uniquely corresponds to a channel-service type pair, and each cell is filled with three data items: a) the number of currently active sessions belonging to the service type corresponding to the cell on the channel corresponding to the cell; b) the real-time interference profile of the channel corresponding to the cell; c) the dynamic service weight: for monitoring services, this weight is a bitstream strength factor calculated based on the monitoring service characteristics (resolution, frame rate, I / P frame ratio) (bitstream strength factor = resolution × frame rate × I-frame ratio × (1 + I / P frame ratio)). For WiFi user data services, this weight is based on a preset priority factor for each WiFi user data sub-service type. This priority factor directly quantifies the channel resource usage and stability requirements of each sub-service type. In essence, DPI identifies the application type (e.g., video conferencing, web browsing, file downloading). Service dimension scoring standards are set (each item scored from 1 to 5 points), specifically: Latency Sensitivity: Video conferencing = 5, Web browsing = 3, File downloading = 1; Bandwidth Requirement: Video conferencing = 4, File downloading = 5, Web browsing = 2; Packet Loss Sensitivity: Video conferencing = 5, Web browsing = 2, File downloading = 1; Session Persistence: Long-connection services (e.g., downloading) score higher, short-connection services (e.g., web browsing) score lower. The priority factor is calculated based on the set service dimension scoring standards. In the formula, Weights are assigned to each dimension (latency sensitivity, bandwidth requirement, packet loss sensitivity, and session persistence). Scoring is done for each dimension. The total number of dimensions for evaluating business priorities. An index for business priority evaluation dimensions.

[0049] It should be further explained that, in the specific implementation process, the service compatibility mapping analysis module determines various services based on the service-channel state matrix; the process of calculating the interference conflict probability of different service types coexisting in the same or adjacent channels through the interference compatibility analysis algorithm, and then obtaining the service quality level-channel compatibility mapping table, includes:

[0050] The central controller reads the service-channel state matrix and analyzes the dynamic service weight of each cell in the matrix: For monitoring services, when its bitstream strength factor is higher than the preset bitstream strength threshold, its service quality level is defined as high service quality level, requiring transmission latency ≤50ms and jitter ≤20ms; For WiFi user data services, the service quality level is divided according to the preset priority factor corresponding to its sub-service type: High-priority services (such as video conferencing) guarantee downlink 2Mbps or uplink 512Kbps, corresponding to high service quality level; Standard-priority services (such as web browsing) guarantee downlink 1Mbps, corresponding to medium service quality level; Low-priority services (such as file download) correspond to basic service quality level.

[0051] The real-time interference profile of each channel in the integrated service-channel state matrix is ​​determined based on the dynamic weights of each service defined in the matrix (i.e., the bitstream strength factor of the monitoring service and the priority factor of the WiFi user data service). Services with dynamic weights higher than a preset weight threshold are identified as high-weight services, and vice versa. For co-channel compatibility, mutual interference is analyzed: the ratio of the signal power of the high-weight service to the interference power of the low-weight service (i.e., the signal-to-interference ratio, SIR) is calculated, and it is determined whether this ratio is lower than a preset dynamic adjustment threshold (e.g., based on 15dB, with an additional 0-5dB compensation value based on the priority factor of the high-weight service). The probability of occurrence is calculated, i.e., the co-channel interference collision probability p1. Then, the compatibility coefficient in the co-channel scenario = 1 - p1. For adjacent channel compatibility, out-of-band interference is analyzed. Calculate the out-of-band leakage power of high-weight services in adjacent channels, determine whether this power exceeds the sensitivity threshold of the receiver for low-weight services, and calculate its occurrence probability, i.e., the adjacent channel leakage interference probability p2. Then, the compatibility coefficient in the adjacent channel scenario = 1 - p2. Here, the preset dynamic adjustment threshold value is a threshold set for the signal-to-interference ratio (SIR) and dynamically adjusted based on the priority of high-weight services (e.g., a base value of 15dB, which can be increased by 0-5dB with priority), used to determine whether the co-channel interference reaches an unacceptable level. The receiver sensitivity threshold refers to the minimum input power level required for the receiver to correctly demodulate the signal. If the out-of-band leakage power of adjacent channels exceeds the sensitivity threshold, it will cause non-negligible interference to the receiver. Finally, the minimum value of the compatibility coefficients of the two scenarios is taken as the compatibility coefficient of the service-channel pair.

[0052] Generate a service quality level-channel compatibility mapping table; where the row dimension of the table is set to service type, and the column dimension is set to physical channel number (e.g., CH1, CH6, CH11, etc.). Each data item in the table corresponds to a service-channel pair and is presented in the form of a tuple of {service quality level; channel compatibility coefficient}.

[0053] It should be further explained that, in the specific implementation process, the resource block pre-allocation slicing module forms virtual resource blocks by dividing the available spectrum into three-dimensional slices based on the service quality level-channel compatibility mapping table from the frequency, spatial, and temporal dimensions, and uses a constrained optimization algorithm to pre-allocate resources. The process of generating the initial three-dimensional resource slicing scheme includes:

[0054] The central controller acquires the service quality level-channel compatibility mapping table and extracts the defined service quality levels and compatibility coefficients with each channel. Based on the mapping table, the total available spectrum is divided in the frequency dimension: Based on the full-channel interference profile obtained from spectrum scanning, and according to a preset interference power spectral density threshold, contaminated channels (e.g., continuously occupied ZigBee channels) whose interference power spectral density consistently exceeds the threshold are removed, thus determining the actually usable and relatively clean spectrum range. Within the determined actually usable spectrum range, the total available spectrum is divided into several sub-bands according to the compatibility coefficient requirements between adjacent sub-bands to avoid high-interference services being concentrated in adjacent frequency bands. In the spatial dimension, the beam coverage range of each AP is calculated based on its deployment location and antenna configuration parameters (e.g., half-power beamwidth), and the campus is divided into multiple virtual cells. Resources are independently allocated within each virtual cell, and the division is based on the service type and corresponding compatibility constraints within each virtual cell. A virtual cell refers to a unit composed of the AP's coverage range used for spatial resource reuse. Specifically, the formula for calculating the beam coverage range is: In the formula, For the first The coverage radius of each AP For carrier wavelength, This refers to the AP's transmit power. For time variables, For antenna gain, This is the antenna's half-power beamwidth. For receiving sensitivity, The path loss index is used; in the time dimension, the wireless channel transmission time is divided into dynamic time slots, and the time slot length is dynamically adjusted according to the service quality level in the mapping table. For example, short time slots (such as 2ms) are allocated to high-level low-latency services, and standard time slots (such as 5ms) are allocated to flexible services; through the coordinated division of spectrum resources in the three dimensions of frequency, space and time, the basic framework of the campus network virtual resource block is constructed.

[0055] Based on the constructed virtual resource block framework of the campus network, a constrained optimization algorithm is used for resource pre-allocation: The compatibility coefficient and service quality level of each service-channel pair are input into the mapping table; when allocating resources for monitoring services, the service quality level (high service quality level) in the mapping table and the compatibility coefficient with each channel are read, and channels with high compatibility coefficients are selected, allocating dedicated resource blocks with short time slot periods to ensure low latency requirements; the bitrate strength factor of the monitoring service is calculated during allocation to ensure that the allocated resource block capacity meets its bandwidth requirements; when allocating resources for WiFi user data services, shared resource block combinations are flexibly allocated based on the service quality level corresponding to its sub-service type in the mapping table and the compatibility coefficient with each channel; for example, a continuous set of resource blocks is allocated to video conferencing services that belong to the same high service quality level and have high compatibility coefficients, while scattered resource blocks with lower compatibility coefficient requirements are allocated to background download services.

[0056] The final generated 3D resource slicing scheme is presented in the form of a structured data table, which records the allocation results of each virtual resource block. Each resource block is uniquely identified by three coordinates: frequency band index (corresponding to frequency sub-band), cell identifier (corresponding to virtual cell), and time slot number (corresponding to transmission time window). It also records the pre-allocated service type and its corresponding compatibility coefficient for each resource block. The 3D resource slicing scheme is stored in the central controller in the form of a database table.

[0057] It should be further explained that, in the specific implementation process, the resource block scheduling optimization module, based on the initial three-dimensional resource slicing scheme and the service-channel state matrix, performs load monitoring and dynamic scheduling optimization of virtual resource blocks, and outputs the optimized resource scheduling instruction set. The process includes:

[0058] The central controller performs real-time monitoring of the service load rate of each virtual resource block. It collects dynamic service weights and channel interference profiles within each resource block in real-time using a service-channel state matrix. Load rate calculation is performed independently for each virtual resource block: D1. Obtain all dynamic service weights and calculate the current total weight requirement based on these weights: for monitoring services, this is the sum of the bitrate strength factors of all video streams within the resource block; for WiFi user data services, it is the sum of the preset priority factors of all sessions. D2. Combine the real-time interference profile of the channel where the virtual resource block is located to assess its current actual available capacity: this assessment is based on Shannon's formula, whose theoretical maximum capacity is positively correlated with the resource block bandwidth; the actual available capacity needs to be determined based on this, according to the real-time interference profile of the channel where the resource block is located. Interference profile reduction calculation is performed based on two key parameters from the service channel feature acquisition module: d1, the busy / idle ratio of the Wi-Fi network's CCA, which directly reflects the proportion of time the channel is occupied by co-channel services; d2, the interference power spectral density of non-Wi-Fi signals, which reflects the strength of unavoidable co-channel interference signals; the specific formula for calculating the actual available capacity is: Actual available capacity = Theoretical maximum capacity × (1 - CCA busy / idle ratio) × [1 - (Interference power spectral density / Interference power tolerance)], where the interference power tolerance is a constant preset based on receiver performance; D3, the real-time load rate of the resource block is calculated based on the current total weight requirement and the current actual available capacity: Real-time load rate = Current total weight requirement / Current actual available capacity;

[0059] When a resource allocation status deviates from the preset optimal operating range, the dynamic scheduling engine is triggered. This deviation includes the following two scenarios: 1) The real-time load rate of any virtual resource block exceeds the preset load threshold corresponding to its pre-allocated service type (this threshold is determined by the service quality level defined in the service quality level-channel compatibility mapping table; for example, the load threshold for an exclusive resource block allocated for monitoring services is set to 0.85); 2) The service-channel state matrix constructed and maintained by the service channel feature acquisition module detects a transmission conflict caused by an excessively low co-channel or adjacent channel compatibility coefficient (calculated and recorded in the mapping table by the service compatibility mapping analysis module). The specific criteria for determining the transmission conflict are that performance indicators such as the bit error rate continuously exceed the service quality tolerance threshold defined in the service compatibility mapping analysis module for that service type.

[0060] Once the dynamic scheduling engine is triggered, it uses the service quality level-channel compatibility mapping table as a constraint to ensure that after scheduling changes, the compatibility coefficient of the new service-channel pair is not lower than the minimum value required for the service quality level of that service in the mapping table. The optimization objective is to find a new resource block allocation combination that minimizes the load rate of high-load resource blocks and optimizes the overall network throughput, while satisfying the constraint.

[0061] Meanwhile, the dynamic scheduling engine uses a heuristic search algorithm to obtain the resource scheduling instruction set: E1, with the current three-dimensional resource slicing scheme as the initial state, load the service-channel state matrix and the service quality level-channel compatibility mapping table;

[0062] E2. Generate candidate resource adjustment operations according to predefined resource optimization rules, including: Service migration: migrate high-service quality-of-service (SQS) level services in high-load resource blocks to resource blocks with compatibility coefficients and lower current load rates; Resource merging: merge multiple non-contiguous resource blocks occupied by scattered low-service SQS level services (such as background download services) into contiguous time slot resource blocks to reduce channel switching overhead and interference accumulation; Resource splitting: subdivide overloaded resource blocks in the frequency or time dimension and introduce new resource blocks to share the load.

[0063] E3. Perform compatibility verification and load impact assessment for each candidate operation: In each operation assessment, call the compatibility coefficient calculation method defined by the service compatibility mapping analysis module to recalculate the compatibility coefficient of the new service-channel pair and verify whether it still meets the mapping table constraints; and estimate the load rate change of the relevant resource blocks after the operation to ensure that it does not exceed the preset load threshold corresponding to the service quality level of the service.

[0064] E4. Based on preset optimization objectives (e.g., prioritizing the experience of high-quality services while minimizing the maximum system load and maximizing overall network throughput), select the current optimal operation from the candidate operations and update the resource allocation status.

[0065] E5. Repeat steps E2 to E4 until the load distribution tends to be balanced (e.g., the load rate of all resource blocks is lower than the load rate threshold corresponding to their service quality level) or the preset maximum number of iterations is reached.

[0066] E6. Transform the final optimized resource block allocation scheme into an operation sequence as the optimized resource scheduling instruction set.

[0067] The generated resource scheduling instruction set sets the new ternary coordinates (new frequency band index, new cell identifier, and new time slot number) of the service type to be adjusted and its target virtual resource block. At the same time, the instruction set is sent by the central controller to the relevant APs and streaming media servers through the southbound interface to complete this optimization scheduling.

[0068] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0069] It should be understood that determining B based on A does not mean determining B solely based on A; it also means determining B based on A and / or other information.

[0070] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0071] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A high-density surveillance WiFi coverage integrated system for a park, characterized in that: It includes a service channel feature acquisition module, a service compatibility mapping analysis module, a resource block pre-allocation slicing module, and a resource block scheduling optimization module; Service channel feature acquisition module: Collects features of all service flows within the campus, including features of monitoring service flows and WiFi user data service flows, and simultaneously scans the interference spectrum of each channel to generate a service-channel state matrix; Service compatibility mapping analysis module: Based on the service-channel state matrix, it identifies various services; through interference compatibility analysis algorithms, it calculates the probability of interference collisions between different service types coexisting in the same or adjacent channels, thereby obtaining a service quality level-channel compatibility mapping table, specifically including: The central controller reads the service-channel state matrix and analyzes the dynamic weight of each service in the matrix: For monitoring services, when their bitstream strength factor is higher than the preset bitstream strength threshold, their service quality level is defined as high service quality level; for WiFi user data services, the service quality level is divided according to the preset priority factor corresponding to their sub-service type: high priority services correspond to high service quality level; standard priority services correspond to medium service quality level; low priority services correspond to basic service quality level; the real-time interference profile of each channel in the service-channel state matrix is ​​synthesized and judged according to the dynamic weight of each service defined in the matrix: services with dynamic weights higher than the preset weight threshold are identified as high-weight services, and vice versa; for co-channel compatibility, the mutual interference situation is analyzed: high... The ratio of the signal power of the weighted service to the interference power of the low-weighted service is used to determine whether this ratio is lower than a preset dynamic adjustment threshold. The probability of this ratio occurring, i.e., the probability of co-channel interference collision p1, is calculated. Then, the compatibility coefficient in the co-channel scenario is 1-p1. For adjacent channel compatibility, the out-of-band interference situation is analyzed: the out-of-band leakage power of the high-weighted service in the adjacent channel is calculated, and it is determined whether this power exceeds the sensitivity threshold of the receiver of the low-weighted service. The probability of this ratio occurring, i.e., the probability of adjacent channel leakage interference p2, is calculated. Then, the compatibility coefficient in the adjacent channel scenario is 1-p2. Finally, the minimum value of the compatibility coefficients of the two scenarios is taken as the compatibility coefficient of the service-channel pair. A service quality level-channel compatibility mapping table is generated. In the table, the row dimension is set as the service type, the column dimension is set as the physical channel number, and each data item in the table corresponds to each service-channel pair. Resource block pre-allocation slicing module: Based on the service quality level-channel compatibility mapping table, virtual resource blocks are formed by three-dimensional slicing of available spectrum from the frequency, spatial and temporal dimensions, and a constraint optimization algorithm is used to pre-allocate resources to generate an initial three-dimensional resource slicing scheme; Resource block scheduling optimization module: Based on the initial three-dimensional resource slicing scheme and service-channel state matrix, it performs load monitoring and dynamic scheduling optimization on virtual resource blocks and outputs the optimized resource scheduling instruction set.

2. The integrated high-density surveillance WiFi coverage system for a park according to claim 1, characterized in that: The service channel feature acquisition module collects features of all service flows within the campus, including features of monitoring service flows and WiFi user data service flows, and simultaneously scans the interference spectrum of each channel. The process of generating the service-channel state matrix includes: For monitoring service streams, APs deployed in various areas of the park establish a collaborative mechanism with the monitoring streaming media server: real-time stream information of the monitoring service stream is obtained through the API interface of the streaming media server, the SDP protocol description field is parsed, and the encoding resolution and target frame rate of each video stream are extracted. The I / P frame ratio is obtained by calculating the I frame and P frame interval, thereby obtaining the characteristics of all monitoring services in the park. For WiFi user data service flows, the uplink data packets sent from WiFi user terminals are received through the network access function of the AP; the DPI engine is used to perform load depth analysis on the data packets at the AP uplink, and the WiFi user data service flows are classified into different sub-service types by combining the transport layer port number, application layer protocol fingerprint and load feature pattern matching. The AP with spectrum analysis capability performs a synchronous scanning task of the interference spectrum of each channel, measures and generates a channel interference profile, which includes the busy / idle ratio of the Wi-Fi network and the interference power spectral density of non-Wi-Fi signals. Finally, the central controller generates an initial service-channel state matrix: the row index of the matrix is ​​the physical channel number, and the column index is the service type, which includes monitoring services and WiFi user data services; each matrix cell uniquely corresponds to a channel-service type pair, and each cell is filled with three data items: a) the number of currently active sessions belonging to the service type corresponding to the cell on the channel corresponding to the cell; b) the real-time interference profile of the channel corresponding to the cell; c) the dynamic service weight: for monitoring services, this weight is a bitstream strength factor calculated based on the monitoring service characteristics; for WiFi user data services, this weight is a priority factor preset according to the WiFi user data sub-service type.

3. The integrated high-density surveillance WiFi coverage system for a park according to claim 1, characterized in that: The process by which the resource block pre-allocation slicing module forms virtual resource blocks from available spectrum three-dimensional slices in terms of frequency, space, and time dimensions based on the service quality level-channel compatibility mapping table includes: The central controller acquires the service quality level-channel compatibility mapping table and extracts the defined service quality levels and compatibility coefficients with each channel. Based on the mapping table, the total available spectrum is divided in the frequency dimension: based on the full-channel interference profile obtained from spectrum scanning, and according to a preset interference power spectral density threshold, contaminated channels whose interference power spectral density continuously exceeds the threshold are removed. Based on the compatibility coefficient requirements between adjacent sub-bands, the total available spectrum is divided into several sub-bands. In the spatial dimension, the beam coverage of each AP is calculated based on its deployment location and antenna configuration parameters, and the campus is divided into multiple virtual cells. Resources are independently allocated within each virtual cell, and the division is based on the service type and corresponding compatibility constraints within each virtual cell. In the temporal dimension, the wireless channel transmission time is divided into dynamic time slots, and the time slot length is dynamically adjusted according to the service quality level in the mapping table. Through the coordinated division of spectrum resources in the frequency, spatial, and temporal dimensions, the basic framework of the campus network virtual resource block is constructed.

4. The integrated high-density surveillance WiFi coverage system for a park according to claim 3, characterized in that: The resource block pre-allocation slicing module uses a constrained optimization algorithm to pre-allocate resources, and the process of generating the initial 3D resource slicing scheme includes: Based on the constructed virtual resource block framework of the campus network, a constrained optimization algorithm is used for resource pre-allocation: the compatibility coefficient and service quality level of each service-channel pair in the input mapping table are used; when allocating resources for monitoring services, the service quality level of the service and the compatibility coefficient with each channel in the mapping table are read, and channels with high compatibility coefficients are selected to allocate exclusive resource blocks with short time slot periods; the bit rate strength factor of the monitoring service is calculated during allocation; when allocating resources for WiFi user data services, the shared resource block combination is flexibly allocated according to the service quality level corresponding to its sub-service type in the mapping table and the compatibility coefficient with each channel. The final generated 3D resource slicing scheme is presented in the form of a structured data table, which records the allocation results of each virtual resource block. Each resource block is uniquely identified by three coordinates: frequency band index, cell identifier, and time slot number. The scheme also records the pre-allocated service type and its corresponding compatibility coefficient for each resource block.

5. The integrated system for high-density WiFi coverage monitoring in a park according to claim 1, characterized in that: The resource block scheduling optimization module, based on the initial three-dimensional resource slicing scheme and the service-channel state matrix, performs load monitoring and dynamic scheduling optimization on virtual resource blocks, and outputs the optimized resource scheduling instruction set. The process includes: The central controller performs real-time monitoring of the service load rate of each virtual resource block. It collects dynamic service weights and channel interference profiles within each resource block in real time using a service-channel state matrix. Load rate calculation is performed independently for each virtual resource block: D1. Obtain all dynamic service weights and calculate the current total weight requirement based on these weights: for monitoring services, this is the sum of the bitrate strength factors of all video streams within the resource block; for WiFi user data services, it is the sum of the preset priority factors of all sessions. D2. Evaluate the current actual available capacity by combining the real-time interference profile of the channel where the virtual resource block is located. D3. Calculate the real-time load rate of the resource block based on the current total weight requirement and the current actual available capacity. When the resource allocation status is detected to deviate from the preset optimal operating range, the dynamic scheduling engine is triggered; Once the dynamic scheduling engine is triggered, it uses the service quality level-channel compatibility mapping table as a constraint. At the same time, the dynamic scheduling engine uses a heuristic search algorithm to obtain the resource scheduling instruction set.

6. The integrated system for high-density WiFi coverage monitoring in a park according to claim 5, characterized in that: The dynamic scheduling engine uses a heuristic search algorithm to obtain the resource scheduling instruction set. The process includes: E1. Using the current 3D resource slicing scheme as the initial state, load the service-channel state matrix and the service quality level-channel compatibility mapping table; E2. Generate candidate resource adjustment operations according to predefined resource optimization rules; E3. Perform compatibility verification and load impact assessment for each candidate operation: In each operation assessment, call the compatibility coefficient calculation method defined by the service compatibility mapping analysis module to recalculate the compatibility coefficient of the new service-channel pair, verify whether it still meets the mapping table constraints, and estimate the load rate change of the relevant resource blocks after the operation. E4. Based on the preset optimization goal, select the current optimal operation from the candidate operations and update the resource allocation status; E5. Repeat steps E2 to E4 until the load distribution tends to be balanced or the preset maximum number of iterations is reached; E6. The final optimized resource block allocation scheme is transformed into an operation sequence, which serves as the optimized resource scheduling instruction set.

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