A robot control instruction analysis method and system integrating continuous instructions

Abstract

The present application provides a robot control instruction analysis method and system that integrates continuous instructions, which relates to the field of electronic digital data processing technology, including: quantitatively evaluating the instruction execution quality and resource consumption based on predicted data to generate quality scores and consumption estimates; constructing a multi-objective optimization framework based on the quality scores and consumption estimates to determine a set of candidate paths; calculating the switching cost and execution time for the candidate path set, and screening the optimized path solutions that meet the constraints; adjusting the instruction execution priority and resource allocation ratio in combination with real-time environmental data to generate a scheduling strategy. The present invention monitors environmental changes and task quality in real time, dynamically adjusts instruction priority and resource allocation, and when the execution efficiency is lower than a threshold, triggers an online update of the prediction model and fine-tunes the model parameters to adapt to the new situation. Through continuous optimization and monitoring, the present invention can improve the execution efficiency and task quality of the instruction sequence and realize intelligent instruction scheduling and resource allocation.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to a robot control instruction analysis method and system integrating continuous instructions. Background Art

[0002] In the field of robotic control, the study of integrating continuous instructions is of vital importance. It directly affects a robot's ability to efficiently and accurately complete complex tasks. This is especially important in scenarios requiring rapid response, such as autonomous navigation or human-robot collaboration. By analyzing and optimizing continuous instructions, robots can not only improve the smoothness of task execution but also demonstrate greater adaptability in dynamic environments.

[0003] However, current approaches still have significant shortcomings. Many systems often fail to effectively balance task quality and execution efficiency when processing continuous instructions, leading to frequent instruction switching and resource waste. These limitations make robots less stable in complex and changing environments, making it difficult to meet the dual requirements of real-time performance and accuracy.

[0004] Looking deeper, the core challenge facing this field lies first in accurately predicting the execution effects of different instruction sequences. Due to the interdependencies between instructions and environmental uncertainty, it is difficult for the system to determine the potential impact of each instruction in advance, which directly leads to blindness in the decision-making process. This prediction difficulty further raises another key issue: how to find an optimized path among numerous instruction sequences that both ensures task quality and reduces switching costs and execution time. This uncertainty in prediction makes optimization decisions extremely complex, especially when resources are limited. The system needs to balance the pros and cons under multiple constraints, creating unique technical challenges.

[0005] Therefore, how to design a mechanism that can optimize the decision path to reduce costs and improve efficiency based on the prediction of instruction execution effects has become a key issue that needs to be urgently addressed in this study. Summary of the Invention

[0006] The present invention provides a robot control instruction analysis method integrating continuous instructions, which mainly includes:

[0007] Obtain instruction effect prediction data from historical execution records, and build a prediction model by analyzing the correlation of instruction sequences and the influence of environmental variables; quantitatively evaluate instruction execution quality and resource consumption based on the prediction data to generate quality scores and consumption estimates; build a multi-objective optimization framework based on quality scores and consumption estimates to determine a set of candidate paths; calculate switching costs and execution times for the candidate path sets, and screen optimized path solutions that meet the constraints; adjust instruction execution priorities and resource allocation ratios based on real-time environmental data to generate scheduling strategies; monitor environmental changes and task quality feedback during execution to obtain dynamic evaluation data; if the dynamic evaluation data is lower than the preset threshold, trigger the prediction model update and fine-tune the model parameters through real-time data; recalculate the quality score and switching cost based on the updated prediction data to determine whether to adjust the optimized path solution; continuously monitor the instruction sequence responsiveness and task quality indicators to generate comprehensive execution effect evaluation data.

[0008] Furthermore, the method of obtaining instruction effect prediction data from historical execution records includes: extracting instruction execution data from historical records, using statistical methods to eliminate outliers and fill in missing values ​​to generate a basic data set; for the instruction sequence in the basic data set, calculating the lag correlation between instructions and extracting key pattern features; screening effective environmental factors based on the correlation between environmental variables and execution time; combining the key pattern features and effective environmental factors into an input matrix, and outputting preliminary prediction data through a pre-trained model; if the residual between the prediction result and the actual effect exceeds a preset range, adjusting the feature weight through an iterative method and recalculating the prediction result in the future time window; and storing the final prediction result and related features in a database.

[0009] Furthermore, the quantitative evaluation of instruction execution quality and resource consumption based on the predicted data includes: extracting execution indicators of instructions under various environmental parameters from historical records to construct an original data set; fitting the data set through regression analysis methods to obtain performance weights of instructions in different environments; extracting sub-samples to calculate adaptability indicators based on the influence of specific environmental parameters; generating quality scores based on the adaptability indicators; using the quality scores and environmental parameters as input to predict resource utilization; if the difference between the predicted value and the historical data exceeds a preset range, re-collecting the execution records for verification to generate a configuration plan containing recommended environmental parameters.

[0010] Furthermore, the multi-objective optimization framework is constructed based on quality scores and consumption estimates, including: processing quality scores and resource consumption data through a regression model to generate a target score matrix; sorting the score matrix using an optimization algorithm to obtain preliminary optimization results; constructing the instruction sequence corresponding to the result into a dependency graph, calculating node weights, and screening key nodes; if the key node sorting does not meet the preset conditions, adjusting the dependency graph structure; generating an optimization sequence based on the adjusted dependency graph, and verifying performance indicators through test scenarios; extracting paths from the compliance scenarios, removing conflicting paths after clustering and stratification, and generating a set of candidate paths; selecting paths that meet the preset conditions as execution plans.

[0011] Furthermore, the switching cost and execution time are calculated for the candidate path set, including: obtaining the cost matrix and time series of the candidate path through a path calculation method; if the total cost of a path exceeds a preset range, it is removed from the set; for the time series of the remaining paths, if the execution time does not meet the preset conditions, it is marked as a path to be optimized; resource competition nodes are detected through dependency modeling, and reordered to generate an updated path set; a comprehensive score is calculated based on the cost and time data of the updated path set; the path is verified through a simulation tool and abnormal paths are eliminated; path branches with the same dependency are merged to output the final execution path plan.

[0012] Furthermore, the method of adjusting the instruction execution priority and resource allocation ratio in combination with real-time environmental data includes: obtaining environmental status data through sensors, removing outliers by filtering methods, and generating an environmental data set; outputting resource demand weights through a pre-trained model to generate an initial allocation ratio; if the resource ratio exceeds a preset range, adjusting the quota to generate a final allocation table; marking path nodes according to the allocation table, calculating the shortest path to generate an execution order list; detecting resource contention through a scheduling tool, and reordering nodes if there is a conflict; updating policy parameters if environmental data fluctuations exceed a preset range; regularly collecting environmental data, and if the change exceeds a threshold, adjusting the resource quota to generate an updated scheduling plan.

[0013] Furthermore, the environmental changes and task quality feedback during the monitoring execution process include: collecting environmental data and task status through a sensor network to generate a set of original data; marking high-priority data groups according to preset rules; calculating the difference in environmental indicators for the high-priority data groups, and performing denoising if it exceeds a preset range; matching the denoised data with the task indicators, calculating the change in task completion volume, and determining that the efficiency is abnormal if it continuously decreases; extracting characteristics of abnormal time periods, and outputting resource adjustment parameters through a model; applying parameters through a scheduling tool, regularly collecting execution data, and triggering a new round of data collection if the backlog exceeds a preset range.

[0014] Furthermore, if the dynamic evaluation data is lower than a preset threshold, the prediction model update is triggered, including: screening a qualified data subset from the dynamic evaluation data stream; if the execution efficiency is lower than the preset threshold, triggering the model online update; standardizing the data subset to generate a feature matrix; outputting the weight adjustment coefficient through the regression model to update the prediction model parameters; if the error change after the update is lower than the preset range, extracting additional indicators to merge the data, adjusting the feature weights and retraining the model; comparing the updated model prediction results with the preset conditions to generate scheduling instructions; recording the execution data to the cache area for subsequent analysis.

[0015] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0016] The present invention discloses a robot control instruction analysis method that integrates continuous instructions. The method analyzes the correlation and environmental impact of instruction sequences by constructing a prediction model, obtains instruction effect prediction data, and uses regression analysis to quantitatively evaluate instruction execution quality and resource consumption. Based on a multi-objective optimization framework, combined with a dynamic programming algorithm, the switching cost is calculated to generate an optimized path plan that meets the constraints. During the execution process, the present invention monitors environmental changes and task quality in real time, and dynamically adjusts instruction priority and resource allocation. When the execution efficiency is lower than the threshold, the prediction model is triggered to update online, and the model parameters are fine-tuned to adapt to the new situation. Through continuous optimization and monitoring, the present invention can improve the execution efficiency and task quality of instruction sequences, and realize intelligent instruction scheduling and resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a robot control instruction analysis method and system that integrates continuous instructions of the present invention. DETAILED DESCRIPTION

[0018] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] like Figure 1 In this embodiment, a robot control instruction analysis method integrating continuous instructions may specifically include:

[0020] Step S101 : constructing a pre-established instruction effect prediction model based on historical execution records, analyzing the correlation between consecutive instruction sequences and the influence of environmental variables through feature extraction, and obtaining preliminary instruction effect prediction data.

[0021] A basic dataset was generated using historical records and execution data, using the interquartile range method to remove outliers that deviated from the median by 1.5 times. Missing values ​​were forward-filled. Granger causality tests were used to calculate the lagged correlation between instructions in the basic dataset, and sequences with p-values ​​less than 0.05 were extracted as key pattern features. When the number of key pattern features exceeded a preset threshold of 5, the Pearson correlation coefficient between environmental variables and instruction execution time was calculated, and variables with absolute values ​​greater than 0.3 were selected as effective environmental factors. The effective environmental factors and key pattern features were combined into a 24-dimensional input matrix and input into a support vector machine model for performance prediction. If the residual between the predicted and actual performance exceeded three standard deviations of the historical error, feature weights were adjusted using gradient descent, reducing the learning rate by 0.1 with each iteration until the residual was within the acceptable range. Based on the adjusted weights, the moving average of the instruction performance for the next 10 time windows was recalculated, and the final prediction results were output. The prediction results, along with the corresponding environmental factors and sequence features, were stored in the instruction_optimization table in the MySQL database.

[0022] For example, when processing historical records and execution data, outliers can be removed using the interquartile range method. Suppose that execution time data for the past 1,000 instructions on a system is collected. First, the median and interquartile range are calculated. The outlier standard is set to 1.5 times the median. Data points outside this range, such as extremely long or short execution times, are removed to ensure data stability. Next, a forward filling method is used for missing values. For example, if data at a certain point in time is missing, the value at the previous point in time is used to fill in the missing data to ensure the integrity of the dataset. This approach has the advantage of effectively smoothing data noise and laying a solid foundation for subsequent analysis.

[0023] In one possible implementation, for the instruction sequence of the basic data set, the Granger causality test can be used to analyze the lagged correlation between instructions. Assuming there are instructions A and B, and the test finds that A has a significant impact on B when it lags 2 time units, and the p-value is less than 0.05, then this sequence is extracted as a key pattern feature. If 8 key pattern features are extracted, which exceeds the preset threshold of 5, the correlation between environmental variables and instruction execution time is further analyzed. When calculating the Pearson correlation coefficient, if it is found that the absolute value of the correlation coefficient between the temperature variable and the execution time is 0.35, which is greater than 0.3, then it is selected as an effective environmental factor. The advantage of this method is that it screens out variables that have a greater impact on the results and improves the pertinence of the model input.

[0024] For example, after combining effective environmental factors and key pattern features into a 24-dimensional input matrix, it is input into the support vector machine model for effect prediction. Suppose the predicted instruction execution effect is 85%, while the actual value is 80%, and the residual is 5%. If the historical error standard deviation is 1% and the residual exceeds 3 times the standard deviation, the gradient descent method is activated to adjust the feature weights. The learning rate is reduced by 0.1 times each iteration until the residual falls within the allowable range. This dynamic adjustment mechanism can effectively improve prediction accuracy and adapt to data changes. After adjustment, the moving average of the instruction effect for the next 10 time windows is calculated based on the new weights, such as the predicted value of 82%, and is output as the final result. Its advantage is that it provides predictions that are closer to reality and assists in decision-making.

[0025] In a possible implementation, the final prediction results, environmental factors, and sequence features are stored in a designated table of a MySQL database.

[0026] For example, a predicted value of 82%, a temperature variable value of 25 degrees, and the AB hysteresis relationship of key pattern features are stored in the instruction_optimization table for easy query and optimization analysis. This storage method ensures data traceability and improves system management efficiency. Through this series of methods, a complete closed loop is formed from data cleaning to feature extraction, model prediction, and result storage, effectively improving instruction optimization results and reducing execution risks.

[0027] In step S102 , based on the preliminary instruction effect prediction data, a regression analysis method is used to quantitatively evaluate the potential execution impact of each instruction, thereby obtaining an execution quality score and resource consumption estimate for each instruction under different environments.

[0028] We extracted instruction execution latency and success rate data under three environmental parameters, temperature, humidity, and load, from historical execution records to construct an original dataset containing environmental parameters and execution metrics. We used sklearn's LinearRegression function to fit the original dataset, obtaining the regression coefficient for each instruction under different environmental parameters as a performance weight. When the absolute value of the humidity coefficient for an instruction exceeded a preset threshold of 0.5, we used scipy's stratified_sample function to extract subsamples stratified by the humidity gradient. Within these subsamples, we calculated the Pearson correlation coefficient between the instruction success rate and humidity variation as an indicator of adaptability. This adaptability indicator was fed into a percentile ranking algorithm, outputting a quality score ranging from 0 to 100. Using the quality score as the dependent variable and the environmental parameters as the independent variables, we reran LinearRegression to predict CPU and memory usage. If the 95% confidence interval of the predicted value differed from the historical mean by more than 15, we recollected 200 execution records under the same environmental parameters for verification. Finally, we combined the performance weight, quality score, and resource prediction results to generate a configuration plan containing a recommended environmental parameter range.

[0029] For example, when extracting instruction data from historical execution records, one can focus on the impact of three environmental parameters, temperature, humidity, and load, on execution latency and success rate. Suppose a system records instruction execution for the past year and extracts 10,000 data points, with temperatures ranging from 10 to 35 degrees Celsius, humidity ranging from 30% to 80%, and load ranging from 20% to 90%. Preliminary statistics reveal that execution latency tends to increase with rising temperature, while humidity has a more significant impact on success rate. This data extraction method helps build a comprehensive raw data set, laying the foundation for subsequent analysis.

[0030] For example, when using linear regression to fit a dataset, environmental parameters can be used as independent variables, and execution latency and success rate as dependent variables to analyze the performance weight of each instruction under different environmental conditions. For example, suppose the regression coefficient for a certain instruction under varying humidity is 0.6, exceeding the preset threshold of 0.5, indicating a significant humidity impact. This method can intuitively reflect the impact of environmental factors on instruction performance, facilitating subsequent targeted optimization.

[0031] For example, for instructions with humidity coefficients exceeding a threshold, stratified sampling can be used to extract subsamples. These subsamples can be divided into three intervals: low, medium, and high, based on the humidity gradient. 100 data points are extracted from each interval for analysis. The correlation coefficient between the success rate and humidity change in these subsamples is calculated. Assuming a value of 0.4 indicates a positive correlation between humidity and success rate. This stratified sampling method can more accurately capture the impact of environmental variables and enhance the level of detail in the analysis.

[0032] For example, when calculating the adaptability level indicator and inputting it into the percentile ranking algorithm, suppose a certain instruction's correlation coefficient corresponds to a quality score of 75, which is in the upper-middle range. This means that the instruction has good adaptability to humidity fluctuations, but there is still room for improvement. The use of quality scores helps quantify instruction performance and provides a reference for subsequent resource forecasting.

[0033] For example, when predicting CPU and memory usage using quality scores as dependent variables, if the predicted CPU usage for a particular instruction is 60% while the historical average is 45%, a discrepancy of more than 15%, then re-collecting data for verification is necessary. This verification mechanism effectively avoids prediction bias and ensures reliable results, which is particularly important in resource allocation scenarios.

[0034] For example, when performance weights, quality scores, and resource predictions are finally combined, a recommended environmental parameter range can be generated. For example, if a certain instruction performs best at a temperature of 20 to 25 degrees Celsius, humidity of 40% to 50%, and a load of 30% to 50%, this will be output as a configuration plan. This plan provides specific guidance for system operation, reducing execution risks caused by inappropriate environments while optimizing resource utilization efficiency. This complete process, from data extraction to the final configuration plan, significantly improves the stability and adaptability of instruction execution.

[0035] Step S103 , constructing a multi-objective optimization framework based on the execution quality score and the resource consumption estimate, with the goal of improving the execution quality score and reducing the resource consumption estimate, and combining the relevance of the instruction sequence to obtain a set of candidate paths for the optimal instruction sequence.

[0036] A random forest regression model processes execution quality and resource consumption data, outputting a target score matrix. The NSGA-II algorithm performs non-dominated sorting on the score matrix, obtaining a Pareto front solution set as the initial optimization result. The instruction sequence corresponding to the Pareto solution set is input into the NetworkX tool, which constructs an instruction dependency graph based on the control flow graph. The PageRank algorithm is run on the dependency graph to calculate node weights, selecting instructions with weights above a preset threshold of 0.7 as key nodes. If the ordering of key nodes does not conform to the topological order, the network is restructured using an edge weight adjustment strategy, and the updated directed graph is used as the optimized instruction sequence. The optimized sequence is then input into a predefined set of test scenarios, and instrumentation testing is performed to obtain latency and throughput metrics. Latency exceeding 50ms or throughput below 1000 ops / s is marked as a constraint. Control flow paths are extracted from the qualified scenarios, and path weights are calculated using the Dijkstra algorithm. K-means clustering is then performed to stratify the paths. Path pairs in the stratified path set are detected for resource contention or timing violations, and conflicting paths are removed to generate a candidate path set. Based on the two-dimensional coordinates of delay and energy consumption of candidate paths, the top three paths with the smallest Euclidean distance to the ideal point (20ms, 5J) are selected as the final execution plan.

[0037] For example, when processing execution quality and resource consumption data through a random forest regression model, it can be regarded as an integrated learning method based on multiple decision trees, which aims to improve prediction accuracy. Suppose in a system instruction execution scenario, instruction operation data for the past year has been collected, including environmental parameters such as temperature, humidity, and load, as well as the corresponding execution delay and resource occupancy. Through this model analysis, a target score matrix can be output. Each element in the matrix represents the comprehensive performance score of an instruction in a specific environment. For example, the score of an instruction at a temperature of 25 degrees and a humidity of 50% is 80 points. This method can effectively capture the nonlinear relationship between environmental variables and execution performance.

[0038] For example, when using the NSGA-II algorithm to perform a non-dominated sort on a rating matrix, the goal is to find a set of solutions that balance multiple objectives. Assuming the goal is to optimize both execution latency and energy consumption, the algorithm generates a Pareto frontier solution set, where each solution represents an instruction configuration, such as one with a latency of 30ms but an energy consumption of 8J, and another with a latency of 40ms but an energy consumption of 6J. This multi-objective optimization provides a variety of options for subsequent decision-making.

[0039] For example, when constructing a directed instruction dependency graph using the NetworkX tool, the control flow relationships between instructions can be visualized as nodes and edges. For example, if instruction A must execute after instruction B completes, then A points to B, forming a directed edge. After calculating node weights using the PageRank algorithm, if instruction A's weight is 0.8, which is higher than the threshold of 0.7, it is marked as a critical node. This method can identify instructions that have a significant impact on overall system execution.

[0040] For example, when the order of key nodes doesn't conform to the topological order, the network can be restructured using an edge weight adjustment strategy. For example, if there's a dependency conflict between instructions A and C, adjusting the edge weights regenerates the directed graph to ensure the correct execution order. This restructure can avoid execution deadlocks or incorrect sequencing.

[0041] For example, when using instrumentation testing to obtain latency and throughput metrics in a test scenario, if a certain instruction sequence has a latency of 60ms in scenario 1, exceeding the 50ms threshold, it will be marked as failing. This type of testing can filter out sequences that do not meet performance expectations.

[0042] For example, when using the Dijkstra algorithm to calculate control flow path weights and perform K-means clustering, suppose a path has a total weight of 15 and belongs to the low-latency group. This stratification can group paths by performance characteristics, facilitating subsequent optimization.

[0043] For example, when detecting resource contention or timing violations, if two paths access the same resource simultaneously, causing a conflict, one of them is removed to generate a set of candidate paths. This screening can reduce potential risks in execution.

[0044] For example, when selecting the final execution plan based on the two-dimensional coordinates of latency and energy consumption, if a path with coordinates of 25ms and 6J is closest to the ideal point, it will be selected as the optimal plan. This selection can achieve a good balance between performance and resources.

[0045] In step S104, for the candidate path set of the optimal instruction sequence, a dynamic programming algorithm is used to calculate the switching cost and execution time of each path layer by layer. If the switching cost of a path exceeds a preset threshold, the path is eliminated to obtain an optimized path solution that meets the cost constraint.

[0046] The Dijkstra algorithm is used to perform a hierarchical calculation of candidate paths in the instruction sequence, obtaining a switching cost matrix and execution time series for each path. Based on the cost matrix, if a path's total cost exceeds 1.2 times the average cost, it is removed from the candidate path set, resulting in the first path set. Time series data is extracted from the first path set. If a path's execution time is not within 80% to 120% of the instruction deadline, the path is marked as a path to be optimized. A directed acyclic graph is used to model the dependencies of the first path set, and path nodes with resource contention are detected through topological sorting. Path nodes with resource contention are reordered to generate the second path set. Based on the cost matrix and time series of the second path set, a weighted summation formula is used to calculate a comprehensive score, with a weighting of 60% for cost and 40% for time, resulting in a sorted path list. This sorted path list is then fed into the QEMU virtualization tool for instruction-level simulation, where instrumentation is used to collect memory access exceptions and instruction conflict data. Paths with exceptions during the simulation are removed to generate the third path set. According to the dependency graph of the third path set, path branches with the same predecessor node are merged, and a final execution path plan is output.

[0047] For example, when using the Dijkstra algorithm to perform hierarchical calculations on candidate paths for an instruction sequence, it can be considered a graph-based path search method, aiming to find the path with the lowest cost from the starting point to the end point. Consider a system instruction scheduling scenario with 10 candidate paths. After calculating the switching cost matrix and execution time series for each path, it is found that path A has a total cost of 50 units and an average cost of 40 units, exceeding the threshold of 1.2 times (40 x 1.2, or 48 units). Therefore, path A is eliminated, forming the first path set. This approach effectively screens out paths with excessively high costs.

[0048] For example, when extracting time series data for the first path set, assuming the instruction deadline is 100ms, and path B's execution time is 130ms, it exceeds 120% of the upper limit of 120ms and is therefore marked as a path to be optimized. This screening mechanism helps identify paths that do not meet time constraints, providing a basis for subsequent optimization.

[0049] For example, when modeling the dependencies of the first set of paths using a directed acyclic graph, the execution order of instructions can be abstracted as nodes and directed edges. Suppose there is resource contention between nodes C1 and C2 in path C. A topological sort reveals that C1 and C2 access the same memory area simultaneously, leading to a potential conflict. To address this issue, the execution order of C1 and C2 is reordered to generate a second set of paths. This adjustment effectively avoids resource contention.

[0050] For example, when calculating the comprehensive score based on the cost matrix and time series of the second path set, assuming that path D has a cost of 30 units and an execution time of 90ms, the weights are 60% for cost and 40% for time, and the score is obtained by weighted summation. This scoring mechanism comprehensively considers multiple indicators and selects more balanced paths.

[0051] For example, when the sorted path list is fed into the QEMU virtualization tool for instruction-level simulation, suppose path E encounters memory access anomalies during simulation, manifesting as frequent page faults. Therefore, it is eliminated and a third set of paths is generated. This type of simulation testing can identify potential execution issues in advance and ensure path reliability.

[0052] For example, when merging path branches based on the dependency graph of the third path set, assuming that paths F and G share the same predecessor node N1, a new execution path solution is formed by merging the branches. This merging approach simplifies the path structure, reduces redundant execution, and improves overall scheduling efficiency. Through the above multifaceted analysis and examples, it can be seen that every step from path screening to final solution output is closely centered around instruction sequence optimization, proceeding step by step to ensure the efficiency and reliability of the path.

[0053] Step S105 , based on the optimized path plan that meets the cost constraint and in combination with the real-time collected dynamic environment data, adjust the instruction execution priority and resource allocation ratio to determine the instruction execution sequence and resource scheduling strategy in the current environment.

[0054] Sensors collect ambient temperature, humidity, and light data, and use a median filter to remove outliers, generating a timestamp-containing environmental status dataset. This dataset is fed into a pre-trained random forest model, which outputs resource demand weights for each region. An initial allocation table is generated, sorted by descending weight. The CPU and memory usage in the allocation table is read. If any resource exceeds 70% of the budget, the quota for the higher-weighted region is proportionally reduced to generate the final allocation table. Nodes corresponding to the region numbers in the allocation table are marked in the path planning graph. The Dijkstra algorithm is used to calculate the shortest delivery path and generate a node execution order list. This execution order list is imported into the Kubernetes scheduler to detect resource contention between nodes. If the same GPU resources are requested during the same time period, nodes are reordered by path distance. A scheduling policy is generated based on the sorting results. Policy parameter updates are triggered when real-time light intensity fluctuations exceed 20% of the historical average. After the policy is deployed, environmental data is collected every five minutes, and the humidity variance within the sliding window is calculated. If the variance exceeds a threshold three times in a row, the priority queue is invoked to adjust resource quotas, generating an updated scheduling plan.

[0055] For example, when obtaining ambient temperature, humidity, and light data through sensors.

[0056] It's understandable that sensors are deployed at key locations across different areas to ensure comprehensive data collection. For example, in a greenhouse resource scheduling scenario, sensors collect data hourly, with temperatures ranging from 20 to 30 degrees Celsius, humidity between 50% and 80%, and light intensity varying according to day and night. This data collection method provides a multi-dimensional data foundation for subsequent analysis.

[0057] For example, when using median filtering to remove outliers.

[0058] It's easy to understand why this method smooths out abnormal fluctuations by sorting a set of data and taking the median value. For example, suppose the collected temperature data contains values ​​of 22, 23, 45, 24, and 25 degrees Celsius. Clearly, 45 degrees Celsius is an outlier. Using median filtering, it can be replaced with a value near 23 degrees Celsius. This process effectively improves the reliability of the dataset.

[0059] For example, when feeding a dataset into a pre-trained random forest model.

[0060] It's understandable that the model uses historical data to learn the relationship between environmental factors and resource demand. For example, let's say the model predicts that high temperature and humidity in one region have a CPU resource demand weight of 0.8, while in another region, the weight is only 0.3. This weighted output provides a scientific basis for subsequent allocation.

[0061] For example, when generating the initial allocation ratio table based on weight and performing resource compression, if the CPU budget is 100 units and a region accounts for 80%, exceeding the 70% threshold, it will be compressed to 70 units, and the remaining resources will be reallocated to the next highest weighted region. This adjustment ensures that resources are not overloaded.

[0062] For example, when using the Dijkstra algorithm to calculate the shortest delivery path in a path planning graph.

[0063] As you can understand, the algorithm uses the graph structure to find the shortest distance from the starting point to each node. For example, if the path distances from the control center to areas A, B, and C are 5, 8, and 10 units, respectively, the algorithm will prioritize A, which has the shortest distance, as the first node. This approach optimizes the delivery sequence.

[0064] For example, when importing the execution list into the Kubernetes scheduler to detect resource contention, if two nodes simultaneously request the same GPU resource, the system will prioritize scheduling the closer node based on path distance. This dynamic adjustment avoids resource conflicts.

[0065] For example, when light intensity fluctuations trigger a policy update, assuming the historical average is 1,000 units and the real-time value is 1,250 units, exceeding the 20% threshold, the system will automatically adjust scheduling parameters, such as increasing nighttime resource quotas. This mechanism adapts to environmental changes.

[0066] For example, when calculating humidity variance within a sliding window, if the variances for three consecutive windows are 15, 18, and 20, all exceeding the threshold of 10, the system will use the priority queue to allocate resources to areas with large humidity fluctuations. This response mechanism ensures timely resource allocation.

[0067] Step S106 , based on the instruction execution sequence and resource scheduling strategy in the current environment, monitor the environment changes and task quality feedback during the execution process in real time to obtain dynamic evaluation data of execution efficiency and task quality.

[0068] The sensor network collects environmental data and task completion status once per second, with millisecond timestamps, to generate a time-stamped raw data set. Based on pre-set rules, data with an environmental data change rate greater than 5% per second or with more than three task failures is marked as a high-priority group. For this high-priority data group, the difference between the environmental metric and the baseline value is calculated. If the difference exceeds twice the standard deviation of the historical data, median filtering is initiated to obtain a denoised environmental status subset. This environmental status subset is matched with the task completion time and error code of the same timestamp, and a moving average of the number of tasks completed per minute is calculated. A decrease in efficiency for three consecutive times is considered a decline in efficiency. The mean environmental metric, peak resource utilization, and task queue length during the period of decline are extracted as features and fed into a pre-trained random forest regression model. The model outputs parameters for increasing CPU allocation and decreasing memory allocation. These parameters are fed into the Kubernetes scheduler, which modifies the container resource limits and restarts the pod instance. The scheduler collects the pod's CPU usage and task backlog every 30 seconds and transmits this data back to the sensor network database. When the task backlog exceeds 80% of the total queue capacity, a new round of environmental data collection and prioritization is triggered.

[0069] For example, in smart greenhouse management scenarios, a sensor network collects environmental data and task completion status once per second to ensure real-time data. Sensors are deployed at key locations in the greenhouse, such as planting areas, ventilation openings, and near irrigation equipment, collecting data such as temperature, humidity, and light intensity, with millisecond timestamp accuracy.

[0070] For example, data collected at one time included a temperature of 25.3 degrees Celsius, a humidity of 65.2%, and a light intensity of 800 lux, with a timestamp of 2025-05-24 08:00:00.123. This high-frequency data collection provides a continuous data foundation for subsequent analysis.

[0071] In one possible implementation, high-priority data sets are filtered based on pre-set rules. For example, if the temperature in a certain area rises from 25.0°C to 26.5°C within one second, with a rate of change of 6%, exceeding the 5% threshold; or if a task fails four times in a row, exceeding the three-time threshold, then the task is marked as high priority.

[0072] For example, an irrigation task failed four times due to a pipe blockage, and the humidity dropped to 50%, and was marked as high priority. This filtering mechanism can quickly identify abnormal conditions.

[0073] Specifically, the high priority data group needs to calculate the difference from the baseline value.

[0074] For example, a historical temperature baseline value of 25 degrees Celsius with a standard deviation of 0.8 degrees Celsius. A single collected value of 27 degrees Celsius, a 2-degree difference exceeding twice the standard deviation, triggers median filtering. For example, suppose a set of temperature data is 24.8, 25.2, 27.0, 25.1, and 24.9 degrees Celsius. Median filtering uses 25.1 degrees Celsius as the denoising result. This process ensures data smoothing and reduces the impact of abnormal fluctuations.

[0075] For example, when matching a subset of environmental states with task data, assuming a task completion time of 2 seconds at a certain timestamp and an error code of "Insufficient Pipeline Pressure," we calculate the moving average of task completions per minute. If this number drops from 100 to 80, 60, and 50 three times in a row, we determine a decrease in efficiency. This analysis can accurately identify performance bottlenecks.

[0076] In one possible implementation, features of periods of decreased efficiency, such as an average temperature of 26°C, a peak resource utilization of 90%, and a task queue length of 50, are extracted and fed into a random forest regression model. The model outputs a CPU allocation increment of 20 units and a memory reduction of 10 units. These parameters guide resource optimization and ensure efficient task execution.

[0077] Specifically, the Kubernetes scheduler adjusts container resource limits based on parameters.

[0078] For example, a pod's CPU limit is increased from 100 units to 120 units, its memory limit is reduced from 50 units to 40 units, and the instance is restarted. The scheduler collects pod data every 30 seconds. If the task backlog reaches 85% of the queue capacity, a new round of environmental data collection is triggered.

[0079] For example, if a Pod has a backlog of 60 tasks, exceeding the 80% threshold, the system automatically increases the frequency of sensor collection. This dynamic feedback mechanism can respond to task pressure in a timely manner and improve system stability.

[0080] In one possible implementation, the collected data is transmitted back to the sensor network database to form a closed-loop management.

[0081] For example, if database records indicate a backlog of tasks in a particular area due to insufficient sunlight, the system automatically adjusts resource quotas to prioritize tasks related to lighting equipment. This data-driven scheduling approach optimizes greenhouse resource utilization.

[0082] In step S107, based on the dynamic evaluation data of execution efficiency and task quality, if it is detected that the execution efficiency is lower than the preset threshold, the online update mechanism of the prediction model is triggered, and the model parameters are fine-tuned through real-time data to obtain updated instruction effect prediction data.

[0083] A real-time data collection system captures dynamic evaluation data streams of execution efficiency and task quality. A sliding window method is used to select a subset within the window with a variance greater than 0.1 as analysis data. If the execution efficiency in the data subset falls below the 30th percentile threshold of the historical data, an online update of the XGBoost model is triggered. The filtered data subset is fed into Scikit-learn's StandardScaler for feature normalization, outputting a normalized feature matrix. A pre-established LinearRegression model is used to process the feature matrix, outputting weight adjustment coefficients that serve as a basis for parameter fine-tuning. Based on the weight adjustment coefficients, the leaf node weight parameters of the XGBoost model are updated using gradient descent. The mean squared error of the updated model is calculated on the test set. If the error reduction rate is less than 5%, task completion rate and error rate metrics are extracted from the dynamic evaluation data. The newly added metrics are merged with the execution efficiency data, and the Pearson correlation coefficient is used to determine feature importance ranking. The model is retrained after weighting features with an importance below 0.2 is reset to zero. The prediction results of the re-adjusted model are compared with the preset error tolerance to generate scheduling instructions containing CPU core allocation and memory quota parameters. After the instruction is executed, the actual execution efficiency data is recorded and written into the efficiency indicator cache area of ​​the Redis database.

[0084] For example, in smart greenhouse management scenarios, real-time data collection systems are widely used to monitor environmental data and task execution status to support the acquisition of dynamic evaluation data streams. To analyze execution efficiency and task quality, the system uses sensors to collect key indicators such as temperature and humidity once per second to ensure data continuity and timeliness. If a certain collection shows large temperature fluctuations in a certain area, the system will analyze the data from the past 5 minutes using a sliding window method and select a subset with a variance greater than 0.1 as the focus. For example, the temperature data within a certain window fluctuates from 24.5 degrees to 26.0 degrees, with a variance of 0.15, which meets the screening criteria.

[0085] Specifically, if execution efficiency falls below the 30th percentile of historical data, the system triggers the model's online update mechanism. For example, if the 30th percentile of historical execution efficiency is 80 tasks completed per minute, but only 60 tasks are completed within the current window, the system will use the relevant data subset for subsequent processing. This approach allows for the timely identification of efficiency bottlenecks, providing a basis for subsequent optimization.

[0086] In one possible implementation, feature normalization is a crucial step in data processing. Collected data such as temperature and humidity are fed into a normalization tool, where they are dimensionalized for easier model analysis.

[0087] For example, the temperature data range is between 20 and 30 degrees, and the humidity is between 40% and 80%. After standardization, the data is converted into a distribution with a mean of 0 and a variance of 1, which is convenient for subsequent model input.

[0088] For example, to generate weight adjustment coefficients, the system processes the standardized feature matrix based on a preset model. For example, if, in an analysis, temperature has a higher weight on task efficiency, the system will output an adjustment coefficient to guide subsequent parameter optimization. This approach ensures that the model is more adaptable to current environmental changes.

[0089] Specifically, during the model update process, the system fine-tunes parameters based on the weight adjustment coefficients. For example, by gradually adjusting model parameters, the system can optimize its ability to predict a decrease in the efficiency of the current task. Suppose, after a certain update, the prediction error decreases from 0.2 to 0.18. While the error reduction rate may not meet expectations, metrics such as task completion rate and error rate will still be analyzed for further analysis.

[0090] In one possible implementation, feature importance ranking is crucial for model optimization. Suppose, through correlation analysis, we find that humidity's impact on task efficiency is only 0.15. If this falls below the 0.2 threshold, the system resets its weight to zero, reducing interference from irrelevant factors. This screening allows the model to focus more closely on key variables.

[0091] For example, after a scheduling instruction is generated, the system adjusts resource allocation based on the predicted results. For example, if a certain area faces a significant backlog of tasks, the system might generate an instruction to increase the number of processing cores from two to three and adjust the memory quota. This dynamic adjustment can effectively alleviate task pressure.

[0092] Specifically, execution efficiency data is recorded in a database for subsequent analysis. For example, after a task is executed, efficiency data is updated every minute and cached for quick access. This approach provides data support for long-term optimization.

[0093] Step S108 , recalculating the execution quality score and switching cost of the instruction sequence based on the updated instruction effect prediction data, determining whether the optimization path plan needs to be adjusted based on the calculation results, and determining the final path adjustment decision.

[0094] The timestamp, response code, and resource consumption fields are extracted from the instruction effect prediction data as raw evaluation information. This raw evaluation information is fed into a pre-established Pandas data processing module, where missing value filling and Z-score normalization are performed to output a first data set. For this first data set, instruction execution latency, success rate, and CPU usage are extracted as execution quality features, and context switch count and memory reallocation amount are extracted as switching cost features. These are combined into a 5-dimensional feature vector group. If the execution latency in a feature vector group exceeds 200ms or the success rate is less than 95%, the Analytic Hierarchy Process (AHP) method is used to calculate the switching cost weight. If the weighted total score is less than 70, the path optimization process is triggered. The feature vector group is fed into the scikit-learn RandomForestClassifier to predict three sets of optimized path parameters as candidate solutions. The TOPSIS ranking algorithm is used to perform normalized weighted ranking of the candidate solutions based on execution quality and switching cost, outputting a priority list. Based on the priority list, the solution with the highest overall score is selected and parameter replacement is performed using the rule engine. The adjusted instruction parameters are written to the Redis cache to generate a second data set for subsequent instruction scheduling.

[0095] For example, in smart greenhouse management scenarios, the processing and optimization of command effect prediction data is a critical step. The extraction of timestamp, response code, and resource consumption fields can be considered fundamental information for evaluating command execution effectiveness. For example, suppose a command execution record shows a timestamp of 10:00 AM, a response code of 200 indicating success, and a resource consumption of 30% CPU usage. This raw data is input into a data processing module. Using a missing value filling method, records with missing response codes are supplemented with historical averages to ensure data integrity.

[0096] Specifically, Z-Score normalization transforms resource consumption data into a distribution with a mean of 0 and a variance of 1, facilitating subsequent analysis. For example, CPU utilization originally ranged from 20% to 50%. Normalization eliminates dimensional differences and improves model input quality. This processing helps more accurately reflect the true state of instruction execution.

[0097] In one possible implementation, when extracting execution quality and switching cost features from the first data set, instruction execution latency and success rate are the core indicators. Assuming the execution latency of an instruction is 250ms, exceeding the preset threshold of 200ms, and the success rate is 93%, which is lower than the standard of 95%, the system will automatically trigger the hierarchical analysis method to calculate the switching cost weight. The number of context switches is assumed to be 50 per second, and the amount of memory reallocation is 100MB. These data are comprehensively evaluated. If the weighted total score is only 65 points, which is lower than the standard of 70 points, the path optimization process will be activated. This mechanism can promptly identify potential problems and take countermeasures.

[0098] For example, after inputting a set of feature vectors into a random forest classifier, the system generates three sets of optimized path parameters as candidate solutions. Suppose the first set of parameters focuses on reducing latency to 180ms, the second set improves the success rate to 97%, and the third set balances switching costs. These solutions are then normalized and weighted using the TOPSIS ranking algorithm, ultimately outputting a prioritized list. Assuming the first set of solutions has the highest overall score, the system uses the rules engine to replace the instruction parameters with the configuration of that solution. This approach ensures the optimal path is selected, improving overall execution efficiency.

[0099] Specifically, the adjusted command parameters are written to the Redis cache to form a second data set. Suppose the new parameters increase the CPU allocation from 30% to 40% to support higher-load task scheduling. This caching mechanism enables rapid response to subsequent command requests and reduces latency. The entire process, from data extraction to parameter optimization, forms a closed loop, ensuring efficient and stable command execution. This is particularly important in greenhouse environments, where environmental responsiveness is crucial, effectively guaranteeing task continuity and reliability.

[0100] Step S109: Based on the final path adjustment decision, continuously monitor the real-time response capability and task quality assurance indicators of the instruction sequence to obtain comprehensive execution effect evaluation data for subsequent instruction sequence prediction and optimization iteration.

[0101] From the command sequence execution environment, a monitoring dataset containing response latency, CPU utilization, and task success rate was obtained using the Prometheus monitoring tool. For this monitoring dataset, Pandas' drop_duplicates function was used to remove duplicate records, and missing fields were filled with the mean to generate a base dataset. The 90th percentile of response latency and the standard deviation of task success rate were extracted from the base dataset as key features, and a feature dataset was generated by numerical concatenation. This feature dataset was fed into a pre-set random forest classifier. If the predicted response latency exceeded 200ms or the task success rate fell below 95%, the optimization process was triggered. Based on the classification results, high-latency command sequence segments were identified, and the corresponding thread pool size and batch size were extracted from the run logs as path adjustment parameters. A decision tree rule engine was used to generate candidate solutions, including parameter increases and decreases of 10% and 20%. The TOPSIS tool was used to rank the solutions based on execution time and resource consumption, selecting those with a comprehensive score exceeding 0.8. The selected thread pool parameters were updated in the execution environment using the Ansible automation tool, and monitoring data was recollected after the updates. The response latency and task success rate metrics before and after the adjustments were continuously compared.

[0102] For example, in smart greenhouse management scenarios, using monitoring tools to acquire and optimize operating environment data is a critical step. By collecting monitoring data such as response latency, CPU utilization, and task success rate, a real-time monitoring system can capture the operating status of equipment. For example, in a given monitoring session, the average response latency was 180ms, the CPU utilization was 35%, and the task success rate was 96%. This data provides a foundation for subsequent analysis. The monitoring tool stores these metrics in a time series format to facilitate tracking of changing trends.

[0103] For example, during data preprocessing, duplicate records are removed and missing values ​​are filled. If duplicate response delay records are found at certain time points in the collected data set, the system will automatically remove the redundant records to ensure data uniqueness. Missing task success rate data can be filled based on the average value over the past 24 hours, for example, by filling missing values ​​to 95%, to ensure dataset integrity. This helps avoid data bias and improves the reliability of subsequent feature extraction.

[0104] For example, during the feature extraction phase, the 90th percentile of response latency might be 220ms, indicating high latency in some command executions, while the standard deviation of the task success rate is 2.5%, suggesting that execution stability needs improvement. By combining these features to form a feature dataset, we can intuitively identify the bottlenecks in the execution of the command sequence and provide a basis for subsequent classifier predictions. This feature selection approach focuses on core issues and improves the targeted nature of the analysis.

[0105] For example, if the prediction results for a random forest classifier indicate that response latency exceeds a threshold of 200ms, the system will automatically flag the relevant instruction sequence as a high-risk area and trigger an optimization process. This predictive mechanism can promptly identify potential issues, providing decision support for subsequent adjustments and ensuring system stability.

[0106] For example, when locating a high-latency instruction sequence, the system extracts task configuration information from the run log, including a thread pool size of 10 and a batch size of 50. These parameters, used as a basis for adjustment, can help analyze the cause of the delay, such as an undersized thread pool causing task queuing. This analysis method helps pinpoint the root cause of the problem.

[0107] For example, when generating candidate solutions for a decision tree rule engine, suppose the system proposes increasing the thread pool size by 10% and 20%, adjusting it to 11 and 12, respectively, and adjusting the batch size to 55 and 60 accordingly. By comparing the execution time and resource usage of different solutions, the optimal configuration can be selected. This multi-solution comparison method provides flexible selection options.

[0108] For example, when using the ranking tool to evaluate solutions, the solution with the highest overall score is 0.85, which exceeds the preset threshold of 0.8 and is therefore selected as the final solution. This evaluation mechanism ensures the scientific nature and feasibility of the solution.

[0109] For example, after updating parameters through automated tools, newly collected monitoring data shows that response latency has dropped to 190ms and the task success rate has increased to 97%, indicating significant improvement. Continuously comparing metrics before and after the adjustment verifies the effectiveness of the optimization plan and provides data support for subsequent improvements. This closed-loop mechanism continuously improves system operational efficiency and ensures the continuity and reliability of greenhouse task scheduling.

[0110] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

Claims

1. A robot control instruction analysis method integrating continuous instructions, characterized in that: include: Obtain instruction effect prediction data from historical execution records and build a prediction model by analyzing the correlation between instruction sequences and the influence of environmental variables. The specific implementation of the prediction model includes extracting instruction execution data from historical records, using statistical methods to eliminate outliers and fill in missing values ​​to generate a basic data set; for the instruction sequences in the basic data set, calculate the lag correlation between instructions and extract key pattern features; Screen effective environmental factors based on the correlation between environmental variables and execution time; Based on the predicted data, the quality of instruction execution and resource consumption are quantitatively evaluated to generate quality scores and resource consumption estimates. The specific evaluation method includes extracting instruction execution indicators under various environmental parameters from historical records to construct an original data set; fitting the data set through regression analysis to obtain the performance weights of instructions in different environments; and calculating adaptability indicators by extracting subsamples based on the impact of specific environmental parameters. Based on quality scores and consumption estimates, a multi-objective optimization framework is constructed to determine a set of candidate paths. The specific steps for constructing the multi-objective optimization framework include processing quality scores and resource consumption data through a regression model to generate a target scoring matrix; using an optimization algorithm to sort the scoring matrix and obtain preliminary optimization results; constructing the instruction sequence corresponding to the results into a dependency graph, calculating node weights, and screening key nodes; Calculate the switching cost and execution time for the candidate path set and select the optimal path solution that meets the constraints; Combine real-time environment data to adjust instruction execution priority and resource allocation ratio to generate scheduling strategies; Monitor environmental changes and task quality feedback during execution to obtain dynamic evaluation data; If the dynamic evaluation data is lower than the preset threshold, the prediction model update is triggered and the model parameters are fine-tuned based on real-time data; Recalculate the quality score and switching cost based on the updated prediction data to determine whether to adjust the optimization path plan; Continuously monitor instruction sequence responsiveness and task quality indicators to generate comprehensive execution effectiveness evaluation data.

2. A robot control instruction analysis method integrating continuous instructions according to claim 1, characterized in that: The obtaining of instruction effect prediction data from historical execution records includes: Combine key pattern features and effective environmental factors into an input matrix and output preliminary prediction data through the pre-trained model; If the residual between the predicted result and the actual effect exceeds the preset range, the feature weights are adjusted through an iterative method to recalculate the predicted results in the future time window; The final prediction results and related features are stored in the database.

3. The robot control instruction analysis method integrating continuous instructions according to claim 1, characterized in that: The quantitative evaluation of instruction execution quality and resource consumption based on the predicted data includes: Generate a quality score based on the adaptability metrics; Using quality scores and environmental parameters as input, predict resource occupancy; If the difference between the predicted value and the historical data exceeds the preset range, the execution record is collected again for verification and a configuration plan containing recommended environmental parameters is generated.

4. A robot control instruction analysis method integrating continuous instructions as claimed in claim 1, characterized in that: The multi-objective optimization framework based on quality scoring and consumption estimation includes: If the order of key nodes does not meet the preset conditions, the dependency graph structure is adjusted; Generate an optimized sequence based on the adjusted dependency graph and verify the performance indicators through test scenarios; Extract paths from the target scenarios, remove conflicting paths after clustering and stratification, and generate a set of candidate paths; Select the path that meets the preset conditions as the execution plan.

5. The robot control instruction analysis method integrating continuous instructions according to claim 1, characterized in that: The calculating of the switching cost and execution time for the candidate path set includes: Obtain the cost matrix and time series of candidate paths through path calculation methods; If the total cost of a path exceeds the preset range, it will be removed from the set; For the time series of the remaining paths, if the execution time does not meet the preset conditions, it will be marked as a path to be optimized; Detect resource competition nodes through dependency modeling and reorder them to generate an updated path set; Calculate a comprehensive score based on the cost and time data of the updated path set; Verify the path through simulation tools and eliminate abnormal paths; Merge path branches with the same dependencies and output the final execution path plan.

6. A robot control instruction analysis method integrating continuous instructions as claimed in claim 1, characterized in that: The adjusting of instruction execution priority and resource allocation ratio in combination with real-time environment data includes: Acquire environmental status data through sensors, use filtering methods to remove outliers, and generate environmental data sets; Output resource demand weights through the pre-trained model to generate the initial allocation ratio; If the resource ratio exceeds the preset range, the quota will be adjusted to generate the final allocation table; Mark the path nodes according to the allocation table, calculate the shortest path and generate the execution order list; Use scheduling tools to detect resource contention and reorder nodes if conflicts exist; If the environmental data fluctuation exceeds the preset range, the strategy parameters are updated; Environmental data is collected regularly. If the change exceeds the threshold, resource quotas are adjusted to generate an updated scheduling plan.

7. The robot control instruction analysis method integrating continuous instructions according to claim 1, characterized in that: The monitoring of environmental changes and task quality feedback during execution includes: Collect environmental data and task status through sensor networks to generate raw data sets; Mark high-priority data groups according to preset rules; Calculate the difference of environmental indicators for high-priority data groups, and perform denoising if it exceeds the preset range; Match the denoised data with the task indicators and calculate the changes in task completion. If there is a continuous decrease, it is considered an efficiency anomaly. Extract abnormal period characteristics and output resource adjustment parameters through the model; By applying parameters through the scheduling tool, execution data is collected regularly, and if the backlog exceeds the preset range, a new round of data collection is triggered.

8. The robot control instruction analysis method integrating continuous instructions according to claim 1, characterized in that: If the dynamic evaluation data is lower than a preset threshold, triggering the prediction model update includes: Filtering a subset of data that meets the criteria from the dynamic evaluation data stream; If the execution efficiency is lower than the preset threshold, the model online update is triggered; Standardize the data subset to generate a feature matrix; Output weight adjustment coefficients through regression model to update prediction model parameters; If the error change after the update is lower than the preset range, additional indicators are extracted and merged, and the feature weights are adjusted before retraining the model; Compare the updated model prediction results with the preset conditions and generate scheduling instructions; Record execution data to the cache for subsequent analysis.

9. A robot control instruction analysis system integrating continuous instructions, based on the robot control instruction analysis method integrating continuous instructions according to any one of claims 1 to 8, characterized in that: Data analysis module, evaluation module, multi-objective optimization framework module, path selection and optimization module, real-time scheduling and resource allocation module, dynamic monitoring and feedback module, and model updating and self-learning module; The data analysis module is used to obtain instruction effect prediction data from historical execution records and build a prediction model by analyzing the correlation between instruction sequences and the impact of environmental variables; An evaluation module, used to quantitatively evaluate instruction execution quality and resource consumption based on the prediction data, and generate quality scores and consumption estimates; Multi-objective optimization framework module, used to build a multi-objective optimization framework based on quality scores and consumption estimates to determine the set of candidate paths; The path selection and optimization module is used to calculate the switching cost and execution time for the candidate path set and select the optimized path solution that meets the constraints; Real-time scheduling and resource allocation module, which is used to adjust instruction execution priority and resource allocation ratio based on real-time environment data and generate scheduling strategies; The dynamic monitoring and feedback module is used to monitor environmental changes and task quality feedback during execution, obtain dynamic evaluation data, and trigger the update of the prediction model if the dynamic evaluation data is lower than the preset threshold, and fine-tune the model parameters based on real-time data; The model update and self-learning module is used to recalculate the quality score and switching cost based on the updated prediction data, determine whether to adjust the optimization path plan, and continuously monitor the instruction sequence responsiveness and task quality indicators to generate comprehensive execution effect evaluation data.

10. A computer device comprising: memory and processor; The memory stores a computer program, characterized in that when the processor executes the computer program, the steps of the robot control instruction analysis method for integrating continuous instructions according to any one of claims 1 to 8 are implemented.

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