A method for optimizing power consumption of iTOF ranging

Abstract

The present invention relates to the technical field of indirect time-of-flight (iTOF) ranging technology and discloses a method for optimizing iTOF ranging power consumption. The method comprises dynamically adjusting the signal sampling frequency based on changes in ambient light intensity; comparing the real-time sampling frequency with a preset interval and generating an adjustment signal if the range is exceeded; dividing the power supply into zones based on the adjustment signal, calculating the zone power supply impact value and comparing it with a threshold value to generate a power supply adjustment-related signal; obtaining the load fluctuation value and the collaborative efficiency value based on the power supply adjustment execution signal, and calculating the zone optimization coefficient; and finally obtaining dynamic power supply strategy parameters, which are superimposed on the current power supply zone configuration to generate an optimization solution. By dynamically adjusting the sampling frequency and power supply strategy, the method comprehensively considers multiple influencing factors to optimize the power consumption of the iTOF ranging system, improve system performance and stability, and is suitable for a variety of iTOF ranging application scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of indirect time-of-flight (iTOF) ranging, and in particular to a method for optimizing power consumption of iTOF ranging. Background Art

[0002] With the rapid development of technologies such as the Internet of Things (IoT), autonomous driving, and robotic vision, indirect time-of-flight (iTOF) ranging technology has gained widespread application in numerous fields due to its advantages such as non-contact measurement, high accuracy, and fast response speed. iTOF ranging technology calculates distance by measuring the time it takes for a light signal to travel back and forth between the target and the sensor. However, in practical applications, the power consumption of ranging systems has become a key factor limiting its further development and application.

[0003] On the one hand, traditional iTOF ranging systems typically use a fixed signal sampling frequency, which fails to account for the impact of varying ambient light intensity on ranging signals. Under varying lighting conditions, ambient light noise can interfere with ranging signal accuracy. When ambient light intensity is high, a sampling frequency that is too low may not effectively capture accurate ranging signals, resulting in reduced ranging accuracy. Conversely, when ambient light intensity is low, an excessively high sampling frequency will not only fail to improve ranging accuracy, but may actually increase system power consumption and waste energy. Furthermore, ranging systems employ a relatively crude power supply management approach, typically employing fixed power partitioning configurations and strategies without dynamic adjustments based on the system's actual operating status. During system operation, the load and collaborative efficiency of each partition can vary. For example, when performing different ranging tasks, each partition has varying computational loads and data processing requirements. Improper power distribution can result in insufficient power to some partitions, impacting performance, or excessive power to others, increasing power consumption.

[0004] In the process of obtaining and calculating parameters related to power supply, the existing technology also has many deficiencies. When calculating the impact value of partition power supply, only a single current or voltage factor is often considered, without comprehensively evaluating the impact of current fluctuations and voltage stability on the power supply status, and it is impossible to accurately reflect the actual power supply situation of each partition, resulting in a lack of accuracy and effectiveness in power supply adjustment. When determining the partition optimization coefficient, the characteristics of load fluctuations and collaborative efficiency are not fully considered, so that the optimized power supply scheme cannot adapt well to changes in the system operating status, and it is difficult to achieve effective control of power consumption. In addition, when faced with complex actual application scenarios, such as environments with objects of different reflectivity, the existing technology is unable to perform targeted optimization and adjustment of the sampling frequency and power supply strategy, resulting in high power consumption and unstable ranging accuracy of the system in complex environments. Therefore, there is an urgent need for an iTOF ranging power consumption optimization method that can be dynamically adjusted according to the environment and system operating status to reduce system power consumption and improve ranging performance and stability. Summary of the Invention

[0005] The object of the present invention is to provide a method for optimizing the power consumption of an iTOF ranging device to solve the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing iTOF ranging power consumption, the method comprising:

[0007] Step 1: Dynamically adjust the signal sampling frequency of the ranging system and obtain the dynamic sampling frequency value according to the change in ambient light intensity;

[0008] Step 2: Based on the dynamic sampling frequency value, the real-time sampling frequency is compared with the preset sampling frequency range, and if the real-time sampling frequency exceeds the preset sampling frequency range, a sampling frequency adjustment signal is generated;

[0009] Step 3: Based on the sampling frequency adjustment signal, the ranging system is divided into power supply zones. During the division period, the power supply status data of each zone is obtained, the zone power supply impact value is calculated, and the value is compared with the zone power supply impact threshold to generate a power supply adjustment signal.

[0010] If the partition power supply impact value is greater than or equal to the partition power supply impact threshold, a power supply adjustment execution signal is generated;

[0011] Step 4: Based on the power supply adjustment execution signal, obtain the partition load fluctuation value and the partition coordination efficiency value, calculate the difference between the load fluctuation value and the coordination efficiency value, and obtain the partition optimization coefficient;

[0012] Step 5: Based on the power supply adjustment execution signal, obtain the dynamic power supply strategy parameters, superimpose the current power supply partition configuration and the dynamic power supply strategy parameters, generate an optimized partition power supply plan, and complete the power supply adjustment of the ranging system.

[0013] Preferably, the dynamic sampling frequency value is obtained in the following manner:

[0014] The ranging environment is divided into multiple light intensity monitoring areas, the light intensity change rate of each area is obtained, and the change rate of each area is weighted and summed to obtain the comprehensive light fluctuation value;

[0015] The ratio operation of the comprehensive light fluctuation value and the preset fluctuation threshold is performed to obtain the dynamic sampling frequency adjustment coefficient.

[0016] Preferably, the partition power supply impact value is obtained in the following manner:

[0017] Extract the current fluctuation impact value and voltage stability impact value of each partition, multiply the current fluctuation impact value and the voltage stability impact value to obtain the partition power supply impact value;

[0018] The power supply zones are divided as follows:

[0019] Based on the real-time changes in partition load fluctuation values ​​and collaborative efficiency values, the power supply status of adjacent partitions is evaluated for synergy. If the synergy evaluation value is higher than the preset threshold, the adjacent partitions are merged into a joint power supply unit to generate a dynamic partition configuration plan.

[0020] Preferably, the current fluctuation impact value is obtained in the following manner:

[0021] During the monitoring period, the current peak and valley change slopes of each partition are extracted, and the difference between the current change slope and the preset slope reference value is calculated to obtain the current fluctuation deviation value;

[0022] The current fluctuation deviation value is normalized with the number of partitions to obtain the current fluctuation impact value.

[0023] Preferably, the voltage stability impact value is obtained in the following manner:

[0024] During the division period, the number and amplitude of voltage fluctuations in each partition are recorded, and the number and amplitude of fluctuations are weighted and accumulated to obtain the voltage stability index.

[0025] The voltage stability index is proportional to the total number of partitions to obtain the voltage stability impact value.

[0026] Preferably, the partition optimization coefficient is obtained as follows:

[0027] According to the partition load fluctuation value, the standard deviation and the average value of the load fluctuation value are extracted, and the ratio of the standard deviation to the average value is calculated to obtain the load fluctuation characteristic value;

[0028] According to the partition collaborative efficiency value, the distribution dispersion and concentration of the efficiency value are extracted, and the dispersion and concentration are multiplied to obtain the collaborative efficiency characteristic value;

[0029] Perform difference calculation on the load fluctuation characteristic value and the collaborative efficiency characteristic value to obtain the partition optimization coefficient;

[0030] The verification method of the partition optimization coefficient is:

[0031] After generating the optimized partition power supply plan, the partition load fluctuation value and the collaborative efficiency value are reversely checked. If the check deviation value exceeds the preset tolerance range, the partition configuration backtracking mechanism is triggered and step four is executed again.

[0032] Preferably, the dynamic power supply strategy parameters are generated in the following manner:

[0033] Obtain the partition optimization coefficients from the historical power supply optimization records, calculate the sliding average of the optimization coefficients according to the time series, and obtain the dynamic power supply strategy benchmark value;

[0034] The current partition optimization coefficient is superimposed on the dynamic power supply strategy benchmark value to generate the dynamic power supply strategy parameters.

[0035] Preferably, the load fluctuation characteristic value is corrected in the following manner:

[0036] When the partition load fluctuation value continuously exceeds the preset fluctuation range, the load fluctuation characteristic value is exponentially attenuated and the attenuation factor is dynamically adjusted according to the historical fluctuation data.

[0037] Preferably, the correction method of the collaborative efficiency characteristic value is:

[0038] When the collaborative efficiency value is lower than the preset efficiency threshold, a linear compensation correction is performed on the collaborative efficiency characteristic value, and the compensation weight is dynamically determined according to the task correlation between partitions.

[0039] Preferably, the dynamic sampling frequency value is corrected in the following manner:

[0040] The material identification parameters of reflective objects are introduced into the light intensity monitoring area, and the comprehensive light fluctuation value is normalized and compensated according to the material reflectivity difference to generate the corrected dynamic sampling frequency adjustment coefficient.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The iTOF ranging power consumption optimization method provided by the present invention effectively solves the problems of high power consumption and poor adaptability existing in traditional iTOF ranging systems through a multi-dimensional and multi-level optimization strategy. In terms of signal sampling frequency adjustment, the sampling frequency is dynamically obtained based on the change value of the ambient light intensity, and the ranging environment is finely divided into multiple light intensity monitoring areas. The dynamic sampling frequency adjustment coefficient is accurately determined by weighted summation of the light intensity change rate of each area and calculation with the preset fluctuation threshold. This method enables the sampling frequency to be flexibly adjusted with changes in ambient light. When the light intensity is strong, the sampling frequency is increased to ensure the accuracy of the ranging signal; when the light intensity is weak, the sampling frequency is reduced to avoid unnecessary power consumption, thereby effectively reducing the power consumption of the system caused by unreasonable sampling frequency while ensuring the ranging accuracy.

[0043] In power supply partition management and optimization, the power supply partitions are dynamically divided based on the sampling frequency adjustment signal. The partition power supply impact value is calculated by comprehensively considering the current fluctuation impact value and the voltage stability impact value, and the power supply status of each partition is comprehensively and accurately evaluated. At the same time, based on the real-time changes in the partition load fluctuation value and the collaborative efficiency value, the power supply status of adjacent partitions is evaluated for synergy. Partitions with high synergy are merged into joint power supply units to generate a dynamic partition configuration plan. This dynamic power supply partition management method can reasonably allocate power resources according to the actual operation needs of the system, avoid power consumption waste caused by unreasonable power supply in some partitions, and improve power supply efficiency.

[0044] During the process of determining the optimization coefficient and power supply strategy, the partition optimization coefficient is obtained through scientific calculation methods and then verified and corrected. Eigenvalues ​​are extracted based on the load fluctuation value and the synergistic efficiency value, and the partition optimization coefficient is obtained through difference calculation. After the power supply plan is generated, it is reverse-checked to ensure the effectiveness of the plan. When the load fluctuation value or synergistic efficiency value is abnormal, the corresponding eigenvalue is dynamically corrected to ensure that the optimization coefficient more accurately reflects the actual system conditions. On this basis, historical power supply optimization records are used to generate dynamic power supply strategy parameters. The current power supply partition configuration is superimposed with the dynamic power supply strategy parameters to generate an optimized partition power supply plan, achieving dynamic optimization of the power supply strategy and ensuring that the system maintains low power consumption and good performance under various operating conditions.

[0045] In addition, reflective object material identification parameters are introduced during the correction process of the dynamic sampling frequency value, and the comprehensive illumination fluctuation value is normalized and compensated according to the material reflectivity difference, which further improves the accuracy of the sampling frequency adjustment and enables the system to operate stably and efficiently even in complex lighting and reflective object environments. It reduces the increased power consumption and ranging errors caused by environmental factors, and greatly enhances the overall performance and application scope of the iTOF ranging system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a working principle diagram of the iTOF ranging power consumption optimization method according to the present invention;

[0047] Figure 2 Design diagram for obtaining dynamic sampling frequency value;

[0048] Figure 3 Schematic diagram for calculating the impact value of power supply to the partition. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0050] See also Figure 1-Figure 3 The present invention relates to an iTOF ranging power consumption optimization method, and the specific implementation steps are as follows:

[0051] Step 1: Dynamically adjust the signal sampling frequency of the ranging system and obtain the dynamic sampling frequency value based on the change value of the ambient light intensity. The ranging environment is divided into multiple light intensity monitoring areas. Each monitoring area is equipped with a corresponding light intensity sensor. These sensors are used to obtain the light intensity change rate of each area in real time. Based on factors such as the importance and size of each area, different weights are assigned to the change rate of each area. The weighted sum of the change rates of each area is calculated to obtain the comprehensive light fluctuation value. The comprehensive light fluctuation value is then compared with the preset fluctuation threshold to obtain the dynamic sampling frequency adjustment coefficient. Combined with the initial sampling frequency, the dynamic sampling frequency value is obtained.

[0052] Step 2: Based on the dynamic sampling frequency value, the real-time sampling frequency is compared with the preset sampling frequency range. If the real-time sampling frequency exceeds the preset sampling frequency range, a sampling frequency adjustment signal is generated. The preset sampling frequency range is pre-set based on the performance parameters of the ranging system, the operating environment, and other factors. By comparing the real-time sampling frequency with this range, the appropriateness of the current sampling frequency is determined. If it exceeds the range, a sampling frequency adjustment signal is immediately generated for subsequent adjustment of the ranging system.

[0053] Step 3: Based on the sampling frequency adjustment signal, the power supply partitions of the ranging system are divided. During the division period, the power supply status data of each partition is obtained through current sensors, voltage sensors and other devices, and the current fluctuation impact value and voltage stability impact value of each partition are extracted. The current fluctuation impact value and the voltage stability impact value are multiplied to obtain the partition power supply impact value, and compared with the partition power supply impact threshold to generate a power supply adjustment signal; if the partition power supply impact value is greater than or equal to the partition power supply impact threshold, a power supply adjustment execution signal is generated. When dividing the power supply partitions, based on the real-time changes in the partition load fluctuation value and the collaborative efficiency value, the power supply status of adjacent partitions is evaluated for synergy. If the synergy evaluation value is higher than the preset threshold, the adjacent partitions are merged into a joint power supply unit to generate a dynamic partition configuration plan.

[0054] Step 4: Based on the power supply adjustment execution signal, obtain the partition load fluctuation value and the partition collaborative efficiency value, perform a difference calculation between the load fluctuation value and the collaborative efficiency value, and obtain the partition optimization coefficient. According to the partition load fluctuation value, extract the standard deviation and the average value of the load fluctuation value, perform a ratio calculation between the standard deviation and the average value to obtain the load fluctuation characteristic value; according to the partition collaborative efficiency value, extract the distribution dispersion and concentration of the efficiency value, multiply the dispersion and the concentration to obtain the collaborative efficiency characteristic value; perform a difference calculation between the load fluctuation characteristic value and the collaborative efficiency characteristic value to obtain the partition optimization coefficient. After generating the optimized partition power supply plan, perform a reverse check on the partition load fluctuation value and the collaborative efficiency value. If the check deviation value exceeds the preset tolerance range, the partition configuration backtracking mechanism is triggered and step 4 is re-executed.

[0055] Step 5: Based on the power supply adjustment execution signal, obtain the dynamic power supply strategy parameters, superimpose the current power supply partition configuration with the dynamic power supply strategy parameters, generate an optimized partition power supply plan, and complete the power supply adjustment of the ranging system. Obtain the partition optimization coefficients from the historical power supply optimization records, perform a sliding average calculation on the optimization coefficients over time series, and obtain the dynamic power supply strategy baseline value; superimpose the current partition optimization coefficients with the dynamic power supply strategy baseline value to generate the dynamic power supply strategy parameters.

[0056] Example 1: In the iTOF ranging system, the process of obtaining the dynamic sampling frequency value is as follows. First, the ranging environment needs to be reasonably divided into multiple light intensity monitoring areas. When dividing, the spatial layout of the ranging scene, the characteristics of light distribution and other factors are comprehensively considered. For example, for indoor ranging environments, different functional areas or areas where there may be large differences in lighting are divided into independent monitoring areas based on the structure of the room, the position of windows, etc.; for outdoor environments, areas are divided according to topography, building obstruction, etc. High-precision light intensity sensors are installed in each monitoring area. These sensors have the ability to collect light intensity data in real time and stably, and can continuously collect light intensity data at extremely short time intervals to provide raw data support for the subsequent calculation of the light intensity change rate.

[0057] To calculate the rate of change of light intensity per unit time for each area, the light intensity sensor collects data at consecutive time points. The ratio of the difference in light intensity data between adjacent time points to the time interval is the rate of change of light intensity per unit time. To ensure that the resulting comprehensive light fluctuation value more accurately reflects the light variations in the entire ranging environment, weighting coefficients are determined based on factors such as the role of each area in the overall ranging environment and its coverage. Areas that play a key role in the ranging process, such as those where the primary target is located or where light variations have a significant impact on the ranging results, are assigned higher weights. Auxiliary areas or areas with relatively stable light that have a smaller impact on the ranging results are assigned lower weights. The weighting coefficients can be determined using pre-defined rules or algorithms that take into account multiple factors, such as the area's geometric location, functional importance, and historical light variations.

[0058] By multiplying the rate of change for each region by its corresponding weight and summing the results, we can obtain the comprehensive light fluctuation value. This comprehensive light fluctuation value integrates the light variation information for different regions in the entire ranging environment, reflecting the overall degree of ambient light fluctuation. Next, the comprehensive light fluctuation value is divided by the preset fluctuation threshold to obtain the dynamic sampling frequency adjustment coefficient. The preset fluctuation threshold is a pre-set benchmark value based on the performance parameters of the ranging system and the characteristics of the operating environment. It is used to measure the magnitude of the comprehensive light fluctuation value and determine the degree of adjustment required for the sampling frequency. By calculating the ratio of the comprehensive light fluctuation value to the preset fluctuation threshold, the dynamic sampling frequency adjustment coefficient directly reflects the proportional relationship in which the sampling frequency should be adjusted under the current ambient light changes.

[0059] The dynamic sampling frequency adjustment coefficient is combined with the initial sampling frequency of the ranging system to ultimately obtain a dynamic sampling frequency value. Specifically, the initial sampling frequency is multiplied or subjected to other logical operations based on the dynamic sampling frequency adjustment coefficient to obtain a dynamic sampling frequency value that adapts to changes in the current ambient light intensity. This process dynamically adjusts the sampling frequency, allowing it to vary with changes in ambient light intensity. When ambient light intensity fluctuates significantly, the dynamic sampling frequency value is adjusted accordingly to ensure that the ranging system can obtain sufficiently accurate signal data. When ambient light intensity is relatively stable, the sampling frequency is appropriately reduced to avoid unnecessary power consumption caused by excessively high sampling frequencies.

[0060] To further improve the accuracy of sampling frequency adjustment, a material identification parameter for reflective objects is introduced into the light intensity monitoring area. A dedicated material identification module is integrated into the ranging system. This module uses advanced technologies such as spectral analysis and image recognition to identify the material of reflective objects. Spectral analysis analyzes the spectral characteristics of the light reflected from an object and compares them with a pre-established material spectrum database to determine the object's material. Image recognition uses image acquisition equipment to capture image information of the object and determine the object's material type through algorithms such as image feature extraction and pattern recognition.

[0061] Different materials have different reflectivities. These differences in reflectivity can affect light intensity to varying degrees, thereby affecting the accuracy of the integrated light fluctuation value. Therefore, a normalized compensation calculation is performed on the integrated light fluctuation value based on the differences in material reflectivity. In specific implementation, a table is first established that maps material reflectivity to compensation coefficients. This table, derived through extensive experimentation and statistical data analysis, details the reflectivity of various common materials and their corresponding compensation coefficients. After the material recognition module determines the material of the reflective object, it retrieves the corresponding compensation coefficient from the table based on that material. This compensation coefficient is then calculated with the integrated light fluctuation value. This calculation can involve multiplication, addition, or other methods designed specifically for the specific situation. This calculation generates a corrected dynamic sampling frequency adjustment coefficient. Based on this corrected adjustment coefficient, a more accurate dynamic sampling frequency value is recalculated, enabling the ranging system to operate at a more reasonable sampling frequency under varying lighting environments and reflective object conditions. This ensures ranging accuracy while effectively reducing system power consumption, thus optimizing the power consumption of the iTOF ranging system.

[0062] Example 2: In the power consumption optimization process of the iTOF ranging system, the acquisition of partition power supply impact values ​​and the dynamic configuration of power supply partitions are key links. During the division period, the system uses high-precision current sensors and voltage sensors to monitor the current and voltage data of each partition in real time. These sensors must have high sampling rate and high precision characteristics, and be able to capture small changes in current and voltage. During the monitoring period, the current peak and valley change slopes of each partition are extracted by analyzing the current data. Specifically, the system records the changes in current at adjacent time points and calculates the rate of current rise or fall, that is, the change slope. The difference between these current change slopes and the preset slope reference value is calculated to obtain the current fluctuation deviation value. The preset slope reference value is a reference value pre-set according to the current change characteristics under the normal working state of the system, which is used to measure the degree of abnormality of the current fluctuation.

[0063] To ensure comparability of current fluctuation impact values ​​across different zones, the current fluctuation deviation values ​​must be normalized against the number of zones. Normalization adjusts the current fluctuation deviation values ​​for each zone based on the total number of zones, eliminating the impact of differences in the number of zones on the result. This yields a current fluctuation impact value. This value accurately reflects the impact of each zone's current fluctuation on the entire power supply system. The system also records the number and amplitude of voltage fluctuations for each zone. The number of voltage fluctuations refers to the number of times the voltage exceeds the normal range during the monitoring period, while the amplitude refers to the degree to which the voltage deviates from the normal range. The number and amplitude of fluctuations are weighted and accumulated according to pre-set weights to produce the voltage stability index. The weights are determined based on the importance of the number and amplitude of fluctuations to system performance. The voltage stability index is then proportionally calculated with the total number of zones to produce the voltage stability impact value.

[0064] Finally, the current fluctuation impact value is multiplied by the voltage stability impact value to obtain the partition power supply impact value. This multiplication comprehensively considers both current and voltage fluctuations, enabling a comprehensive assessment of the impact of each partition's power supply stability on the entire ranging system. Regarding power supply partitioning, the system establishes a dedicated evaluation model to assess the power supply compatibility of adjacent partitions based on real-time changes in partition load fluctuation and synergy efficiency. When the system generates a sampling frequency adjustment signal (often triggered by the real-time sampling frequency exceeding a preset range), it first assesses the current system workload characteristics. For example, if an increase in sampling frequency increases the load on modules such as data acquisition and processing, the system then adjusts the signal type based on the sampling frequency, analyzing power consumption changes in modules such as data acquisition, signal processing, and output. The system then evaluates the power supply status of each partition based on the current partition load fluctuation value and synergy efficiency value. If an increase in sampling frequency increases the load fluctuation value of the data acquisition partition, while the synergy efficiency value of the adjacent data processing partition is high, the power supply compatibility between the two partitions should be considered. The synergy evaluation model determines whether the synergy between adjacent partitions exceeds a preset threshold. If coordination is high, such as when partitions frequently exchange data due to an increased sampling frequency, they can be merged into a joint power supply unit. If coordination is low, such as when a partition's load decreases suddenly due to a reduced sampling frequency, and the task correlation with adjacent partitions decreases, it can be split to avoid power supply redundancy. This allows for dynamic matching of power supply partitions with sampling frequency adjustment signals, meeting power consumption requirements while reducing overall energy consumption. This evaluation model comprehensively considers factors such as power transmission loss between partitions and task execution correlation. Power transmission loss is related to factors such as the physical distance between partitions and line impedance. Greater distance and greater impedance result in greater transmission loss. Task execution correlation refers to the degree of collaboration between adjacent partitions during ranging tasks, as measured by data exchange frequency and resource sharing.

[0065] If the synergy evaluation value is higher than the preset threshold, it indicates that the adjacent partitions have strong synergy in their power supply status, and merging them can achieve more efficient power supply management. In this case, the system merges the adjacent partitions into a joint power supply unit and generates a dynamic partition configuration plan. This dynamic partition configuration method can adjust the power supply partitions based on the real-time operating status of the system, achieving dynamic optimization of the power supply partitions. By merging adjacent partitions with strong synergy, power transmission losses between partitions can be reduced, power supply efficiency can be improved, and the overall power consumption of the system can be reduced. In actual operation, the evaluation model continuously monitors and evaluates the power supply status of adjacent partitions and adjusts the partition configuration based on real-time data. When the system operating status changes, such as changes in ambient light intensity leading to adjustments in the sampling frequency, or changes in the distribution of ranging tasks, the evaluation model recalculates the synergy evaluation values ​​of adjacent partitions to determine whether the partition configuration needs to be adjusted.

[0066] This dynamic partitioning configuration scheme is highly adaptable and can flexibly adjust the power supply partitions according to the actual needs of the system, ensuring that power supply efficiency is maximized and power consumption is minimized under various operating conditions. Throughout the entire process, the system achieves refined management of the power supply of the iTOF ranging system by accurately obtaining the partition power supply impact value and dynamically adjusting the power supply partition configuration, effectively improving the system's energy efficiency and extending the device's battery life, while ensuring the performance and stability of the ranging system. In the process of calculating the current fluctuation impact value, the sampling frequency and accuracy of the high-precision current sensor directly affect the accuracy of the calculation of the current change slope. To ensure the reliability of the calculation results, the sensor needs to have a sufficiently high sampling frequency to be able to capture rapid changes in current. At the same time, the system will also filter the collected current data to remove noise interference and improve data quality.

[0067] When calculating the voltage stability impact value, the number and amplitude of fluctuations must be accurately recorded. The system sets a reasonable voltage normal range threshold. When the voltage exceeds this threshold, the fluctuation event is promptly recorded and the amplitude of the fluctuation is accurately measured. Pre-set weighting parameters are also adjusted based on the actual system operation to ensure that the voltage stability index accurately reflects the impact of voltage fluctuations on the system. In the process of establishing a collaborative evaluation model, multiple factors need to be comprehensively considered. For power transmission loss factors, the system establishes a detailed power transmission model, considering the impact of factors such as line impedance, transmission distance, and current on loss. For task execution correlation factors, the system analyzes the role and interaction of each partition in the ranging task, quantifying indicators such as data flow and synchronization requirements between them.

[0068] By incorporating these factors into the evaluation model, the synergy between adjacent partitions can be more accurately assessed. Setting the preset threshold is also a key step in the evaluation model. Setting this threshold requires balancing the efficiency gains from partition merging with the increased management complexity. If the threshold is set too high, it may make it difficult to merge adjacent partitions, failing to fully leverage the benefits of dynamic partition configuration. If the threshold is set too low, it may lead to excessive partition merging, increasing management complexity and even reducing power supply efficiency. Therefore, the preset threshold needs to be appropriately set based on the system's characteristics and actual operating environment, and adjusted based on feedback during system operation.

[0069] After generating a dynamic partition configuration plan, the system also monitors its implementation in real time. The effectiveness of the plan is evaluated by comparing indicators such as the partition power supply impact value and system power consumption before and after the configuration plan is implemented. If the implementation effect is found to be unsatisfactory, the system will promptly adjust the evaluation model parameters or preset thresholds and regenerate the dynamic partition configuration plan to ensure that the system is always in the optimal power supply state. The entire process of obtaining partition power supply impact values ​​and dynamically configuring power supply partitions is a closed-loop control system. Through continuous monitoring, evaluation, and adjustment, it achieves continuous optimization of the iTOF ranging system's power supply, effectively reducing system power consumption and improving system performance and reliability.

[0070] Example 3: After the system obtains the partition load fluctuation value based on the power supply adjustment execution signal, it begins to process it using statistical methods. The system records the load fluctuation data of each partition within a time period. Suppose the load fluctuation data sequence of a partition within the time period is . First calculate the mean of the series ,average value It reflects the average level of load fluctuation in the partition, and its calculation formula is: ,in is the number of data points, Indicates the The load fluctuation value at each moment. Then calculate the standard deviation , standard deviation It is used to measure the degree of dispersion of data relative to the mean value. The calculation formula is: . The standard deviation Divide by the mean , get the load fluctuation characteristic value ,Right now This characteristic value can intuitively reflect the discrete degree and relative size of the load fluctuation. The larger the value, the more severe the load fluctuation and the higher the deviation from the average level.

[0071] For the partition collaborative efficiency value, the system will analyze its data distribution over a period of time. A special data acquisition module is used to collect collaborative efficiency data of each partition during the execution of ranging tasks and other operations. The specific data collected by the special data acquisition module include: communication delay data between partitions (such as the time it takes to transmit data from partition A to partition B), task execution timestamps (the start and end time of each partition completing subtasks), data transmission volume between partitions (such as the size of the ranging raw data or processed data transmitted from partition A to partition B), shared resource occupancy rate (such as the usage ratio of shared memory, computing cores and other resources between partitions), etc. These data are used to quantitatively evaluate the collaborative efficiency between partitions. Suppose the collaborative efficiency data sequence of a partition is The collaborative efficiency value is a quantitative indicator to measure the collaborative efficiency between power supply partitions in the ranging system. Specifically, it refers to the comprehensive evaluation value of data interaction efficiency, task synchronization and resource sharing utilization rate between partitions when performing ranging tasks. For example, the smaller the data transmission delay between partitions, the higher the consistency of task completion time, and the more balanced the efficiency of shared computing resources, the higher the collaborative efficiency value; otherwise, the lower it is. First, the data distribution analysis algorithm is used to calculate its distribution dispersion. , discreteness Reflects the degree of dispersion of collaborative efficiency data; then calculates the concentration , concentration It reflects the trend of collaborative efficiency data concentrating on a certain value. and concentration Perform product operation to obtain the collaborative efficiency characteristic value ,Right now This characteristic value is used to characterize the stability and effectiveness of collaborative efficiency. A larger value means that the stability of collaborative efficiency is worse or the concentration is not ideal.

[0072] The load fluctuation characteristic value and synergistic efficiency characteristic value Perform difference operation to obtain the partition optimization coefficient ,Right now . Partition optimization coefficient The two factors of load fluctuation and coordinated efficiency are comprehensively considered to provide core parameter basis for the optimization of subsequent power supply solutions.

[0073] After generating the optimized partition power supply plan, the system will reversely check the partition load fluctuation value and the collaborative efficiency value. The specific operation is to substitute the actual collected partition load fluctuation value and collaborative efficiency value into the optimized power supply plan calculation model. The calculation model is constructed based on multiple factors such as the system's circuit structure, power consumption characteristics, task execution logic, etc., and contains a series of operation rules and logical judgments. The corresponding theoretical output value is calculated through the model and then compared with the theoretical value based on which the plan was designed. A tolerance range is set in advance. , This is the tolerance threshold set based on system accuracy requirements. If the deviation value obtained during verification exceeds this preset tolerance range, the partition configuration backtracking mechanism is triggered. The system will re-execute step 4, re-obtain the partition load fluctuation value and partition collaborative efficiency value, recalculate the partition optimization coefficient, and re-optimize the power supply plan to ensure its effectiveness and rationality.

[0074] When the load fluctuation value of a partition exceeds the preset fluctuation range continuously, the system will perform exponential attenuation correction on the load fluctuation characteristic value. The system stores historical load fluctuation data internally, and uses the data analysis module to deeply mine these historical data and analyze their changing trends and patterns. Based on the analysis results, the appropriate attenuation factor calculation formula is determined. Assume that the current load fluctuation characteristic value is , the corrected load fluctuation characteristic value is , the attenuation factor is , then the correction formula is ,in The number of times the load fluctuation value exceeds the preset range continuously. The value range is The system dynamically adjusts the load fluctuation characteristic value based on factors such as the amplitude and frequency of historical fluctuation data. This exponential decay correction ensures that the load fluctuation characteristic value more accurately reflects actual load fluctuations, avoiding excessive deviations in the calculation of the partition optimization coefficient due to extreme load fluctuations. This provides a more reliable basis for optimizing the partition power supply solution. Throughout the entire process, the system ensures the accuracy and reliability of the partition optimization coefficient through a series of operations including real-time monitoring, precise calculation, rigorous verification, and dynamic correction. This effectively optimizes the power supply solution for the iTOF ranging system, achieving the goals of reducing system power consumption and improving operational efficiency. Furthermore, the system continuously updates and improves data collection, analysis algorithms, and computational models to adapt to system operating conditions under different working environments and task requirements, ensuring the continued effectiveness of the optimization process. Regarding data collection, the system adjusts the frequency and accuracy of data collection based on the characteristics of each partition to ensure that the acquired load fluctuation and collaborative efficiency values ​​truly reflect the actual system operation. For partitions with frequent load fluctuations, the data collection frequency is appropriately increased; for critical partitions with high collaborative efficiency requirements, the data collection accuracy is improved. In terms of data analysis algorithms, as system operation data continues to accumulate, more advanced machine learning algorithms will be used to analyze data distribution and improve the accuracy of characteristic value calculations such as dispersion and concentration. Regarding the calculation model, the power supply solution calculation model will be updated and improved based on new hardware features, circuit optimization solutions, and adjustments to task execution strategies. This will enable it to more accurately simulate system operating conditions and provide more reliable support for reverse verification and solution optimization. The system will also establish a comprehensive logging and fault diagnosis mechanism, detailing every key step in obtaining partition optimization coefficients and optimizing power supply solutions. When an anomaly occurs, such as triggering the partition configuration backtracking mechanism, log data analysis can quickly pinpoint the cause of the problem, determining whether it is due to inaccurate data collection, deviations in the calculation process, or improper model parameter settings. This allows for timely repairs and adjustments to ensure stable system operation and power consumption optimization.

[0075] Example 4: This example describes in detail the generation of dynamic power supply strategy parameters, specifically including:

[0076] Suppose the iTOF ranging system has completed multiple power supply optimization operations over a period of time. The system's storage module records the partition optimization coefficients obtained from each optimization, forming a historical power supply optimization record. For example, in the past 10 power supply optimization operations, the partition optimization coefficients obtained were 0.2, 0.3, 0.25, 0.4, 0.35, 0.28, 0.32, 0.38, 0.42, and 0.36, respectively. The system arranges these coefficients into a time series in the order in which the optimization operations occurred. Next, the system processes this time series using a sliding average calculation method. The sliding average calculation requires setting an appropriate sliding window size, which takes into account both system operational stability and responsiveness to new data. If the window is too small, the calculation results may be significantly affected by individual anomalies; if the window is too large, the calculation results will be less responsive to real-time system changes. For example, based on the system's operating characteristics, the sliding window size is set to 5. At this point, the system selects five consecutive partition optimization coefficients (0.2, 0.3, 0.25, 0.4, and 0.35) from the beginning of the time series and calculates their average. This average becomes the dynamic power supply strategy baseline for that phase. As time passes, the sliding window slides back one data position, meaning the next calculation will select the five data points (0.3, 0.25, 0.4, 0.35, and 0.28) to calculate the average. This continuously updates the dynamic power supply strategy baseline, ensuring that it consistently reflects the historical trends and average levels of power supply optimization.

[0077] Based on the power supply adjustment execution signal, the system calculates the current partition optimization coefficient, such as 0.33. The system then adds the calculated partition optimization coefficient of 0.33 to the latest dynamic power supply policy baseline value. For example, if the latest dynamic power supply policy baseline value is 0.3, the resulting dynamic power supply policy parameter is 0.63. This dynamic power supply policy parameter is further added to the current power supply partition configuration to generate an optimized partition power supply solution.

[0078] During the operation of the system, the collaborative efficiency value of each partition will be monitored in real time. Assuming that the preset efficiency threshold set by the system is 0.6, when the collaborative efficiency value of a certain partition is monitored to be 0.5, which is lower than the preset efficiency threshold, the system will perform a linear compensation correction on the collaborative efficiency characteristic value of the partition. The determination of the compensation weight requires first analyzing the task correlation between partitions. Taking an actual scenario as an example, when the iTOF ranging system performs an environmental modeling task, there are partitions A, B, and C. Partition A is responsible for data collection, partition B performs preliminary data processing, and partition C completes the final model construction. There is a close data interaction and resource sharing relationship between the three partitions.

[0079] The system quantifies the task correlation between partitions by analyzing the data flow, data exchange frequency, type of shared resources, and usage duration during task execution. For example, statistics show that over the past period, the amount of data transferred from partition A to partition B accounted for 70% of its total data output, while the amount of data transferred from partition B to partition C accounted for 80% of its total data output. Furthermore, the three partitions shared the same computing resources for 60% of the time. Based on this data, the system applied a specific quantification algorithm to determine that the task correlation between partitions A and B is 0.7, and the task correlation between partitions B and C is 0.8.

[0080] Based on these quantified task correlations, the system further determines reasonable compensation weights. For example, when correcting the collaborative efficiency characteristic value of partition B, due to its high task correlation with partitions A and C, the system will assign it a higher compensation weight, such as 0.8. Through this compensation weight, the collaborative efficiency characteristic value of partition B is linearly compensated. The corrected collaborative efficiency characteristic value will be used to recalculate the partition optimization coefficient, thereby affecting the generation of dynamic power supply strategy parameters, so that the dynamic power supply strategy parameters can better adapt to the current operating status changes of the system. In the subsequent power supply solution optimization process, for partitions with low collaborative efficiency, the power supply strategy is reasonably adjusted to improve the optimization effect of the power supply solution, thereby achieving effective control of the power consumption of the iTOF ranging system and performance improvement.

[0081] Throughout the dynamic power supply strategy parameter generation and collaborative efficiency characteristic value correction process, the system continuously monitors each partition's operational data, including but not limited to partition optimization coefficients, collaborative efficiency values, and task correlations. If any unusual data fluctuations or changes in system operating status are detected, such as changes in inter-partition task correlations caused by the addition of a new ranging task, the system promptly adjusts relevant calculation parameters, such as the sliding window size, preset efficiency thresholds, and quantization algorithms, to ensure that the dynamic power supply strategy parameters accurately reflect the actual system requirements. The generated power supply plan optimizes the iTOF ranging system under various operating conditions, balancing system performance and power consumption to ensure stable and efficient system operation. Furthermore, the system regularly cleans and organizes historical power supply optimization records, deleting expired or invalid data to improve data processing efficiency. It also continuously optimizes data storage and retrieval methods to ensure fast and accurate data acquisition when generating dynamic power supply strategy parameters. Furthermore, the system continuously optimizes the inter-partition task correlation quantization algorithm, incorporating advanced data analysis and machine learning techniques to improve the accuracy and real-time performance of task correlation quantification, thereby further enhancing the quality of dynamic power supply strategy parameter generation and the effectiveness of power supply plan optimization.

[0082] Example 5: In the process of dynamically adjusting the signal sampling frequency of the iTOF ranging system, in order to improve the accuracy of the dynamic sampling frequency value, Example 5 optimizes the calculation process by introducing the reflective object material identification parameter. The specific implementation method is as follows:

[0083] At the hardware level of the ranging system, a dedicated material recognition module must be integrated. This module can employ a variety of technical means to identify the material of a reflective object. For example, the implementation of spectral analysis technology relies on a spectral sensor, which can collect spectral information from reflective objects. When light strikes an object's surface and reflects back, the spectral sensor captures the intensity distribution of light at different wavelengths. Each material has its own unique spectral reflectance characteristics. The system compares this collected spectral information with a pre-built database of material spectra. This database stores spectral data for a large amount of common materials under different lighting conditions. An algorithm calculates the similarity between the collected spectra and the spectra in the database to determine the material type of the reflective object.

[0084] Another common technical approach is image recognition. This system uses high-definition image acquisition devices, such as cameras, to capture images of reflective objects. Using image processing algorithms, the images are pre-processed, including noise reduction and contrast enhancement, to improve image quality. Key features in the image, such as texture, color distribution, and shape, are then extracted. Pattern recognition algorithms are then applied to match these features with a library of material image features to determine the object's material. In practical applications, spectral analysis and image recognition technologies can be combined to leverage their complementary strengths, further improving the accuracy and reliability of material identification.

[0085] After determining the material of the reflective object, different materials have different reflectivities. These differences in reflectivity can significantly affect the ambient light intensity, and thus the accuracy of the integrated light fluctuation value. Therefore, it is necessary to perform a normalized compensation calculation for the integrated light fluctuation value based on the differences in material reflectivity. First, the system establishes a corresponding table between material reflectivity and compensation coefficients. This table is constructed based on a large amount of experimental data and theoretical analysis. During the experimental phase, the reflectivity of various common materials under different lighting conditions is measured, and a compensation coefficient is assigned to each material based on the degree to which the reflectivity affects light intensity. For example, metal materials with high reflectivity have a strong ability to reflect light and have a greater impact on ambient light intensity, so they are assigned a larger compensation coefficient; on the other hand, fabric materials with low reflectivity have a relatively small compensation coefficient.

[0086] Once the material recognition module determines the material of the reflective object, the system immediately obtains the corresponding compensation coefficient from the correspondence table. Assuming that the currently identified reflective object is made of metal, the compensation coefficient obtained from the table is 0.8. Next, the compensation coefficient is calculated with the previously calculated comprehensive illumination fluctuation value. The calculation process is performed according to the rules set by the system. For example, the compensation coefficient can be multiplied by the comprehensive illumination fluctuation value, or calculated through a more complex function relationship to achieve the correction of the comprehensive illumination fluctuation value. After the calculation, the corrected comprehensive illumination fluctuation value is obtained, and the dynamic sampling frequency adjustment coefficient is recalculated based on this correction value.

[0087] In practice, the system substitutes the corrected integrated light fluctuation value into the dynamic sampling frequency adjustment coefficient calculation process, replacing the original integrated light fluctuation value. For example, the original calculation method is to divide the integrated light fluctuation value by the preset fluctuation threshold to obtain the adjustment coefficient. Now, the same division operation is performed using the corrected integrated light fluctuation value to generate the corrected dynamic sampling frequency adjustment coefficient. Based on this new adjustment coefficient, combined with the initial sampling frequency of the ranging system, a more accurate dynamic sampling frequency value is ultimately obtained.

[0088] In actual application scenarios, such as indoor environments, when the iTOF ranging system measures the distance of a room with metal furniture and fabric curtains, the material recognition module will identify the metal and fabric materials respectively and obtain the corresponding compensation coefficients. By compensating for the comprehensive light fluctuation value, the dynamic sampling frequency value calculated by the system can more accurately reflect the impact of the actual lighting environment on the ranging signal. In outdoor environments, when facing reflective objects such as buildings and vegetation made of different materials, this mechanism can also be used to make precise adjustments based on the differences in material reflectivity, ensuring that the ranging system can operate at a reasonable sampling frequency under different lighting environments and reflective object conditions. A reasonable sampling frequency can not only ensure that a sufficiently accurate ranging signal is obtained to meet the ranging accuracy requirements, but also avoid the increase in system power consumption due to excessively high sampling frequency, or the impact of excessively low sampling frequency on the ranging effect, thereby achieving a good balance between power consumption and performance of the iTOF ranging system and improving the overall efficiency of the system. In addition, the system will continuously update the material spectrum database, image feature library, and the correspondence table between material reflectivity and compensation coefficient. With the emergence of new materials and environmental changes, it will continuously optimize the material identification and compensation calculation process to ensure the accuracy and effectiveness of dynamic sampling frequency adjustment.

[0089] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0090] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing power consumption of iTOF ranging, characterized in that: The following steps are involved: Step 1: Dynamically adjust the signal sampling frequency of the ranging system and obtain the dynamic sampling frequency value according to the change in ambient light intensity; Step 2: Based on the dynamic sampling frequency value, the real-time sampling frequency is compared with the preset sampling frequency range, and if the real-time sampling frequency exceeds the preset sampling frequency range, a sampling frequency adjustment signal is generated; Step 3: Based on the sampling frequency adjustment signal, the ranging system is divided into power supply zones. During the division period, the power supply status data of each zone is obtained, the zone power supply impact value is calculated, and the value is compared with the zone power supply impact threshold to generate a power supply adjustment signal. If the partition power supply impact value is greater than or equal to the partition power supply impact threshold, a power supply adjustment execution signal is generated; Step 4: Based on the power supply adjustment execution signal, obtain the partition load fluctuation value and the partition coordination efficiency value, calculate the difference between the load fluctuation value and the coordination efficiency value, and obtain the partition optimization coefficient; Step 5: Based on the power supply adjustment execution signal, obtain the dynamic power supply strategy parameters, superimpose the current power supply partition configuration and the dynamic power supply strategy parameters, generate an optimized partition power supply plan, and complete the power supply adjustment of the ranging system; The partition power supply impact value is obtained in the following way: Extract the current fluctuation impact value and voltage stability impact value of each partition, multiply the current fluctuation impact value and the voltage stability impact value to obtain the partition power supply impact value; The power supply zones are divided as follows: Based on the real-time changes in partition load fluctuation values ​​and collaborative efficiency values, the power supply status of adjacent partitions is evaluated for synergy. If the synergy evaluation value is higher than the preset threshold, the adjacent partitions are merged into a joint power supply unit to generate a dynamic partition configuration plan.

2. The iTOF ranging power consumption optimization method according to claim 1, wherein: The dynamic sampling frequency value is obtained as follows: The ranging environment is divided into multiple light intensity monitoring areas, the light intensity change rate of each area is obtained, and the change rate of each area is weighted and summed to obtain the comprehensive light fluctuation value; The ratio operation of the comprehensive light fluctuation value and the preset fluctuation threshold is performed to obtain the dynamic sampling frequency adjustment coefficient.

3. The iTOF ranging power consumption optimization method according to claim 1, wherein: The current fluctuation impact value is obtained as follows: During the monitoring period, the current peak and valley change slopes of each partition are extracted, and the difference between the current change slope and the preset slope reference value is calculated to obtain the current fluctuation deviation value; The current fluctuation deviation value is normalized with the number of partitions to obtain the current fluctuation impact value.

4. The iTOF ranging power consumption optimization method according to claim 3, wherein: The voltage stability impact value is obtained as follows: During the division period, the number and amplitude of voltage fluctuations in each partition are recorded, and the number and amplitude of fluctuations are weighted and accumulated to obtain the voltage stability index. The voltage stability index is proportional to the total number of partitions to obtain the voltage stability impact value.

5. The iTOF ranging power consumption optimization method according to claim 4, wherein: The partition optimization coefficient is obtained as follows: According to the partition load fluctuation value, the standard deviation and the average value of the load fluctuation value are extracted, and the ratio of the standard deviation to the average value is calculated to obtain the load fluctuation characteristic value; According to the partition collaborative efficiency value, the distribution dispersion and concentration of the efficiency value are extracted, and the dispersion and concentration are multiplied to obtain the collaborative efficiency characteristic value; Perform difference calculation on the load fluctuation characteristic value and the collaborative efficiency characteristic value to obtain the partition optimization coefficient; The verification method of the partition optimization coefficient is: After generating the optimized partition power supply plan, the partition load fluctuation value and the collaborative efficiency value are reversely checked. If the check deviation value exceeds the preset tolerance range, the partition configuration backtracking mechanism is triggered and step four is executed again.

6. The iTOF ranging power consumption optimization method according to claim 5, wherein: The dynamic power supply strategy parameters are generated as follows: Obtain the partition optimization coefficients from the historical power supply optimization records, calculate the sliding average of the optimization coefficients according to the time series, and obtain the dynamic power supply strategy benchmark value; The current partition optimization coefficient is superimposed on the dynamic power supply strategy benchmark value to generate the dynamic power supply strategy parameters.

7. The iTOF ranging power consumption optimization method according to claim 6, wherein: The correction method of the load fluctuation characteristic value is: When the partition load fluctuation value continuously exceeds the preset fluctuation range, the load fluctuation characteristic value is exponentially attenuated and the attenuation factor is dynamically adjusted according to the historical fluctuation data.

8. The iTOF ranging power consumption optimization method according to claim 7, wherein: The correction method of the collaborative efficiency characteristic value is: When the collaborative efficiency value is lower than the preset efficiency threshold, a linear compensation correction is performed on the collaborative efficiency characteristic value, and the compensation weight is dynamically determined according to the task correlation between partitions.

9. The iTOF ranging power consumption optimization method according to claim 1, wherein: The correction method of the dynamic sampling frequency value is: The material identification parameters of reflective objects are introduced into the light intensity monitoring area, and the comprehensive light fluctuation value is normalized and compensated according to the material reflectivity difference to generate the corrected dynamic sampling frequency adjustment coefficient.

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