Multi-data-source intelligent student psychological data acquisition and analysis system

Abstract

The invention discloses a multi-data-source intelligent student psychological data acquisition and analysis system, and relates to the technical field of electric digital data processing. The multi-data-source intelligent student psychological data acquisition and analysis system comprises a data acquisition delay analysis module, a data transmission delay analysis module and a signal processing delay analysis module. According to the invention, data acquisition delay analysis is carried out on the acquisition process of the local preprocessing layer through the acquired first interference data, whether filtering parameter optimization is carried out is determined according to the data acquisition delay analysis result, and whether sampling frequency optimization is carried out is determined based on the acquired data transmission delay analysis result. Finally, whether dynamic window optimization is carried out or not is judged based on the obtained signal processing delay analysis result, and the obtaining timeliness of the psychological signals corresponding to the psychological data of the students in the dynamic load area is improved. The problem that in the prior art, obtaining timeliness of psychological signals corresponding to student psychological data in a dynamic load area is not high is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to an intelligent student psychological data acquisition and analysis system for multiple data sources. Background Art

[0002] Existing intelligent student psychological data acquisition and analysis systems collect multi-modal indicators such as heart rate variability, voice emotional characteristics, and cognitive behavior data in real time through intelligent wearable devices, mobile applications, and clinical assessment tools, and perform dynamic analysis in combination with machine learning algorithms. In the existing technology, it is impossible to obtain the psychological data of students in the dynamic load area in real time.

[0003] In the existing technology, multi-source multi-modal data fusion is adopted, physiological data and behavioral data are combined, the data is preprocessed to reduce noise interference, manual intervention is reduced, and the degree of process automation is improved.

[0004] For example, the invention patent announcement with the announcement number: CN117633329B discloses a data acquisition method and system for multiple data sources, including: being responsible for generating and managing data acquisition, configuring acquisition templates according to data types, executing specific acquisition tasks, obtaining data through technology using the configured templates, equipped with a monitoring module to monitor the task progress and abnormal scenarios, as well as a data cleaning module and a data persistence module, adopting multi-data source adaptation, and supporting heterogeneous data acquisition through data type identification and template configuration.

[0005] For example, the patent application with the publication number: CN116303699A discloses a method and system for data extraction and analysis based on multiple data sources, including: constructing a database and designing a three-level analysis model of the U layer, C layer, and S layer, assigning a unified identifier ID to multi-source heterogeneous data and completing data mapping, and generating structured and effective integrated data through steps such as standardized preprocessing, rule engine configuration, and redundancy information deduplication to solve the problems of scattered multi-source data and inconsistent formats.

[0006] However, in the process of implementing the technical solution of the present invention in the embodiments of the present application, it is found that the above technologies have at least the following technical problems:

[0007] In the existing technology, data acquisition needs to be completely acquired or transmitted in batches, and after being uploaded to the cloud, it is uniformly processed, resulting in high latency in obtaining data sources; the batch processing architecture depends on traditional database storage and then analysis, and it is impossible to stream and fuse multi-source data, and the network round-trip latency accumulates, resulting in low timeliness in obtaining the psychological signals corresponding to students' psychological data in the dynamic load area. Summary of the Invention

[0008] By providing an intelligent student psychological data acquisition and analysis system with multiple data sources in an embodiment of the present application, the problem that the timeliness of obtaining psychological signals corresponding to student psychological data is not high in a dynamic load area in the prior art is solved, and the improvement of the timeliness of obtaining psychological signals corresponding to student psychological data in the dynamic load area is realized.

[0009] An embodiment of the present application provides an intelligent student psychological data acquisition and analysis system with multiple data sources, including: a data acquisition delay analysis module, a data transmission delay analysis module, and a signal processing delay analysis module; wherein, the data acquisition delay analysis module is used to perform data acquisition delay analysis on the acquisition process of the local preprocessing layer according to the obtained first interference data to obtain a data acquisition delay analysis result, and the data acquisition delay analysis is used to quantify the data response delay degree of the local preprocessing layer in the process of processing student psychological data; the data transmission delay analysis module is used to determine whether to optimize the filtering parameters according to the data acquisition delay analysis result, and at the same time perform data transmission delay analysis on the obtained student psychological data in the process of collaborative transmission between the cloud and the terminal to obtain a data transmission delay analysis result, and the filtering parameter optimization means to improve the data acquisition efficiency of student psychological data in the preprocessing layer by adjusting the filtering intensity and buffer capacity, and the data transmission delay analysis is used to quantify the collaborative transmission efficiency of student psychological data in the process of transmission between the cloud and the terminal; the signal processing delay analysis module is used to determine whether to optimize the sampling frequency based on the obtained data transmission delay analysis result, and at the same time perform signal processing delay analysis on the processing process of psychological signals in the dynamic load area based on the obtained second interference data to obtain a signal processing delay analysis result, and determine whether to perform dynamic window optimization based on the obtained signal processing delay analysis result. The sampling frequency optimization means to reduce the delay of student psychological data in the process of transmission between the cloud and the terminal by adjusting the sampling frequency amplitude and data compression activation, and the signal processing delay analysis is used to quantify the load balance of student psychological data in the dynamic load area, and the dynamic window optimization means to improve the efficiency and real-time performance of psychological signal processing by adjusting the backpressure evaluation level.

[0010] One or more technical solutions provided in an embodiment of the present application have at least the following technical effects or advantages:

[0011] 1. Analyze the data acquisition delay of the acquisition process of the local preprocessing layer through the obtained first interference data, then determine whether to optimize the sampling frequency based on the obtained data transmission delay analysis results. At the same time, analyze the signal processing delay of the psychological signal processing process in the dynamic load area through the obtained second interference data. Finally, judge whether to perform dynamic window optimization based on the obtained signal processing delay analysis results, thereby improving the accuracy of optimizing the filtering parameters and frequency sampling parameters, and further improving the real-time performance of obtaining multi-data source student psychological data in the dynamic load area, effectively solving the problem of low timeliness of obtaining the psychological signal corresponding to the student psychological data in the existing technology.

[0012] 2. Correct the difference degree between the filtering delay duration and the reference filtering delay duration in the database through the filtering delay duration correction amount to obtain the filtering delay duration interference value. Similarly, obtain the feature extraction delay duration interference value and the circular buffer waiting duration interference value through the same steps, and couple the filtering delay duration interference value, the feature extraction delay duration interference value and the circular buffer waiting duration interference value to obtain the data acquisition delay analysis index, thereby improving the accuracy of obtaining the data acquisition delay analysis index, and further realizing a more accurate evaluation of the delay degree in the process of obtaining student psychological data.

[0013] 3. Correct the difference degree between the low-frequency signal ratio and the reference low-frequency signal ratio in the database through the low-frequency signal ratio correction amount to obtain the low-frequency signal ratio interference value. Similarly, obtain the noise signal ratio interference value and the inter-node communication delay duration interference value through the same steps, and couple the low-frequency signal ratio interference value, the noise signal ratio interference value and the inter-node communication delay duration interference value to obtain the signal processing analysis index, thereby improving the accuracy of obtaining the signal processing analysis index, and further realizing a more accurate evaluation of the delay degree of cloud processing of student psychological data. Description of the Drawings

[0014] Figure 1 It is a schematic structural diagram of an intelligent student psychological data acquisition and analysis system with multiple data sources provided by an embodiment of the present application;

[0015] Figure 2 It is a flowchart of the filtering parameter optimization work corresponding to the data acquisition delay analysis module provided by an embodiment of the present application;

[0016] Figure 3 It is a specific flowchart of the buffer capacity optimization corresponding to the data acquisition delay analysis module provided by an embodiment of the present application;

[0017] Figure 4 It is a flowchart of the sampling frequency optimization work corresponding to the data transmission delay analysis module provided by an embodiment of the present application;

[0018] Figure 5 This is the data compression activation optimization workflow diagram corresponding to the data transmission delay analysis module provided by the embodiment of the present application;

[0019] Figure 6 This is the workflow diagram corresponding to the signal processing delay analysis module provided by the embodiment of the present application. Detailed implementation manners

[0020] In the embodiment of the present application, by providing an intelligent student psychological data acquisition and analysis system with multiple data sources, the problem that the timeliness of obtaining psychological signals corresponding to student psychological data in the prior art is not high in the dynamic load area is solved. The data acquisition delay analysis module performs data acquisition delay analysis on the acquisition process of the local preprocessing layer according to the acquired first interference data to obtain a data acquisition delay analysis result. Then, the data transmission delay analysis module determines whether to optimize the filtering parameters according to the data acquisition delay analysis result, and at the same time performs data transmission delay analysis on the process of collaborative transmission of the acquired student psychological data between the cloud and the terminal to obtain a data transmission delay analysis result. Finally, the signal processing delay analysis determines whether to optimize the sampling frequency based on the obtained data transmission delay analysis result, and at the same time performs signal processing delay analysis on the process of processing psychological signals in the dynamic load area based on the acquired second interference data to obtain a signal processing delay analysis result, and determines whether to perform dynamic window optimization based on the obtained signal processing delay analysis result, realizing the improvement of the timeliness of obtaining psychological signals corresponding to student psychological data in the dynamic load area.

[0021] The technical solution in the embodiment of the present application for solving the problem that the timeliness of obtaining psychological signals corresponding to the above-mentioned student psychological data in the dynamic load area is not high has the following general idea:

[0022] Determine whether to optimize the filtering parameters according to the data acquisition delay analysis result, then determine whether to optimize the sampling frequency based on the obtained data transmission delay analysis result, and at the same time perform signal processing delay analysis on the process of processing psychological signals in the dynamic load area based on the acquired second interference data. Finally, determine whether to perform dynamic window optimization based on the obtained signal processing delay analysis result, achieving the effect of improving the timeliness of obtaining psychological signals corresponding to student psychological data in the dynamic load area.

[0023] To better understand the above technical solution, the above technical solution will be described in detail below in combination with the accompanying drawings of the specification and specific implementation manners.

[0024] As Figure 1As shown in the figure, it is a schematic structural diagram of an intelligent student psychological data acquisition and analysis system with multiple data sources provided by an embodiment of the present application. An intelligent student psychological data acquisition and analysis system with multiple data sources provided by an embodiment of the present application includes: a data acquisition delay analysis module, a data transmission delay analysis module, and a signal processing delay analysis module; the data acquisition delay analysis module is used to perform data acquisition delay analysis on the acquisition process of the local preprocessing layer according to the acquired first interference data, and obtain a data acquisition delay analysis result. The data acquisition delay analysis is used to quantify the data response delay degree of the local preprocessing layer in the process of processing student psychological data; the data transmission delay analysis module is used to determine whether to optimize the filtering parameters according to the data acquisition delay analysis result, and at the same time perform data transmission delay analysis on the acquired student psychological data during the collaborative transmission process between the cloud and the terminal, and obtain a data transmission delay analysis result. Filtering parameter optimization means adjusting the filtering intensity and buffer capacity to improve the data acquisition efficiency of student psychological data in the preprocessing layer. The data transmission delay analysis module is used to quantify the collaborative transmission efficiency of student psychological data during the transmission process between the cloud and the terminal; signal processing delay analysis is used to determine whether to optimize the sampling frequency based on the acquired data transmission delay analysis result, and at the same time perform signal processing delay analysis on the psychological signal processing process in the dynamic load area based on the acquired second interference data, and obtain a signal processing delay analysis result. Based on the acquired signal processing delay analysis result, it is judged whether to perform dynamic window optimization. Sampling frequency optimization means adjusting the sampling frequency amplitude and data compression activation to reduce the delay of student psychological data during the transmission process between the cloud and the terminal. Signal processing delay analysis is used to quantify the load balance of student psychological data in the dynamic load area. Dynamic window optimization means adjusting the backpressure evaluation level to improve the efficiency and real-time performance of psychological signal processing.

[0025] In this embodiment, as Figure 2 shown, it is a flowchart of the filtering parameter optimization work corresponding to the data acquisition delay analysis module provided by an embodiment of the present application. As Figure 3 shown, it is a specific flowchart of the buffer capacity optimization corresponding to the data acquisition delay analysis module provided by an embodiment of the present application. The connection points are explained as follows: 1 means that when the filtering intensity optimization does not meet the improvement threshold, the buffer capacity optimization is started. 2 means that after the buffer capacity optimization is completed, the main process is returned and ended.

[0026] Figure 2 Is the data delay > threshold? It means comparing the acquired data delay index with the set data acquisition delay analysis index in the database. If the data acquisition delay analysis index is greater than the set data acquisition delay analysis index in the database, filtering parameter optimization needs to be started. Figure 2The improvement in > threshold? It means to judge whether the reduction amplitude of the data acquisition delay analysis index obtained after reducing this filtering intensity is greater than the set reduction amplitude in the database. Input the parallel processing duration deviation and CPU utilization rate into the circular buffer adjustment area to output the increase amplitude of the circular buffer capacity. If the data acquisition delay analysis index obtained again within the set number of adjustment times is not greater than the data acquisition delay analysis index set in the database, it indicates that the buffer capacity optimization has been completed; otherwise, send a buffer warning instruction.

[0027] As Figure 4 shown, it is the sampling frequency optimization flowchart corresponding to the data transmission delay analysis module provided by the embodiment of the present application. As Figure 5 shown, it is the data compression activation optimization flowchart corresponding to the data transmission delay analysis module provided by the embodiment of the present application. The description of the connection points therein: 3 means that when the frequency sampling optimization is judged to be unqualified, it jumps to the data compression activation process through this connection point. 4 means that after the data compression activation is completed, it returns to the main process end node through the connection point; judge whether to perform sampling frequency optimization based on the obtained data transmission delay analysis result. When the obtained data transmission delay analysis is less than the data transmission delay duration set in the database, perform signal processing delay analysis; if it is greater, perform sampling frequency optimization. Input the data transmission delay duration deviation and data sampling frequency deviation into the linear regression algorithm to output the reduction amplitude of the pressure sampling frequency. Judge whether the reduction amplitude of the obtained data transmission delay duration deviation after reduction is greater than the set reduction amplitude in the database. If it is greater, continue to reduce the sampling frequency; if it is less, send a data compression activation instruction. Figure 4 The number of times of reaching the standard in ? It means to judge that when the data transmission delay duration obtained again within the preset sampling frequency adjustment times in the database is not greater than the data transmission delay duration set in the database, the sampling frequency optimization is completed; otherwise, send a sampler warning instruction. Input the obtained data transmission delay duration deviation into the symmetric decompression to calculate the data compression activation parameter. Judge whether the compressed data transmission delay duration obtained within the preset sampling frequency adjustment times is greater than the compressed data transmission delay duration set in the database. If it is less, the data compression activation is completed; if it is greater, send a data compression warning instruction.

[0028] As Figure 6As shown in the figure, it is the working flowchart corresponding to the signal processing delay analysis module provided by the embodiment of the present application. If the obtained signal processing delay analysis index is less than the signal processing delay analysis index set in the database, a cloud storage instruction is sent. If it is greater, dynamic window parameter adjustment is performed; the signal processing delay analysis index deviation and the backpressure evaluation level deviation are input into the dynamic window adjustment mechanism to output the backpressure evaluation level adjustment amplitude. It is judged whether the reduction amplitude of the signal processing delay analysis index obtained again after adjustment is greater than the reduction amplitude set in the database. If not, it is uploaded to the cloud for data storage. If so, dynamic window parameter adjustment is performed. It is judged whether the signal processing delay analysis index obtained again within the preset number of dynamic window adjustment times is not greater than the set signal processing delay analysis index through the dynamic window adjustment amplitude. If so, the dynamic window adjustment is completed. If not, a dynamic window warning instruction is sent.

[0029] Through the data acquisition delay index, the system can accurately quantify the interference degree of the first interference data on the acquisition of students' psychological data by the local preprocessing layer. Similarly, through the obtained signal processing delay index, the system can accurately quantify the interference degree of the second interference data on the process of processing data in the cloud. This quantification provides objective and accurate data support for the acquisition and analysis process, which helps to understand the interference degree of this process. Secondly, through the obtained index, the optimization effect can be dynamically evaluated, and it can be judged whether it is necessary to re-perform dynamic window adjustment according to the optimization effect. This dynamic adjustment mechanism ensures the continuous optimization and improvement of the acquisition and analysis of students' psychological data, and thus improves the timeliness of obtaining psychological signals corresponding to students' psychological data in the dynamic load area.

[0030] Further, the first interference data includes the filtering delay duration, the feature extraction delay duration, and the ring buffer waiting duration; according to the obtained first interference data, data acquisition delay analysis is performed in the local preprocessing layer. The specific steps include:

[0031] First, obtain the difference degree between the filtering delay duration and the reference filtering delay duration in the database, and correct it through the filtering delay duration correction amount to obtain the filtering delay duration interference value. The specific expression of the filtering delay duration interference value is: , where represents the filtering delay duration interference value of the students' psychological data in the local preprocessing layer at the end of the current acquisition period, represents the filtering delay duration of the students' psychological data in the local preprocessing layer at the end of the current acquisition period, represents the reference filtering delay duration, represents the filtering delay duration correction amount.

[0032] Then, obtain the degree of difference between the feature extraction delay duration and the reference feature extraction delay duration in the database, and correct it through the feature extraction delay duration correction amount to obtain the feature extraction delay duration interference value. The feature extraction delay duration interference value has the following specific expression: , where in the formula, represents the feature extraction delay duration interference value of the student psychological data in the local preprocessing layer at the end of the current acquisition period, represents the feature extraction delay duration of the student psychological data in the local preprocessing layer at the end of the current acquisition period, represents the reference feature extraction delay duration, represents the feature extraction delay duration correction amount.

[0033] Next, obtain the degree of difference between the circular buffer waiting duration and the reference circular buffer waiting duration in the database, and correct it through the circular buffer waiting duration correction amount to obtain the circular buffer waiting duration interference value. The circular buffer waiting duration interference value has the following specific expression: , where in the formula, represents the circular buffer waiting duration interference value of the student psychological data in the local preprocessing layer at the end of the current acquisition period, represents the circular buffer waiting duration of the student psychological data in the local preprocessing layer at the end of the current acquisition period, represents the reference circular buffer waiting duration, represents the circular buffer waiting duration correction amount.

[0034] Finally, perform coupling processing on the obtained wave delay duration interference value, feature extraction delay duration interference value, and circular buffer waiting duration interference value to obtain a data acquisition delay analysis index. The data acquisition delay analysis index is used to quantify the interference degree of the first interference data on the acquisition process of the local preprocessing layer. The data acquisition delay analysis index has the following specific expression: , where in the formula, represents the data acquisition delay analysis index of the student psychological data in the local preprocessing layer at the end of the current acquisition period.

[0035] In this embodiment, the reference filtering delay duration is represented by the result of summing and averaging the historical filtering delay durations of the students' psychological data in the local preprocessing layer of the database at the end of the historical acquisition period. The reference feature extraction delay duration is represented by the result of summing and averaging the historical reference feature extraction delay durations of the students' psychological data in the local preprocessing layer of the database at the end of the historical acquisition period. The reference circular buffer waiting duration is represented by the result of summing and averaging the historical reference circular buffer waiting durations of the students' psychological data in the local preprocessing layer of the database at the end of the historical acquisition period. The filtering delay duration, the feature extraction delay duration, and the circular buffer waiting duration are monitored by a time sensor, and the unit is millisecond (ms).

[0036] The correction amounts of the filtering delay duration, the feature extraction delay duration, and the circular buffer waiting duration are respectively the influence degrees of the preset filtering delay duration, the feature extraction delay duration, and the circular buffer waiting duration in the database on the acquisition process of the data acquisition delay analysis index. Specifically, the database stores the preset correction amounts corresponding to the filtering delay duration, the feature extraction delay duration, and the circular buffer waiting duration. There is a preset mapping relationship between these correction amounts and the filtering delay duration, the feature extraction delay duration, and the circular buffer waiting duration. This mapping relationship can be one-to-one or many-to-one. For example, in practical applications, the real-time filtering delay duration, the feature extraction delay duration, and the circular buffer waiting duration can be input into this mapping relationship to quickly obtain the corresponding correction amounts, which provides an important quantitative index for evaluating the timeliness of the psychological signal corresponding to the students' psychological data in the dynamic load area, and further calculates the data acquisition delay analysis index more accurately.

[0037] In this example, the value ranges of the correction amounts of the filtering delay duration, the feature extraction delay duration, and the circular buffer waiting duration are all limited to between 0 and 1, and the sum of the three is 1.

[0038] It should be noted that there is an interrelationship among the three factors of the filtering delay duration, the feature extraction delay duration, and the circular buffer waiting duration. An increase in the filtering delay duration means that the data has experienced longer waiting and processing before entering the feature extraction link, resulting in a decrease in the amount of data received by the feature extraction module per unit time, so that the feature extraction process takes longer to complete, thereby increasing the feature extraction delay duration.

[0039] Similarly, changes in the filtering delay will affect the pace of data entering the ring buffer. When the filtering delay increases, the speed at which data reaches the ring buffer slows down, resulting in a decrease in the amount of data in the ring buffer, thereby affecting the buffer's scheduling and waiting mechanism; the increase in the feature extraction delay will be fed back to the filtering link, resulting in a large amount of resources being occupied, which in turn affects the processing efficiency of the filtering link and increases the filtering delay. Therefore, changes in the feature extraction delay will directly affect the speed at which data enters the ring buffer from the feature extraction link.

[0040] The increase in the waiting time of the ring buffer means that the backlog of data in the buffer will be fed back to the upstream, affecting the processing rhythm of the filtering link. The ring buffer is in a high occupancy state for a long time, resulting in the data output by the filtering module unable to enter the buffer in time, thereby forcing the filtering module to adjust the processing speed, which in turn affects the filtering delay time; when the waiting time of the ring buffer increases, the time for data to reach the feature extraction module is delayed, which may cause the feature extraction module to be idle and waiting.

[0041] By considering the above-mentioned mutual influence mechanism, we can have a more comprehensive understanding of the relationship between the data acquisition delay analysis indicators and each variable. These relationships are crucial for the timeliness evaluation of the acquisition of psychological signals corresponding to students' psychological data in the dynamic load area. By optimizing the filtering delay time, feature extraction delay time and ring buffer waiting time, the real-time performance of the student psychological data acquisition process is improved, and then the timeliness of the acquisition of psychological signals corresponding to students' psychological data in the dynamic load area is improved, effectively solving the problem of low timeliness of the acquisition of psychological signals corresponding to students' psychological data in the dynamic load area.

[0042] Furthermore, whether to perform filtering parameter optimization is determined based on the data acquisition delay analysis results. The specific steps include: comparing the acquired data acquisition delay analysis index with the data acquisition delay analysis index set in the database: if the acquired data acquisition delay analysis index is greater than the data acquisition delay analysis index set in the database, the data acquisition delay analysis result is recorded as unqualified and filtering parameter optimization is performed; if the acquired data acquisition delay analysis index is not greater than the data acquisition delay analysis index set in the database, the data acquisition delay analysis result is recorded as qualified and psychological signal delay analysis is performed; the filtering parameters include filtering strength and ring buffer capacity.

[0043] Specifically, the specific steps for optimizing the filtering parameters are as follows: jointly input the obtained data acquisition delay analysis index deviation and feature extraction delay duration deviation into the real-time filtering algorithm of the adaptive filter to output the actual reduction amplitude of the filtering intensity. The data acquisition delay analysis index deviation is used to quantify the difference degree between the obtained data acquisition delay analysis index and the set data acquisition delay index in the database. The feature extraction delay duration deviation is used to quantify the difference degree between the obtained feature extraction delay duration and the set feature extraction delay duration in the database. After a reduction in the filtering intensity, determine whether the reduction amplitude of the obtained data acquisition delay analysis index deviation is greater than the set reduction amplitude in the database. If so, continue to reduce the filtering intensity of the adaptive filter; otherwise, send a buffer capacity optimization instruction and perform buffer capacity optimization. If the newly obtained data acquisition delay analysis index within the preset number of filtering intensity adjustment times is not greater than the set data acquisition delay analysis index in the database, the filtering intensity optimization is completed; otherwise, send a filter warning instruction and prompt the preset personnel to intervene. The set data acquisition delay analysis index is represented by the result of summing and averaging the historical data acquisition delay indexes of the student psychological data in the local preprocessing layer of the database at the end of the historical acquisition period.

[0044] Among them, the specific steps for optimizing the buffer capacity are as follows: jointly input the obtained parallel processing duration deviation and CPU resource utilization rate into the circular buffer adjustment area to output the actual increase amplitude of the circular buffer capacity. If the newly obtained data acquisition delay analysis index within the preset number of filtering intensity adjustment times is not greater than the set data acquisition delay analysis index in the database, the buffer capacity optimization is completed and data delay analysis is performed; otherwise, send a buffer warning instruction and prompt the preset personnel to intervene. The parallel processing duration deviation is used to quantify the difference degree between the obtained parallel processing duration and the set parallel processing duration in the database.

[0045] In this embodiment, the parallel processing duration deviation represents the difference between the parallel processing duration and the set parallel processing duration in the database. The feature extraction delay duration represents the difference between the feature extraction delay duration and the set feature extraction delay duration in the database. The reduction amplitude of the data acquisition delay analysis index deviation represents the difference between the obtained data delay analysis index deviation and the newly obtained data acquisition delay analysis index deviation.

[0046] By integrating the deviation of the data acquisition delay analysis index and the deviation of the feature extraction delay duration, and taking them as input quantities into the real-time algorithm of the adaptive filter to dynamically generate the adjustment amplitude of the filtering intensity, it helps to realize the dynamic adjustment of the filtering intensity. After performing a single intensity attenuation, compare the actual attenuation amount of the delay index deviation with the preset reduction amplitude in the database. If it exceeds, reduce the filtering intensity; otherwise, trigger the buffer capacity adjustment mechanism. By setting a warning instruction, the preset personnel can take measures in advance to avoid efficiency decline, thereby reducing the probability of data acquisition timeliness caused by changes in filtering parameters.

[0047] Secondly, by comparing the numerical relationship between the actual number of filtering intensity adjustments and the preset number of filtering intensity adjustments, it helps to determine the actual increase amplitude of the circular buffer capacity, realize the optimization of the buffer capacity and conduct data delay analysis. At the same time, by sending a warning instruction, the preset personnel can intervene in advance to avoid efficiency decline, thereby reducing the risk of system performance loss caused by filtering parameter adjustment problems and ensuring the high real-time performance of the data processing process.

[0048] Furthermore, based on the obtained data transmission delay analysis results, determine whether to optimize the sampling frequency. The specific steps include: if the obtained data transmission delay duration is greater than the data transmission delay duration set in the database, record the data transmission delay analysis result as unqualified and optimize the sampling frequency; if the obtained data transmission delay duration is less than the data transmission delay duration set in the database, record the data transmission delay analysis result as qualified and conduct signal processing delay analysis; the data transmission delay duration is used to quantify the time delay degree experienced by the students' psychological data from the sending end to the receiving end during the transmission process.

[0049] Specifically, the specific steps for optimizing the sampling frequency are as follows: Y1, input the deviation of the data transmission delay duration and the deviation of the data sampling frequency obtained into the linear regression algorithm to output the actual reduction amplitude of the pressure sampling frequency; Y2, after reducing the sampling frequency once, determine whether the reduction amplitude of the deviation of the data transmission delay duration obtained is greater than the set reduction amplitude in the database. If so, continue to reduce the sampling frequency; otherwise, send a data compression activation instruction and execute Y4; Y3, if the re-obtained data transmission delay duration within the preset number of sampling frequency adjustment times is not greater than the set data transmission delay duration in the database, the sampling frequency optimization is completed; otherwise, send a sampler warning instruction and prompt the preset personnel to intervene; Y4, the specific steps for data compression activation are as follows: input the re-obtained deviation of the data transmission delay duration into the symmetric decompression algorithm to output the data compression activation parameter; if the re-obtained compressed data transmission delay duration within the preset number of sampling frequency adjustment times is not greater than the set compressed data transmission delay duration in the database, the data compression activation is completed; otherwise, send a data compression warning instruction and prompt the preset personnel to intervene; the deviation of the data transmission delay duration is used to quantify the difference degree between the obtained data transmission delay duration and the preset data transmission delay duration in the database.

[0050] In this embodiment, the set data transmission delay duration is represented by the sum average of the historical data transmission delay durations at the end of the historical transmission period of the students' psychological data in the dynamic load area of the database. By comparing the dynamic change relationship between the deviation of the data transmission delay duration and the deviation of the sampling frequency, the linear regression algorithm is used to accurately deduce the dynamic adjustment amplitude of the pressure sampling frequency, so as to realize the regulation of data acquisition. When the reduction amplitude of the delay deviation exceeds the limit, the sampling frequency is optimized; on the contrary, the data compression mechanism is activated to compress redundant information. If the delay index returns to the standard threshold within the limited number of adjustment times, it is determined that the optimization process is completed; if it continues to exceed the limit, the warning mechanism is triggered to prompt the preset personnel to intervene and check, so as to reduce the imbalance between data acquisition and transmission efficiency and ensure the high timeliness of the acquisition and analysis of students' psychological data. The deviation of the data transmission delay duration represents the difference between the data transmission delay duration and the set data transmission delay duration in the database. The deviation of the data sampling frequency represents the difference between the data sampling frequency and the set data sampling frequency in the database. The reduction amplitude of the deviation of the data transmission delay duration represents the difference between the deviation of the data transmission delay duration obtained this time and the deviation of the data transmission delay duration obtained last time.

[0051] Furthermore, the second interference data includes the low-frequency signal ratio, the noise signal ratio, and the communication delay duration between nodes; based on the obtained second interference data, signal processing delay analysis is carried out on the psychological signal processing process in the dynamic load area. The specific steps include:

[0052] First, the difference between the low-frequency signal ratio and the reference low-frequency signal ratio in the database is corrected by the low-frequency signal ratio correction amount to obtain the low-frequency signal ratio interference value, and the low-frequency signal interference value The specific expression is: , where represents the low-frequency signal ratio interference value of the mental signal in the dynamic load area at the end of the current signal processing period, represents the low-frequency signal ratio of the mental signal in the dynamic load area at the end of the current signal processing period, represents the reference low-frequency signal ratio, represents the low-frequency signal ratio correction amount.

[0053] Then, the difference between the noise signal ratio and the reference noise signal ratio in the database is corrected by the noise signal ratio correction amount to obtain the noise signal ratio interference value, and the noise signal ratio interference value The specific expression is: , where represents the noise signal ratio interference value of the mental signal in the dynamic load area at the end of the current signal processing period, represents the noise signal ratio of the mental signal in the dynamic load area at the end of the current signal processing period, represents the reference noise signal ratio, represents the noise signal ratio correction amount.

[0054] Next, the difference between the inter-node communication delay duration and the reference inter-node communication delay duration in the database is corrected by the inter-node communication delay duration correction amount to obtain the inter-node communication delay duration interference value, and the inter-node communication delay duration interference value The specific expression is: , where represents the inter-node communication delay duration interference value of the mental signal in the dynamic load area at the end of the current signal processing period, represents the inter-node communication delay duration of the mental signal in the dynamic load area at the end of the current signal processing period, represents the reference inter-node communication delay duration, represents the inter-node communication delay duration correction amount.

[0055] Finally, the obtained low-frequency signal ratio interference value, noise signal ratio interference value, and inter-node communication delay duration interference value are coupled to obtain the signal processing delay analysis index, which is used to quantify the interference degree of the second interference data on the cloud data processing process. The signal processing delay analysis index The specific expression is: , where The signal processing delay analysis index of the psychological signal representing the dynamic load area at the end of the current signal processing period.

[0056] In this embodiment, the reference low-frequency signal ratio is represented by the result of summing and averaging the historical low-frequency signal ratios at the end of the historical signal processing periods of the student psychological data in the dynamic load area in the database, and the low-frequency signal ratio is detected by a voltage acceleration sensor; the reference noise signal ratio is represented by the result of summing and averaging the historical noise signal ratios at the end of the historical signal processing periods of the student psychological data in the dynamic load area in the database, and the noise signal ratio is obtained by a spectrum analyzer and threshold integration measurement of the ratio. The units of both the low-frequency signal ratio and the noise signal ratio are percentages (%); the reference node-to-node communication delay duration is represented by the result of summing and averaging the historical node-to-node communication delay durations at the end of the historical signal processing periods of the student psychological data in the dynamic load area in the database, and the node-to-node communication delay duration is obtained by network probes and differential calculation, with the unit being milliseconds (ms).

[0057] The correction amount of the low-frequency signal ratio, the correction amount of the noise signal ratio, and the correction amount of the node-to-node communication delay duration are respectively the influence degrees of the preset low-frequency signal ratio, noise signal ratio, and node-to-node communication delay duration in the database on the process of obtaining the signal processing analysis index. Specifically, the database stores the preset correction amounts corresponding to the low-frequency signal ratio, noise signal ratio, and node-to-node communication delay duration. There is a preset mapping relationship between these correction amounts and the low-frequency signal ratio, noise signal ratio, and node-to-node communication delay duration. This mapping relationship can be one-to-one or many-to-one. For example, in practical applications, the real-time low-frequency signal ratio, noise signal ratio, and node-to-node communication delay duration can be input into this mapping relationship to quickly obtain the corresponding correction amounts, providing important quantitative indicators for evaluating the timeliness of the psychological signal corresponding to the student psychological data in the dynamic load area signal processing, and thus more accurately calculating the signal processing analysis index.

[0058] In this example, the value ranges of the correction amount of the low-frequency signal ratio, the correction amount of the noise signal ratio, and the correction amount of the node-to-node communication delay duration are all limited to between 0 and 1, and the sum of the three is 1.

[0059] It should be noted that the three factors of the low-frequency signal ratio, the noise signal ratio, and the node-to-node communication delay duration affect each other. When the noise signal ratio increases, the purity of the data decreases, which will cause trouble for the analysis and interpretation of the data; the increase in the node-to-node communication delay duration means that more time is required for data transmission between nodes, resulting in a slower rhythm of data reaching the processing module; the change in the low-frequency signal ratio may affect the recognition and processing of the noise signal, and at the same time also affect the data characteristics of the node-to-node communication.

[0060] Changes in the communication delay duration between nodes can affect the real-time processing of low-frequency signals and noise signals. When the delay duration increases, the timeliness of data decreases, which in turn affects the accurate analysis of low-frequency signals and noise signals. Changes in the proportion of low-frequency signals and the proportion of noise signals will be fed back to the communication link between nodes, affecting the transmission and processing efficiency of data. Changes in the communication delay duration between nodes will also be fed back upstream, affecting the acquisition and analysis processes of low-frequency signals and noise signals.

[0061] The psychological signals of students are essentially the comprehensive manifestation of bioelectrical signals (such as electroencephalogram, electrocardiogram, etc.) or characteristic signals (such as speech intonation, limb movement frequency, etc.). Usually, they contain multiple frequency band components. The proportion of low-frequency signals reflects the manifestation degree of specific characteristics in the psychological state of students. When the proportion of low-frequency signals increases, it indicates that there are more invalid signals in the psychological signals. At this time, additional time is required to process these invalid signals, resulting in an increase in the amount of calculation, thus increasing the duration of signal processing delay.

[0062] By considering the above mutual influence mechanism, the relationship between the signal processing delay analysis index and each variable can be more comprehensively understood. This helps to improve the efficiency in the process of processing students' psychological signals in the cloud, provides a reliable basis for accurately capturing changes in students' psychological states and timely adjusting intervention strategies, ensures that the cloud psychological signal processing system can still maintain efficient and stable operation under complex interactive influences, and then realizes the improvement of the timeliness of obtaining psychological signals corresponding to students' psychological data in the dynamic load area, effectively solving the problem of low timeliness of obtaining psychological signals corresponding to students' psychological data in the dynamic load area.

[0063] Furthermore, based on the obtained signal processing delay analysis results, it is judged whether to perform dynamic window optimization. The specific steps include: comparing the obtained signal processing delay analysis index with the signal processing delay analysis index set in the database: if the obtained signal processing delay analysis index is greater than the signal processing delay analysis index set in the database, the data processing delay analysis result is recorded as unqualified and the dynamic window parameters are adjusted; if the obtained signal processing delay analysis index is not greater than the signal processing delay analysis index set in the database, the data processing delay analysis result is recorded as qualified and a cloud storage instruction is sent; the dynamic window parameter adjustment includes dynamic window adjustment and backpressure evaluation level; the backpressure evaluation level is used to quantify the degree of obstruction of the obtained students' psychological data during the processing process.

[0064] In this embodiment, the specific strategy of the dynamic window is to flexibly adjust the window size according to the level of the backpressure evaluation. When the backpressure evaluation level increases, it means that the processing of students' psychological data is severely blocked. At this time, the dynamic window will be appropriately reduced to reduce the amount of data entering the processing flow per unit time, relieve the processing pressure, and avoid further delays caused by data backlog. When the backpressure evaluation level decreases, indicating that the data processing is relatively smooth, the dynamic window will be moderately enlarged to improve the data processing efficiency and make full use of system resources. During the process of adjusting the dynamic window parameters, the system will continuously monitor the changes of various indicators and make dynamic fine-tuning according to the actual situation. In addition, the qualified data processing delay analysis results will be sent to the cloud for storage, which is not only convenient for subsequent review and analysis of the signal processing performance, but also can improve the real-time performance when the cloud processes signals, thereby improving the real-time performance of the overall system.

[0065] Among them, the specific steps for optimizing the dynamic window parameters are as follows: jointly input the deviation of the signal processing delay analysis index and the deviation of the backpressure evaluation level obtained into the dynamic window adjustment mechanism to output the adjustment range of the backpressure evaluation level; after one adjustment of the backpressure evaluation level, judge whether the reduction range of the deviation of the signal processing delay analysis index obtained again is greater than the reduction range set in the database. If so, adjust the dynamic window parameters; otherwise, upload it to the cloud for data storage; jointly input the deviation of the signal processing delay analysis index and the deviation of the backpressure evaluation level obtained into the dynamic window adjustment mechanism to output the adjustment range of the backpressure evaluation level; the dynamic window adjustment means automatically switching the window strategy according to the fluctuation type of the signal processing delay analysis index obtained again to output the window adjustment range; if the signal processing delay analysis index obtained again within the preset number of dynamic window adjustment times is not greater than the signal processing delay analysis index set in the database, the dynamic window adjustment is completed; otherwise, send a warning instruction for the dynamic window area to remind the preset personnel to intervene; the deviation of the signal processing delay analysis index is used to quantify the difference between the obtained signal processing delay analysis index and the preset signal processing delay analysis index in the database; the deviation of the backpressure evaluation level is used to quantify the difference between the obtained backpressure evaluation level and the preset backpressure evaluation level in the database.

[0066] In this embodiment, the deviation of the signal processing delay analysis metric represents the difference between the signal processing delay analysis metric and the signal processing delay analysis metric set in the database, and the deviation of the backpressure evaluation level represents the difference between the backpressure evaluation level and the backpressure evaluation level set in the database; the backpressure evaluation level is obtained by quantifying the real-time monitoring of system resources (such as memory occupancy) and the task queue length. The original backpressure evaluation level is set by a preset person. Usually, for the consideration of saving resource utilization, it is too conservative (triggering backpressure too early). However, the load fluctuation of the psychological signal in the actual processing process may exhibit non-linear characteristics (such as periodic peaks). If the backpressure evaluation level is not optimized at this time, it may lead to data backlog or packet loss because the system may overly limit the throughput at low load or fail to trigger the protection mechanism in time at high load.

[0067] By jointly inputting the deviation of the signal processing delay analysis metric and the deviation of the backpressure evaluation level into the dynamic window adjustment mechanism to output the adjustment amplitude of the backpressure evaluation level, comprehensively considering the signal processing delay and the backpressure situation. After one adjustment of the backpressure evaluation level, judge the relationship between the reduction amplitude of the re-obtained signal processing delay analysis metric deviation and the set reduction amplitude in the database. If it is greater, continue to adjust the dynamic window parameters, which helps to continuously optimize the signal processing effect until a more ideal state is reached; if it is not greater, upload it to the cloud for data storage, which is convenient for subsequent analysis and traceability of the adjustment process and results, helps to ensure the stability and accuracy of the processing of students' psychological data signals, and further improves the high real-time performance of the entire system in processing students' psychological states.

[0068] In summary, the embodiment of this application performs data acquisition delay analysis on the acquisition process of the local preprocessing layer through the obtained first interference data, then determines whether to optimize the sampling frequency based on the obtained data transmission delay analysis results, and at the same time performs signal processing delay analysis on the psychological signal processing process in the dynamic load area through the obtained second interference data. Finally, based on the obtained signal processing delay analysis results, it judges whether to perform dynamic window optimization, thereby improving the accuracy of optimizing the filtering parameters and frequency sampling parameters, and further improving the real-time performance of obtaining multi-source students' psychological data in the dynamic load area, effectively solving the problem of low timeliness in obtaining the psychological signals corresponding to students' psychological data in the prior art in the dynamic load area.

[0069] Those skilled in the art will appreciate that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0070] The present invention is described with reference to the flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices produce means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0071] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0072] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0073] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present invention.

[0074] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent student psychological data collection and analysis system with multiple data sources, characterized in that Including: A data acquisition delay analysis module, a data transmission delay analysis module, and a signal processing delay analysis module; Among them, the data acquisition delay analysis module is used to perform data acquisition delay analysis on the acquisition process of the local preprocessing layer according to the acquired first interference data, and obtain a data acquisition delay analysis result. The data acquisition delay analysis is used to quantify the data response delay degree of the local preprocessing layer in the process of processing students' psychological data; The data transmission delay analysis module is used to determine whether to optimize the filtering parameters according to the data acquisition delay analysis result, and at the same time perform data transmission delay analysis on the collaborative transmission process of the acquired students' psychological data between the cloud and the terminal, and obtain a data transmission delay analysis result. The filtering parameter optimization means improving the data acquisition efficiency of students' psychological data in the preprocessing layer by adjusting the filtering intensity and buffer capacity. The data transmission delay analysis is used to quantify the collaborative transmission efficiency of students' psychological data during the transmission between the cloud and the terminal; The signal processing delay analysis module is used to determine whether to optimize the sampling frequency based on the acquired data transmission delay analysis result, and at the same time perform signal processing delay analysis on the psychological signal processing process in the dynamic load area based on the acquired second interference data, and obtain a signal processing delay analysis result. Based on the acquired signal processing delay analysis result, it is judged whether to perform dynamic window optimization. The sampling frequency optimization means reducing the delay of students' psychological data during the transmission between the cloud and the terminal by adjusting the sampling frequency amplitude and data compression activation. The signal processing delay analysis is used to quantify the load balance of students' psychological data in the dynamic load area. The dynamic window optimization means improving the efficiency and real-time performance of psychological signal processing by adjusting the backpressure evaluation level.

2. The intelligent student psychological data collection and analysis system with multiple data sources according to claim 1, wherein The first interference data includes filtering delay duration, feature extraction delay duration, and circular buffer waiting duration; The specific steps of performing data acquisition delay analysis on the acquisition process of the local preprocessing layer according to the acquired first interference data include: Obtain the difference degree between the filtering delay duration and the reference filtering delay duration in the database, and correct it through the filtering delay duration correction amount to obtain the filtering delay duration interference value; Obtain the difference degree between the feature extraction delay duration and the reference feature extraction delay duration in the database, and correct it through the feature extraction delay duration correction amount to obtain the feature extraction delay duration interference value; Obtain the difference degree between the obtained circular buffer waiting duration and the reference circular buffer waiting duration in the database, and correct it through the circular buffer waiting duration correction amount to obtain the circular buffer waiting duration interference value; Couple the obtained filtering delay duration interference value, feature extraction delay duration interference value, and circular buffer waiting duration interference value to obtain a data acquisition delay analysis index, which is used to quantify the interference degree of the first interference data on the acquisition process of the local preprocessing layer.

3. The intelligent student psychological data collection and analysis system with multiple data sources according to claim 2, characterized in that, The specific steps of determining whether to optimize the filtering parameters according to the data acquisition delay analysis result include: Compare the obtained data acquisition delay analysis index with the data acquisition delay analysis index set in the database: If the obtained data acquisition delay analysis index is greater than the data acquisition delay analysis index set in the database, record the data acquisition delay analysis result as unqualified and optimize the filtering parameters; If the obtained data acquisition delay analysis index is not greater than the data acquisition delay analysis index set in the database, record the data acquisition delay analysis result as qualified and perform psychological signal delay analysis; The filtering parameters include filtering intensity and circular buffer capacity.

4. The intelligent student psychological data collection and analysis system with multiple data sources according to claim 3, characterized in that, The specific steps for optimizing the filtering parameters are as follows: Input the deviation of the obtained data acquisition delay analysis index and the deviation of the feature extraction delay duration into the real-time filtering algorithm of the adaptive filter to output the actual reduction amplitude of the filtering intensity; After reducing the filtering intensity once, determine whether the reduction amplitude of the obtained delay analysis index deviation is greater than the set reduction amplitude in the database. If so, continue to reduce the filtering intensity of the adaptive filter. Otherwise, send a buffer capacity optimization instruction and perform buffer capacity optimization; If the newly obtained data acquisition delay analysis index within the preset number of filtering intensity adjustment times is not greater than the data acquisition delay analysis index set in the database, the filtering intensity optimization is completed. Otherwise, send a filter warning instruction and prompt the preset personnel to intervene.

5. The intelligent student psychological data acquisition and analysis system with multiple data sources according to claim 4, characterized in that, The deviation of the data acquisition delay analysis index is used to quantify the difference degree between the obtained data acquisition delay analysis index and the data acquisition delay index set in the database; The deviation of the feature extraction delay duration is used to quantify the difference degree between the obtained feature extraction delay duration and the feature extraction delay duration set in the database; The specific steps for optimizing the buffer capacity are as follows: Input the obtained parallel processing duration deviation and CPU resource utilization rate into the circular buffer adjustment area to output the actual increase amplitude of the circular buffer capacity; The parallel processing duration deviation is used to quantify the difference degree between the obtained parallel processing duration and the parallel processing duration set in the database; If the newly obtained data acquisition delay analysis index within the preset number of filtering intensity adjustment times is not greater than the data acquisition delay analysis index set in the database, the buffer capacity optimization is completed and data delay analysis is performed. Otherwise, send a buffer warning instruction and prompt the preset personnel to intervene.

6. The intelligent student psychological data collection and analysis system with multiple data sources according to claim 1, characterized in that Determine whether to optimize the sampling frequency based on the obtained data transmission delay analysis result. The specific steps include: If the obtained data transmission delay duration is greater than the data transmission delay duration set in the database, record the data transmission delay analysis result as unqualified and optimize the sampling frequency; If the obtained data transmission delay duration is less than the data transmission delay duration set in the database, record the data transmission delay analysis result as qualified and perform signal processing delay analysis; The data transmission delay duration is used to quantify the time delay degree experienced by the student psychological data from the sending end to the receiving end during the transmission process.

7. The intelligent student psychological data acquisition and analysis system with multiple data sources according to claim 6, characterized in that The specific steps for optimizing the sampling frequency are as follows: Y1, input the deviation of data transmission delay duration and the deviation of data sampling frequency obtained into a linear regression algorithm to output the actual reduction amplitude of the pressure sampling frequency; Y2, after reducing the sampling frequency once, determine whether the reduction amplitude of the deviation of the obtained data transmission delay duration is greater than the set reduction amplitude in the database. If so, continue to reduce the sampling frequency; otherwise, send a data compression activation instruction and execute Y4; Y3, if the re-obtained data transmission delay duration within the preset number of sampling frequency adjustment times is not greater than the set data transmission delay duration in the database, the sampling frequency optimization is completed; otherwise, send a sampler warning instruction and prompt the preset personnel to intervene; Y4, the specific steps of data compression activation are: Input the deviation of the re-obtained data transmission delay duration into a symmetric decompression algorithm to output data compression activation parameters; If the re-obtained data transmission delay duration within the preset number of sampling frequency adjustment times is not greater than the set data transmission delay duration in the database, the data compression activation is completed; otherwise, send a data compression warning instruction and prompt the preset personnel to intervene; The deviation of the data transmission delay duration is used to quantify the difference degree between the obtained data transmission delay duration and the preset data transmission delay duration in the database.

8. The intelligent student psychological data collection and analysis system with multiple data sources according to claim 1, characterized in that, The second interference data includes the low-frequency signal ratio, the noise signal ratio, and the inter-node communication delay duration; The signal processing delay analysis of the mental signal processing process in the dynamic load area based on the obtained second interference data, the specific steps include: Correct the difference degree between the low-frequency signal ratio and the reference low-frequency signal ratio in the database through the low-frequency signal ratio correction amount to obtain the low-frequency signal ratio interference value; Correct the difference degree between the noise signal ratio and the reference noise signal ratio in the database through the noise signal ratio correction amount to obtain the noise signal ratio interference value; Correct the difference degree between the inter-node communication delay duration and the reference inter-node communication delay duration in the database through the inter-node communication delay duration correction amount to obtain the inter-node communication delay duration interference value; Couple the obtained low-frequency signal ratio interference value, noise signal ratio interference value, and inter-node communication delay duration interference value to obtain a signal processing delay analysis index, and the signal processing delay analysis index is used to quantify the interference degree of the second interference data on the cloud data processing process.

9. The intelligent student psychological data collection and analysis system with multiple data sources according to claim 8, characterized in that, The judgment on whether to perform dynamic window optimization based on the obtained signal processing delay analysis result, the specific steps include: Compare the obtained signal processing delay analysis index with the set signal processing delay analysis index in the database: If the obtained signal processing delay analysis index is greater than the set signal processing delay analysis index in the database, record the data processing delay analysis result as unqualified and adjust the dynamic window parameters; If the obtained signal processing delay analysis index is not greater than the set signal processing delay analysis index in the database, record the data processing delay analysis result as qualified and send a cloud storage instruction; The dynamic window parameter adjustment includes dynamic window adjustment and backpressure evaluation level; The backpressure evaluation level is used to quantify the degree of obstruction in the process of processing the obtained student psychological data.

10. The intelligent student psychological data collection and analysis system with multiple data sources according to claim 9, characterized in that, The specific steps for adjusting the dynamic window parameters are as follows: Input the deviation of the obtained signal processing delay analysis index and the deviation of the backpressure evaluation level into the dynamic window adjustment mechanism to output the adjustment range of the backpressure evaluation level; After one adjustment of the backpressure evaluation level, determine whether the reduction amplitude of the deviation of the re-obtained signal processing delay analysis index is greater than the set reduction amplitude in the database. If so, perform dynamic window parameter adjustment; otherwise, upload it to the cloud for data storage; The dynamic window adjustment means that the re-obtained signal processing delay analysis index is output with the window adjustment range through the automatic switching window strategy of the fluctuation type; If the re-obtained signal processing delay analysis index is not greater than the set signal processing delay analysis index in the database within the preset number of dynamic window adjustment times, the dynamic window adjustment is completed; otherwise, send a dynamic window area warning instruction to remind the preset personnel to intervene; The deviation of the signal processing delay analysis index is used to quantify the difference degree between the obtained signal processing delay analysis index and the preset signal processing delay analysis index in the database; The deviation of the backpressure evaluation level is used to quantify the difference degree between the obtained backpressure evaluation level and the preset backpressure evaluation level in the database.

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