Intelligent mine heterogeneous network cooperative anti-interference method and system

Abstract

The invention provides an intelligent mine heterogeneous network cooperative anti-interference method and system, and the method comprises the steps: monitoring a communication signal in a communication channel in real time through each heterogeneous network node of an intelligent mine, carrying out the generalized weighted inverse discrete Fourier transform processing of the obtained communication signal, obtaining a weighted frequency domain signal, and carrying out the interference detection; sharing the detected interference information to all heterogeneous networks; and dynamically adjusting generalized weighted inverse discrete Fourier transform parameters and spectrum resources of each heterogeneous network according to the shared interference information. According to the method, the generalized WFRFT technology is applied, a heterogeneous network cooperative anti-interference mechanism and a dynamic spectrum allocation strategy are combined, various complex interferences in the mine heterogeneous network are effectively suppressed, and the communication stability and the anti-interference capability of the network are improved.

Description

Technical Field

[0001] The present invention relates to the field of mine communication technology, and in particular to a method and system for collaborative anti-interference of intelligent mine heterogeneous networks. Background Art

[0002] As the intelligentization of the mining industry continues to advance, communication networks have become an indispensable infrastructure in mining operations. The communication needs of smart mines cover a wide range of areas from underground to the ground, involving a variety of applications such as equipment control, remote monitoring, and data collection. In order to meet these diverse needs, mines usually adopt a heterogeneous network architecture, that is, a communication network composed of multiple different types of network systems, including wireless sensor networks, WiFi networks, cellular networks (such as 4G, 5G) and dedicated communication systems (such as LoRa, ZigBee, etc.). These networks work together to ensure the stability and safety of mining operations.

[0003] However, the particularity of the mining environment has brought huge challenges. There are a lot of electromagnetic interference, environmental noise, terrain shielding and other problems in the mine, which makes the existing communication system vulnerable to interference, especially in heterogeneous networks, where various networks are prone to mutual interference due to overlapping spectrum resources or differences in communication protocols. In addition, the dynamic nature of mining operations also increases the uncertainty of communication, such as equipment movement and mine car scheduling. These interferences and environmental complexities have put forward higher requirements on the communication reliability of heterogeneous networks in mines.

[0004] At present, anti-interference technology in wireless communication systems mainly focuses on conventional means such as frequency hopping, time-frequency coding, and diversity reception. These technologies are effective in a single network system, but their effectiveness is often limited in the complex heterogeneous network environment of mines. The reasons are: (1) Limitations of spectrum resources: Existing anti-interference technologies are mostly based on fixed spectrum resources, while in heterogeneous network environments, spectrum resources are often shared. The overlap and congestion of the spectrum lead to increased mutual interference between networks. (2) Lack of collaborative working mechanism between networks: Different networks use different protocols and modulation methods. Existing anti-interference technologies are usually only applicable to a single network and fail to fully utilize the synergy of heterogeneous networks. In a mining environment, the anti-interference ability of a single network is limited and it is difficult to cope with multi-source interference. (3) Complexity of interference sources: The interference sources in mines are diverse, including electromagnetic interference, broadband noise, equipment transmission interference, etc. Traditional anti-interference technologies cannot fully cope with these complex interference sources.

[0005] The introduction of the generalized weighted inverse discrete Fourier transform (WFRFT) technology provides a new direction for solving this problem. WFRFT is a flexible signal transformation method that can redistribute signal energy and increase the signal's anti-interference ability by weighted processing of the signal. The WFRFT transform has the characteristic of adjustable order, which makes it more flexible in dealing with different types of interference. Compared with traditional frequency hopping or coded modulation technology, WFRFT technology can more effectively disperse interference energy, thereby improving the robustness of the communication system.

[0006] However, the existing WFRFT technology is mainly applied to a single network or system, and is mostly carried out in theoretical research and simulation, lacking practical application exploration in heterogeneous networks. In a heterogeneous network environment, different networks have different spectrum requirements and anti-interference requirements. Therefore, extending the WFRFT technology to the anti-interference field of heterogeneous networks is an innovative direction with important application prospects. Summary of the invention

[0007] The purpose of the present invention is to provide a method and system for collaborative anti-interference of intelligent mine heterogeneous networks, perform weighted processing on signals in heterogeneous networks through generalized weighted inverse discrete Fourier transform (WFRFT), and design a cross-network collaborative anti-interference mechanism to achieve dynamic spectrum allocation and resource optimization, thereby significantly improving the anti-interference performance and communication reliability of mine heterogeneous networks.

[0008] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0009] The present invention provides a smart mine heterogeneous network collaborative anti-interference method, comprising:

[0010] The communication signals in the communication channel are monitored in real time by the heterogeneous network nodes of the intelligent mine, and the acquired communication signals are processed by generalized weighted inverse discrete Fourier transform to obtain weighted frequency domain signals, and interference detection is performed; the heterogeneous networks in the mine environment include WiFi networks, wireless sensor networks and cellular networks; the communication signals include: signal strength, signal-to-noise ratio and bit error rate;

[0011] Share detected interference information to all heterogeneous networks;

[0012] The generalized weighted inverse discrete Fourier transform order and spectrum resources of each heterogeneous network are dynamically adjusted according to the shared interference information.

[0013] Preferably, the performing interference detection includes:

[0014] If the interference information detected exceeds the preset threshold, the interference reporting mechanism is triggered.

[0015] Preferably, the interference detection further includes:

[0016] For WiFi networks, the electromagnetic interference intensity in the communication channel is detected. When the electromagnetic interference intensity is detected to be higher than -80dBm, the interference reporting mechanism is triggered and spectrum resource adjustment is requested;

[0017] For wireless sensor networks, narrowband noise or device noise in the environment is detected. When the noise interference intensity exceeds -80dBm, the interference reporting mechanism is triggered and the interference information is shared with other networks.

[0018] Preferably, the sharing of the detected interference information to all heterogeneous networks includes:

[0019] The interference information is transmitted to other heterogeneous networks through a cooperative protocol, wherein the interference information includes: interference frequency band, interference intensity and interference source type.

[0020] Preferably, the dynamically adjusting the generalized weighted inverse discrete Fourier transform order and spectrum resources of each heterogeneous network according to the shared interference information includes:

[0021] Switch the communication tasks of heterogeneous networks that are severely interfered to the frequency band with the least interference;

[0022] For broadband interference environments, increase the order of the generalized weighted inverse discrete Fourier transform;

[0023] For narrowband interference environments, the order of the generalized weighted inverse discrete Fourier transform is reduced.

[0024] Preferably, before the real-time monitoring of the communication signals in the communication channel by the heterogeneous network nodes of the intelligent mine, the method further comprises:

[0025] Real-time monitoring of the usage of each spectrum resource, including channel quality, interference intensity and communication demand;

[0026] According to the communication needs and task urgency of each heterogeneous network, priorities are assigned to different heterogeneous networks, and priority use rights are allocated to heterogeneous networks for emergency tasks.

[0027] Preferably, after dynamically adjusting the generalized weighted inverse discrete Fourier transform order and spectrum resources of each heterogeneous network according to the shared interference information, the method further includes:

[0028] After the interference is eliminated, the allocated spectrum resources are recovered.

[0029] Preferably, the method further comprises:

[0030] Obtain historical disturbance data of the mining environment and build a predictive model through machine learning algorithms to predict future disturbance patterns;

[0031] Based on the monitored spectrum resource usage of the current heterogeneous network, a prediction model is used to obtain the predicted interference pattern;

[0032] Based on the prediction results, the communication tasks of each heterogeneous network are scheduled to ensure timely adjustment of resource allocation before or during interference.

[0033] The present invention also provides a smart mine heterogeneous network collaborative anti-interference system, which is used to implement the above-mentioned smart mine heterogeneous network collaborative anti-interference method, and the system includes:

[0034] Heterogeneous network module: includes various heterogeneous networks in the mine environment, including WiFi networks, wireless sensor networks and cellular networks; various heterogeneous networks constitute the mine's heterogeneous communication system, and each network undertakes different communication tasks; each heterogeneous network node monitors the communication signal in the communication channel in real time; the communication signal includes: signal strength, signal-to-noise ratio and bit error rate;

[0035] WFRFT signal processing module: used to perform generalized weighted discrete Fourier inverse transform processing on the communication signal transmitted by each heterogeneous network, output weighted frequency domain signal; and adjust the order of generalized weighted discrete Fourier inverse transform;

[0036] Interference monitoring module: deployed on each heterogeneous network node, used to monitor the communication signal of the communication channel in real time and perform interference detection; the communication signal includes signal strength, signal-to-noise ratio and bit error rate;

[0037] Collaborative anti-interference module: used to process the interference information received from the interference monitoring module, transmit it to other heterogeneous networks through the cross-network collaborative protocol, and dynamically adjust the generalized weighted discrete Fourier transform order and spectrum resources of each heterogeneous network according to the shared interference information.

[0038] Preferably, the collaborative anti-interference module is specifically used to:

[0039] Interference information is exchanged in real time between heterogeneous networks through a cross-network collaboration protocol, wherein the interference information includes interference source type, interference frequency band, and interference intensity;

[0040] According to the shared interference information, the generalized weighted inverse discrete Fourier transform order and spectrum resources of each heterogeneous network are dynamically adjusted, including: switching the communication tasks of the heterogeneous network that is severely interfered to the frequency band with the least interference; and adjusting the generalized weighted inverse discrete Fourier transform order.

[0041] Preferably, the system further comprises: a resource optimization module, configured to adjust resources in advance based on interference patterns that may occur in the future,

[0042] The resource optimization module includes:

[0043] Prediction model: used to predict the interference pattern that will occur in the future based on the current monitoring of spectrum resource usage of heterogeneous networks; the prediction model is trained using a machine learning algorithm using historical interference data of the mining environment;

[0044] Resource allocation module: used to schedule the communication tasks of various heterogeneous networks based on the prediction results, ensuring timely adjustment of resource allocation before or during interference.

[0045] Preferably, the resource optimization module is specifically used to:

[0046] Based on the prediction model, the future interference pattern is obtained, which is expressed as the joint channel matrix ,

[0047] Take the channel state matrix The pseudo-inverse matrix of ,

[0048] Multiply the user information by the weight vector , after power allocation, it is sent and expressed as:

[0049] ,

[0050] in, is the signal received by the receiving end. is assigned to The transmission power of each user, is the element in the joint channel matrix, is the weight vector The elements in It is The transmission data of each user, is additive white Gaussian noise.

[0051] The beneficial effects of the present invention are:

[0052] The present invention provides a method and system for collaborative anti-interference of intelligent mine heterogeneous networks. The frequency domain distribution of the signal can be flexibly adjusted through the WFRFT technology, which can better adapt to changing interference scenarios and maintain the transmission stability of the signal. At the same time, the collaborative anti-interference mechanism works in coordination among multiple heterogeneous networks, which significantly improves the overall reliability of the network, while traditional single network anti-interference technology is difficult to achieve this effect. The method of the present invention applies generalized WFRFT technology, combines heterogeneous network collaborative anti-interference mechanism and dynamic spectrum allocation strategy, and realizes effective suppression of various complex interferences in mine heterogeneous networks, thereby improving the communication stability and anti-interference capability of the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1A schematic diagram of a process flow of a smart mine heterogeneous network collaborative anti-interference method provided by an embodiment of the present invention;

[0054] Figure 2 A schematic diagram of a smart mine heterogeneous network collaborative anti-interference system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0056] It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, only structures and / or processing steps closely related to the solutions according to the present invention are shown in the accompanying drawings, while other details that are not closely related to the present invention are omitted.

[0057] It should be emphasized that the term “include / comprises” when used herein refers to the presence of features, elements, steps or components, but does not exclude the presence or addition of one or more other features, elements, steps or components.

[0058] It should also be noted that, unless otherwise specified, the term “connection” herein may refer not only to a direct connection but also to an indirect connection involving an intermediate.

[0059] It should be emphasized here that the step marks mentioned below are not intended to limit the order of the steps, but it should be understood that the steps can be executed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be executed simultaneously.

[0060] The core of WFRFT application in mine heterogeneous networks is its flexible transformation order setting. Different communication signals can select suitable orders for weighted processing according to their characteristics. In the case of broadband signals, high-order WFRFT transformation can be used to maximize the diffusion effect of frequency distribution and reduce the concentrated influence of interference; in the case of narrowband signals, lower-order transformation can be selected to ensure the frequency domain concentration of the signal, thereby improving the signal strength and anti-interference ability.

[0061] The heterogeneous networks in the mining environment are composed of a variety of different communication networks, such as WiFi, wireless sensor networks, cellular networks, etc. The spectrum usage of these networks overlaps and crosses, and due to the complex environment, the types and intensities of interference vary. In such an environment, the anti-interference ability of a single network is limited, and it is difficult to effectively deal with multi-source and diversified interference. Therefore, an embodiment of the present invention proposes a collaborative anti-interference method for intelligent mine heterogeneous networks.

[0062] The core idea of ​​the collaborative anti-interference mechanism is to coordinate the anti-interference behaviors of different networks by sharing interference information across networks. For example, when a network is subject to strong interference, the network can pass the interference information to other networks through a sharing protocol. Other networks adjust their spectrum usage or change parameters based on the information to avoid entering the interference frequency band or area. This approach can effectively reduce mutual interference between heterogeneous networks and enhance the overall anti-interference capability of each network.

[0063] Based on the above invention concept, the first embodiment of the present invention provides a method for collaborative anti-interference of a heterogeneous network in an intelligent mine, see Figure 1 , including the following main steps:

[0064] S1. Interference detection through intelligent mine heterogeneous network nodes.

[0065] Nodes in each heterogeneous network monitor the channel quality, signal strength and interference of their frequency bands in real time. Once interference is detected to exceed the preset threshold, the interference reporting mechanism is triggered.

[0066] S2, share the interference information to all heterogeneous networks,

[0067] When a heterogeneous network detects strong interference, it transmits interference information such as interference frequency band, interference intensity, and interference type to other heterogeneous networks through a collaborative protocol.

[0068] S3. Each heterogeneous network adjusts the spectrum usage of its signal according to the shared interference information, or reduces the interference impact by adjusting the transformation order of WFRFT.

[0069] It should be noted that other heterogeneous networks make corresponding adjustments based on the shared interference information, including changing the communication frequency band, optimizing WFRFT transformation parameters, or switching to low-interference mode. In this way, different heterogeneous networks can achieve collaborative anti-interference in a dynamic environment and avoid interference conduction and superposition effects.

[0070] Due to the complexity of the environment, heterogeneous networks in mines often face the problem of spectrum resource congestion or overlapping interference frequency bands. To solve this problem, this embodiment designs a dynamic spectrum allocation method based on real-time monitoring. The method can dynamically adjust the allocation of spectrum resources according to the real-time needs of different networks, and preferentially allocate spectrum resources with less interference to networks that need high-reliability communication.

[0071] The process of dynamic spectrum allocation mainly includes the following steps:

[0072] 1. Spectrum usage monitoring: Real-time monitoring of the usage of each spectrum resource, including channel quality, interference intensity and communication needs.

[0073] 2. Spectrum priority setting: Assign priorities to different heterogeneous networks based on their communication needs and mission urgency. For example, priority is given to heterogeneous networks for emergency missions.

[0074] 3. Spectrum reallocation: When it is detected that the spectrum resources of a heterogeneous network are severely interfered with, the communication task of the heterogeneous network is switched to a frequency band with less interference, and the signal is optimized in combination with WFRFT technology to adapt to the new spectrum environment.

[0075] 4. Spectrum recovery and reallocation: After the task is completed or the interference is eliminated, the allocated spectrum resources are recovered for use by other heterogeneous networks. This spectrum recovery and reallocation mechanism ensures efficient use of resources.

[0076] In another preferred embodiment, resource optimization is not limited to the dynamic allocation of spectrum, but also includes the optimal use of various communication resources in heterogeneous networks. By combining machine learning algorithms, based on historical data and current heterogeneous network conditions, possible interference in the future is predicted, and the spectrum allocation strategy is adjusted in advance to avoid the negative impact of interference on communication quality.

[0077] In this implementation, the resource optimization mechanism is mainly implemented in the following ways:

[0078] Obtain historical disturbance data of the mining environment and build a prediction model through machine learning algorithms to predict possible disturbance patterns in the future;

[0079] Based on the current monitoring of the use of spectrum resources in heterogeneous networks, a prediction model is used to obtain possible interference patterns in the future. For example, the operation of certain devices may cause periodic interference, and the system can adjust the communication frequency band in advance before the interference occurs.

[0080] Based on the prediction results, the communication tasks of each heterogeneous network are scheduled to ensure that resource allocation can be adjusted in time before or during interference to avoid the impact of interference on the communication system.

[0081] The intelligent mine heterogeneous network collaborative anti-interference method proposed in the embodiment of the present invention is now described in detail in conjunction with specific embodiments, as follows.

[0082] In mining environments, WiFi networks are usually used for high-speed data transmission between mining equipment, such as surveillance video transmission and remote control, while wireless sensor networks (WSNs) are used to collect data from various sensors in mines, such as temperature, humidity, gas concentration, etc. These two types of networks overlap in spectrum resources and are more seriously affected by environmental interference. Common interference sources include broadband electromagnetic interference, equipment noise, and other environmental noise. Since WiFi networks have high transmission rates and large demands for spectrum resources, they are easily affected by broadband interference. Wireless sensor networks have limited power consumption and usually work on narrowband signals, making them susceptible to narrowband noise and sudden interference.

[0083] Based on the above intelligent mine heterogeneous network collaborative anti-interference method, the specific implementation steps are as follows:

[0084] (1) Obtain the original communication signal f(x) generated by various heterogeneous networks (such as WiFi, WSN, 4G / 5G, etc.) in the mining environment and process it through the WFRFT signal processing module. The signal can be data transmission, monitoring images, or control instructions.

[0085] The original signal f(x) is processed by generalized WFRFT, and the output signal is is the weighted frequency domain signal. Different networks use different WFRFT orders to adapt to their respective interference environments. For WiFi networks with severe broadband interference, multi-parameter WFRFT transformation is selected to increase the frequency domain diffusion of the signal; for WSNs with narrowband signals, single-parameter WFRF is used to enhance the concentration of the signal in the frequency domain.

[0086] The weighting coefficients of MP-WFRFT are:

[0087] ,

[0088] in, is the transformation order, is the weighting coefficient, , is the counting sequence number, is an imaginary unit,

[0089] ,

[0090] matrix is the parameter matrix of MP-WFRFT, , are the elements in the parameter matrix, is the matrix size, belongs to the set of natural numbers and ;when , When , it degenerates into a single parameter 4-WFRFT. In order to satisfy the boundary conditions of weighted fractional Fourier transform, it is necessary to make:

[0091] ,

[0092] is the impulse function, so and are all integer vectors, and their corresponding eigenvalues ​​are:

[0093] ,

[0094] When the values ​​in the parameter matrix are not all zero, the transformation parameters of 4-WFRFT can be expanded to 9, and the corresponding state function will be obtained by performing the classical FRFT transformation of f(x) Order 4-WFRFT generation.

[0095] The definition of 4-WFRFT can be described as:

[0096] ,

[0097] In the formula, for Order WFRFT operator, is the weight coefficient corresponding to each state function, which is defined as:

[0098] ,

[0099] Among them, when the third index is "+", it means that the representation axis of WFRFT rotates counterclockwise around the origin. When the index is "-", it means that the representation axis of WFRFT rotates clockwise around the origin. The weighting coefficients defined in the two ways are conjugate to each other and rotate in opposite directions.

[0100] After the WFRFT transformation, the frequency domain energy distribution of the signal is more dispersed or concentrated, depending on the selected WFRFT order. This processed signal is then transmitted through the network and has a stronger anti-interference ability than the interference signal.

[0101] (2) WFRFT parameter adjustment mechanism:

[0102] MP-WFRFT (more than 4 parameters): Applicable to broadband interference environments. By increasing the number of transformation parameters, the frequency domain distribution of the signal is more diffuse, thereby reducing the concentrated impact of the interference signal. For example, when a WiFi network is subject to electromagnetic broadband interference, using MP-WFRFT can effectively disperse the impact of the interference source, making the signal energy more distributed in a wider s-time-frequency plane, reducing the bit error rate.

[0103] 4-WFRFT (4-weighted): Applicable to narrowband interference environments. By reducing the number of transformation c parameters, the frequency domain concentration of the signal is enhanced, making its energy stronger in a specific frequency band, thereby combating narrowband interference. For example, WSNs usually have low power consumption and narrow signal bandwidth. Using 4-WFRFT can make the signal more concentrated in the time-frequency plane and enhance anti-interference capabilities.

[0104] (3) Heterogeneous network collaborative anti-interference mechanism

[0105] The core of the heterogeneous network collaborative anti-interference mechanism lies in the collaboration between heterogeneous networks, which coordinates their anti-interference measures by sharing interference information in real time. The mechanism includes the following steps:

[0106] (31) Interference detection and reporting

[0107] Each heterogeneous network is equipped with an interference monitoring module to monitor the signal quality in the communication channel in real time, including signal strength, signal-to-noise ratio, bit error rate, etc. When the interference monitoring module detects that the interference signal strength exceeds the preset threshold (such as -80 dBm), the interference detection reporting mechanism is triggered.

[0108] WiFi network interference detection: The WiFi network monitoring module detects the electromagnetic interference intensity in the channel. When the interference intensity is detected to be higher than -80dBm, the WiFi network will report the interference situation through the collaborative anti-interference module and request spectrum resource adjustment.

[0109] WSN interference detection: Nodes in wireless sensor networks detect narrowband noise or device noise in the environment. When the interference intensity exceeds -80dBm, WSN will share the interference information with other networks.

[0110] (32) Interference information sharing and collaborative optimization

[0111] The interference monitoring module transmits the detected interference information to the collaborative anti-interference module. The system performs the following collaborative optimization steps based on the feedback data of each network:

[0112] Interference information sharing: Heterogeneous networks exchange interference information in real time through a sharing protocol, including interference source type, interference frequency band, and interference intensity. The shared information enables each heterogeneous network to be aware of the overall interference environment, thereby avoiding interference superposition between each other.

[0113] Spectrum avoidance and WFRFT parameter adjustment: Based on the shared interference information, the system will dynamically adjust the WFRFT parameters and spectrum resources of each network. For example, the broadband interference information detected by the WiFi network can be used by the wireless sensor network to adjust the WFRFT parameters on the basis of avoiding interference, that is, adjust the rotation angle of the coordinate axis to make the signal time-frequency distribution orthogonal to ensure the signal transmission quality.

[0114] (33) Collaborative anti-interference execution

[0115] Through the collaborative work between heterogeneous networks, the spectrum resources and WFRFT parameters of each heterogeneous network will be dynamically adjusted in real time, and the system will ultimately achieve the following goals:

[0116] Interference avoidance: When broadband interference is detected on the WiFi network, the system adjusts the spectrum resources of the WiFi network to a frequency band with less interference, and the WSN avoids working on that frequency band.

[0117] WFRFT parameter optimization: Adjust the WFRFT transformation order so that each network can optimize the signal processing effect and enhance the anti-interference ability under the new spectrum resources.

[0118] (4) Dynamic spectrum allocation and resource optimization

[0119] The dynamic spectrum allocation and resource optimization module is responsible for managing and allocating limited spectrum resources in the mining environment. The system dynamically adjusts the allocation of spectrum resources by monitoring the communication needs, interference and spectrum usage of each heterogeneous network in real time.

[0120] The implementation process of dynamic spectrum allocation is as follows:

[0121] (41) Interference monitoring

[0122] The system first monitors the signal quality of each frequency band in real time through the interference monitoring module. When a frequency band of a heterogeneous network is seriously interfered with (such as channel noise exceeding -80 dBm), the system will immediately trigger spectrum allocation adjustment.

[0123] (42) Priority allocation

[0124] The system allocates spectrum resources based on the urgency and priority of the tasks in the heterogeneous networks. For example, the real-time communication task of controlling a mine car has a higher priority, while the sensor data collection task has a lower priority. When resources are tight, the system will prioritize the communication needs of the high-priority network.

[0125] (43) Spectrum Adjustment

[0126] When the spectrum resources of a heterogeneous network are affected by interference, the system will switch the communication tasks of the network to a frequency band with less interference. At the same time, the system will optimize the signal by combining WFRFT technology to adapt to the new spectrum environment. For example, broadband interference of WiFi networks can be avoided by switching the spectrum to a low-interference frequency band, combined with WFRFT transformation to optimize signal quality.

[0127] (44) Resource recovery and redistribution

[0128] When the task is completed or the interference is eliminated, the system will reclaim the allocated spectrum resources for use by other heterogeneous networks. This mechanism ensures efficient use of spectrum resources and reduces spectrum waste.

[0129] The resource optimization strategies are as follows:

[0130] Through machine learning algorithms, historical interference data is analyzed to predict possible interference in the future and to pre-adjust spectrum allocation strategies. For example, a mining vehicle device may generate periodic interference during operation. The system can switch the spectrum before the interference occurs to ensure that communication is not affected by the interference.

[0131] Through the precoding algorithm, the user's information will be sent to the precoding module, multiplied by the weight vector, and sent after power allocation. for:

[0132] ,

[0133] The signal sent Power limit conditions must be met , is assigned to The transmission power of each user, is the weight coefficient, To transfer data, represents the trace of the matrix, for The conjugate vector of is the total transmit power.

[0134] The weight vector matrix obtained by beamforming for:

[0135] ,

[0136] Assumptions The base station and The channel gain between users, the independent channel conforms to the zero-mean cyclic symmetric complex Gaussian random distribution (expressed as CN), then the joint channel matrix is ​​expressed as:

[0137] ,

[0138] represents the transposed matrix,

[0139] In this embodiment, based on the machine learning algorithm, the future interference pattern is predicted to obtain the joint channel matrix .

[0140] No. The signal received by each user is:

[0141] , ,

[0142] in is the signal received by the receiving end. is additive white Gaussian noise, is the total number of users.

[0143] Will Substituting the expression of into , we can get:

[0144] ,

[0145] No. The signal received by a user can be divided into three parts, namely, the valid data part, the interference part of other users and the noise part. To eliminate interference, the channel state matrix The pseudo-inverse matrix of ,

[0146] .

[0147] In one embodiment, the experimental environment simulates the communication between the WiFi network and WSN in a mine under a complex interference environment. The WiFi network is used to monitor video transmission, and the WSN is used to collect environmental data such as temperature and humidity. In the experiment, the system uses traditional anti-interference technology (frequency hopping technology) and a collaborative anti-interference method based on WFRFT for comparison.

[0148] Experimental results show that the average throughput of the WiFi network under the traditional anti-interference method is 20 Mbps and the bit error rate is 5.2%. After using the WFRFT-based collaborative anti-interference method, the average throughput of the WiFi network is increased to 42 Mbps and the bit error rate is reduced to 1.8%. The packet loss rate of the WSN is reduced from 12.5% ​​to 4.3%, and the bit error rate is reduced from 4.8% to 1.2%.

[0149] In another embodiment of the present invention, a smart mine heterogeneous network collaborative anti-interference system is provided. Figure 2 ,include:

[0150] Heterogeneous network module: includes various heterogeneous networks in the mining environment, such as WiFi networks, wireless sensor networks (WSN), cellular networks (4G / 5G, etc.), etc. These networks constitute the heterogeneous communication system of the mine, and each network undertakes different communication tasks, such as monitoring data transmission, equipment control, and environmental monitoring.

[0151] WFRFT signal processing module: used to perform generalized WFRFT transformation on the original signal transmitted by each network, output weighted frequency domain signal; and adjust WFRFT parameters to optimize the frequency domain distribution of the signal and enhance its anti-interference ability.

[0152] Interference monitoring module: deployed on each heterogeneous network node, used to monitor the signal quality of the network communication channel in real time, including signal strength, signal-to-noise ratio, bit error rate, etc. Once the interference signal strength is detected to exceed the preset threshold, the interference monitoring module will immediately report the interference information.

[0153] Collaborative anti-interference module: used to process the interference information received from the interference monitoring module, adjust spectrum resources and WFRFT transformation parameters through cross-network collaboration, and reduce the impact of interference on communications.

[0154] Resource optimization module: used to analyze historical interference data through machine learning algorithms, predict possible interference situations in the future, and pre-adjust spectrum allocation strategies.

[0155] In this embodiment, the WFRFT signal processing module adjusts the WFRFT parameters, specifically including:

[0156] MP-WFRFT (more than 4 parameters): Applicable to broadband interference environments. By increasing the number of transformation parameters, the frequency domain distribution of the signal is more diffuse, thereby reducing the concentrated impact of the interference signal.

[0157] 4-WFRFT (4-weighted): Applicable to narrowband interference environments. By reducing the number of transformation parameters, the frequency domain concentration of the signal is enhanced, making its energy in a specific frequency band stronger, thereby counteracting narrowband interference.

[0158] In this embodiment, the interference monitoring module detects the interference signal, specifically including:

[0159] WiFi network interference detection: The interference monitoring module in the WiFi network will detect the electromagnetic interference intensity in the channel. When the interference intensity is detected to be higher than -80dBm, the WiFi network will report the interference situation through the collaborative anti-interference module and request spectrum resource adjustment.

[0160] WSN interference detection: The interference monitoring module in the wireless sensor network detects narrowband noise or device noise in the environment. When the interference intensity exceeds -80dBm, the WSN will share the interference information with other networks.

[0161] In this embodiment, the collaborative anti-interference module is specifically used to:

[0162] Interference information sharing: Heterogeneous networks exchange interference information in real time through a sharing protocol, including interference source type, interference frequency band, and interference intensity. The shared information enables each heterogeneous network to be aware of the overall interference environment, thereby avoiding interference superposition between each other.

[0163] Spectrum avoidance and WFRFT parameter adjustment: Based on the shared interference information, the system will dynamically adjust the WFRFT parameters and spectrum resources of each network. This includes: when the WiFi network detects broadband interference, the system adjusts the spectrum resources of the WiFi network to a frequency band with less interference, and the WSN avoids working on this frequency band; and adjusts the WFRFT transformation order so that each network can optimize the signal processing effect under the new spectrum resources and enhance the anti-interference ability.

[0164] In one embodiment, the resource optimization module includes:

[0165] Prediction model: It is used to predict the possible interference patterns in the future based on the current monitoring of spectrum resource usage in heterogeneous networks. It should be noted that the prediction model is trained using a machine learning algorithm using historical interference data from the mining environment.

[0166] Resource allocation module: It is used to schedule the communication tasks of various heterogeneous networks based on the prediction results of the prediction model, ensuring that resource allocation can be adjusted in time before or during interference to avoid the impact of interference on the communication system.

[0167] Based on WFRFT technology, the present invention can flexibly adjust the frequency domain distribution of the signal, better adapt to changing interference scenarios, and maintain the transmission stability of the signal. At the same time, combined with the heterogeneous network collaborative anti-interference mechanism and dynamic spectrum allocation strategy, it can effectively suppress various complex interferences in the mine heterogeneous network and improve the communication stability and anti-interference ability of the network.

[0168] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for collaborative anti-interference of intelligent mine heterogeneous networks, characterized in that: include: The communication signals in the communication channel are monitored in real time by the heterogeneous network nodes of the intelligent mine, and the acquired communication signals are processed by generalized weighted inverse discrete Fourier transform to obtain weighted frequency domain signals, and interference detection is performed; the heterogeneous networks in the mine environment include WiFi networks, wireless sensor networks and cellular networks; the communication signals include: signal strength, signal-to-noise ratio and bit error rate; Share detected interference information to all heterogeneous networks; The generalized weighted inverse discrete Fourier transform order and spectrum resources of each heterogeneous network are dynamically adjusted according to the shared interference information.

2. According to claim 1, a smart mine heterogeneous network collaborative anti-interference method is characterized in that: The interference detection comprises: If the interference information detected exceeds the preset threshold, the interference reporting mechanism is triggered.

3. The method for collaborative anti-interference of heterogeneous networks in intelligent mines according to claim 2 is characterized in that: The interference detection further includes: For WiFi networks, the electromagnetic interference intensity in the communication channel is detected. When the electromagnetic interference intensity is detected to be higher than -80dBm, the interference reporting mechanism is triggered and spectrum resource adjustment is requested; For wireless sensor networks, narrowband noise or device noise in the environment is detected. When the noise interference intensity exceeds -80dBm, the interference reporting mechanism is triggered and the interference information is shared with other networks.

4. The method for collaborative anti-interference of intelligent mine heterogeneous networks according to claim 1 is characterized in that: The sharing of the detected interference information to all heterogeneous networks includes: The interference information is transmitted to other heterogeneous networks through a cooperative protocol, wherein the interference information includes: interference frequency band, interference intensity and interference source type.

5. The method for collaborative anti-interference of intelligent mine heterogeneous networks according to claim 1 is characterized in that: The dynamically adjusting the generalized weighted inverse discrete Fourier transform order and spectrum resources of each heterogeneous network according to the shared interference information includes: Switch the communication tasks of heterogeneous networks that are severely interfered to the frequency band with the least interference; For broadband interference environments, increase the order of the generalized weighted inverse discrete Fourier transform; For narrowband interference environments, the order of the generalized weighted inverse discrete Fourier transform is reduced.

6. The method for collaborative anti-interference of intelligent mine heterogeneous networks according to claim 1 is characterized in that: Before the real-time monitoring of the communication signals in the communication channel by the heterogeneous network nodes of the intelligent mine, the method further includes: Real-time monitoring of the usage of each spectrum resource, including channel quality, interference intensity and communication demand; According to the communication needs and task urgency of each heterogeneous network, priorities are assigned to different heterogeneous networks, and priority use rights are allocated to heterogeneous networks for emergency tasks.

7. The method for collaborative anti-interference of intelligent mine heterogeneous networks according to claim 1 is characterized in that: After dynamically adjusting the generalized weighted inverse discrete Fourier transform order and spectrum resources of each heterogeneous network according to the shared interference information, the method further includes: After the interference is eliminated, the allocated spectrum resources are recovered.

8. The method for collaborative anti-interference of intelligent mine heterogeneous networks according to claim 1 is characterized in that: The method further comprises: Obtain historical disturbance data of the mining environment and build a predictive model through machine learning algorithms to predict future disturbance patterns; Based on the monitored spectrum resource usage of the current heterogeneous network, a prediction model is used to obtain the predicted interference pattern; Based on the prediction results, the communication tasks of each heterogeneous network are scheduled to ensure timely adjustment of resource allocation before or during interference.

9. An intelligent mine heterogeneous network collaborative anti-interference system, characterized in that: The system is used to implement the intelligent mine heterogeneous network collaborative anti-interference method according to any one of claims 1 to 8, and comprises: Heterogeneous network module: includes various heterogeneous networks in the mine environment, including WiFi networks, wireless sensor networks and cellular networks; various heterogeneous networks constitute the mine's heterogeneous communication system, and each network undertakes different communication tasks; each heterogeneous network node monitors the communication signal in the communication channel in real time; the communication signal includes: signal strength, signal-to-noise ratio and bit error rate; WFRFT signal processing module: used to perform generalized weighted discrete Fourier inverse transform processing on the communication signal transmitted by each heterogeneous network, output weighted frequency domain signal; and adjust the order of generalized weighted discrete Fourier inverse transform; Interference monitoring module: deployed on each heterogeneous network node, used to monitor the communication signal of the communication channel in real time and perform interference detection; the communication signal includes signal strength, signal-to-noise ratio and bit error rate; Collaborative anti-interference module: used to process the interference information received from the interference monitoring module, transmit it to other heterogeneous networks through the cross-network collaborative protocol, and dynamically adjust the generalized weighted discrete Fourier transform order and spectrum resources of each heterogeneous network according to the shared interference information.

10. The intelligent mine heterogeneous network collaborative anti-interference system according to claim 9 is characterized in that: The collaborative anti-interference module is specifically used for: Interference information is exchanged in real time between heterogeneous networks through a cross-network collaboration protocol, wherein the interference information includes interference source type, interference frequency band, and interference intensity; According to the shared interference information, the generalized weighted inverse discrete Fourier transform order and spectrum resources of each heterogeneous network are dynamically adjusted, including: switching the communication tasks of the heterogeneous network that is severely interfered to the frequency band with the least interference; and adjusting the generalized weighted inverse discrete Fourier transform order.

11. The intelligent mine heterogeneous network collaborative anti-interference system according to claim 9, characterized in that: The system also includes: a resource optimization module, which is used to adjust resources in advance based on interference patterns that may occur in the future. The resource optimization module includes: Prediction model: used to predict the interference pattern that will occur in the future based on the current monitoring of spectrum resource usage of heterogeneous networks; the prediction model is trained using a machine learning algorithm using historical interference data of the mining environment; Resource allocation module: used to schedule the communication tasks of various heterogeneous networks based on the prediction results, ensuring timely adjustment of resource allocation before or during interference.

12. The intelligent mine heterogeneous network collaborative anti-interference system according to claim 11, characterized in that: The resource optimization module is specifically used to: Based on the prediction model, the future interference pattern is obtained, which is expressed as the joint channel matrix , Take the channel state matrix The pseudo-inverse matrix of , Multiply the user information by the weight vector , after power allocation, it is sent and expressed as: ， in, is the signal received by the receiving end. is assigned to The transmission power of each user, is the element in the joint channel matrix, is the weight vector The elements in It is The transmission data of each user, is additive white Gaussian noise.

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