StarFlash Connection Anti-Interference Dynamic Optimization Method for Multi-Device Collaboration

Abstract

The present invention discloses a SparkLink connection anti-interference dynamic optimization method for multi-device collaboration, specifically relating to the technical field of data transmission. Through 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 to output the real-time factor of multi-device collaborative communication. Sorting is carried out according to the real-time factor, and high and low interference intervals are divided by a preset threshold. For the high interference interval, erasure coding technology and timing reconstruction mechanism are used to reduce the packet loss ratio. At the same time, fuzzy logic is adopted to dynamically adjust channel selection and transmission power. This method can effectively maintain high-precision timing synchronization between devices in application scenarios with high concurrency and multi-device collaboration, optimize communication strategies, improve anti-interference ability and network stability, and ensure the reliability and efficiency of data transmission.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and particularly to a SparkLink connection anti-interference dynamic optimization method for multi-device collaboration. Background Art

[0002] In today's wireless communication environment, multi-device collaborative communication poses higher requirements for stability and anti-interference ability. SparkLink technology, as an emerging short-range wireless communication protocol, has received extensive attention for its high efficiency, low latency, and low power consumption characteristics. However, in a complex wireless environment, signal interference problems remain a key challenge affecting its performance. To improve the stability and anti-interference ability 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 existing technologies have the following deficiencies:

[0004] Due to the dynamic adjustment of the adaptive optimization strategy, it may lead to the failure of timing synchronization between devices. In a high-interference environment, devices need to frequently adjust communication parameters to avoid interference sources, but this dynamic change may disrupt the original timing synchronization mechanism, resulting in an increase in the packet retransmission rate, a decrease in throughput, and even device connection loss in extreme cases. This problem is particularly prominent in application scenarios with high concurrency and multi-device collaboration, such as industrial automation, vehicle networking, 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 SparkLink connection anti-interference optimization methods. Summary of the Invention

[0005] The purpose of the present invention is to provide a SparkLink connection anti-interference dynamic optimization method for multi-device collaboration to solve the deficiencies in the background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions: A SparkLink connection anti-interference dynamic optimization method for multi-device collaboration, including the following steps:

[0007] Through multi-channel real-time monitoring technology, obtain external interference source data and network communication quality data in the SparkLink network respectively;

[0008] Substitute the obtained 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;

[0009] Rank the real-time performance of multi-device collaborative communication according to the real-time factor, generate a real-time list, and divide the real-time list into a high-interference interval and a low-interference interval according to a preset threshold;

[0010] For the high-interference interval, use erasure coding technology and timing reconstruction mechanism to reduce the packet loss ratio, and based on the fluctuation of the packet loss ratio and the real-time factor of the high-interference interval, use fuzzy logic to dynamically update and optimize the strategy, and continuously adjust the channel selection and transmission power.

[0011] Preferably, substitute the obtained external interference source data and network communication quality data into the data analysis model, and the data analysis model respectively outputs the real-time factors of multi-device collaborative communication, including:

[0012] Normalize the data transmission interference ratio and the network signal interference degree value so that they are both in the range of [0,1], and calculate the real-time factor of multi-device collaborative communication according to the normalized data transmission interference ratio and the network signal interference degree value.

[0013] Preferably, the method for obtaining the data transmission interference ratio is: obtain the signal strength indication value , and establish a data set , n is a positive integer, obtain the maximum signal strength indication value , the minimum signal strength indication value , calculate the average signal strength indication value , obtain the number of times g greater than the average signal strength indication value and the number of times p less than the average signal strength indication value, calculate the data transmission interference ratio, and the calculation expression is: = , where is the data transmission interference ratio.

[0014] Preferably, after real-time monitoring and analysis of the communication network signals of each device in the SparkLink network, obtain the network signal interference degree value. The method for obtaining the network signal interference degree value is: obtain the number of correct bits in the transmission result during data transmission , and the total number of bits transmitted during data transmission , calculate the data transmission efficiency, and the calculation expression is: = , where is the data transmission efficiency, is the data transmission frequency, obtain the standard data transmission efficiency under normal network signal conditions , calculate the network signal interference degree value, and the calculation expression is: = , where is the network signal interference degree value.

[0015] Preferably, according to the real-time factor, the real-time performance of multi-device collaborative communication 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 obtained real-time factors of multi-device collaborative communication to generate a real-time list, comparing the real-time factors 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, it is divided 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, it is divided into the high-interference interval.

[0016] Preferably, 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.

[0017] Preferably, the method for obtaining the packet loss ratio fluctuation index is as follows:

[0018] Within a preset time window, count the number of lost packets L(t) and the total number of packets T(t), and calculate the loss ratio for each time period: ; where P(t) is the packet loss ratio at the t-th moment, calculate the mean and standard deviation of the packet loss ratio within a fixed time window, and generate a packet loss ratio fluctuation index PLFI according to the calculated packet loss ratio standard deviation σP, and the expression is: .

[0019] Preferably, the packet loss ratio fluctuation index and the real-time factor of multi-device collaborative communication are used as input items of fuzzy logic, and they are respectively divided into different fuzzy sets;

[0020] The channel selection and transmission power are used as output items of fuzzy logic, and they are divided into different fuzzy sets;

[0021] Formulate fuzzy rules to describe the influence of the packet loss ratio fluctuation index and the real-time factor of multi-device collaborative communication on the channel selection and transmission power;

[0022] Infer according to the fuzzy rules and update the optimization strategy.

[0023] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0024] 1. The present invention introduces multi-channel real-time monitoring technology to dynamically collect external interference source data and network communication quality data, and calculates real-time factors through a data analysis model, which can effectively identify high-interference intervals and low-interference intervals. For high-interference intervals, the erasure code technology and the timing reconstruction mechanism are used to reduce the packet loss rate, and the channel selection and transmission power are dynamically adjusted using fuzzy logic, 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 communication, greatly reducing the problems of packet retransmission and device connection loss caused by interference.

[0025] 2. The present invention not only improves the reliability of communication between devices, but also ensures efficient collaborative operation in a complex interference environment. The introduction of the packet loss ratio fluctuation index enables quantitative analysis and optimization of the network stability, thereby significantly improving the overall network communication throughput and the anti-interference ability of the system, effectively avoiding connection loss in extreme cases. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0027] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0029] Embodiment, please refer to Figure 1 As shown, the SparkLink connection anti-interference dynamic optimization method for multi-device collaboration in this embodiment includes the following steps:

[0030] Obtain external interference source data and network communication quality data in the SparkLink network through multi-channel real-time monitoring technology;

[0031] Substitute the obtained 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;

[0032] Sort the real-time performance of multi-device collaborative communication according to the real-time factor to generate a real-time list, and divide the real-time list into a high-interference interval and a low-interference interval according to a preset threshold;

[0033] For the high-interference interval, use erasure coding technology and timing reconstruction mechanism to reduce the packet loss ratio, and based on the fluctuation of the packet loss ratio and the real-time factor of the high-interference interval, use fuzzy logic on it and dynamically update the optimization strategy to continuously adjust the channel selection and transmission power.

[0034] The multi-channel real-time monitoring technology first uses a multi-spectrum analyzer or a wireless signal monitoring device to comprehensively scan the spectrum in the communication environment. This process includes:

[0035] Frequency scanning: By collecting signals in different frequency bands, identify possible interference sources (such as other wireless devices, industrial devices or environmental noise). These signals can be divided into broadband interference, narrowband interference or burst interference at specific frequencies.

[0036] Intensity evaluation: Monitor the intensity change of the interference source signal, identify the frequency and fluctuation range of the interference, and evaluate its potential impact on the star flash communication. Intensity evaluation can be achieved through the received signal strength indication value. The basic calculation of the received signal strength indication value st is usually automatically completed by wireless hardware or software, but it is estimated based on the relationship between signal power and distance behind. A common calculation method is: ; where: is the received signal power (unit: watt or milliwatt). is the reference power, usually 1 milliwatt (0 dBm).

[0037] Bandwidth analysis: By analyzing the bandwidth characteristics of the interference source, judge its occupancy of the communication bandwidth, and further infer the possible interference degree.

[0038] Time-domain analysis: Through time synchronization, capture the time change of the interference signal in real time, and identify short-term interference or periodic interference sources. Through time-domain analysis, the periodic pattern or burst fluctuation of the interference source appearance can be judged.

[0039] The data of these external interference sources are crucial for identifying the interference types and interference source characteristics in the wireless environment.

[0040] The network communication quality data collects the following key indicators by real-time monitoring and recording the communication performance of each device in the star flash network:

[0041] Signal strength: Evaluate the signal strength between devices, reflecting the quality and stability of the wireless link. Signal strength directly affects the communication coverage and connection stability.

[0042] Latency: Real-time monitor the latency of data packet transmission, and identify latency changes caused by interference or network congestion, etc. Excessive latency may affect data synchronization and real-time performance.

[0043] Packet loss rate: Record the loss situation of data packets, and evaluate 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.

[0044] Throughput: Measure the amount of successful data transmission per unit time, which reflects the efficiency of the communication system. A decrease in throughput may indicate the presence of interference or high network load.

[0045] Connection stability: Monitor the connection holding time of the device and the frequency of disconnections, and identify connection interruptions or frequent reconnections caused by interference.

[0046] 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 status of the communication network and its relationship with interference sources in real time, thus providing data support for subsequent optimization and adjustment.

[0047] In this application, through multi-channel real-time monitoring technology, the SparkLink network can simultaneously collect detailed information of external interference sources (such as interference intensity, frequency, bandwidth and time-domain characteristics) and key data of network communication quality (such as signal strength, latency, packet loss rate, throughput, etc.). The combination of these data can help to comprehensively understand the specific impact of interference on network performance, providing an accurate basis for subsequent dynamic optimization.

[0048] Monitor the data transmission process, judge the interference degree of data transmission efficiency during the data transmission process, and calculate the data transmission interference ratio, including:

[0049] The method for obtaining the data transmission interference ratio is: Obtain the signal strength indication value , and establish a data set , n is a positive integer, obtain the maximum signal strength indication value , the minimum signal strength indication value , calculate the average signal strength indication value , obtain the number of times g greater than the average signal strength indication value, the number of times p less than the average signal strength indication value, calculate the data transmission interference ratio, and the calculation expression is: = , in the formula, is the data transmission interference ratio.

[0050] The larger the data transmission interference ratio, the higher the intensity of the interference signal relative to the effective data, and the more significant the impact on the effective data. In this case, the communication signal may experience severe attenuation or distortion, resulting in packet loss, increased retransmission times, and even connection disconnection, thereby significantly reducing the data transmission efficiency. At this time, the network throughput decreases, the latency increases, and the user experience and communication quality are greatly affected. Therefore, the larger the interference ratio, the more severe the interference degree and the lower the efficiency during the data transmission process.

[0051] The smaller the data transmission interference ratio, the weaker the intensity of the interference signal, the effective signal dominates, the error rate during the data transmission process is lower, and the communication quality is better. At this time, the signal can be transmitted more stably, the packet loss rate is low, and the number of retransmissions is also small, thus improving the data transmission efficiency. The smaller the interference ratio, the more effectively the communication system can utilize the bandwidth, providing higher throughput and lower latency, ensuring a more reliable communication experience.

[0052] After real-time monitoring and analysis of the communication network signals of each device in the SparkLink network, the network signal interference degree value is obtained. The method for obtaining the network signal interference degree value is as follows: Obtain the number of correct bits in the transmission result during the data transmission process , and the total number of bits transmitted during the data transmission process , calculate the data transmission efficiency, and the calculation expression is: = , where, is the data transmission efficiency, is the data transmission frequency, obtain the standard data transmission efficiency under the normal state of the network signal , calculate the network signal interference degree value, and the calculation expression is: = , where, is the network signal interference degree value.

[0053] Substitute the obtained external interference source data and network communication quality data into the data analysis model, and the data analysis model respectively outputs the real-time factors of multi-device collaborative communication, including:

[0054] Normalize the data transmission interference ratio and the network signal interference degree value so that they are both within [0,1], and calculate the real-time factors of multi-device collaborative communication according to the normalized data transmission interference ratio and network signal interference degree value.

[0055] For example, the present invention can use the following formula to calculate the real-time factor of multi-device collaborative communication, and the calculation expression is: ; where, is the real-time factor of multi-device collaborative communication, is the data transmission interference ratio, is the network signal interference degree value, are the weight coefficients of the data transmission interference ratio and the network signal interference degree value (which can be optimized according to experimental experience or machine learning), and both are greater than 0.

[0056] According to the real-time factor, sort the real-time performance of multi-device collaborative communication to generate a real-time list, and divide the real-time list into a high-interference interval and a low-interference interval according to a preset threshold, specifically including:

[0057] Sort the real-time factors of the obtained multi-device collaborative communication to generate a real-time list, compare the real-time factors of the multi-device collaborative communication in the real-time list with the 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, divide it into the 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, divide it into the high-interference interval.

[0058] For the high-interference interval, use the erasure code technology and the timing reconstruction mechanism to reduce the packet loss ratio, and based on the fluctuation of the packet loss ratio and the real-time factor of the high-interference interval, use fuzzy logic on it and then dynamically update and optimize the strategy, continuously adjusting the channel selection and transmission power.

[0059] Error Correction Code (ECC) is a method that ensures the integrity and reliability of data by introducing redundant information in data transmission. Even if some data is lost or incorrect during data transmission, the receiving end can still recover the lost information through the redundant data.

[0060] In a high-interference environment, the communication link is prone to packet loss or damage. Using the erasure code technology, the packet loss rate can be reduced through the following steps:

[0061] Encoding process: At the sending end, the data is encoded by the erasure code to generate packets with redundant information. Common erasure codes include Reed-Solomon code, LDPC code, Turbo code, etc.

[0062] Transmission process: The encoded data is transmitted through an unstable wireless link.

[0063] Decoding process: The receiving end recovers the lost data through the received partial packets and redundant information, reducing the data loss caused by interference.

[0064] The role of the erasure code: Through the transmission of redundant data, it can reduce the need for retransmission in case of packet loss, reduce the packet loss rate, and maintain a high transmission efficiency.

[0065] The timing reconstruction mechanism is mainly used to compensate for the delay changes caused by interference or channel switching, thus avoiding packet sequence disorder or loss. The specific steps are as follows:

[0066] Timing synchronization: In a high-interference environment, due to factors such as signal attenuation and interference, the arrival time of packets may change. The timing reconstruction mechanism corrects the delay by real-time monitoring and predicting the delay fluctuations of packets and using algorithms (such as predictive timing compensation algorithms).

[0067] Packet sequence recovery: In a high-interference environment, some packets may arrive early or late due to excessive delay, causing difficulties in processing at the receiving end. Through the timing reconstruction mechanism, the receiving timing of packets can be adjusted to ensure the sequential continuity of data transmission and avoid data loss or chaos caused by timing problems.

[0068] In a high-interference environment, the packet loss ratio may fluctuate greatly, affecting the stability of the network. 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.

[0069] The packet loss ratio fluctuation index PLFI can be calculated through the following steps:

[0070] Within a preset time window (such as every second, every minute, or each time window), count the number of lost packets L(t) and the total number of packets T(t), and calculate the loss ratio for each time period: ; where P(t) is the packet loss ratio at the t-th moment. Calculate the mean and standard deviation of the packet loss ratio within a fixed time window. Based on the calculated packet loss ratio standard deviation σP, the packet loss ratio fluctuation index PLFI can be generated, and the expression is: ; Through this fluctuation index, the degree of packet loss fluctuation can be quantified. The higher the value, the greater the fluctuation of the packet loss rate and the worse the stability of network communication.

[0071] A low PLFI value indicates that the packet loss rate is relatively stable and the network communication quality is high, which 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 problems such as communication interruption, video stuttering, or data loss. In this case, the system may need further optimization (such as increasing the redundancy degree of erasure codes, enhancing the timing reconstruction ability, etc.).

[0072] Based on the fluctuation of the packet loss ratio and the real-time factor in the high-interference interval, use fuzzy logic to dynamically update and optimize the strategy, and continuously adjust the channel selection and transmission power. Specifically:

[0073] Take the packet loss ratio fluctuation index PLFI and the real-time factor BF of multi-device cooperative communication as the input items of fuzzy logic, and take channel selection and transmit power as the output items of fuzzy logic, and use fuzzy logic to dynamically update and optimize the strategy;

[0074] In a fuzzy logic control system, the input real values need to be converted into fuzzy sets. In this example, 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 it belongs to these levels according to the value of each variable.

[0075] PLFI (Packet Loss Ratio Fluctuation Index): Low: Indicates that the packet loss fluctuation is small and the network is relatively stable. Medium: Indicates that the packet loss fluctuation is medium and the network quality is average. High: Indicates that the packet loss fluctuation is large and the network quality is unstable.

[0076] BF (Real-time Factor): Low interference: Indicates good real-time performance and low interference. Medium interference: Indicates average real-time performance and medium interference level. High interference: Indicates poor real-time performance and high interference.

[0077] Next, a set of fuzzy rules need to be defined. These rules will determine how to adjust the output (channel selection and transmit power) according to the input of the signals (PLFI and BF). The construction of fuzzy rules is based on experience, experiments, or the results of machine learning. The following is a simple example of a rule base:

[0078] Rule 1: If PLFI is high and BF is high, then the channel selection is "poor" and the transmit power is "high".

[0079] Rule 2: If PLFI is low and BF is low, then the channel selection is "good" and the transmit power is "low".

[0080] Rule 3: If PLFI is medium and BF is medium, then the channel selection is "medium" and the transmit power is "medium".

[0081] Rule 4: If PLFI is high and BF is low, then the channel selection is "poor" and the transmit power is "medium".

[0082] Rule 5: If PLFI is low and BF is high, then the channel selection is "medium" and the transmit power is "high".

[0083] Rule 6: If PLFI is low and BF is medium, then the channel selection is "medium" and the transmit power is "low".

[0084] Rule 7: If PLFI is high and BF is medium, the channel selection is "poor" and the transmit power is "high".

[0085] These rules can be refined and adjusted by tuning the optimization objectives and experimental data of the system.

[0086] Fuzzy inference is the core step of a fuzzy logic control system. It infers based on the fuzzified input data and the fuzzy rule base, outputs a fuzzy value, and then obtains a specific control signal through defuzzification. The inference process includes the following steps:

[0087] Based on the input PLFI and BF, evaluate the matching degree of each rule. Determine the membership degree of the input value in each fuzzy set through the "membership function", such as using triangular, trapezoidal and other membership functions. The output of each rule (channel selection and transmit power) will be weighted and calculated according to the membership degree of the input value.

[0088] For each rule, derive the corresponding output fuzzy value according to the membership degree value. For example, if the PLFI of rule 1 is high and the BF is high, then the output of channel selection is "poor" and the output of transmit power is "high". Through the inference process of all rules, multiple output fuzzy values are obtained.

[0089] The result of fuzzy inference is a fuzzy value. In order to be able to perform specific control operations, the fuzzy value needs to be converted into a specific numerical value. The commonly used methods of defuzzification are the Centroid Method or the Max Membership Method. Centroid Method: Obtain the final output value by weighted averaging the values of all output fuzzy sets. For example, if there are multiple output fuzzy values, use the membership degree to weight and obtain a comprehensive result.

[0090] Through the channel selection and transmit power values obtained by defuzzification, the system dynamically adjusts the channel selection and transmit power according to these output values:

[0091] Channel selection: According to the inference result, the system will select a suitable channel. For example, in a high interference situation, the system may select a less commonly used channel, or select a channel with less interference in a low interference situation.

[0092] Transmit power: Adjust the transmit power of the device according to the real-time channel selection and interference situation. If the signal interference is strong, the system may increase the transmit power to enhance the anti-interference ability of the signal; if the environment is good, the transmit power is reduced to reduce interference and save energy.

[0093] 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 into the fuzzy control system for re-fuzzification, inference, defuzzification, and update of the optimization strategy, thereby continuously adjusting the communication parameters to ensure the stability and efficiency of communication.

[0094] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0095] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using 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 processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains a set of one or more available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0096] It should be understood that the term "and / or" in this text is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. Additionally, the character " / " in this text generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, and the specific meaning can be understood by referring to the context. Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this text can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians 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.

[0097] As described above, this is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered within the protection scope of this 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. The method for obtaining the packet loss ratio fluctuation index is as follows: within a preset 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 is generated. The expression is: .

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 1 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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