Method and system for improving the efficiency of electronic detonator communication

Abstract

The present invention relates to the field of electronic communication technology, and in particular to a method and system for improving the communication efficiency of electronic detonators. The method comprises the following steps: obtaining electronic detonator communication metadata and analyzing it to obtain abnormal communication data of the electronic detonator; constructing a detonator communication network model based on the electronic detonator communication metadata, and dividing the detonator communication network model to obtain abnormal communication node data; optimizing the detonator communication network model to obtain an optimized detonator communication network model; simulating the electronic detonator communication metadata to obtain chaotic disturbance enhanced transmission signal data, and performing spectrum allocation to obtain an optimized transmission signal spectrum; performing electrical signal transmission simulation on the optimized transmission signal spectrum to obtain electrical signal transmission simulation data, and performing analysis to obtain a chaotic disturbance factor; fusing the optimized detonator communication network model to obtain a chaotic detonator communication network model. The present invention can improve the communication efficiency of electronic detonators.

Description

Technical Field

[0001] The present invention relates to the field of electronic communication technology, and in particular to a method and system for improving the communication efficiency of electronic detonators. Background Art

[0002] As the core element in blasting engineering, electronic detonators are responsible for detonating explosives and achieving precise blasting. In modern mining, construction blasting and other blasting fields, their application has become an essential tool for improving production efficiency, ensuring safety and protecting the environment. However, traditional electronic detonator communication systems face many challenges in practical applications. First of all, traditional systems often use wired connections, which limits the layout and scope of use of detonators and increases the difficulty of construction. The operating environment of electronic detonators is complex, and there are many interference sources such as geological structures and electromagnetic signals. Although the communication method based on simple wireless radio frequency technology can meet the needs under certain circumstances, its data transmission rate is limited, and its communication stability and anti-interference ability are weak. It is easily affected by surrounding environmental factors such as electromagnetic interference and topography, resulting in low communication efficiency of electronic detonators. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a method and system for improving the communication efficiency of electronic detonators to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for improving the communication efficiency of electronic detonators comprises the following steps:

[0005] Step S1: acquiring electronic detonator communication metadata, and performing electronic detonator abnormal communication analysis on the electronic detonator communication metadata, thereby obtaining electronic detonator abnormal communication data;

[0006] Step S2: constructing a detonator communication network model based on the electronic detonator communication metadata, and dividing the detonator communication network model into abnormal communication nodes according to the electronic detonator abnormal communication data, thereby obtaining abnormal communication node data;

[0007] Step S3: optimizing abnormal communication node channels on abnormal communication node data according to the detonator communication network model, thereby obtaining optimized communication node channel data, and performing communication node fusion on the optimized communication node channel data and the detonator communication network model, thereby obtaining the optimized detonator communication network model;

[0008] Step S4: performing a chaotic disturbance enhancement simulation on the electronic detonator communication metadata to obtain chaotic disturbance enhanced transmission signal data, and performing transmission signal spectrum allocation on the chaotic disturbance enhanced transmission signal data by optimizing the detonator communication network model to obtain an optimized transmission signal spectrum;

[0009] Step S5: performing an electrical signal transmission simulation on the optimized transmission signal spectrum by optimizing the detonator communication network model, thereby obtaining electrical signal transmission simulation data, and performing adaptive chaotic perturbation factor analysis on the electrical signal transmission simulation data according to the electronic detonator communication metadata, thereby obtaining a receiving end chaotic perturbation factor and a sending end chaotic perturbation factor;

[0010] Step S6: Perform disturbance factor encoding fusion on the optimized detonator communication network model according to the chaotic disturbance factor of the receiving end and the chaotic disturbance factor of the sending end, so as to obtain the chaotic detonator communication network model, and upload it to the electronic detonator communication cloud platform.

[0011] By analyzing the electronic detonator communication metadata, the present invention can identify abnormal communication modes, including abnormal frequencies, abnormal time periods, etc., so as to timely discover potential communication problems and provide data support for subsequent optimization. Establishing a detonator communication network model helps to understand the relationship between the system structure and the communication nodes, and the division of abnormal communication nodes can help locate the source of communication problems and provide specific goals for subsequent optimization. Optimizing the communication node channel can improve the stability and reliability of data transmission, and the fusion of communication node data can integrate the information of different nodes, comprehensively consider the overall performance of the system, and thus improve communication efficiency. Using chaotic perturbations to enhance the transmission signal can increase the randomness of the communication signal, improve the anti-interference ability and security; by optimizing the transmission signal spectrum allocation, spectrum resources can be effectively utilized to improve communication efficiency. Electrical signal transmission simulation can evaluate communication performance, and analyzing chaotic perturbation factors can understand the influencing factors in the signal transmission process, providing a basis for system optimization. Through the perturbation factor coding fusion, the stability and security of the communication system can be improved, and the impact of interference can be reduced; uploading to the communication cloud platform can realize data centralized management and real-time monitoring, further improving the controllability and reliability of the system. In summary, these steps together constitute a complete optimization process. By analyzing, optimizing and integrating the electronic detonator communication system, the performance and reliability of the system are improved, further ensuring the safety and efficiency of blasting projects.

[0012] Optionally, step S1 specifically includes:

[0013] Step S11: acquiring electronic detonator communication metadata, and extracting communication frequency features of the electronic detonator communication metadata, thereby obtaining electronic detonator communication frequency data;

[0014] Step S12: performing Fourier transform on the electronic detonator communication frequency data, thereby obtaining the electronic detonator communication spectrum;

[0015] Step S13: performing frequency distribution density calculation on the electronic detonator communication spectrum to obtain communication frequency distribution density data, and performing distribution density statistics on the communication frequency distribution density data to obtain high-value communication frequency distribution density data and low-value communication frequency distribution density data;

[0016] Step S14: performing cluster calculation on the electronic detonator communication frequency data according to the high-value communication frequency distribution density data, thereby obtaining abnormal electronic detonator communication frequency data, and performing abnormal communication association on the electronic detonator communication metadata according to the abnormal electronic detonator communication frequency data, thereby obtaining abnormal communication data;

[0017] Step S15: performing cluster calculation on the electronic detonator communication frequency data according to the low-rate communication frequency distribution density data, thereby obtaining normal electronic detonator communication frequency data, and performing normal communication association on the electronic detonator communication metadata according to the normal electronic detonator communication frequency data, thereby obtaining normal communication data;

[0018] Step S16: Time-sequentially merge the abnormal communication data and the normal communication data to obtain the abnormal communication data of the electronic detonator.

[0019] The present invention obtains electronic detonator communication metadata and extracts communication frequency features. This can help capture the basic features of electronic detonator communication, such as changes and patterns in communication frequency. By extracting these features, basic data can be provided for subsequent analysis. Fourier transform is performed on the communication frequency data to obtain the communication spectrum. Fourier transform converts the communication frequency data in the time domain into spectrum data in the frequency domain. This can help discover the regularity of frequency distribution and prominent frequency components, and provide a basis for subsequent frequency distribution density calculation. Calculate the frequency distribution density of the communication spectrum. By performing frequency distribution density calculation on the communication spectrum, the distribution of communication frequency can be understood, including the distribution of high frequency and low frequency. This can be used to identify abnormal modes and normal modes of communication frequency. Cluster calculation is performed based on high-value communication frequency distribution density data to obtain abnormal electronic detonator communication frequency data, and the abnormal communication data is associated. Through cluster calculation, the data of high-value communication frequency distribution density can be aggregated into abnormal communication modes, thereby identifying abnormal electronic detonator communication data, and further analyzing the association of abnormal communication. Abnormal communication data and normal communication data are time-series merged to obtain abnormal communication data of electronic detonators. By merging the abnormal communication data and the normal communication data in time sequence, the timing information of the abnormal communication can be combined with the timing information of the normal communication, so as to more comprehensively analyze the communication situation of the electronic detonator and discover the abnormal communication mode.

[0020] Optionally, step S2 specifically includes:

[0021] Step S21: estimating the detonator communication channel based on the electronic detonator communication metadata, thereby obtaining detonator channel estimation data;

[0022] Step S22: performing network topology analysis on the electronic detonator communication metadata based on the detonator channel estimation data, thereby obtaining a detonator communication network topology structure;

[0023] Step S23: dividing the detonator communication network topology structure by communication edge weights based on the electronic detonator communication metadata, thereby obtaining a detonator communication network model;

[0024] Step S24: dividing the detonator communication network model into abnormal communication nodes according to the abnormal communication data of the electronic detonator, so as to obtain abnormal communication node data.

[0025] The present invention can estimate the communication channel by analyzing the electronic detonator communication metadata. Communication channel estimation refers to analyzing and evaluating the characteristics and conditions of the communication channel, such as the noise level of the channel, the fading of the channel, etc. The detonator channel estimation data obtained can help understand the quality and reliability of the communication channel and provide a basis for subsequent network analysis. Based on the detonator channel estimation data, the network topology structure analysis of the electronic detonator communication metadata is performed. This means analyzing the connection relationship and structural characteristics between each node in the communication network, so as to understand the overall layout and topology of the communication network. This helps to identify the main nodes and communication paths in the communication network, and provides a basis for subsequent analysis. Based on the electronic detonator communication metadata, the communication edge weight division of the detonator communication network topology is performed to construct a detonator communication network model. Communication edge weight division refers to assigning weights to each connection in the communication network according to the characteristics and importance of the communication data. In this way, a more accurate communication network model can be established to reflect the relationship and importance between the communication data. According to the abnormal communication data of the electronic detonator, the detonator communication network model is divided into abnormal communication nodes. This means identifying abnormal nodes in the communication network, that is, nodes with abnormal or abnormal communication behavior. By dividing abnormal communication nodes, abnormal situations in the communication network can be discovered in time, so that corresponding measures can be taken to handle and repair them.

[0026] Optionally, step S22 is specifically:

[0027] Step S221: extracting communication record features from the electronic detonator communication metadata, thereby obtaining the electronic detonator historical communication data;

[0028] Step S222: determining the detonator communication node based on the electronic detonator historical communication data, thereby obtaining the communication node data;

[0029] Step S223: classifying the communication node data by communication frequency according to the historical communication data of the electronic detonator, thereby obtaining high-frequency communication node data and low-frequency communication node data;

[0030] Step S224: defining the core communication node for the high frequency communication node data, thereby obtaining the core communication node data;

[0031] Step S225: performing node communication relationship analysis on the electronic detonator historical communication data based on the core communication node data, thereby obtaining node communication relationship data;

[0032] Step S226: constructing a network topology structure for the core communication node data and the low-frequency communication node data based on the node communication relationship data, thereby obtaining a communication network topology structure;

[0033] Step S227: Perform channel attribute weight mapping on the communication network topology structure based on the detonator channel estimation data, so as to obtain the detonator communication network topology structure.

[0034] The present invention can obtain the historical communication data of the electronic detonator by extracting the characteristics of the electronic detonator communication metadata. These data include information such as the time, frequency, and both parties of the communication, which are helpful to understand the communication mode and the behavior of the communication node. Based on the historical communication data, the communication nodes in the detonator communication network are determined. These communication nodes can be electronic detonator devices or other communication devices, which are the basic components of the communication network. According to the historical communication data, the communication nodes are divided into high-frequency communication nodes and low-frequency communication nodes. High-frequency communication nodes refer to nodes with higher communication frequencies, and low-frequency communication nodes refer to nodes with lower communication frequencies. This helps to identify active and inactive nodes in the communication network. The high-frequency communication node data is analyzed to define the core communication nodes. The core communication nodes are usually the nodes with the highest communication frequency or play a key role in the communication network. Identifying the core communication nodes helps to understand the key nodes of the communication network. Based on the core communication node data, the communication relationship between the nodes is analyzed. This helps to understand the connection and communication mode between the nodes in the communication network, and provides a reference for network optimization. Based on the node communication relationship data, the topological structure of the communication network is constructed. The topological structure reflects the connection relationship and layout between the nodes in the communication network, which helps to optimize the network structure and communication efficiency. Based on the detonator channel estimation data, the communication network topology is mapped to channel attribute weights. This helps to optimize the allocation and management of communication channels and improve the quality and reliability of communications.

[0035] Optionally, step S24 is specifically:

[0036] Step S241: extracting abnormal communication features from abnormal communication data of the electronic detonator, thereby obtaining abnormal communication feature data;

[0037] Step S242: Correlating node communication characteristics of the detonator communication network model according to the abnormal communication characteristic data, thereby obtaining node communication characteristic data;

[0038] Step S243: performing similarity calculation on the abnormal communication feature data and the node communication feature data, thereby obtaining node similarity data;

[0039] Step S244: performing high-similarity node clustering calculation on the node communication feature data according to the node similarity data, thereby obtaining abnormal communication feature node data;

[0040] Step S245: marking abnormal communication nodes of the detonator communication network model according to the abnormal communication characteristic node data, thereby obtaining abnormal communication node data.

[0041] The present invention can identify the characteristic pattern of abnormal communication by extracting features from abnormal communication data of electronic detonators. These features include information such as the time, frequency, and communication content of abnormal communication, which helps to distinguish normal and abnormal communication. According to the abnormal communication characteristic data, it is associated with the corresponding node in the detonator communication network model to obtain the abnormal communication characteristic data of the node. This helps to associate the abnormal feature with a specific communication node, providing a basis for further analysis. The abnormal communication characteristic data and the node communication characteristic data are similarly calculated to determine the similarity between the nodes. This helps to find nodes with similar behaviors in abnormal communication, so as to better understand the abnormal behavior patterns in the communication network. Based on the node similarity data, high-similarity node clustering calculations are performed on the node communication characteristic data. This means that nodes with similar abnormal communication characteristics are aggregated together to form a cluster of abnormal communication characteristic nodes, so as to more clearly identify abnormal communication patterns. According to the abnormal communication characteristic node data, the abnormal communication nodes in the detonator communication network model are marked. This helps to clearly identify and mark abnormal nodes in the communication network for further investigation and processing.

[0042] Optionally, step S3 specifically includes:

[0043] Step S31: extracting abnormal communication features from abnormal communication node data, thereby obtaining node abnormal communication feature data;

[0044] Step S32: performing channel planning for the highest stability of the communication node according to the node abnormal communication characteristic data and the detonator communication network model, thereby obtaining channel planning data for the communication node;

[0045] Step S33: performing channel optimization calculation on the detonator communication network model based on the communication node channel planning data, thereby obtaining optimized communication node channel data;

[0046] Step S34: performing communication node fusion on the optimized communication node channel data and the detonator communication network model, thereby obtaining the optimized detonator communication network model.

[0047] The present invention can identify abnormal patterns in inter-node communication by extracting features from abnormal communication node data. These abnormal features include communication frequency, delay, data packet size, etc., which are helpful to identify potential problem nodes in the communication network. The highest stability channel planning of the communication node is performed in combination with the node abnormal communication feature data and the detonator communication network model. This helps to identify the communication channel with the highest stability under the current communication network conditions to ensure the reliability and stability of the communication. Based on the communication node channel planning data, the detonator communication network model is subjected to channel optimization calculation. This includes adjusting channel parameters, optimizing channel allocation, etc. to improve the efficiency and performance of the communication network. The optimized communication node channel data is merged with the detonator communication network model to form an optimized detonator communication network model. This means that the optimized channel configuration is applied to the entire communication network to ensure that the communication between each node can be carried out under the best conditions.

[0048] Optionally, step S4 is specifically:

[0049] Step S41: extracting the sending signal data from the electronic detonator communication metadata, thereby obtaining the sending signal data of the electronic detonator;

[0050] Step S42: obtaining random chaotic signal data through a preset Henon mapping system;

[0051] Step S43: performing chaotic signal coding simulation on the signal data sent by the electronic detonator according to the random chaotic signal data, thereby obtaining mixed communication signal simulation data;

[0052] Step S44: performing signal clarity calculation on the mixed communication signal simulation data to obtain mixed signal clarity data, and performing maximum clarity mixed communication signal selection on the mixed communication signal simulation data according to the signal clarity data to obtain chaotic disturbance enhanced transmission signal data;

[0053] Step S45: performing transmission signal spectrum allocation on the chaotic disturbance enhanced transmission signal data by optimizing the detonator communication network model, thereby obtaining an optimized transmission signal spectrum.

[0054] The present invention can obtain the relevant data of the electronic detonator sending signal by extracting the electronic detonator communication metadata. These data include information such as the frequency, amplitude, duration, etc. of the sending, which is helpful to understand the communication behavior of the detonator. The random chaotic signal data is obtained by using the preset Henon mapping system. Chaotic signals are widely used in the field of communication. They have high complexity and randomness and can enhance the security and anti-interference ability of communication signals. The obtained random chaotic signal data is applied to the electronic detonator sending signal data to perform chaotic signal coding simulation. This means that the chaotic signal is embedded in the detonator sending signal to enhance the security and concealment of the communication signal. The signal clarity calculation is performed on the mixed communication signal simulation data to evaluate the signal quality. Then, the mixed communication signal with the maximum clarity is selected according to the signal clarity data to ensure the stability and reliability of the communication signal. By optimizing the detonator communication network model, the chaotic disturbance enhanced sending signal data is optimized for the sending signal spectrum allocation. This means that according to the characteristics and requirements of the communication network, the spectrum of the sending signal is adjusted and allocated to maximize the communication efficiency and performance.

[0055] Optionally, step S5 specifically includes:

[0056] Step S51: performing electrical signal transmission simulation on the optimized transmission signal spectrum by optimizing the detonator communication network model, thereby obtaining electrical signal transmission simulation data;

[0057] Step S52: performing noise reduction processing on the electrical signal transmission analog data, thereby obtaining noise-reduced signal transmission analog data;

[0058] Step S53: performing phase space reconstruction demodulation on the electronic detonator transmission signal data and the chaotic disturbance enhanced transmission signal data, so as to obtain the transmission end chaotic disturbance factor;

[0059] Step S54: Based on the chaotic disturbance factor at the sending end and the electronic detonator communication metadata, the chaotic disturbance factor at the receiving end is analyzed on the noise reduction signal transmission simulation data, so as to obtain the chaotic disturbance factor at the receiving end.

[0060] The present invention can evaluate the performance of the signal under different conditions by simulating the transmission process of the signal in the communication link. This includes considering factors such as the transmission distance of the signal, the transmission medium, the signal attenuation and distortion. By simulating these situations, the behavior of the signal in the actual communication environment can be better understood, and data support can be provided for system design and optimization. In the process of signal transmission, it is often affected by various interferences and noises from the environment and equipment. The purpose of noise reduction processing is to reduce the influence of these interferences and noises, thereby improving the clarity and reliability of the signal. Through effective noise reduction processing, it is easier for the receiving end to interpret and identify the signal, reducing the possibility of miscoding and misjudgment. The phase space reconstruction technology is used to demodulate the transmitted signal data. Phase space reconstruction is a signal processing method that extracts the dynamic characteristics of the signal by mapping the signal to the trajectory in the phase space. This method can effectively extract the chaotic disturbance factor in the signal, thereby enhancing the stability and anti-interference ability of the signal. At the receiving end, by analyzing the chaotic disturbance factor of the transmitting end and the received signal data, the changes and influences of the signal during the transmission process can be better understood. This helps to adjust the parameters and algorithms of the receiving end to more effectively decode and process the received signal, and improve the performance and reliability of the communication system.

[0061] Optionally, step S54 is specifically:

[0062] Step S541: extracting reception signal features from the noise reduction signal transmission analog data, thereby obtaining analog reception signal data;

[0063] Step S542: performing chaotic demodulation on the simulated received signal data by using the chaotic disturbance factor of the transmitting end, thereby obtaining demodulated received signal data;

[0064] Step S543: extracting corresponding received signal data from the electronic detonator communication metadata according to the electronic detonator sent signal data, thereby obtaining the electronic detonator received signal data;

[0065] Step S544: performing received signal error calculation on the demodulated received signal data and the electronic detonator received signal data, thereby obtaining received signal error data;

[0066] Step S545: performing signal transmission error correction on the chaotic disturbance factor of the transmitting end according to the received signal error data, thereby obtaining the chaotic disturbance factor of the receiving end.

[0067] The present invention can capture important features of the signal during the transmission process, such as frequency, amplitude, phase, etc., by extracting features from the noise reduction signal transmission analog data. These features are helpful for accurately demodulating and processing the signal in the subsequent steps. By demodulating the simulated received signal data using the chaotic disturbance factor of the transmitting end, the original signal can be effectively extracted. The chaotic demodulation technology has a strong adaptability to the nonlinear characteristics of the signal and can effectively deal with the interference and distortion in the signal transmission process. According to the electronic detonator sending signal data, metadata related to the electronic detonator communication is extracted from the noise reduction signal. These metadata include the identification, format, and verification information of the sending signal, which are key information for correctly parsing and processing the received signal. By comparing and analyzing the demodulated received signal data and the electronic detonator receiving signal data, the error of the received signal can be calculated. These error data reflect the loss, distortion or interference in the signal transmission process, and provide a basis for subsequent error correction. According to the received signal error data, the chaotic disturbance factor of the transmitting end is adjusted and corrected to reduce the error influence in the signal transmission process. This can improve the reliability and anti-interference ability of the signal, ensuring that the receiving end can accurately parse and process the received signal.

[0068] Optionally, the present specification also provides a system for improving the communication efficiency of electronic detonators, which is used to execute the method for improving the communication efficiency of electronic detonators as described above. The system for improving the communication efficiency of electronic detonators includes:

[0069] The abnormal communication analysis module is used to obtain electronic detonator communication metadata and perform electronic detonator abnormal communication analysis on the electronic detonator communication metadata, thereby obtaining electronic detonator abnormal communication data;

[0070] An abnormal communication node division module is used to construct a detonator communication network model based on the electronic detonator communication metadata, and divide the detonator communication network model into abnormal communication nodes according to the electronic detonator abnormal communication data, so as to obtain abnormal communication node data;

[0071] A channel optimization module is used to optimize abnormal communication node channels for abnormal communication node data according to the detonator communication network model, thereby obtaining optimized communication node channel data, and to perform communication node fusion on the optimized communication node channel data and the detonator communication network model, thereby obtaining the optimized detonator communication network model;

[0072] A chaotic disturbance enhancement module is used to perform chaotic disturbance enhancement simulation on the electronic detonator communication metadata to obtain chaotic disturbance enhanced transmission signal data, and to perform transmission signal spectrum allocation on the chaotic disturbance enhanced transmission signal data by optimizing the detonator communication network model to obtain an optimized transmission signal spectrum;

[0073] A chaos disturbance factor analysis module is used to perform an electrical signal transmission simulation on the optimized transmission signal spectrum by optimizing the detonator communication network model, thereby obtaining electrical signal transmission simulation data, and to perform adaptive chaos disturbance factor analysis on the electrical signal transmission simulation data according to the electronic detonator communication metadata, thereby obtaining a receiving end chaos disturbance factor and a sending end chaos disturbance factor;

[0074] The disturbance factor coding fusion module is used to perform disturbance factor coding fusion on the optimized detonator communication network model according to the chaotic disturbance factor of the receiving end and the chaotic disturbance factor of the sending end, so as to obtain the chaotic detonator communication network model and upload it to the electronic detonator communication cloud platform.

[0075] The system for improving the communication efficiency of electronic detonators of the present invention can implement any one of the methods for improving the communication efficiency of electronic detonators of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the method for improving the communication efficiency of electronic detonators. The internal modules of the system cooperate with each other to improve the communication efficiency of electronic detonators. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0077] Figure 1 A schematic flow chart of the steps of the method for improving the communication efficiency of electronic detonators according to the present invention;

[0078] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0079] Figure 3 Detailed step flow diagram of step S2 in the present invention.

[0080] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0081] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0082] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0083] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0084] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for improving the communication efficiency of electronic detonators, the method comprising the following steps:

[0085] Step S1: acquiring electronic detonator communication metadata, and performing electronic detonator abnormal communication analysis on the electronic detonator communication metadata, thereby obtaining electronic detonator abnormal communication data;

[0086] In this embodiment, metadata of electronic detonator communication is collected from the electronic detonator communication system, including information such as communication nodes, communication frequency, and communication time period. Then, abnormal communication analysis is performed on these metadata to detect whether there are abnormal communication modes or nodes. For example, through data mining technology and statistical analysis methods, it is found that the communication frequency of certain communication nodes is abnormally high or abnormally low, or the communication time period does not match the normal mode. In this way, abnormal communication data of electronic detonators can be obtained for subsequent processing.

[0087] Step S2: constructing a detonator communication network model based on the electronic detonator communication metadata, and dividing the detonator communication network model into abnormal communication nodes according to the electronic detonator abnormal communication data, thereby obtaining abnormal communication node data;

[0088] In this embodiment, the collected electronic detonator communication metadata is used to construct a detonator communication network model. This model can be a network structure in graph theory, where nodes represent detonators and edges represent communication connections. Then, based on the electronic detonator abnormal communication data, the detonator communication network model is divided into abnormal communication nodes to identify abnormal communication nodes. This division process can use cluster analysis or anomaly detection algorithm.

[0089] Step S3: optimizing abnormal communication node channels on abnormal communication node data according to the detonator communication network model, thereby obtaining optimized communication node channel data, and performing communication node fusion on the optimized communication node channel data and the detonator communication network model, thereby obtaining the optimized detonator communication network model;

[0090] In this embodiment, the abnormal communication node data is channel optimized according to the detonator communication network model. This includes adjusting the communication channels between the communication nodes to improve the communication efficiency and stability. The optimized communication node channel data is fused with the detonator communication network model to form an optimized detonator communication network model. In the fusion process, the interconnection relationship between the communication nodes and the allocation of the communication channels need to be considered.

[0091] Step S4: performing a chaotic disturbance enhancement simulation on the electronic detonator communication metadata to obtain chaotic disturbance enhanced transmission signal data, and performing transmission signal spectrum allocation on the chaotic disturbance enhanced transmission signal data by optimizing the detonator communication network model to obtain an optimized transmission signal spectrum;

[0092] In this embodiment, according to the requirements and performance indicators of the communication system, suitable chaotic mapping functions such as Logistic mapping, Henon mapping, Lorenz mapping, etc. are selected, and the parameters of the chaotic mapping function, such as initial conditions, control parameters, etc., are adjusted to obtain ideal chaotic signal characteristics. These parameter settings will affect the spectrum distribution, periodicity and other characteristics of the chaotic signal. The selected chaotic mapping function is applied to the electronic detonator communication data to enhance the chaotic disturbance of the data. This includes mixing the original communication data with the chaotic signal or performing an XOR operation on a random sequence generated by the chaotic mapping function to introduce chaotic disturbance. This can increase the randomness and unpredictability of the communication signal and improve the security of the communication. Then, using the optimized detonator communication network model, the spectrum of the transmission signal data enhanced by the chaotic disturbance is allocated to reduce signal overlap and interference, and obtain an optimized transmission signal spectrum.

[0093] Step S5: performing an electrical signal transmission simulation on the optimized transmission signal spectrum by optimizing the detonator communication network model, thereby obtaining electrical signal transmission simulation data, and performing adaptive chaotic perturbation factor analysis on the electrical signal transmission simulation data according to the electronic detonator communication metadata, thereby obtaining a receiving end chaotic perturbation factor and a sending end chaotic perturbation factor;

[0094] In this embodiment, the detonator communication network model is optimized to simulate the transmission of electrical signals on the optimized transmission signal spectrum to simulate the communication signal transmission process after the chaotic disturbance is enhanced. By simulating the changes of the signal during the transmission process, the performance indicators of the communication system, such as bit error rate, signal-to-noise ratio, transmission rate, etc., are evaluated. Then, the transmission simulation data is adaptively analyzed for chaotic disturbance factors according to the electronic detonator communication metadata. The chaotic disturbance characteristics under different communication scenarios and the degree of influence of chaotic disturbance on the signal are considered. Through analysis, the chaotic disturbance factor of the current communication scenario is determined, thereby obtaining the chaotic disturbance factors of the receiving end and the sending end respectively.

[0095] Step S6: Perform disturbance factor encoding fusion on the optimized detonator communication network model according to the chaotic disturbance factor of the receiving end and the chaotic disturbance factor of the sending end, so as to obtain the chaotic detonator communication network model, and upload it to the electronic detonator communication cloud platform.

[0096] In the present embodiment, the chaotic disturbance factors of the receiving end and the transmitting end are respectively encoded into the receiving end node communication signal decryption part and the encryption part of the transmitting end node communication signal in the optimized detonator communication network model by coding algorithms such as hash coding, differential coding, convolution coding, etc. The chaotic sequence and the communication data can also be subjected to operations such as XOR operation and encryption algorithm to ensure the accuracy of the chaotic disturbance factors of the receiving end and the transmitting end and the communication data being coded and fused. The chaotic disturbance factors of the receiving end and the transmitting end are coded and fused with the communication data. The coding operation is performed on the adjacent nodes or hardware of the receiving end node and the transmitting end node in the communication system. The chaotic detonator communication network model coded and fused with the disturbance factor is uploaded to the electronic detonator communication cloud platform. This can be completed through a secure network connection and authentication mechanism to ensure that the uploaded model can be accessed and used by legitimate users.

[0097] By analyzing the electronic detonator communication metadata, the present invention can identify abnormal communication modes, including abnormal frequencies, abnormal time periods, etc., so as to timely discover potential communication problems and provide data support for subsequent optimization. Establishing a detonator communication network model helps to understand the relationship between the system structure and the communication nodes, and the division of abnormal communication nodes can help locate the source of communication problems and provide specific goals for subsequent optimization. Optimizing the communication node channel can improve the stability and reliability of data transmission, and the fusion of communication node data can integrate the information of different nodes, comprehensively consider the overall performance of the system, and thus improve communication efficiency. Using chaotic perturbations to enhance the transmission signal can increase the randomness of the communication signal, improve the anti-interference ability and security; by optimizing the transmission signal spectrum allocation, spectrum resources can be effectively utilized to improve communication efficiency. Electrical signal transmission simulation can evaluate communication performance, and analyzing chaotic perturbation factors can understand the influencing factors in the signal transmission process, providing a basis for system optimization. Through the perturbation factor coding fusion, the stability and security of the communication system can be improved, and the impact of interference can be reduced; uploading to the communication cloud platform can realize data centralized management and real-time monitoring, further improving the controllability and reliability of the system. In summary, these steps together constitute a complete optimization process. By analyzing, optimizing and integrating the electronic detonator communication system, the performance and reliability of the system are improved, further ensuring the safety and efficiency of blasting projects.

[0098] Optionally, step S1 specifically includes:

[0099] Step S11: acquiring electronic detonator communication metadata, and extracting communication frequency features of the electronic detonator communication metadata, thereby obtaining electronic detonator communication frequency data;

[0100] In this embodiment, metadata is obtained from the electronic detonator communication system, including information such as communication timestamp and communication frequency. Then, frequency features of these metadata are extracted using signal processing technology, such as power spectrum density analysis or related technology to extract communication frequency data. This can be achieved through digital signal processing algorithms, such as fast Fourier transform (FFT), to obtain high-precision frequency information.

[0101] Step S12: performing Fourier transform on the electronic detonator communication frequency data, thereby obtaining the electronic detonator communication spectrum;

[0102] In this embodiment, the time domain signal is converted into a frequency domain signal by performing Fourier transform on the electronic detonator communication frequency data, thereby obtaining the electronic detonator communication spectrum. This involves applying digital signal processing technology, such as discrete Fourier transform (DFT) or fast Fourier transform (FFT), to effectively analyze frequency domain characteristics.

[0103] Step S13: performing frequency distribution density calculation on the electronic detonator communication spectrum to obtain communication frequency distribution density data, and performing distribution density statistics on the communication frequency distribution density data to obtain high-value communication frequency distribution density data and low-value communication frequency distribution density data;

[0104] In this embodiment, the frequency distribution density of the electronic detonator communication spectrum is calculated, and the communication frequency distribution density data is obtained by counting the number of occurrences or probability distribution of each frequency. Then, the distribution density statistics of these data are performed to identify high-value and low-value communication frequency distribution density data, which may involve probability density function fitting, statistical analysis and other methods.

[0105] Step S14: performing cluster calculation on the electronic detonator communication frequency data according to the high-value communication frequency distribution density data, thereby obtaining abnormal electronic detonator communication frequency data, and performing abnormal communication association on the electronic detonator communication metadata according to the abnormal electronic detonator communication frequency data, thereby obtaining abnormal communication data;

[0106] In this embodiment, clustering calculation is performed based on the high-value communication frequency distribution density data, and a clustering algorithm such as K-means or density clustering is used to separate the abnormal communication frequency data from the total electronic detonator communication frequency data. Then, based on the abnormal communication frequency data, the electronic detonator communication metadata is subjected to an association analysis related to abnormal communication, and communication features with a high correlation with abnormal communication frequency are extracted, which can be achieved by using machine learning or pattern recognition technologies such as decision trees and random forests.

[0107] Step S15: performing cluster calculation on the electronic detonator communication frequency data according to the low-rate communication frequency distribution density data, thereby obtaining normal electronic detonator communication frequency data, and performing normal communication association on the electronic detonator communication metadata according to the normal electronic detonator communication frequency data, thereby obtaining normal communication data;

[0108] In this embodiment, clustering calculation is performed based on the low-rate communication frequency distribution density data, and a clustering algorithm such as K-means or density clustering is used to separate the normal communication frequency data from the total electronic detonator communication frequency data. Then, based on the normal communication frequency data, the electronic detonator communication metadata is subjected to association analysis, and the communication data containing the normal communication frequency is extracted, which can be achieved by machine learning or pattern recognition techniques such as decision trees and random forests.

[0109] Step S16: Time-sequentially merge the abnormal communication data and the normal communication data to obtain the abnormal communication data of the electronic detonator.

[0110] In this embodiment, the abnormal communication data and the normal communication data are combined in time series to obtain the abnormal communication data of the electronic detonator, which includes time series alignment and integration, as well as marking and recording of abnormal data for subsequent analysis and processing.

[0111] The present invention obtains electronic detonator communication metadata and extracts communication frequency features. This can help capture the basic features of electronic detonator communication, such as changes and patterns in communication frequency. By extracting these features, basic data can be provided for subsequent analysis. Fourier transform is performed on the communication frequency data to obtain the communication spectrum. Fourier transform converts the communication frequency data in the time domain into spectrum data in the frequency domain. This can help discover the regularity of frequency distribution and prominent frequency components, and provide a basis for subsequent frequency distribution density calculation. Calculate the frequency distribution density of the communication spectrum. By performing frequency distribution density calculation on the communication spectrum, the distribution of communication frequency can be understood, including the distribution of high frequency and low frequency. This can be used to identify abnormal modes and normal modes of communication frequency. Cluster calculation is performed based on high-value communication frequency distribution density data to obtain abnormal electronic detonator communication frequency data, and the abnormal communication data is associated. Through cluster calculation, the data of high-value communication frequency distribution density can be aggregated into abnormal communication modes, thereby identifying abnormal electronic detonator communication data, and further analyzing the association of abnormal communication. Abnormal communication data and normal communication data are time-series merged to obtain abnormal communication data of electronic detonators. By merging the abnormal communication data and the normal communication data in time sequence, the timing information of the abnormal communication can be combined with the timing information of the normal communication, so as to more comprehensively analyze the communication situation of the electronic detonator and discover the abnormal communication mode.

[0112] Optionally, step S2 specifically includes:

[0113] Step S21: estimating the detonator communication channel based on the electronic detonator communication metadata, thereby obtaining detonator channel estimation data;

[0114] In this embodiment, relevant parameters such as communication frequency and signal strength are extracted from the electronic detonator communication metadata. Then, using these parameters, a channel estimation algorithm, such as the least mean square (LMS) algorithm or the Kalman filter, is used to estimate the detonator communication channel. These algorithms can estimate the characteristics of the detonator communication channel, such as transmission delay, signal attenuation, etc., based on the received signal data, thereby generating detonator channel estimation data.

[0115] Step S22: performing network topology analysis on the electronic detonator communication metadata based on the detonator channel estimation data, thereby obtaining a detonator communication network topology structure;

[0116] In this embodiment, based on the detonator channel estimation data, graph theory and network analysis techniques can be used to perform network topology analysis on the electronic detonator communication metadata. This includes identifying the connection relationship between detonators, communication paths, and communication frequencies between network nodes. By analyzing these structures, the topology of the detonator communication network can be revealed, including whether there is a central node, centralized or decentralized communication, and other features.

[0117] Step S23: dividing the detonator communication network topology structure by communication edge weights based on the electronic detonator communication metadata, thereby obtaining a detonator communication network model;

[0118] In this embodiment, the detonator communication network topology is divided into communication edges according to the electronic detonator communication metadata, and a weight is assigned to each edge. These weights can be divided according to factors such as communication frequency and communication quality to reflect the importance of different communication edges. For example, a higher frequency or more stable communication path is assigned a higher weight, while a lower frequency or unstable communication path is assigned a lower weight.

[0119] Step S24: dividing the detonator communication network model into abnormal communication nodes according to the abnormal communication data of the electronic detonator, so as to obtain abnormal communication node data.

[0120] In this embodiment, based on the abnormal communication data of the electronic detonator, an abnormal detection algorithm, such as an isolation forest or an abnormal detection based on statistical methods, can be used to divide the abnormal communication nodes of the detonator communication network model. These algorithms can identify abnormal nodes that are inconsistent with normal communication behavior, such as frequent communication, abnormal communication frequency, etc. The identified abnormal communication node data can be used for further security analysis and processing.

[0121] The present invention can estimate the communication channel by analyzing the electronic detonator communication metadata. Communication channel estimation refers to analyzing and evaluating the characteristics and conditions of the communication channel, such as the noise level of the channel, the fading of the channel, etc. The detonator channel estimation data obtained can help understand the quality and reliability of the communication channel and provide a basis for subsequent network analysis. Based on the detonator channel estimation data, the network topology structure analysis of the electronic detonator communication metadata is performed. This means analyzing the connection relationship and structural characteristics between each node in the communication network, so as to understand the overall layout and topology of the communication network. This helps to identify the main nodes and communication paths in the communication network, and provides a basis for subsequent analysis. Based on the electronic detonator communication metadata, the communication edge weight division of the detonator communication network topology is performed to construct a detonator communication network model. Communication edge weight division refers to assigning weights to each connection in the communication network according to the characteristics and importance of the communication data. In this way, a more accurate communication network model can be established to reflect the relationship and importance between the communication data. According to the abnormal communication data of the electronic detonator, the detonator communication network model is divided into abnormal communication nodes. This means identifying abnormal nodes in the communication network, that is, nodes with abnormal or abnormal communication behavior. By dividing abnormal communication nodes, abnormal situations in the communication network can be discovered in time, so that corresponding measures can be taken to handle and repair them.

[0122] Optionally, step S22 is specifically:

[0123] Step S221: extracting communication record features from the electronic detonator communication metadata, thereby obtaining the electronic detonator historical communication data;

[0124] In this embodiment, the electronic detonator communication metadata is feature extracted, including communication timestamp, communication frequency, communication duration, etc., to form the electronic detonator historical communication data. For example, for feature extraction, time series analysis can be used to arrange the communication data in chronological order and extract features such as frequency distribution and communication period.

[0125] Step S222: determining the detonator communication node based on the electronic detonator historical communication data, thereby obtaining the communication node data;

[0126] In this embodiment, the communication nodes between the detonators are determined by using the historical communication data of the electronic detonators through a clustering algorithm (such as k-means clustering) or a network analysis technique. These nodes can be determined based on indicators such as communication frequency and communication strength, thereby forming communication node data.

[0127] Step S223: classifying the communication node data by communication frequency according to the historical communication data of the electronic detonator, thereby obtaining high-frequency communication node data and low-frequency communication node data;

[0128] In this embodiment, the communication nodes are divided into high-frequency communication nodes and low-frequency communication nodes by classifying the communication frequency of the electronic detonator history communication data. For example, nodes with a communication frequency higher than a certain threshold can be defined as high-frequency communication nodes, and nodes with a frequency lower than the threshold can be defined as low-frequency communication nodes.

[0129] Step S224: defining the core communication node for the high frequency communication node data, thereby obtaining the core communication node data;

[0130] In this embodiment, the high-frequency communication node data is defined as a core communication node. This can be done by screening the high-frequency communication node data through a network centrality analysis method such as betweenness centrality or degree centrality to identify core nodes that have an important position in the communication network. These nodes form the core communication node data.

[0131] Step S225: performing node communication relationship analysis on the electronic detonator historical communication data based on the core communication node data, thereby obtaining node communication relationship data;

[0132] In this embodiment, the core communication node data is used to analyze the node communication relationship of the electronic detonator historical communication data, which includes analyzing the communication frequency, communication strength, and communication mode between the communication nodes, thereby forming the node communication relationship data.

[0133] Step S226: constructing a network topology structure for the core communication node data and the low-frequency communication node data based on the node communication relationship data, thereby obtaining a communication network topology structure;

[0134] In this embodiment, based on the node connection relationship in the node communication relationship data, the core communication node is connected to the low-frequency communication node, and the topology of the communication network can be constructed using graph theory and network modeling technology. This includes determining the connection relationship between nodes, the communication path, and the substructure in the network, thereby forming a complete communication network topology.

[0135] Step S227: Perform channel attribute weight mapping on the communication network topology structure based on the detonator channel estimation data, so as to obtain the detonator communication network topology structure.

[0136] In this embodiment, the detonator channel estimation data is used to perform channel attribute weight mapping on the communication network topology. For example, according to different channel attributes (such as transmission delay, signal strength), corresponding weights are assigned to different connections in the communication network to reflect their impact on the communication quality, thereby forming a detonator communication network topology with channel attribute weights.

[0137] The present invention can obtain the historical communication data of the electronic detonator by extracting the characteristics of the electronic detonator communication metadata. These data include information such as the time, frequency, and both parties of the communication, which are helpful to understand the communication mode and the behavior of the communication node. Based on the historical communication data, the communication nodes in the detonator communication network are determined. These communication nodes can be electronic detonator devices or other communication devices, which are the basic components of the communication network. According to the historical communication data, the communication nodes are divided into high-frequency communication nodes and low-frequency communication nodes. High-frequency communication nodes refer to nodes with higher communication frequencies, and low-frequency communication nodes refer to nodes with lower communication frequencies. This helps to identify active and inactive nodes in the communication network. The high-frequency communication node data is analyzed to define the core communication nodes. The core communication nodes are usually the nodes with the highest communication frequency or play a key role in the communication network. Identifying the core communication nodes helps to understand the key nodes of the communication network. Based on the core communication node data, the communication relationship between the nodes is analyzed. This helps to understand the connection and communication mode between the nodes in the communication network, and provides a reference for network optimization. Based on the node communication relationship data, the topological structure of the communication network is constructed. The topological structure reflects the connection relationship and layout between the nodes in the communication network, which helps to optimize the network structure and communication efficiency. Based on the detonator channel estimation data, the communication network topology is mapped to channel attribute weights. This helps to optimize the allocation and management of communication channels and improve the quality and reliability of communications.

[0138] Optionally, step S24 is specifically:

[0139] Step S241: extracting abnormal communication features from abnormal communication data of the electronic detonator, thereby obtaining abnormal communication feature data;

[0140] In this embodiment, a series of abnormal communication features can be extracted from the abnormal communication data of the electronic detonator, such as sudden change in communication frequency, abnormal communication duration, abnormal communication mode, etc. For example, by using time series analysis technology, mutation detection and pattern recognition are performed on the communication data to obtain abnormal communication feature data.

[0141] Step S242: Correlating node communication characteristics of the detonator communication network model according to the abnormal communication characteristic data, thereby obtaining node communication characteristic data;

[0142] In this embodiment, by using abnormal communication feature data, these features can be associated with the node communication features in the detonator communication network model to form node communication feature data. This includes matching and associating the communication attributes and node communication mode of each node according to the abnormal communication features, thereby obtaining the communication features of each node that are similar to the abnormal communication features, that is, the abnormal communication feature data of the node.

[0143] Step S243: performing similarity calculation on the abnormal communication feature data and the node communication feature data, thereby obtaining node similarity data;

[0144] In this embodiment, similarity calculation is performed on abnormal communication feature data and node communication feature data. Various similarity calculation methods, such as Euclidean distance, cosine similarity, etc., can be used to obtain communication feature similarity data between nodes for subsequent node clustering and anomaly detection.

[0145] Step S244: performing high-similarity node clustering calculation on the node communication feature data according to the node similarity data, thereby obtaining abnormal communication feature node data;

[0146] In this embodiment, based on the node similarity data, the node communication feature data is clustered and calculated for nodes with high similarity. This can use clustering algorithms, such as k-means clustering, DBSCAN clustering, etc., to aggregate nodes with high similarity into abnormal communication feature node data, and these nodes have similar abnormal communication behaviors.

[0147] Step S245: marking abnormal communication nodes of the detonator communication network model according to the abnormal communication characteristic node data, thereby obtaining abnormal communication node data.

[0148] In this embodiment, the abnormal communication nodes of the detonator communication network model are marked according to the abnormal communication feature node data. A supervised learning method, such as a support vector machine (SVM) or a decision tree, can be used to mark and classify the abnormal nodes according to their characteristics, thereby obtaining data marked as abnormal communication nodes for subsequent abnormality detection and processing.

[0149] The present invention can identify the characteristic pattern of abnormal communication by extracting features from abnormal communication data of electronic detonators. These features include information such as the time, frequency, and communication content of abnormal communication, which helps to distinguish normal and abnormal communication. According to the abnormal communication characteristic data, it is associated with the corresponding node in the detonator communication network model to obtain the abnormal communication characteristic data of the node. This helps to associate the abnormal feature with a specific communication node, providing a basis for further analysis. The abnormal communication characteristic data and the node communication characteristic data are similarly calculated to determine the similarity between the nodes. This helps to find nodes with similar behaviors in abnormal communication, so as to better understand the abnormal behavior patterns in the communication network. Based on the node similarity data, high-similarity node clustering calculations are performed on the node communication characteristic data. This means that nodes with similar abnormal communication characteristics are aggregated together to form a cluster of abnormal communication characteristic nodes, so as to more clearly identify abnormal communication patterns. According to the abnormal communication characteristic node data, the abnormal communication nodes in the detonator communication network model are marked. This helps to clearly identify and mark abnormal nodes in the communication network for further investigation and processing.

[0150] Optionally, step S3 specifically includes:

[0151] Step S31: extracting abnormal communication features from abnormal communication node data, thereby obtaining node abnormal communication feature data;

[0152] In this embodiment, data mining and machine learning techniques can be used to extract abnormal communication features for abnormal communication node data. For example, by analyzing data such as node communication frequency, communication mode, and communication period, the features of abnormal communication nodes, such as frequent communication, abnormal communication mode, etc., are identified, thereby obtaining node abnormal communication feature data.

[0153] Step S32: performing channel planning for the highest stability of the communication node according to the node abnormal communication characteristic data and the detonator communication network model, thereby obtaining channel planning data for the communication node;

[0154] In this embodiment, the node abnormal communication feature data and the detonator communication network model are used to plan the communication node's highest stability channel. This includes considering factors such as the distance between nodes, channel quality, and communication frequency band, and determining the best communication channel for each node through an optimization algorithm (such as a genetic algorithm, a simulated annealing algorithm, etc.) to ensure the stability and reliability of communication, thereby obtaining communication node channel planning data.

[0155] Step S33: performing channel optimization calculation on the detonator communication network model based on the communication node channel planning data, thereby obtaining optimized communication node channel data;

[0156] In this embodiment, based on the communication node channel planning data, the channel optimization calculation of the detonator communication network model is performed. This may include channel allocation algorithm, channel interference analysis and other technologies to maximize channel utilization, reduce communication interference, thereby improving communication efficiency and reliability, and obtaining optimized communication node channel data.

[0157] Step S34: performing communication node fusion on the optimized communication node channel data and the detonator communication network model, thereby obtaining the optimized detonator communication network model.

[0158] In this embodiment, communication node fusion is performed on the optimized communication node channel data and the detonator communication network model. This involves integrating the optimized communication node channel data into the detonator communication network model, updating the communication parameters and topology of the nodes to form an optimized detonator communication network model, ensuring that the communication connection between each node is stable and efficient, thereby obtaining an optimized detonator communication network model.

[0159] The present invention can identify abnormal patterns in inter-node communication by extracting features from abnormal communication node data. These abnormal features include communication frequency, delay, data packet size, etc., which are helpful to identify potential problem nodes in the communication network. The highest stability channel planning of the communication node is performed in combination with the node abnormal communication feature data and the detonator communication network model. This helps to identify the communication channel with the highest stability under the current communication network conditions to ensure the reliability and stability of the communication. Based on the communication node channel planning data, the detonator communication network model is subjected to channel optimization calculation. This includes adjusting channel parameters, optimizing channel allocation, etc. to improve the efficiency and performance of the communication network. The optimized communication node channel data is merged with the detonator communication network model to form an optimized detonator communication network model. This means that the optimized channel configuration is applied to the entire communication network to ensure that the communication between each node can be carried out under the best conditions.

[0160] Optionally, step S4 is specifically:

[0161] Step S41: extracting the sending signal data from the electronic detonator communication metadata, thereby obtaining the sending signal data of the electronic detonator;

[0162] In this embodiment, the electronic detonator communication metadata is subjected to transmission signal data extraction. This step involves using dedicated hardware or software tools to extract transmission signal data from the electronic detonator, including parameters such as frequency, amplitude, and pulse width, to ensure that the extracted data is accurate and reliable, and to provide accurate input for subsequent communication signal processing.

[0163] Step S42: obtaining random chaotic signal data through a preset Henon mapping system;

[0164] In this embodiment, random chaotic signal data is obtained through a preset Henon mapping system. Henon mapping is a nonlinear dynamic system that can generate a data sequence with high randomness and chaotic properties. By setting relevant parameters for electronic detonator communication in a preset Henon mapping system and performing a series of iterative calculations, random chaotic signal data is obtained for subsequent chaotic signal coding simulation.

[0165] Step S43: performing chaotic signal coding simulation on the signal data sent by the electronic detonator according to the random chaotic signal data, thereby obtaining mixed communication signal simulation data;

[0166] In this embodiment, random chaotic signal data is used to perform chaotic signal coding simulation on the electronic detonator transmission signal data. This includes mixing the extracted transmission signal data with the random chaotic signal data to increase the randomness and anti-interference ability of the signal. The coding simulation process can be implemented by mathematical operations or chaotic circuits to ensure that the generated mixed communication signal simulation data has high security and reliability.

[0167] Step S44: performing signal clarity calculation on the mixed communication signal simulation data to obtain mixed signal clarity data, and performing maximum clarity mixed communication signal selection on the mixed communication signal simulation data according to the signal clarity data to obtain chaotic disturbance enhanced transmission signal data;

[0168] In this embodiment, the signal clarity calculation is performed on the mixed communication signal simulation data to obtain the mixed signal clarity data. This involves using signal processing algorithms, such as power spectrum density analysis, correlation analysis, etc., to evaluate the clarity and quality of the mixed communication signal. According to the clarity data, the mixed communication signal simulation data with the maximum clarity is selected as the chaotic perturbation enhanced transmission signal data to improve the transmission quality and reliability of the communication signal.

[0169] Step S45: performing transmission signal spectrum allocation on the chaotic disturbance enhanced transmission signal data by optimizing the detonator communication network model, thereby obtaining an optimized transmission signal spectrum.

[0170] In this embodiment, the chaotic disturbance enhanced transmission signal data is subjected to transmission signal spectrum allocation by optimizing the detonator communication network model to obtain an optimized transmission signal spectrum. This includes using a spectrum allocation algorithm (such as a dynamic spectrum allocation algorithm, an adaptive spectrum allocation algorithm, etc.) according to the topological structure and communication requirements of the detonator communication network model to reasonably allocate transmission signal spectrum resources to maximize the transmission efficiency and coverage of the communication signal and ensure the stability and reliability of the communication.

[0171] The present invention can obtain the relevant data of the electronic detonator sending signal by extracting the electronic detonator communication metadata. These data include information such as the frequency, amplitude, duration, etc. of the sending, which is helpful to understand the communication behavior of the detonator. The random chaotic signal data is obtained by using the preset Henon mapping system. Chaotic signals are widely used in the field of communication. They have high complexity and randomness and can enhance the security and anti-interference ability of communication signals. The obtained random chaotic signal data is applied to the electronic detonator sending signal data to perform chaotic signal coding simulation. This means that the chaotic signal is embedded in the detonator sending signal to enhance the security and concealment of the communication signal. The signal clarity calculation is performed on the mixed communication signal simulation data to evaluate the signal quality. Then, the mixed communication signal with the maximum clarity is selected according to the signal clarity data to ensure the stability and reliability of the communication signal. By optimizing the detonator communication network model, the chaotic disturbance enhanced sending signal data is optimized for the sending signal spectrum allocation. This means that according to the characteristics and requirements of the communication network, the spectrum of the sending signal is adjusted and allocated to maximize the communication efficiency and performance.

[0172] Optionally, step S5 specifically includes:

[0173] Step S51: performing electrical signal transmission simulation on the optimized transmission signal spectrum by optimizing the detonator communication network model, thereby obtaining electrical signal transmission simulation data;

[0174] In this embodiment, it is necessary to perform electrical signal transmission simulation on the optimized transmission signal spectrum. This can be achieved by optimizing the detonator communication network model and the communication spectrum allocation algorithm. For example, digital signal processing technology such as discrete Fourier transform (DFT) or discrete wavelet transform (DWT) can be used to simulate the transmission process of the transmission signal in a simulation environment by optimizing the detonator communication network model, simulate the spectrum of the transmission signal, and generate electrical signal transmission simulation data in combination with the channel characteristics (such as Rayleigh fading and multipath effect) contained in the optimized detonator communication network model. These data will contain information such as signal power, spectrum characteristics, time domain waveform, etc., which are used to simulate the signal transmission process in actual communication. These data will reflect the transmission characteristics and performance of electrical signals in actual communication, and provide basic data support for subsequent steps.

[0175] Step S52: performing noise reduction processing on the electrical signal transmission analog data, thereby obtaining noise-reduced signal transmission analog data;

[0176] In this embodiment, the electrical signal transmission analog data is subjected to noise reduction processing. In order to simulate the noise reduction operation in a real communication environment to improve the quality and reliability of the receiving end signal, the specific implementation may include using noise reduction technologies such as digital filters and wavelet transforms to remove the noise interference during the transmission process and obtain clear noise reduction signal transmission analog data.

[0177] Step S53: performing phase space reconstruction demodulation on the electronic detonator transmission signal data and the chaotic disturbance enhanced transmission signal data, so as to obtain the transmission end chaotic disturbance factor;

[0178] In this embodiment, phase space reconstruction demodulation is performed for the electronic detonator transmission signal data and the chaotic disturbance enhanced transmission signal data. The phase space reconstruction technology can be used to demodulate and recover the transmission signal data to obtain the chaotic disturbance factor of the transmitting end. Phase space reconstruction is a method for reconstructing the phase space trajectory in a chaotic system. The characteristic parameters of the chaotic signal can be obtained by demodulation for subsequent analysis and processing. Methods such as delayed reconstruction and phase space projection can be used to obtain the chaotic disturbance factor of the transmitting end. These factors are very critical for demodulation and recovery in chaotic communication systems.

[0179] Step S54: Based on the chaotic disturbance factor at the sending end and the electronic detonator communication metadata, the chaotic disturbance factor at the receiving end is analyzed on the noise reduction signal transmission simulation data, so as to obtain the chaotic disturbance factor at the receiving end.

[0180] In this embodiment, based on the chaotic disturbance factor of the transmitting end and the electronic detonator communication metadata, the noise reduction signal transmission simulation data is analyzed for the chaotic disturbance factor of the receiving end. This includes using a chaotic analysis method, combining the chaotic disturbance factor of the receiving end and the characteristics of the transmitted signal data, analyzing and demodulating the receiving end signal, thereby obtaining the chaotic disturbance factor of the receiving end. This includes performing a chaotic analysis on the receiving end signal, such as using Lyapunov exponents, phase space reconstruction and other methods to obtain the chaotic disturbance factor of the receiving end. This can help evaluate the stability and reliability of the receiving end signal and provide data support for the performance optimization of the communication system.

[0181] The present invention can evaluate the performance of the signal under different conditions by simulating the transmission process of the signal in the communication link. This includes considering factors such as the transmission distance of the signal, the transmission medium, the signal attenuation and distortion. By simulating these situations, the behavior of the signal in the actual communication environment can be better understood, and data support can be provided for system design and optimization. In the process of signal transmission, it is often affected by various interferences and noises from the environment and equipment. The purpose of noise reduction processing is to reduce the influence of these interferences and noises, thereby improving the clarity and reliability of the signal. Through effective noise reduction processing, it is easier for the receiving end to interpret and identify the signal, reducing the possibility of miscoding and misjudgment. The phase space reconstruction technology is used to demodulate the transmitted signal data. Phase space reconstruction is a signal processing method that extracts the dynamic characteristics of the signal by mapping the signal to the trajectory in the phase space. This method can effectively extract the chaotic disturbance factor in the signal, thereby enhancing the stability and anti-interference ability of the signal. At the receiving end, by analyzing the chaotic disturbance factor of the transmitting end and the received signal data, the changes and influences of the signal during the transmission process can be better understood. This helps to adjust the parameters and algorithms of the receiving end to more effectively decode and process the received signal, and improve the performance and reliability of the communication system.

[0182] Optionally, step S54 is specifically:

[0183] Step S541: extracting reception signal features from the noise reduction signal transmission analog data, thereby obtaining analog reception signal data;

[0184] In this embodiment, time domain and frequency domain feature extraction techniques are used for noise reduction signal transmission analog data. In the time domain, the mean, variance, peak value, waveform characteristics, etc. of the signal can be extracted, for example, using time domain statistics and autocorrelation functions. In the frequency domain, Fourier transform or wavelet transform can be performed to extract the spectral characteristics of the signal, such as the main frequency, frequency distribution, etc. Through these feature extraction techniques, analog received signal data can be obtained from the noise reduction signal transmission analog data, providing a basis for the next processing steps.

[0185] Step S542: performing chaotic demodulation on the simulated received signal data by using the chaotic disturbance factor of the transmitting end, thereby obtaining demodulated received signal data;

[0186] In this embodiment, chaotic demodulation is performed on the simulated received signal data by using the chaotic disturbance factor of the transmitting end, and a chaotic synchronization demodulation technology can be used. First, the received simulated received signal data needs to be synchronized with the chaotic disturbance factor of the transmitting end. This can be achieved through phase space reconstruction technology and cross-correlation function. Once the synchronization state is reached, the chaotic demodulation equation can be used to demodulate the received signal and extract the original information. For example, a Lorentz chaotic system can be used to generate a chaotic signal, and a Lorentz chaotic mapping function can be used for demodulation.

[0187] Step S543: extracting corresponding received signal data from the electronic detonator communication metadata according to the electronic detonator sent signal data, thereby obtaining the electronic detonator received signal data;

[0188] In this embodiment, the communication protocol parsing technology can be used to extract the corresponding received signal data from the electronic detonator communication metadata according to the electronic detonator sending signal data. First, it is necessary to parse the communication protocol of the electronic detonator sending signal to identify key information such as synchronization words and data frame structure. Then, based on this information, the corresponding communication elements are extracted from the received analog signal. For example, if the electronic detonator communication protocol adopts Manchester encoding, the data bit can be identified by the edge of the clock signal. In this way, the electronic detonator receiving signal data can be effectively extracted.

[0189] Step S544: performing received signal error calculation on the demodulated received signal data and the electronic detonator received signal data, thereby obtaining received signal error data;

[0190] In this embodiment, the received signal error calculation is performed on the demodulated received signal data and the electronic detonator received signal data, and a bit error rate calculation method can be used. First, the demodulated received signal is compared with the original transmitted signal to calculate the bit error rate. At the same time, for the electronic detonator received signal data, we can detect whether there is data loss or error. Taking these factors into consideration, the error of the received signal can be obtained for subsequent correction and optimization.

[0191] Step S545: performing signal transmission error correction on the chaotic disturbance factor of the transmitting end according to the received signal error data, thereby obtaining the chaotic disturbance factor of the receiving end.

[0192] In this embodiment, the signal transmission error correction is performed on the chaotic disturbance factor of the transmitting end according to the received signal error data, and an adaptive control algorithm can be used. The chaotic parameters of the transmitting end, that is, the chaotic disturbance factor of the receiving end, are dynamically adjusted according to the error situation of the receiving end. For example, a PID controller can be used to adjust the parameters of the chaotic system so that the error of the receiving end is reduced as much as possible. Through continuous monitoring and adjustment, the accurate correction of the chaotic disturbance factor of the transmitting end can be achieved, and the stability and reliability of the communication system can be improved.

[0193] The present invention can capture important features of the signal during the transmission process, such as frequency, amplitude, phase, etc., by extracting features from the noise reduction signal transmission analog data. These features are helpful for accurately demodulating and processing the signal in the subsequent steps. By demodulating the simulated received signal data using the chaotic disturbance factor of the transmitting end, the original signal can be effectively extracted. The chaotic demodulation technology has a strong adaptability to the nonlinear characteristics of the signal and can effectively deal with the interference and distortion in the signal transmission process. According to the electronic detonator sending signal data, metadata related to the electronic detonator communication is extracted from the noise reduction signal. These metadata include the identification, format, and verification information of the sending signal, which are key information for correctly parsing and processing the received signal. By comparing and analyzing the demodulated received signal data and the electronic detonator receiving signal data, the error of the received signal can be calculated. These error data reflect the loss, distortion or interference in the signal transmission process, and provide a basis for subsequent error correction. According to the received signal error data, the chaotic disturbance factor of the transmitting end is adjusted and corrected to reduce the error influence in the signal transmission process. This can improve the reliability and anti-interference ability of the signal, ensuring that the receiving end can accurately parse and process the received signal.

[0194] Optionally, the present specification also provides a system for improving the communication efficiency of electronic detonators, which is used to execute the method for improving the communication efficiency of electronic detonators as described above. The system for improving the communication efficiency of electronic detonators includes:

[0195] The abnormal communication analysis module is used to obtain electronic detonator communication metadata and perform electronic detonator abnormal communication analysis on the electronic detonator communication metadata, thereby obtaining electronic detonator abnormal communication data;

[0196] An abnormal communication node division module is used to construct a detonator communication network model based on the electronic detonator communication metadata, and divide the detonator communication network model into abnormal communication nodes according to the electronic detonator abnormal communication data, so as to obtain abnormal communication node data;

[0197] A channel optimization module is used to optimize abnormal communication node channels for abnormal communication node data according to the detonator communication network model, thereby obtaining optimized communication node channel data, and to perform communication node fusion on the optimized communication node channel data and the detonator communication network model, thereby obtaining the optimized detonator communication network model;

[0198] A chaotic disturbance enhancement module is used to perform chaotic disturbance enhancement simulation on the electronic detonator communication metadata to obtain chaotic disturbance enhanced transmission signal data, and to perform transmission signal spectrum allocation on the chaotic disturbance enhanced transmission signal data by optimizing the detonator communication network model to obtain an optimized transmission signal spectrum;

[0199] A chaos disturbance factor analysis module is used to perform an electrical signal transmission simulation on the optimized transmission signal spectrum by optimizing the detonator communication network model, thereby obtaining electrical signal transmission simulation data, and to perform adaptive chaos disturbance factor analysis on the electrical signal transmission simulation data according to the electronic detonator communication metadata, thereby obtaining a receiving end chaos disturbance factor and a sending end chaos disturbance factor;

[0200] The disturbance factor coding fusion module is used to perform disturbance factor coding fusion on the optimized detonator communication network model according to the chaotic disturbance factor of the receiving end and the chaotic disturbance factor of the sending end, so as to obtain the chaotic detonator communication network model and upload it to the electronic detonator communication cloud platform.

[0201] The system for improving the communication efficiency of electronic detonators of the present invention can implement any one of the methods for improving the communication efficiency of electronic detonators of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the method for improving the communication efficiency of electronic detonators. The internal modules of the system cooperate with each other to improve the communication efficiency of electronic detonators.

[0202] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0203] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for improving the communication efficiency of electronic detonators, characterized in that: The following steps are involved: Step S1: acquiring electronic detonator communication metadata, and performing electronic detonator abnormal communication analysis on the electronic detonator communication metadata, thereby obtaining electronic detonator abnormal communication data; Step S2: constructing a detonator communication network model based on the electronic detonator communication metadata, and dividing the detonator communication network model into abnormal communication nodes according to the electronic detonator abnormal communication data, thereby obtaining abnormal communication node data; Step S3: Optimizing abnormal communication node channels on abnormal communication node data according to the detonator communication network model, thereby obtaining optimized communication node channel data, and performing communication node fusion on the optimized communication node channel data and the detonator communication network model, thereby obtaining the optimized detonator communication network model. Step S3 is specifically as follows: Step S31: extracting abnormal communication features from abnormal communication node data, thereby obtaining node abnormal communication feature data; Step S32: performing channel planning for the highest stability of the communication node according to the node abnormal communication characteristic data and the detonator communication network model, thereby obtaining channel planning data for the communication node; Step S33: performing channel optimization calculation on the detonator communication network model based on the communication node channel planning data, thereby obtaining optimized communication node channel data; Step S34: performing communication node fusion on the optimized communication node channel data and the detonator communication network model, thereby obtaining the optimized detonator communication network model; Step S4: performing a chaotic disturbance enhancement simulation on the electronic detonator communication metadata to obtain chaotic disturbance enhanced transmission signal data, and performing transmission signal spectrum allocation on the chaotic disturbance enhanced transmission signal data by optimizing the detonator communication network model to obtain an optimized transmission signal spectrum. Step S4 is specifically as follows: Step S41: extracting the sending signal data from the electronic detonator communication metadata, thereby obtaining the sending signal data of the electronic detonator; Step S42: obtaining random chaotic signal data through a preset Henon mapping system; Step S43: performing chaotic signal coding simulation on the signal data sent by the electronic detonator according to the random chaotic signal data, thereby obtaining mixed communication signal simulation data; Step S44: performing signal clarity calculation on the mixed communication signal simulation data to obtain mixed signal clarity data, and performing maximum clarity mixed communication signal selection on the mixed communication signal simulation data according to the signal clarity data to obtain chaotic disturbance enhanced transmission signal data; Step S45: performing transmission signal spectrum allocation on the chaotic disturbance enhanced transmission signal data by optimizing the detonator communication network model, thereby obtaining an optimized transmission signal spectrum; Step S5: performing an electrical signal transmission simulation on the optimized transmission signal spectrum by optimizing the detonator communication network model, thereby obtaining electrical signal transmission simulation data, and performing adaptive chaotic perturbation factor analysis on the electrical signal transmission simulation data according to the electronic detonator communication metadata, thereby obtaining a receiving end chaotic perturbation factor and a sending end chaotic perturbation factor; Step S6: Perform disturbance factor encoding fusion on the optimized detonator communication network model according to the chaotic disturbance factor of the receiving end and the chaotic disturbance factor of the sending end, so as to obtain the chaotic detonator communication network model, and upload it to the electronic detonator communication cloud platform.

2. The method for improving the communication efficiency of electronic detonators according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: acquiring electronic detonator communication metadata, and extracting communication frequency features of the electronic detonator communication metadata, thereby obtaining electronic detonator communication frequency data; Step S12: performing Fourier transform on the electronic detonator communication frequency data, thereby obtaining the electronic detonator communication spectrum; Step S13: performing frequency distribution density calculation on the electronic detonator communication spectrum to obtain communication frequency distribution density data, and performing distribution density statistics on the communication frequency distribution density data to obtain high-value communication frequency distribution density data and low-value communication frequency distribution density data; Step S14: performing cluster calculation on the electronic detonator communication frequency data according to the high-value communication frequency distribution density data, thereby obtaining abnormal electronic detonator communication frequency data, and performing abnormal communication association on the electronic detonator communication metadata according to the abnormal electronic detonator communication frequency data, thereby obtaining abnormal communication data; Step S15: performing cluster calculation on the electronic detonator communication frequency data according to the low-rate communication frequency distribution density data, thereby obtaining normal electronic detonator communication frequency data, and performing normal communication association on the electronic detonator communication metadata according to the normal electronic detonator communication frequency data, thereby obtaining normal communication data; Step S16: Time-sequentially merge the abnormal communication data and the normal communication data to obtain the abnormal communication data of the electronic detonator.

3. The method for improving the communication efficiency of electronic detonators according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: estimating the detonator communication channel based on the electronic detonator communication metadata, thereby obtaining detonator channel estimation data; Step S22: performing network topology analysis on the electronic detonator communication metadata based on the detonator channel estimation data, thereby obtaining a detonator communication network topology structure; Step S23: dividing the detonator communication network topology structure by communication edge weights based on the electronic detonator communication metadata, thereby obtaining a detonator communication network model; Step S24: dividing the detonator communication network model into abnormal communication nodes according to the abnormal communication data of the electronic detonator, thereby obtaining abnormal communication node data.

4. The method for improving the communication efficiency of electronic detonators according to claim 3, characterized in that: Step S22 is specifically as follows: Step S221: extracting communication record features from the electronic detonator communication metadata, thereby obtaining the electronic detonator historical communication data; Step S222: determining the detonator communication node based on the electronic detonator historical communication data, thereby obtaining the communication node data; Step S223: classifying the communication node data by communication frequency according to the historical communication data of the electronic detonator, thereby obtaining high-frequency communication node data and low-frequency communication node data; Step S224: defining the core communication node for the high frequency communication node data, thereby obtaining the core communication node data; Step S225: performing node communication relationship analysis on the electronic detonator historical communication data based on the core communication node data, thereby obtaining node communication relationship data; Step S226: constructing a network topology structure for the core communication node data and the low-frequency communication node data based on the node communication relationship data, thereby obtaining a communication network topology structure; Step S227: Perform channel attribute weight mapping on the communication network topology structure based on the detonator channel estimation data, so as to obtain the detonator communication network topology structure.

5. The method for improving the communication efficiency of electronic detonators according to claim 3, characterized in that: Step S24 is specifically as follows: Step S241: extracting abnormal communication features from abnormal communication data of the electronic detonator, thereby obtaining abnormal communication feature data; Step S242: Correlating node communication characteristics of the detonator communication network model according to the abnormal communication characteristic data, thereby obtaining node communication characteristic data; Step S243: performing similarity calculation on the abnormal communication feature data and the node communication feature data, thereby obtaining node similarity data; Step S244: performing high-similarity node clustering calculation on the node communication feature data according to the node similarity data, thereby obtaining abnormal communication feature node data; Step S245: marking abnormal communication nodes of the detonator communication network model according to the abnormal communication characteristic node data, thereby obtaining abnormal communication node data.

6. The method for improving the communication efficiency of electronic detonators according to claim 1, characterized in that: Step S5 is specifically as follows: Step S51: performing electrical signal transmission simulation on the optimized transmission signal spectrum by optimizing the detonator communication network model, thereby obtaining electrical signal transmission simulation data; Step S52: performing noise reduction processing on the electrical signal transmission analog data, thereby obtaining noise-reduced signal transmission analog data; Step S53: performing phase space reconstruction demodulation on the electronic detonator transmission signal data and the chaotic disturbance enhanced transmission signal data, so as to obtain the transmission end chaotic disturbance factor; Step S54: Based on the chaotic disturbance factor at the sending end and the electronic detonator communication metadata, the chaotic disturbance factor at the receiving end is analyzed on the noise reduction signal transmission simulation data, so as to obtain the chaotic disturbance factor at the receiving end.

7. The method for improving the communication efficiency of electronic detonators according to claim 6, characterized in that: Step S54 is specifically as follows: Step S541: extracting reception signal features from the noise reduction signal transmission analog data, thereby obtaining analog reception signal data; Step S542: performing chaotic demodulation on the simulated received signal data by using the chaotic disturbance factor of the transmitting end, thereby obtaining demodulated received signal data; Step S543: extracting corresponding received signal data from the electronic detonator communication metadata according to the electronic detonator sent signal data, thereby obtaining the electronic detonator received signal data; Step S544: performing received signal error calculation on the demodulated received signal data and the electronic detonator received signal data, thereby obtaining received signal error data; Step S545: performing signal transmission error correction on the chaotic disturbance factor of the transmitting end according to the received signal error data, thereby obtaining the chaotic disturbance factor of the receiving end.

8. A system for improving the communication efficiency of electronic detonators, characterized in that: Used to execute the method for improving the communication efficiency of electronic detonators as claimed in claim 1, the system for improving the communication efficiency of electronic detonators comprises: The abnormal communication analysis module is used to obtain electronic detonator communication metadata and perform electronic detonator abnormal communication analysis on the electronic detonator communication metadata, thereby obtaining electronic detonator abnormal communication data; An abnormal communication node division module is used to construct a detonator communication network model based on the electronic detonator communication metadata, and divide the detonator communication network model into abnormal communication nodes according to the electronic detonator abnormal communication data, so as to obtain abnormal communication node data; A channel optimization module is used to optimize abnormal communication node channels for abnormal communication node data according to the detonator communication network model, thereby obtaining optimized communication node channel data, and to perform communication node fusion on the optimized communication node channel data and the detonator communication network model, thereby obtaining the optimized detonator communication network model; A chaotic disturbance enhancement module is used to perform chaotic disturbance enhancement simulation on the electronic detonator communication metadata to obtain chaotic disturbance enhanced transmission signal data, and to perform transmission signal spectrum allocation on the chaotic disturbance enhanced transmission signal data by optimizing the detonator communication network model to obtain an optimized transmission signal spectrum; A chaos disturbance factor analysis module is used to perform an electrical signal transmission simulation on the optimized transmission signal spectrum by optimizing the detonator communication network model, thereby obtaining electrical signal transmission simulation data, and to perform adaptive chaos disturbance factor analysis on the electrical signal transmission simulation data according to the electronic detonator communication metadata, thereby obtaining a receiving end chaos disturbance factor and a sending end chaos disturbance factor; The disturbance factor coding fusion module is used to perform disturbance factor coding fusion on the optimized detonator communication network model according to the chaotic disturbance factor of the receiving end and the chaotic disturbance factor of the sending end, so as to obtain the chaotic detonator communication network model and upload it to the electronic detonator communication cloud platform.

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