Communication signal optimization method and system for wireless initiation of drill and blast tunneling

Abstract

The application provides a communication signal optimization method and system for wireless initiation of a drill-and-blast tunnel, and relates to the technical field of communication signal optimization. The application collects multi-source data of each initiation detonator in the tunnel initiation area, constructs a channel feature vector, and synchronously obtains a signal attenuation value and a signal-to-noise ratio. Based on the channel feature vector and the signal attenuation value, a sample is constructed, a kernel matrix model is built, and a channel attenuation value at the next moment is predicted. According to the historical signal-to-noise ratio, a communication success rate is calculated, the initiation priority is determined in combination with the key degree of the drilling position, the optimal expected transmission power is obtained by solving through a particle swarm algorithm, and before formal initiation, simulation initiation testing is performed based on the optimal expected transmission power, power compensation is implemented on the weak signal detonator, and the actual transmission power is determined, so as to realize optimization of the tunnel wireless initiation communication signal. The application can significantly improve the communication reliability of wireless initiation in a complex tunnel environment, and realize the collaborative balance of high reliability and low power consumption.

Description

Technical Field

[0001] This invention relates to the field of communication signal optimization technology, specifically to a communication signal optimization method and system for wireless initiation of tunnels using the drill-and-blast method. Background Technology

[0002] In drill-and-blast tunnel construction, wireless detonation systems are gradually replacing traditional wired detonation methods due to their high flexibility and adaptability to complex working conditions. However, tunnel detonation areas are typically located in complex underground environments, experiencing multipath effects, severe signal attenuation, and strong electromagnetic interference, leading to unstable wireless communication signal quality and affecting the reliable transmission of detonation commands. Currently, detonation systems usually employ fixed transmission power or simple channel feedback mechanisms, which are difficult to adapt to the dynamically changing channel environment within tunnels, easily resulting in communication interruptions, detonation failures, or accidental triggering, posing safety hazards. Furthermore, the location and attitude differences of different detonators also significantly impact communication quality. Traditional methods fail to comprehensively consider these multi-source information, leading to unreasonable power allocation, potentially causing energy waste or reduced detonation reliability due to insufficient power. Therefore, how to effectively optimize the detonation communication signal in complex tunnel environments and improve the reliability and energy efficiency of the detonation system has become an urgent technical problem to be solved.

[0003] In the prior art, CN120512152A discloses a method and system for optimizing communication signals in power distribution automation in underground environments. The method includes: conducting multi-band data transmission tests based on perturbation frequency test packets to acquire multi-band power distribution communication signals; performing dynamic spatiotemporal attenuation analysis to construct a multi-band electromagnetic spatiotemporal attenuation matrix; calculating the response delay of each node based on the multi-band power distribution communication signals to generate a full-band delay sensitivity curve; evaluating safe and effective frequency bands based on the multi-band power distribution communication signals, and eliminating high-delay frequency bands based on the full-band delay sensitivity curve to construct a frequency band transition optimization pool; and performing real-time environmental interference attenuation analysis on the frequency band transition optimization pool according to the multi-band electromagnetic spatiotemporal attenuation matrix to construct communication frequency band transition driving logic, thereby optimizing the communication signals.

[0004] The existing solution relies primarily on frequency band test data, failing to comprehensively consider various physical factors affecting communication quality. Its adaptability to complex interference sources is weak, making it difficult to meet the requirements of complex operating conditions. Furthermore, its method of establishing an attenuation matrix through dynamic spatiotemporal attenuation analysis is a typical post-hoc analysis approach. Because it lacks a prediction mechanism, communication parameter adjustments cannot keep pace with channel changes, resulting in a significant lag.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a communication signal optimization method and system for wireless initiation of tunnels using the drill-and-blast method, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A method for optimizing communication signals for wireless initiation of blasting in drill-and-blast tunnels, comprising the following steps: Step 1: In the tunnel detonation area, set a data acquisition time window, acquire multi-source data of each detonator within the data acquisition time window along the borehole axis, construct the channel feature vector of each detonator, and simultaneously acquire the signal attenuation value and signal-to-noise ratio of each detonator. Step 2: Construct samples using the channel feature vectors and signal attenuation values ​​of each detonator, calculate the channel similarity of the samples using a kernel function, build a kernel matrix model, and predict the channel attenuation value of each detonator at the next acquisition time. Step 3: Calculate the communication success rate based on the historical signal-to-noise ratio of each detonator, and comprehensively determine the detonation priority of each detonator by combining the criticality of the borehole location. Using the predicted channel attenuation value and detonation priority as inputs, and with the minimum power consumption and the highest detonation reliability as optimization objectives, the optimal expected transmission power of each detonator at the next acquisition time is obtained by using the particle swarm optimization algorithm. Step 4: Before the actual detonation, simulated detonation tests are conducted on each detonator based on the optimal expected transmission power. The simulated received signal strength and signal-to-noise ratio of each detonator are collected, the comprehensive channel quality coefficient is calculated, the channel quality is determined by a preset threshold, power compensation is implemented for detonators with weak signals, and the actual transmission power is determined by combining the optimal expected transmission power and the power compensation amount, thereby optimizing the wireless detonation communication signal in the tunnel.

[0008] Furthermore, multi-source data of each detonator within the acquisition time window is obtained along the borehole axis, and the channel feature vector of each detonator is constructed. The principle underlying this is: Using the current acquisition time as the end time, backtrack a acquisition time window and divide the acquisition time window into several historical acquisition times at equal intervals. The current acquisition time and the historical acquisition times together constitute the acquisition times within the acquisition time window. At each acquisition time, multi-source data of each detonator is acquired, including the distance between the detonator and the detonation controller, the pitch angle, roll angle and heading angle of the detonator. Based on the above four parameters, the channel feature vector of each detonator at the corresponding acquisition time is constructed.

[0009] Furthermore, the principle of simultaneously acquiring the signal attenuation value and signal-to-noise ratio of each detonator is as follows: Based on the communication data collected at each acquisition time within the acquisition time window, and taking the original communication signal strength emitted by the detonator as a benchmark, the actual communication signal strength received by each detonator is compared. The signal attenuation value of each detonator is calculated by the difference between the actual communication signal strength and the original communication signal strength. At the same time, the effective signal power and noise power in the received signal of each detonator are extracted, and the corresponding signal-to-noise ratio is calculated by the ratio of the effective signal power to the noise power.

[0010] Furthermore, samples are constructed using the channel feature vectors and signal attenuation values ​​of each detonator. The channel similarity of the samples is calculated using a kernel function, and a kernel matrix model is built. The specific principle is as follows: Each detonator at a single acquisition moment is used as a set of independent samples, with the channel feature vector corresponding to a single acquisition moment and the signal attenuation value at the same moment being the same as the number of acquisition moments within the acquisition time window. A composite kernel function combining a squared exponential kernel and a white noise kernel is used as the basis for similarity calculation. The kernel function parameters are pre-initialized, including signal variance and noise variance. Based on the channel feature vectors of each group of samples, the Mahalanobis distance between samples is calculated. The Mahalanobis distance and the initialized signal variance and noise variance are substituted into the composite kernel function to solve for the channel similarity between any two groups of samples. The similarity between all samples is calculated by iterating through all samples, and a kernel matrix model is constructed based on the channel similarity of all samples.

[0011] Furthermore, the principle underlying the prediction of the channel attenuation value of each detonator at the next acquisition time is as follows: A Gaussian process regression prediction model is constructed, with the kernel matrix used as the covariance matrix of the Gaussian process regression prediction model. At the same time, the historical channel attenuation values ​​corresponding to each sample are used as the model observation values. Iterative calculations are performed through the posterior prediction distribution of the Gaussian process regression to obtain the predicted mean and predicted variance of the channel attenuation values ​​at the next acquisition time. The predicted mean of the channel attenuation values ​​with the smallest predicted variance is used as the channel attenuation value of each detonator at the next acquisition time.

[0012] Furthermore, the communication success rate is calculated based on the historical signal-to-noise ratio of each detonator, and the detonation priority of each detonator is determined comprehensively by combining the criticality of the borehole location. The specific principle is as follows: A preset signal-to-noise ratio threshold is set, and the number of data acquisitions for each detonator within the acquisition time window is obtained, along with the number of valid acquisitions in which the historical signal-to-noise ratio is higher than the preset signal-to-noise ratio threshold. The ratio of the number of valid acquisitions to the number of data acquisitions is used to obtain the historical communication success rate of each detonator. Calculate the average distance between all detonators and detonators, and then divide the distance between each detonator and detonator by this average distance to obtain the normalized distance coefficient for each detonator. The angle between the axis of the detonator and the main detonation direction is calculated based on the pitch angle, roll angle and heading angle of the detonator. After normalizing the included angle, the normalized included angle is multiplied by the preset adjustment coefficient and the normalized distance coefficient in sequence to obtain the position adjustment factor. The exponential function with the natural constant as the base and the negative value of the position adjustment factor as the exponent is calculated. 1 is summed with the exponential function, and then 1 is divided by the sum to obtain the position criticality of each detonator. The historical communication success rate and location criticality of each detonator are normalized separately. The results of the normalization are then weighted and summed to obtain a quantitative score representing the detonation priority. The weight of location criticality is greater than that of historical communication success rate, and the sum of their weights is 1.

[0013] Furthermore, the optimal expected emission power for each detonator at the next acquisition time is obtained using the particle swarm optimization algorithm. The specific principle is as follows: The maximum allowable transmission power of the detonation controller and the minimum transmission power that meets communication requirements are used as the boundary constraints for power calculation. With the optimization objectives of minimum power consumption and maximum detonation reliability, the optimal expected launch power is obtained through iterative optimization using a particle swarm optimization algorithm. The specific process is as follows: For a single detonator, the detonation power consumption of a single detonator is obtained by multiplying the emission power by the corresponding emission duration. The optimal operating power and receiving sensitivity parameters of each detonator are obtained. Based on the predicted channel attenuation value, the actual receiving power of each detonator is obtained by subtracting the channel attenuation value from the controller's transmit power. Calculate the difference between the actual received power and the received sensitivity, and the difference between the optimal operating power and the received sensitivity, respectively. Take the quotient of the two sets of differences to obtain the detonation reliability of a single detonator. The detonation reliability and detonation power consumption of a single detonator are calculated by weighted subtraction. The objective function value is obtained by traversing all the detonator cumulative calculation results, where the detonation reliability weight is greater than the overall power consumption weight, and the sum of the two weights is 1. The combined emission power of each detonator is constructed into a power vector, and a single power vector is used as a single particle. The particle swarm optimization algorithm iteratively updates the particle positions to search for the optimal particle with the largest objective function value. Extracting the emission power of each group corresponding to the optimal particle is the optimal expected emission power of the controller at the next acquisition time for each detonator.

[0014] Furthermore, based on the optimal expected transmission power, simulated detonation tests were conducted on each detonator. The simulated received signal strength and simulated signal-to-noise ratio of each detonator were collected, and the comprehensive channel quality coefficient was calculated. The specific principle is as follows: The optimal expected transmission power was used to simulate the detonation test of each detonator, and the simulated received signal strength and simulated signal-to-noise ratio of each detonator were measured. The analog received signal strength of each detonator is divided by the corresponding received signal strength threshold, and the analog signal-to-noise ratio is divided by the corresponding signal-to-noise ratio threshold. The two sets of quotients are weighted and summed to obtain the comprehensive channel quality index of each detonator. Among them, the weight corresponding to the received signal strength term is greater than the weight corresponding to the signal-to-noise ratio term, and the sum of the two weights is always 1.

[0015] Furthermore, the channel quality is determined, power compensation is applied to detonators with weak signals, and the actual transmission power is determined by combining the optimal expected transmission power with the power compensation amount. The specific principle is as follows: If the overall channel quality index of the detonator is greater than or equal to the preset threshold, its channel quality is deemed qualified and its communication status is stable. No power compensation is required, and the optimal expected transmission power is directly determined as the actual transmission power of the detonator. If the overall channel quality index of the detonator is less than the preset threshold, the detonator is determined to be a detonator with weak signal and there is a risk of detonation communication failure. The deviation between the integrated channel quality index threshold and the measured integrated channel quality index is calculated. This deviation is then input into a preset PID control model, and the matching power compensation amount is obtained through proportional-integral-derivative coordinated control operations. The optimal expected transmission power is superimposed with the corresponding power compensation amount to obtain the final actual transmission power of the detonator with weak signal.

[0016] This invention also provides a communication signal optimization system for wireless initiation of drill-and-blast tunnels, the system being used to implement the communication signal optimization method for wireless initiation of drill-and-blast tunnels as described in any of the above claims, specifically including: The data acquisition module is used to set an acquisition time window in the tunnel detonation area, acquire multi-source data of each detonator within the acquisition time window along the borehole axis, construct the channel feature vector of each detonator, and simultaneously acquire the signal attenuation value and signal-to-noise ratio of each detonator. The information prediction module is used to construct samples based on the channel feature vectors and signal attenuation values ​​of each detonator, calculate the channel similarity of the samples using a kernel function, build a kernel matrix model, and predict the channel attenuation value of each detonator at the next acquisition time. The power allocation module is used to calculate the communication success rate based on the historical signal-to-noise ratio of each detonator, and to comprehensively determine the detonation priority of each detonator by combining the criticality of the drilling location. It takes the predicted channel attenuation value and detonation priority as input, and takes the minimum power consumption and the highest detonation reliability as optimization objectives. The optimal expected transmission power of each detonator at the next acquisition time is obtained by solving the particle swarm algorithm. The power compensation module is used to conduct simulated detonation tests on each detonator based on the optimal expected transmission power before the actual detonation. It collects the simulated received signal strength and signal-to-noise ratio of each detonator, calculates the comprehensive channel quality coefficient, determines the channel quality through a preset threshold, implements power compensation for detonators with weak signals, and determines the actual transmission power by combining the optimal expected transmission power and the power compensation amount, thereby optimizing the wireless detonation communication signal in the tunnel.

[0017] Compared with the prior art, the beneficial effects of the present invention are: This invention collects multi-source data and constructs a channel feature vector to more comprehensively reflect the intrinsic characteristics of detonators in complex tunnel environments. It extracts channel attenuation values ​​from historical channel state information, constructs a sample containing channel feature vectors and channel attenuation values, and builds a prediction model based on Gaussian process regression. This model can proactively predict channel attenuation values ​​at future moments, achieving forward prediction and providing a forward-looking and quantifiable input for expected transmission power configuration.

[0018] This invention also incorporates detonation priority into the objective function of transmission power configuration. Detonation priority is calculated based on historical communication success rate and location criticality to achieve differentiated power allocation, forming a differentiated allocation mechanism where higher priority results in more skewed power allocation. This overcomes the one-sidedness of traditional methods that solely optimize power consumption or reliability. Global optimization is performed using a particle swarm optimization algorithm, with minimum power consumption and maximum detonation reliability as optimization objectives to obtain the optimal expected transmission power. Before the detonation command is issued, simulated detonation tests are conducted on each detonator based on the optimal expected transmission power. The simulated received signal strength and signal-to-noise ratio of each detonator are collected, and channel quality is determined through a preset threshold. Power compensation is implemented for detonators with weak signals. The actual transmission power is determined by combining the optimal expected transmission power and the power compensation amount, thus optimizing the tunnel wireless detonation communication signal. This allows the communication system to confirm channel quality before actual detonation, avoiding detonation failures caused by prediction deviations or instantaneous interference under open-loop control. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the fitting curve of the positional criticality of the present invention as a function of the positional adjustment factor; Figure 3 This is a schematic diagram of the system modules of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0021] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0022] Example: Please see Figures 1 to 2 The present invention provides a technical solution: A method for optimizing communication signals for wireless initiation of blasting in drill-and-blast tunnels, comprising the following steps: Step 1: In the tunnel detonation area, set a data acquisition time window, acquire multi-source data of each detonator within the data acquisition time window along the borehole axis, construct the channel feature vector of each detonator, and simultaneously acquire the signal attenuation value and signal-to-noise ratio of each detonator. In this embodiment, multi-source data of each detonator within the acquisition time window is acquired along the borehole axis, and the channel feature vector of each detonator is constructed. The principle behind this is as follows: Using the current acquisition time as the end time, a acquisition time window is traced back, and the acquisition time window is divided into several historical acquisition times at equal intervals. The current acquisition time and the historical acquisition times together constitute the acquisition times within the acquisition time window. The value range of the acquisition time window is 2 to 10 seconds. If the acquisition time window is too short, it will result in insufficient data and failure to effectively capture the channel change trend. If the acquisition time window is too long, it may contain too much outdated or non-representative data, introduce noise, and reduce prediction sensitivity and real-time performance. At each acquisition time, multi-source data of each detonator is acquired, including the distance between the detonator and the detonation controller, the pitch angle, roll angle and heading angle of the detonator, and a channel feature vector containing four dimensions of data is constructed at each acquisition time.

[0023] The channel feature vector at each acquisition moment includes the distance between the detonator and the detonation controller, the pitch angle, roll angle, and heading angle of the detonator. The distance between the detonator and the detonation controller is obtained using a two-way ranging method. Specifically, the detonation controller sends a timestamped ranging request signal, and the detonator immediately replies with a locally timestamped response signal. The round-trip time is calculated, and the distance between the detonator and the detonation controller is calculated using a dynamic formula based on the electromagnetic wave propagation speed and the round-trip time. The pitch angle is the angle between the detonator's axis and the horizontal plane, the roll angle is the rotation angle of the detonator around its own axis, and the heading angle is the angle between the projection of the detonator's axis onto the horizontal plane and the main propagation direction, with the main propagation direction being the tunnel axis direction. MEMS inertial sensors are used to acquire the pitch angle, roll angle, and heading angle of the detonator.

[0024] The attenuation of wireless signals from the controller to the detonator is mainly due to geometric propagation loss and radiation direction mismatch loss. Tunnels are long and narrow spaces, and the signal attenuation varies significantly with different drilling depths. The distance between the detonator and the detonator controller is the main factor affecting signal attenuation. The detonator antenna has directional radiation characteristics, and the polarization matching degree between the antenna and the detonator controller varies under different attitudes. Moreover, multipath reflection in the tunnel is strongly correlated with the detonator attitude. The pitch angle affects the change in the antenna's vertical gain, the roll angle affects the antenna's horizontal polarization direction, and the heading angle affects the angle between the antenna's pointing direction and the propagation direction. The pitch angle, roll angle, and heading angle together reflect the additional attenuation caused by the direction and attitude of the detonator antenna relative to the controller antenna.

[0025] The principle of synchronously acquiring the signal attenuation value and signal-to-noise ratio of each detonator is as follows: Based on the communication data collected at each acquisition time within the acquisition time window, and taking the original communication signal strength emitted by the detonator as a benchmark, the actual communication signal strength received by each detonator is compared. The signal attenuation value of each detonator is calculated by the difference between the actual communication signal strength and the original communication signal strength. At the same time, the effective signal power and noise power in the received signal of each detonator are extracted, and the corresponding signal-to-noise ratio is calculated by the ratio of the effective signal power to the noise power.

[0026] The original communication signal strength transmitted by the detonator represents the actual wireless signal power output by the detonator at its transmission port. It is a known and controllable reference value used to compare with the signal strength received by the detonator, thereby calculating the signal attenuation value, which is directly read from the detonator. The actual communication signal strength is the total radio frequency signal power actually measured by the detonator, including the effective signal and noise, and is obtained from the RSSI register of each detonator. When the detonator is not transmitting a communication signal, the detonator is used to receive it. Multiple signal power data are continuously used, and the average power of these signal power data is calculated and recorded as the noise power. Each time the detonator receives a communication signal from the detonator, the noise power is subtracted to obtain the effective signal power.

[0027] Channel attenuation reflects the energy loss of the wireless signal from the detonator to the detonator. Its value reflects the severity of the tunnel environment's impact on the signal; a larger channel attenuation indicates more severe signal loss. Signal-to-noise ratio (SNR) characterizes the quality of the communication link and directly determines whether the receiver can correctly demodulate the signal in a noisy environment. A higher SNR value indicates a more significant advantage of the effective signal over noise, making it easier for the receiver to separate and extract the effective information.

[0028] Combining channel attenuation and signal-to-noise ratio, the quality of the communication link between each detonator and the detonation controller is comprehensively reflected.

[0029] Step 2: Construct samples using the channel feature vectors and signal attenuation values ​​of each detonator, calculate the channel similarity of the samples using a kernel function, build a kernel matrix model, and predict the channel attenuation value of each detonator at the next acquisition time. In this embodiment, samples are constructed using the channel feature vectors and signal attenuation values ​​of each detonator. The channel similarity of the samples is calculated using a kernel function, and a kernel matrix model is built. The specific principle is as follows: Each detonator at a single acquisition moment is used as a set of independent samples, with the channel feature vector corresponding to a single acquisition moment and the signal attenuation value at the same moment being the same as the number of acquisition moments within the acquisition time window. A composite kernel function combining a squared exponential kernel and a white noise kernel is used as the basis for similarity calculation. The kernel function parameters are pre-initialized, including signal variance and noise variance. Based on the channel feature vectors of each group of samples, the Mahalanobis distance between samples is calculated. Combined with the preset signal variance and noise variance, the channel similarity between any two groups of samples is solved by substituting them into the composite kernel function. The similarity between all samples is calculated by traversing all samples, and a kernel matrix model is constructed based on the channel similarity values ​​of all samples.

[0030] The formula for calculating the channel similarity between samples is: in, Indicates the first Channel feature vectors corresponding to each sample Indicates the first Channel feature vectors corresponding to each sample Both represent the index of the sample. , Indicates the number of samples. Indicates the first The channel feature vector corresponding to the nth sample, the nth sample Channel similarity between channel feature vectors corresponding to each sample Indicates the signal variance. Indicates the first The channel feature vector corresponding to the nth sample, the nth sample Mahalanobis distance between the channel feature vectors corresponding to each sample Indicates the noise variance. Represent the Kronecker function, when hour, ,when hour, ; For the sample and The formula for the Mahalanobis distance between the corresponding channel feature vectors is: in, express The transpose of , Let represent the inverse of the covariance matrix, and the dimension of the matrix is ​​4×4; wherein, for each detonator, based on the multi-source data of all acquisition times within the acquisition time window of the detonator, the covariance of these data in the four dimensions of spacing, pitch angle, roll angle and heading angle is calculated to obtain the covariance matrix of the detonator.

[0031] Since the dimensions of pitch, roll, and heading are different, the Mahalanobis distance calculation has normalized the four types of multi-source data. Furthermore, there is a coupling relationship between attitude change and effective communication distance. Mahalanobis distance can capture this correlation, so it is used to calculate channel similarity.

[0032] Channel similarity is used to measure the similarity between the channel states of the same detonator in two different samples. The higher the channel similarity, the closer the position and attitude of the detonating detonator are, and the more similar the channel propagation conditions are. Essentially, it uses position and attitude information as proxy features of the channel state and quantifies its similarity through a kernel function. A composite kernel function is used to calculate the channel similarity. The square exponential kernel is used to reflect the continuous and smooth changes in tunnel channel state with the position and attitude of detonator, without abrupt changes. The square exponential kernel is infinitely differentiable and can reflect this smooth change. The larger the Mahalanobis distance, the more the similarity decays exponentially to approach 0, reflecting that the greater the difference in channel state, the weaker the similarity. The white noise kernel is used to describe independent and uncorrelated random noise within each sample, rather than describing the relationship between different samples. Therefore, it is only used when... The value is non-zero, representing independent Gaussian noise for each sample.

[0033] Signal variance represents the variance of the channel attenuation value itself, reflecting the overall fluctuation range of the channel attenuation value. In the initial stage of system deployment, a pre-test is conducted for a period of time, and the variance of the channel attenuation value actually observed for all detonators during this period is calculated as... The value of noise variance represents the measurement error or instantaneous random interference during the observation process. Its value is much smaller than that of signal variance and is usually set to 1% to 10% of signal variance. The specific value is set by those skilled in the art based on the actual situation.

[0034] The kernel matrix is ​​a matrix of size A symmetric matrix, where the th is a symmetric matrix. line, number The elements of the column are A kernel matrix is ​​constructed based on the channel similarity among all the calculated samples.

[0035] In this embodiment, the principle underlying the prediction of the channel attenuation value of each detonator at the next acquisition time is as follows: A Gaussian process regression prediction model is constructed, with the kernel matrix used as the covariance matrix of the Gaussian process regression prediction model. At the same time, the historical channel attenuation values ​​corresponding to each sample are used as the model observation values. Through the posterior prediction distribution characteristics of the Gaussian process regression, the predicted mean and predicted variance of the channel attenuation values ​​at the next acquisition time are obtained by iterative solution. The predicted mean of the channel attenuation value with the smallest predicted variance is used as the channel attenuation value of each detonator at the next acquisition time.

[0036] In the scenario of wireless detonation in drill-and-blast tunnels, the position and attitude of the detonator, as well as the on-site environment, will cause dynamic fluctuations in the channel attenuation value. In order to achieve pre-configuration of the transmit power, it is necessary to predict the channel attenuation value at the next acquisition time.

[0037] A Gaussian process regression prediction model is constructed, which is jointly determined by the mean function and the covariance matrix. The kernel matrix obtained earlier is used as the model covariance matrix, and the historical channel attenuation values ​​corresponding to each sample are set as the model observations. The mean function is initialized to 0, representing that the model has no prior data bias. Based on the posterior prediction distribution of the Gaussian process regression, the predicted mean and predicted variance of the channel attenuation value at the next acquisition time are obtained.

[0038] Here, the prediction mean is the expected estimate of the channel attenuation value, and the prediction variance is used to measure the uncertainty of the corresponding prediction result; the smaller the prediction variance, the higher the accuracy of the prediction result. Therefore, the prediction mean corresponding to the minimum prediction variance is selected as the channel attenuation value of each detonator at the next acquisition time.

[0039] Step 3: Calculate the communication success rate based on the historical signal-to-noise ratio of each detonator, and comprehensively determine the detonation priority of each detonator by combining the criticality of the borehole location. Using the predicted channel attenuation value and detonation priority as inputs, and with the minimum power consumption and the highest detonation reliability as optimization objectives, the optimal expected transmission power of each detonator at the next acquisition time is obtained by using the particle swarm optimization algorithm. In this embodiment, the communication success rate is calculated based on the historical signal-to-noise ratio of each detonator, and the detonation priority of each detonator is determined by combining the criticality of the borehole location. The specific principle is as follows: A preset signal-to-noise ratio threshold is set, and the number of data acquisitions for each detonator within the acquisition time window is obtained, along with the number of valid acquisitions in which the historical signal-to-noise ratio is higher than the preset signal-to-noise ratio threshold. The ratio of the number of valid acquisitions to the number of data acquisitions is used to obtain the historical communication success rate of each detonator. Calculate the average distance between all detonators and detonators, and then divide the distance between each detonator and detonator by this average distance to obtain the normalized distance coefficient for each detonator. The angle between the detonator axis and the main detonation direction is calculated based on the pitch, roll, and yaw angles of the detonator. After normalizing the angle, the position adjustment factor is obtained by multiplying the normalized angle by the adjustment coefficient and the normalized distance coefficient in sequence. An exponential function with the natural constant as the base and the negative value of the position adjustment factor as the exponent is calculated. The sum of 1 and the exponential function is then divided by the sum to obtain the position criticality of each detonator, expressed by the following formula: in, Indicates the first Normalized distance coefficient for each detonator, Indicates the index of the detonator, and , Indicates the number of detonators. Indicates the first The distance between the detonator and the detonation controller This represents the average distance between all detonators and the detonation controller. Indicates the position adjustment factor. This represents the adjustment coefficient. Indicates the first The normalized value of the angle between the axis of the detonator and the main detonation direction. Indicates the first The criticality of the location of the detonator; The unit vector of the detonator is determined based on its pitch and yaw angles. The main detonation direction is then determined, and the unit vector of the main detonation direction is taken. The dot product of the two unit vectors is calculated, and the inverse cosine of the dot product is taken to obtain the angle between the axis of the detonator and the main detonation direction.

[0040] The position adjustment factor reflects the degree of communication disadvantage caused by the location of each detonator, specifically the signal attenuation risk resulting from the distance between the detonator and the detonation controller, and the angle between the detonator axis and the main detonation direction. A larger position adjustment factor indicates a more unfavorable location for reliable communication, requiring higher transmission power or a higher detonation priority. The normalized distance coefficient is used to determine the relative size of the distance between the detonator and the detonation controller. This indicates that the distance between the detonator and the detonator controller is greater than the average distance, resulting in a longer signal propagation path and greater attenuation. This indicates that the distance between the detonator and the detonation controller is relatively close, resulting in better signal conditions; This is used to reflect the additional attenuation caused by antenna directional deviation. The main propagation direction is the optimal signal propagation direction. The larger the angle between the main propagation direction and this direction, the more severe the antenna directional mismatch, the more significant the decrease in polarization loss and directivity gain, and the worse the communication quality. When the distance is long and the angle is large, the probability of communication failure is much higher than in the case of single-factor adverse conditions. Therefore, a product form is used to capture this coupling effect. The adjustment coefficient is used to adjust the overall sensitivity of distance and angle. The larger the adjustment coefficient, the better. right and The more sensitive it is to minute changes; as an implementation method, the adjustment coefficient can range from [value missing]. The specific value is set by those skilled in the art based on the actual situation, and is not limited here.

[0041] Location criticality reflects the degree to which the position of the detonator in the tunnel borehole negatively impacts the reliability of wireless communication. It is determined by the distance between the detonator and the detonation controller, as well as the angle between the detonator's axis and the main detonation direction. A higher location criticality indicates a more unfavorable location for reliable communication, requiring higher transmission power or a higher detonation priority. In actual tunnel environments, location criticality does not increase indefinitely but has an upper limit. The sigmoid function is used to adjust this limit. Mapped to interval, The larger, The closer the value is to 1, the more unfavorable the location of the detonator, the greater the signal loss during propagation, and the more closely the detonator needs to be monitored. The smaller, The closer to 0, the more favorable the position of the detonator is for signal propagation, and the less loss occurs during signal propagation.

[0042] As shown in Table 1, the nonlinear correlation between the position adjustment factor and the position criticality is reflected. As the position adjustment factor increases from 0.12 to 4.02, the growth rate of the position criticality changes from fast to slow, exhibiting a sigmoid saturation characteristic. This indicates that in the tunnel detonation scenario, when the detonator changes from a favorable scenario with close distance and small directional deviation to an unfavorable scenario with far distance and large directional deviation, its communication risk will first rise rapidly, and then rise slowly until it reaches the upper limit.

[0043] Table 1. Changes in Location Keyness with Location Adjustment Factor The historical communication success rate and location criticality of each detonator are normalized separately. The results of the normalization are then weighted and summed to obtain the detonation priority of each detonator. The weight of location criticality is greater than that of historical communication success rate, and the sum of their weights is 1.

[0044] The formula for calculating the detonation priority is: in, Indicates the first Detonation priority of each detonator Indicates the first Historical communication success rate of detonators These represent the weights of historical communication success rate and location criticality, respectively. Detonation priority reflects the importance and communication risk level of the detonator in the entire detonation network, taking into account the adverse signal propagation caused by historical communication quality and positional orientation. A higher detonation priority indicates a worse historical communication quality, lower communication success rate, and unfavorable position for reliable signal transmission. Therefore, detonators with higher detonation priorities need to be assigned higher power to compensate for their adverse conditions. As historical communication success rate and positional criticality increase, detonation priority also increases. Historical communication success rate and positional criticality are two independent risk factors. Historical success rate reflects actual communication quality but is susceptible to transient interference, while positional criticality characterizes the inherent channel adverseness determined by distance and orientation and has long-term stability. A weighted summation is used to allow the disadvantage of one component to be moderately compensated by the advantage of another component, avoiding the neglect of parameters with lower values.

[0045] Location criticality reflects structural, long-term stable, and uncompensable communication risks, while historical communication success rate has the characteristics of being instantaneous, recoverable, and compensable. In scenarios with extremely high safety requirements, such as detonation, structural conditions should be prioritized. Therefore, the weight of location criticality is greater than that of historical communication success rate. As one implementation method, the value range of the location criticality weight is: The range of values ​​for the historical communication success rate weight is: The specific value is to be set by those skilled in the art based on the actual situation, and is not limited here.

[0046] In this embodiment, the optimal expected emission power of each detonator at the next acquisition time is obtained by the particle swarm optimization algorithm. The specific principle is as follows: The maximum allowable transmission power of the detonation controller and the minimum transmission power that meets communication requirements are used as the boundary constraints for power calculation. With the optimization objectives of minimum power consumption and maximum detonation reliability, the optimal expected launch power is obtained through iterative optimization using a particle swarm optimization algorithm. The specific process is as follows: For a single detonator, the detonation power consumption is obtained by multiplying the emission power by the corresponding emission duration. ,in, Indicates the first The detonation power consumption of a single detonator Indicates the detonation controller for the first The firing power of a detonator, Indicates the detonation controller for the first Signal transmission duration of each detonator; The optimal operating power and receiver sensitivity parameters of each detonator are obtained. Based on the predicted channel attenuation value, the actual received power of the detonator is obtained by subtracting the channel attenuation value from the controller's transmit power. Specifically: ,in, Indicates the first The actual received power of each detonator. Indicates the first Predicted channel attenuation value for each detonator; Calculate the difference between the actual received power and the received sensitivity, and the difference between the optimal operating power and the received sensitivity, respectively. Quotient these two differences to obtain the detonation reliability of a single detonator. ,in, Indicates the first The detonation reliability of an individual detonator Indicates the first The receiving sensitivity of a detonator. Indicates the first The optimal operating power of a detonator; The detonation reliability and detonation power consumption of a single detonator are calculated by weighted subtraction. The objective function value is obtained by traversing all the detonator cumulative calculation results, where the weight of detonation reliability is greater than the weight of detonation power consumption, and the sum of the two weights is 1. The objective function value is: in, This represents the objective function value for all detonators. These represent the weights of the detonation reliability and detonation power consumption of the detonator, respectively. The wireless detonation system faces two conflicting optimization objectives: maximizing detonation reliability and minimizing overall power consumption. These two objectives involve a Pareto trade-off: increasing transmission power improves reliability but increases power consumption; reducing power saves energy but may lead to detonation failure due to signal attenuation. Therefore, a weighted combination is used to transform the dual-objective problem into a single-objective problem. The detonation reliability and power consumption of each detonator are weighted and subtracted. Higher detonation reliability and lower detonation power consumption are both desirable. To ensure consistency in the optimization direction, a weighted subtraction of detonation reliability and power consumption is used. A larger subtraction result indicates higher detonation reliability and lower detonation power consumption, which aligns with the optimization objectives. A linear subtraction method ensures that the joint optimization of multi-detonator power can be completed within milliseconds, improving computational efficiency. Using a non-linear method would cause the algorithm to get stuck in local optima or significantly increase the number of iterations.

[0047] During the detonation process, the principle of safety first should be followed. Detonation failure will have serious consequences, while the impact of increased power consumption is generally economic cost and has a smaller impact. Therefore, the reliability of detonation should be given priority, and the weight of detonation reliability is greater than the weight of detonation power consumption.

[0048] As one implementation method, the weight range for detonation reliability is: The weight range for the detonation power consumption is: The specific value shall be set by those skilled in the art based on the actual situation, and is not limited here.

[0049] The combined emission power of each detonator is constructed into a power vector, and a single power vector is used as a single particle. Each particle is a vector, which is equal to a power allocation scheme. The parameters in the vector represent the power allocation of the detonator to the first to the nth detonators. Several particles are randomly generated, which correspond to several power allocation schemes.

[0050] The particle swarm optimization algorithm iteratively updates the particle positions to search for the optimal particle with the largest objective function value. The objective function value of each particle is calculated sequentially as the particle's fitness. If the current fitness of a particle is greater than its historical best fitness, then the current fitness is taken as the best fitness of the particle. From the best fitness of all particles, the maximum value is selected as the global best fitness. The maximum number of iterations is set to 100. When the maximum number of iterations is reached, the particle corresponding to the global best fitness at this time is output as the best particle.

[0051] Extracting the emission power of each group corresponding to the optimal particle is the optimal expected emission power of the controller at the next acquisition time for each detonator.

[0052] The particle swarm optimization algorithm can efficiently handle the power allocation problem among multiple detonators in the wireless detonation scenario of drill-and-blast tunnels. It can naturally adapt to the complex trade-off between detonation reliability and power consumption. Through information sharing among particles and guidance from individual historical bests, it can quickly converge to the global approximate optimal solution within the feasible power boundary, avoiding getting trapped in local optima.

[0053] Step 4: Before the actual detonation, simulated detonation tests are conducted on each detonator based on the optimal expected transmission power. The simulated received signal strength and signal-to-noise ratio of each detonator are collected, the comprehensive channel quality coefficient is calculated, the channel quality is determined by a preset threshold, power compensation is implemented for detonators with weak signals, and the actual transmission power is determined by combining the optimal expected transmission power and the power compensation amount, thereby optimizing the wireless detonation communication signal in the tunnel.

[0054] In this embodiment, simulated detonation tests are conducted on each detonator based on the optimal expected transmission power. The simulated received signal strength and simulated signal-to-noise ratio of each detonator are collected, and the comprehensive channel quality coefficient is calculated. The specific principle is as follows: The optimal expected transmission power was used to simulate the detonation test of each detonator, and the simulated received signal strength and simulated signal-to-noise ratio of each detonator were measured. The analog received signal strength of each detonator is divided by the corresponding received signal strength threshold, and the analog signal-to-noise ratio is divided by the corresponding signal-to-noise ratio threshold. The two sets of quotients are weighted and summed to obtain the comprehensive channel quality index of each detonator. Among them, the weight corresponding to the received signal strength term is greater than the weight corresponding to the signal-to-noise ratio term, and the sum of the two weights is always 1.

[0055] Obtain the received signal strength and signal-to-noise ratio corresponding to the detonator at the minimum received power, and use the received signal strength and signal-to-noise ratio at this time as the received signal strength threshold and signal-to-noise ratio threshold, respectively.

[0056] The formula for calculating the overall channel quality index is as follows: in, Indicates the first The overall channel quality index of a detonator Indicates the first The simulated received signal strength of a detonator. Indicates the received signal strength threshold. Indicates the first The simulated signal-to-noise ratio of a detonator. Indicates the signal-to-noise ratio threshold. These represent the weights of the received signal strength term and the signal-to-noise ratio term, respectively. The comprehensive channel quality index reflects the overall quality of the channel in which a single detonator is located under simulated detonation test conditions. Specifically, it is reflected in the degree to which the received signal strength and signal-to-noise ratio simultaneously meet their respective minimum acceptable thresholds. This is the received signal strength item, used to reflect whether the actual signal strength received by the receiver meets the minimum operating requirements. The signal-to-noise ratio (SNR) term reflects whether a signal can be demodulated normally in a noisy environment; and Dividing each variable by its respective minimum acceptable threshold, a result greater than 1 indicates that the requirements are met, while a result less than 1 indicates that there is a risk. This maps two variables with different dimensions to the same scale. The received signal strength and signal-to-noise ratio jointly determine the reliability of the communication link from two independent dimensions: the magnitude of signal energy and the relative strength of signal and noise, respectively. Both must reach their respective acceptable thresholds to ensure the correct demodulation of the detonation command. However, the deficiency of one can be partially compensated to a certain extent by the surplus of the other. The two are weighted and summed to reflect this compensation relationship.

[0057] During the detonation process, the strength of the received signal is the fundamental condition for the successful triggering of the detonator. Only when the received signal is strong enough can sufficient energy be ensured to demodulate the signal. The signal-to-noise ratio (SNR) mainly affects the bit error rate during demodulation. Under the premise that the signal strength has met the standard, appropriate error correction coding or retransmission mechanism can partially tolerate a low SNR. Therefore, from the perspective of detonation safety and engineering reliability, the weight of the received signal strength term is greater than that of the SNR term.

[0058] As an example, the weight range of the received signal strength term is: The weight range of the signal-to-noise ratio term is: The specific value is to be set by those skilled in the art based on the actual situation, and is not limited here.

[0059] The principle for determining channel quality, implementing power compensation for detonators with weak signals, and determining the actual transmission power by combining the optimal expected transmission power and the power compensation amount is as follows: If the overall channel quality index of the detonator is greater than or equal to the preset threshold, its channel quality is deemed qualified and its communication status is stable. No power compensation is required, and the optimal expected transmission power is directly determined as the actual transmission power of the detonator. If the overall channel quality index of the detonator is less than the preset threshold, the detonator is determined to be a detonator with weak signal and there is a risk of detonation communication failure. The deviation between the integrated channel quality index threshold and the measured integrated channel quality index is calculated. This deviation is then input into a preset PID control model, and the matching power compensation amount is obtained through proportional-integral-derivative coordinated control operations. The optimal expected transmission power is superimposed with the corresponding power compensation amount to obtain the final actual transmission power of the detonator with weak signal.

[0060] For detonators with weak signals, calculate the deviation between their integrated channel quality index and the integrated channel quality index threshold, and then calculate the power compensation amount. The specific formula is as follows: in, Representing time variables The corresponding power compensation amount for detonators with weak signals. Represents a time variable. Representing time variables The deviation value, Indicates the weak signal of the detonator at time The overall channel quality index, This represents the threshold for the overall channel quality index. This represents the deviation value from the initial time to the time variable. The error integral, Time interval Integral variables within, This represents the rate of change of the deviation value over time. These represent the proportional coefficient, integral coefficient, and differential coefficient, respectively. The comprehensive channel quality index threshold is the dividing line for judging whether the channel quality is acceptable. , At this point, the system is in a critical operating state, and the comprehensive channel quality index is set to 1. In practice, a certain safety margin needs to be maintained, so it is set to slightly greater than 1. A higher comprehensive channel quality index means more detonators are judged to have weak signals, requiring power compensation, which increases overall detonation power consumption, but also increases safety. Conversely, a lower comprehensive channel quality index means more detonators can pass the channel quality assessment without additional power compensation, resulting in lower overall power consumption, but relatively lower safety. As one implementation method, the comprehensive channel quality index threshold range is... The specific value is to be set by those skilled in the art based on the actual situation, and is not limited here.

[0061] The proportional gain, integral gain, and derivative gain determine the power compensation effect. These coefficients are obtained through an engineering integer method, and the integral gain is then... and differential coefficients The initial values ​​are all set to 0; the scaling factor is set to... The initial value is set to 0.2, and it is gradually increased in increments of 0.1. The corresponding comprehensive channel quality index is obtained, and the steady-state error between the comprehensive channel quality index and the comprehensive channel quality index threshold is less than 0.05. The result is then recorded. ; use and order ,from Initially, the increments were gradually increased in steps of 0.05. Obtain the corresponding composite channel quality index, and continue until the composite channel quality index equals the composite channel quality index, then record the result at this point. Based on the obtained and ,from Initially, the increments were gradually increased in steps of 0.01. Obtain the corresponding comprehensive channel quality index. When the deviation between the corresponding comprehensive channel quality index and the comprehensive channel quality index threshold is less than or equal to 5%, record the value at this time. The obtained As parameters for a PID controller.

[0062] Please see Figure 3 The present invention also provides a communication signal optimization system for wireless initiation of drill-and-blast tunnels, the system being used to implement the communication signal optimization method for wireless initiation of drill-and-blast tunnels as described in any of the above claims, specifically including: The data acquisition module is used to set an acquisition time window in the tunnel detonation area, acquire multi-source data of each detonator within the acquisition time window along the borehole axis, construct the channel feature vector of each detonator, and simultaneously acquire the signal attenuation value and signal-to-noise ratio of each detonator. The information prediction module is used to construct samples based on the channel feature vectors and signal attenuation values ​​of each detonator, calculate the channel similarity of the samples using a kernel function, build a kernel matrix model, and predict the channel attenuation value of each detonator at the next acquisition time. The power allocation module is used to calculate the communication success rate based on the historical signal-to-noise ratio of each detonator, and to comprehensively determine the detonation priority of each detonator by combining the criticality of the drilling location. It takes the predicted channel attenuation value and detonation priority as input, and takes the minimum power consumption and the highest detonation reliability as optimization objectives. The optimal expected transmission power of each detonator at the next acquisition time is obtained by solving the particle swarm algorithm. The power compensation module is used to conduct simulated detonation tests on each detonator based on the optimal expected transmission power before the actual detonation. It collects the simulated received signal strength and signal-to-noise ratio of each detonator, calculates the comprehensive channel quality coefficient, determines the channel quality through a preset threshold, implements power compensation for detonators with weak signals, and determines the actual transmission power by combining the optimal expected transmission power and the power compensation amount, thereby optimizing the wireless detonation communication signal in the tunnel.

[0063] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0064] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0065] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0066] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes 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.

Claims

1. A method for optimizing communication signals for wireless initiation of blasting in drill-and-blast tunnels, characterized in that, The specific steps include: Step 1: In the tunnel detonation area, set a data acquisition time window, acquire multi-source data of each detonator within the data acquisition time window along the borehole axis, construct the channel feature vector of each detonator, and simultaneously acquire the signal attenuation value and signal-to-noise ratio of each detonator. Step 2: Construct samples using the channel feature vectors and signal attenuation values ​​of each detonator, calculate the channel similarity of the samples using a kernel function, build a kernel matrix model, and predict the channel attenuation value of each detonator at the next acquisition time. Step 3: Calculate the communication success rate based on the historical signal-to-noise ratio of each detonator, and comprehensively determine the detonation priority of each detonator by combining the criticality of the borehole location. Using the predicted channel attenuation value and detonation priority as inputs, and with the minimum power consumption and the highest detonation reliability as optimization objectives, the optimal expected transmission power of each detonator at the next acquisition time is obtained by using the particle swarm optimization algorithm. Step 4: Before the actual detonation, simulated detonation tests are conducted on each detonator based on the optimal expected transmission power. The simulated received signal strength and signal-to-noise ratio of each detonator are collected, the comprehensive channel quality coefficient is calculated, the channel quality is determined by a preset threshold, power compensation is implemented for detonators with weak signals, and the actual transmission power is determined by combining the optimal expected transmission power and the power compensation amount, so as to optimize the tunnel wireless detonation communication signal. Multi-source data of each detonator within the acquisition time window is acquired along the borehole axis, and the channel feature vector of each detonator is constructed. The principle behind this is as follows: Using the current acquisition time as the end time, backtrack a acquisition time window and divide the acquisition time window into several historical acquisition times at equal intervals. The current acquisition time and the historical acquisition times together constitute the acquisition times within the acquisition time window. At each acquisition time, multi-source data of each detonator is acquired, including the distance between the detonator and the detonation controller, the pitch angle, roll angle and heading angle of the detonator. Based on the above four-dimensional parameters, the channel feature vector of each detonator at the corresponding acquisition time is constructed. Samples are constructed using the channel feature vectors and signal attenuation values ​​of each detonator. The channel similarity of the samples is calculated using a kernel function, and a kernel matrix model is built. The specific principle is as follows: Each detonator at a single acquisition moment is used as a set of independent samples, with the channel feature vector corresponding to a single acquisition moment and the signal attenuation value at the same moment being the same as the number of acquisition moments within the acquisition time window. A composite kernel function combining a squared exponential kernel and a white noise kernel is used as the basis for similarity calculation. The kernel function parameters are pre-initialized, including signal variance and noise variance. Based on the channel feature vectors of each group of samples, the Mahalanobis distance between samples is calculated. The Mahalanobis distance and the initialized signal variance and noise variance are substituted into the composite kernel function to solve for the channel similarity between any two groups of samples. All samples are traversed to complete pairwise similarity calculations, and a kernel matrix model is constructed based on the channel similarity of all samples; The communication success rate is calculated based on the historical signal-to-noise ratio of each detonator, and the detonation priority of each detonator is determined by combining the criticality of the borehole location. The specific principle is as follows: A preset signal-to-noise ratio threshold is set, and the number of data acquisitions for each detonator within the acquisition time window is obtained, along with the number of valid acquisitions in which the historical signal-to-noise ratio is higher than the preset signal-to-noise ratio threshold. The ratio of the number of valid acquisitions to the number of data acquisitions is used to obtain the historical communication success rate of each detonator. Calculate the average distance between all detonators and detonators, and then divide the distance between each detonator and detonator by this average distance to obtain the normalized distance coefficient for each detonator. The angle between the axis of the detonator and the main detonation direction is calculated based on the pitch angle, roll angle and heading angle of the detonator. After normalizing the included angle, the normalized included angle is multiplied by the preset adjustment coefficient and the normalized distance coefficient in sequence to obtain the position adjustment factor. The exponential function with the natural constant as the base and the negative value of the position adjustment factor as the exponent is calculated. 1 is summed with the exponential function, and then 1 is divided by the sum to obtain the position criticality of each detonator. The historical communication success rate and location criticality of each detonator are normalized separately. The results of the normalization are then weighted and summed to obtain a quantitative score representing the detonation priority. The weight of location criticality is greater than that of historical communication success rate, and the sum of their weights is 1.

2. The communication signal optimization method for wireless initiation of drill-and-blast tunnels according to claim 1, characterized in that: The principle of synchronously acquiring the signal attenuation value and signal-to-noise ratio of each detonator is as follows: Based on the communication data collected at each acquisition time within the acquisition time window, and taking the original communication signal strength emitted by the detonator as a benchmark, the actual communication signal strength received by each detonator is compared. The signal attenuation value of each detonator is calculated by the difference between the actual communication signal strength and the original communication signal strength. At the same time, the effective signal power and noise power in the received signal of each detonator are extracted, and the corresponding signal-to-noise ratio is calculated by the ratio of the effective signal power to the noise power.

3. The communication signal optimization method for wireless initiation of drill-and-blast tunnels according to claim 1, characterized in that: The principle underlying the prediction of the channel attenuation value of each detonator at the next acquisition time is as follows: A Gaussian process regression prediction model is constructed, with the kernel matrix used as the covariance matrix of the Gaussian process regression prediction model. At the same time, the historical channel attenuation values ​​corresponding to each sample are used as the model observation values. Iterative calculations are performed through the posterior prediction distribution of the Gaussian process regression to obtain the predicted mean and predicted variance of the channel attenuation values ​​at the next acquisition time. The predicted mean of the channel attenuation values ​​with the smallest predicted variance is used as the channel attenuation value of each detonator at the next acquisition time.

4. The communication signal optimization method for wireless initiation of drill-and-blast tunnels according to claim 1, characterized in that: The optimal expected emission power of each detonator at the next acquisition time is obtained by using the particle swarm optimization algorithm. The specific principle is as follows: The maximum allowable transmission power of the detonation controller and the minimum transmission power that meets communication requirements are used as the boundary constraints for power calculation. With the optimization objectives of minimum power consumption and maximum detonation reliability, the optimal expected launch power is obtained through iterative optimization using a particle swarm optimization algorithm. The specific process is as follows: For a single detonator, the detonation power consumption of a single detonator is obtained by multiplying the emission power by the corresponding emission duration. The optimal operating power and receiving sensitivity parameters of each detonator are obtained. Based on the predicted channel attenuation value, the actual receiving power of each detonator is obtained by subtracting the channel attenuation value from the controller's transmit power. Calculate the difference between the actual received power and the received sensitivity, and the difference between the optimal operating power and the received sensitivity, respectively. Take the quotient of the two sets of differences to obtain the detonation reliability of a single detonator. The detonation reliability and detonation power consumption of a single detonator are calculated by weighted subtraction. The objective function value is obtained by traversing all the detonator cumulative calculation results, where the detonation reliability weight is greater than the overall power consumption weight, and the sum of the two weights is 1. The combined emission power of each detonator is constructed into a power vector, and a single power vector is used as a single particle. The particle swarm optimization algorithm iteratively updates the particle positions to search for the optimal particle with the largest objective function value. Extracting the emission power of each group corresponding to the optimal particle is the optimal expected emission power of the controller at the next acquisition time for each detonator.

5. The communication signal optimization method for wireless initiation of drill-and-blast tunnels according to claim 4, characterized in that: Simulated detonation tests were conducted on each detonator based on the optimal expected transmission power. The simulated received signal strength and simulated signal-to-noise ratio of each detonator were collected, and the comprehensive channel quality coefficient was calculated. The specific principle is as follows: The optimal expected transmission power was used to simulate the detonation test of each detonator, and the simulated received signal strength and simulated signal-to-noise ratio of each detonator were measured. The analog received signal strength of each detonator is divided by the corresponding received signal strength threshold, and the analog signal-to-noise ratio is divided by the corresponding signal-to-noise ratio threshold. The two sets of quotients are weighted and summed to obtain the comprehensive channel quality index of each detonator. Among them, the weight corresponding to the received signal strength term is greater than the weight corresponding to the signal-to-noise ratio term, and the sum of the two weights is always 1.

6. The communication signal optimization method for wireless initiation of drill-and-blast tunnels according to claim 5, characterized in that: The principle for determining channel quality, implementing power compensation for detonators with weak signals, and determining the actual transmission power by combining the optimal expected transmission power and the power compensation amount is as follows: If the overall channel quality index of the detonator is greater than or equal to the preset threshold, its channel quality is deemed qualified and its communication status is stable. No power compensation is required, and the optimal expected transmission power is directly determined as the actual transmission power of the detonator. If the overall channel quality index of the detonator is less than the preset threshold, the detonator is determined to be a detonator with weak signal and there is a risk of detonation communication failure. The deviation between the integrated channel quality index threshold and the measured integrated channel quality index is calculated. This deviation is then input into a preset PID control model, and the matching power compensation amount is obtained through proportional-integral-derivative coordinated control operations. The optimal expected transmission power is superimposed with the corresponding power compensation amount to obtain the final actual transmission power of the detonator with weak signal.

7. A communication signal optimization system for wireless initiation of blasting in drill-and-blast tunnels, characterized in that: The system is used to implement the communication signal optimization method for wireless initiation of drill-and-blast tunnels as described in any one of claims 1-6, specifically including: The data acquisition module is used to set an acquisition time window in the tunnel detonation area, acquire multi-source data of each detonator within the acquisition time window along the borehole axis, construct the channel feature vector of each detonator, and simultaneously acquire the signal attenuation value and signal-to-noise ratio of each detonator. The information prediction module is used to construct samples based on the channel feature vectors and signal attenuation values ​​of each detonator, calculate the channel similarity of the samples using a kernel function, build a kernel matrix model, and predict the channel attenuation value of each detonator at the next acquisition time. The power allocation module is used to calculate the communication success rate based on the historical signal-to-noise ratio of each detonator, and to comprehensively determine the detonation priority of each detonator by combining the criticality of the drilling location. It takes the predicted channel attenuation value and detonation priority as input, and takes the minimum power consumption and the highest detonation reliability as optimization objectives. The optimal expected transmission power of each detonator at the next acquisition time is obtained by solving the particle swarm algorithm. The power compensation module is used to conduct simulated detonation tests on each detonator based on the optimal expected transmission power before the actual detonation. It collects the simulated received signal strength and signal-to-noise ratio of each detonator, calculates the comprehensive channel quality coefficient, determines the channel quality through a preset threshold, implements power compensation for detonators with weak signals, and determines the actual transmission power by combining the optimal expected transmission power and the power compensation amount, thereby optimizing the wireless detonation communication signal in the tunnel.

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