Robot control instruction analysis method and system fusing continuous instructions

Abstract

The invention provides a robot control instruction analysis method and system fusing continuous instructions, and relates to the technical field of electric digital data processing, and the method comprises the steps: carrying out the quantitative evaluation of the instruction execution quality and resource consumption according to prediction data, and generating a quality score and a consumption estimation; constructing a multi-objective optimization framework based on the quality score and the consumption estimation, and determining a candidate path set; switching cost and execution time are calculated according to the candidate path set, and optimized path schemes meeting constraint conditions are screened; the instruction execution priority and the resource allocation proportion are adjusted in combination with the real-time environment data, and a scheduling strategy is generated. According to the method, environment changes and task quality are monitored in real time, instruction priorities and resource allocation are dynamically adjusted, when execution efficiency is lower than a threshold value, online updating of a prediction model is triggered, model parameters are finely adjusted to adapt to new conditions, and through continuous optimization and monitoring, the execution efficiency and task quality of an instruction sequence can be improved, and the execution efficiency is improved. And intelligent instruction scheduling and resource allocation are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of electric digital data processing, and particularly to a method and system for analyzing robot control instructions integrating continuous instructions. Background Art

[0002] In the field of robot control, the research on integrating continuous instructions is of crucial significance, which is directly related to whether the robot can efficiently and accurately complete complex tasks. Especially in scenarios that require rapid response, such as autonomous navigation or human-robot collaboration, its importance is self-evident. Through the analysis and optimization of continuous instructions, the robot can not only improve the fluency of task execution but also demonstrate stronger adaptability in a dynamic environment.

[0003] However, the current related methods still have obvious deficiencies. When many systems process continuous instructions, they often cannot effectively balance task quality and execution efficiency, and are prone to problems such as frequent instruction switching or resource waste. These limitations make the robot perform unstably when facing a complex and changeable environment, and it is difficult to meet the dual requirements of real-time performance and accuracy.

[0004] Looking deeper, the core challenge in this field lies first in how to accurately predict the execution effects of different instruction sequences. Due to the correlation between instructions and the uncertainty of the environment, it is very difficult for the system to judge in advance the possible impacts of each instruction, which directly leads to blindness in the decision-making process. And this prediction problem further gives rise to another key issue, that is, how to find an optimized path among numerous instruction sequences that can not only ensure task quality but also reduce switching costs and execution time. The uncertainty of prediction makes the optimization decision extremely complex. Especially in the case of limited resources, the system needs to weigh the pros and cons under multiple constraints, forming a unique technical problem.

[0005] Therefore, how to design a mechanism that can optimize the decision-making path to reduce costs and improve efficiency based on predicting the execution effects of instructions has become the key problem that needs to be solved urgently in this research. Summary of the Invention

[0006] The present invention provides a method for analyzing robot control instructions integrating continuous instructions, mainly including:

[0007] Obtain instruction effect prediction data from historical execution records, and build a prediction model by analyzing the correlation of instruction sequences and the impact of environmental variables; quantitatively evaluate instruction execution quality and resource consumption based on the prediction data, and generate quality scores and consumption estimates; build a multi-objective optimization framework based on quality scores and consumption estimates, and determine a set of candidate paths; calculate switching costs and execution times for the candidate path sets, and screen out optimization path solutions that meet the constraints; adjust instruction execution priorities and resource allocation ratios in combination with real-time environmental data, and generate scheduling strategies; monitor environmental changes and task quality feedback during execution, and obtain dynamic evaluation data; if the dynamic evaluation data is lower than the preset threshold, trigger the update of the prediction model, and fine-tune the model parameters through real-time data; recalculate quality scores and switching costs based on updated prediction data to determine whether to adjust the optimization path solution; continuously monitor instruction sequence responsiveness and task quality indicators, and 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 a regression analysis method 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 a quality score based on the adaptability indicator; predicting resource occupancy using the quality score and environmental parameters as input; if the difference between the predicted value and the historical data exceeds a preset range, re-collecting the execution record for verification, and generating a configuration plan containing recommended environmental parameters.

[0010] Furthermore, constructing a multi-objective optimization framework based on quality scoring and consumption estimation includes: processing quality scoring and resource consumption data through a regression model to generate an objective scoring matrix; using an optimization algorithm to sort the scoring matrix to obtain a preliminary optimization result; constructing a dependency graph for the instruction sequence corresponding to the result, calculating node weights, and screening key nodes; if the sorting of the key nodes 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 a test scenario; extracting paths from the qualified scenarios, clustering and layering, and removing conflicting paths to generate a candidate path set; selecting paths that meet the preset conditions as the execution plan.

[0011] Furthermore, calculating the switching cost and execution time for the candidate path set includes: obtaining the cost matrix and time series of the candidate paths through a path calculation method; if the total cost of a certain path exceeds the preset range, removing it from the set; for the time series of the remaining paths, if the execution time does not meet the preset conditions, marking it as a path to be optimized; detecting resource competition nodes through dependency relationship modeling, and reordering to generate an updated path set; calculating a comprehensive score based on the cost and time data of the updated path set; verifying the paths through a simulation tool and removing abnormal paths; merging path branches with the same dependency relationship and outputting the final execution path plan.

[0012] Furthermore, adjusting the instruction execution priority and resource allocation ratio in combination with real-time environmental data includes: obtaining environmental status data through sensors, using a filtering method to remove outliers, and generating an environmental data set; outputting resource requirement weights through a pre-trained model to generate an initial allocation ratio; if the resource occupancy exceeds the 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 situations through a scheduling tool, and reordering the nodes if there are conflicts; if the environmental data fluctuation exceeds the preset range, updating the policy parameters; regularly collecting environmental data, and if the change exceeds the threshold, adjusting the resource quota to generate an updated scheduling plan.

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

[0014] Further, the triggering of the prediction model update when the dynamic evaluation data is lower than the preset threshold includes: screening a qualified data subset from the dynamic evaluation data stream; triggering online model update if the execution efficiency is lower than the preset threshold; performing normalization processing on the data subset to generate a feature matrix; outputting a weight adjustment coefficient through a regression model to update the prediction model parameters; if the error change after update is lower than the preset range, extracting additional metrics to merge data, adjusting feature weights and then retraining the model; comparing the prediction results of the updated model with the preset conditions to generate a scheduling instruction; recording the execution data in a buffer for subsequent analysis.

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

[0016] The present invention discloses a method for analyzing robot control instructions integrating continuous instructions. The method analyzes the relevance of instruction sequences and environmental impacts by constructing a prediction model, obtains prediction data of instruction effects, and uses regression analysis to quantitatively evaluate the execution quality and resource consumption of instructions. Based on a multi-objective optimization framework, the switching cost is calculated by combining with a dynamic programming algorithm 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 the instruction priority and resource allocation. When the execution efficiency is lower than the threshold, the online update of the prediction model is triggered to fine-tune 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 instruction sequences, and achieve intelligent instruction scheduling and resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of a method and system for analyzing robot control instructions integrating continuous instructions according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0019] Such as Figure 1 This embodiment of a method for analyzing robot control instructions integrating continuous instructions may specifically include:

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

[0021] Using historical records and execution data, the interquartile range method is adopted to remove outliers that deviate from the median by 1.5 times the interquartile range, and forward filling is used for missing values to generate a basic dataset. For the instruction sequences in the basic dataset, Granger causality test is used to calculate the lag correlation between instructions, and the sequences with p-values less than 0.05 are extracted as key pattern features. When the number of key pattern features exceeds the preset threshold of 5, the Pearson correlation coefficient between environmental variables and instruction execution duration is calculated, and the variables with absolute values greater than 0.3 are selected as effective environmental factors. The effective environmental factors and key pattern features are combined into a 24-dimensional input matrix and input into the support vector machine model for effect prediction. If the residual between the predicted result and the actual effect exceeds 3 times the standard deviation of the historical error, the gradient descent method is used to adjust the feature weights, and the learning rate is reduced by 0.1 times in each iteration until the residual enters the allowable range. According to the adjusted weights, the moving average of the instruction effects in the next 10 time windows is recalculated, and the final prediction result is output. The prediction result, corresponding environmental factors, and sequence features are jointly stored in the instruction_optimization table of the MySQL database.

[0022] For example, when processing historical records and execution data, outliers can be removed first by the interquartile range method. Suppose the execution duration data of 1000 past instructions of a certain system is collected. First, the median and interquartile range are calculated, and the outlier standard is set to deviate from the median by 1.5 times the interquartile range. Data points outside this range, such as extremely long or short execution times, are removed to ensure data stability. Then, the forward filling method is used for missing values. For example, if the data at a certain time point is missing, it is filled with the value of the previous time point to ensure the integrity of the dataset. The advantage of doing this is to effectively smooth data noise and lay a reliable foundation for subsequent analysis.

[0023] In a possible implementation, for the instruction sequences in the basic dataset, the lag correlation between instructions can be analyzed through Granger causality test. Suppose there are instruction A and instruction B. Through the test, it is found that A has a significant impact on B at a lag of 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, exceeding the preset threshold of 5, the correlation between environmental variables and instruction execution duration 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 duration is 0.35, which is greater than 0.3, then it is selected as an effective environmental factor. The advantage of this method is to screen out variables that have a greater impact on the result and improve the pertinence of the model input.

[0024] For example, after combining the 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 execution effect of the instruction 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 initiated to adjust the feature weights. The learning rate is decreased by 0.1 times for each iteration until the residual falls within the allowable range. This dynamic adjustment mechanism can effectively improve the prediction accuracy and adapt to data changes. After adjustment, the moving average of the instruction effects for the next 10 time windows is calculated based on the new weights. For example, the predicted value is 82% and it is output as the final result. Its advantage lies in providing a prediction closer to the actual situation to assist in decision-making.

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

[0026] For example, the predicted value of 82%, the temperature variable value of 25 degrees, the lag relationship of key pattern feature A - B, etc. are all stored in the instruction_optimization table for subsequent query and optimization analysis. This storage method ensures data traceability and improves the system management efficiency. Through the above series of methods, from data cleaning to feature extraction, model prediction, and result storage, a complete closed-loop is formed, effectively improving the instruction optimization effect and reducing the execution risk.

[0027] Step S102, for the preliminary instruction effect prediction data, the regression analysis method is used to quantitatively evaluate the potential execution impact of each instruction, and the execution quality score value and resource consumption estimation value of each instruction in different environments are obtained.

[0028] Extract the execution delay and success rate data of instructions under three environmental parameters: temperature, humidity, and load from the historical execution records, and construct an original dataset containing environmental parameters and execution metrics. Use LinearRegression in sklearn to fit the original dataset, and obtain the regression coefficients of each instruction under different environmental parameters as performance weights. When the absolute value of the humidity coefficient of a certain instruction is greater than the preset threshold of 0.5, use stratified_sample in scipy to extract subsamples according to the humidity gradient. Calculate the Pearson correlation coefficient between the instruction success rate and humidity change in the subsamples as the adaptability level index. Input the adaptability level index into the percentile ranking algorithm to output a quality score in the range of 0 - 100. Use the quality score as the dependent variable and the environmental parameters as the independent variables, and execute LinearRegression again to predict the CPU and memory occupancy. If the difference between the 95% confidence interval of the predicted value and the historical data mean exceeds 15, re-collect 200 execution records under this environmental parameter for verification. Finally, merge the performance weights, quality scores, and resource prediction results to generate a configuration plan including the recommended environmental parameter range.

[0029] For example, when extracting instruction data from historical execution records, the impacts of three environmental parameters, namely temperature, humidity, and load, on execution delay and success rate can be focused on. Suppose a certain system has recorded the instruction execution situations in the past year and 10,000 pieces of data have been extracted, where the temperature range is from 10 to 35 degrees, the humidity range is from 30% to 80%, and the load range is from 20% to 90%. Through preliminary statistics, it is found that the execution delay shows an increasing trend when the temperature rises, and the impact of humidity on the success rate is relatively significant. This data extraction method helps to construct a comprehensive original dataset and lays a foundation for subsequent analysis.

[0030] For example, when using linear regression to fit the dataset, the environmental parameters can be used as independent variables, and the execution delay and success rate can be used as dependent variables to analyze the performance weights of each instruction under different environmental conditions. Suppose the regression coefficient of a certain instruction under humidity change is 0.6, which exceeds the preset threshold of 0.5, indicating that humidity has a greater impact on it. This method can intuitively reflect the intensity of the influence of environmental factors on instruction performance and is convenient for subsequent targeted optimization.

[0031] For example, for instructions with humidity coefficients exceeding the threshold, when using the stratified sampling method to extract subsamples, they can be divided into three intervals: low, medium, and high according to the humidity gradient, and 100 pieces of data are extracted from each interval for analysis. Calculate the correlation coefficient between the success rate and humidity change in the subsamples. Suppose the obtained value is 0.4, indicating a certain positive correlation between humidity and the success rate. This stratified sampling can more accurately capture the influence differences of environmental variables and improve the meticulousness of the analysis.

[0032] For example, when calculating the adaptability level index and inputting it into the percentile ranking algorithm, assume that the quality score corresponding to the correlation coefficient of a certain instruction is 75 points, which is at an upper-middle level. This means that the instruction has good adaptability under humidity changes, but there is still room for improvement. The use of quality scores helps to quantify the performance of instructions and provides a reference basis for subsequent resource prediction.

[0033] For example, when predicting the CPU and memory occupancy rates with the quality score as the dependent variable, if the predicted CPU occupancy rate of a certain instruction is 60% while the historical average is 45% and the difference exceeds 15%, then data needs to be re-collected for verification. This verification mechanism can effectively avoid prediction biases and ensure the reliability of the results, which is particularly significant in resource allocation scenarios.

[0034] For example, when finally combining the performance weights, quality scores, and resource prediction results, a recommended range of environmental parameters can be generated. Assume that a certain instruction performs best at a temperature of 20 to 25 degrees, a humidity of 40% to 50%, and a load of 30% to 50%, then it is output as a configuration plan. Such a plan can provide specific guidance for system operation, reduce the execution risks caused by improper environments, and at the same time optimize the resource usage efficiency. Through the complete process from data extraction to the final configuration plan, the stability and adaptability of instruction execution can be significantly improved.

[0035] Step S103: According to the execution quality score value and the resource consumption estimation value, construct a multi-objective optimization framework. With the goal of improving the execution quality score value and reducing the resource consumption estimation value, and combining the relevance of the instruction sequence, obtain a set of candidate paths for the optimal instruction sequence.

[0036] Process the execution quality and resource consumption data through a random forest regression model to output a target score matrix. Use the NSGA-II algorithm to perform non-dominated sorting on the score matrix to obtain the Pareto front solution set as the preliminary optimization result. Input the instruction sequence corresponding to the Pareto solution set into the NetworkX tool, and construct a directed instruction dependency graph based on the control flow graph. Run the PageRank algorithm in the directed dependency graph to calculate the node weights, and select the instructions with weights higher than the preset threshold of 0.7 as the key nodes. When the sorting of the key nodes does not conform to the topological order, adopt an edge weight adjustment strategy to reconstruct the network, and use the updated directed graph as the optimized instruction sequence. Input the optimized sequence into a predefined test scenario set, and obtain the latency and throughput metrics through instrumentation testing. When the latency exceeds 50 ms or the throughput is lower than 1000 ops / s, it is marked as a constraint condition. Extract the control flow paths from the compliant scenarios, use the Dijkstra algorithm to calculate the path weights and perform K-means clustering stratification. Detect the path pairs with resource competition or timing violations in the stratified path set, and generate a candidate path set after removing the conflicting paths. Based on the two-dimensional coordinates of latency-energy consumption of the candidate paths, select the top three paths with the smallest Euclidean distance from the ideal point (20 ms, 5 J) as the final execution plan.

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

[0038] For example, when using the NSGA-II algorithm to perform non-dominated sorting on the score matrix, the purpose is to find a set of solution sets that balance multiple objectives. Suppose the goal is to optimize both the execution delay and energy consumption simultaneously. The algorithm will generate a Pareto front solution set, where each solution represents an instruction configuration plan. For example, one plan has a delay of 30 ms but an energy consumption of 8 J, and another plan has a delay of 40 ms but an energy consumption of 6 J. This multi-objective optimization can provide diverse choices for subsequent decision-making.

[0039] For example, when constructing a directed graph of instruction dependencies based on the NetworkX tool, the control flow relationships between instructions can be visualized as nodes and edges. Suppose an instruction A must be executed after instruction B is completed, then a directed edge is formed from A to B. After calculating the node weights through the PageRank algorithm, if the weight of instruction A 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 greater impact on the overall execution of the system.

[0040] For example, when the sorting of critical nodes does not conform to the topological order, the network can be reconstructed through an edge weight adjustment strategy. Suppose there is a dependency conflict between instructions A and C. After adjusting the weights of the edges, a new directed graph is generated to ensure the correct logic of the execution order. This reconstruction can avoid execution deadlocks or incorrect orders.

[0041] For example, when obtaining latency and throughput metrics through instrumentation testing in a test scenario set, if the latency of a certain instruction sequence in scenario one is 60 ms, exceeding the threshold of 50 ms, it is marked as non-compliant. This kind of testing can screen out sequences that do not meet the performance expectations.

[0042] For example, when calculating the weights of control flow paths using the Dijkstra algorithm and performing K-means clustering for layering, if the total weight of a certain path is 15 and it belongs to the low-latency group. This layering can group paths according to their performance characteristics, facilitating subsequent optimization.

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

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

[0045] Step S104, for the set of candidate paths of the optimal instruction sequence, use the dynamic programming algorithm to calculate the switching cost and execution time of each path layer by layer. If the switching cost of a certain path exceeds the preset threshold, then this path is removed to obtain an optimized path plan that meets the cost constraints.

[0046] Use the Dijkstra algorithm to perform hierarchical calculations on the candidate paths of the instruction sequence, and obtain the switching cost matrix and execution time series of each path. According to the cost matrix, if the total cost of a certain path exceeds 1.2 times the average cost, it is excluded from the candidate path set to obtain the first path set. Extract the time series data from the first path set. If the execution time of a certain path is not within the range of 80% to 120% of the instruction deadline, mark this path as a path to be optimized. Use a directed acyclic graph to model the dependencies of the first path set, and detect path nodes with resource contention through topological sorting. Reorder the path nodes with resource contention to generate the second path set. According to the cost matrix and time series of the second path set, use the weighted summation formula to calculate the comprehensive score, with the scoring weights being 60% for cost and 40% for time, to obtain the sorted path list. Input the sorted path list into the QEMU virtualization tool for instruction-level simulation, and collect memory access exception and instruction conflict data through instrumentation. Exclude the paths with exceptions in the simulation to generate the third path set. According to the dependency graph of the third path set, merge the path branches with the same predecessor nodes and output the final execution path plan.

[0047] For example, when using the Dijkstra algorithm to perform hierarchical calculations on the candidate paths of the instruction sequence, it can be regarded as a graph-structure-based path search method aimed at finding the lowest-cost path from the starting point to the ending point. Suppose in a system instruction scheduling scenario, there are 10 candidate paths. After calculating the switching cost matrix and execution time series of each path, it is found that the total cost of path A is 50 units, while the average cost is 40 units, exceeding the 1.2-fold threshold of 40 x 1.2, which is 48 units. Therefore, path A is excluded to form the first path set. This method can effectively filter out paths with excessively high costs.

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

[0049] For example, when using a directed acyclic graph to model the dependencies of the first path set, the execution order between instructions can be abstracted as nodes and directed edges. Suppose there is a resource contention between nodes C1 and C2 of path C. After detection through topological sorting, it is found that C1 and C2 access the same memory area simultaneously, resulting in a potential conflict. To address this issue, reorder the execution order of C1 and C2 to generate the second path set. This adjustment can effectively avoid resource contention problems.

[0050] For example, when calculating the comprehensive score based on the cost matrix and time series of the second path set, assume that the cost of path D is 30 units, the execution time is 90 ms, and the weights are 60% for cost and 40% for time. The score is obtained by weighted summation. This scoring mechanism can comprehensively consider multi-dimensional indicators and screen out more balanced paths.

[0051] For example, when inputting the sorted path list into the QEMU virtualization tool for instruction-level simulation, assume that path E has a memory access exception during the simulation, manifested as frequent page faults, so it is excluded, and the third path set is generated. This simulation test can detect potential execution problems in advance and ensure path reliability.

[0052] For example, when merging path branches according to the dependency graph of the third path set, assume that path F and path G have the same predecessor node N1, and a new execution path scheme is formed by merging the branches. This merging method can simplify the path structure, reduce redundant execution, and improve the overall scheduling efficiency. Through the above multi-faceted analysis and examples, it can be seen that each step from path screening to the final scheme output closely revolves around instruction sequence optimization, progressing step by step to ensure the efficiency and reliability of the path.

[0053] Step S105: According to the optimized path scheme that meets the cost constraint, combined with the real-time collected environmental dynamic data, adjust the priority of instruction execution and the resource allocation ratio, and determine the instruction execution order and resource scheduling strategy in the current environment.

[0054] Obtain environmental temperature, humidity, and light data through sensors, use median filtering to remove outliers, and generate an environmental status data set containing timestamps. Input the data set into a pre-trained random forest model, output the resource demand weights for each region, and generate an initial allocation ratio table in descending order of weights. Read the CPU and memory occupancy ratios in the allocation ratio table. If any resource exceeds 70% of the budget, compress the quotas for high-weight regions proportionally to generate the final allocation table. Mark the corresponding nodes in the path planning diagram according to the region numbers in the allocation table, use the Dijkstra algorithm to calculate the shortest delivery path, and generate a node execution order list. Import the execution list into the Kubernetes scheduler to detect resource contention between nodes. If the same GPU resource is requested in the same time period, reorder the nodes according to the path distance. Generate a scheduling strategy based on the sorting result. When the real-time light intensity fluctuation exceeds 20% of the historical average, trigger the update of policy parameters. After deploying the strategy, collect environmental data every five minutes and calculate the humidity variance within the sliding window. If the variance exceeds the threshold three times in a row, call the priority queue to adjust the resource quota and generate an updated scheduling scheme.

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

[0056] It is understandable that sensors are deployed at key points in different regions to ensure the comprehensiveness of data collection. Suppose in a resource scheduling scenario of a greenhouse, the sensors collect data once an hour, with the temperature ranging from 20 to 30 degrees, the humidity ranging from 50% to 80%, and the light intensity varying according to day and night. Such a collection method can provide a multi-dimensional data basis for subsequent analysis.

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

[0058] It is understandable that this method smooths out abnormal fluctuations by sorting a set of data and taking the median value. Suppose a set of temperature data values are 22, 23, 45, 24, 25 degrees. Obviously, 45 degrees is an outlier, and after median filtering, it can be replaced with a value near 23 degrees. This processing can effectively improve the reliability of the data set.

[0059] For example, when inputting a data set into a pre-trained random forest model.

[0060] It is understandable that the model has learned the relationship between environmental factors and resource requirements through historical data. Suppose the model predicts that the weight of CPU resource requirements for a certain area due to high temperature and humidity is 0.8, while the weight for another area is only 0.3. Such weight output provides a scientific basis for subsequent allocation.

[0061] For example, when generating an initial allocation ratio table by weight and performing resource compression, assume the CPU budget is 100 units, and the allocation ratio for a certain area is 80%, exceeding the threshold of 70%. Then it will be compressed to 70 units, and the remaining resources will be reallocated to the area with the second-highest weight. This adjustment ensures that resources are not overloaded.

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

[0063] It is understandable that the algorithm finds the shortest distance from the starting point to each node through a graph structure. Suppose the path distances from the control center to regions A, B, and C are 5, 8, and 10 units respectively. The algorithm will give priority to selecting A with the shortest distance as the first node. This way optimizes the delivery order.

[0064] For example, when importing an execution list into the Kubernetes scheduler to detect resource contention, assume that two nodes request the same GPU resource simultaneously. The system will preferentially schedule the closer node according to the path distance. This dynamic adjustment avoids resource conflicts.

[0065] For example, when the light intensity fluctuation triggers the strategy update, assuming the historical mean is 1000 units and the real-time value is 1250 units, exceeding the 20% threshold, the system will automatically adjust the scheduling parameters, such as increasing the night resource quota. This mechanism adapts to environmental changes.

[0066] For example, when calculating the humidity variance within the sliding window, assuming the continuous three window variances are 15, 18, and 20 respectively, all exceeding the threshold of 10, the system will tilt the resources to the areas with large humidity fluctuations through the priority queue. This response mechanism ensures the timeliness of resource allocation.

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

[0068] Collect environmental data and task completion status at a frequency of once per second through the sensor network, with the timestamp accurate to milliseconds, and generate a set of original data with time stamps. According to the preset rules, mark the data with an environmental data change rate greater than 5% per second or the number of task failures greater than 3 as the high-priority group. For the high-priority data group, calculate the difference between the environmental indicators and the reference value. When the difference exceeds 2 times the standard deviation of the historical data, start the median filtering process to obtain the denoised environmental state subset. Match the environmental state subset with the task completion duration and error code at the same timestamp, calculate the moving average of the number of tasks completed per minute, and if it decreases continuously for 3 times, it is determined that the efficiency has decreased. Extract the mean value of the environmental indicators, the peak resource occupancy rate, and the task queue length during the efficiency decrease period as features, and input them into the pre-trained random forest regression model. The model outputs the CPU allocation increment and memory allocation decrement parameters. Input the parameters into the Kubernetes scheduler, modify the container resource limits, and restart the Pod instance. The scheduler collects the CPU usage rate and task backlog of the Pod every 30 seconds, and the data is transmitted back to the sensor network database. When the task backlog exceeds 80% of the total queue capacity, trigger a new round of environmental data collection and priority division.

[0069] For example, in the intelligent management scenario of a greenhouse, collect environmental data and task completion status at a frequency of once per second through the sensor network to ensure data real-time. The sensors are deployed at key points in the greenhouse, such as near the planting area, ventilation openings, and irrigation equipment, to collect data such as temperature, humidity, and light intensity, with the timestamp accurate to milliseconds.

[0070] For example, the data collected in a certain time is temperature 25.3 degrees, humidity 65.2%, light 800 lux, and the timestamp is 2025-05-24 08:00:00.123. Such high-frequency collection provides a continuous data basis for subsequent analysis.

[0071] In a possible implementation, high-priority data groups are screened according to preset rules. If the temperature in a certain area rises from 25.0 degrees to 26.5 degrees within 1 second, with a change rate of 6%, exceeding the 5% threshold; or if a certain task fails continuously 4 times, exceeding the 3 - time threshold, it is marked as high - priority.

[0072] For example, the irrigation task fails 4 times due to a blocked pipeline, accompanied by a humidity drop to 50%, and is marked as high - priority. This screening mechanism can quickly lock in abnormal states.

[0073] Specifically, the difference from the reference value needs to be calculated for high - priority data groups.

[0074] For example, the historical temperature reference value is 25 degrees, the standard deviation is 0.8 degrees, and a certain collected value is 27 degrees. The difference of 2 degrees exceeds 2 times the standard deviation, triggering median filtering. Suppose a set of temperature data is 24.8, 25.2, 27.0, 25.1, 24.9 degrees, and the median filtering takes 25.1 degrees as the denoising result. This processing ensures data smoothness and reduces the impact of abnormal fluctuations.

[0075] For example, when the environmental status subset matches the task data, assume that at a certain timestamp, the task completion duration is 2 seconds and the error code is "insufficient pipeline pressure". Calculate the moving average of the number of tasks completed per minute. If it drops from 100 to 80, 60, 50 continuously for 3 times, it is determined that the efficiency has decreased. This kind of analysis can accurately identify performance bottlenecks.

[0076] In a possible implementation, extract the characteristics of the period with decreased efficiency, such as the average temperature of 26 degrees, the peak resource occupancy rate of 90%, and the task queue length of 50, and input them into the random forest regression model. The model outputs a CPU allocation increment of 20 units and a memory decrement of 10 units. These parameters guide resource optimization to ensure efficient task execution.

[0077] Specifically, the Kubernetes scheduler adjusts the container resource limits according to the parameters.

[0078] For example, the CPU limit of a certain Pod is increased from 100 units to 120 units, and the memory is decreased 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, a certain Pod has 60 backlogged tasks, exceeding the 80% threshold, and the system automatically increases the sensor collection frequency. This dynamic feedback mechanism can respond to task pressure in a timely manner and improve system stability.

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

[0081] For example, database records show that there is a backlog of tasks in a certain area due to insufficient lighting. The system automatically adjusts the resource quota and preferentially schedules tasks for lighting equipment. This data-driven scheduling method optimizes the utilization efficiency of greenhouse resources.

[0082] Step S107: According to the dynamic evaluation data of execution efficiency and task quality, if it is detected that the execution efficiency is lower than the preset threshold, trigger the online update mechanism of the prediction model, and fine-tune the model parameters through real-time data to obtain the updated instruction effect prediction data.

[0083] Obtain the dynamic evaluation data stream of execution efficiency and task quality through the real-time acquisition system, and use the sliding window method to screen the subset with a data variance greater than 0.1 within the window as the analysis data. If the execution efficiency in the data subset is lower than the 30th percentile threshold of historical data, trigger the online update of the XGBoost model. Input the screened data subset into StandardScaler of Scikit-learn for feature standardization, and output the standardized feature matrix. Use the pre-established LinearRegression model to process the feature matrix, and output the weight adjustment coefficient as the basis for parameter fine-tuning. According to the weight adjustment coefficient, update the leaf node weight parameters of the XGBoost model by the gradient descent method. Calculate the mean squared error of the updated model on the test set. If the error reduction rate is less than 5%, extract the task completion rate and error rate indicators from the dynamic evaluation data. Combine the new indicators with the execution efficiency data, use the Pearson correlation coefficient to determine the importance ranking of features, set the weights of features with importance lower than 0.2 to zero, and retrain the model. Compare the prediction result of the secondarily adjusted model with the preset error tolerance to generate a scheduling instruction including CPU core number allocation and memory quota parameters. Record the actual execution efficiency data after the instruction is executed and write it into the efficiency index buffer area of the Redis database.

[0084] For example, in the intelligent management scenario of a greenhouse, the real-time acquisition system is widely used to monitor environmental data and task execution status to support the acquisition of dynamic evaluation data streams. For the analysis of execution efficiency and task quality, the system collects key indicators such as temperature and humidity once per second through sensors to ensure the continuity and timeliness of the data. Suppose a certain acquisition shows that the temperature in a certain area fluctuates greatly. The system will analyze the data in the past 5 minutes based on the sliding window method and screen out the subset with a variance greater than 0.1 as the key concern object. For example, the temperature data in a certain window fluctuates from 24.5 degrees to 26.0 degrees, and the variance reaches 0.15, meeting the screening conditions.

[0085] Specifically, when the execution efficiency is lower than the 30th percentile of historical data, the system will trigger the online update mechanism of the model. Suppose the 30th percentile of execution efficiency in historical data is 80 tasks completed per minute, while only 60 tasks are completed within the current window. The system will use the relevant data subset for subsequent processing. This approach can promptly identify efficiency bottlenecks and provide a basis for subsequent optimization.

[0086] In a possible implementation, feature standardization is an important part of data processing. The collected data such as temperature and humidity will be input into the standardization tool to unify the dimension for model analysis.

[0087] For example, the temperature data ranges from 20 to 30 degrees, and the humidity ranges from 40% to 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, for the generation of the weight adjustment coefficient, the system will process the standardized feature matrix based on a preset model. Suppose in a certain analysis, the influence weight of temperature on task efficiency is relatively high. The system will output an adjustment coefficient to guide subsequent parameter optimization. This method can ensure that the model better fits the current environmental changes.

[0089] Specifically, during the model update process, the system will fine-tune the parameters according to the weight adjustment coefficient. For example, by gradually adjusting the model parameters, optimize its prediction ability for the current task efficiency decline. Suppose after a certain update, the prediction error drops from 0.2 to 0.18, and the error decline rate does not reach the expectation, but still extract indicators such as task completion rate and error rate for further analysis.

[0090] In a possible implementation, the ranking of feature importance is crucial for model optimization. Suppose through correlation analysis, it is found that the importance of humidity on task efficiency is only 0.15, lower than the threshold of 0.2. The system will set its weight to zero to reduce the interference of irrelevant factors. This screening can make the model focus more on key variables.

[0091] For example, after the scheduling instruction is generated, the system will adjust the resource allocation according to the prediction result. Suppose there is a serious backlog of tasks in a certain area, the system will generate an instruction to increase the number of processing cores from 2 to 3 and adjust the memory quota at the same time. This dynamic adjustment can effectively relieve the task pressure.

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

[0093] Step S108: For the updated instruction effect prediction data, recalculate the execution quality score value and switching cost of the instruction sequence, determine whether to adjust the optimization path plan based on the calculation results, and determine the final path adjustment decision.

[0094] Extract the timestamp, response code, and resource consumption fields from the instruction effect prediction data as the original evaluation information. Input the original evaluation information into the pre-established pandas data processing module, perform missing value filling and Z-Score standardization, and output the first data set. For the first data set, extract the instruction execution delay, success rate, and CPU occupancy rate as execution quality features, extract the number of context switches and memory reallocation amount as switching cost features, and combine them into a feature vector group with a dimension of 5. If the execution delay in the feature vector group exceeds 200 ms or the success rate is lower than 95%, then call the analytic hierarchy process to calculate the switching cost weight. When the weighted total score is lower than 70 points, trigger the path optimization process. Input the feature vector group into the RandomForestClassifier of scikit-learn, and predict 3 groups of optimized path parameters as the candidate solution set. Use the TOPSIS sorting algorithm to perform normalized weighted sorting on the execution quality and switching cost of the candidate solutions, and output the priority list. Select the solution with the highest comprehensive score according to the priority list, and perform parameter replacement through the rule engine. Write the adjusted instruction parameters into the Redis cache, and generate the second data set for subsequent instruction scheduling.

[0095] For example, in the intelligent management scenario of greenhouse greenhouses, the processing and optimization of instruction effect prediction data is a key link. The extraction of the timestamp, response code, and resource consumption fields can be regarded as the basic information for evaluating the instruction execution effect. Suppose in a certain instruction execution record, the timestamp shows 10:00 am, the response code of 200 indicates success, and the resource consumption is 30% CPU occupancy. These original data will be input into a data processing module, and through the missing value filling method, the records with missing response codes will be filled with the historical average value to ensure data integrity.

[0096] Specifically, the Z-Score standardization process will convert the resource consumption data into a distribution with a mean of 0 and a variance of 1, which is convenient for subsequent analysis. For example, the original range of CPU occupancy rate is between 20% and 50%. After standardization, the dimension difference can be eliminated, and the input quality of the model can be improved. Such a processing method helps to more accurately reflect the true state of instruction execution.

[0097] In a possible implementation, when extracting execution quality features and switching cost features for the first data set, instruction execution latency and success rate are the core metrics. Suppose the instruction execution latency for a certain time is 250 ms, exceeding the preset threshold of 200 ms, and at the same time the success rate is 93%, lower than the standard of 95%. The system will automatically trigger the analytic hierarchy process to calculate the switching cost weight. The number of context switches is assumed to be 50 times per second, and the memory reallocation amount is 100 MB. These data will be comprehensively evaluated. If the weighted total score is only 65 points, lower than the standard of 70 points, the path optimization process will be activated. This mechanism can timely detect potential problems and take corresponding measures.

[0098] For example, after the feature vector group is input into the random forest classifier, the system will generate 3 groups of optimized path parameters as candidate solutions. Suppose the first group of parameters focuses on reducing the latency to 180 ms, the second group improves the success rate to 97%, and the third group balances the switching cost. These solutions are normalized and weighted sorted through the TOPSIS sorting algorithm, and finally a priority list is output. Suppose the first group of solutions has the highest comprehensive score, the system will replace the instruction parameters with the configuration of this solution through the rule engine. This way can ensure the selection of the optimal path and improve the overall execution efficiency.

[0099] Specifically, after the adjusted instruction parameters are written into the Redis cache, a second data set is formed. Suppose the new parameters adjust the CPU allocation ratio from 30% to 40% to support higher-load task scheduling. This caching mechanism can quickly respond to subsequent instruction requirements and reduce latency. The entire process from data extraction to parameter optimization forms a closed loop, ensuring the efficiency and stability of instruction execution. Especially in scenarios such as greenhouse sheds where the environmental response requirements are extremely high, it can effectively guarantee the continuity and reliability of tasks.

[0100] Step S109, according to the final path adjustment decision, continuously monitor the real-time response ability of the instruction sequence and the task quality guarantee metrics, obtain the comprehensive execution effect evaluation data, and use it for the prediction and optimization iteration of the subsequent instruction sequence.

[0101] From the instruction sequence running environment, obtain a monitoring data set containing response latency, CPU utilization, and task success rate through the Prometheus monitoring tool. For the monitoring data set, use drop_duplicates in Pandas to remove duplicate records and fill missing fields with the mean value to generate a basic data set. Extract the 90th percentile of the response latency and the standard deviation of the task success rate from the basic data set as key features, and generate a feature data set through numerical concatenation. Input the feature data set into a preset random forest classifier. If the predicted response latency exceeds 200 ms or the task success rate is lower than 95%, trigger the optimization process. Locate the high-latency instruction sequence segment according to the classification result, and extract the corresponding thread pool size and batch processing volume from the running log as path adjustment parameters. Use a decision tree rule engine to generate candidate solutions with parameter increases and decreases of 10% and 20%. Use the TOPSIS tool to sort the solutions based on execution time and resource consumption, and select the solutions with a comprehensive score exceeding 0.8. Update the selected thread pool parameters to the running environment through the Ansible automation tool, and re-collect the updated monitoring data. Continuously compare the response latency and task success rate indicators before and after adjustment.

[0102] For example, in the intelligent management scenario of a greenhouse, obtaining and optimizing the running environment data through a monitoring tool is a key link. For the collection of monitoring data such as response latency, CPU utilization, and task success rate, the running status of the device can be captured through a real-time monitoring system. Suppose in a certain monitoring, the average response latency is 180 ms, the CPU utilization is 35%, and the task success rate is 96%. These data provide a basis for subsequent analysis. The monitoring tool will store these metrics in a time series format for easy tracking of the change trend.

[0103] For example, for the part of removing duplicate records and filling missing values in data preprocessing, suppose duplicate response latency records are found at some time points in the collected data set. The system will automatically remove the redundant records to ensure data uniqueness. For the missing task success rate data, it can be filled based on the average value in the past 24 hours. For example, fill the missing value with 95% to ensure the integrity of the data set. This helps to avoid data deviation and improve the reliability of subsequent feature extraction.

[0104] For example, in the feature extraction stage, the 90th percentile of the response latency may be shown as 220 ms, indicating that there is a relatively high latency in the execution of some instructions, while the standard deviation of the task success rate is 2.5%, reflecting that the execution stability needs to be improved. After these features are concatenated to form a feature data set, they can intuitively reflect the operation bottleneck of the instruction sequence and provide a basis for the prediction of the subsequent classifier. Such a feature selection method can focus on the core issues and improve the pertinence of the analysis.

[0105] For example, for the application of a random forest classifier, assuming that the prediction result shows that the response delay exceeds the threshold of 200 ms, the system will automatically mark the relevant instruction sequence segment as a high-risk area and trigger the optimization process. This prediction mechanism can timely detect potential problems, provide decision-making support for subsequent adjustments, and ensure the stability of system operation.

[0106] For example, when locating the high-latency instruction sequence segment, the system extracts the task configuration information with a thread pool size of 10 and a batch processing volume of 50 from the operation log. These parameters are used as the basis for adjustment and can help analyze the cause of the delay, such as the task queueing due to a too-small thread pool. Such an analysis method helps to accurately locate the root cause of the problem.

[0107] For example, for the part where the decision tree rule engine generates candidate solutions, assuming that the system proposes to increase the thread pool size by 10% and 20% respectively, that is, adjust it to 11 and 12, and the batch processing volume is also adjusted to 55 and 60 accordingly. By comparing the execution time and resource occupancy of different solutions, a more optimal configuration can be selected. This method of comparing multiple solutions can provide a flexible selection space.

[0108] For example, when using a sorting tool to evaluate the solutions, assuming that the solution with the highest comprehensive score is a thread pool size of 12 and a batch processing volume of 60, with a score of 0.85, exceeding the preset threshold of 0.8, so it is selected as the final solution. Such an evaluation mechanism ensures the scientificity and feasibility of the solution.

[0109] For example, after updating the parameters through an automated tool, assuming that the newly collected monitoring data shows that the response delay drops to 190 ms and the task success rate increases to 97%, indicating that the adjustment effect is significant. Continuously comparing the indicators before and after the adjustment can verify the effectiveness of the optimization solution and provide data support for subsequent improvements. This closed-loop mechanism can continuously improve the system operation efficiency and ensure the continuity and reliability of the greenhouse task scheduling.

[0110] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined by this claim book, they should all fall within the protection scope of the present invention.

Claims

1. A method for analyzing robot control instructions that integrates continuous instructions, characterized in that, include: Obtain instruction effect prediction data from historical execution records and build a prediction model by analyzing instruction sequence correlation and environmental variable influence; Quantitatively evaluate instruction execution quality and resource consumption based on the predicted data to generate quality scores and consumption estimates; A multi-objective optimization framework is constructed based on quality scores and consumption estimates to determine the set of candidate paths; 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 and 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 through 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. The robot control instruction analysis method integrating continuous instructions according to claim 1, characterized in that, The obtaining instruction effect prediction data from the historical execution records includes: Extract instruction execution data from historical records, use statistical methods to remove outliers and fill in missing values to generate a basic data set; For the instruction sequences in the basic data set, the lag correlation between instructions is calculated and the key pattern features are extracted; Screen effective environmental factors based on the correlation between environmental variables and execution time; Combine key pattern features and effective environmental factors into an input matrix and output preliminary prediction data through a 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 method for analyzing robot control instructions integrating continuous instructions according to claim 1, characterized in that, The quantitative evaluation of instruction execution quality and resource consumption according to the prediction data includes: Extract the execution indicators of instructions under various environmental parameters from historical records and construct the original data set; Fit the data set through regression analysis method to obtain the performance weight of instructions in different environments; According to the influence of specific environmental parameters, subsamples were selected to calculate the adaptability index; Generating a quality score based on the adaptability indicators; Taking the quality score 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. The robot control instruction analysis method integrating continuous instructions according to claim 1, characterized in that, The multi-objective optimization framework based on quality score and consumption estimation includes: Process the quality score and resource consumption data through regression model to generate the target score matrix; Use the optimization algorithm to sort the scoring matrix and obtain the preliminary optimization results; The instruction sequence corresponding to the result is constructed into a dependency graph, the node weights are calculated, and the key nodes are screened; If the key node ordering does not meet the preset conditions, adjust the dependency graph structure; Generate optimized sequences based on the adjusted dependency graph and verify performance indicators through test scenarios; Extract paths from the target-reaching 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 the switching cost and execution time for the candidate path set includes: Obtain the cost matrix and time series of candidate paths through the path calculation method; If the total cost of a certain path exceeds the preset range, exclude it from the set; For the time series of the remaining paths, if the execution time does not meet the preset conditions, mark them as paths to be optimized; Detect resource competition nodes through dependency relationship modeling, and reorder to generate an updated path set; Calculate the comprehensive score based on the cost and time data of the updated path set; Verify the paths through a simulation tool and exclude abnormal paths; Merge path branches with the same dependency relationship and output the final execution path plan.

6. The method for analyzing robot control instructions integrating continuous instructions according to claim 1, characterized in that, The adjustment of the instruction execution priority and resource allocation ratio in combination with real-time environmental data includes: Obtain environmental status data through sensors, use filtering methods to remove outliers, and generate an environmental data set; Output resource requirement weights through a pre-trained model to generate an initial allocation ratio; If the resource occupancy ratio exceeds the preset range, adjust the quota to generate the final allocation table; Mark path nodes according to the allocation table, calculate the shortest path to generate an execution order list; Detect resource contention through a scheduling tool, and reorder nodes if there are conflicts; If the environmental data fluctuation exceeds the preset range, update the policy parameters; Regularly collect environmental data. If the change exceeds the threshold, adjust the resource quota to generate an updated scheduling plan.

7. The method for analyzing robot control instructions integrating continuous instructions according to claim 1, wherein The monitoring of environmental changes and task quality feedback during the execution process includes: Collect environmental data and task status through a sensor network to generate an original data set; Mark high-priority data groups according to preset rules; Calculate the difference in environmental indicators for high-priority data groups. If it exceeds the preset range, perform denoising processing; Match the denoised data with task indicators, calculate the change in task completion volume. If it continuously decreases, determine that the efficiency is abnormal; Extract the characteristics of abnormal periods and output resource adjustment parameters through the model; Apply the parameters through a scheduling tool, regularly collect execution data, and trigger a new round of data collection if the backlog exceeds the preset range.

8. The method for analyzing robot control instructions integrating continuous instructions according to claim 1, wherein, If the dynamic evaluation data is lower than the preset threshold, trigger the update of the prediction model, including: Screen a qualified data subset from the dynamic evaluation data stream; If the execution efficiency is lower than the preset threshold, trigger the online update of the model; Standardize the data subset to generate a feature matrix; Output the weight adjustment coefficient through a regression model and update the prediction model parameters; If the error change after the update is lower than the preset range, extract additional indicators to merge the data, adjust the feature weights and then retrain the model; Compare the prediction results of the updated model with the preset conditions to generate a scheduling instruction; Record the execution data in the buffer 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 1-8, 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 update and self-learning module; The data analysis module is used to obtain instruction effect prediction data from historical execution records and construct a prediction model by analyzing the correlation of instruction sequences and the influence of environmental variables; The evaluation module is used to quantitatively evaluate the instruction execution quality and resource consumption based on the prediction data, and generate a quality score and consumption estimate; A multi-objective optimization framework module, which is used to construct a multi-objective optimization framework based on quality scoring and consumption estimation, and determine a set of candidate paths; A path selection and optimization module, which is used to calculate the switching cost and execution time for the set of candidate paths, and screen out the optimized path solutions that meet the constraint conditions; A real-time scheduling and resource allocation module, which is used to adjust the instruction execution priority and resource allocation ratio in combination with real-time environmental data, and generate a scheduling strategy; A dynamic monitoring and feedback module, which is used to monitor the environmental changes and task quality feedback during the execution process, obtain dynamic evaluation data, and if the dynamic evaluation data is lower than the preset threshold, trigger the update of the prediction model and fine-tune the model parameters through real-time data; A model update and self-learning module, which is used to recalculate the quality score and switching cost according to the updated prediction data, determine whether to adjust the optimized path solution, and continuously monitor the instruction sequence response ability and task quality indicators, and generate comprehensive execution effect evaluation data.

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

Images