Deep exploration method based on 6g air-ground integrated network integrated with nearlink technology

Abstract

The present invention relates to the technical field of communication applications and exploration, and relates to a deep exploration method based on a 6G air-ground integrated network integrated with a NearLink technology, comprising the following steps: setting a luminous flux threshold, and receiving an optical signal by means of a receiving end of a VLC communication system; and if the optical signal is lower than the luminous flux threshold, by means of a collision model, calculating and searching for whether signal reachability of other optical signal nodes can be achieved, and if the signal is not reachable, converting the optical signal into a wireless signal by means of the VLC communication system, and then by means of a switching model, transmitting data in a UWB signal manner. By constructing an intelligent energy system mainly characterized by device intelligence, multi-energy coordination, information symmetry, decentralized supply-demand, a flat system architecture, an open transaction and the like, the present invention can be applied to all links of energy production, transmission, storage, and service, solves the problems of severe wireless signal attenuation and coverage dead zones in coal mine exploration, and solves the problems of reduced radio wave transmission efficiency and high bit error rate caused by factors such as natural environment interference in an application scenario.

Description

A deep exploration method based on 6G space-ground integration combined with star flash technology Technical Field

[0001] The present invention belongs to the field of communication applications and exploration technology, and in particular relates to a deep exploration method based on 6G space-ground integration combined with star flash technology. Background Art

[0002] At present, the world is in urgent need of realizing energy transformation, reshaping the energy landscape, and realizing safe, stable, clean and sustainable energy utilization. Under this general trend, one of the hot topics in the energy field is smart energy. This is a way to develop human wisdom, promote technological development and system improvement, integrate human wisdom into energy development and production and consumption in all aspects, build an energy technology and energy system under the construction of ecological civilization and sustainable development, and promote the birth of new forms of energy. At present, the application of smart energy is becoming increasingly rich, and its social and economic benefits are constantly highlighted, which has also accelerated the energy layout of global energy companies.

[0003] Smart energy is a new form of energy production and utilization. It is based on technologies such as the Internet and the Internet of Things to achieve real-time monitoring and analysis of energy production, storage, transmission and utilization, and uses big data and cloud computing for real-time detection, reporting and optimization processing to form an open, transparent and widely voluntary integrated management system. Smart energy is a new model and new business model formed by the deep integration of energy utilization technology with artificial intelligence and big data technology.

[0004] Summary of the Invention

[0005] In view of the above shortcomings of the existing technology, the purpose of the invention is to provide a deep exploration method based on 6G space-ground integration combined with star flash technology, innovatively construct a smart energy system based on the four key characteristics of smart energy to obtain four feature data sets from the log and merge them for anomaly prediction. The anomaly prediction values ​​of the four features are used as signal anomaly trigger conditions for coal exploration. According to the anomaly prediction values, the VLC communication system and the UWB signal transmission device are switched to complete the application of wireless signals in the exploration process. At the same time, a mathematical model of UWB is constructed to generate a signal measurement matrix, and according to the characteristics of DA suitable for compressing UWB signals, the UWB signal is compressed and output, which is responsible for efficiently transmitting the wireless signal to the ground receiving end and has the ability to send signals into the air, thereby realizing the integration of artificial intelligence technology for smart energy and 6G space-ground integration combined with coal exploration.

[0006] The present invention proposes a depth exploration method based on 6G space-ground integration combined with star flash technology, including the following steps:

[0007] S1. Set the light flux threshold and receive the optical signal through the VLC communication system receiving end;

[0008] If the optical signal falls below the light flux threshold, the collision model is used to calculate and determine if other optical signal nodes are reachable. If not, the optical signal is converted into a wireless signal via the VLC communication system, and then the data is transmitted using the switching model UWB signal method.

[0009] S3. Obtain data sets of four characteristics from the log based on the four key characteristics of smart energy, and extract the data sets of the most recent 10, 100, and 1000 times from each of the four data sets;

[0010] S4. Perform a weighted average of the historical data to obtain an initial baseline for prediction. The number of times the threshold exceeds and falls below the initial baseline is then counted. If the threshold exceeds the threshold, add 1; if the threshold falls below the threshold, add 1. Forecasts are obtained for the last 10, 100, and 1000 times, respectively. If the probability intervals for the predicted values ​​are consistent and within a 10% fluctuation range, the initial threshold for prediction is updated; otherwise, the threshold is not updated.

[0011] S5. Obtain the abnormal prediction values ​​of the four features through S4 respectively, use the abnormal prediction values ​​as the signal abnormality trigger conditions for exploration, and use the prediction values ​​that meet the above four data sets as the resonance index. If the resonance index is not higher than 50% of the features at the same time, the exploration is judged to be normal. If one indicator is abnormal, the exploration is judged to be abnormal and marked.

[0012] Furthermore, in S1, setting the luminous flux threshold specifically includes the following steps:

[0013] S11. Obtain historical luminous flux data from the log, obtain the most recent 10, 100, and 1000 times, and then take a weighted average of all historical luminous flux data to obtain the luminous flux threshold average;

[0014] S12. Count the data of the last 10, 100, and 1000 times respectively as the denominator, and the number of times the luminous flux threshold value exceeds the average line as the number of abnormalities and the numerator. The ratio of the luminous flux abnormality to the last 10, 100, and 1000 times is used as the predicted value of the luminous flux abnormality passing through the LED next time;

[0015] S13. If the difference between the 10th, 100th, and 1000th luminous flux prediction values ​​is within 10%, the weighted average is used to obtain the final luminous flux prediction value. If the difference is greater than 10%, the 10th short-term luminous flux prediction value is used as the basis.

[0016] Furthermore, the VLC communication system consists of an LED light, a collision and migration model, and a photoelectric detector.

[0017] Furthermore, the four characteristics include systematicity, safety, cleanliness and economy.

[0018] Furthermore, the data set includes a systematic data set, a safety data set, a cleanliness data set and an economic data set.

[0019] Furthermore, the systematic data set includes a short-term systematic data set, a medium-term systematic data set, and a long-term systematic data set;

[0020] The safety data set includes a short-term safety data set, a mid-term safety data set, and a long-term safety data set;

[0021] The clean data set includes a short-term clean data set, a mid-term clean data set, and a long-term clean data set.

[0022] The economic data set includes a short-term economic data set, a medium-term economic data set, and a long-term economic data set.

[0023] Furthermore, the calculation formula of the collision model is:

[0024] Among them, f i (x, t)-t represents the probability density function of the moment;

[0025] f i (x, t+Δt)-t+Δt represents the probability density function of the moment;

[0026] represents the equilibrium distribution function at the moment;

[0027] Where x is the spatial coordinate in the x-direction, t is the time, Δt is the time change, and τ is the relaxation factor.

[0028] Furthermore, the migration formula of the migration model is: i (x+e i Δt, t) = f i (x, t);

[0029] Among them, e i The collision node.

[0030] Furthermore, τ=0.5+3v, where v is the kinematic viscosity.

[0031] Furthermore, the value range of τ is 0.5<τ<3.

[0032] The beneficial effects of the present invention are as follows:

[0033] (1) The main features of building a smart energy system include equipment intelligence, multi-energy collaboration, information symmetry, decentralized supply and demand, flat system, and open transactions. It can be applied to all aspects of energy production, transmission, storage, and services. Innovatively, based on the four key characteristics of smart energy, the data sets of the four characteristics are obtained from the log and merged to make anomaly predictions. The anomaly prediction values ​​of the four characteristics are used as signal anomaly trigger conditions for coal exploration. Innovatively, based on the anomaly prediction values, the VLC-based optical communication system and device are switched to solve the serious fading and coverage dead angle problems of wireless signals in coal mine exploration, and solve the problem of reduced radio wave transmission efficiency and high bit error rate caused by factors such as natural environment interference in application scenarios. At the same time, a mathematical model of UWB is constructed to generate a signal measurement matrix, and according to the characteristics of DA suitable for compressing UWB signals, the UWB signal is compressed and output. It is responsible for efficiently transmitting wireless signals to the ground receiving end and has the ability to send signals to the air, thereby realizing the integration of smart energy and 6G ground-to-ground integration combined with coal exploration to realize artificial intelligence technology integration.

[0034] (2) According to the abnormal prediction value, the VLC communication system and the UWB signal transmission device are switched to complete the application of wireless signals in the exploration process. The VLC-based optical communication system and device are innovatively used to solve the serious fading and coverage dead angle problems of wireless signals in coal mine exploration, and solve the problems of reduced radio wave transmission efficiency and high bit error rate caused by factors such as natural environment interference in application scenarios. At the same time, a mathematical model of UWB is constructed to generate a signal measurement matrix, and according to the characteristics of DA suitable for compressing UWB signals, the UWB signal is compressed and output, which is responsible for efficiently transmitting the wireless signal to the ground receiving end and has the ability to send signals into the air. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings are only for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. Throughout the drawings, the same reference numerals represent the same components. Obviously, the drawings described below are only some of the embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings.

[0036] FIG1 is a flow chart of an embodiment of the present invention;

[0037] FIG2 is a diagram illustrating the composition of a VLC communication system according to an embodiment of the present invention;

[0038] FIG3 is a migration diagram of an embodiment of the present invention;

[0039] FIG4 is a diagram showing the probability density direction and probability density distribution according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are part of the embodiments of the present invention, rather than all of the embodiments. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work should fall within the scope of protection of the present invention.

[0041] Furthermore, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily obscuring the concepts disclosed in the present invention.

[0042] In the description of the present invention, it should be noted that, unless otherwise expressly specified and limited, the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second" and "third" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance. The terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be a communication between the internal parts of two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0043] Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present invention. Rather, they are merely examples of methods and systems consistent with certain aspects of the present invention, as detailed in the appended claims.

[0044] The present invention proposes a deep exploration method based on 6G space-ground integration combined with star flash technology, which is used to solve the problem that the current exploration process lacks integration of artificial intelligence technology, Internet technology, cloud computing technology, communication technology, control technology and future new technologies, and cannot give full play to the comprehensive advantages of energy production, transmission and utilization. In addition, it solves the problems of severe fading and coverage blind spots of wireless signals in coal mine exploration, and solves the problems of reduced radio wave transmission efficiency and high bit error rate due to factors such as natural environment interference.

[0045] Method Example

[0046] In order to assist people in understanding the contents of this invention, the following nouns appearing in the text are explained:

[0047] VLC: Visible light communication (VLC) technology uses high-speed, visible light and dark flickering signals emitted by fluorescent lamps or light-emitting diodes to transmit information. Connecting high-speed Internet wiring to a lighting fixture and plugging it into a power source allows for immediate use. Systems built using this technology can cover the range of indoor lighting, eliminating the need for wired connections to computers, and therefore have broad development prospects.

[0048] Smart energy: Smart energy is to fully develop human intelligence and capabilities, integrate human unique wisdom into the entire process and all links of energy development, utilization, production and consumption through continuous technological innovation and institutional reform, establish and improve energy technology and energy system that meet the requirements of ecological civilization and sustainable development, thus presenting a new energy form. In short, smart energy refers to an energy form that has human brain functions such as self-organization, self-inspection, self-balancing, and self-optimization, and meets the requirements of system, safety, cleanliness and economy.

[0049] Ultra-wideband (UWB) is one of the major breakthroughs in wireless communications in recent years. Because UWB signals are highly sparse in the time domain, it's natural to introduce CS into UWB systems to improve UWB signal acquisition performance. A major breakthrough in UWB technology is the invention of micropower impulse radar (MIR), which for the first time demonstrated the ability to operate UWB signals at very low power using inexpensive hardware.

[0050] DA (also known as transverse filter) is a microwave component.

[0051] The Latice Boltzmann Method (LBM) is a simulation calculation method based on the mesoscopic simulation scale.

[0052] Luminous flux refers to the radiant power that the human eye can perceive. It is equal to the product of the radiant energy of a certain wavelength per unit time and the relative visibility of that wavelength. Because the relative visibility of the human eye is different for different wavelengths of light, when the radiant power of light of different wavelengths is equal, the luminous flux is not equal.

[0053] Star Flash's innovative technology: First, in the VLC communication system, the LED link manages optical signals through the luminous flux indicator. In the receiver link, the collision model and migration model constructed by the LBM algorithm are used to optimize the optical signal path and convert the short-distance transmission of wireless signals. It has made outstanding contributions in solving the problems of severe fading and coverage blind spots of wireless signals in coal mines / geological surveys. It is achieved from the perspective of transmission and energy consumption by combining the speed of light with ultra-wideband technology.

[0054] In addition, regarding how to build a smart energy system, in the present invention, the main features of building a smart energy system include equipment intelligence, multi-energy collaboration, information symmetry, decentralized supply and demand, system flatness, open transactions, etc., which can be applied to various links of energy production, transmission, storage, and services. Smart energy should have the following four key features. Innovatively, based on the four key features of smart energy, the data sets of the four features are obtained from the log and merged for anomaly prediction. One anomaly in the predicted values ​​of the four features is judged as an anomaly, which is used as a signal anomaly trigger condition for coal or geological exploration.

[0055] Regarding the above content, in the present invention, as shown in Figures 1 to 4, a depth exploration method based on 6G ground-ground integration combined with star flash technology is provided, including the following steps:

[0056] S1. Set the light flux threshold and receive the optical signal through the VLC communication system receiving end;

[0057] If the optical signal falls below the light flux threshold, the collision model is used to calculate and determine if other optical signal nodes are reachable. If not, the optical signal is converted into a wireless signal via the VLC communication system, and then the data is transmitted using the switching model UWB signal method.

[0058] S3. Obtain data sets of four characteristics from the log based on the four key characteristics of smart energy, and extract the data sets of the most recent 10, 100, and 1000 times from each of the four data sets;

[0059] S4. Perform a weighted average of the historical data to obtain an initial baseline for prediction. The number of times the threshold exceeds and falls below the initial baseline is then counted. If the threshold exceeds the threshold, add 1; if the threshold falls below the threshold, add 1. Forecasts are obtained for the last 10, 100, and 1000 times, respectively. If the probability intervals for the predicted values ​​are consistent and within a 10% fluctuation range, the initial threshold for prediction is updated; otherwise, the threshold is not updated.

[0060] S5. Obtain the abnormal prediction values ​​of the four features through S4 respectively, use the abnormal prediction values ​​as the signal abnormality trigger conditions for exploration, and use the prediction values ​​that meet the above four data sets as the resonance index. If the resonance index is not higher than 50% of the features at the same time, the exploration is judged to be normal. If one indicator is abnormal, the exploration is judged to be abnormal and marked.

[0061] The four characteristics include systematicity, safety, cleanliness and economy.

[0062] The data set includes a systematic data set, a safety data set, a cleanliness data set and an economic data set.

[0063] The systematic data set includes a short-term systematic data set, a medium-term systematic data set, and a long-term systematic data set;

[0064] The safety data set includes a short-term safety data set, a mid-term safety data set, and a long-term safety data set;

[0065] The clean data set includes a short-term clean data set, a mid-term clean data set, and a long-term clean data set.

[0066] The economic data set includes a short-term economic data set, a medium-term economic data set, and a long-term economic data set.

[0067] Regarding the above-mentioned systemicity, safety, cleanliness and economy, in the present invention:

[0068] (1) Systematic: Smart energy technology organically integrates artificial intelligence technology, Internet technology, cloud computing technology, communication technology, control technology, and future new technologies to achieve comprehensive advantages in energy production, transmission, and utilization. The function of smart energy technology is to meet the needs of ecological civilization development and to establish a comprehensive system combining environment, society, humanities, and politics.

[0069] (2) Safety: Smart energy technology needs to provide society with safe, stable, and continuous energy, and solve the hazards of energy in uncontrollable situations such as fire, flood, electric shock, and traffic accidents, and completely tame the "wildness" of energy.

[0070] (3) Cleanliness: Smart energy should have minimal impact on the natural environment. Clean energy is an inevitable requirement for the future. Its production and use should not produce harmful substances and should not destroy the natural balance. Smart energy should not only control visible tangible pollutants but also eliminate the hazards of radiation and electromagnetic waves.

[0071] (4) Economical: Smart energy technology seeks more efficient energy, improves energy density, and achieves high efficiency, low cost, and high output.

[0072] By analyzing the meaning and characteristics of smart energy, we can conclude that the smart energy industry is not the product of new energy companies, traditional energy-consuming enterprises, traditional energy-saving service industries, or the IT / ICT industry alone. Rather, it is a complex industry formed through the integration of these industries. Current distributed energy resources and smart grids have not reached the level of smart energy, and the development and application of wind, solar, and biomass energy have also fallen short of meeting the requirements of smart energy.

[0073] Furthermore, regarding how to address the severe fading and coverage blind spots of wireless signals in coal mine surveys, this invention utilizes an innovative VLC-based optical communication system and device to address the reduced radio wave transmission efficiency and high bit error rates caused by factors such as natural environmental interference. This system utilizes the LBM algorithm to construct a collision and migration model to monitor and switch incoming optical signals.

[0074] In addition, through the cooperation of Star Flash innovative technology, first, the optical signal received by the receiving end of the VLC communication system is lower than the light flux threshold, and then the collision model is used to calculate and find other optical signal nodes to see if the signal is reachable. If it is not reachable, the optical signal is converted into a wireless signal through the switching model and the data is transmitted using the UWB signal. UWB compresses and outputs the UWB signal according to the characteristics of DA suitable for compressing UWB signals. It is responsible for efficiently transmitting the wireless signal to the ground receiving end and has the ability to send signals into the air, thereby realizing the integration of artificial intelligence technology for 6G integrated space and ground exploration, and providing a VLC communication system using Star Flash innovative technology that combines light flux indicators with UWB signal switching.

[0075] In S1, setting the luminous flux threshold specifically includes the following steps:

[0076] S11. First, obtain historical luminous flux data from the log, obtaining the most recent 10, 100, and 1000 times, respectively. Then, take a weighted average of all historical luminous flux data to obtain the luminous flux threshold average.

[0077] S12. The data of the most recent 10, 100, and 1000 times are then counted as the denominator, the number of times the luminous flux threshold average line is exceeded as the number of abnormalities and the numerator. The ratio of luminous flux abnormality to the most recent 10, 100, and 1000 times is obtained as the predicted value of the next luminous flux abnormality passing through the LED;

[0078] S13. If the difference between the 10th, 100th, and 1000th luminous flux prediction values ​​is within 10%, the weighted average is used to obtain the final luminous flux prediction value. If the difference is greater than 10%, the 10th short-term luminous flux prediction value is used as the basis.

[0079] The VLC communication system consists of an LED light, a collision and migration model, and a photoelectric detector.

[0080] Regarding the coverage area of ​​the LED lamp, in the present invention, the optical signal carrying the multi-stream transmission data transmitted by FTN overlapping within the LED lamp coverage area is transmitted through free space to the photodetector at the receiving end. After the photodetector at the receiving end receives the optical signal, the signal is converted into an electrical signal, and the electrical signal is post-equalized, demodulated, and decoded to restore it to the original transmission signal.

[0081] In addition, in the present invention, regarding the mathematical model of UWB:

[0082] Let h(t) represent the impulse response of the channel and x(t) represent the signal to be transmitted. Then, the signal received by the receiver is y(t) = x(t) * h(t) (Equation 1).

[0083] Among them, * represents convolution, where thermal noise and interference are ignored. If the channel is a multipath channel, that is, The received signal can be written as

[0084] Among them, h i and τ i is the channel gain and path delay of path 1, and L is the number of multipaths. The typical transmission waveform is a Gauss pulse, that is,

[0085] Among them, x n (t) is a polynomial, σ 2 It is the parameter that controls the pulse width.

[0086] Because the sampling rate is limited, the vector y cannot be obtained directly. After downsampling to M consistent sample points, the signal obtained by the sampler is y = Φ x .

[0087] To simplify the analysis, the quantization error is ignored here, and the measurement matrix is ​​obtained by the following detailed expression:

[0088] where B is the divergence of the synthesis filter f·h.

[0089] The relationship between the original signal x, the measurement matrix Φ, and the downsampled signal y. As we can see, the matrix Φ is a quasi-Toeplitz matrix, with its non-zero elements distributed in a strip-shaped region, with B elements per column. Intuitively, we should design an FIR filter with a strong divergence, that is, making B very large. This will allow the measurement matrix to have more non-zero elements and be more dispersed in the time domain.

[0090] Advantages of this structure:

[0091] (1) This hybrid structure can be implemented using an FIR filter;

[0092] (2) The divergence B can be flexibly designed;

[0093] (3) The structure can process signal flow.

[0094] In addition, in the present invention, regarding the above-mentioned collision model:

[0095] Refers to the evolution of the probability density function of a node in the time layer. Specifically, it can be described as: at a certain moment and a certain spatial point, based on the equilibrium state function at that time and place, the correction amount of the probability distribution of the velocity in all directions at that point is obtained, thereby realizing the evolution of the probability density function of this point. For the single relaxation model, the collision term is controlled by only one relaxation factor, and the formula is as follows:

[0096] Where, f i (x, t)-t represents the probability density function of the moment;

[0097] f i (x, t+Δt)-t+Δt represents the probability density function of the moment;

[0098] represents the equilibrium distribution function at the moment;

[0099] Where x is the spatial coordinate in the x-direction, t is the time, Δt is the time variation, and τ is the relaxation factor:

[0100] Relaxation factor, τ = 0.5 + 3v, v is the kinematic viscosity. Here, the value of τ has a certain range. In principle, 0.5 < τ < 3. This is the required range for LBM simulation of incompressible fluids. If you do not pay attention to this, it may lead to incorrect calculation results.

[0101] The MATLAB code for the collision step is as follows: F = omega*FEQ+(1-omega)*E

[0102] In addition, in the present invention, regarding the above-mentioned migration model:

[0103] Migration is the spatial evolution of the flow field. As shown in Figure 3, the effects of migration are:

[0104] At a specific moment and point in space, the probability density function of the point after collision is transferred to the corresponding position of the adjacent grid node, replacing the value at that position; at the same time, the point also accepts the values ​​transferred from other adjacent grid nodes and replaces the corresponding local values. After all values ​​are migrated and replaced, the density, velocity, etc. of the flow field can be updated, thereby realizing the evolution of the flow field in space. The migration formula is:

[0105] f i (x+ei Δt, t) = f i (x, t), where e i For collision nodes, as shown in Figure 4, (a) is the probability density direction, (b) is the probability density distribution, and the velocity distribution in the 9th direction (at the center point) does not change during the migration. Therefore, when implementing the migration steps, only the changes in the velocity distribution in 8 directions are considered.

[0106] In the present invention, the following application scenarios are provided to ensure safe production in coal mines:

[0107] (1) Improvement of the structure and performance of coal mine safety instruments and meters. With the development of intelligent automation technology, the application and development prospects of coal mine safety monitoring instruments and meters are broader. Based on historical and real-time data information, software and hardware intelligence, each instrument and meter can be analyzed at any time, and the measurement process can be abstractly displayed from three levels: low, medium and high. The application of artificial intelligence technology has greatly expanded the functions of traditional measurement systems, effectively improved the efficiency and performance of measurement and verification systems, and made the functions of coal mine safety monitoring instruments and meters more complete, with the characteristics of sensitivity, efficiency and high speed.

[0108] Microchip technologies such as microcontrollers and microprocessors are applied to instruments and meters for coal mine safety monitoring. Fuzzy reasoning and other technologies are used to set critical values ​​of various measurement data and fuzzy control programs to establish various fuzzy relationships of fuzzy decision-making things. The unique feature of fuzzy technology is that it does not require a large amount of test data, nor does it require the establishment of a mathematical model of the control object. It should be noted that the use of fuzzy technology requires very rich experience, and requires on-site chip debugging and offline calculation to analyze and research appropriate control rules to achieve precise control and analysis of equipment based on a given degree of accuracy.

[0109] Mining plan decision-making and parameter optimization design. At present, the development of expert systems has greatly improved the mine excavation and parameter selection technology of related coal mining enterprises, which is getting closer and closer to reality and more in line with actual conditions. In recent years, coal mine safety production has become a key research and attention area of ​​artificial intelligence research institutions, and related application research has continued to deepen. For example, expert systems can use artificial intelligence to effectively select coal cutting plans for longwall mining and shortwall mining based on actual conditions. Fuzzy mathematics theory is actively used in coal mine production, and the designed expert system can intelligently select the best blasting strategy and optimize the plan parameters.

[0110] (3) Related applications in the networking of coal mine safety instruments and meters. In order to better integrate artificial intelligence technology into coal mine safety instruments and meters and promote the maximum use and realization of the functions of safety instruments, the calculation function of the computer can be used to quickly and accurately calculate the parameters. For example, the network can be used to connect digital safety instruments and apply effective pattern recognition software to accurately analyze the relevant attributes of the instruments and the actual conditions in which they are located so as to make appropriate processing. By installing the intelligent system on the data acquisition equipment, automatic classification can be achieved, and intelligent data acquisition and remote measurement can be realized without the network.

[0111] In the current era of great scientific and technological progress, the computer field has also achieved rapid development and innovation. The functions of human-computer interaction and calculation based on computers have been gradually improved, and more humane services have been realized. According to the specific needs of the task, the instruments and meters can be interconnected with computers through the design of artificial intelligence software to realize remote control of the instruments and instruments. Based on relevant applications, a database for storing measurement results that can be viewed at any time can be established, and the data collected on the instruments can be collected and copied, and the data can be shared with relevant departments. For the data collection and monitoring of the same task or the same instrument, the application of artificial intelligence enables different users to obtain the required data at different locations at the same time, without the need for users to go to the equipment site to collect the data in person. Based on this solution, the simultaneity of data acquisition can be ensured, so that relevant personnel can analyze and solve sudden problems in a timely manner, make corresponding suggestions and implement them, prevent delays caused by data asymmetry, and effectively shorten the time required to solve the problem.

[0112] (4) Underground fault diagnosis and disaster prevention and control. In order to maximize economic benefits, effectively solve the safety problems of underground operations, and reduce damage to the environment, it is necessary to solve the rationality and optimization problems of mining plans based on the characteristics of the coal mine production process. To address these problems, artificial intelligence technology can be considered for fault diagnosis and disaster prevention and control. The intelligent diagnosis expert system built based on neural networks (as shown in Figure 2) can use neural networks with strong learning capabilities to summarize the safety problems and their solutions that have occurred in previous coal mine operations. When similar problems occur again, the expert system can be used for rapid diagnosis, rapid response, and finding appropriate response plans.

[0113] Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the embodiments of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they may still modify the technical solutions described in the aforementioned embodiments, or replace some of the technical features therein with equivalents; and such modifications or replacements do not deviate from the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention. Any changes or replacements that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be covered by the scope of protection of the present invention.

Claims

1. A deep exploration method based on 6G space-ground integration combined with star flash technology, characterized in that: The steps include: S1. Setting the light flux threshold, receiving the optical signal through the receiving end of the VLC communication system; S2. If the optical signal is lower than the light flux threshold, the collision model is used to calculate and find other optical signal nodes to see if the signal is reachable. If the signal is not reachable, the optical signal is converted into a wireless signal through the VLC communication system, and then the switching model UWB signal is used to transmit the data; S3. Obtain data sets of four characteristics from the log according to the four key characteristics of smart energy, and extract the data sets of the most recent 10, 100, and 1000 times from each of the four data sets; S4. Perform weighted averaging on historical data to obtain the initial baseline for prediction. Then count the number of times above and below the initial baseline for prediction. If it is abnormal, add 1. Otherwise, add 1. Obtain the prediction values ​​for the last 10, 100, and 1000 times respectively. If the probability interval of the prediction value tends to be consistent within 10% fluctuation, update the initial threshold for prediction. Otherwise, do not update. S5. Obtain the abnormal prediction values ​​of the four features respectively through S4, use the abnormal prediction values ​​as the signal abnormality triggering conditions of exploration, and use the prediction values ​​that meet the above four data sets as resonance indicators. If the resonance indicators are not higher than 50% of the features at the same time, the exploration is judged to be normal. If one indicator is abnormal, the exploration is judged to be abnormal and marked.

2. According to claim 1, a depth exploration method based on 6G space-ground integration combined with star flash technology is characterized in that: In S1, setting the luminous flux threshold specifically includes the following steps: S11. Obtain historical luminous flux data from the log, obtain the most recent 10 times, 100 times, and 1000 times respectively, and then perform weighted average of all historical luminous flux data to obtain the luminous flux threshold average; S12. Count the data of the last 10, 100, and 1000 times respectively as the denominator, and the number of times exceeding the average line of the luminous flux threshold as the number of abnormalities and the numerator, and obtain the ratio of the luminous flux abnormality to the last 10, 100, and 1000 times as the predicted value of the luminous flux abnormality passing through the LED next time; S13. If the difference between the three luminous flux prediction values ​​of 10 times, 100 times, and 1000 times is within 10%, the weighted average is used to obtain the final luminous flux prediction value. If the difference is greater than 10%, the luminous flux prediction value of 10 times shall prevail.

3. According to claim 1, a depth exploration method based on 6G space-ground integration combined with star flash technology is characterized in that: The VLC communication system consists of an LED lamp, a collision and migration model, and a photoelectric detector.

4. According to claim 1, a depth exploration method based on 6G space-ground integration combined with star flash technology is characterized in that: The four characteristics include systematicity, safety, cleanliness and economy.

5. A method for deep exploration based on 6G space-ground integration combined with star flash technology according to claim 4, characterized in that: The data sets include a systematic data set, a safety data set, a cleanliness data set and an economic data set.

6. A depth exploration method based on 6G space-ground integration combined with star flash technology according to claim 5, characterized in that: The systematic data set includes a short-term systematic data set, a medium-term systematic data set, and a long-term systematic data set; The safety data set includes a short-term safety data set, a medium-term safety data set, and a long-term safety data set; The cleanliness data set includes a short-term cleanliness data set, a mid-term cleanliness data set, and a long-term cleanliness data set; The economic data set includes a short-term economic data set, a medium-term economic data set, and a long-term economic data set.

7. The method for deep exploration based on 6G space-ground integration combined with star flash technology according to claim 5 is characterized in that: The calculation formula of the collision model is: Among them, f i (x, t) - t represents the probability density function at the moment; f i (x, t+Δt)-t+Δt represents the probability density function of the moment; represents the equilibrium distribution function at time; Among them, x is the spatial coordinate in the x direction, t is the time, Δt is the time change, and τ is the relaxation factor.

8. The method for deep exploration based on 6G space-ground integration combined with star flash technology according to claim 7 is characterized in that: The migration formula of the migration model is: i (x+e i ·Δt, t) = f i (x, t); Among them, e i The collision node.

9. The method for deep exploration based on 6G space-ground integration combined with star flash technology according to claim 7 is characterized in that: The τ=0.5+3υ, where υ is the kinematic viscosity.

10. A depth exploration method based on 6G space-ground integration combined with star flash technology according to claim 9, characterized in that: The value range of τ is 0.5<τ<3.

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