A mars explorer temperature monitoring method and system based on time control factor optimization

Abstract

The application belongs to the technical field of data processing, and discloses a Mars probe temperature monitoring method and system based on time control factor optimization, which comprises the following steps: obtaining simulated soil, and performing screening treatment and dense state preparation treatment to obtain a plurality of relatively dense state samples; performing simulated temperature monitoring on the plurality of relatively dense state samples to obtain a plurality of temperature-time curves, and performing piecewise linear fitting to obtain the thermal response characteristics of the simulated soil; performing optimal monitoring time period analysis and optimal monitoring time length analysis according to the thermal response characteristics to obtain an optimal passive monitoring time window and time length strategy; determining a target to-be-measured soil, and performing real temperature monitoring on the target to-be-measured soil according to the optimal passive monitoring time window and time length strategy to obtain the best temperature monitoring result. The application can realize accurate monitoring of the temperature of the to-be-measured soil, thereby effectively improving the evaluation accuracy of the passability of the Mars rover and ensuring the driving safety of the Mars rover.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, system, terminal, and computer-readable storage medium for monitoring the temperature of a Mars probe based on time-controlled factors. Background Technology

[0002] As human deep space exploration continues to advance, Mars, with its similar day-night cycle and geological structure to Earth, has become one of the most valuable planets for exploration. Its surface environment characteristics have a crucial impact on the safe movement and scientific operations of probes. In Mars exploration missions, mobility assessment is a core technical aspect ensuring that Mars rovers can safely and efficiently traverse complex terrain. Thermal inertia is a key parameter characterizing the thermophysical properties of soil, reflecting its responsiveness to temperature changes. Thermal inertia is closely related to the mechanical characteristics of soil. By systematically studying the temperature variation patterns of soil, its thermal inertia characteristics can be inverted, thereby obtaining the physical and mechanical properties of soil, which are crucial for the mobility of Mars rovers. Since the calculation of thermal inertia requires temperature data over a certain period, and temperature data has a significant impact on the calculation results, accurate acquisition of temperature data is essential.

[0003] Currently, existing technologies mainly rely on two methods for monitoring Martian soil temperature: orbital remote sensing observation and in-situ temperature monitoring. However, these two methods are mostly based on fixed time windows and do not fully consider the impact of Martian day-night cycle, dynamic changes in solar radiation, and atmospheric transmittance fluctuations on thermal response. This results in low accuracy of Martian soil temperature monitoring data, affecting the calculation of thermal inertia and the driving safety of the Mars rover.

[0004] Therefore, existing technologies still need to be improved and developed. Summary of the Invention

[0005] The main objective of this invention is to provide a Mars probe temperature monitoring method, system, terminal, and computer-readable storage medium optimized based on time-control factors. This aims to solve the problem that existing technologies rely on orbital remote sensing observation and in-situ temperature monitoring based on fixed time windows for Martian soil temperature monitoring, without fully considering the influence of Martian day-night cycle, dynamic changes in solar radiation, and atmospheric transmittance fluctuations on thermal response. This results in low accuracy of temperature monitoring data, affecting the calculation of thermal inertia and the safety of Mars rover operation.

[0006] To achieve the above objectives, the present invention provides a Mars probe temperature monitoring method optimized based on time-control factors, the method comprising the following steps:

[0007] Simulated fire soil was obtained, and the simulated fire soil was subjected to sieving and compaction treatment to obtain multiple relatively compacted samples.

[0008] Multiple relatively dense samples were subjected to simulated temperature monitoring to obtain multiple temperature-time curves. Piecewise linear fitting was performed on the multiple temperature-time curves to obtain the thermal response characteristics of the simulated fire soil.

[0009] Based on the thermal response characteristics, the optimal monitoring period and optimal monitoring duration are analyzed to obtain the optimal passive monitoring time window and duration strategy.

[0010] The target soil to be tested is identified, and the actual temperature of the target soil to be tested is monitored according to the optimal passive monitoring time window and duration strategy to obtain the best temperature monitoring results.

[0011] Optionally, the Mars probe temperature monitoring method based on time-controlled factor optimization includes, in part, obtaining simulated fire soil and performing sieving and compaction processing on the simulated fire soil to obtain multiple relatively compacted samples, specifically including:

[0012] A simulated fire soil was obtained, and the simulated fire soil was initially screened using a sieve with a preset mesh size to obtain the first simulated fire soil.

[0013] The first simulated fire soil was re-sieved using a laser particle size analyzer to obtain the second simulated fire soil.

[0014] The second simulated fire soil was prepared into a dense state by using a vibration compaction method, resulting in multiple relatively dense samples.

[0015] The relatively dense sample includes fine-grained soft sample, fine-grained dense sample, coarse-grained soft sample, and coarse-grained dense sample.

[0016] Optionally, in the Mars probe temperature monitoring method optimized based on time-control factors, the expression for calculating the relative density of the relatively dense sample is:

[0017] ;

[0018] in, For relative density, For maximum dry loose density, The density is the dry, loose density under natural conditions. It is the minimum dry loose density.

[0019] Optionally, the Mars probe temperature monitoring method based on time-controlled factor optimization includes, in part, simulating temperature monitoring of multiple relatively dense samples to obtain multiple temperature-time curves, and performing piecewise linear fitting on the multiple temperature-time curves to obtain the thermal response characteristics of the simulated fire soil, specifically including;

[0020] A preset number of temperature sensor arrays are set in each of the relatively dense state samples. The temperature of each of the relatively dense state samples is collected through the temperature sensor arrays at preset sampling intervals to obtain a temperature-time curve of temperature change in each of the relatively dense state samples over time.

[0021] Based on the temperature-time curves, the heating curves corresponding to the heating stage and the cooling curves corresponding to the cooling stage of each relatively dense sample are obtained, and piecewise linear fitting is performed on the heating curves and the cooling curves to obtain the thermal response characteristics of each relatively dense sample.

[0022] Optionally, the Mars probe temperature monitoring method based on time-controlled factors optimization, wherein the step of performing optimal monitoring period analysis and optimal monitoring duration analysis based on the thermal response characteristics to obtain the optimal passive monitoring time window and duration strategy specifically includes:

[0023] Based on the thermal response characteristics, the influence of different monitoring start times and different monitoring durations on the intensity of the extracted temperature difference signal is analyzed, and the optimal passive monitoring time window and duration strategy are obtained.

[0024] The optimal passive monitoring time window and duration strategy includes a daytime optimal passive monitoring time window and duration strategy and a nighttime optimal passive monitoring time window and duration strategy.

[0025] The optimal passive monitoring time window and duration strategy during the day is to monitor within 1 to 2 hours after sunrise and within 1 to 2 hours before sunset;

[0026] The optimal passive monitoring time window and duration strategy for nighttime is to start monitoring within 1 to 2 hours after sunset, where the monitoring duration is set to a preset detection duration.

[0027] Optionally, the Mars probe temperature monitoring method based on time-controlled factors optimization, wherein the step of performing optimal monitoring period analysis and optimal monitoring duration analysis based on the thermal response characteristics to obtain the optimal passive monitoring time window and duration strategy, further includes:

[0028] If the temperature monitoring time does not meet the optimal passive monitoring time window and duration strategy, then an active thermal radiation mode is set to collect the transient temperature response of the relatively dense sample during heating and natural cooling.

[0029] Optionally, the Mars probe temperature monitoring method based on time-controlled factor optimization, wherein determining the target soil to be tested and performing real temperature monitoring on the target soil according to the optimal passive monitoring time window and duration strategy to obtain the best temperature monitoring result specifically includes:

[0030] Identify the target fire soil to be tested, and obtain the current location and current solar time of the target fire soil to be tested. Then, determine whether the optimal passive monitoring time window and duration strategy is satisfied based on the current location and current solar time.

[0031] If the daytime optimal passive monitoring time window and duration strategy is satisfied, then the actual temperature of the target fire soil to be tested is monitored according to the daytime optimal passive monitoring time window and duration strategy to obtain the best temperature monitoring result.

[0032] If the optimal passive monitoring time window and duration strategy for nighttime is satisfied, then the target fire soil to be tested is subjected to real temperature monitoring according to the optimal passive monitoring time window and duration strategy for nighttime, and the best temperature monitoring result is obtained.

[0033] If the optimal passive monitoring time window and duration strategy is not met, the active thermal radiation mode is used to monitor the actual temperature of the target soil to be tested, and the best temperature monitoring result is obtained.

[0034] Furthermore, to achieve the above objectives, the present invention also provides a Mars probe temperature monitoring system optimized based on time-control factors, wherein the Mars probe temperature monitoring system optimized based on time-control factors includes:

[0035] The sample preparation module is used to obtain simulated fire soil and to perform sieving and compaction processing on the simulated fire soil to obtain multiple relatively compacted samples.

[0036] The simulated temperature monitoring module is used to simulate the temperature of multiple relatively dense samples, obtain multiple temperature-time curves, and perform piecewise linear fitting on the multiple temperature-time curves to obtain the thermal response characteristics of the simulated fire soil.

[0037] The optimal monitoring strategy generation module is used to perform optimal monitoring period analysis and optimal monitoring duration analysis based on the thermal response characteristics to obtain the optimal passive monitoring time window and duration strategy.

[0038] The monitoring result output module is used to determine the target fire soil to be tested, and to perform real temperature monitoring on the target fire soil to be tested according to the optimal passive monitoring time window and duration strategy, so as to obtain the best temperature monitoring result.

[0039] Furthermore, to achieve the above objectives, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and a Mars probe temperature monitoring program optimized based on time control factors stored in the memory and executable on the processor. When the Mars probe temperature monitoring program optimized based on time control factors is executed by the processor, it implements the steps of the Mars probe temperature monitoring method optimized based on time control factors as described above.

[0040] Furthermore, to achieve the above objectives, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a Mars probe temperature monitoring program optimized based on time control factors, and when the Mars probe temperature monitoring program optimized based on time control factors is executed by a processor, it implements the steps of the Mars probe temperature monitoring method optimized based on time control factors as described above.

[0041] In this invention, simulated fire soil is obtained and subjected to sieving and compaction processes to obtain multiple relatively compacted samples. Simulated temperature monitoring is performed on these samples to obtain multiple temperature-time curves. Piecewise linear fitting is then performed on these temperature-time curves to obtain the thermal response characteristics of the simulated fire soil. Based on these thermal response characteristics, optimal monitoring time periods and durations are analyzed to obtain the optimal passive monitoring time window and duration strategy. A target fire soil sample is identified, and its actual temperature is monitored according to the optimal passive monitoring time window and duration strategy to obtain the best temperature monitoring results. This invention, through simulation experiments, constructs an optimal passive monitoring time window and duration strategy, enabling accurate monitoring of the fire soil temperature and accurate acquisition of the fire soil temperature difference signal. This effectively improves the accuracy of the Mars rover's passability assessment and ensures the rover's driving safety. Attached Figure Description

[0042] Figure 1 This is a flowchart of a preferred embodiment of the Mars probe temperature monitoring method optimized based on time control factors according to the present invention;

[0043] Figure 2 This is a schematic diagram of the simulated soil particle size distribution of a preferred embodiment of the Mars probe temperature monitoring method optimized based on time control factors according to the present invention.

[0044] Figure 3 This is a schematic diagram of simulated volcanic morphology features of a preferred embodiment of the Mars probe temperature monitoring method optimized based on time control factors of the present invention. Figure 3 Medium a represents small-particle-density fire soil. Figure 3 Medium b represents small-grained, loose, fiery soil. Figure 3 Medium c represents large-particle-density fire soil. Figure 3(middle d is large-particle-soft fire soil).

[0045] Figure 4 This is a schematic diagram of the thermocouple distribution of the temperature monitoring system in a preferred embodiment of the Mars probe temperature monitoring method optimized based on time control factors according to the present invention. Figure 4 In the diagram, 'a' represents the distribution of thermocouples placed in dense, flammable soil. Figure 4 In the diagram, b represents the distribution of thermocouples placed in soft, flammable soil. Figure 4 (where 'c' is a schematic diagram of the distribution of temperature sensors in the simulated fire soil).

[0046] Figure 5 This is a schematic diagram of the temperature monitoring system of the Mars probe temperature monitoring method optimized based on time control factors, representing a preferred embodiment of the present invention. It is a simulation of the temperature response of simulated fire soil with different particle sizes on the first day.

[0047] Figure 6 This is a schematic diagram of the temperature monitoring system of the Mars probe temperature monitoring method optimized based on time control factors, representing a preferred embodiment of the present invention. It is a simulation analysis of the temperature response of simulated fire soil with different particle sizes on the second day.

[0048] Figure 7 This is a schematic diagram of the temperature monitoring system of the Mars probe temperature monitoring method optimized based on time control factors, which is a preferred embodiment of the present invention. It is a simulation of the temperature response of simulated fire soil with different particle sizes on the third day.

[0049] Figure 8 This is a schematic diagram of the temperature response of a simulated fire soil at different time periods with different particle sizes, based on a preferred embodiment of the temperature monitoring system of the Mars probe based on time-controlled factor optimization of the present invention. Figure 8 In the figure, 'a' represents the temperature response of coarse-grained, loose volcanic soil from 6:00 AM to 6:00 PM over three days. Figure 8 (b in the diagram represents the temperature response of coarse-grained, loose volcanic soil from 18:00 to 6:00 the following morning over three days).

[0050] Figure 9 This is a schematic diagram of the simulated soil warming rate with different particle sizes in a temperature monitoring system of a preferred embodiment of the Mars probe temperature monitoring method optimized based on time control factors of the present invention. Figure 9 (a) in the figure is a schematic diagram of the warming rate of large-particle-soft volcanic soil and small-particle-soft volcanic soil from 05:00 to 06:30 over three days; Figure 9 (b) in the figure is a schematic diagram of the warming rate of large-particle-soft volcanic soil and small-particle-soft volcanic soil from 08:00 to 16:00 over three days; Figure 9 (c) is a schematic diagram showing the warming rate of large-particle-soft pyrophyllite and small-particle-soft pyrophyllite from 16:00 to 16:30 over three days.

[0051] Figure 10 This is a schematic diagram of the temperature monitoring system with different particle sizes simulating the cooling rate of fire soil, representing a preferred embodiment of the Mars probe temperature monitoring method optimized based on time control factors according to the present invention. Figure 10 (a) in the figure is a schematic diagram of the cooling rate of large-particle-soft volcanic soil and small-particle-soft volcanic soil from 16:30 to 00:00 over three days. Figure 10 (b) is a schematic diagram showing the cooling rate of large-particle-soft volcanic soil and small-particle-soft volcanic soil from 18:00 to 04:00 the next day over three days.

[0052] Figure 11 This is a schematic diagram of the temperature difference of a simulated fire soil with different particle sizes over a 30-minute monitoring period, representing a preferred embodiment of the temperature monitoring method for Mars probes based on time control factors according to the present invention.

[0053] Figure 12 This is a schematic diagram of the temperature difference of a simulated fire soil with different particle sizes over a 2-hour monitoring period, which is a preferred embodiment of the temperature monitoring method for Mars probes based on time control factors of the present invention.

[0054] Figure 13 This is a schematic diagram of the temperature difference of a simulated fire soil with different particle sizes over a 4-hour monitoring period, which is a preferred embodiment of the temperature monitoring method for Mars probes based on time control factors of the present invention.

[0055] Figure 14 This is a schematic diagram of the temperature difference over a 6-hour monitoring period of simulated fire soil with different particle sizes, representing a preferred embodiment of the temperature monitoring method for Mars probes based on time control factors according to the present invention.

[0056] Figure 15 This is a schematic diagram of the temperature difference of a simulated fire soil with different particle sizes over an 8-hour monitoring period, which is a preferred embodiment of the temperature monitoring method for Mars probes based on time control factors of the present invention.

[0057] Figure 16 This is a schematic diagram of the average temperature difference of a simulated fire soil with different particle sizes and different monitoring times, based on a preferred embodiment of the temperature monitoring method for Mars probes optimized by time control factors according to the present invention. Figure 16 (a) is a bar graph showing the average temperature difference between 18:00 in the afternoon and 5:00 the next morning over five days in coarse-grained, loose volcanic soil. Figure 16 (b) is a bar graph showing the average temperature difference between 18:00 in the afternoon and 5:00 the next morning over five days in fine-grained, loose volcanic soil. Figure 16 (c) is a radar diagram showing the average temperature difference of coarse-grained, loose volcanic soil from 18:00 in the afternoon to 5:00 the next morning over five days. Figure 16(d) is a radar diagram showing the average temperature difference of fine-grained, loose volcanic soil from 18:00 to 5:00 the following morning over five days.

[0058] Figure 17 This is a schematic diagram of the temperature response of the temperature monitoring system of the Mars probe at different densities during different time periods on the first day, based on a preferred embodiment of the temperature monitoring method for Mars probes optimized by time control factors according to the present invention.

[0059] Figure 18 This is a schematic diagram of the temperature response of the temperature monitoring system of the Mars probe at different time periods with different densities on the second day, which is a preferred embodiment of the temperature monitoring method of the Mars probe based on time control factors optimization of the present invention.

[0060] Figure 19 This is a schematic diagram of the temperature response of the temperature monitoring system of the Mars probe at different time periods on the third day, simulating different densities of the soil at different times, based on the preferred embodiment of the temperature monitoring method of the Mars probe optimized by time control factors according to the present invention.

[0061] Figure 20 This is a schematic diagram of the temperature monitoring system of the Mars probe temperature monitoring method optimized based on time control factors, from 05:00 to 06:30, simulating the heating rate of the soil at different densities.

[0062] Figure 21 This is a schematic diagram of the temperature monitoring system of the Mars probe temperature monitoring method optimized based on time control factors, from 08:00 to 16:00, simulating the heating rate of the soil at different densities. This is a preferred embodiment of the temperature monitoring system of the Mars probe temperature monitoring method optimized based on time control factors of the present invention.

[0063] Figure 22 This is a schematic diagram of the temperature monitoring system of the Mars probe temperature monitoring method optimized based on time control factors, from 16:00 to 16:30, simulating the heating rate of the soil at different densities. This is a preferred embodiment of the temperature monitoring system of the Mars probe temperature monitoring method optimized based on time control factors of the present invention.

[0064] Figure 23 This is a schematic diagram of the temperature monitoring system simulating the cooling rate of fire soil with different densities, based on a preferred embodiment of the Mars probe temperature monitoring method optimized by time control factors according to the present invention. Figure 23 (a) in the figure is a schematic diagram of the cooling rate of large-particle-loose fire soil and large-particle-dense fire soil from 16:30 to 00:00 over three days. Figure 23 (b) is a schematic diagram showing the cooling rate of large-particle-loose volcanic soil and large-particle-dense volcanic soil from 18:30 to 04:00 the next day over three days.

[0065] Figure 24 This is a schematic diagram of the temperature difference of a simulated fire soil with different densities over a 30-minute monitoring period, representing a preferred embodiment of the temperature monitoring method for Mars probes based on time control factors according to the present invention.

[0066] Figure 25 This is a schematic diagram of the temperature difference of a simulated fire soil with different densities over a 2-hour monitoring period, representing a preferred embodiment of the temperature monitoring method for Mars probes based on time control factors according to the present invention.

[0067] Figure 26 This is a schematic diagram of the temperature difference of a Mars probe temperature monitoring system with different densities simulating 4 hours of fire soil temperature monitoring, which is a preferred embodiment of the temperature monitoring method for Mars probes based on time control factors optimized according to the present invention.

[0068] Figure 27 This is a schematic diagram of the temperature difference of a Mars probe temperature monitoring system with different densities simulating 6 hours of fire soil temperature monitoring, which is a preferred embodiment of the temperature monitoring method for Mars probes based on time control factors optimized according to the present invention.

[0069] Figure 28 This is a schematic diagram of the temperature difference of a simulated fire soil with different densities over an 8-hour monitoring period, representing a preferred embodiment of the temperature monitoring method for Mars probes based on time control factors according to the present invention.

[0070] Figure 29 This is a schematic diagram of the active thermal radiation system of the temperature monitoring system of the Mars probe temperature monitoring method optimized based on time control factors according to the present invention. Figure 29 In the diagram, 'a' represents a schematic representation of the active thermal radiation system. Figure 29 In the diagram, b is a schematic of a soil sample box in an active thermal radiation system; Figure 29 In the diagram, 'c' represents the depth to which the temperature sensor in the active thermal radiation system is inserted into the fire soil. Figure 29 (d in the diagram represents a simulated distribution of fire and soil in an active thermal radiation system).

[0071] Figure 30 This is a schematic diagram of the temperature monitoring system's response to active thermal radiation under fire and soil temperature, representing a preferred embodiment of the Mars probe temperature monitoring method optimized based on time control factors according to the present invention.

[0072] Figure 31 This is a structural diagram of a preferred embodiment of the Mars probe temperature monitoring system optimized based on time control factors according to the present invention;

[0073] Figure 32 This is a structural diagram of a preferred embodiment of the terminal of the present invention. Detailed Implementation

[0074] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0075] As human exploration of deep space continues to advance, Mars, with its similar day-night cycle and geological structure to Earth, has become one of the most valuable planets for exploration. Its surface environment has a crucial impact on the safe movement and scientific operations of probes. In Mars exploration missions, mobility assessment is a core technical aspect that ensures the rover can safely and efficiently traverse complex terrain.

[0076] The mobility of a Mars rover includes both geometric mobility and support mobility. While significant progress has been made in predicting geometric mobility, the prediction of support mobility still requires refinement and optimization. Predicting vehicle support mobility solely by inferring soil properties from surface morphology is insufficient. Relying on traces and slip ratios to infer soil mechanical properties only applies to surfaces already traversed by the rover and fails to accurately reflect the mechanical properties of untouched soils, which is crucial for predicting vehicle mobility. In recent years, mobility assessment based on thermal inertia has become a research hotspot. Compared to visual images, thermal measurements provide information on overall mechanical properties both at and below the surface. This correlation has been used to infer global geological composition from Earth, Moon, and Mars orbits. Thermal inertia is a key parameter characterizing soil thermophysical properties, reflecting the soil's responsiveness to temperature changes. Thermal inertia is closely related to the mechanical characteristics of soils. By systematically studying the temperature variation patterns of soils, their thermal inertia characteristics can be inferred, thereby obtaining the physical and mechanical properties of soils, which are crucial for the mobility of Mars rovers.

[0077] Without considering the latitude and longitude of the observation location, the angle of the sun, the duration of sunshine, the Earth-Sun distance, etc., thermal inertia It can be simplified to: ,in: Albedo; This refers to the temperature difference, which is the temperature data over a period of time.

[0078] Since the calculation of thermal inertia requires temperature data over a certain period of time, and temperature data has a significant impact on the calculation results, accurate acquisition of temperature data is crucial. Currently, the acquisition of Martian surface temperature mainly relies on orbital remote sensing observations (such as the THEMIS thermal imaging system) and in-situ temperature monitoring (such as the thermal conductivity detector in the InSight mission).

[0079] However, existing methods still face the following significant technical bottlenecks when applied to detector passability assessment:

[0080] 1. Assessment bias due to lack of timing control strategy: Existing temperature monitoring schemes are mostly based on fixed time windows (such as local midnight or noon), failing to fully consider the impact of Martian day-night cycle, dynamic changes in solar radiation, and atmospheric transmittance fluctuations on thermal response. In real missions, rovers are limited by communication windows and mission sequence, making continuous monitoring difficult during ideal periods. The arbitrariness in selecting monitoring periods and the insufficient monitoring duration result in weak representativeness of extracted thermal inertial parameters, thus affecting the accuracy of passability assessments.

[0081] 2. Disconnect between short-term monitoring data and long-term thermal response characteristics: Current research focuses primarily on diurnal temperature variations, lacking systematic analysis of the dynamic characteristics of thermal response within key short-term windows (e.g., 30 minutes to 8 hours). In actual operations, exploration vehicles often need to complete local topographic assessments within several hours. Directly using short-term monitoring data to infer soil mechanical properties may introduce significant errors due to the insufficient development of the thermal response.

[0082] 3. Lack of adaptive monitoring strategies for different soil types: Martian surface soils are diverse (such as fine-grained loose soil, coarse-grained gravel, etc.), and their thermal response characteristics vary significantly. Existing monitoring schemes often use a "one-size-fits-all" time window without adaptive adjustments based on key parameters such as soil particle size distribution and density, leading to assessment failures or even misjudgments in certain terrains.

[0083] 4. Signal limitation: In environments with weak temperature changes during the day, the natural temperature difference signal is insufficient, which will cause the temperature difference to fail in the calculation of thermal inertia.

[0084] In summary, current temperature monitoring technologies used in Mars probe capability assessments still have systemic shortcomings in terms of runaway strategies, data representativeness, environmental adaptability, and engineering feasibility. Therefore, there is an urgent need for a temperature monitoring method and system that can optimize monitoring periods and durations, integrate active heating, and adapt to different soil types, in order to improve the accuracy of capability assessments and mission execution efficiency.

[0085] To address the aforementioned issues, this invention provides a Mars probe temperature monitoring method and system optimized based on time-control factors, belonging to the fields of deep space exploration and planetary science. Through ground-based simulation experiments, this invention establishes for the first time the influence relationship between the "monitoring time period" and "monitoring duration" on the temperature difference signal of simulated Martian soil, and proposes an optimization strategy: during the day, monitoring is conducted during the period with the highest temperature change rate and the highest temperature difference signal (1-2 hours after sunrise and before sunset); at night, monitoring begins 1-2 hours after sunset, employing long-term monitoring (≥6 hours) to significantly enhance the temperature difference signal (the monitoring duration can be flexibly selected according to the probe's mission; for example, if the Mars mission is urgent, a 2-hour temperature monitoring duration is selected; if the probe is conducting long-term scientific exploration, a 4-8 hour monitoring duration is selected). This enhancement is more than 90% higher than that of short-term monitoring of 30 minutes. Meanwhile, this invention provides a supplementary method for rapidly stimulating soil thermal response characteristics through short-term active heating to address scenarios where natural temperature difference signals are weak or mission time is tight. This method can solve the technical problem in existing Mars surface temperature monitoring where the fixed monitoring time window and lack of scientific basis for the selection of monitoring duration lead to incomplete and insufficient representative thermal response data.

[0086] The preferred embodiment of the Mars probe temperature monitoring method based on time-controlled factors optimization described in this invention, such as... Figure 1 As shown, the Mars probe temperature monitoring method optimized based on time control factors includes the following steps:

[0087] Step S10: Obtain simulated fire soil, and perform sieving and compaction processing on the simulated fire soil to obtain multiple relatively compacted samples.

[0088] This invention belongs to the field of deep space exploration and planetary science, and specifically relates to a time control strategy for optimizing temperature monitoring. Furthermore, this invention provides a technical solution for obtaining higher quality soil thermal response data by optimizing the start time and duration of temperature monitoring.

[0089] This invention addresses the limitations in the accuracy of Mars rover passability assessment caused by fixed temperature monitoring periods, insufficient duration, and neglect of the impact of time control factors on thermal response characteristics in existing technologies. It provides a time-controlled optimized method and system for temperature monitoring and passability assessment of Mars rover. By rationally selecting the monitoring period and duration, this invention effectively improves the quality of temperature data, obtains accurate temperature difference data, and enables accurate calculation of thermal inertia, thereby improving the accuracy of passability assessment.

[0090] The system components of this invention include: 1. a Mars-simulated soil preparation unit for configuring standardized samples with specific particle size distribution and relative density; 2. at least one temperature sensor array for being arranged or inserted into Mars-simulated soil or in-situ Mars soil; 3. a data acquisition unit connected to the temperature sensor array for recording temperature time-series data; 4. an active thermal excitation unit for generating and emitting controllable thermal radiation toward the target soil region; and 5. a control module for coordinating the working timing of the active thermal excitation unit with the data recording of the data acquisition unit.

[0091] The present invention first requires the construction of a simulated Martian soil system. The process is as follows: prepare simulated Martian soil samples that match the thermophysical properties of Martian soil. The samples include at least two typical state combinations with different median particle size and different relative density.

[0092] Specifically, simulated fire soil is obtained, and the simulated fire soil is initially screened using a sieve with a preset mesh size to obtain a first simulated fire soil; the first simulated fire soil is then screened again using a laser particle size analyzer to obtain a second simulated fire soil; the second simulated fire soil is then subjected to a compaction process using a vibration compaction method to obtain multiple relatively compacted samples; wherein, the relatively compacted samples include fine-grained soft samples, fine-grained dense samples, coarse-grained soft samples, and coarse-grained dense samples.

[0093] The expression for calculating the relative density of the sample in a relatively dense state is as follows:

[0094] ;

[0095] in, For relative density, For maximum dry loose density, The density is the dry, loose density under natural conditions. It is the minimum dry loose density.

[0096] Understandably, this invention addresses the need for surface soil thermal response information in Mars rover mobility assessment by providing a time-controlled optimization-based temperature monitoring method. Its core lies in revealing and quantifying the impact of "monitoring period" and "monitoring duration" on soil thermal response feature extraction through systematic ground simulation experiments. This leads to the establishment of a scientific and engineering-applicable time-optimized monitoring strategy. First, a simulated fire-soil system needs to be constructed and a baseline thermal response database established. The specific implementation process is as follows:

[0097] First, select mechanical parameters (cohesion) that are consistent with those of real Martian soil. c internal friction angle φThe JLU Mars series of simulated soils were used as experimental materials (i.e., simulated fire soil in this invention), specifically two samples with significant differences in particle size: JLU Mars 1 (fine-grained, D...). 50 =0.041 mm) and JLU Mars 3 (coarse-grained, D 50 =0.69 mm).

[0098] The relative density Dr is prepared using the relative density calculation formula, which is expressed as:

[0099] ;

[0100] in, For relative density, For maximum dry loose density, The density is the dry, loose density under natural conditions. It is the minimum dry loose density.

[0101] The dry loose density was measured using a self-designed vibration compaction device and electronic scale, and the minimum dry loose density was measured using the tilting method. The simulated fire soil was slowly and evenly poured into the soil sample box, and the mass of the simulated fire soil in the soil sample box was measured. The expression for calculating the dry loose density is:

[0102] ;

[0103] in: To simulate the weight of fire soil, The total volume of the simulated dry volcanic soil. Maximum dry loose density. Measurements were taken using a vibratory compaction device. First, simulated scorched soil was placed in a soil sample box, then placed on the self-designed vibratory compaction device. By adjusting the frequency and vibration duration, the mass of the simulated scorched soil in the sample box was measured sequentially. The mass that tended to stabilize was then calculated using a formula. Calculate and use as the maximum dry loose density The dry, loose density under natural conditions is measured using the ring cutter method. First, the mass of the empty ring cutter is weighed. The ring cutter is then vertically pressed into the sample to be tested, and a complete soil column is removed. Its mass is then weighed, and the density is determined according to the formula... Calculate the dry loose density under natural conditions The two density states are: soft state (relative density Dr = 30%) and dense state (relative density Dr = 77%), for a total of four typical sample combinations.

[0104] The simulated volcanic soil sample includes a median particle size D. 50 For fine-grained samples with a diameter of 0.041 mm and a median particle size D 50The sample was a coarse-grained sample with a particle size of 0.69 mm. For each particle size, a soft sample with a relative density Dr of 30% and a dense sample with a relative density Dr of 77% were prepared.

[0105] In the specific implementation process:

[0106] like Figures 2 to 3 As shown ( Figure 3 Medium a represents small-particle-density fire soil. Figure 3 Medium b represents small-grained, loose, fiery soil. Figure 3 Medium c represents large-particle-density fire soil. Figure 3 (where d represents large-particle-size, loose Martian soil). This invention uses JLU Mars series simulated Martian soil as experimental material. The material's formulation is designed based on mission-measured Martian soil mechanical parameters. The specific preparation steps include:

[0107] 1. Screening: The simulated fire soil with a particle size greater than 0.1 mm is screened using screens with different mesh sizes (i.e., the initial screening process in this invention), and then the simulated fire soil with a particle size less than 0.1 mm is screened using a laser particle size analyzer.

[0108] 2. Particle size analysis: The particle size distribution was accurately determined using a laser particle size analyzer (i.e., the re-sieving process in this invention); small particle size simulates fire soil JLU Mars 1, median particle size D 50 =0.041mm; Large-particle-size simulated fire soil JLU Mars 3, median particle size D 50 =0.69mm.

[0109] 3. State Preparation (i.e., the dense state preparation process in this invention): Two types of engineering samples with relative densities are prepared by vibration compaction method. The relative density of the soft sample is Dr=30%; the relative density of the dense state is Dr=77%. The formula for calculating the relative density is as follows:

[0110] .

[0111] 4. Moisture Content Control: The main interfering factor in soil temperature measurement is humidity. Given that the actual humidity of Martian surface soil is almost zero, this invention uses a drying method to determine the moisture content of the simulated Martian soil to be 0%. The simulated Martian soil materials used (volcanic rock and volcanic ash) themselves have extremely low humidity. Furthermore, the air humidity at the target location (northeastern region of a certain country) is low and fluctuates little, further ensuring the stability of the moisture content during the experiment. To verify its long-term stability, the prepared simulated Martian soil was initially placed in a laboratory environment, and the moisture content was remeasured after 3 and 6 months. The results showed that it was basically consistent with the initial moisture content, thus effectively eliminating the influence of soil humidity on the temperature measurement results.

[0112] Step S20: Simulate temperature monitoring on multiple relatively dense samples to obtain multiple temperature-time curves, and perform piecewise linear fitting on the multiple temperature-time curves to obtain the thermal response characteristics of the simulated fire soil.

[0113] This invention obtains natural temperature response benchmark data by simulating temperature monitoring. The specific implementation process includes: placing the simulated fire soil sample in a ground simulation environment and conducting long-term continuous temperature monitoring to obtain its natural temperature response data in a complete day-night cycle.

[0114] Specifically, a preset number of temperature sensor arrays are set in each of the relatively dense samples. The temperature of each relatively dense sample is collected by the temperature sensor arrays at preset sampling intervals to obtain a temperature-time curve of temperature change over time for each relatively dense sample. Based on the temperature-time curves, the heating curve corresponding to the heating stage and the cooling curve corresponding to the cooling stage of each relatively dense sample are obtained. Piecewise linear fitting is performed on the heating curves and the cooling curves to obtain the thermal response characteristics of each relatively dense sample.

[0115] In this invention, the temperature sensor array is a thermocouple, and its sensing node is configured to be completely buried below the soil surface. The specific monitoring process involves placing four prepared sample containers (45cm × 33cm × 15cm in volume) horizontally on a flat outdoor experimental site, ensuring sufficient spacing between the containers to avoid mutual obstruction of sunlight and thermal interference. Figure 4 As shown ( Figure 4 In the diagram, 'a' represents the distribution of thermocouples placed in dense, flammable soil. Figure 4 In the diagram, b represents the distribution of thermocouples placed in soft, flammable soil. Figure 4 (C in the diagram represents the distribution of temperature sensors in the simulated fire soil). Five thermocouples (i.e., the preset number of temperature sensor arrays in this invention) are evenly buried in each sample container, with the sensing nodes of the thermocouples precisely buried at a depth of 3.0 ± 0.5 cm below the soil surface. All thermocouples (4 samples × 5 thermocouples = 20 channels) are connected to an EX4000 high-precision temperature acquisition instrument. In the data acquisition software, a label ("Fine-Loose-1") is set for each channel, and uniform acquisition parameters are set (the sampling interval is 1 minute, i.e., the preset sampling time in this invention).

[0116] The core of the data acquisition is unattended continuous monitoring: the EX4000 data acquisition instrument continuously stores data in its built-in memory or a connected computer according to a preset sampling interval (recording the temperature of all 20 channels once per minute). Researchers check the equipment's operating status, battery level, and data storage status on-site every hour to ensure uninterrupted operation.

[0117] The experiment recorded the temperature-time curves of different soil types within a complete day-night cycle (approximately a Martian day, based on a 24-hour Earth cycle). It particularly captured short-term, rapid temperature changes caused by shading gaps around sunrise (e.g., 05:00-06:30) and sunset (e.g., 16:00-16:30). By performing piecewise linear fitting on the temperature-time curves, the heating or cooling rates (K-values) of different soil types at different time periods (e.g., rapid warming, sustained warming, and cooling periods) were quantified. This allowed for the establishment of a basic thermal response characteristic database for different soil types (particle size, density) under natural conditions (the database stores the thermal response characteristics of different soil types).

[0118] The fitting process is as follows: Figure 5-8 The figure shows the temperature rise and fall time curves monitored continuously over three days. Analysis reveals that the temperature changes in the volcanic soil can be divided into two phases: a warming phase from 05:00 to 6:30 (short-term rapid warming), 08:00 to 16:00 (continuous warming), and 16:00 to 16:30 (two short-term rapid warming); and a cooling phase from 16:00 to 04:00 the following day (continuous cooling). Therefore, the warming and cooling curves are fitted using piecewise linear curves to characterize the thermal response of the volcanic soil. The formula for the linear fitting is shown below:

[0119] ;

[0120] in, The equation is a linear fit. For the rate of heating or cooling, For time, This is the intercept.

[0121] The quantification process is as follows: Figure 9 and Figure 10As shown, to simulate the heating and cooling rates of volcanic soil, significant differences were observed between the heating and cooling rates of loose, small-particle-size (JLU Mars 1) and large-particle-size (JLU Mars 3) volcanic soils over three consecutive days. Comparison revealed that, under the same environmental conditions, the heating and cooling rates of large-particle-size volcanic soil were significantly higher than those of small-particle-size volcanic soil. During the periods of 05:00 to 6:30 (short-term rapid heating) and 16:00 to 16:30 (secondary short-term rapid heating), the heating rate of large-particle-size soil was more than 60% higher than that of small-particle-size soil. This indicates that, compared to small-particle-size soil, large-particle-size volcanic soil is more sensitive to the thermal response of ambient temperature.

[0122] In the specific implementation process, the construction and calibration of the temperature monitoring system are as follows:

[0123] This invention constructs a distributed multi-point temperature monitoring system, with the following specific configuration:

[0124] 1. Sensor arrangement scheme: Five thermocouples are arranged in each sample container (45cm×33cm×15cm); the thermocouple sensing nodes are completely buried in the soil at a depth of 3.0±0.5cm; the sensors are evenly distributed in a grid pattern with an adjacent spacing of 10cm; the outermost sensor is at least 8cm away from the edge of the container.

[0125] 2. Data acquisition system: The EX4000 high-precision temperature monitor is used; the measurement accuracy is ±0.1℃ and the resolution is 0.01℃.

[0126] The optimization process for the time window under natural lighting conditions is as follows:

[0127] This invention determines the optimal monitoring time window through natural light experiments. The specific implementation steps are as follows:

[0128] 1. Place four typical samples (fine-grained soft state, fine-grained dense state, coarse-grained soft state, and coarse-grained dense state) in an outdoor test field.

[0129] 2. The experimental site has partial obstruction (mainly due to the limitations of the experimental site; there are local buildings around the simulated fire soil placement area that block direct sunlight for a specific period of time. It was precisely because of this brief obstruction that the temperature of the simulated fire soil dropped sharply, providing a basis for the subsequent active thermal radiation), which can generate intermittent sunlight during specific periods.

[0130] 3. Continuous monitoring for 72 hours (three complete daily cycles).

[0131] The experimental results are as follows:

[0132] 1. The optimal monitoring window during the day is: 1 to 2 hours after sunrise (06:00 to 08:00) and 1 to 2 hours before sunset (15:00 to 16:30);

[0133] 2. Optimal start time for nighttime monitoring: 1 to 2 hours after sunset (16:30 to 18:30);

[0134] 3. Optimal during the day: Because the temperature rises rapidly in the 1 to 2 hours after sunrise, for example... Figure 8 As observed in image b, between 05:00 and 06:30, there was a significant difference in the heating rates between large and small particle sizes, for example, in... Figure 8 As observed in Figure a, there were also significant differences between the 16:00 and 16:30 periods. This is because the temperature monitoring of different simulated volcanic soils is mainly to distinguish the characteristics of different volcanic soils, and these characteristics are more pronounced in these two periods.

[0135] 4. Optimal nighttime: The temperature difference between 16:30 and 00:00 is significant, but this period is considerably too long. Figure 11-16 The analysis shows that there is already a significant temperature difference within 2 hours, so choosing 1 to 2 hours after sunset is optimal.

[0136] like Figure 17-28 This refers to statistical data from a temperature monitoring system on the temperature response, heating rate, cooling rate, and temperature difference over different monitoring durations of simulated fire soil with varying densities at different time points. Figure 17-19 A schematic diagram illustrating the temperature response of simulated fire soil with different densities at different time periods; Figure 20-22 Schematic diagram illustrating the simulated heating rate of volcanic soil with different densities; Figure 23 Schematic diagram of simulated cooling rate data for fire soil with different densities; Figure 24-28 A schematic diagram illustrating the temperature difference over different monitoring periods for simulated fire soil with varying densities.

[0137] Step S30: Analyze the optimal monitoring period and the optimal monitoring duration based on the thermal response characteristics to obtain the optimal passive monitoring time window and duration strategy.

[0138] Based on the natural temperature response data, this invention analyzes the impact of different monitoring start times and different monitoring durations on the intensity of the extracted temperature difference signal, thereby determining the optimal daytime monitoring window, the nighttime monitoring start time, and the recommended minimum monitoring duration (i.e., the optimal monitoring period analysis and optimal monitoring duration analysis in this invention).

[0139] Specifically, based on the thermal response characteristics, the influence of different monitoring start times and different monitoring durations on the intensity of the extracted temperature difference signal is analyzed to obtain the optimal passive monitoring time window and duration strategy. The optimal passive monitoring time window and duration strategy includes a daytime optimal passive monitoring time window and duration strategy and a nighttime optimal passive monitoring time window and duration strategy. The daytime optimal passive monitoring time window and duration strategy involves monitoring within 1 to 2 hours after sunrise and within 1 to 2 hours before sunset. The nighttime optimal passive monitoring time window and duration strategy involves starting monitoring within 1 to 2 hours after sunset, with the monitoring duration set to a preset detection duration.

[0140] In this invention, the process of determining the optimal monitoring duration specifically includes the following: within the nighttime monitoring window, comparative analysis is performed on monitoring data with durations of 30 minutes, 2 hours, 4 hours, 6 hours, and 8 hours; among them, the maximum soil temperature difference obtained from monitoring with a duration of 6 hours or 8 hours is significantly higher than the maximum soil temperature difference obtained from monitoring with a duration of 30 minutes.

[0141] In other words, during the daytime (solar cycle), monitoring is selected within 1 to 2 hours after sunrise and 1 to 2 hours before sunset. The monitoring duration can be flexibly selected according to the probe's mission. For example, if the Mars mission is urgent, a 2-hour temperature monitoring duration is selected. If the probe is conducting long-term scientific exploration, a 4-8 hour monitoring duration is selected. If the environment is under weak temperature change, active thermal radiation is used. During the non-solar cycle (night), active thermal radiation is selected to achieve rapid heating in a short time and improve the accuracy of temperature difference acquisition.

[0142] The specific process for determining the optimal passive monitoring time window and duration strategy is as follows: Based on the experimental data of simulated volcanic soil collected earlier (i.e., the thermal response characteristics in this invention), the influence of the two time control variables, "monitoring period" and "monitoring duration," on the extracted temperature difference (ΔT) is analyzed to form an operable engineering strategy:

[0143] 1. Optimal Monitoring Period Selection Strategy: Experiments show that soil thermal response exhibits distinct stages. During the day, it is recommended to monitor during the period of highest temperature change rate, namely 1 to 2 hours after sunrise and 1 to 2 hours before sunset (i.e., the optimal passive monitoring time window and duration strategy for the daytime in this invention). At night, to avoid the complex coupling effects of daytime solar radiation, it is recommended to begin monitoring 1 to 2 hours after sunset, when soil temperature differences begin to appear significantly (i.e., the optimal passive monitoring time window and duration strategy for the nighttime in this invention).

[0144] 2. Optimal Monitoring Duration Guidance Strategy: For the nighttime monitoring window (18:00 to 05:00 the next day), the impact of different monitoring durations (30 min, 2 h, 4 h, 6 h, 8 h) on the temperature difference was systematically studied. Experiments confirmed that monitoring duration is a key factor affecting the temperature difference amplitude. Short-term monitoring (e.g., 30 min) yielded small temperature differences (approximately -0.5℃) with large fluctuations, while long-term monitoring (8 h) yielded significantly larger temperature differences (92.19% increase in fine-grained soil and 93.5% increase in coarse-grained soil), resulting in more stable and representative data. Therefore, given sufficient time, long-term monitoring of ≥6 hours is recommended to obtain reliable temperature difference data. Simultaneously, the experiment established a model where the time of maximum temperature difference occurrence is regularly delayed with increasing monitoring duration, providing a time benchmark for result interpretation (the analysis method for "simulated fire soil with different densities" is the same as that for "simulated fire soil with different particle sizes").

[0145] In the specific implementation process:

[0146] This invention studies the effect of monitoring duration on temperature difference signals, and the specific implementation is as follows:

[0147] Experimental plan:

[0148] 1. Set five monitoring durations for the optimal nighttime monitoring window (starting at 18:00): 30 minutes, 2 hours, 4 hours, 6 hours, and 8 hours; 2. Repeat the experiment for each duration for 5 days; 3. Record the temperature difference curves of various soil types under different durations.

[0149] Data processing:

[0150] 1. Extract the maximum temperature difference value for each monitoring duration; 2. Calculate the trend of the average temperature difference with the monitoring duration.

[0151] Experiments have shown that:

[0152] 1. The monitoring time was extended from 30 minutes to 8 hours, and the temperature difference value increased significantly; 2. Fine-grained soil: increased from -0.46℃ to -5.89℃, an increase of 92.19%; 3. Coarse-grained soil: increased from -0.51℃ to -7.93℃, an increase of 93.5%.

[0153] Furthermore, if the temperature monitoring time does not meet the optimal passive monitoring time window and duration strategy, an active thermal radiation mode is set to collect the transient temperature response of the relatively dense sample during heating and natural cooling.

[0154] In the specific implementation process:

[0155] like Figure 29 and 30As shown, this invention verifies the effectiveness of the active thermal excitation method (active thermal radiation experiment), and the specific implementation process is as follows:

[0156] Experimental equipment:

[0157] 1. Establish a closed experimental environment with the temperature controlled at 20±2℃; 2. The heating unit should be 50cm away from the sample surface.

[0158] Experimental steps:

[0159] 1. Place four typical samples in the experimental chamber; 2. Start the heating system and record the temperature rise curve; 3. After heating for 20 minutes, turn off the heat source and record the natural cooling curve; 4. Repeat the heating-cooling cycle 4 times for each sample.

[0160] The results are shown in Table 1 below:

[0161] Table 1: Rate of soil warming during active thermal radiation stage

[0162]

[0163] Comparison of heating rates: coarse-grained, loose simulated fire soil: 1.09-1.30℃ / min; coarse-grained, dense simulated fire soil: 0.88-0.99℃ / min; fine-grained, loose simulated fire soil: 0.76-0.87℃ / min; fine-grained, dense simulated fire soil: 0.73-0.81℃ / min.

[0164] Conclusion regarding natural cooling characteristics: Coarse-grained soils have a significantly higher cooling rate than fine-grained soils.

[0165] It is understandable that this invention develops and applies an active thermal excitation rapid characterization method: to address scenarios where passive monitoring is limited, such as tight mission timelines and weak natural temperature difference signals (e.g., Martian winter or cloudy days), this invention develops an active thermal excitation method as a core supplementary strategy. This method, in a controlled environment, uses a controlled heat source at a distance of 50 cm from the soil sample to apply radiative heating for a short period of 20 minutes, monitoring the transient temperature response of the soil during heating and subsequent natural cooling. Experiments show that this active excitation can effectively elicit differences in the thermal response of soils, especially showing higher excitation efficiency for coarse-grained and soft soils. During the heating phase, the heating rate of coarse-grained soft soils (1.09 to 1.30 °C / min) is significantly higher than that of fine-grained dense soils (0.73 to 0.81 °C / min). Dynamic slope analysis during the cooling phase further reveals the differences in heat dissipation characteristics among different soils. This invention compresses traditional observations that rely on long-term natural cycles into rapid active testing that can be completed in about one hour, providing an efficient and reliable emergency means to obtain key thermal sensitivity characteristics of soil within engineering time windows.

[0166] Step S40: Determine the target fire soil to be tested, and perform real temperature monitoring on the target fire soil to be tested according to the optimal passive monitoring time window and duration strategy to obtain the best temperature monitoring results.

[0167] During a Mars exploration mission, based on the local time at the probe's location (i.e., the location near the target Martian soil to be tested), one of the following temperature monitoring modes will be executed:

[0168] 1. Daytime monitoring mode: Temperature monitoring is initiated and executed within a window of 1 to 2 hours after sunrise or 1 to 2 hours before sunset;

[0169] 2. Nighttime monitoring mode: 1 to 2 hours after local sunset, with a continuous monitoring duration of no less than 6 hours (the monitoring duration can be flexibly selected according to the probe's exploration mission. For example, if the Mars mission is urgent, a 2-hour temperature monitoring duration can be selected. If the probe is conducting long-term scientific exploration, a 4-8 hour monitoring duration can be selected).

[0170] 3. When the activation conditions of the daytime monitoring mode or nighttime monitoring mode are not met, and it is necessary to quickly obtain the soil thermal response characteristics, perform the following steps: apply active thermal radiation to the target soil area once or multiple times for a duration of 15 to 25 minutes; measure and record the transient temperature response curve of the target soil area during the heating period and during the natural cooling process after the heating is stopped (i.e., using the active thermal radiation mode set in this invention).

[0171] Specifically, the target volcanic soil to be tested is determined, and its current location and current solar time are obtained. Based on the current location and current solar time, it is determined whether the optimal passive monitoring time window and duration strategy is satisfied. If the daytime optimal passive monitoring time window and duration strategy is satisfied, the target volcanic soil is subjected to real-time temperature monitoring according to the daytime optimal passive monitoring time window and duration strategy to obtain the optimal temperature monitoring result. If the nighttime optimal passive monitoring time window and duration strategy is satisfied, the target volcanic soil is subjected to real-time temperature monitoring according to the nighttime optimal passive monitoring time window and duration strategy to obtain the optimal temperature monitoring result. If the optimal passive monitoring time window and duration strategy is not satisfied, the active thermal radiation mode is used to monitor the target volcanic soil for real-time temperature to obtain the optimal temperature monitoring result.

[0172] The following demonstrates the application of the complete time-optimized monitoring strategy in a real-world exploration mission (mission scenario: Mars rover needs to quickly assess soil properties in complex terrain areas). The implementation process is as follows:

[0173] 1. Determine if the current location is within the optimal monitoring window based on the local solar time:

[0174] If the time period is 06:00-08:00 or 15:00-16:30, activate the daytime monitoring mode;

[0175] If the time period is between 16:30 and 18:30, activate the night monitoring mode;

[0176] At other times, depending on the urgency of the task, choose to wait or activate the active heating mode.

[0177] For example, when a Mars rover needs to conduct a passability assessment during the daytime, if it is between 06:00-08:00, 16:00-18:00, or 16:30-18:30, the characteristics of different simulated soils are significant during these periods. This is because the thermal inertia of the soil is significant during these periods, and thermal inertia reflects the mechanical properties of the soil. Thus, the Mars rover can determine whether a certain area is passable based on thermal inertia. At other times, the temperature difference between different soils is not significant. Therefore, during these periods, active thermal radiation is applied to the soil to raise its temperature, thereby determining the thermal inertia of different soils and then judging passability based on the thermal inertia.

[0178] 2. Select the monitoring duration according to the task time constraints: when there is enough time, select 6-8 hours of long-term monitoring; when time is tight, select 2-4 hours of medium-term monitoring.

[0179] 3. Perform temperature monitoring and record data: Deploy temperature sensors or use vehicle-mounted temperature measurement equipment; continuously monitor temperature changes for selected durations; record complete temperature-time curves.

[0180] Technical effects:

[0181] 1. The "time control factor" is systematically established as an optimizable design variable for Martian surface temperature monitoring, and a complete quantitative impact model is developed. In traditional methods, monitoring time is often considered a fixed engineering constraint or selected based on experience. This invention, through ground simulation experiments, reveals for the first time the independent and significant quantitative impact of two time variables, "monitoring start time" and "monitoring duration," on the thermal response characteristics of simulated Martian soil, and establishes a correlation model of "soil type-time window-monitoring duration-temperature difference response."

[0182] 2. An optimal monitoring window selection strategy based on thermal response dynamics is proposed. Daytime window: Focus on the period of greatest temperature change rate (1-2 hours after sunrise and before sunset) to capture the most sensitive response signal. Nighttime window: Clearly begin 1-2 hours after sunset to avoid residual sunset heat disturbance, and it is recommended to continue for at least 6 hours (the monitoring duration can be flexibly selected according to the probe's mission; for example, if the Mars mission is urgent, a 2-hour temperature monitoring duration is selected; if the probe is conducting long-term scientific exploration, a 4-8 hour monitoring duration is selected) to ensure sufficient accumulation of characteristic temperature differences.

[0183] 3. This invention proposes that "monitoring duration" is a key factor determining the intensity of the temperature difference signal. Through experiments, this invention clearly demonstrates that, within the same time window, simply extending the monitoring duration can increase the intensity of the extracted temperature difference signal by over 90%. This discovery elevates "monitoring duration" from a often overlooked, simple parameter to a critical parameter determining data quality.

[0184] 4. Integrating "passive monitoring optimization" with "active thermal excitation" methods. Addressing the limitations of passive monitoring in specific scenarios, this invention introduces active thermal radiation excitation as a supplement. This is not simply heating, but rather rapidly stimulating and extracting the transient thermal response characteristics of the soil through a short-term (20-minute) controllable heating-cooling cycle, which is particularly effective for coarse-grained and soft soils sensitive to permeability.

[0185] In summary, the above technical solutions have achieved an optimal temperature monitoring strategy. This invention transforms the questions of "when to monitor" and "for how long to monitor" from empirical engineering choices into an optimization decision-making process based on experimental evidence, thus providing systematic methodological guidance for Mars rovers to acquire high-quality surface thermal response data when performing complex tasks.

[0186] Furthermore, such as Figure 31 As shown, based on the above-mentioned Mars probe temperature monitoring method optimized by time control factors, the present invention also provides a Mars probe temperature monitoring system optimized by time control factors, wherein the Mars probe temperature monitoring system optimized by time control factors includes:

[0187] The sample preparation module 51 is used to obtain simulated fire soil and to perform sieving and compaction processing on the simulated fire soil to obtain multiple relatively compacted samples.

[0188] The simulated temperature monitoring module 52 is used to simulate the temperature of multiple relatively dense samples, obtain multiple temperature-time curves, and perform piecewise linear fitting on the multiple temperature-time curves to obtain the thermal response characteristics of the simulated fire soil.

[0189] The optimal monitoring strategy generation module 53 is used to perform optimal monitoring period analysis and optimal monitoring duration analysis based on the thermal response characteristics to obtain the optimal passive monitoring time window and duration strategy.

[0190] The monitoring result output module 54 is used to determine the target fire soil to be tested, and to perform real temperature monitoring on the target fire soil to be tested according to the optimal passive monitoring time window and duration strategy, so as to obtain the best temperature monitoring result.

[0191] Furthermore, such as Figure 32 As shown, based on the above-mentioned Mars probe temperature monitoring method and system optimized by time control factors, the present invention also provides a terminal, which includes a processor 10, a memory 20 and a display 30. Figure 32 Only some of the terminal components are shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0192] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard drive or memory. In other embodiments, the memory 20 may be an external storage device of the terminal, such as a plug-in hard drive, smart media card (SMC), secure digital card (SD), flash card, etc. Further, the memory 20 may include both internal and external storage devices. The memory 20 is used to store application software and various types of data installed on the terminal, such as the program code installed on the terminal. The memory 20 can also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 20 stores a Mars probe temperature monitoring program 40 optimized based on time-control factors. This time-control factor-optimized Mars probe temperature monitoring program 40 can be executed by the processor 10, thereby implementing the time-control factor-optimized Mars probe temperature monitoring method of this application.

[0193] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in the memory 20 or process data, such as executing the Mars probe temperature monitoring method optimized based on time control factors.

[0194] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display 30 is used to display information on the terminal and to display a visual user interface.

[0195] In one embodiment, when the processor 10 executes the Mars probe temperature monitoring program 40 optimized based on time control factors in the memory 20, the steps of the Mars probe temperature monitoring method optimized based on time control factors as described above are implemented.

[0196] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a Mars probe temperature monitoring program optimized based on time control factors, and the Mars probe temperature monitoring program optimized based on time control factors, when executed by a processor, implements the steps of the Mars probe temperature monitoring method optimized based on time control factors as described above.

[0197] In summary, this invention provides a method, system, terminal, and storage medium for Mars rover temperature monitoring based on time-controlled factor optimization. The method includes: acquiring simulated fire soil, and performing sieving and compaction processing on the simulated fire soil to obtain multiple relatively compacted samples; performing simulated temperature monitoring on the multiple relatively compacted samples to obtain multiple temperature-time curves, and performing piecewise linear fitting on the multiple temperature-time curves to obtain the thermal response characteristics of the simulated fire soil; performing optimal monitoring period analysis and optimal monitoring duration analysis based on the thermal response characteristics to obtain the optimal passive monitoring time window and duration strategy; determining the target fire soil to be tested, and performing real temperature monitoring on the target fire soil to be tested according to the optimal passive monitoring time window and duration strategy to obtain the best temperature monitoring result. This invention constructs the optimal passive monitoring time window and duration strategy through simulation experiments, which can achieve accurate monitoring of the temperature of the fire soil to be tested and accurate acquisition of the fire soil temperature difference signal, thereby effectively improving the accuracy of the rover's passability assessment and ensuring the rover's driving safety.

[0198] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal that includes that element.

[0199] Of course, those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.). The program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The computer-readable storage medium can be a memory, magnetic disk, optical disk, etc.

[0200] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for monitoring the temperature of a Mars rover based on time-controlled factors, characterized in that, The Mars probe temperature monitoring method optimized based on time control factors includes: Simulated fire soil was obtained, and the simulated fire soil was subjected to sieving and compaction treatment to obtain multiple relatively compacted samples. Multiple relatively dense samples were subjected to simulated temperature monitoring to obtain multiple temperature-time curves. Piecewise linear fitting was performed on the multiple temperature-time curves to obtain the thermal response characteristics of the simulated fire soil. Based on the thermal response characteristics, the optimal monitoring period and optimal monitoring duration are analyzed to obtain the optimal passive monitoring time window and duration strategy. The step of performing optimal monitoring period analysis and optimal monitoring duration analysis based on the thermal response characteristics to obtain the optimal passive monitoring time window and duration strategy specifically includes: Based on the thermal response characteristics, the influence of different monitoring start times and different monitoring durations on the intensity of the extracted temperature difference signal is analyzed, and the optimal passive monitoring time window and duration strategy are obtained. The optimal passive monitoring time window and duration strategy includes a daytime optimal passive monitoring time window and duration strategy and a nighttime optimal passive monitoring time window and duration strategy. The optimal passive monitoring time window and duration strategy during the day is to monitor within 1 to 2 hours after sunrise and within 1 to 2 hours before sunset; The optimal passive monitoring time window and duration strategy for nighttime is to start monitoring within 1 to 2 hours after sunset, wherein the monitoring duration is set to a preset detection duration; The target soil to be tested is identified, and the actual temperature of the target soil to be tested is monitored according to the optimal passive monitoring time window and duration strategy to obtain the best temperature monitoring results.

2. The Mars probe temperature monitoring method based on time-controlled factor optimization according to claim 1, characterized in that, The process of obtaining simulated fire soil and then performing sieving and compaction treatments on the simulated fire soil to obtain multiple relatively compacted samples specifically includes: A simulated fire soil was obtained, and the simulated fire soil was initially screened using a sieve with a preset mesh size to obtain the first simulated fire soil. The first simulated fire soil was re-sieved using a laser particle size analyzer to obtain the second simulated fire soil; The second simulated fire soil was prepared into a dense state by using a vibration compaction method, resulting in multiple relatively dense samples. The relatively dense sample includes fine-grained soft sample, fine-grained dense sample, coarse-grained soft sample, and coarse-grained dense sample.

3. The Mars probe temperature monitoring method based on time-controlled factor optimization according to claim 2, characterized in that, The expression for calculating the relative density of the sample in a relatively dense state is as follows: ； in, For relative density, For maximum dry loose density, The density is the dry, loose density under natural conditions. It is the minimum dry loose density.

4. The Mars probe temperature monitoring method based on time-controlled factor optimization according to claim 1, characterized in that, The step of simulating temperature monitoring on multiple relatively dense samples to obtain multiple temperature-time curves, and performing piecewise linear fitting on the multiple temperature-time curves to obtain the thermal response characteristics of the simulated fire soil, specifically includes: A preset number of temperature sensor arrays are set in each of the relatively dense state samples. The temperature of each of the relatively dense state samples is collected through the temperature sensor arrays at preset sampling intervals to obtain a temperature-time curve of temperature change in each of the relatively dense state samples over time. Based on the temperature-time curves, the heating curves corresponding to the heating stage and the cooling curves corresponding to the cooling stage of each relatively dense sample are obtained, and piecewise linear fitting is performed on the heating curves and the cooling curves to obtain the thermal response characteristics of each relatively dense sample.

5. The Mars probe temperature monitoring method based on time-controlled factor optimization according to claim 1, characterized in that, The step of performing optimal monitoring period analysis and optimal monitoring duration analysis based on the thermal response characteristics to obtain the optimal passive monitoring time window and duration strategy further includes: If the temperature monitoring time does not meet the optimal passive monitoring time window and duration strategy, then an active thermal radiation mode is set to collect the transient temperature response of the relatively dense sample during heating and natural cooling.

6. The Mars probe temperature monitoring method based on time-controlled factor optimization according to claim 5, characterized in that, The process of identifying the target volcanic soil to be tested and conducting real temperature monitoring on the target volcanic soil according to the optimal passive monitoring time window and duration strategy to obtain the best temperature monitoring results specifically includes: Identify the target fire soil to be tested, and obtain the current location and current solar time of the target fire soil to be tested. Then, determine whether the optimal passive monitoring time window and duration strategy is satisfied based on the current location and current solar time. If the daytime optimal passive monitoring time window and duration strategy is satisfied, then the actual temperature of the target fire soil to be tested is monitored according to the daytime optimal passive monitoring time window and duration strategy to obtain the best temperature monitoring result. If the optimal passive monitoring time window and duration strategy for nighttime is satisfied, then the target fire soil to be tested is subjected to real temperature monitoring according to the optimal passive monitoring time window and duration strategy for nighttime, and the best temperature monitoring result is obtained. If the optimal passive monitoring time window and duration strategy is not met, the active thermal radiation mode is used to monitor the actual temperature of the target soil to be tested, and the best temperature monitoring result is obtained.

7. A Mars probe temperature monitoring system optimized based on time-controlled factors, characterized in that, The Mars probe temperature monitoring system optimized based on time control factors is used to implement the Mars probe temperature monitoring method optimized based on time control factors as described in any one of claims 1-6, wherein the Mars probe temperature monitoring system optimized based on time control factors includes: The sample preparation module is used to obtain simulated fire soil and to perform sieving and compaction processing on the simulated fire soil to obtain multiple relatively compacted samples. The simulated temperature monitoring module is used to simulate the temperature of multiple relatively dense samples, obtain multiple temperature-time curves, and perform piecewise linear fitting on the multiple temperature-time curves to obtain the thermal response characteristics of the simulated fire soil. The optimal monitoring strategy generation module is used to perform optimal monitoring period analysis and optimal monitoring duration analysis based on the thermal response characteristics to obtain the optimal passive monitoring time window and duration strategy. The monitoring result output module is used to determine the target fire soil to be tested, and to perform real temperature monitoring on the target fire soil to be tested according to the optimal passive monitoring time window and duration strategy, so as to obtain the best temperature monitoring result.

8. A terminal, characterized in that, The terminal includes: a memory, a processor, and a Mars probe temperature monitoring program optimized based on time control factors, stored in the memory and executable on the processor. When the Mars probe temperature monitoring program optimized based on time control factors is executed by the processor, it implements the steps of the Mars probe temperature monitoring method optimized based on time control factors as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a Mars probe temperature monitoring program optimized based on time control factors, which, when executed by a processor, implements the steps of the Mars probe temperature monitoring method optimized based on time control factors as described in any one of claims 1-6.

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