ITOF ranging power consumption optimization method

Abstract

The invention relates to the technical field of indirect time of flight (iTOF) ranging, and discloses an iTOF ranging power consumption optimization method, which comprises the following steps of: dynamically adjusting signal sampling frequency according to the change of environment illumination intensity; comparing the real-time sampling frequency with a preset interval, and generating an adjusting signal if the real-time sampling frequency exceeds the range; dividing power supply partitions based on the adjustment signal, calculating a partition power supply influence value, comparing the partition power supply influence value with a threshold value, and generating a power supply adjustment related signal; obtaining a load fluctuation value and a cooperative efficiency value according to the power supply adjustment execution signal, and calculating a partition optimization coefficient; and obtaining dynamic power supply strategy parameters, and superposing the dynamic power supply strategy parameters with the current power supply partition configuration to generate an optimization scheme. According to the method, the sampling frequency and the power supply strategy are dynamically adjusted, multiple influence factors are comprehensively considered, power consumption optimization of the iTOF ranging system is achieved, the system performance and stability are improved, and the method is suitable for multiple iTOF ranging application scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of indirect time-of-flight (iTOF) ranging, and specifically to an iTOF ranging power consumption optimization method. Background Art

[0002] With the rapid development of technologies such as the Internet of Things, autonomous driving, and robot vision, the indirect time-of-flight (iTOF) ranging technology has been widely used in many fields due to its advantages of non-contact measurement, high accuracy, and fast response speed. The iTOF ranging technology calculates the distance by measuring the round-trip time of the optical signal between the target to be measured and the sensor. In practical applications, the power consumption problem of the ranging system has become one of the key factors restricting its further development and application.

[0003] On the one hand, traditional iTOF ranging systems usually adopt a fixed signal sampling frequency, and this method does not consider the influence of changes in ambient light intensity on the ranging signal. Under different lighting conditions, ambient light noise will interfere with the accuracy of the ranging signal. When the ambient light intensity is strong, if the sampling frequency is too low, it may not be able to effectively capture the accurate ranging signal, resulting in a decrease in ranging accuracy; while when the ambient light intensity is weak, too high a sampling frequency will not only improve the ranging accuracy, but will increase the power consumption of the system, causing energy waste. On the other hand, the power supply management method of the ranging system is relatively crude, generally adopting a fixed power supply partition configuration and power supply strategy, without dynamic adjustment according to the actual operating state of the system. During the operation of the system, the load conditions and collaborative working efficiency of each partition will change. For example, when performing different ranging tasks, the computing loads and data processing requirements of each partition are different. If the power supply allocation is unreasonable, it will lead to insufficient power supply in some partitions affecting performance, or excessive power supply in some partitions resulting in increased power consumption.

[0004] In the process of obtaining the parameter calculation related to power supply, there are also many deficiencies in the prior art. For the calculation of the influence value of partition power supply, only a single current or voltage factor is often considered, without comprehensively evaluating the influence of current fluctuations and voltage stability on the power supply state, and it is impossible to accurately reflect the actual power supply situation of each partition, resulting in the lack of accuracy and effectiveness of 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 well adapt to the changes in the system operating state, and it is difficult to effectively control the power consumption. In addition, the prior art cannot perform targeted optimization and adjustment of the sampling frequency and power supply strategy in the face of complex actual application scenarios, such as an environment with objects of different reflectivities, 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 state to reduce the system power consumption and improve the ranging performance and stability. Summary of the Invention

[0005] The object of the present invention is to provide an iTOF ranging power consumption optimization method to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: an iTOF ranging power consumption optimization method, the method comprising: Step 1: Dynamically adjust the signal sampling frequency of the ranging system, and obtain a dynamic sampling frequency value according to the change value of the ambient light intensity; Step 2: Based on the dynamic sampling frequency value, compare the real-time sampling frequency with a preset sampling frequency range. If the real-time sampling frequency exceeds the preset sampling frequency range, generate a sampling frequency adjustment signal; Step 3: Based on the sampling frequency adjustment signal, divide the power supply areas of the ranging system. During the division period, obtain the power supply state data of each area, calculate the power supply influence value of the area, and compare it with the power supply influence threshold to generate a power supply adjustment signal; If the power supply influence value of the area is greater than or equal to the power supply influence threshold, generate a power supply adjustment execution signal; Step 4: Based on the power supply adjustment execution signal, obtain the load fluctuation value and the cooperation efficiency value of the area, calculate the difference between the load fluctuation value and the cooperation efficiency value to obtain the area optimization coefficient; Step 5: Based on the power supply adjustment execution signal, obtain the dynamic power supply strategy parameters, perform superposition calculation on the current power supply area configuration and the dynamic power supply strategy parameters, generate an optimized area power supply plan, and complete the power supply adjustment of the ranging system.

[0007] Preferably, the obtaining method of the dynamic sampling frequency value is: Divide the ranging environment into multiple light intensity monitoring areas, obtain the light intensity change rate of each area, perform weighted summation calculation on the change rates of each area to obtain a comprehensive light fluctuation value; Perform ratio operation on the comprehensive light fluctuation value and a preset fluctuation threshold to obtain a dynamic sampling frequency adjustment coefficient.

[0008] Preferably, the obtaining method of the power supply influence value of the area is: Extract the current fluctuation influence value and the voltage stability influence value of each area, perform product operation on the current fluctuation influence value and the voltage stability influence value to obtain the power supply influence value of the area; The division method of the power supply area is: Based on the real-time changes of the load fluctuation value and the cooperation efficiency value of the area, perform cooperation evaluation on the power supply states of adjacent areas. If the cooperation evaluation value is higher than the preset threshold, merge the adjacent areas into a combined power supply unit to generate a dynamic area configuration plan.

[0009] Preferably, the acquisition method of the current fluctuation influence value is as follows: During the monitoring period, extract the change slopes of the current peak and valley values in each partition, calculate the difference between the current change slope and the preset slope reference value to obtain the current fluctuation deviation value; Normalize the current fluctuation deviation value with the number of partitions to obtain the current fluctuation influence value.

[0010] Preferably, the acquisition method of the voltage stability influence value is as follows: During the division period, record the number of voltage fluctuations and the fluctuation amplitude in each partition, perform weighted cumulative calculation on the number of fluctuations and the fluctuation amplitude to obtain the voltage stability index; Perform proportional operation on the voltage stability index and the total number of partitions to obtain the voltage stability influence value.

[0011] Preferably, the acquisition method of the partition optimization coefficient is as follows: According to the partition load fluctuation value, extract the standard deviation and the average value of the load fluctuation value, calculate the ratio of the standard deviation to the average value to obtain the load fluctuation characteristic value; According to the partition cooperation efficiency value, extract the distribution dispersion and concentration of the efficiency value, perform product operation on the dispersion and the concentration to obtain the cooperation efficiency characteristic value; Perform difference operation on the load fluctuation characteristic value and the cooperation efficiency characteristic value to obtain the partition optimization coefficient; The verification method of the partition optimization coefficient is as follows: After generating the optimized partition power supply scheme, perform reverse verification on the partition load fluctuation value and the cooperation efficiency value. If the verification deviation value exceeds the preset tolerance range, trigger the partition configuration backtracking mechanism and re-execute step four.

[0012] Preferably, the generation method of the dynamic power supply strategy parameter is as follows: Obtain the partition optimization coefficient in the historical power supply optimization record, perform moving average calculation on the optimization coefficient according to the time series to obtain the dynamic power supply strategy reference value; Superimpose the current partition optimization coefficient and the dynamic power supply strategy reference value to generate the dynamic power supply strategy parameter.

[0013] Preferably, the correction method of the load fluctuation characteristic value is as follows: When the partition load fluctuation value continuously exceeds the preset fluctuation range, perform exponential decay correction on the load fluctuation characteristic value, and the decay factor is dynamically adjusted according to the historical fluctuation data.

[0014] Preferably, the correction method of the cooperation efficiency characteristic value is as follows: When the collaborative efficiency value is lower than the preset efficiency threshold, linear compensation and correction are performed on the collaborative efficiency eigenvalue, and the compensation weight is dynamically determined according to the task correlation degree in different intervals.

[0015] Preferably, the correction method of the dynamic sampling frequency value is as follows: Introduce the reflection object material recognition parameter in the light intensity monitoring area, perform normalized compensation calculation on the comprehensive light intensity fluctuation value according to the material reflectivity difference, and generate the corrected dynamic sampling frequency adjustment coefficient.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 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 the traditional iTOF ranging system through multi-dimensional and multi-level optimization strategies. In terms of signal sampling frequency adjustment, the sampling frequency is dynamically obtained according to the change value of the ambient light intensity. The ranging environment is carefully divided into multiple light intensity monitoring areas. By weighted summing the light intensity change rates of each area and operating with the preset fluctuation threshold, the dynamic sampling frequency adjustment coefficient is accurately determined. This method can flexibly adjust the sampling frequency according to the ambient light change. 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 decreased to avoid unnecessary power consumption, thereby effectively reducing the power consumption of the system caused by unreasonable sampling frequency while ensuring the ranging accuracy.

[0017] In terms of power supply partition management and optimization, the power supply partitions are dynamically divided based on the sampling frequency adjustment signal, and the partition power supply influence value is calculated by comprehensively considering the current fluctuation influence value and the voltage stability influence value, comprehensively and accurately evaluating the power supply status of each partition. At the same time, according to the real-time changes of the partition load fluctuation value and the collaborative efficiency value, the power supply status of adjacent partitions is evaluated for collaboration, and the partitions with high collaboration are merged into a combined power supply unit to generate a dynamic partition configuration plan. This dynamic power supply partition management method can reasonably allocate power resources according to the actual operation requirements of the system, avoid power consumption waste caused by unreasonable power supply in some partitions, and improve the power supply efficiency.

[0018] In the process of determining the optimization coefficient and power supply strategy, the zonal optimization coefficient is obtained through a scientific calculation method, and is verified and corrected. Eigenvalues are extracted based on the load fluctuation value and the collaborative efficiency value respectively, and the zonal optimization coefficient is obtained through difference operation. At the same time, after generating the power supply plan, a reverse check is carried out to ensure the effectiveness of the plan; when the load fluctuation value or the collaborative efficiency value is abnormal, the corresponding eigenvalue is dynamically corrected to make the optimization coefficient more accurately reflect the actual situation of the system. On this basis, dynamic power supply strategy parameters are generated using historical power supply optimization records, and the current power supply zone configuration is superimposed and calculated with the dynamic power supply strategy parameters to generate an optimized zonal power supply plan, realizing the dynamic optimization of the power supply strategy, so that the system can maintain low power consumption and good performance under various operating conditions.

[0019] In addition, in the process of correcting the dynamic sampling frequency value, a parameter for identifying the material of the reflective object is introduced, and the comprehensive light fluctuation value is normalized and compensated according to the difference in material reflectivity, further improving the accuracy of sampling frequency adjustment, enabling the system to operate stably and efficiently even in complex lighting and reflective object environments, reducing the power consumption increase and ranging error caused by environmental factors, and greatly improving the overall performance and application range of the iTOF ranging system. Brief Description of the Drawings

[0020] Figure 1 It is the working principle diagram of the iTOF ranging power consumption optimization method described in the present invention; Figure 2 It is the design diagram for obtaining the dynamic sampling frequency value; Figure 3 It is the schematic diagram for calculating the influence value of zonal power supply. Detailed Embodiments

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] Please refer to Figures 1 - 3 , an iTOF ranging power consumption optimization method involved in the present invention, and the specific implementation steps are as follows: Step 1: Dynamically adjust the signal sampling frequency of the ranging system. Based on the change value of the ambient light intensity, obtain the dynamic sampling frequency value. Divide the ranging environment into multiple light intensity monitoring areas, and set corresponding light intensity sensors in each monitoring area. Through these sensors, obtain the light intensity change rate of each area in real time. Based on factors such as the importance and area size of each area, assign different weights to the change rates of each area, and perform weighted summation calculation on the change rates of each area to obtain the comprehensive light fluctuation value; then perform a ratio operation on the comprehensive light fluctuation value and the preset fluctuation threshold to obtain the dynamic sampling frequency adjustment coefficient, and combine it with the initial sampling frequency to further obtain the dynamic sampling frequency value.

[0023] Step 2: Based on the dynamic sampling frequency value, compare the real-time sampling frequency with the preset sampling frequency interval. If the real-time sampling frequency exceeds the preset sampling frequency interval, generate a sampling frequency adjustment signal. The preset sampling frequency interval is preset according to factors such as the performance parameters and working environment of the ranging system. By comparing the real-time sampling frequency with this interval, judge whether the current sampling frequency is reasonable. Once it exceeds the range, immediately generate a sampling frequency adjustment signal for subsequent adjustment of the ranging system.

[0024] Step 3: Based on the sampling frequency adjustment signal, divide the power supply areas of the ranging system. During the division period, obtain the power supply status data of each area through devices such as current sensors and voltage sensors, extract the current fluctuation influence value and voltage stability influence value of each area, perform a product operation on the current fluctuation influence value and the voltage stability influence value to obtain the area power supply influence value, and compare it with the area power supply influence threshold to generate a power supply adjustment signal; if the area power supply influence value is greater than or equal to the area power supply influence threshold, generate a power supply adjustment execution signal. When dividing the power supply areas, based on the real-time changes of the area load fluctuation value and the cooperation efficiency value, perform a cooperation evaluation on the power supply status of adjacent areas. If the cooperation evaluation value is higher than the preset threshold, merge adjacent areas into a combined power supply unit to generate a dynamic area configuration plan.

[0025] Step 4: Based on the power supply adjustment execution signal, obtain the area load fluctuation value and the area cooperation efficiency value, perform a difference calculation on the load fluctuation value and the cooperation efficiency value to obtain the area optimization coefficient. According to the area load fluctuation value, extract the standard deviation and average value of the load fluctuation value, perform a ratio calculation on the standard deviation and the average value to obtain the load fluctuation characteristic value; according to the area cooperation efficiency value, extract the distribution dispersion degree and concentration degree of the efficiency value, perform a product operation on the dispersion degree and the concentration degree to obtain the cooperation efficiency characteristic value; perform a difference operation on the load fluctuation characteristic value and the cooperation efficiency characteristic value to obtain the area optimization coefficient. After generating the optimized area power supply plan, perform a reverse check on the area load fluctuation value and the cooperation efficiency value. If the check deviation value exceeds the preset tolerance range, trigger the area configuration backtracking mechanism and re-execute Step 4.

[0026] Step 5: Based on the power supply adjustment execution signal, obtain the dynamic power supply policy parameters, perform superposition calculation on the current power supply partition configuration and the dynamic power supply policy parameters, generate an optimized partition power supply plan, and complete the power supply adjustment of the ranging system. Obtain the partition optimization coefficient in the historical power supply optimization record, perform a moving average calculation on the optimization coefficient according to the time series to obtain the dynamic power supply policy reference value; superimpose the current partition optimization coefficient and the dynamic power supply policy reference value to generate the dynamic power supply policy parameters.

[0027] Embodiment 1: In the iTOF ranging system, the process of obtaining the dynamic sampling frequency value is as follows. First, it is necessary to reasonably divide the ranging environment into multiple light intensity monitoring areas. When dividing, factors such as the spatial layout of the ranging scene and the characteristics of light distribution are comprehensively considered. For example, for the indoor ranging environment, according to the structure of the room, the position of the window, etc., different functional areas or areas with large differences in light may be divided into independent monitoring areas; for the outdoor environment, the area is divided according to the terrain, building occlusion, etc. High-precision light intensity sensors are installed in each monitoring area, and these sensors have the ability to collect light intensity data in real time and stably, and can continuously collect light intensity data at very short time intervals, providing raw data support for subsequent calculation of the light intensity change rate.

[0028] When calculating the light intensity change rate of each area per unit time, the light intensity sensor collects data at continuous time nodes, and the ratio of the difference between the light intensity data at adjacent time nodes to the time interval is the light intensity change rate per unit time. In order to make the subsequent obtained comprehensive light fluctuation value more accurately reflect the light change situation of the entire ranging environment, it is necessary to determine the corresponding weight coefficients according to factors such as the role of each area in the entire ranging environment and the coverage range. For areas that play a key role in the ranging process, such as the area where the main target is located and the area where the light change has a greater impact on the ranging result, a higher weight is given; while for some auxiliary areas or areas with relatively stable light and less impact on the ranging result, a lower weight is given. The determination of the weight coefficient can be achieved through a pre-set rule or algorithm, and this rule or algorithm comprehensively considers multiple factors such as the geometric position of the area, the functional importance, and the historical light change situation.

[0029] By multiplying the change rate of each region by the corresponding weight and then summing them up, the comprehensive light intensity fluctuation value can be obtained. This comprehensive light intensity fluctuation value integrates the light intensity change information of different regions in the entire ranging environment and reflects the fluctuation degree of the environmental light intensity as a whole. Then, divide the comprehensive light intensity fluctuation value by the preset fluctuation threshold to obtain the dynamic sampling frequency adjustment coefficient. The preset fluctuation threshold is a reference value preset according to the performance parameters of the ranging system, the characteristics of the working environment, etc., and is used to measure the size of the comprehensive light intensity fluctuation value to determine the degree of adjustment required for the sampling frequency. By performing a ratio operation on the comprehensive light intensity fluctuation value and the preset fluctuation threshold, the obtained dynamic sampling frequency adjustment coefficient can directly reflect the proportional relationship by which the sampling frequency should be adjusted under the current environmental light intensity change situation.

[0030] Combine the dynamic sampling frequency adjustment coefficient with the initial sampling frequency of the ranging system to finally obtain the dynamic sampling frequency value. Specifically, perform corresponding multiplication operations or other logical operation operations on the initial sampling frequency according to the dynamic sampling frequency adjustment coefficient, so as to obtain the dynamic sampling frequency value that adapts to the current environmental light intensity change. This process realizes the dynamic adjustment of the sampling frequency, enabling the sampling frequency to change with the change of the environmental light intensity. When the environmental light intensity changes greatly, the dynamic sampling frequency value will be adjusted accordingly to ensure that the ranging system can obtain sufficient and accurate signal data; when the environmental light intensity is relatively stable, the sampling frequency will be appropriately reduced to avoid unnecessary power consumption waste caused by too high a sampling frequency.

[0031] In order to further improve the accuracy of the sampling frequency adjustment, a reflection object material recognition parameter is introduced in the light intensity monitoring area. Integrate a special material recognition module in the ranging system, and this module uses advanced technical means such as spectral analysis and image recognition to identify the material of the reflection object. The spectral analysis technology determines the material of the object by analyzing the spectral characteristics of the light reflected by the object and comparing it with the pre-established material spectral database; the image recognition technology uses an image acquisition device to obtain the image information of the object and judges the material type of the object through algorithms such as image feature extraction and pattern recognition.

[0032] Different materials have different reflectivities. The differences in reflectivities will have varying degrees of influence on the light intensity, thereby affecting the accuracy of the comprehensive light intensity fluctuation value. Therefore, normalization compensation calculation is performed on the comprehensive light intensity fluctuation value according to the material reflectivity differences. During specific implementation, first, a correspondence table between material reflectivities and compensation coefficients is established. This table is obtained through a large number of experiments and data statistical analyses, and details the reflectivities of various common materials and their corresponding compensation coefficients. When the material recognition module determines the material of the reflecting object, the corresponding compensation coefficient is obtained from the correspondence table according to this material, and then this compensation coefficient is operated with the comprehensive light intensity fluctuation value. The operation process can be multiplication operation, addition operation, or other operation methods designed according to actual situations. Through this operation, a corrected dynamic sampling frequency adjustment coefficient is generated. Based on the corrected adjustment coefficient, a more accurate dynamic sampling frequency value is recalculated, enabling the ranging system to work at a more reasonable sampling frequency under different lighting environments and reflecting object conditions, effectively reducing the system power consumption while ensuring the ranging accuracy, and achieving the optimization of the power consumption of the iTOF ranging system.

[0033] Embodiment 2: In the process of optimizing the power consumption of the iTOF ranging system, the acquisition of the power supply partition influence value and the dynamic configuration of the 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 need to have the characteristics of high sampling rate and high precision, and can capture the minute changes in current and voltage. During the monitoring period, by analyzing the current data, the change slopes of the current peak and valley values of each partition are extracted. Specifically, the system records the changes in current at adjacent time points and calculates the rate of increase or decrease of the current, that is, the change slope. The difference between these current change slopes and a preset slope reference value is calculated to obtain the current fluctuation deviation value. The preset slope reference value is a reference value preset according to the current change characteristics under the normal operating state of the system, and is used to measure the abnormality degree of the current fluctuation.

[0034] To make the current fluctuation impact values of different partitions comparable, it is necessary to normalize the current fluctuation deviation values with the number of partitions. The normalization process is to adjust the current fluctuation deviation value of each partition according to the total number of partitions, eliminating the influence of the difference in the number of partitions on the results, so as to obtain the current fluctuation impact value. This value can accurately reflect the impact degree of the current fluctuation in each partition on the entire power supply system. At the same time, the system also records the voltage fluctuation times and fluctuation amplitudes of each partition. The voltage fluctuation times refer to the number of times the voltage exceeds the normal range within the monitoring period, and the fluctuation amplitude refers to the degree to which the voltage deviates from the normal range. The fluctuation times and fluctuation amplitudes are weighted and accumulated according to the preset weights to obtain the voltage stability index. The setting of the weights is determined based on the importance of the fluctuation times and fluctuation amplitudes to the system performance. Then, the voltage stability index is subjected to a proportional operation with the total number of partitions to obtain the voltage stability impact value.

[0035] Finally, multiply the current fluctuation impact value by the voltage stability impact value to obtain the partition power supply impact value. This multiplication operation comprehensively considers the fluctuations of both current and voltage, and can comprehensively evaluate the impact of the power supply stability of each partition on the entire ranging system. In terms of power supply partition division, based on the real-time changes of the partition load fluctuation value and the cooperation efficiency value, the system conducts a collaborative evaluation of the power supply status of adjacent partitions through a specially established evaluation model. When the system generates a sampling frequency adjustment signal (usually triggered because the real-time sampling frequency exceeds the preset interval), first judge the current system workload characteristics. For example, an increase in the sampling frequency will increase the workload of modules such as data acquisition and processing. Combining the real-time power consumption requirements of each functional module, and based on the type of sampling frequency adjustment signal, analyze the power consumption changes of modules such as data acquisition, signal processing, and result output. Then, combine the current partition load fluctuation value and the cooperation efficiency value to evaluate the power supply status of each partition. If an increase in the sampling frequency causes an increase in the partition load fluctuation value of the data acquisition partition, and the cooperation efficiency value of the adjacent data processing partition is high, it is necessary to focus on considering the power supply cooperation between the two. Through the cooperation evaluation model, judge whether the cooperation between adjacent partitions is higher than the preset threshold. If the cooperation is high, such as frequent data interaction is required between partitions due to an increase in the sampling frequency, they can be combined into a joint power supply unit; if the cooperation is low, such as a sudden reduction in the load of a partition due to a decrease in the sampling frequency and a decrease in the task association degree with the adjacent partition, it is split to avoid power supply redundancy. In this way, the power supply partition is dynamically matched with the sampling frequency adjustment signal, which not only meets the power consumption requirements but also reduces the overall energy consumption. The evaluation model comprehensively considers factors such as the power transmission loss and task execution association degree between partitions. The power transmission loss is related to factors such as the physical distance and line impedance between partitions. The farther the distance and the greater the impedance, the greater the transmission loss. The task execution association degree refers to the degree of cooperation tightness between adjacent partitions during the execution of the ranging task, such as the data interaction frequency and resource sharing situation.

[0036] If the synergy evaluation value is higher than the preset threshold, it indicates that adjacent partitions have strong synergy in the power supply state, and more efficient power supply management can be achieved after merging. At this time, the system merges adjacent partitions into a combined power supply unit and generates a dynamic partition configuration scheme. This dynamic partition configuration method can adjust the power supply partitions according to the real-time operation state of the system, realizing the dynamic optimization of power supply partitions. By merging adjacent partitions with strong synergy, the power transmission loss between partitions can be reduced, the power supply efficiency can be improved, and at the same time, the overall power consumption of the system can be reduced. In actual operation, the evaluation model continuously monitors and evaluates the power supply state of adjacent partitions and adjusts the partition configuration according to real-time data. When the operation state of the system changes, such as the sampling frequency is adjusted due to the change of environmental light intensity or the distribution of ranging tasks changes, the evaluation model recalculates the synergy evaluation value of adjacent partitions and decides whether to adjust the partition configuration.

[0037] This dynamic partition configuration scheme has strong adaptability and can flexibly adjust the power supply partitions according to the actual needs of the system, ensuring the maximization of power supply efficiency and the minimization of power consumption under various working conditions. Throughout the process, the system realizes the refined management of the power supply of the iTOF ranging system by accurately obtaining the partition power supply influence value and dynamically adjusting the power supply partition configuration, effectively improving the energy efficiency ratio of the system, extending the battery life of the device, and at the same time ensuring the performance and stability of the ranging system. In the calculation process of the current fluctuation influence value, the sampling frequency and accuracy of the high-precision current sensor directly affect the calculation accuracy 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 the rapid changes in current. At the same time, the system also filters the collected current data to remove noise interference and improve the data quality.

[0038] In the calculation of the voltage stability influence value, the recording of the number of fluctuations and the fluctuation amplitude needs to be accurate. The system will set a reasonable voltage normal range threshold. When the voltage exceeds this threshold, the fluctuation event will be recorded in a timely manner, and the fluctuation amplitude will be accurately measured. The preset weight parameters will also be adjusted according to the actual operation situation of the system to ensure that the voltage stability index can accurately reflect the impact of voltage fluctuations on the system. In the establishment process of the synergy evaluation model, various factors need to be comprehensively considered. For the power transmission loss factor, the system will establish a detailed power transmission model, considering the impact of factors such as line impedance, transmission distance, and current magnitude on the loss. For the task execution correlation factor, the system will analyze the roles and interaction relationships of each partition in the ranging task, and quantify indicators such as data flow and synchronization requirements between them.

[0039] By taking these factors into account in the evaluation model, the synergy of adjacent partitions can be evaluated more accurately. The setting of the preset threshold is also a key link in the evaluation model. The setting of this threshold needs to balance the relationship between the efficiency improvement brought by merging partitions and the increased management complexity. If the threshold is set too high, it may make it difficult to merge adjacent partitions and fail to give full play to the advantages of dynamic partition configuration; if the threshold is set too low, it may lead to too many partitions being merged, increasing management complexity and even reducing power supply efficiency. Therefore, the preset threshold needs to be reasonably set according to the characteristics of the system and the actual working environment, and adjusted according to feedback information during system operation.

[0040] After generating the dynamic partition configuration plan, the system will also monitor the implementation effect of the plan in real time. The effectiveness of the plan is evaluated by comparing the partition power supply impact value, system power consumption and other indicators before and after the implementation of the configuration plan. If the implementation effect is found to be unsatisfactory, the system will promptly adjust the parameters of the evaluation model or the preset threshold, 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 the partition power supply impact value and dynamically configuring the power supply partition is a closed-loop control system. Through continuous monitoring, evaluation, and adjustment, the continuous optimization of the power supply of the iTOF ranging system is achieved, which effectively reduces the system power consumption and improves the system performance and reliability.

[0041] 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 load fluctuation value at each moment. Then calculate the standard deviation , standard deviation It is used to measure the 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 load fluctuations. The larger the value, the more severe the load fluctuation and the higher the deviation from the average level.

[0042] For the partition cooperation efficiency value, the system will analyze its data distribution over a period of time. The dedicated data acquisition module collects the cooperation efficiency data of each partition during operations such as range measurement tasks. The specific data collected by the dedicated data acquisition module includes: communication delay data between partitions (such as the time taken for data to be transmitted from partition A to partition B), task execution timestamps (the start and end times of each partition to complete subtasks), data transfer volume between partitions (such as the size of the original range measurement data or processed data transferred from partition A to partition B), occupancy rate of shared resources (such as the usage ratio of resources shared between partitions like memory and computing cores), etc. These data are used to quantitatively evaluate the cooperation efficiency between partitions. Suppose the cooperation efficiency data sequence of a certain partition is . The cooperation efficiency value is a quantitative indicator to measure the cooperation efficiency between power supply partitions in the range measurement system, specifically referring to the comprehensive evaluation value of data interaction efficiency, task synchronization, and resource sharing utilization rate when partitions execute range measurement tasks. For example, the smaller the data transfer delay between partitions, the higher the consistency of task completion time, and the more balanced the usage efficiency of shared computing resources, the higher the cooperation efficiency value; otherwise, it is lower. First, use the data distribution analysis algorithm to calculate its distribution dispersion , and the dispersion reflects the degree of dispersion of the cooperation efficiency data; then calculate the concentration , and the concentration reflects the trend of the cooperation efficiency data concentrating towards a certain value. Multiply the dispersion by the concentration to obtain the cooperation efficiency characteristic value , that is . This characteristic value is used to characterize the stability and effectiveness of the cooperation efficiency. The larger the value, the worse the stability of the cooperation efficiency or the less ideal the concentration.

[0043] Subtract the cooperation efficiency characteristic value from the load fluctuation characteristic value to obtain the partition optimization coefficient , that is . The partition optimization coefficient comprehensively considers the two factors of load fluctuation and cooperation efficiency, providing a core parameter basis for the subsequent optimization of the power supply scheme.

[0044] After generating the optimized partition power supply scheme, the system will perform a reverse verification on the partition load fluctuation value and the collaboration efficiency value. The specific operation is to substitute the actually collected partition load fluctuation value and collaboration efficiency value into the calculation model of the optimized power supply scheme. This calculation model is constructed based on various factors such as the circuit structure, power consumption characteristics, and task execution logic of the system, and includes a series of operation rules and logical judgments. By calculating with the model, the corresponding theoretical output value is obtained, and then it is compared with the theoretical value on which the scheme design is based. A tolerance range is preset , is the tolerance threshold set according to the system accuracy requirements. If the deviation value obtained from the verification exceeds the preset tolerance range, the partition configuration backtracking mechanism will be triggered, and the system will re-execute step four, obtain the partition load fluctuation value and the partition collaboration efficiency value again, recalculate the partition optimization coefficient, and re-optimize the power supply scheme to ensure the effectiveness and rationality of the partition power supply scheme.

[0045] When the partition load fluctuation value continuously exceeds the preset fluctuation range, the system will perform an exponential decay correction on the load fluctuation characteristic value. The system internally stores historical load fluctuation data, and through the data analysis module, these historical data are deeply mined to analyze their change trends and patterns. According to the analysis results, a suitable decay factor calculation formula is determined. Let the current load fluctuation characteristic value be , and the corrected load fluctuation characteristic value be , and the decay factor be , then the correction formula is , where is the number of times the load fluctuation value continuously exceeds the preset range, and the value range of is in And it is dynamically adjusted according to factors such as the fluctuation amplitude and frequency of historical fluctuation data. Through this exponential decay correction, the load fluctuation eigenvalue can more accurately reflect the actual load fluctuation situation, avoiding excessive calculation deviation of the partition optimization coefficient caused by extreme load fluctuation situations, thereby providing a more reliable basis for optimizing the partition power supply scheme. Throughout the process, the system ensures the accuracy and reliability of the partition optimization coefficient through a series of operations such as real-time monitoring, precise calculation, strict verification, and dynamic correction, and then effectively optimizes the power supply scheme of the iTOF ranging system to achieve the purpose of reducing system power consumption and improving operation efficiency. At the same time, the system will continuously update and improve the data acquisition, analysis algorithm, and calculation model to adapt to the system operation state under different working environments and task requirements, and ensure the continuous effectiveness of the optimization process. In terms of data acquisition, the system will adjust the data acquisition frequency and accuracy according to the characteristics of different partitions to ensure that the obtained load fluctuation values and cooperation efficiency values can truly reflect the actual operation of the system. For some partitions with relatively frequent load changes, the data acquisition frequency is appropriately increased; for key partitions with higher cooperation efficiency requirements, the data acquisition accuracy is improved. In terms of data analysis algorithms, as the system operation data accumulates continuously, more advanced machine learning algorithms will be used to analyze the data distribution to improve the accuracy of calculating eigenvalue such as dispersion and concentration. In terms of the calculation model, in combination with new hardware characteristics, circuit optimization schemes, and adjustments to task execution strategies, the power supply scheme calculation model will be updated and improved to enable it to more accurately simulate the system operation state and provide more reliable support for reverse verification and scheme optimization. The system will also establish a complete log recording and fault diagnosis mechanism to record in detail each key step in the process of obtaining the partition optimization coefficient and optimizing the power supply scheme. When an abnormal situation occurs, such as triggering the partition configuration backtracking mechanism, by analyzing the log data, the problem can be quickly located, whether it is inaccurate data acquisition, deviation in the calculation process, or unreasonable model parameter settings, etc., so as to repair and adjust in time to ensure the stable operation of the system and the power consumption optimization effect.

[0046] Embodiment 4: This embodiment details the generation of dynamic power supply strategy parameters, specifically including: Suppose the iTOF ranging system has completed multiple power supply optimization operations within a certain period of time. The storage module in the system will record the partition optimization coefficients obtained from each optimization to form a historical power supply optimization record. For example, in the past 10 power supply optimizations, the sequentially obtained partition optimization coefficients are 0.2, 0.3, 0.25, 0.4, 0.35, 0.28, 0.32, 0.38, 0.42, and 0.36 respectively. The system will arrange these coefficients into a time series in the order of the time when the optimization operations occurred. Next, the system uses the moving average calculation method to process this time series. The moving average calculation requires setting a suitable moving window size, and the setting of this size needs to comprehensively consider the operation stability of the system and the response speed to new data. If the window is too small, the calculation result may be greatly affected by individual abnormal data; if the window is too large, the response of the calculation result to the real-time changes of the system will be relatively lagged. For example, according to its own operation characteristics, the system sets the moving window size to 5. At this time, the system will select 5 consecutive partition optimization coefficients (0.2, 0.3, 0.25, 0.4, 0.35) from the beginning of the time series and calculate their average value, which is the dynamic power supply strategy reference value for this stage. As time goes by, the moving window will slide backward by one data bit in turn, that is, the next calculation will select these 5 data (0.3, 0.25, 0.4, 0.35, 0.28) to calculate the average value, continuously updating the dynamic power supply strategy reference value to enable it to continuously reflect the trend and average level of historical power supply optimization.

[0047] When the system calculates the current partition optimization coefficient based on the power supply adjustment execution signal, such as 0.33. At this time, the system will perform a superposition operation on the currently calculated partition optimization coefficient 0.33 and the latest obtained dynamic power supply strategy reference value. For example, if the latest dynamic power supply strategy reference value is 0.3, the dynamic power supply strategy parameter obtained after superposition is 0.63. This dynamic power supply strategy parameter will be further superposed and calculated with the current power supply partition configuration to generate an optimized partition power supply plan.

[0048] During the operation of the system, the collaborative efficiency values of each partition will be monitored in real time. Suppose the preset efficiency threshold set by the system is 0.6. When it is monitored that the collaborative efficiency value of a certain partition is 0.5, which is lower than the preset efficiency threshold, the system will perform linear compensation and correction on the collaborative efficiency characteristic value of this partition. The determination of the compensation weight needs to first analyze the task correlation degree between partitions. Taking an actual scenario as an example, when the iTOF ranging system performs an environment modeling task, there are partitions A, B, and C. Partition A is responsible for data acquisition, partition B performs preliminary data processing, and partition C completes the final model construction. There is a close data interaction and resource sharing relationship among the three partitions.

[0049] The system quantifies the task correlation degree between partitions by analyzing the data traffic, data interaction frequency, types of shared resources, and usage duration during the task execution process in each partition. For example, through statistics, it is found that within a certain period of time in the past, the data volume transmitted from partition A to partition B accounts for 70% of its total data output, the data volume transmitted from partition B to partition C accounts for 80% of its total data output, and the time proportion of the three partitions sharing the same computing resource reaches 60%. Based on these data, the system uses a specific quantification algorithm to determine that the task correlation degree between partition A and partition B is 0.7, and the task correlation degree between partition B and partition C is 0.8.

[0050] According to these quantified task correlation degrees, the system further determines reasonable compensation weights. For example, when correcting the collaborative efficiency eigenvalue of partition B, since its task correlation degrees with partition A and partition C are relatively high, the system will assign a relatively high compensation weight to it, such as 0.8. Through this compensation weight, the collaborative efficiency eigenvalue of partition B is linearly compensated and corrected. The corrected collaborative efficiency eigenvalue will be used to recalculate the partition optimization coefficient, which in turn affects the generation of dynamic power supply strategy parameters, enabling the dynamic power supply strategy parameters to better adapt to the current operating state changes of the system. Thus, in the subsequent power supply scheme optimization process, for partitions with lower collaborative efficiency, the power supply strategy is reasonably adjusted to improve the optimization effect of the power supply scheme, achieving effective control of the power consumption and performance improvement of the iTOF ranging system.

[0051] During the entire process of generating dynamic power supply strategy parameters and correcting the collaborative efficiency eigenvalue, the system continuously monitors the operating data of each partition, including but not limited to the partition optimization coefficient, collaborative efficiency value, task correlation degree, etc. Once abnormal fluctuations in the data are detected or the operating state of the system changes, such as the change in the task correlation degree between partitions due to the addition of a ranging task, the system will promptly adjust relevant parameters in the calculation process, such as the sliding window size, preset efficiency threshold, quantification algorithm, etc., to ensure that the dynamic power supply strategy parameters can always accurately reflect the actual needs of the system, and the generated power supply scheme can optimize the power supply of the iTOF ranging system under various working conditions, balance the relationship between system performance and power consumption, and ensure the stable and efficient operation of the system. At the same time, the system will also regularly clean and organize the historical power supply optimization records, delete expired or invalid data, improve the data processing efficiency, and continuously optimize the data storage and reading methods to ensure that the required data can be quickly and accurately obtained when generating dynamic power supply strategy parameters. In addition, the system will continuously optimize the quantification algorithm for the task correlation degree between partitions, introduce more advanced data analysis and machine learning technologies, improve the accuracy and real-time performance of task correlation degree quantification, and thus further improve the generation quality of dynamic power supply strategy parameters and the optimization effect of the power supply scheme.

[0052] Example 5: During the dynamic adjustment of the signal sampling frequency in the iTOF ranging system, to improve the accuracy of the dynamic sampling frequency value, Example 5 optimizes the calculation process by introducing the reflection object material recognition parameter. The specific implementation is as follows: At the hardware level of the ranging system, a dedicated material recognition module needs to be integrated. This module can use a variety of technical means to achieve the recognition of the reflection object material. For example, the implementation of spectral analysis technology relies on a spectral sensor, which can collect the spectral information of the reflection object. When light irradiates the object surface and reflects back, the spectral sensor will capture the light intensity distribution of different wavelengths. Each material has its unique spectral reflection characteristics. The system compares the collected spectral information with the pre-constructed material spectral database. The database stores a large amount of spectral data of common materials under different lighting conditions. By calculating the similarity between the collected spectrum and the spectra in the database through algorithms, the material type of the reflection object can be determined.

[0053] Another common technical means is image recognition. The system uses a high-definition image acquisition device, such as a camera, to obtain the image information of the reflection object. Using image processing algorithms, the image is first pre-processed, including operations such as noise reduction and contrast enhancement, to improve the image quality. Then, key features in the image, such as texture, color distribution, shape, etc., are extracted, and pattern recognition algorithms are used to match the extracted features with the existing material image feature library to determine the material of the object. In practical applications, spectral analysis and image recognition technologies can also be combined to complement each other's advantages and further improve the accuracy and reliability of material recognition.

[0054] After determining the material of the reflection object, since different materials have different reflectivities, and the difference in reflectivity will significantly affect the ambient light intensity, and thus affect the accuracy of the comprehensive light fluctuation value. Therefore, it is necessary to perform normalization compensation calculation on the comprehensive light fluctuation value according to the material reflectivity difference. First, the system will establish a correspondence table between the material reflectivity and the compensation coefficient. The construction of this table is based on a large amount of experimental data and theoretical analysis. In the experimental stage, the reflectivities of various common materials under different lighting conditions are measured, and a compensation coefficient is assigned to each material according to the degree of influence of the reflectivity on the light intensity. For example, a metal material with a high reflectivity, which has a strong ability to reflect light and has a greater impact on the ambient light intensity, will be given a larger compensation coefficient; while a fabric material with a low reflectivity will have a relatively small compensation coefficient.

[0055] When the material recognition module determines the material of the reflective object, the system will immediately obtain the corresponding compensation coefficient from the correspondence table. Suppose the currently recognized reflective object is made of metal, and the compensation coefficient obtained from the table is 0.8. Next, this compensation coefficient is operated on with the previously calculated comprehensive light intensity fluctuation value. The operation process is carried out according to the rules set by the system. For example, it can be multiplying the compensation coefficient by the comprehensive light intensity fluctuation value, or calculating through a more complex functional relationship to achieve the correction of the comprehensive light intensity fluctuation value. After the operation, the corrected comprehensive light intensity fluctuation value is obtained, and then the dynamic sampling frequency adjustment coefficient is recalculated based on this corrected value.

[0056] In specific operations, the system will substitute the corrected comprehensive light intensity fluctuation value into the calculation process of the dynamic sampling frequency adjustment coefficient, replacing the original comprehensive light intensity fluctuation value for calculation. For example, the original calculation method was to divide the comprehensive light intensity fluctuation value by the preset fluctuation threshold to obtain the adjustment coefficient. Now, the corrected comprehensive light intensity fluctuation value is used for the same division operation 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, the final more accurate dynamic sampling frequency value is obtained.

[0057] In actual application scenarios, such as in an indoor environment, when the iTOF ranging system measures the distance in a room with metal furniture and fabric curtains, the material recognition module will respectively identify the metal and fabric materials and obtain the corresponding compensation coefficients. Through the compensation and correction of the comprehensive light intensity fluctuation value, the dynamic sampling frequency value calculated by the system can more accurately reflect the influence of the actual light environment on the ranging signal. In an outdoor environment, in the face of reflective objects such as buildings and vegetation of different materials, the same mechanism can be used to make precise adjustments according to the material reflectivity differences, ensuring that the ranging system can work at a reasonable sampling frequency under different light environments and conditions of reflective objects. A reasonable sampling frequency can not only ensure that sufficient accurate ranging signals are obtained to meet the ranging accuracy requirements, but also avoid the increase in system power consumption caused by too high a sampling frequency or the impact on the ranging effect due to too low a sampling frequency, thus 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 spectral database, image feature library, and the correspondence table between material reflectivity and compensation coefficients, and continuously optimize the material recognition and compensation calculation process with the emergence of new materials and environmental changes to ensure the accuracy and effectiveness of dynamic sampling frequency adjustment.

[0058] It should be noted that in this text, relational terms such as first and second are only used 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 "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device.

[0059] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An iTOF ranging power consumption optimization method, characterized in that It includes the following steps: Step 1: Dynamically adjust the signal sampling frequency of the ranging system, and obtain the dynamic sampling frequency value according to the change value of the ambient light intensity; Step 2: Based on the dynamic sampling frequency value, compare the real-time sampling frequency with the preset sampling frequency range. If the real-time sampling frequency exceeds the preset sampling frequency range, generate a sampling frequency adjustment signal; Step 3: Based on the sampling frequency adjustment signal, divide the power supply area of the ranging system. During the division period, obtain the power supply state data of each area, calculate the power supply influence value of the area, and compare it with the power supply influence threshold to generate a power supply adjustment signal; If the power supply influence value of the area is greater than or equal to the power supply influence threshold, generate a power supply adjustment execution signal; Step 4: Based on the power supply adjustment execution signal, obtain the load fluctuation value and the collaborative efficiency value of the area, calculate the difference between the load fluctuation value and the collaborative efficiency value to obtain the area optimization coefficient; Step 5: Based on the power supply adjustment execution signal, obtain the dynamic power supply strategy parameters, superimpose and calculate the current power supply area configuration and the dynamic power supply strategy parameters to generate an optimized area power supply plan, and complete the power supply adjustment of the ranging system.

2. The iTOF ranging power consumption optimization method according to claim 1, wherein The obtaining method of the dynamic sampling frequency value is: divide the ranging environment into multiple light intensity monitoring areas, obtain the light intensity change rate of each area, and perform a weighted sum calculation on the change rates of each area to obtain the comprehensive light fluctuation value; Perform a ratio operation on the comprehensive light fluctuation value and the preset fluctuation threshold to obtain the dynamic sampling frequency adjustment coefficient.

3. The iTOF ranging power consumption optimization method according to claim 2, wherein The obtaining method of the power supply influence value of the area is: extract the current fluctuation influence value and the voltage stability influence value of each area, and perform a product operation on the current fluctuation influence value and the voltage stability influence value to obtain the power supply influence value of the area; The division method of the power supply area is: based on the real-time changes of the load fluctuation value and the collaborative efficiency value of the area, perform a collaborative evaluation on the power supply states of adjacent areas. If the collaborative evaluation value is higher than the preset threshold, merge the adjacent areas into a combined power supply unit to generate a dynamic area configuration plan.

4. The iTOF ranging power consumption optimization method according to claim 3, characterized in that The obtaining method of the current fluctuation influence value is: during the monitoring period, extract the change slopes of the current peak value and the valley value of each area, calculate the difference between the current change slope and the preset slope reference value to obtain the current fluctuation deviation value; Perform a normalization process on the current fluctuation deviation value and the number of areas to obtain the current fluctuation influence value.

5. The iTOF ranging power consumption optimization method according to claim 4, wherein The obtaining method of the voltage stability influence value is: during the division period, record the voltage fluctuation times and the fluctuation amplitude of each area, perform a weighted accumulation calculation on the fluctuation times and the fluctuation amplitude to obtain the voltage stability index; Perform a proportional operation on the voltage stability index and the total number of areas to obtain the voltage stability influence value.

6. The iTOF ranging power consumption optimization method according to claim 5, characterized in that The obtaining method of the area optimization coefficient is: according to the load fluctuation value of the area, extract the standard deviation and the average value of the load fluctuation value, and perform a ratio calculation on the standard deviation and the average value to obtain the load fluctuation characteristic value; According to the partition cooperation efficiency value, extract the distribution dispersion and concentration degree of the efficiency value, perform a product operation on the dispersion and the concentration degree to obtain the cooperation efficiency eigenvalue; perform a difference operation on the load fluctuation eigenvalue and the cooperation efficiency eigenvalue to obtain the partition optimization coefficient. The verification method of the partition optimization coefficient is as follows: after generating the optimized partition power supply plan, perform a reverse check on the partition load fluctuation value and the cooperation efficiency value. If the check deviation value exceeds the preset tolerance range, trigger the partition configuration backtracking mechanism and re-execute step four.

7. The iTOF ranging power consumption optimization method according to claim 6, wherein, The generation method of the dynamic power supply strategy parameter is as follows: obtain the partition optimization coefficient in the historical power supply optimization record, perform a moving average calculation on the optimization coefficient according to the time series to obtain the dynamic power supply strategy reference value. Superimpose the current partition optimization coefficient and the dynamic power supply strategy reference value to generate the dynamic power supply strategy parameter.

8. The iTOF ranging power consumption optimization method according to claim 7, characterized in that The correction method of the load fluctuation eigenvalue is as follows: when the partition load fluctuation value continuously exceeds the preset fluctuation range, perform an exponential decay correction on the load fluctuation eigenvalue, and the decay factor is dynamically adjusted according to the historical fluctuation data.

9. The iTOF ranging power consumption optimization method according to claim 8, characterized in that The correction method of the cooperation efficiency eigenvalue is as follows: when the cooperation efficiency value is lower than the preset efficiency threshold, perform a linear compensation correction on the cooperation efficiency eigenvalue, and the compensation weight is dynamically determined according to the task correlation degree between partitions.

10. The iTOF ranging power consumption optimization method according to claim 1, wherein The correction method of the dynamic sampling frequency value is as follows: introduce a reflection object material recognition parameter in the light intensity monitoring area, perform a normalization compensation calculation on the comprehensive light fluctuation value according to the material reflectivity difference, and generate a corrected dynamic sampling frequency adjustment coefficient.

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