Distributed IO expansion module control system and method for mobile devices

Abstract

The present invention discloses a distributed IO expansion module control system and method for mobile devices, which specifically relates to the field of electronic embedded technology, and includes the following steps: by obtaining the distributed IO expansion module application data in the current scenario, determine whether there is a hidden danger in its flexibility; if there is a hidden danger, activate the performance detection mechanism, obtain the scalability quality index and the adaptability quality index respectively, and generate a single scenario application coefficient and a joint coefficient. According to the joint coefficient, the module is divided into high-quality or low-quality monomers. Time series analysis is performed on low-quality monomers to determine whether the problem is accidental or non-accidental, and an optimization strategy is executed accordingly; the present invention can monitor and evaluate the operating performance of the system in real time, can promptly discover the problem of insufficient flexibility of the system in a specific application scenario, and automatically adjust it through a performance detection mechanism. This flexibility ensures that the system can operate stably in various complex and changeable environments.

Description

Technical Field

[0001] The present invention relates to the field of electronic embedded technology, and more particularly to a distributed IO expansion module control system and method for mobile devices. Background Art

[0002] With the widespread use of mobile devices in various application scenarios, higher requirements are placed on the input / output capabilities of these devices. Especially in the fields of industrial automation, Internet of Things, smart home, robotics and automotive electronics, the system usually needs to connect to a variety of external devices and sensors, which poses a severe challenge to the IO expansion capabilities of mobile devices and the flexibility of the control system.

[0003] In the prior art, the IO expansion modules of mobile devices often adopt a fixed configuration design and cannot be flexibly adjusted according to actual needs. This fixed design may meet the requirements in simple application scenarios, but it is often inadequate when faced with complex and changeable application environments. For example, in some scenarios, it may be necessary to add additional sensors or actuators, but the existing IO modules cannot provide sufficient expansion conditions, resulting in the system being unable to operate normally or limited expansion. In addition, the existing IO expansion module control system usually lacks the ability to dynamically adjust system performance when processing expansion operations. When the system is expanding or modifying modules, it may encounter problems such as excessive resource utilization, delayed response time, increased error rate, and decreased stability. These problems not only reduce the operating efficiency of the system, but may also cause a significant decrease in the reliability and maintainability of the entire system. Therefore, a distributed IO expansion module control system and method for mobile devices are proposed herein. Summary of the invention

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A distributed IO expansion module control method for a mobile device comprises the following steps:

[0006] Obtain application data of distributed IO expansion modules for mobile devices in the current scenario, and then conduct preliminary analysis to determine whether all distributed IO expansion modules as a whole have the risk of insufficient flexibility in the current application scenario;

[0007] When all distributed IO expansion modules as a whole have the risk of insufficient flexibility in the current application scenario, the performance detection mechanism is activated to detect and analyze the operation of each IO expansion module respectively to obtain the scalability quality index and adaptability quality index;

[0008] Comprehensively analyze the scalability quality index and adaptability quality index to generate a single scenario application coefficient for each IO expansion module.

[0009] Obtain the single-scenario application coefficient of the target IO expansion module in all application scenarios, then perform joint analysis to generate the joint coefficient, and divide the target IO expansion module into a high-quality application monomer or a low-quality application monomer based on the joint coefficient;

[0010] Conduct time series analysis on low-quality application entities to determine whether the lack of application quality is accidental or non-accidental, and ultimately execute corresponding optimization strategies based on the judgment results.

[0011] In a preferred embodiment, the preliminary analysis refers to:

[0012] Get the overall system response time to the new module from the application data , that is, the time it takes for the system to run stably after an IO expansion module is added or removed. The shorter the response time, the more flexible the system is. The time required to add or remove an IO expansion module , the error rate when adding or removing an IO expansion module , the compatibility ratio of modules of different types (types or brands) , the time required With error ratio Represents the module's connection time and error rate, the product of which is inversely proportional to the module's operating efficiency;

[0013] Then substitute it into the following calculation formula: ; N represents the total number of distributed IO expansion modules, Represents the flexibility factor. The higher the value, the better the flexibility of the system. Then calculate its ratio with the preset standard flexibility threshold to get the flexibility ratio .

[0014] In a preferred embodiment, during the preliminary analysis, the two data obtained after adding or removing need to be averaged, and the average value corresponds to the indicator data of the project, for example: the time required to add or remove an IO expansion module It is obtained by taking the average of the two data obtained after adding or removing them.

[0015] In a preferred embodiment, judging whether all distributed IO expansion modules as a whole have the risk of insufficient flexibility in the current application scenario refers to:

[0016] The flexibility ratio Compare with the preset hidden danger threshold, if the flexibility ratio is met If the risk is greater than or equal to the preset hidden danger threshold, a normal signal is generated. If the flexibility ratio is not met, If it is greater than or equal to the preset hidden danger threshold, an abnormal signal is generated. When the abnormal signal is generated, it indicates that all distributed IO expansion modules as a whole have the hidden danger of insufficient flexibility.

[0017] In a preferred embodiment, the logic for obtaining the scalability quality index is as follows:

[0018] Get the proportion of system resources (such as CPU, memory, bandwidth, etc.) occupied by the target IO expansion module in the current scenario during the expansion process and mark it as , the time required to complete the expansion operation, i.e., add the module, and is marked as , during the expansion operation, the decrease in system stability (such as service response time, error rate, etc.) is marked as , the success rate of module extension operations and marked as , and then perform the following calculation: ; W1, W2, W3, W4 are all preset influence coefficients, Indicates the scalability quality index of the target I / O expansion module.

[0019] In a preferred embodiment, the logic for obtaining the adaptability quality index is as follows:

[0020] A standard operation scenario is preset. The time required for the target IO expansion module to run stably after switching from the standard scenario to the current scenario is obtained and marked as , the performance change after switching from the standard scene to the current scene is marked as , the proportion of machines that can operate normally under different environmental conditions (such as temperature, humidity, vibration, etc.) in the current scene and marked as , the ratio of stable running time in the current scenario to the total running time and marked as , and then perform the following calculation: ; Indicates the adaptability quality index of the target IO expansion module in the current scenario. All are preset proportional factors;

[0021] ; ; is a constant used to prevent the calculation formula from being invalid.

[0022] In a preferred embodiment, the single scenario application coefficient is obtained through a pre-trained machine learning model, the single scenario application coefficient is the output result of the pre-trained machine learning model, and the scalability quality index and adaptability quality index are both input data of the pre-trained machine learning model.

[0023] In a preferred embodiment, the logic for obtaining the joint coefficient is: obtain the single scenario application coefficients of the target IO expansion module in all application scenarios respectively, and then perform weighted summation to obtain the joint coefficient of the target IO expansion module, and compare the joint coefficient with the preset joint threshold. If the joint coefficient is greater than or equal to the preset joint threshold, it is a high-quality application monomer. If the joint coefficient is less than the preset joint threshold, it is a low-quality application monomer.

[0024] In a preferred embodiment, performing time series analysis on low-quality application monomers to determine whether the insufficient application quality is accidental or non-accidental means: collecting historical performance data of target low-quality application monomers such as the value of the joint coefficient at multiple time points to obtain a time series set X, dividing the time series set X into a trend component, a seasonal component, and a residual component, and then extracting the residual component, using ACF and Ljung-Box tests to determine whether the residual sequence corresponding to the residual component is white noise, if the residual sequence appears as white noise, then it is determined that the insufficient application quality is accidental, and if the residual sequence does not appear as white noise, then it is determined that the insufficient application quality is non-accidental.

[0025] In a preferred embodiment, a distributed IO expansion module control system for a mobile device includes an information collection module, a preliminary analysis module, a performance detection module, a comprehensive analysis module, a joint analysis module, and a judgment optimization module;

[0026] The information collection module is used to obtain application data of the distributed IO expansion module for the mobile device in the current scenario;

[0027] The preliminary analysis module is used to perform preliminary analysis based on the application data of the information collection module to determine whether all distributed IO expansion modules as a whole have the risk of insufficient flexibility in the current application scenario;

[0028] When all distributed IO expansion modules as a whole have the risk of insufficient flexibility in the current application scenario, the performance detection mechanism in the performance detection module is activated. The performance detection module detects and analyzes the operation of each IO expansion module respectively to obtain the scalability quality index and the adaptability quality index.

[0029] The comprehensive analysis module is used to perform comprehensive analysis based on the scalability quality index and the adaptability quality index, and generate a single scenario application coefficient for each IO expansion module;

[0030] The joint analysis module is used to obtain the single scenario application coefficient of the target IO expansion module in all application scenarios, and then perform joint analysis to generate the joint coefficient, and divide the target IO expansion module into a high-quality application monomer or a low-quality application monomer based on the joint coefficient;

[0031] The judgment optimization module is used to perform time series analysis on low-quality application monomers to determine whether the insufficient application quality is accidental or non-accidental, and finally execute the corresponding optimization strategy based on the judgment result.

[0032] Technical effects and advantages of the present invention:

[0033] The distributed IO expansion module control system and method proposed in the present invention, through flexible modular design, allows the system to dynamically add or remove IO expansion modules according to demand. This scalability enables the system to quickly adapt to changing application scenarios and meet input / output requirements in different environments. By real-time monitoring and analysis of the operating status of the IO expansion modules, the present invention can promptly discover the problem of insufficient flexibility of the system in specific application scenarios, and automatically adjust it through a performance detection mechanism. This flexibility ensures that the system can operate stably in various complex and changeable environments.

[0034] The present invention monitors and evaluates the system's operating performance in real time by introducing evaluation indicators such as the scalability quality index (EQI) and the adaptability quality index (AQI). When it is detected that the system performance has degraded or there are hidden dangers, the system can automatically optimize and adjust to ensure efficient and stable performance in various expansion operations. Since the system can automatically detect and adjust performance parameters, the present invention reduces the need for manual intervention and reduces the complexity and cost of system maintenance. The automated optimization mechanism not only improves the reliability of the system, but also extends the service life of the system and reduces the overall cost of ownership.

[0035] The present invention comprehensively monitors low-quality application monomers through accurate time series analysis, and can effectively distinguish whether the insufficient application quality is accidental or non-accidental, and adopt corresponding optimization strategies accordingly. This targeted optimization method helps to maintain the long-term stable operation of the system and improve the reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0037] Figure 1 It is a schematic diagram of the distributed IO expansion module control method for mobile devices in the present invention.

[0038] Figure 2 The schematic diagram of the distributed IO expansion module control system for mobile devices in the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Reference Figure 1 - Figure 2 The following examples are obtained:

[0041] Embodiment 1: A distributed IO expansion module control method for a mobile device comprises the following steps:

[0042] Obtain application data of distributed IO expansion modules for mobile devices in the current scenario, and then conduct preliminary analysis to determine whether all distributed IO expansion modules as a whole have the risk of insufficient flexibility in the current application scenario;

[0043] When all distributed IO expansion modules as a whole have the risk of insufficient flexibility in the current application scenario, the performance detection mechanism is activated to detect and analyze the operation of each IO expansion module respectively to obtain the scalability quality index and adaptability quality index;

[0044] Comprehensively analyze the scalability quality index and adaptability quality index to generate a single scenario application coefficient for each IO expansion module.

[0045] Obtain the single-scenario application coefficient of the target IO expansion module in all application scenarios, then perform joint analysis to generate the joint coefficient, and divide the target IO expansion module into a high-quality application monomer or a low-quality application monomer based on the joint coefficient;

[0046] Conduct time series analysis on low-quality application entities to determine whether the lack of application quality is accidental or non-accidental, and ultimately execute corresponding optimization strategies based on the judgment results.

[0047] The initial analysis refers to:

[0048] Get the overall system response time to the new module from the application data , which is the time it takes for the system to run stably after an IO expansion module is added or removed. The shorter this time is, the more flexible the system is, because it can adapt to changes more quickly. The time required to add or remove an IO expansion module The time required to complete the physical or logical connection when adding or removing an IO expansion module. This time reflects the complexity of the module operation and the system's response speed to the operation. The error rate when adding or removing an IO expansion module The lower the error rate, the higher the reliability of the system when performing module expansion operations. The compatibility ratio of modules of different types (types or brands) , indicating the system's compatibility with modules of different types or brands. The higher this ratio is, the more adaptable the system is to different hardware or software environments. The time required With error ratio Represents the module's connection time and error rate. The product of the two is inversely proportional to the module's operating efficiency. Then substitute it into the following calculation formula:

[0049] ; N represents the total number of distributed IO expansion modules, Represents the flexibility factor. The higher the value, the better the flexibility of the system. The product of the denominator represents the combined impact of connection time, error rate, and response time. The larger the product, the more complex the operation, the more errors, and the slower the response, which is not conducive to the flexibility of the system. After taking the reciprocal, a larger product will be converted into a smaller value, and vice versa, reflecting the inverse relationship of the system operation efficiency. The average of all modules (take the sum of N modules and divide it by N) is obtained to obtain the overall flexibility, and finally multiplied by To reflect the impact of compatibility on flexibility, the higher the compatibility, the better the system flexibility. When applied, the ratio of the number of successfully compatible and normally operated module types Mcompatible to the total number of types M is used as the compatibility ratio. , reflects the compatibility of the system. Only when a type of module can be recognized, initialized, and stably operated by the system, it will be counted into Mcompatible. Assuming that the total number of module types or brands supported by the system is 10 (M=10), it is found through testing that the distributed IO expansion module control system for mobile devices can normally be compatible with and operate 8 of them (Mcompatible=8). The final calculated compatibility ratio The flexibility factor is 0.8, and then the ratio of the flexibility factor to the preset standard flexibility threshold is calculated to obtain the flexibility ratio. ,The larger value of the flexibility ratio is, the higher level of flexibility of the system in actual operation.

[0050] During the preliminary analysis, the two data obtained after adding or removing need to be averaged. The average value corresponds to the indicator data of the project. For example, the time required to add or remove an IO expansion module It is obtained by taking the average of two data points obtained after adding or removing. During the analysis process, when a module is added or removed, two key data points may be generated. For example, the time required when the module is added and the time required when the module is removed. These two data points represent the response time or operation efficiency of the system under different operations. The two data points are averaged to obtain a more representative indicator data, and the time required is finally obtained. .

[0051] To determine whether all distributed IO expansion modules have insufficient flexibility in the current application scenario, we need to set the flexibility ratio Compare with the preset hidden danger threshold, if the flexibility ratio is met If the risk is greater than or equal to the preset hidden danger threshold, a normal signal is generated. If the flexibility ratio is not met, If it is greater than or equal to the preset hidden danger threshold, an abnormal signal is generated. When the abnormal signal is generated, it means that all distributed IO expansion modules as a whole have the hidden danger of insufficient flexibility. The hidden danger threshold is a pre-set standard used to judge whether the system is flexible enough. In different application scenarios, this threshold may be different, depending on the system requirements and design goals. Through this comparison method, the flexibility performance of the system can be dynamically monitored in actual operation, and potential hidden dangers can be discovered in time. Especially in complex or changeable application scenarios, this real-time monitoring and judgment mechanism can effectively improve the reliability of the system.

[0052] The logic for obtaining the scalability quality index is as follows:

[0053] Get the proportion of system resources (such as CPU, memory, bandwidth, etc.) occupied by the target IO expansion module in the current scenario during the expansion process and mark it as The percentage of system resources occupied during the expansion operation is usually expressed as a percentage. The lower the resource utilization rate, the less impact the system has on existing resources during the expansion operation. When the resource utilization rate is increased to the W2 power, a larger resource utilization rate will reduce , indicating poor scalability, the time required to complete the expansion operation, i.e., add a module, is marked as The time required to complete the module expansion operation is usually measured in seconds. The shorter the time, the faster the system can complete the expansion operation and the better the scalability. Use the exponential function exp(Texpand) to amplify the time parameter. The longer the time, the The negative impact is greater. Then it is increased to W3, making the expansion operation time The influence of the system stability (such as service response time, error rate, etc.) during the expansion operation is adjustable and marked as , use the natural logarithm ln(1+Sdrop) to process this parameter to ensure that when the stability decreases slightly, The effect of is limited; otherwise, the effect increases. When it is raised to the power of W4, this parameter has The influence can also be adjusted according to actual needs, and the success rate of module expansion operation is marked as , which indicates the proportion of successful module expansion operations, usually expressed as a percentage. The higher the success rate, the better the reliability of the system during expansion operations. The success rate is processed using natural logarithms to smooth the data and ensure its accuracy. The contribution of is stable. If the success rate is close to 100%, the logarithmic value will be large, otherwise it will be small; then the following calculation is performed:

[0054] ; W1, W2, W3, and W4 are all preset influence coefficients, which can emphasize or weaken the influence of certain factors according to actual needs. Indicates the scalability quality index of the target IO expansion module; It is a comprehensive indicator used to evaluate the performance of the target IO expansion module in the expansion operation, and can fully reflect the expansion capability of the system. The higher the value, the better the scalability of the system, and the more efficient and stable the expansion operation can be completed.

[0055] The logic for obtaining the adaptability quality index is as follows:

[0056] A standard operation scenario is preset. The time required for the target IO expansion module to run stably after switching from the standard scenario to the current scenario is obtained and marked as The shorter the switching time, the faster the system adapts to the new environment. The performance change after switching from the standard scene to the current scene is marked as , performance change range, indicates the degree of fluctuation of system performance under different environmental conditions (such as the rate of change of response time, processing speed, etc.), compares the performance indicators of the module in different scenarios, and calculates the rate of change of the performance indicator values ​​in two scenarios. The smaller the performance change rate, the more stable the performance of the module can be maintained in different scenarios, and the stronger the adaptability is. The proportion of modules that can operate normally under different environmental conditions (such as temperature, humidity, vibration, etc.) in the current scenario is marked as Through environmental testing, the module's operating conditions under various conditions are recorded, and the proportion of its normal operating time is calculated to obtain Logarithmic processing is performed to smooth the influence of environmental adaptability. When it is higher, its The proportion of stable running time in the current scenario to the total running time is marked as , using square processing in the formula, so that high stability has a stronger positive impact, and then the following calculation is performed: ; Indicates the adaptability quality index of the target IO expansion module in the current scenario. are preset proportional coefficients, which can be adjusted according to system requirements to highlight or weaken the influence of certain factors; TT and PP are intermediate results of the calculation, representing the composite indicators of system switching time and performance change respectively; A low value indicates that the system has strong adaptability in a changing or complex environment. A low value indicates that the system may need to be adjusted or optimized to improve its adaptability.

[0057] ; ; is a constant used to prevent the calculation formula from being invalid. By calculating the composite index TT, the efficiency of the system when switching scenes is reflected. The larger the value, the higher the switching efficiency. PP comprehensively considers the exponent and square root of performance change to reflect the performance stability of the system in the face of environmental changes. The larger the value, the smaller the performance change of the system in different environments and the stronger the adaptability.

[0058] The single scenario application coefficient is obtained through a pre-trained machine learning model. The single scenario application coefficient is the output result of the pre-trained machine learning model. The scalability quality index and the adaptability quality index are both input data of the pre-trained machine learning model. The machine learning model is not specifically limited here. Any model that can comprehensively analyze the scalability quality index and the adaptability quality index to generate the single scenario application coefficient of the current scenario is acceptable. In order to implement the technical solution of the present invention, the present invention provides a specific implementation method: select a suitable machine learning model, for example: SSAC=r1* +r2* SSAC represents the single scenario application coefficient. r1 and r2 are both preset quality coefficients, which can be adjusted according to demand to highlight or weaken the impact of the scalability quality index and adaptability quality index. The larger the single scenario application coefficient, the higher the application performance of the target IO expansion module in the current scenario.

[0059] The logic for obtaining the joint coefficient is: obtain the single scenario application coefficient of the target IO expansion module in all application scenarios respectively, and then perform weighted summation. The weighted summation formula is:

[0060] ; Indicates the single scenario application coefficient under the application scenario number i, reflecting the performance of the target IO expansion module in this specific scenario. represents the joint coefficient, k represents the total number of application scenarios, Indicates the weight coefficient of the application scenario numbered i, reflecting the importance of the scenario in the entire system. The weight coefficient of each scenario is set according to the importance or frequency of use of the scenario. , according to the joint coefficient The size of the joint coefficient is used to evaluate the overall performance of the target IO expansion module in all application scenarios. If the joint coefficient is high, it means that the target IO expansion module performs well in most scenarios; if the joint coefficient is low, the target IO expansion module may need to be optimized or adjusted. Get the joint coefficient of the target IO expansion module and compare the joint coefficient with the preset joint threshold. If the joint coefficient is greater than or equal to the preset joint threshold, it is a high-quality application monomer. If the joint coefficient is less than the preset joint threshold, it is a low-quality application monomer.

[0061] For low-quality application monomers, time series analysis is performed to determine whether the insufficient application quality is accidental or non-accidental. This means: collecting historical performance data of the target low-quality application monomers, such as the value of the joint coefficient at multiple time points, to obtain a time series set X, dividing the time series set X into trend components, seasonal components, and residual components, and then extracting the residual components. ACF and Ljung-Box tests are used to determine whether the residual sequence corresponding to the residual component is white noise. If the residual sequence is white noise, it is determined that the insufficient application quality is accidental. If the residual sequence is not white noise, it is determined that the insufficient application quality is non-accidental. Specifically, the trend component represents the long-term growth or decrease trend in the time series. For example, over time, the system performance may gradually decline due to aging. This long-term change is the trend component; the seasonal component represents the short-term periodic fluctuations in the time series. For example, the system performance may fluctuate periodically at certain time points (such as peak hours or specific seasons). This is the seasonal component; the residual component is the random component, which represents the random fluctuations in the time series that cannot be explained and is usually regarded as noise. This part of the fluctuation is not regular and can be considered as the unpredictable part of the data. In order to better analyze the data, seasonal difference method is usually used to eliminate the seasonal component. The seasonal difference method eliminates the influence of seasonal components by subtracting the data at the same time point in the previous cycle, making the series more stable. This treatment helps to separate the remaining trend components and residual components, so as to more clearly analyze the hidden rules in the series. After eliminating the trend and seasonal components, what remains is the residual component, that is, the random fluctuations in the time series.

[0062] In order to determine whether the residual component is white noise, it is necessary to use the autocorrelation function (ACF) and Ljung-Box test. These methods are used to detect the autocorrelation in the residual sequence, that is, to determine whether there is a significant regularity in the sequence.

[0063] ACF analysis: ACF can measure the correlation of the residual sequence at different time lags. If the value of ACF in all lags is close to zero, it means that the sequence has no significant autocorrelation and may be white noise.

[0064] Ljung-Box test: This is a statistical test for the residual sequence, which is used to further confirm whether the residual sequence is white noise. The result of the Ljung-Box test can tell us whether the sequence has significant overall autocorrelation. The residual sequence extraction method, ACF analysis method, and Ljung-Box test method are all existing technologies and will not be elaborated here.

[0065] If the residual sequence appears as white noise: it means that the random fluctuations in the time series do not have a significant trend or periodicity, and the insufficient application quality of the system may be accidental. This means that the problem may be caused by random events or occasional external factors, and no major system adjustments are required.

[0066] If the residual sequence does not appear as white noise, it means that there are regularities in the time series, which may be related to the long-term trend or cyclical fluctuations of the system, suggesting that there are structural problems in the system. In this case, the lack of application quality may be non-accidental.

[0067] Embodiment 2: A distributed IO expansion module control system for a mobile device, comprising an information collection module, a preliminary analysis module, a performance detection module, a comprehensive analysis module, a joint analysis module, and a judgment optimization module; the information collection module, the preliminary analysis module, the performance detection module, the comprehensive analysis module, the joint analysis module, and the judgment optimization module are connected in communication, and the IO expansion module refers to a hardware module for expanding input / output capabilities. Such modules are generally used in computers or other electronic systems to increase the number of input / output ports of the system or enhance its input / output functions. Specifically, the IO expansion module can provide additional interfaces to connect more devices, sensors, actuators or other peripherals, and is generally used in complex embedded systems or industrial control systems. Expanding input / output capabilities: The main function of the IO expansion module is to expand the input and output capabilities of the system, so that the system can process more data inputs or output more control signals. Modular design: These expansion modules are generally modular and can be added or removed as needed so that the system has flexible expansion capabilities. Interface standards: IO expansion modules generally support a variety of interface standards, such as I2C, SPI, CAN, UART, GPIO, etc., allowing them to communicate with different types of sensors, actuators and other peripherals. These modules are widely used in industrial automation, Internet of Things (IoT), robotics, automotive electronics, smart homes, and other fields that require flexible IO configuration.

[0068] The information collection module is used to obtain application data of the distributed IO expansion module for the mobile device in the current scenario;

[0069] The preliminary analysis module is used to perform preliminary analysis based on the application data of the information collection module to determine whether all distributed IO expansion modules as a whole have the risk of insufficient flexibility in the current application scenario;

[0070] When all distributed IO expansion modules as a whole have the risk of insufficient flexibility in the current application scenario, the performance detection mechanism in the performance detection module is activated. The performance detection module detects and analyzes the operation of each IO expansion module respectively to obtain the scalability quality index and the adaptability quality index.

[0071] The comprehensive analysis module is used to perform comprehensive analysis based on the scalability quality index and the adaptability quality index, and generate a single scenario application coefficient for each IO expansion module;

[0072] The joint analysis module is used to obtain the single scenario application coefficient of the target IO expansion module in all application scenarios, and then perform joint analysis to generate the joint coefficient, and divide the target IO expansion module into a high-quality application monomer or a low-quality application monomer based on the joint coefficient;

[0073] The judgment optimization module is used to perform time series analysis on low-quality application monomers to determine whether the lack of application quality is accidental or non-accidental. Finally, according to the judgment results and the preset optimization strategy, the corresponding optimization strategy is executed, specifically: Short-term adjustment: If the problem is accidental, there is usually no need for systematic adjustment, only short-term parameter adjustment or resource reallocation. For example, the system's load balancing strategy can be adjusted to temporarily increase resource allocation to cope with the current abnormal situation. Monitoring and observation: After performing short-term adjustments, continuously monitor the performance of the system to ensure that the problem does not recur. If the problem recurs, it may be necessary to re-evaluate the judgment of insufficient accidental. System reconstruction: If the problem is non-accidental, it means that there is a potential structural problem in the system, and the system architecture may need to be deeply optimized or reconstructed. This may include adjusting the design of the IO expansion module, improving hardware performance, etc.

[0074] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by software simulation of a large amount of data. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0075] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0076] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0077] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0078] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A distributed IO expansion module control method for a mobile device, characterized in that: The following steps are involved: Obtain application data of distributed IO expansion modules for mobile devices in the current scenario, and then conduct preliminary analysis to determine whether all distributed IO expansion modules as a whole have the risk of insufficient flexibility in the current application scenario; When all distributed IO expansion modules as a whole have the risk of insufficient flexibility in the current application scenario, the performance detection mechanism is activated to detect and analyze the operation of each IO expansion module respectively to obtain the scalability quality index and adaptability quality index; Comprehensively analyze the scalability quality index and adaptability quality index to generate a single scenario application coefficient for each IO expansion module. Obtain the single-scenario application coefficient of the target IO expansion module in all application scenarios, then perform joint analysis to generate the joint coefficient, and divide the target IO expansion module into a high-quality application monomer or a low-quality application monomer based on the joint coefficient; Conduct time series analysis on low-quality application monomers to determine whether the poor quality of the application is accidental or non-accidental, and finally implement the corresponding optimization strategy based on the judgment result; The logic for obtaining the scalability quality index is as follows: Get the proportion of system resources occupied by the target IO expansion module in the current scenario during the expansion process and mark it as , the time required to complete the expansion operation and marked as , during the expansion operation, the system stability decreases and is marked as , the success rate of module extension operations and marked as , and then perform the following calculation: ； W1, W2, W3, and W4 are all preset influence coefficients. Indicates the scalability quality index of the target IO expansion module; The logic for obtaining the adaptability quality index is as follows: A standard operation scenario is preset. The time required for the target IO expansion module to run stably after switching from the standard scenario to the current scenario is obtained and marked as , the performance change after switching from the standard scene to the current scene is marked as , the proportion of normal operation under different environmental conditions in the current scene is marked as , the ratio of stable running time in the current scenario to the total running time and marked as , and then perform the following calculation: ; Indicates the adaptability quality index of the target IO expansion module in the current scenario. All are preset proportional factors; ; ; is a constant used to prevent the calculation formula from being invalid; The single scenario application coefficient is obtained through a pre-trained machine learning model. The single scenario application coefficient is the output result of the pre-trained machine learning model. The scalability quality index and adaptability quality index are both input data of the pre-trained machine learning model.

2. The distributed IO expansion module control method for mobile devices according to claim 1, characterized in that: The initial analysis refers to: Get the overall system response time to the new module from the application data , the time required to add or remove an IO expansion module , the error rate when adding or removing an IO expansion module , compatibility ratio of different types of modules ; Then substitute it into the following calculation formula: ; N represents the total number of distributed IO expansion modules, The flexibility factor is then calculated to compare it with the preset standard flexibility threshold to obtain the flexibility ratio. .

3. The distributed IO expansion module control method for mobile devices according to claim 2, characterized in that: During the preliminary analysis, the two data obtained after adding or removing need to be averaged. The average corresponds to the indicator data of the project, which refers to the operation of adding or removing an IO expansion module.

4. The distributed IO expansion module control method for mobile devices according to claim 3, characterized in that: The following are the hidden dangers of insufficient flexibility in all distributed IO expansion modules in the current application scenario: The flexibility ratio Compare with the preset hidden danger threshold, if the flexibility ratio is met If the risk is greater than or equal to the preset hidden danger threshold, a normal signal is generated. If the flexibility ratio is not met, If it is greater than or equal to the preset hidden danger threshold, an abnormal signal is generated. When the abnormal signal is generated, it indicates that all distributed IO expansion modules as a whole have the hidden danger of insufficient flexibility.

5. The distributed IO expansion module control method for mobile devices according to claim 4, characterized in that: The logic for obtaining the joint coefficient is as follows: obtain the single scenario application coefficients of the target IO expansion module in all application scenarios respectively, and then perform weighted summation to obtain the joint coefficient of the target IO expansion module, and compare the joint coefficient with the preset joint threshold. If the joint coefficient is greater than or equal to the preset joint threshold, it is a high-quality application monomer. If the joint coefficient is less than the preset joint threshold, it is a low-quality application monomer.

6. The distributed IO expansion module control method for mobile devices according to claim 5, characterized in that: Conducting time series analysis on low-quality application monomers to determine whether the insufficient application quality is accidental or non-accidental means: collecting historical performance data of target low-quality application monomers to obtain a time series set X, dividing the time series set X into a trend component, a seasonal component, and a residual component, and then extracting the residual component, using ACF and Ljung-Box tests to determine whether the residual sequence corresponding to the residual component is white noise. If the residual sequence appears to be white noise, it is determined that the insufficient application quality is accidental. If the residual sequence does not appear to be white noise, it is determined that the insufficient application quality is non-accidental.

7. A distributed IO expansion module control system for mobile devices, based on the distributed IO expansion module control method for mobile devices according to any one of claims 1 to 6, characterized in that: It includes information collection module, preliminary analysis module, performance detection module, comprehensive analysis module, joint analysis module and judgment optimization module; The information collection module is used to obtain application data of the distributed IO expansion module for the mobile device in the current scenario; The preliminary analysis module is used to perform preliminary analysis based on the application data of the information collection module to determine whether all distributed IO expansion modules as a whole have the risk of insufficient flexibility in the current application scenario; When all distributed IO expansion modules as a whole have the risk of insufficient flexibility in the current application scenario, the performance detection mechanism in the performance detection module is activated. The performance detection module detects and analyzes the operation of each IO expansion module respectively to obtain the scalability quality index and the adaptability quality index. The comprehensive analysis module is used to perform comprehensive analysis based on the scalability quality index and the adaptability quality index, and generate a single scenario application coefficient for each IO expansion module; The joint analysis module is used to obtain the single scenario application coefficient of the target IO expansion module in all application scenarios, and then perform joint analysis to generate the joint coefficient, and divide the target IO expansion module into a high-quality application monomer or a low-quality application monomer based on the joint coefficient; The judgment optimization module is used to perform time series analysis on low-quality application monomers to determine whether the insufficient application quality is accidental or non-accidental, and finally execute the corresponding optimization strategy based on the judgment result.

Images