Star flash connection anti-interference dynamic optimization method for multi-device cooperation

Abstract

The invention discloses a multi-device cooperation-oriented star flash connection anti-interference dynamic optimization method, and particularly relates to the technical field of data transmission. Through a multi-channel real-time monitoring technology, external interference source data and network communication quality data are respectively obtained and substituted into a data analysis model, real-time factors of multi-device cooperative communication are output, sorting is carried out according to the real-time factors, high and low interference intervals are divided through a preset threshold value, and for the high interference interval, the real-time performance of multi-device cooperative communication is improved. An erasure code technology and a time sequence reconstruction mechanism are utilized to reduce the data packet loss proportion, meanwhile, fuzzy logic is adopted to dynamically adjust channel selection and transmitting power, the method can effectively keep high-precision time sequence synchronization between devices in a high-concurrency and multi-device cooperation application scene, meanwhile, a communication strategy is optimized, and the communication efficiency is improved. The anti-interference capability and the network stability are improved, and the reliability and the efficiency of data transmission are ensured.

Description

Technical Field

[0001] The present invention relates to the field of data transmission technology, and in particular to a star flash connection anti-interference dynamic optimization method for multi-device collaboration. Background Art

[0002] In today's wireless communication environment, multi-device collaborative communication has put forward higher requirements for stability and anti-interference capabilities. As an emerging short-range wireless communication protocol, SparkLink technology has attracted widespread attention for its high efficiency, low latency and low power consumption. However, in complex wireless environments, signal interference is still a key challenge affecting its performance. In order to improve the stability and anti-interference capability of SparkLink connections, it is particularly important to study dynamic optimization methods. By adaptively adjusting communication parameters, multi-path selection and intelligent scheduling strategies, the impact of external interference on multi-device collaborative communication can be effectively reduced, and the reliability and performance of the network can be improved.

[0003] The prior art has the following deficiencies: Due to the dynamic adjustment of the adaptive optimization strategy, the timing synchronization between devices may fail. In a high-interference environment, devices need to frequently adjust communication parameters to avoid interference sources, but this dynamic change may destroy the original timing synchronization mechanism, resulting in an increase in packet retransmission rate, a decrease in throughput, and even in extreme cases, causing device connection loss. This problem is particularly prominent in high-concurrency, multi-device collaborative application scenarios, such as industrial automation, Internet of Vehicles, or smart home scenarios. Therefore, how to ensure high-precision timing synchronization between devices while dynamically optimizing the anti-interference strategy is a major problem in the current research on anti-interference optimization methods for Star Flash connections. Summary of the invention

[0004] The purpose of the present invention is to provide a dynamic optimization method for anti-interference of star flash connection for multi-device collaboration to solve the shortcomings of the background technology.

[0005] In order to achieve the above object, the present invention provides the following technical solution: a dynamic optimization method for anti-interference of star flash connection for multi-device collaboration, comprising the following steps: Through multi-channel real-time monitoring technology, the external interference source data and network communication quality data in the Xingshan network are obtained respectively; Substitute the acquired external interference source data and network communication quality data into the data analysis model, and the data analysis model outputs the real-time factors of multi-device collaborative communication respectively; According to the real-time factor, the real-time of the collaborative communication of multiple devices is sorted to generate a real-time list, and the real-time list is divided into a high-interference interval and a low-interference interval according to a preset threshold; For high-interference intervals, erasure coding technology and timing reconstruction mechanism are used to reduce the packet loss ratio. Based on the fluctuation of the packet loss ratio and the real-time factor of the high-interference interval, fuzzy logic is used to dynamically update the optimization strategy and continuously adjust the channel selection and transmission power.

[0006] Preferably, the acquired external interference source data and network communication quality data are substituted into a data analysis model, and the data analysis model outputs real-time factors of multi-device collaborative communication respectively, including: The data transmission interference ratio and the network signal interference degree values ​​are normalized so that they are both between [0,1]. The real-time factor of multi-device collaborative communication is calculated based on the normalized data transmission interference ratio and the network signal interference degree values.

[0007] Preferably, the method for obtaining the data transmission interference ratio is: obtaining the signal strength indicator value , and create a data set , n is a positive integer, get the maximum signal strength indication value , minimum signal strength indicator value , calculate the average signal strength indicator value , obtain the number of times greater than the average signal strength indication value g, the number of times less than the average signal strength indication value p, and calculate the data transmission interference ratio. The calculation expression is: = , where is the data transmission interference ratio.

[0008] Preferably, after real-time monitoring and analysis of the communication network signals of each device in the Xingshan network, the network signal interference degree value is obtained. The method for obtaining the network signal interference degree value is: obtaining the number of bits with correct transmission results during the data transmission process. , and the total number of bits transmitted during data transmission , calculate the data transmission efficiency, the calculation expression is: = , where For data transmission efficiency, The data transmission frequency is used to obtain the standard data transmission efficiency under normal network signal conditions. , calculate the network signal interference value, the calculation expression is: = , where It is the value of network signal interference degree.

[0009] Preferably, the real-time of multi-device collaborative communication is sorted according to the real-time factor to generate a real-time list, and the real-time list is divided into a high-interference interval and a low-interference interval according to a preset threshold, including: sorting the acquired real-time factors of multi-device collaborative communication and generating a real-time list, comparing the real-time factor of multi-device collaborative communication in the real-time list with the preset threshold, if the real-time factor of multi-device collaborative communication in the real-time list is greater than or equal to the preset threshold, dividing it into the low-interference interval; if the real-time factor of multi-device collaborative communication in the real-time list is less than the preset threshold, dividing it into the high-interference interval.

[0010] Preferably, the network stability is quantitatively analyzed by calculating the data packet loss ratio fluctuation index, and the optimization strategy is updated in combination with the real-time factor.

[0011] Preferably, the method for obtaining the data packet loss ratio fluctuation index is: In the preset time window, count the number of lost data packets L(t) and the total number of data packets T(t), and calculate the loss ratio in each time period: ; Where P(t) is the packet loss ratio at the tth moment. The mean and standard deviation of the packet loss ratio within the fixed time window are calculated. According to the calculated standard deviation of the packet loss ratio σP, the packet loss ratio fluctuation index PLFI is generated. The expression is: .

[0012] Preferably, the data packet loss ratio fluctuation index and the real-time factor of multi-device collaborative communication are used as input items of fuzzy logic, and are divided into different fuzzy sets respectively; Channel selection and transmission power are taken as output items of fuzzy logic and divided into different fuzzy sets; Formulate fuzzy rules to describe the impact of the packet loss ratio fluctuation index and the real-time factor of multi-device collaborative communication on channel selection and transmission power; Reasoning is performed based on fuzzy rules to update the optimization strategy.

[0013] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention introduces multi-channel real-time monitoring technology, dynamically collects external interference source data and network communication quality data, and calculates real-time factors through data analysis models, which can effectively identify high-interference intervals and low-interference intervals. For high-interference intervals, the packet loss rate is reduced through erasure code technology and timing reconstruction mechanism, and fuzzy logic is used to dynamically adjust channel selection and transmission power, thereby optimizing the communication quality and network stability of the device. This method can effectively balance the requirements of dynamic anti-interference strategies and high-precision timing synchronization in multi-device collaborative communications, greatly reducing the problems of packet retransmission and device connection loss caused by interference.

[0014] 2. The present invention not only improves the reliability of communication between devices, but also ensures efficient collaborative operation in complex interference environments. The introduction of the packet loss ratio fluctuation index enables the stability of the network to be quantitatively analyzed and optimized, thereby significantly improving the overall network communication throughput and the system's anti-interference ability, and effectively avoiding the phenomenon of connection loss in extreme situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0016] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] For examples, see Figure 1 As shown, the anti-interference dynamic optimization method for star flash connection for multi-device collaboration described in this embodiment includes the following steps: Through multi-channel real-time monitoring technology, the external interference source data and network communication quality data in the Xingshan network are obtained respectively; Substitute the acquired external interference source data and network communication quality data into the data analysis model, and the data analysis model outputs the real-time factors of multi-device collaborative communication respectively; According to the real-time factor, the real-time of the collaborative communication of multiple devices is sorted to generate a real-time list, and the real-time list is divided into a high-interference interval and a low-interference interval according to a preset threshold; For high-interference intervals, erasure coding technology and timing reconstruction mechanism are used to reduce the packet loss ratio. Based on the fluctuation of the packet loss ratio and the real-time factor of the high-interference interval, fuzzy logic is used to dynamically update the optimization strategy and continuously adjust the channel selection and transmission power.

[0019] Multi-channel real-time monitoring technology first uses a multi-spectrum analyzer or wireless signal monitoring equipment to comprehensively scan the spectrum in the communication environment. The process includes: Frequency scanning: By collecting signals in different frequency bands, possible interference sources (such as other wireless devices, industrial equipment, or environmental noise) can be identified. These signals can be classified as broadband interference, narrowband interference, or sudden interference at a specific frequency.

[0020] Strength assessment: monitor the intensity changes of interference source signals, identify the frequency and fluctuation range of interference, and assess its potential impact on star flash communication. Strength assessment can be achieved by receiving the signal strength indicator value. The basic calculation of the signal strength indicator value st is usually completed automatically by wireless hardware or software, but behind it is an estimate based on the relationship between signal power and distance. A common calculation method is: ;in: is the received signal power in watts or milliwatts. is the reference power, typically 1 milliwatt (0 dBm).

[0021] Bandwidth analysis: By analyzing the bandwidth characteristics of the interference source, we can determine its occupancy of the communication bandwidth and further infer the degree of interference it may cause.

[0022] Time domain analysis: Through time synchronization, the time changes of interference signals can be captured in real time to identify short-term interference or periodic interference sources. Time domain analysis can be used to determine the periodic pattern or sudden fluctuations of interference sources.

[0023] Data on these external interference sources is critical to identifying the types of interference and their characteristics in the wireless environment.

[0024] The network communication quality data collects the following key indicators by real-time monitoring and recording the communication performance of each device in the Xingshan network: Signal strength: Evaluates the signal strength between devices and reflects the quality and stability of the wireless link. Signal strength directly affects the coverage and connection stability of communications.

[0025] Latency: Real-time monitoring of data packet transmission latency, identifying latency changes due to interference or network congestion. Excessive latency may affect data synchronization and real-time performance.

[0026] Packet loss rate: records the loss of data packets and evaluates the reliability of the network and the stability of data transmission by calculating the packet loss rate. A high packet loss rate is usually caused by interference, signal attenuation or congestion.

[0027] Throughput: Measures the amount of successful data transfer per unit time, reflecting the efficiency of the communication system. A decrease in throughput may indicate interference or excessive network load.

[0028] Connection stability: Monitors the device's connection retention time and frequent disconnections to identify frequent connection interruptions or reconnections due to interference.

[0029] Through multi-channel technology, multiple devices and different network parameters can be monitored simultaneously to obtain more comprehensive communication quality data. These data help to evaluate the health of the communication network and its relationship with interference sources in real time, thus providing data support for subsequent optimization and adjustment.

[0030] In this application, through multi-channel real-time monitoring technology, Xingshan Network can simultaneously collect detailed information on external interference sources (such as interference intensity, frequency, bandwidth and time domain characteristics) and key data on network communication quality (such as signal strength, latency, packet loss rate, throughput, etc.). The combination of these data can help fully understand the specific impact of interference on network performance and provide an accurate basis for subsequent dynamic optimization.

[0031] Monitor the data transmission process, determine the degree of interference in data transmission efficiency during the data transmission process, and calculate the data transmission interference ratio, including: The method for obtaining the data transmission interference ratio is as follows: obtaining the signal strength indicator value , and create a data set , n is a positive integer, get the maximum signal strength indication value , minimum signal strength indicator value , calculate the average signal strength indicator value , obtain the number of times greater than the average signal strength indication value g, the number of times less than the average signal strength indication value p, and calculate the data transmission interference ratio. The calculation expression is: = , where is the data transmission interference ratio.

[0032] The larger the data transmission interference ratio, the higher the strength of the interference signal and the more significant the impact on valid data. In this case, the communication signal may suffer severe attenuation or distortion, resulting in packet loss, increased retransmissions, or even disconnection, which significantly reduces data transmission efficiency. At this point, the network throughput decreases, latency increases, and user experience and communication quality are greatly affected. Therefore, the larger the interference ratio, the more severe the interference during data transmission and the lower the efficiency.

[0033] The smaller the data transmission interference ratio is, the weaker the interference signal is, the more effective the signal is, the lower the error rate is during data transmission, and the better the communication quality is. At this time, the signal can be transmitted more stably, the packet loss rate is low, and the number of retransmissions is also low, thereby improving data transmission efficiency. The smaller the interference ratio is, the more effectively the communication system can use the bandwidth, provide higher throughput and lower latency, and ensure a more reliable communication experience.

[0034] After real-time monitoring and analysis of the communication network signals of each device in the Xingshan network, the network signal interference degree value is obtained. The method for obtaining the network signal interference degree value is: obtain the number of bits with correct transmission results during the data transmission process , and the total number of bits transmitted during data transmission , calculate the data transmission efficiency, the calculation expression is: = , where For data transmission efficiency, The data transmission frequency is used to obtain the standard data transmission efficiency under normal network signal conditions. , calculate the network signal interference value, the calculation expression is: = , where It is the value of network signal interference degree.

[0035] Substitute the acquired external interference source data and network communication quality data into the data analysis model, and the data analysis model outputs the real-time factors of multi-device collaborative communication, including: The data transmission interference ratio and the network signal interference degree values ​​are normalized so that they are both between [0,1]. The real-time factor of multi-device collaborative communication is calculated based on the normalized data transmission interference ratio and the network signal interference degree values.

[0036] For example, the present invention may use the following formula to calculate the real-time factor of multi-device collaborative communication, and the calculation expression is: ; In the formula, It is the real-time factor of multi-device collaborative communication. is the data transmission interference ratio, is the network signal interference level value, is the weight coefficient of the data transmission interference ratio and the network signal interference degree value (which can be optimized based on experimental experience or machine learning), and Both are greater than 0.

[0037] According to the real-time factor, the real-time of multi-device collaborative communication is sorted to generate a real-time list, and the real-time list is divided into high-interference intervals and low-interference intervals according to a preset threshold, including: The acquired real-time factors of the multi-device collaborative communication are sorted and a real-time list is generated. The real-time factors of the multi-device collaborative communication in the real-time list are compared with a preset threshold. If the real-time factor of the multi-device collaborative communication in the real-time list is greater than or equal to the preset threshold, it is divided into a low interference interval; if the real-time factor of the multi-device collaborative communication in the real-time list is less than the preset threshold, it is divided into a high interference interval.

[0038] For high-interference intervals, erasure coding technology and timing reconstruction mechanism are used to reduce the packet loss ratio. Based on the fluctuation of the packet loss ratio and the real-time factor of the high-interference interval, fuzzy logic is used to dynamically update the optimization strategy and continuously adjust the channel selection and transmission power.

[0039] Error Correction Code (ECC) is a method that ensures data integrity and reliability by introducing redundant information during data transmission. Even if some data is lost or errors occur during data transmission, the receiver can still recover the lost information through redundant data.

[0040] In a high-interference environment, communication links are prone to packet loss or corruption. Using erasure coding technology, the packet loss rate can be reduced by following the steps below: Coding process: At the sending end, data is encoded using erasure codes to generate data packets with redundant information. Common erasure codes include Reed-Solomon codes, LDPC codes, and Turbo codes.

[0041] Transmission process: The encoded data is transmitted through unstable wireless links.

[0042] Decoding process: The receiving end recovers the lost data through the received partial data packets and redundant information, reducing the data loss caused by interference.

[0043] The role of erasure codes: By transmitting redundant data, it can reduce the need for retransmission in the event of packet loss, reduce the packet loss rate, and maintain a high transmission efficiency.

[0044] The timing reconstruction mechanism is mainly used to compensate for the delay changes caused by interference or channel switching, thereby avoiding the disorder or loss of data packets. The specific steps are as follows: Timing synchronization: In a high-interference environment, the arrival time of data packets may change due to factors such as signal attenuation and interference. The timing reconstruction mechanism uses algorithms (such as predictive timing compensation algorithms) to correct the delay by monitoring and predicting the delay fluctuations of data packets in real time.

[0045] Packet sequence recovery: In a high-interference environment, some packets may arrive early or late due to long delays, making it difficult for the receiving end to process them. Through the timing reconstruction mechanism, the receiving timing of the packet can be adjusted to ensure the sequential continuity of data transmission and avoid data loss or confusion caused by timing problems.

[0046] In a high-interference environment, the packet loss ratio may fluctuate greatly, affecting the stability of the network. In order to quantify this fluctuation, a packet loss ratio fluctuation index can be introduced to describe the fluctuation characteristics of the packet loss rate over time.

[0047] The packet loss ratio fluctuation index PLFI can be calculated by the following steps: In a preset time window (such as every second, every minute, or every time window), count the number of lost packets L(t) and the total number of packets T(t), and calculate the loss ratio in each time period: ; Where P(t) is the packet loss ratio at the tth moment. The mean and standard deviation of the packet loss ratio within the fixed time window are calculated. According to the calculated standard deviation of the packet loss ratio σP, the packet loss ratio fluctuation index PLFI can be generated. The expression is: ; Through this fluctuation index, the fluctuation degree of packet loss can be quantified. The higher the value, the greater the fluctuation of packet loss rate and the worse the stability of network communication.

[0048] A low PLFI value indicates that the packet loss rate is relatively stable, the network communication quality is high, and it is suitable for high real-time applications (such as video conferencing, online games, etc.). A high PLFI value indicates that the packet loss rate fluctuates greatly and the communication quality is unstable, which may lead to communication interruption, video freeze, or data loss. In this case, the system may need further optimization (such as increasing the redundancy of erasure codes and enhancing timing reconstruction capabilities).

[0049] Based on the fluctuation of packet loss ratio and the real-time factor of high interference interval, fuzzy logic is used to dynamically update the optimization strategy and continuously adjust channel selection and transmission power. Specifically: The packet loss ratio fluctuation index PLFI and the real-time factor BF of multi-device collaborative communication are used as the input items of fuzzy logic, and the channel selection and transmission power are used as the output items of fuzzy logic. The optimization strategy is dynamically updated after using fuzzy logic. In fuzzy logic control systems, the real values ​​of the input need to be converted into fuzzy sets. In this case, both PLFI and BF need to be fuzzified. Suppose we divide each input variable into multiple levels (such as low, medium, high, etc.) and determine the degree to which each variable belongs to these levels based on its value.

[0050] PLFI (Packet Loss Fraction Fluctuation Index): Low (Low): Indicates that the packet loss fluctuation is small and the network is relatively stable. Medium (Medium): Indicates that the packet loss fluctuation is medium and the network quality is average. High (High): Indicates that the packet loss fluctuation is large and the network quality is unstable.

[0051] BF (Real-time Factor): Low interference (Low): indicates good real-time performance and less interference. Medium interference (Medium): indicates average real-time performance and medium interference. High interference (High): indicates poor real-time performance and greater interference.

[0052] Next, you need to define a set of fuzzy rules that will determine how to adjust the output (channel selection and transmit power) based on the signal input (PLFI and BF). The construction of fuzzy rules is based on the results of experience, experiments, or machine learning. The following is an example of a simple rule base: Rule 1: If PLFI is high and BF is high, the channel selection is "poor" and the transmit power is "high".

[0053] Rule 2: If PLFI is low and BF is low, the channel selection is "good" and the transmit power is "low".

[0054] Rule 3: If PLFI is medium and BF is medium, the channel selection is "medium" and the transmit power is "medium".

[0055] Rule 4: If PLFI is high and BF is low, the channel selection is "poor" and the transmit power is "medium".

[0056] Rule 5: If PLFI is low and BF is high, the channel selection is "Medium" and the transmit power is "High".

[0057] Rule 6: If PLFI is low and BF is medium, the channel selection is "medium" and the transmit power is "low".

[0058] Rule 7: If PLFI is high and BF is medium, the channel selection is "poor" and the transmit power is "high".

[0059] These rules can be refined and adjusted by adjusting the optimization goals of the system and experimental data.

[0060] Fuzzy reasoning is the core step of fuzzy logic control system. It performs reasoning based on the input fuzzified data and fuzzy rule base, outputs a fuzzy value, and then obtains a specific control signal through defuzzification. The reasoning process includes the following steps: The matching degree of each rule is evaluated based on the input PLFI and BF. The membership of the input value in each fuzzy set is determined by the "membership function", such as triangle, trapezoidal, etc. The output of each rule (channel selection and transmit power) is weighted according to the membership of the input value.

[0061] For each rule, the corresponding output fuzzy value is derived according to the membership value. For example, if the PLFI of rule 1 is high and the BF is high, then the output of channel selection is "bad" and the output of transmit power is "high". Through the reasoning process of all rules, multiple output fuzzy values ​​are obtained.

[0062] The result of fuzzy reasoning is a fuzzy value. In order to perform specific control operations, the fuzzy value needs to be converted into a specific numerical value. Common methods for defuzzification are the Centroid Method or the Max Membership Method. Centroid Method: The final output value is obtained by weighted averaging the values ​​of all output fuzzy sets. For example, if there are multiple output fuzzy values, a comprehensive result is obtained by weighting the membership.

[0063] The channel selection and transmit power values ​​obtained by defuzzification are dynamically adjusted by the system based on these output values: Channel selection: Based on the inference results, the system will select an appropriate channel. For example, in high-interference situations, the system may choose a less commonly used channel, or in low-interference situations, a less-interfered channel.

[0064] Transmit power: adjust the device's transmit power based on real-time channel selection and interference conditions. If the signal interference is strong, the system may increase the transmit power to enhance the signal's anti-interference ability; if the environment is good, reduce the transmit power to reduce interference and save energy.

[0065] The system continuously monitors the communication quality of the network and regularly obtains the latest values ​​of PLFI and BF. These new input values ​​are continuously fed back to the fuzzy control system, and the fuzzification, reasoning, defuzzification and optimization strategy are re-updated, so as to continuously adjust the communication parameters and ensure the stability and efficiency of communication.

[0066] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0067] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0068] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the objects associated with each other are in an "or" relationship, but it may also indicate an "and / or" relationship, which can be understood by referring to the context. A person of ordinary skill in the art can appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this article can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0069] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A dynamic optimization method for anti-interference of star flash connection for multi-device collaboration, characterized by: include: Through multi-channel real-time monitoring technology, the external interference source data and network communication quality data in the Xingshan network are obtained respectively; Substitute the acquired external interference source data and network communication quality data into the data analysis model, and the data analysis model outputs the real-time factors of multi-device collaborative communication respectively; According to the real-time factor, the real-time of the collaborative communication of multiple devices is sorted to generate a real-time list, and the real-time list is divided into a high-interference interval and a low-interference interval according to a preset threshold; For high-interference intervals, erasure coding technology and timing reconstruction mechanism are used to reduce the packet loss ratio. Based on the fluctuation of the packet loss ratio and the real-time factor of the high-interference interval, fuzzy logic is used to dynamically update the optimization strategy and continuously adjust the channel selection and transmission power.

2. The method for dynamic optimization of anti-interference of star flash connection for multi-device collaboration according to claim 1 is characterized in that: Substitute the acquired external interference source data and network communication quality data into the data analysis model, and the data analysis model outputs the real-time factors of multi-device collaborative communication, including: The data transmission interference ratio and the network signal interference degree values ​​are normalized so that they are both between [0,1]. The real-time factor of multi-device collaborative communication is calculated based on the normalized data transmission interference ratio and the network signal interference degree values.

3. The method for dynamic optimization of anti-interference of star flash connection for multi-device collaboration according to claim 2 is characterized in that: The method for obtaining the data transmission interference ratio is as follows: obtaining the signal strength indicator value And create a data set , n is a positive integer, get the maximum signal strength indication value , minimum signal strength indicator value , calculate the average signal strength indicator value , obtain the number of times greater than the average signal strength indication value g, the number of times less than the signal strength indication value p, and calculate the data transmission interference ratio. The calculation expression is: = , where is the data transmission interference ratio.

4. The method for dynamic optimization of anti-interference of star flash connection for multi-device collaboration according to claim 3 is characterized in that: After real-time monitoring and analysis of the communication network signals of each device in the Xingshan network, the network signal interference degree value is obtained. The method for obtaining the network signal interference degree value is: obtain the number of bits with correct transmission results during the data transmission process , and the total number of bits transmitted during data transmission , calculate the data transmission efficiency, the calculation expression is: = , where For data transmission efficiency, The data transmission frequency is used to obtain the standard data transmission efficiency under normal network signal conditions. , calculate the network signal interference value, the calculation expression is: = , where It is the value of network signal interference degree.

5. The method for dynamic optimization of anti-interference of star flash connection for multi-device collaboration according to claim 4 is characterized in that: According to the real-time factor, the real-time of the collaborative communication of multiple devices is sorted to generate a real-time list, and the real-time list is divided into a high-interference interval and a low-interference interval according to a preset threshold, including: sorting the acquired real-time factors of the collaborative communication of multiple devices and generating a real-time list, comparing the real-time factor of the collaborative communication of multiple devices in the real-time list with the preset threshold, if the real-time factor of the collaborative communication of multiple devices in the real-time list is greater than or equal to the preset threshold, dividing it into a low-interference interval; if the real-time factor of the collaborative communication of multiple devices in the real-time list is less than the preset threshold, dividing it into a high-interference interval.

6. The method for dynamic optimization of anti-interference of star flash connection for multi-device collaboration according to claim 5 is characterized in that: By calculating the packet loss ratio fluctuation index, the network stability is quantitatively analyzed, and the optimization strategy is updated in combination with the real-time factor.

7. The method for dynamic optimization of anti-interference of star-flash connection for multi-device collaboration according to claim 6 is characterized in that: The method for obtaining the packet loss ratio fluctuation index is as follows: In the preset time window, count the number of lost data packets L(t) and the total number of data packets T(t), and calculate the loss ratio in each time period: ; Where P(t) is the packet loss ratio at the tth moment. The mean and standard deviation of the packet loss ratio within the fixed time window are calculated. According to the calculated standard deviation of the packet loss ratio σP, the packet loss ratio fluctuation index PLFI is generated. The expression is: .

8. The method for dynamic optimization of anti-interference of star flash connection for multi-device collaboration according to claim 7 is characterized in that: The packet loss ratio fluctuation index and the real-time factor of multi-device collaborative communication are used as the input items of fuzzy logic and divided into different fuzzy sets respectively. Channel selection and transmission power are taken as output items of fuzzy logic and divided into different fuzzy sets; Formulate fuzzy rules to describe the impact of the packet loss ratio fluctuation index and the real-time factor of multi-device collaborative communication on channel selection and transmission power; Reasoning is performed based on fuzzy rules to update the optimization strategy.

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