Equipment management method for wireless electroencephalogram equipment acquisition system

Abstract

The invention relates to the technical field of equipment management, in particular to an equipment management method for a wireless electroencephalogram acquisition system, which comprises an upper computer, a router, a database, electroencephalogram equipment and a state monitoring module. According to the system management method provided by the invention, the collected data is subjected to preprocessing such as analysis, accounting and the like; comparing the calculated transmission rate with a preset threshold value to judge a data acquisition management state; the average broadband occupancy and the amended idle ratio are adjusted based on the judgment result, and the minimum distribution distance is further determined according to the mediation result, unified processing of the transmission rate of the electroencephalogram equipment and optimization of analysis performance are achieved, system energy consumption is reduced, and the effectiveness guarantee of data and the resource utilization rate are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of equipment management methods, and in particular to an equipment management method for a wireless EEG device acquisition system. Background Technology

[0002] The transmission speed and battery life of EEG acquisition systems are limited, and subjects cannot maintain free movement for extended periods while wearing the device, causing significant inconvenience for acquisition and maintenance. This leads to static transmission strategies, preventing the system from optimizing based on feedback, and compromising the reliability and stability of the transmission rate. To avoid potential risks, quantify the transmission rate effects of different management measures, optimize system technology, and place higher demands on the scientific rigor and comparability of acquisition and adjustment results. Many technical solutions offer simple star network topologies for communication, and many technicians are beginning to choose laboratory analysis, which can promptly detect anomalies and significantly improve the overall data transmission rate, becoming a necessary and effective method. Therefore, traditional communication between the host computer and the device remains one of the current management methods for EEG acquisition systems.

[0003] However, existing management models mostly adopt a unified and simple communication connection, failing to consider the multi-interface dynamic mode of complex application scenarios. This involves multiple key parameters such as status monitoring, signal strength, electrode detachment, battery power, and network environment. Existing technologies are mostly static, single-interface isolated monitoring, and most devices can only be used to transmit a large amount of invalid and low-quality data, resulting in resource waste and failing to systematically capture the dynamic correlation between various parameters.

[0004] Meanwhile, existing transmission models have many shortcomings. Key parameters involved in the system often rely on empirical settings or fixed standard values, lacking the ability to perceive the device's own status in real time, and failing to form a dynamic adjustment mechanism, thus reducing the data transmission rate.

[0005] Chinese Patent Publication No. CN115022852B discloses an electronic device and method for controlling Bluetooth transmission rate, including developing a BLE driver on a host computer and providing an interface through a communication module, using an adjustable communication interface to acquire data in real time, and setting up a binding system for screen projection mode, thereby improving Bluetooth transmission rate based on reduced screen projection connection latency.

[0006] Therefore, although it can communicate with multiple data acquisition terminals, it still has the following problems: 1. This solution only uses a single intelligent calculation formula for data processing. When the collected data contains outliers or missing values, it cannot intelligently optimize the intelligent calculation formula, which leads to the inability to effectively collect data from various wireless devices, thereby reducing the data transmission rate for wireless acquisition devices. 2. This scheme uses fixed transmission commands. When the distribution distance of each wireless device changes, there will be delays or deviations in data transmission. This will affect the working status of the subsequent status monitoring module and further reduce the data transmission rate of the wireless acquisition device. Summary of the Invention

[0007] To address this issue, the present invention provides a device management method for a wireless EEG acquisition system, which overcomes the problem of reduced data transmission rate in the prior art due to network environment obstacles caused by the inability to generate corresponding update instructions based on changes in data quantity.

[0008] To achieve the above objectives, the present invention provides a device management method for a wireless EEG device acquisition system, comprising: Use several routers to build a local area network; Connect each EEG device and each host computer to the local area network to enable each EEG device and each host computer to establish a network transmission channel; The acquired state parameters generated during the operation of each of the brain electrical devices and the user's brain electrical signals are transmitted to each of the host computers. The state parameters for a single brain electrical device include the electrode impedance data, battery power data and signal strength indication of the brain electrical device. The transmission rate of each of the aforementioned EEG devices during the transmission process was collected; The comprehensive transmission rate of each EEG device during transmission is calculated based on the acquired transmission rate. Based on the comprehensive transmission rate obtained from each transmission rate, it is determined whether the management of each EEG device in the local area network complies with the standard. If it is determined that it does not comply with the standard, corresponding processing instructions are generated according to the determined reasons, including issuing notifications and adjustment instructions. The notifications include device replacement notifications and device restart notifications. The acquisition method based on the transmission rate adjusts the maximum allowable idle ratio for each of the aforementioned EEG devices, wherein the maximum idle ratio is the maximum allowable value of the ratio of the number of idle EEG devices to the total number of EEG devices in the local area network. Re-testing is performed based on the obtained adjusted maximum allowable idle ratio, and continuous testing is conducted if the standard is met; Based on the obtained adjusted maximum allowable idle ratio, a retest is performed. If it still does not meet the standard, the corresponding handling method is selected, including maintenance notification and network update instruction.

[0009] Furthermore, the process by which the host computer calculates the overall transmission rate includes: The host computer calculates the overall transmission rate v of the system using the following formula: ; in, Let be the real-time transmission rate of the i-th EEG device. n is the total number of online devices. Let be the transmission weighting coefficient of the i-th EEG device, which is calculated by the following formula: ; in, Preset weighting coefficients for signal quality. Let be the signal quality index of the i-th EEG device. Preset weighting coefficients for power quality. Let be the power supply quality index of the i-th EEG device. Preset weighting coefficients for connection stability Let i be the connection stability index of the i-th EEG device. is a standardized constant.

[0010] The host computer acquires environmental factors for each EEG device and constructs an environmental influence coefficient k for each device based on the acquired environmental factors. Furthermore, the host computer calculates the transmission rate of each device for the i-th real-time using the following formula. : ; in, The standard transmission rate is pre-stored in the database; the environmental impact coefficient k is determined by the real-time signal strength coefficient between the device and the router. Infinite interference coefficient in the same frequency band as the space where the equipment is located Together, k is determined and calculated using the following formula: ; in, The preset signal strength weighting factor, The signal strength coefficient is a preset interference suppression weight factor. The interference coefficient is calculated using the signal strength indicator mapping value. The background noise intensity of the channel used by the device is calculated from the background noise intensity detected by the host computer or router.

[0011] Furthermore, the process by which the host computer determines whether the management of each EEG acquisition device complies with the standard based on the overall transmission rate v includes: The host computer calculates the overall transmission rate v; The obtained comprehensive transmission rate v is compared with the preset comprehensive transmission rate stored in the database. Perform a comparison; Based on the comparison results, determine whether the management of each EEG acquisition device complies with the standards; If the transmission rate for each EEG device is determined to meet the standard, continuous testing is performed; if the transmission rate for each EEG device is determined to be non-compliant, the reason for non-compliance is determined based on the variance of the transmission rate.

[0012] Furthermore, the host computer determines the variance of the transmission rate. The process of determining why the management of various EEG acquisition devices does not meet standards includes: The host computer calculates the variance of the transmission rate of each of the EEG devices. ; The variance of the transmission rate of each of the EEG devices was obtained. The pre-stored variance in the database A comparison was conducted to determine why the management of each EEG device did not meet the standards. In determining the cause of the malfunction, the cause of the malfunction of the EEG device is determined based on the time-transmission rate curve of each EEG device. If the cause is determined to be that the local area network environment does not meet the standards, the environmental cause will be determined based on the broadband usage.

[0013] Furthermore, the process by which the host computer determines the cause of the EEG device malfunction based on the integral of the time-transmission rate curve includes: EEG devices with a transmission rate lower than the standard rate pre-stored in the database are marked as labeled EEG devices, and when it is determined that the cause is a faulty EEG device, the transmission rate in a single detection cycle is calculated based on each of the acquired labeled EEG devices. Plot the time-transfer rate curves for each labeled EEG device, and calculate the integral for each individual time-transfer rate curve; The integral of the obtained single time-transmission rate curve is compared with the preset integral stored in the database to determine the cause of failure of the labeled EEG device corresponding to the curve. A charging notification will be sent if the cause is determined to be insufficient battery power. If the cause is determined to be a non-compliant network environment, optimize the network transmission parameters.

[0014] Furthermore, the process by which the host computer determines the reason why the network environment does not meet the standard based on the Received Signal Strength Indicator (RSSI) includes: Acquire the Received Signal Strength Indicator (RSSI) and compare the acquired RSSI with a preset Received Signal Strength Indicator stored in the database. Perform a comparison; The environmental reasons for non-standard broadband usage were determined based on the comparison results. Optimize network transmission parameters if the environmental cause is determined to be the network environment. If the environmental cause is determined to be that the number of EEG devices connected does not meet the standard, the maximum allowable idle ratio is adjusted. The maximum allowable idle ratio is the maximum value of the ratio of the number of idle devices to the total number of connected devices.

[0015] Furthermore, the process by which the host computer determines the reasons why the network environment does not meet the standard based on the channel interference index I includes: The channel interference index is obtained by looking up a table based on the detected noise-to-interference ratio. ; The obtained channel interference index Compared with the preset channel interference index stored in the database Perform a comparison; The channel multiplexing ratio is determined based on the comparison results. And the channel multiplexing ratio The increase in magnitude and the channel interference index Positively correlated; wherein, the channel multiplexing ratio It is the ratio of the spacing between co-frequency cells to the cell radius.

[0016] Furthermore, the host computer determines the bandwidth difference based on the bandwidth difference. The process of determining why a network environment does not meet standards includes: Based on the actual available broadband B and standard available broadband within the local area network. The difference The bandwidth difference is obtained; and the obtained bandwidth difference Difference between the preset bandwidth and the database Perform a comparison; The adjusted channel multiplexing ratio was corrected based on the comparison results. And channel multiplexing ratio The reduction rate and the bandwidth difference It is negatively correlated.

[0017] Furthermore, the host computer determines whether the corrected maximum allowable idle ratio meets the standard based on the usage time, and the process includes: The actual usage time t for each of the aforementioned EEG devices is obtained through the status monitoring module; The actual usage time t is compared with the preset usage time T stored in the database; Determine the target adjustment coefficient based on the comparison results. The maximum allowable idle ratio P is used to adjust the modified maximum allowable idle ratio, and the increase in the maximum allowable idle ratio P is positively correlated with the usage duration t.

[0018] Furthermore, if the host computer completes the adjustment of the EEG device and network environment and recalculates that the calculation of the transmission rate v for the online EEG device does not meet the standard, it determines that the reason for non-compliance is insufficient network capacity, and generates an expansion router instruction to optimize the network update. The expansion process includes: The spatial distribution density of EEG devices is calculated by using the real-time coordinates of all current EEG devices obtained from the status monitoring module. ; The obtained spatial distribution density The preset standard spatial distribution density is stored in each of the aforementioned databases. Perform a comparison; Based on the comparison results, a distribution adjustment coefficient λ is determined to adjust the minimum distribution distance h of each of the aforementioned EEG devices, and the reduction in the minimum distribution distance h is related to the spatial distribution density. It is negatively correlated.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: by setting up multiple host computers, which analyze the data transmission rate within each acquisition cycle, the present invention can effectively determine whether there is a problem with the EEG equipment or network environment limitations within the acquisition area. At the same time, the host computers can also output corresponding processing and adjustment methods according to the determined actual situation, thereby effectively eliminating the situation where the data transmission rate is too slow due to equipment damage or network congestion during the transmission process. While effectively improving the transmission rate for different devices, the present invention also effectively avoids the impact of different network environments on the stability of data transmission, thereby effectively improving the data transmission rate of the EEG acquisition system described in the present invention.

[0020] Furthermore, the host computer described in this invention can achieve comprehensive optimization and adjustment of network resources and device operating status by coordinating and analyzing the number of EEG devices, electrode resistance, and battery power. This allows it to prioritize data from devices with good signal quality and sufficient power, thereby effectively improving the overall signal transmission efficiency and data validity under multi-device operating conditions, and thus effectively ensuring the long-term stable and efficient operation of the system.

[0021] Furthermore, the host computer of this invention calculates the actual transmission rate, environmental influence coefficient, and transmission weight coefficient of the EEG device using formulas, which can intuitively quantify the collected data and quickly determine the actual status of the EEG device's acquisition process in this cycle. Through the quickly determined results, the host computer can achieve dynamic evaluation of the collected data and effectively output corresponding processing decisions. While further improving the overall data evaluation of each EEG acquisition device, it also further avoids the impact of the network environment on the data transmission rate, thereby further improving the data transmission rate of the EEG acquisition system described in this invention.

[0022] Furthermore, the host computer of the present invention determines whether the management of each EEG acquisition device complies with the standard by comparing the obtained comprehensive transmission rate v with the standard transmission rate pre-stored in the database. It also continuously monitors the transmission rate of each EEG device if it is determined that the transmission rate complies with the standard; and determines the reason for non-compliance based on the variance of the transmission rate if it is determined that the transmission rate of each EEG device does not comply with the standard. This further makes the standard compliance determination result more scenario-based and intelligent, effectively avoiding misjudgment and omission of the transmission rate by the host computer under a single fixed threshold, while further improving the data transmission rate of the EEG acquisition system described in the present invention.

[0023] Furthermore, the host computer of the present invention obtains the variance of the transmission rate of each EEG device. The pre-stored variance in the database The comparison results determine the reasons for non-compliance with standards, and can quickly determine whether the variance of the transmission rate of each EEG device within the acquisition period is qualified. The host computer can effectively determine the corresponding processing decision based on the obtained comparison results, thereby ensuring that more accurate data can be obtained after updating the processing decision. While further improving accuracy, it further avoids the occurrence of calculation errors, thereby further improving the data transmission rate of the EEG acquisition system described in this invention.

[0024] Furthermore, when the host computer of the present invention determines that the cause is a faulty EEG device, it compares the integral of the acquired time-transmission rate curve with the integral of the standard time-transmission rate curve pre-stored in the database to determine the cause of the EEG device malfunction. Based on the determined cause, it generates a corresponding processing method, including issuing a charging notification if the cause is insufficient battery power, and issuing a notification to optimize network transmission parameters if the cause is a network environment that does not meet the standard. This effectively avoids deviations in the integral of the time-transmission rate curve caused by a single, fixed threshold being too small. While further improving the completeness of the data processing, it also accurately determines the range of processing methods, thereby further improving the data transmission rate of the EEG acquisition system described in the present invention.

[0025] Furthermore, the host computer of the present invention is also used to compare the acquired Received Signal Strength Indicator (RSSI) with a preset Received Signal Strength Indicator (RSSI) stored in the database. By comparing and determining the environmental reasons for non-standard bandwidth usage based on the comparison results, the system can intuitively generate corresponding processing methods for the identified environmental reasons. Simultaneously, it pre-stores multiple standard transmission rates to generate corresponding instructions for different network environments. This includes optimizing network transmission parameters when the environmental reason is determined to be a network environment issue, or correcting the maximum allowable idle ratio when the environmental reason is determined to be a non-standard number of EEG device connections. This further optimizes the local area network environment, making the adjustment process more refined and scenario-based. While further improving processing accuracy, it also avoids deviations in the comparison results, thereby further improving the data transmission rate of the EEG acquisition system described in this invention.

[0026] Furthermore, the host computer of the present invention, based on the acquired channel interference index... Compared with the preset channel interference index stored in the database The comparison results determine the adjustment coefficient m, which is used to adjust the channel reuse ratio Q. This can effectively improve the decision-making efficiency of the system described in this invention under different network environments, thereby effectively avoiding communication obstacles caused by excessive interference. It further improves the compatibility of decision-making for transmission on different channels, thereby further improving the data transmission rate of the EEG acquisition system described in this invention.

[0027] Furthermore, the host computer of the present invention, based on the actual available bandwidth B and the standard available bandwidth within the local area network, The difference The bandwidth difference is obtained, and the obtained bandwidth difference is... Difference between the preset bandwidth and the database By comparing the results, the correction coefficient can be determined intuitively. This is used to correct the adjusted channel reuse ratio Q, thereby ensuring adaptation to the current bandwidth occupancy and obtaining a more accurate correction result. The host computer can dynamically calculate and obtain the bandwidth difference under the current situation, thereby further intelligently judging the relationship between the current channel reuse ratio being excessive, constant, or insufficient. While further improving the system agility, it further avoids resource waste, thereby further improving the data transmission rate of the EEG acquisition system described in this invention.

[0028] Furthermore, the host computer of the present invention corrects the target idle ratio by comparing the actual usage time of each device with the preset usage time stored in the database. Simultaneously, it stores multiple preset values ​​in the database. The host computer adjusts the corrected maximum allowable idle ratio using a target adjustment coefficient, thereby intuitively determining whether the device usage time affects the idle ratio. By adjusting the maximum allowable idle ratio to the corresponding value, more accurate calculation results are obtained, and corresponding processing decisions are generated, ensuring more accurate data acquisition. This further improves response speed and avoids deviations in comparison results, thereby further improving the data transmission rate of the EEG acquisition system described in the present invention.

[0029] Furthermore, when the host computer of the present invention completes the adjustment of the EEG device and network environment and re-determines that the calculation of the transmission rate v for the online EEG device does not meet the standard, it determines that the reason for non-compliance is insufficient network capacity. Therefore, it generates corresponding optimization instructions for network updates and calculates the spatial distribution density ρ of the EEG device based on the real-time coordinates of all current EEG devices, and compares this with the preset standard spatial distribution density stored in each database. The comparison results reduce the maximum distribution distance of each EEG device, further effectively avoiding the situation where the transmission rate is reduced due to the excessive maximum distribution distance of the initial acquisition device. While ensuring the rationality of the scheme in the arrangement of the acquisition device, the accuracy of the acquired data is effectively improved, thereby further improving the data transmission rate of the EEG acquisition system according to the scheme of the present invention. Attached Figure Description

[0030] Figure 1 This is a structural block diagram of the device management system of the device management method for the wireless EEG acquisition system described in this invention; Figure 2 The flowchart below shows the device management method for a wireless EEG acquisition system according to the present invention. Figure 3 This is a flowchart illustrating the system of the present invention determining whether the transmission rate for each EEG device conforms to the standard based on the actual transmission rate. Figure 4 This is an optimized flowchart of the data processing system described in this invention, which determines the data processing based on the variance of the transmission rates of each EEG device. Figure 5 This is a flowchart illustrating the system parameter optimization described in this invention. Detailed Implementation

[0031] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0032] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0033] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0034] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0035] It should be noted that the data in this embodiment are all derived from a comprehensive analysis and evaluation of historical test data and corresponding historical test results from the system described in this invention over the three months prior to this test. Before this test, the system described in this invention comprehensively determines the preset values ​​stored in the database based on the analysis results of 25,863 cumulative tests over the previous three months and the processing results after handling 19,584 specific cases. Those skilled in the art will understand that the system described in this invention can determine the above-mentioned parameters for a single item by selecting the value with the highest proportion based on the data distribution as the preset standard parameter, using weighted summation to obtain the value as the preset standard parameter, substituting each historical data point into a specific formula and using the value obtained by the formula as the preset standard parameter, or other selection methods, as long as the system described in this invention can clearly define different specific situations in the single-item judgment process through the obtained values.

[0036] Please see Figure 1 The diagram shown is a structural block diagram of a device management system using the device management method for a wireless EEG acquisition system according to the present invention. The device management system includes several routers, several EEG devices, a status monitoring module, several host computers, and a database. The router is used to build a data communication network channel by establishing a wireless local area network; The EEG devices are located within the wireless local area networks of each router, and are used to transmit the status parameters of each EEG device and the collected EEG signals of the user. The status parameters of a single EEG device include the electrode impedance data, battery power data and signal strength indication of the EEG device. The status monitoring module is connected to each router to collect the data transmission rate of each EEG device during operation. The host computer is connected to each of the EEG devices and the status monitoring module, respectively, to receive the status parameters and EEG signals output by each EEG device and the data transmission rate output by the status monitoring module; the host computer is also used to determine whether the management of each EEG device in the local area network complies with the standard based on the comprehensive transmission rate obtained based on each transmission rate, and, if it is determined that it does not comply with the standard, to generate corresponding processing instructions based on the determined reasons, including issuing a notification, determining the data collection method of the status monitoring module for the transmission rate, or determining the maximum allowable idle ratio for each EEG device, wherein the notification includes device replacement notification and device restart notification, and the maximum idle ratio is the maximum allowable value of the ratio of the number of idle EEG devices to the total number of EEG devices in the local area network; The database is connected to each of the host computers and is used to receive and store the status parameters, EEG signals, transmission rates and overall transmission rates output by each host computer.

[0037] Specifically, during operation, the system of the present invention collects corresponding data to be monitored within the acquisition cycle of each of the EEG devices, and transmits the collected parameters to the host computer through the router. The host computer preprocesses the received data, calculates and compares the preprocessed data, determines whether the transmission rate of the online EEG devices meets the standard, and generates corresponding processing instructions based on the determined reasons if the data does not meet the standard. The host computer then performs corresponding operations based on the received instructions.

[0038] Specifically, the EEG device includes a signal acquisition front-end, a main control and processing unit, a power management unit, a wireless communication module, and a status monitoring and auxiliary device; It is understood that the signal acquisition front end is an electrode array device used to acquire basic parameters of EEG signals in real time; the main control and processing unit is a main processor used to amplify and convert data in an analog front end; the power management unit is used to provide power to the device; the wireless communication module is used to send the processed data to the host computer; the status monitoring and auxiliary device is integrated in the analog front end chip, which obtains the common-mode rejection ratio of the device by measuring the interference signal at the input end. Specifically, the signal strength indication mainly refers to the relative positional relationship between the router and the device, and a stable and high signal strength is the basis for ensuring high-speed and reliable transmission of EEG data; It is understood that the signal strength indication between routers is an indicator that measures the power of the received radio signal, and in this system it specifically refers to the strength of the wireless signal received by the antenna of the EEG acquisition device from the router or host computer. Specifically, the system described in this invention stores and manages the collected data and processing results, and constructs a database that can be continuously updated.

[0039] Please see Figure 2 The diagram shown is a flowchart of the device management method of the system described in this invention, the device management method comprising: Specifically, a local area network is built using several routers; Connect each EEG device and each host computer to the local area network to enable each EEG device and each host computer to establish a network transmission channel; The acquired state parameters generated during the operation of each of the brain electrical devices and the user's brain electrical signals are transmitted to each of the host computers. The state parameters for a single brain electrical device include the electrode impedance data, battery power data and signal strength indication of the brain electrical device. The transmission rate of each of the aforementioned EEG devices during the transmission process was collected; The comprehensive transmission rate of each EEG device during transmission is calculated based on the acquired transmission rate. Based on the comprehensive transmission rate obtained from each transmission rate, it is determined whether the management of each EEG device in the local area network complies with the standard. If it is determined that it does not comply with the standard, corresponding processing instructions are generated according to the determined reasons, including issuing notifications and adjustment instructions. The notifications include device replacement notifications and device restart notifications. The acquisition method based on the transmission rate adjusts the maximum allowable idle ratio for each of the aforementioned EEG devices, wherein the maximum idle ratio is the maximum allowable value of the ratio of the number of idle EEG devices to the total number of EEG devices in the local area network. Re-testing is performed based on the obtained adjusted maximum allowable idle ratio, and continuous testing is conducted if the standard is met; Based on the obtained adjusted maximum allowable idle ratio, a retest is performed. If it still does not meet the standard, the corresponding handling method is selected, including maintenance notification and network update instruction.

[0040] Please see Figure 3As shown, this is a flowchart illustrating the process by which the system of the present invention determines whether the transmission rate of each EEG device conforms to the standard based on the actual transmission rate. The process of determining whether the data collection and calculation conforms to the standard based on the comparison results of the actual transmission rate and the comparison results of the preset transmission rate stored in the database includes: Specifically, the embodiments of the present invention set the comprehensive transmission rate. ,in, Let be the real-time transmission rate of the i-th EEG device. , where n is the total number of online devices, and is the transmission weight coefficient of the i-th EEG device. ,in, Preset weighting coefficients for signal quality. Let be the signal quality index of the i-th EEG device. Preset weighting coefficients for power quality. Let be the power supply quality index of the i-th EEG device. Preset weighting coefficients for connection stability Let i be the connection stability index of the i-th EEG device. This is a standardized constant. The host computer acquires environmental factors for each EEG device and constructs an environmental influence coefficient k for each device based on these acquired environmental factors. Furthermore, the host computer acquires the real-time transmission rate of each device for i. ,in, The standard transmission rate is pre-stored in the database; the environmental impact coefficient e is jointly determined by the real-time signal strength coefficient er between the device and the router and the co-frequency infinite interference coefficient et of the space where the device is located. ,in, The preset signal strength weighting factor, The signal strength coefficient is a preset interference suppression weight factor. The interference coefficient is calculated using the signal strength indicator mapping value. The signal strength coefficient is calculated from the background noise intensity of the channel used by the device detected by the host computer or router. In this embodiment of the invention, a preset weighting coefficient is read from the system configuration data by the host computer, wherein the weighting coefficient is... Interference suppression coefficient ; Understandably, in actual processing, signal strength has the greatest impact on the environmental impact coefficient, therefore it has the largest weighting, while interference suppression accounts for a smaller proportion. The signal strength coefficient is used in conjunction with historical data to determine its weighting. Interference suppression coefficient Assigning weights can yield more accurate calculation results; In this embodiment of the invention, a preset weighting coefficient is read from the system configuration data by the host computer, wherein the preset weighting coefficient for signal quality is... Preset weighting coefficient for power quality Preset weighting coefficients for connection stability Standardization constant The standard transmission rate was calculated based on the correction factor of the online EEG device. .

[0041] Understandably, in actual calculations, the signal quality index has the greatest impact on transmission rate, thus receiving the largest weighting coefficient. The power supply quality index and connection stability index have smaller weighting coefficients, which are determined by combining historical data and using a preset weighting coefficient for signal quality. Preset weighting coefficient for power quality Preset weighting coefficients for connection stability Assigning weights can yield more accurate calculation results; Specifically, the process by which the host computer determines whether the management of each EEG acquisition device complies with the standard based on the overall transmission rate v includes: The host computer will calculate the comprehensive transmission rate v and compare it with the preset comprehensive transmission rate stored in the database. A comparison is conducted, and the results are used to determine whether the management of each EEG acquisition device complies with the standards. If the transmission rate of each EEG device is found to be compliant with the standards, continuous monitoring is performed. If the transmission rate of each EEG device is found to be non-compliant, the reasons for non-compliance are determined based on the variance of the transmission rate.

[0042] In this embodiment of the invention, the host computer reads a preset standard transmission rate from the system configuration data. .

[0043] If the transmission rate is greater than the standard transmission rate The host computer determines that the calculation for the online EEG device meets the standard; If the transmission rate is less than or equal to the standard transmission rate The host computer determines that the calculation for the online EEG device does not meet the standard, and determines the reason for non-compliance based on the variance of each transmission rate.

[0044] It is understood that the database pre-stores minimum allowable transmission rate thresholds for different types of data. Therefore, the above-mentioned assignment of the standard transmission rate is only a preferred embodiment of the system of the present invention. The present invention does not impose specific restrictions on the value of the preset transmission rate, as long as the comparison result between the obtained transmission rate and the preset standard transmission rate can directly characterize the analysis situation of the host computer.

[0045] Please see Figure 4 As shown, this is an optimized flowchart of the system of the present invention for determining data processing based on the transmission rate of each EEG device. The process includes: The host computer will calculate the variance of the transmission rate of each EEG device. The pre-stored variance in the database A comparison is performed, and the reasons why the management of each EEG device by the host computer does not meet the standards are determined based on the comparison results. In this embodiment, the variance is preset. ; If the variance Greater than the preset variance The host computer determines that the standard is not met because, in the case of a faulty EEG device, the cause of the fault is determined based on the time-transmission rate curve of each EEG device, and instructions are generated to optimize the processing method for device detection. If the variance Less than or equal to the preset variance The host computer determines that the network does not meet the standard because the local area network environment does not meet the standard. The cause is determined based on the bandwidth usage, and instructions are generated to optimize the environmental problem in the network area. Specifically, the process of determining the cause of a malfunction in an EEG device based on the integral of the time-transmission rate curve in this embodiment of the invention includes: EEG devices with transmission rates lower than the pre-stored standard rates in the database are designated as marked EEG devices. When the cause is determined to be a faulty EEG device, the transmission rate within a single detection cycle is calculated based on the acquired marked EEG devices. Time-transmission rate curves are plotted for each marked EEG device, and the integral of each individual time-transmission rate curve is calculated. The integral of the obtained individual time-transmission rate curve is then calculated. Compared with the preset integrals stored in the database A comparison is performed to determine the cause of the malfunction of the labeled EEG device corresponding to the curve, and a corresponding processing method is generated based on the determined cause, including issuing a charging notification if the cause is determined to be insufficient battery power, or optimizing network transmission parameters if the cause is determined to be a network environment that does not meet the standards; in this embodiment, a preset integral value is used. ; If the integral value Greater than the preset integral value The host computer determines that the device does not meet the standard because the brainwave device has insufficient power, and generates a charging notification command and disconnection processing for the brainwave device. If the integral value Greater than or equal to the preset integral value The host computer determines that the network environment does not meet the standard and generates instructions to optimize network transmission parameters. Specifically, the process of determining the reason why the network environment does not meet the standard based on the Received Signal Strength Indication (RSSI) in this embodiment of the invention includes: The host computer is also used to compare the acquired Received Signal Strength Indicator (RSSI) with a preset Received Signal Strength Indicator stored in the database. A comparison is performed, and the environmental reasons for non-compliance with broadband usage standards are determined based on the comparison results. Corresponding processing methods are generated based on the determined environmental reasons, including optimizing network transmission parameters when the environmental reason is determined to be a network environment issue, or correcting the maximum allowable idle ratio when the environmental reason is determined to be a non-compliance with EEG device access standards. The maximum allowable idle ratio is the maximum value of the ratio of the number of idle devices to the total number of connected devices. In this embodiment of the invention, a preset received signal strength indicator is used. ; If the Received Signal Strength Indicator (RSSI) is greater than the preset Received Signal Strength Indicator (RSSI) The host computer determines that the standard is not met because there are obstacles in the network environment, and generates instructions to optimize the channel interference method for the network environment. If the Received Signal Strength Indicator (RSSI) is less than or equal to the preset Received Signal Strength Indicator (RSSI) The host computer determines that the standard is not met because the number of connected EEG devices exceeds the preset number stored in the database, and generates instructions to optimize the idle ratio of the devices, thereby optimizing resource allocation; wherein, the maximum allowable idle ratio of the devices is the maximum value of the ratio of the number of idle devices to the total number of connected devices. It is understood that the reason why the host computer uses the received signal strength indicator to determine non-compliance is that the received signal strength indicator reflects the underlying physical connection quality of the wireless circuit, and it provides a real-time, quantitative indicator for zoning and summarizing the reasons.

[0046] Please see Figure 5 As shown, it is a flowchart of the system parameter optimization described in this invention. The process of determining whether the data collection and calculation meet the standard based on the comparison result of the channel interference index and the preset channel interference index pre-stored in the database includes: Specifically, in this embodiment of the invention, the host computer uses the channel interference index... The process of determining why a network environment does not meet standards includes: The host computer obtains the channel interference index by looking up a table based on the detected interference-to-noise ratio. and the obtained channel interference index The channel multiplexing ratio Q is adjusted by comparing it with the preset channel interference index L stored in the database and determining the adjustment coefficient m based on the comparison result. The increase in the channel multiplexing ratio Q is proportional to the increase in the channel interference index. Positively correlated; wherein, the channel reuse ratio Q is the ratio of the spacing between co-frequency cells to the cell radius; the channel interference index is obtained by looking up a table established experimentally based on a signal strength indication correspondence table; in this embodiment of the invention, the first preset channel interference index... Second preset channel interference index ; If the channel interference index If the interference index L2 is greater than the second preset channel interference index L2 stored in the database, the data analysis module determines to use the third adjustment coefficient m3 to adjust the channel multiplexing ratio Q. If the channel interference index If the channel multiplexing ratio Q is less than or equal to the second preset channel interference index L2 and greater than the first preset channel interference index L1 stored in the database, the data analysis module determines to adjust the channel multiplexing ratio Q using the second adjustment coefficient m2. If the channel interference index If the value is less than or equal to the first preset channel interference index L1, the data analysis module determines to adjust the channel multiplexing ratio Q using the first adjustment coefficient m1. When the host computer adjusts the channel multiplexing ratio Q using the nth processing adjustment coefficient mn, where n = 1, 2, 3, the adjusted channel multiplexing ratio is set. .

[0047] It is understood that the host computer of the present invention re-determines whether the system's overall transmission rate meets the standard based on the channel switching, and issues an instruction to make the brainwave device switch to another router if it is determined that the standard is not met. Specifically, embodiments of the present invention are based on broadband difference. The process of determining why a network environment does not meet standards includes: The host computer compares the actual available bandwidth B and the standard available bandwidth within the local area network. The difference The bandwidth difference is obtained, and the obtained bandwidth difference is... Difference between the preset bandwidth and the database Perform a comparison and determine the correction coefficient based on the comparison results. Used to correct the adjusted channel reuse ratio Q, and the reduction in the channel reuse ratio Q is related to the bandwidth difference. Negatively correlated; wherein, in the embodiments of the present invention, the first preset broadband difference value Second preset broadband difference Correction factor , ; If the actual available bandwidth B is greater than the second preset bandwidth difference value stored in the database. The data analysis module determines to use a third correction coefficient. Adjusted channel reuse ratio ; If the actual available bandwidth B is less than or equal to the second preset bandwidth difference And greater than the first preset bandwidth difference value pre-stored in the database. The data analysis module determines to use the second correction coefficient. Corrected channel multiplexing ratio ; If the actual available bandwidth B is less than or equal to the first preset bandwidth difference The data analysis module determines to use the first correction coefficient. Corrected channel multiplexing ratio ; It is understood that the reason why the host computer in this invention uses the device bandwidth difference calculation to adjust the idle ratio is that the bandwidth difference can measure the proportion of devices entering a low-power sleep state, which is a preventive management measure of the system in a complex network environment; the initial effect achieved after adjustment includes: by obtaining the bandwidth difference, retaining or introducing the necessary difference value, thereby intervening in the blocking effect that the data transmission process itself may bring, thereby effectively eliminating the error caused by data failure in the calculation result of the transmission rate; When the host computer uses the j-th correction coefficient When adjusting the maximum allowable idle ratio P, j=1, 2, 3, and setting the adjusted channel reuse ratio. .

[0048] Specifically, in this embodiment of the invention, the process by which the host computer determines whether the corrected maximum allowable idle ratio meets the standard based on the usage time includes: The host computer obtains the actual usage time t of each EEG device through the status monitoring module, compares the actual usage time t with the preset usage time T stored in the database, and determines the target adjustment coefficient based on the comparison result. Used to adjust the corrected maximum allowable idle ratio P; The target idle ratio is determined based on the comparison between the actual usage time t of the device and the preset usage time T stored in the database. If the actual usage time does not match the corresponding preset usage time stored in the database, the host computer will use an adjustment coefficient. Adjust the maximum allowable idle ratio to the corresponding values, where the first preset usage time T1 = 1h, the second preset usage time T2 = 3h, and the corresponding adjustment coefficients. ; It is understandable that the reason why the host computer in this invention uses the time adjustment to adjust the maximum allowable idle ratio is that the current calculation formula is not applicable to the current actual environment. Adjusting the maximum allowable idle ratio can improve the compatibility of the host computer with the current environment, thereby improving the calculation accuracy. If the actual usage time is greater than the second preset usage time T2 stored in the database, the host computer determines to use the third adjustment coefficient. Adjust the maximum allowable idle ratio P; If the actual usage time is less than or equal to the second preset usage time T2 and greater than the first preset usage time T1 stored in the database, the host computer determines to use the second adjustment coefficient. Adjust the maximum allowable idle ratio P; If the actual usage time is less than or equal to the first preset usage time T1, the host computer determines to use the first adjustment coefficient. Adjust the maximum allowable idle ratio P; When the analysis module uses the p-th processing adjustment coefficient When adjusting the idle ratio P, p = 1, 2, 3, and setting the adjusted standard content. .

[0049] It is understood that the database pre-stores the minimum allowed preset usage time for different EEG devices. Therefore, the above-mentioned assignment of the preset usage time is only a preferred embodiment of the system of the present invention. The present invention does not impose specific restrictions on the value of each preset usage time, as long as the comparison result between the actual usage time and each preset usage time can directly characterize the host computer's analysis of each data.

[0050] Specifically, in this embodiment of the invention, the host computer is further configured to determine that the reason for non-compliance with the standard is insufficient network capacity when the calculation of the transmission rate v for the online EEG device is not in accordance with the standard after completing the adjustment of the EEG device and the network environment, and generating an expansion router instruction to optimize the network update. The expansion process includes: The host computer calculates the spatial distribution density of EEG devices based on the real-time coordinates of all current EEG devices obtained by the status monitoring module. The preset standard spatial distribution density is stored in each of the aforementioned databases. A comparison is performed, and the distribution adjustment coefficient of the minimum distribution distance of the EEG device is determined based on the comparison results. Among them, the first preset spatial distribution density Second preset spatial distribution density ; corresponding distribution adjustment coefficient ; If the average distribution density Greater than the second preset average distribution density stored in the database The host computer uses a third distribution adjustment coefficient for determination. Adjust the minimum distribution distance h of the EEG devices; If the average distribution density Less than or equal to the second preset average distribution density And it is greater than the first preset average distribution density stored in the database. The data analysis module determines to use the second distribution adjustment coefficient. Adjust the minimum distribution distance h of the EEG devices; If the average distribution density Less than or equal to the first preset average distribution density The data analysis module determines to use the first distribution adjustment coefficient. Adjust the minimum distribution distance h of the EEG devices; When the analysis module uses the r-th processing distribution adjustment coefficient When adjusting the minimum distribution distance h of the EEG devices, r=1,2,3, the adjusted distribution distance of the acquisition devices is set. .

[0051] It is understood that the database pre-stores the minimum allowable variance threshold for different types of parameters. Therefore, the above assignment of values ​​for the first preset average distribution density and the second preset average distribution density is only a preferred embodiment of the system described in this invention. This invention does not impose specific restrictions on the values ​​of each preset average distribution density, as long as the comparison results between the obtained average distribution density and each preset average distribution density can directly characterize the host computer's analysis of each parameter. It is understandable that the higher the spatial distribution density, the smaller the distance between devices, and the more dominant the interference between devices. Therefore, it is necessary to increase the adjustment coefficient to increase the distribution distance. Thus, the spatial distribution density is proportional to the in-set coefficient. The present invention does not impose specific restrictions on the values ​​of each preset adjustment coefficient, as long as the minimum distribution interval can be obtained by reading the spatial distribution density.

[0052] The host computer is also used to send a network update command to the router when it has completed the adjustment of the corresponding minimum distribution distance and the transmission rate of the wireless EEG acquisition system is not in compliance with the standard based on the adjusted minimum distribution interval.

[0053] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0054] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A device management method for a wireless EEG acquisition system, characterized in that, include: Use several routers to build a local area network; Connect each EEG device and each host computer to the local area network to enable each EEG device and each host computer to establish a network transmission channel; The acquired state parameters generated during the operation of each of the brain electrical devices and the user's brain electrical signals are transmitted to each of the host computers. The state parameters for a single brain electrical device include the electrode impedance data, battery power data and signal strength indication of the brain electrical device. The transmission rate of each of the aforementioned brain electrical devices during the transmission process was collected; The comprehensive transmission rate of each EEG device during transmission is calculated based on the acquired transmission rate. Based on the comprehensive transmission rate obtained from each transmission rate, it is determined whether the management of each EEG device in the local area network complies with the standard. If it is determined that it does not comply with the standard, corresponding processing instructions are generated according to the determined reasons, including issuing notifications and adjustment instructions. The notifications include device replacement notifications and device restart notifications. The acquisition method based on the transmission rate adjusts the maximum allowable idle ratio for each of the aforementioned EEG devices, wherein the maximum idle ratio is the maximum allowable value of the ratio of the number of idle EEG devices to the total number of EEG devices in the local area network. Re-testing is performed based on the obtained adjusted maximum allowable idle ratio, and continuous testing is conducted if the standard is met; Based on the obtained adjusted maximum allowable idle ratio, a retest is performed. If the standard is still not met, the corresponding processing method is selected, including maintenance notification and network update instruction.

2. The device management method for a wireless EEG acquisition system according to claim 1, characterized in that, The process by which the host computer calculates the overall transmission rate includes: The host computer calculates the overall transmission rate v of the system using the following formula: ； in, Let be the real-time transmission rate of the i-th EEG device. , n, where n is the total number of online devices, Let be the transmission weighting coefficient of the i-th EEG device, which is calculated by the following formula: ； in, Preset weighting coefficients for signal quality. Let be the signal quality index of the i-th EEG device. Preset weighting coefficients for power quality. Let be the power supply quality index of the i-th EEG device. Preset weighting coefficients for connection stability Let i be the connection stability index of the i-th EEG device. These are standardized constants; The host computer acquires environmental factors for each EEG device and constructs an environmental influence coefficient k for each device based on the acquired environmental factors. Furthermore, the host computer calculates the transmission rate of each device for the i-th real-time using the following formula. : ； in, The standard transmission rate pre-stored in the database; the environmental impact coefficient Real-time signal strength coefficient between the device and the router Infinite interference coefficient in the same frequency band as the space where the equipment is located Joint decision, Calculated using the following formula: ； in, The preset signal strength weighting factor, The signal strength coefficient is a preset interference suppression weight factor. The interference coefficient is calculated using the signal strength indicator mapping value. The background noise intensity of the channel used by the device is calculated from the background noise intensity detected by the host computer or router.

3. The device management method for a wireless EEG acquisition system according to claim 2, characterized in that, The process by which the host computer determines whether the management of each EEG acquisition device complies with the standard based on the overall transmission rate v includes: The host computer calculates the overall transmission rate v; The obtained comprehensive transmission rate v is compared with the preset comprehensive transmission rate stored in the database. Perform a comparison; Based on the comparison results, determine whether the management of each EEG acquisition device complies with the standards; If the transmission rate for each EEG device is determined to meet the standard, continuous testing is performed; if the transmission rate for each EEG device is determined to be non-compliant, the reason for non-compliance is determined based on the variance of the transmission rate.

4. The device management method for a wireless EEG acquisition system according to claim 3, characterized in that, The host computer calculates the variance of the transmission rate. The process of determining why the management of various EEG acquisition devices does not meet standards includes: The host computer calculates the variance of the transmission rate of each of the EEG devices. ; The variance of the transmission rate of each of the EEG devices was obtained. The pre-stored variance in the database A comparison was conducted to determine why the management of each EEG device did not meet the standards. In determining the cause of the malfunction, the cause of the malfunction of the EEG device is determined based on the time-transmission rate curve of each EEG device. If the cause is determined to be that the local area network environment does not meet the standards, the environmental cause will be determined based on the broadband usage.

5. The device management method for a wireless EEG acquisition system according to claim 4, characterized in that, The process by which the host computer determines the cause of the brainwave device malfunction based on the integral of the time-transmission rate curve includes: EEG devices with a transmission rate lower than the standard rate pre-stored in the database are marked as labeled EEG devices, and when it is determined that the cause is a faulty EEG device, the transmission rate in a single detection cycle is calculated based on each of the acquired labeled EEG devices. Plot the time-transfer rate curves for each labeled EEG device, and calculate the integral for each individual time-transfer rate curve; The integral of the obtained single time-transmission rate curve is compared with the preset integral stored in the database to determine the cause of failure of the labeled EEG device corresponding to the curve. A charging notification will be sent if the cause is determined to be insufficient battery power. If the cause is determined to be a non-compliant network environment, optimize the network transmission parameters.

6. The device management method for a wireless EEG acquisition system according to claim 4, characterized in that, The process by which the host computer determines the reason why the network environment does not meet the standard based on the Received Signal Strength Indicator (RSSI) includes: Acquire the Received Signal Strength Indicator (RSSI) and compare the acquired RSSI with a preset Received Signal Strength Indicator stored in the database. Perform a comparison; The environmental reasons for non-standard broadband usage were determined based on the comparison results. Optimize network transmission parameters if the environmental cause is determined to be the network environment. If the environmental cause is determined to be that the number of EEG devices connected does not meet the standard, the maximum allowable idle ratio is adjusted. The maximum allowable idle ratio is the maximum value of the ratio of the number of idle devices to the total number of connected devices.

7. The device management method for a wireless EEG acquisition system according to claim 6, characterized in that, The process by which the host computer determines the reasons why the network environment does not meet the standard based on the channel interference index I includes: The channel interference index I is obtained by looking up a table based on the detected interference-to-noise ratio. The acquired channel interference index I is compared with the preset channel interference index stored in the database. Perform a comparison; The channel multiplexing ratio is determined based on the comparison results. And the channel multiplexing ratio The increase in the channel multiplexing ratio is positively correlated with the channel interference index I; wherein, the channel multiplexing ratio It is the ratio of the spacing between co-frequency cells to the cell radius.

8. The device management method for a wireless EEG acquisition system according to claim 6, characterized in that, The host computer calculates the bandwidth difference. The process of determining why a network environment does not meet standards includes: Based on the actual available broadband B and standard available broadband within the local area network. The difference The bandwidth difference is obtained; and the obtained bandwidth difference Difference between the preset bandwidth and the data stored in the database Perform a comparison; The adjusted channel reuse ratio Q is corrected based on the comparison results, and the reduction in the channel reuse ratio Q is related to the bandwidth difference. It is negatively correlated.

9. The device management method for a wireless EEG acquisition system according to claim 8, characterized in that, The host computer determines whether the corrected maximum allowable idle ratio meets the standard based on the usage time. The process includes: The actual usage time t for each of the aforementioned EEG devices is obtained through the status monitoring module; The actual usage time t is compared with the preset usage time T stored in the database; Determine the target adjustment coefficient based on the comparison results. The maximum allowable idle ratio P is used to adjust the modified maximum allowable idle ratio, and the increase in the maximum allowable idle ratio P is positively correlated with the usage duration t.

10. The device management method for a wireless EEG acquisition system according to claim 9, characterized in that, When the host computer completes the adjustment of the EEG device and network environment, if the host computer recalculates that the calculation of the transmission rate v for the online EEG device does not meet the standard, the host computer determines that the reason for the non-compliance is insufficient network capacity, and generates an expansion router instruction to optimize the network update. The expansion process includes: The spatial distribution density of EEG devices is calculated by using the real-time coordinates of all current EEG devices obtained from the status monitoring module. ; The obtained spatial distribution density The preset standard spatial distribution density is stored in each of the aforementioned databases. Perform a comparison; Determine the distribution adjustment coefficient based on the comparison results. The minimum distribution distance h of each of the aforementioned EEG devices is adjusted, and the magnitude of the reduction in the minimum distribution distance h is related to the spatial distribution density. It is negatively correlated.

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