Sanitation robot cluster state monitoring and fault early warning method and system

Abstract

The invention discloses an environmental sanitation robot cluster state monitoring and fault early warning method and system, and the method comprises the steps: optimizing the maintenance task execution sequence of environmental sanitation robot equipment, preferentially bringing the equipment with high fault risk level and high maintenance priority in the environmental sanitation robot equipment into a maintenance task, and carrying out the maintenance task in time; according to the execution effect of the equipment maintenance task, related parameters of the fault forecasting model are dynamically updated, an equipment fault forecasting result is recalculated, and whether the equipment operation state returns to normal or not is judged; and if the operation state of the equipment is recognized to be normal and the operation state data is within the normal operation state threshold range within the set time, the equipment maintenance task is ended, otherwise, the maintenance scheme needs to be adjusted, and the maintenance measures continue to be optimized until the environmental sanitation robot equipment completely returns to normal. According to the invention, high efficiency and accuracy of intelligent monitoring and fault early warning of the environmental sanitation robot cluster state are realized, the equipment reliability and the operation efficiency are improved, and the maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of robot operation control, and in particular discloses a method and system for monitoring the status and fault warning of a sanitation robot cluster. Background Art

[0002] During the long-term operation of the sanitation robot cluster, due to environmental factors and equipment aging, the status monitoring and fault warning methods have the following problems: 1. Sensor accuracy degradation: During long-term operation, the accuracy and reliability of sensors decrease due to environmental influences or equipment aging, resulting in problems such as observation data drift and increased noise, which directly affect the accuracy of the fault prediction model.

[0003] 2. Difficulty in joint monitoring of key components: Batteries, fans, nozzles, sweeping discs, lidar and other key components have different working conditions, aging rates and failure modes, which makes it difficult to uniformly evaluate the status during joint monitoring. In addition, there are potential related failures between different components. How to identify the fault chain through multi-dimensional data fusion is a key technical difficulty.

[0004] 3. Calibration problems due to individual differences: The performance of sensors of different models and batches varies significantly, making it difficult to calibrate and optimize them uniformly. In addition, sensor performance changes dynamically with operating time, requiring targeted adjustment of fault warning parameters, but current methods are difficult to adapt to.

[0005] 4. Data consistency and continuity: As the cluster runs longer, sensor degradation may lead to incomplete and inconsistent observation data, data breakpoints or error accumulation. This problem easily forms a blind spot for fault prediction, seriously affecting the accuracy and real-time performance of monitoring.

[0006] 5. Optimization and computing efficiency bottleneck: Existing methods are difficult to simultaneously take into account the real-time data collection efficiency and fault risk priority processing requirements of large-scale equipment clusters. Especially when the number of equipment increases and the operating environment becomes more complex, the priority sorting and resource allocation of maintenance tasks become more difficult.

[0007] Therefore, the above-mentioned defects in the existing sanitation robot cluster operations, especially in the joint monitoring of multiple components, data consistency maintenance and dynamic optimization, are technical problems that need to be solved urgently. Summary of the invention

[0008] The present invention provides a method and system for monitoring the status and early warning of a sanitation robot cluster, aiming to solve at least one of the above-mentioned defects existing in the existing sanitation robot cluster operation.

[0009] One aspect of the present invention relates to a sanitation robot cluster state monitoring and fault warning method, comprising the following steps: Obtain the operating status data of the sanitation robot cluster; the operating status data includes temperature, voltage and current data; The acquired operation status data is compared with a preset normal operation status threshold. If the operation status data exceeds the normal operation status threshold, the observation frequency of the operation status data is dynamically adjusted to narrow the observation range. Obtain the cumulative running time of the sanitation robot cluster. If the cumulative running time exceeds the preset safety time threshold, adjust the time weight parameter in the fault prediction model. According to the adjusted duration weight parameter, the fault prediction result is recalculated, and the accumulated operation duration and operation status data are comprehensively considered to determine whether each sanitation robot equipment has a fatigue loss risk. If there is a fatigue loss risk, an equipment failure warning signal is generated; Obtain equipment failure warning signals, combine operating status data and accumulated operating time, generate targeted equipment maintenance recommendations, and determine the maintenance priority of each sanitation robot equipment based on the failure risk level; Optimize the execution order of maintenance tasks for each sanitation robot equipment, give priority to equipment with high failure risk level and high maintenance priority in the maintenance tasks and execute them in time; According to the execution effect of the equipment maintenance task, the relevant parameters of the fault prediction model are dynamically updated, the equipment fault prediction results are recalculated, and it is determined whether the equipment operation status has returned to normal; If it is identified that the equipment's operating status has returned to normal and the operating status data are within the normal operating status threshold within the set time, the equipment maintenance task is completed. Otherwise, the maintenance plan needs to be adjusted and the maintenance measures continue to be optimized until the sanitation robot equipment is fully restored to normal.

[0010] Furthermore, the steps of obtaining the running status data of the sanitation robot cluster include: According to the operating status of the sanitation robot cluster, obtain real-time data of the operating status data and build a data collection model; The sparrow algorithm is used to optimize the data collection strategy, and the optimal observation frequency and observation range of each parameter in the operating status data are determined through iterative search; During the optimization process, the data collection model is dynamically adjusted according to the changes in observation frequency and observation range; Preprocess the running status data to remove abnormal values ​​and noise data in the running status data; Use clustering algorithms to analyze the preprocessed operating status data and identify data features and patterns under different operating states; According to the clustering results, a mapping relationship between the operating status and data features is established to achieve real-time monitoring and early warning of the operating status of the sanitation robot cluster; The optimized data collection strategy is applied to the sanitation robot cluster, and the data collection efficiency and quality are continuously monitored. The optimization algorithm is dynamically adjusted according to the feedback results to achieve adaptive optimization of the data collection strategy.

[0011] Furthermore, the acquired operating status data is compared with a preset normal operating status threshold. If the operating status data exceeds the normal operating status threshold, the observation frequency of the operating status data is dynamically adjusted to narrow the observation range. The steps include: Transmitting the operating status data to the data processing module; The data processing module uses a time series analysis algorithm to perform trend analysis on the historical data of each parameter in the operating status data to obtain the change trend curve of each parameter; According to the preset normal threshold range of each parameter, determine whether the real-time data of each parameter in the operating status data exceeds the normal threshold range. If the real-time data of each parameter exceeds the normal threshold range, an alarm is triggered; If it is identified that the real-time data of a certain parameter exceeds the normal threshold range for multiple times in a row, the observation frequency of the current parameter is dynamically adjusted; At the same time, according to the changing trend curve, the observation range of the current parameters is narrowed; The Kalman filter algorithm is used to filter the real-time data of the current parameters to remove noise interference in the operating status data; The adjusted observation frequency, observation range, and real-time data after filtering are transmitted to the data visualization module, and the changes of the current parameters are displayed in real time through the data visualization module.

[0012] Furthermore, the cumulative running time of the sanitation robot cluster is obtained. If the cumulative running time exceeds a preset safety time threshold, the step of adjusting the time weight parameter in the fault prediction model includes: Obtain the real-time operation data of the sanitation robot cluster and transmit the real-time operation data to the ground control center; the real-time operation data includes the operation status and operation time information of each sanitation robot; The sparrow algorithm is used to analyze and calculate the cumulative running time of the sanitation robot cluster, and the total running time and average running time of the cluster are obtained; Determine whether the total operation time of the cluster exceeds the preset safety time threshold. If the total operation time of the cluster exceeds the safety time threshold, the adjustment mechanism of the fault prediction model is triggered; According to the degree of exceeding the safety time threshold, the time weight parameter in the fault prediction model is dynamically adjusted to give the time factor a higher weight; By increasing the duration weight, the fault prediction model is more biased towards the duration factor; Based on the adjusted fault prediction model, the failure risk of the sanitation robot cluster is re-evaluated, and high-risk sanitation robot individuals are identified; For high-risk sanitation robots, return or landing instructions are issued in a timely manner, and backup sanitation robots are dispatched to take over the work tasks of high-risk sanitation robots.

[0013] Furthermore, according to the adjusted duration weight parameter, the fault prediction result is recalculated, and the accumulated operation duration and operation status data are comprehensively considered to determine whether each sanitation robot equipment has a fatigue loss risk. If there is a fatigue loss risk, the step of generating an equipment fault warning signal includes: Obtaining an adjusted duration weight parameter, and inputting the adjusted duration weight parameter into a fault prediction model; According to the fault prediction model, the fault prediction results of the sanitation robot equipment are calculated; Obtain operating time data and status monitoring data of sanitation robot equipment; Input the operating time data and condition monitoring data of the sanitation robot equipment into the fatigue loss risk assessment model; The fatigue loss risk assessment model comprehensively analyzes the operating time data and status monitoring data to determine whether the sanitation robot equipment has fatigue loss risks; If the risk of fatigue loss of the sanitation robot equipment is identified, an equipment failure warning signal is generated; Send the fault warning signal to the sanitation robot equipment management system.

[0014] Furthermore, the steps of obtaining equipment failure warning signals, combining operation status data and accumulated operation time, generating targeted equipment maintenance suggestions, and determining the maintenance priority of each sanitation robot equipment according to the failure risk level include: Perform data cleaning and feature engineering on the running status data and accumulated running time to remove outliers and redundant data and extract key features; Based on the historical operation data of the equipment, the support vector machine algorithm is used to establish a fault warning model, and the fault warning model is trained and optimized; When receiving an equipment failure warning signal, the decision tree algorithm is used to generate a targeted equipment maintenance recommendation plan based on the current equipment status data and operation time data; Use association rule mining algorithm to analyze the relationship between equipment failure type, failure cause and maintenance measures; According to the severity of the equipment fault warning signal and the possibility of fault occurrence, the fault risk level of each sanitation robot equipment is calculated by fuzzy comprehensive evaluation method; Based on the failure risk level of each sanitation robot equipment and the limitation of maintenance resources, an integer programming model is used to determine the maintenance priority information of each sanitation robot equipment; Equipment maintenance recommendations and maintenance priority information are displayed through a visual dashboard.

[0015] Furthermore, the steps of optimizing the execution order of maintenance tasks of each sanitation robot device, and prioritizing the equipment with high fault risk level and high maintenance priority in each sanitation robot device into the maintenance task and executing it in time include: Obtain historical fault data and maintenance records of multiple sanitation robot devices, and calculate the fault risk level and maintenance priority value of each sanitation robot device through big data analysis; According to the fault risk level and maintenance priority value, the sparrow algorithm is used to optimize the execution order of the maintenance tasks of the sanitation robot equipment to obtain the optimal equipment maintenance task execution sequence; Generate an equipment maintenance plan based on the optimized equipment maintenance task execution sequence, and prioritize sanitation robot equipment with high failure risk level and high maintenance priority into the maintenance plan; Acquire the real-time operating status data of the sanitation robot equipment. If it is identified that the key operating parameters of the sanitation robot equipment exceed the preset safety threshold range, the early warning maintenance task of the equipment is triggered, and the maintenance task priority of the current equipment is set to the highest; According to the equipment maintenance plan, maintenance personnel are dispatched in real time to perform equipment maintenance tasks; If a warning maintenance task is identified, personnel will be dispatched to handle the warning maintenance task first; Collect and analyze equipment operation data in real time through the IoT platform, use machine learning algorithms to continuously optimize the fault prediction model, and dynamically update the equipment's fault risk level; According to the latest equipment failure risk level, the sparrow algorithm is re-executed regularly to optimize the execution sequence of equipment maintenance tasks, generate a new equipment maintenance plan, and form a dynamic optimization closed loop of equipment maintenance tasks.

[0016] Furthermore, according to the execution effect of the equipment maintenance task, the steps of dynamically updating the relevant parameters of the fault prediction model, recalculating the equipment fault prediction result, and judging whether the equipment operation status has returned to normal include: Obtaining execution effect data of equipment maintenance tasks; execution effect data includes equipment operating parameters and maintenance operation records; According to the acquired execution effect data, the relevant parameters of the fault prediction model are dynamically adjusted; the relevant parameters of the fault prediction model include model weight and threshold; The adjusted fault prediction model is used to recalculate the failure probability of the sanitation robot equipment and obtain updated equipment failure prediction results; Compare the updated equipment fault prediction results with the preset normal operation threshold to determine whether the equipment operation status has returned to normal; If the equipment operation status returns to normal, it is determined that the equipment maintenance task is executed effectively and the current maintenance strategy is maintained; If the equipment operation status does not return to normal, it is determined that the equipment maintenance task is invalid and the maintenance strategy needs to be further optimized; Based on the judgment result of whether the equipment operating status has returned to normal, the execution frequency and maintenance content of the equipment maintenance tasks are dynamically adjusted to form a closed-loop optimization.

[0017] Furthermore, if it is identified that the equipment operating state has returned to normal, and the operating state data are within the normal operating state threshold range within the set time, the equipment maintenance task is terminated, otherwise the maintenance plan needs to be adjusted and the maintenance measures continue to be optimized until the sanitation robot equipment is completely restored to normal. The steps include: Obtain real-time operating status data of sanitation robot equipment and determine whether the equipment operating status has returned to normal based on the preset safety threshold range; If it is identified that the equipment operation status has returned to normal, the equipment operation data will be continuously monitored, and through big data analysis technology, it will be determined whether the sanitation robot equipment has any signs of failure within the subsequent set time; If it is identified that the sanitation robot equipment has no signs of failure within the subsequent set time, it is determined that the sanitation robot equipment has completely recovered to normal, and an end instruction of the equipment maintenance task is generated to terminate the equipment maintenance task; If it is identified that the equipment's operating status has not returned to normal, or if there are signs of failure within a period of time, the maintenance plan adjustment mechanism will be triggered, and the equipment's maintenance measures will be automatically optimized through machine learning algorithms; In the process of adjusting the maintenance plan, the decision tree algorithm is used to comprehensively consider the historical operation data and current operation status of the equipment as well as the maintenance cost factors to obtain the optimal combination of maintenance measures; The optimized maintenance measures are sent to the sanitation robot equipment. Through the Internet of Things technology, the equipment is remotely controlled to perform corresponding maintenance operations and the operating status changes of the equipment are monitored in real time; According to the changes in the equipment operating status, the above steps are executed repeatedly until the equipment is completely restored to normal and the conditions for ending the maintenance task are met, and a completion signal of the equipment maintenance task is output.

[0018] Another aspect of the present invention relates to a sanitation robot cluster state monitoring and fault warning system, which is applied to the above-mentioned sanitation robot cluster state monitoring and fault warning method. The sanitation robot cluster state monitoring and fault warning system includes: The acquisition module is used to obtain the operating status data of the sanitation robot cluster; the operating status data includes temperature, voltage and current data; A comparison module is used to compare the acquired operation status data with a preset normal operation status threshold. If the operation status data exceeds the normal operation status threshold, the observation frequency of the operation status data is dynamically adjusted to narrow the observation range; An adjustment module is used to obtain the cumulative running time of the sanitation robot cluster. If the cumulative running time exceeds the preset safety time threshold, the time weight parameter in the fault prediction model is adjusted; The first judgment module is used to recalculate the fault prediction result according to the adjusted duration weight parameter, comprehensively consider the accumulated operation time and operation status data, and judge whether each sanitation robot equipment has a fatigue loss risk. If there is a fatigue loss risk, an equipment failure warning signal is generated; The determination module is used to obtain equipment failure warning signals, combine the operating status data and the accumulated operating time, generate targeted equipment maintenance suggestions, and determine the maintenance priority of each sanitation robot equipment according to the failure risk level; The optimization module is used to optimize the execution order of maintenance tasks for each sanitation robot equipment, and give priority to the equipment with high failure risk level and high maintenance priority in the maintenance tasks and execute them in time; The second judgment module is used to dynamically update the relevant parameters of the fault prediction model according to the execution effect of the equipment maintenance task, recalculate the equipment fault prediction result, and judge whether the equipment operation status has returned to normal; The maintenance module is used to end the equipment maintenance task if it is recognized that the equipment operating status has returned to normal and the operating status data are within the normal operating status threshold within the set time. Otherwise, the maintenance plan needs to be adjusted and the maintenance measures continue to be optimized until the sanitation robot equipment is fully restored to normal.

[0019] The beneficial effects achieved by the present invention are: The present invention discloses a sanitation robot cluster state monitoring and fault warning method and system, which obtain the sanitation robot cluster operation state data; compare the obtained operation state data with a preset normal operation state threshold, if the operation state data exceeds the normal operation state threshold, dynamically adjust the observation frequency of the operation state data, and narrow the observation range; obtain the cumulative operation time of the sanitation robot cluster, if the cumulative operation time exceeds the preset safety time threshold, adjust the time weight parameter in the fault prediction model; according to the adjusted time weight parameter, recalculate the fault prediction result, comprehensively consider the cumulative operation time and operation state data, judge whether each sanitation robot equipment has the risk of fatigue loss, if there is a risk of fatigue loss, generate an equipment fault warning signal; obtain the equipment fault warning signal , combined with the operation status data and the accumulated operation time, generate targeted equipment maintenance suggestions, and determine the maintenance priority of each sanitation robot equipment according to the fault risk level; optimize the execution order of the maintenance tasks of each sanitation robot equipment, and give priority to the equipment with high fault risk level and high maintenance priority in each sanitation robot equipment into the maintenance tasks and execute them in time; according to the execution effect of the equipment maintenance task, dynamically update the relevant parameters of the fault prediction model, recalculate the equipment fault prediction results, and judge whether the equipment operation status has returned to normal; if it is identified that the equipment operation status has returned to normal, and the operation status data are within the normal operation status threshold range within the set time, then end the equipment maintenance task, otherwise it is necessary to adjust the maintenance plan and continue to optimize the maintenance measures until the sanitation robot equipment is completely restored to normal. The sanitation robot cluster status monitoring and fault warning method and system disclosed in the present invention realizes the high efficiency and accuracy of intelligent monitoring and fault warning of the sanitation robot cluster status, improves equipment reliability and operation efficiency, and reduces maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 The present invention is a flowchart of an embodiment of a method for monitoring the status and fault warning of a sanitation robot cluster. DETAILED DESCRIPTION

[0021] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0022] like Figure 1 As shown, the first embodiment of the present invention proposes a sanitation robot cluster state monitoring and fault warning method, comprising the following steps: Step S100, obtaining the operating status data of the sanitation robot cluster; the operating status data includes temperature, voltage and current data.

[0023] The temperature, voltage, current and other operating status data of the sanitation robot cluster are obtained. For different types of data parameters, the sparrow algorithm is used to optimize the data collection strategy and determine the optimal observation frequency and range of each parameter.

[0024] Step S200: compare the acquired operation status data with a preset normal operation status threshold. If the operation status data exceeds the normal operation status threshold, dynamically adjust the observation frequency of the operation status data to narrow the observation range.

[0025] Based on the collected temperature, voltage and current data, the change trend of each parameter is judged. If the parameter change exceeds the preset normal threshold range, the observation frequency of the parameter is dynamically adjusted to narrow the observation range and improve the data collection accuracy and real-time performance of the parameter.

[0026] Step S300: Obtain the accumulated running time of the sanitation robot cluster. If the accumulated running time exceeds a preset safety time threshold, adjust the time weight parameter in the fault prediction model.

[0027] The sparrow algorithm is used to analyze the cumulative operating time of the sanitation robot cluster. If the cluster operating time exceeds the preset safety time threshold, the time weight parameter in the fault prediction model is adjusted and a more conservative fault prediction strategy is adopted.

[0028] Step S400: recalculate the fault prediction result according to the adjusted duration weight parameter, comprehensively consider the accumulated operation time and operation status data, and judge whether each sanitation robot equipment has the risk of fatigue loss. If there is a risk of fatigue loss, generate an equipment fault warning signal.

[0029] According to the adjusted duration weight parameter, the fault prediction result is recalculated, and the operating duration and status data are comprehensively considered to determine whether the sanitation robot equipment has the risk of fatigue wear. If there is a risk, an equipment failure warning signal is generated.

[0030] Step S500: Obtain equipment failure warning signals, combine operating status data and accumulated operating time, generate targeted equipment maintenance recommendations, and determine the maintenance priority of each sanitation robot equipment according to the failure risk level.

[0031] Obtain equipment failure warning signals, combine status data such as temperature, voltage, current and operating time data to generate targeted equipment maintenance recommendations, and determine the maintenance priority of each device based on the failure risk level.

[0032] Step S600: optimize the execution order of maintenance tasks for each sanitation robot device, and prioritize the devices with high fault risk level and high maintenance priority in the sanitation robot devices into the maintenance tasks and execute them in a timely manner.

[0033] The sparrow algorithm is used to optimize the execution order of maintenance tasks for multiple devices, and devices with high failure risk levels and high maintenance priorities are given priority in maintenance tasks and executed in a timely manner.

[0034] Step S700: dynamically update relevant parameters of the fault prediction model according to the execution effect of the equipment maintenance task, recalculate the equipment fault prediction result, and determine whether the equipment operation status has returned to normal.

[0035] According to the execution effect of the equipment maintenance task, the relevant parameters of the fault prediction model are dynamically updated, the equipment fault prediction results are recalculated, and it is determined whether the equipment operation status has returned to normal.

[0036] Step S800: If it is identified that the equipment operating status has returned to normal and the operating status data are within the normal operating status threshold range within the set time, the equipment maintenance task is terminated. Otherwise, the maintenance plan needs to be adjusted and the maintenance measures continue to be optimized until the sanitation robot equipment is fully restored to normal.

[0037] If the equipment's operating status returns to normal, key parameters such as temperature, voltage, and current are within the safety threshold range, and there are no signs of repeated failures in the subsequent period of time, the maintenance task of the equipment can be completed. Otherwise, the maintenance plan needs to be adjusted and maintenance measures need to be optimized until the equipment returns to normal.

[0038] Furthermore, the sanitation robot cluster status monitoring and fault warning method provided in this embodiment, step S100 includes: Step S110: According to the operating status of the sanitation robot cluster, real-time data of the operating status data is obtained and a data acquisition model is constructed.

[0039] According to the operating status of the sanitation robot cluster, real-time data of parameters such as temperature, voltage and current are obtained, and a data acquisition model is constructed.

[0040] Step S120: Use the sparrow algorithm to optimize the data collection strategy, and determine the optimal observation frequency and observation range of each parameter in the operating status data through iterative search.

[0041] The sparrow algorithm is used to optimize the data collection strategy, and the optimal observation frequency and range of each parameter are determined through iterative search.

[0042] Step S130: During the optimization process, the data acquisition model is dynamically adjusted according to changes in the observation frequency and the observation range.

[0043] During the optimization process, the data collection model is dynamically adjusted according to changes in observation frequency and observation range to improve the adaptability of data collection.

[0044] Step S140: pre-process the running status data to remove abnormal values ​​and noise data in the running status data.

[0045] The collected parameter data such as temperature, voltage and current are preprocessed to remove abnormal values ​​and noise data and improve data quality.

[0046] Step S150: Analyze the preprocessed operating status data using a clustering algorithm to identify data features and patterns under different operating states.

[0047] The preprocessed data is analyzed using clustering algorithms to identify data features and patterns under different operating conditions.

[0048] Step S160: According to the clustering results, a mapping relationship between the operating status and the data features is established to achieve real-time monitoring and early warning of the operating status of the sanitation robot cluster.

[0049] According to the clustering results, a mapping relationship between operating status and data features is established to achieve real-time monitoring and early warning of the operating status of the sanitation robot cluster.

[0050] Step S170: Apply the optimized data collection strategy to the sanitation robot cluster, continuously monitor the data collection efficiency and quality, dynamically adjust the optimization algorithm according to the feedback results, and realize adaptive optimization of the data collection strategy.

[0051] The optimized data collection strategy is applied to the sanitation robot cluster, and the data collection efficiency and quality are continuously monitored. The optimization algorithm is dynamically adjusted according to the feedback results to achieve adaptive optimization of the data collection strategy.

[0052] Furthermore, the sanitation robot cluster status monitoring and fault warning method provided in this embodiment, step S200 includes: Step S210: Transmit the operating status data to the data processing module.

[0053] Acquire real-time data of multiple parameters such as temperature, voltage and current, and transmit the data to the data processing module.

[0054] Step S220: The data processing module uses a time series analysis algorithm to perform trend analysis on the historical data of each parameter in the operating status data to obtain a change trend curve of each parameter.

[0055] In the data processing module, a time series analysis algorithm is used to perform trend analysis on the historical data of each parameter to obtain the change trend curve of each parameter.

[0056] Step S230: According to the preset normal threshold range of each parameter, determine whether the real-time data of each parameter in the operating status data exceeds the normal threshold range. If the real-time data of each parameter exceeds the normal threshold range, an alarm is triggered.

[0057] According to the preset normal threshold range of each parameter, determine whether the current real-time data of each parameter exceeds the normal threshold range. If exceeded, an alarm is triggered.

[0058] Step S240: If it is identified that the real-time data of a current parameter exceeds the normal threshold range for multiple consecutive times, the observation frequency of the current parameter is dynamically adjusted.

[0059] When the real-time data of a certain parameter exceeds the normal threshold range for multiple consecutive times, the observation frequency of the parameter is dynamically adjusted, for example, the original data collection once a minute is adjusted to data collection once every 10 seconds.

[0060] Step S250: narrowing the observation range of the current parameter according to the change trend curve.

[0061] At the same time, according to the changing trend curve of the parameter, the observation range of the parameter is narrowed, for example, the original observation range of 0-100 degrees is adjusted to 80-100 degrees, so as to improve the data collection accuracy of the parameter.

[0062] Step S260: Use the Kalman filter algorithm to filter the real-time data of the current parameters to remove noise interference in the operating status data.

[0063] The Kalman filter algorithm is used to filter the real-time data of the parameter to remove noise interference in the data and improve the real-time performance and reliability of the data.

[0064] Step S270: The adjusted observation frequency, observation range, and filtered real-time data are transmitted to the data visualization module, and the changes of the current parameters are displayed in real time via the data visualization module.

[0065] The adjusted observation frequency and observation range as well as the filtered real-time data are transmitted to the data visualization module, and the changes in the parameters are displayed in real time in the form of curve graphs and bar charts, providing an intuitive monitoring view for operation and maintenance personnel.

[0066] Furthermore, the sanitation robot cluster status monitoring and fault warning method provided in this embodiment includes step S300: Step S310: Acquire the real-time operation data of the sanitation robot cluster, and transmit the real-time operation data to the ground control center; the real-time operation data includes the operation status and operation time information of each sanitation robot.

[0067] Obtain real-time operating data of the sanitation robot cluster, including the operating status and operating time of each sanitation robot, and transmit the data to the ground control center.

[0068] Step S320: Analyze and calculate the cumulative running time of the sanitation robot cluster by using the sparrow algorithm to obtain the total running time and average running time of the cluster.

[0069] The sparrow algorithm is used to analyze and calculate the cumulative running time of the sanitation robot cluster, and the total running time and average running time of the cluster are obtained.

[0070] Step S330: determine whether the total operation time of the cluster exceeds the preset safety time threshold. If the total operation time of the cluster exceeds the safety time threshold, the adjustment mechanism of the fault prediction model is triggered.

[0071] Determine whether the total running time of the cluster exceeds the preset safety time threshold. If it exceeds the threshold, the adjustment mechanism of the fault prediction model is triggered.

[0072] Step S340: dynamically adjust the duration weight parameter in the fault prediction model according to the degree of exceeding the safety duration threshold, and assign a higher weight to the duration factor.

[0073] According to the degree of exceeding the safety time threshold, the time weight parameter in the fault prediction model is dynamically adjusted to give the time factor a higher weight.

[0074] Step S350: By increasing the duration weight, the fault prediction model is more biased towards the duration factor.

[0075] By increasing the duration weight, the fault prediction model is more biased towards the duration factor, thus adopting a more conservative fault prediction strategy.

[0076] Step S360: Based on the adjusted fault prediction model, the fault risk of the sanitation robot cluster is re-evaluated to identify high-risk sanitation robot individuals.

[0077] Based on the adjusted fault prediction model, the failure risk of the sanitation robot cluster is re-evaluated and high-risk sanitation robot individuals are identified.

[0078] Step S370: For high-risk sanitation robots, a return command or a landing command is issued in a timely manner, and a backup sanitation robot is dispatched to take over the work tasks of the high-risk sanitation robot.

[0079] For high-risk sanitation robots, return or landing instructions are issued in a timely manner, and backup sanitation robots are dispatched to take over their tasks to ensure the continuity and safety of cluster missions.

[0080] Furthermore, the sanitation robot cluster status monitoring and fault warning method provided in this embodiment, step S400 includes: Step S410: Obtain the adjusted duration weight parameter, and input the adjusted duration weight parameter into the fault prediction model.

[0081] The adjusted duration weight parameter is obtained and input into the fault prediction model.

[0082] Step S420: Calculate the fault prediction result of the sanitation robot equipment according to the fault prediction model.

[0083] According to the fault prediction model, the fault prediction results of the sanitation robot equipment are calculated.

[0084] Step S430: Obtain the operating time data and status monitoring data of the sanitation robot equipment.

[0085] Obtain operating time data and status monitoring data of sanitation robot equipment.

[0086] Step S440: input the operating time data and status monitoring data of the sanitation robot equipment into the fatigue loss risk assessment model.

[0087] The operating time data and condition monitoring data of the sanitation robot equipment are input into the fatigue loss risk assessment model.

[0088] Step S450: The fatigue wear risk assessment model comprehensively analyzes the operating time data and the status monitoring data to determine whether the sanitation robot equipment has a fatigue wear risk.

[0089] Through the fatigue loss risk assessment model, the operating time and status data are comprehensively analyzed to determine whether the equipment has the risk of fatigue loss.

[0090] Step S460: If it is identified that the sanitation robot equipment has a risk of fatigue wear, an equipment failure warning signal is generated.

[0091] If there is a risk of fatigue wear and tear on the sanitation robot equipment, the generation of an equipment failure warning signal will be triggered.

[0092] Step S470: Send the fault warning signal to the sanitation robot equipment management system.

[0093] The equipment failure warning signal is sent to the sanitation robot equipment management system, prompting the staff to inspect and maintain the equipment in a timely manner.

[0094] Furthermore, the sanitation robot cluster status monitoring and fault warning method provided in this embodiment includes step S500: Step S510: perform data cleaning and feature engineering on the running status data and the accumulated running time, remove outliers and redundant data, and extract key features.

[0095] Obtain multi-dimensional status data and operating time data such as temperature, voltage, current, etc. of the equipment, remove outliers and redundant data through data cleaning and feature engineering, and extract key features.

[0096] Step S520: Based on the historical operation data of the equipment, a fault warning model is established using a support vector machine algorithm, and the fault warning model is trained and optimized.

[0097] Based on the historical operation data of the equipment, the support vector machine algorithm is used to establish the equipment fault warning model. Through model training and optimization, the accuracy and recall rate of fault warning are improved.

[0098] Step S530: When an equipment failure warning signal is received, a targeted equipment maintenance suggestion plan is generated through a decision tree algorithm in combination with the current status data and operation time data of the equipment.

[0099] When an equipment failure warning signal is received, a decision tree algorithm is used to generate targeted equipment maintenance recommendations based on the equipment's current status data and operating time data.

[0100] Step S540: Use an association rule mining algorithm to analyze the association between equipment failure types, failure causes, and maintenance measures.

[0101] The association rule mining algorithm is used to analyze the relationship between equipment failure types, failure causes and maintenance measures, and optimize the operability and effectiveness of equipment maintenance proposals.

[0102] Step S550: Calculate the fault risk level of each sanitation robot device by a fuzzy comprehensive evaluation method according to the severity of the equipment fault warning signal and the possibility of the fault occurring.

[0103] According to the severity of equipment failure warning signals and the possibility of failure occurrence, the failure risk level of each equipment is calculated by fuzzy comprehensive evaluation method.

[0104] Step S560: Based on the failure risk level of each sanitation robot device and the limitation of maintenance resources, an integer programming model is used to determine the maintenance priority information of each sanitation robot device.

[0105] Based on the equipment failure risk level and maintenance resource limitations, an integer programming model is used to determine the maintenance priority of each device and develop the optimal equipment maintenance plan.

[0106] Step S570: Display equipment maintenance recommendations and maintenance priority information through a visual dashboard.

[0107] Personalized equipment maintenance recommendations and maintenance priority information are displayed to maintenance personnel through a visual dashboard to assist them in making equipment maintenance decisions and improve maintenance efficiency and accuracy.

[0108] Furthermore, the sanitation robot cluster status monitoring and fault warning method provided in this embodiment includes step S600: Step S610: Obtain historical fault data and maintenance records of multiple sanitation robot devices, and calculate the fault risk level and maintenance priority value of each sanitation robot device through big data analysis.

[0109] Obtain historical fault data and maintenance records of multiple devices, and calculate the fault risk level and maintenance priority value of each device through big data analysis.

[0110] Step S620: According to the fault risk level and the maintenance priority value, the sparrow algorithm is used to optimize the execution order of the maintenance tasks of the sanitation robot equipment to obtain the optimal equipment maintenance task execution sequence.

[0111] According to the equipment's fault risk level and maintenance priority value, the sparrow algorithm is used to optimize the execution order of the equipment's maintenance tasks to obtain the optimal equipment maintenance task execution sequence.

[0112] Step S630: Generate an equipment maintenance plan based on the optimized equipment maintenance task execution order, and give priority to incorporating sanitation robot equipment with a high fault risk level and a high maintenance priority into the maintenance plan.

[0113] Generate an equipment maintenance plan based on the optimized equipment maintenance task execution sequence, and prioritize equipment with high failure risk levels and high maintenance priorities into the maintenance plan.

[0114] Step S640: Acquire real-time operating status data of the sanitation robot equipment. If it is identified that the key operating parameters of the sanitation robot equipment exceed the preset safety threshold range, trigger the equipment's early warning maintenance task and set the maintenance task priority of the current equipment to the highest.

[0115] Obtain the real-time operating status data of the equipment. If the key operating parameters of the equipment exceed the preset safety threshold range, the early warning maintenance task of the equipment is triggered and the maintenance task priority of the equipment is set to the highest.

[0116] Step S650: According to the equipment maintenance plan, maintenance personnel are scheduled in real time to perform equipment maintenance tasks.

[0117] According to the equipment maintenance plan, maintenance personnel are dispatched in real time to perform equipment maintenance tasks.

[0118] Step S660: If a warning maintenance task is identified, personnel are preferentially dispatched to handle the warning maintenance task.

[0119] If a warning maintenance task occurs, personnel will be dispatched first to handle the warning maintenance task.

[0120] Step S670: collect and analyze equipment operation data in real time through the Internet of Things platform, use machine learning algorithms to continuously optimize the fault prediction model, and dynamically update the fault risk level of the equipment.

[0121] Through the Internet of Things platform, equipment operation data is collected and analyzed in real time, and machine learning algorithms are used to continuously optimize the equipment failure prediction model and dynamically update the equipment's failure risk level.

[0122] Step S680: According to the latest equipment failure risk level, the sparrow algorithm is periodically re-executed to optimize the execution sequence of equipment maintenance tasks, generate a new equipment maintenance plan, and form a dynamic optimization closed loop of equipment maintenance tasks.

[0123] According to the latest equipment failure risk level, the sparrow algorithm is re-executed regularly to optimize the execution sequence of equipment maintenance tasks, generate a new equipment maintenance plan, and form a dynamic optimization closed loop of equipment maintenance tasks.

[0124] Furthermore, the sanitation robot cluster status monitoring and fault warning method provided in this embodiment includes step S700: Step S710: Acquire execution effect data of the equipment maintenance task; the execution effect data includes equipment operating parameters and maintenance operation records.

[0125] Obtain the execution effect data of equipment maintenance tasks, including equipment operating parameters and maintenance operation records.

[0126] Step S720: dynamically adjust relevant parameters of the fault prediction model according to the acquired execution effect data; the relevant parameters of the fault prediction model include model weights and thresholds.

[0127] According to the obtained execution effect data, the relevant parameters of the fault prediction model are dynamically adjusted, including model weights and thresholds.

[0128] Step S730: Use the adjusted fault prediction model to recalculate the failure probability of the sanitation robot equipment to obtain an updated equipment failure prediction result.

[0129] The adjusted fault prediction model is used to recalculate the failure probability of the equipment and obtain updated equipment failure prediction results.

[0130] Step S740: Compare the updated equipment fault prediction result with the preset normal operation threshold to determine whether the equipment operation status has returned to normal.

[0131] Compare the updated equipment fault prediction results with the preset normal operation threshold to determine whether the equipment operation status has returned to normal.

[0132] Step S750: If the equipment operation status returns to normal, it is determined that the equipment maintenance task is executed effectively and the current maintenance strategy is maintained.

[0133] If the equipment operating status returns to normal, it is determined that the equipment maintenance task is executed effectively and the current maintenance strategy is maintained.

[0134] Step S760: If the equipment operation status has not returned to normal, it is determined that the equipment maintenance task is invalid and the maintenance strategy needs to be further optimized.

[0135] If the equipment operating status does not return to normal, it is determined that the equipment maintenance task is invalid and the maintenance strategy needs to be further optimized.

[0136] Step S770: According to the judgment result of whether the equipment operation status has returned to normal, the execution frequency and maintenance content of the equipment maintenance task are dynamically adjusted to form a closed-loop optimization.

[0137] Based on the judgment result of whether the equipment operating status has returned to normal, the execution frequency and maintenance content of the equipment maintenance tasks are dynamically adjusted to form a closed-loop optimization.

[0138] Furthermore, the sanitation robot cluster status monitoring and fault warning method provided in this embodiment, step S800 includes: Step S810: Acquire the real-time operating status data of the sanitation robot equipment, and determine whether the equipment operating status has returned to normal based on a preset safety threshold range.

[0139] Obtain the real-time operating status data of the equipment, including key parameters such as temperature, voltage and current, and determine whether the equipment operating status has returned to normal based on the preset safety threshold range.

[0140] Step S820: If it is identified that the equipment operating status has returned to normal, the equipment operating data is continuously monitored, and through big data analysis technology, it is determined whether the sanitation robot equipment has any signs of failure within the subsequent set time.

[0141] If the equipment's operating status returns to normal, the equipment's operating data will be continuously monitored, and big data analysis technology will be used to determine whether the equipment shows signs of failure in the subsequent period of time.

[0142] Step S830: If it is identified that the sanitation robot equipment has no signs of failure within the subsequent set time, it is determined that the sanitation robot equipment has fully recovered to normal, and an end instruction for the equipment maintenance task is generated to terminate the equipment maintenance task.

[0143] If the equipment shows no signs of failure within a subsequent period of time, it can be determined that the equipment has fully recovered to normal, and an end instruction for the equipment maintenance task is generated to terminate the equipment maintenance task.

[0144] Step S840: If it is identified that the equipment operation status has not returned to normal, or if there are signs of failure within a subsequent period of time, the adjustment mechanism of the maintenance plan is triggered, and the maintenance measures of the equipment are automatically optimized through the machine learning algorithm.

[0145] If the equipment's operating status does not return to normal, or if signs of failure appear within a period of time, the maintenance plan adjustment mechanism will be triggered, and the equipment's maintenance measures will be automatically optimized through machine learning algorithms.

[0146] Step S850: During the maintenance plan adjustment process, a decision tree algorithm is used to comprehensively consider the historical operation data and current operation status of the equipment and maintenance cost factors to obtain the optimal combination of maintenance measures.

[0147] In the process of adjusting the maintenance plan, the decision tree algorithm is used to comprehensively consider factors such as the historical operating data and current operating status of the equipment and maintenance costs to obtain the optimal combination of maintenance measures.

[0148] Step S860: Send the optimized maintenance measures to the sanitation robot equipment, remotely control the equipment to perform corresponding maintenance operations through the Internet of Things technology, and monitor the changes in the operating status of the equipment in real time.

[0149] The optimized maintenance measures are sent to the equipment. Through the Internet of Things technology, the equipment is remotely controlled to perform corresponding maintenance operations and the changes in the equipment's operating status are monitored in real time.

[0150] Step S870: Based on the change of the equipment operation status, the above steps are executed repeatedly until the equipment is completely restored to normal and the conditions for ending the maintenance task are met, and a completion signal of the equipment maintenance task is output.

[0151] According to the changes in the equipment operating status, the above steps are executed repeatedly until the equipment is completely restored to normal and the conditions for ending the maintenance task are met, and a completion signal of the equipment maintenance task is output.

[0152] The present invention relates to a sanitation robot cluster state monitoring and fault warning system, which is applied to the above-mentioned sanitation robot cluster state monitoring and fault warning method. The sanitation robot cluster state monitoring and fault warning system includes an acquisition module, a comparison module, an adjustment module, a first judgment module, a determination module, an optimization module, a second judgment module and a maintenance module, wherein the acquisition module is used to acquire the running state data of the sanitation robot cluster; the running state data includes temperature, voltage and current data; the comparison module is used to compare the acquired running state data with a preset normal running state threshold value, and if the running state data exceeds the normal running state threshold value, the observation frequency of the running state data is dynamically adjusted to narrow the observation range; the adjustment module is used to acquire the cumulative running time of the sanitation robot cluster, and if the cumulative running time exceeds the preset safety time threshold value, the time weight parameter in the fault prediction model is adjusted; the first judgment module is used to recalculate the fault prediction result according to the adjusted time weight parameter, comprehensively considering the cumulative running time and the running state data, Determine whether each sanitation robot equipment has a fatigue loss risk. If so, generate an equipment failure warning signal; determine the module, which is used to obtain the equipment failure warning signal, combine the operating status data and the accumulated operating time, generate a targeted equipment maintenance recommendation plan, and determine the maintenance priority of each sanitation robot equipment according to the fault risk level; optimize the execution order of the maintenance tasks of each sanitation robot equipment, and give priority to the equipment with high fault risk level and high maintenance priority in each sanitation robot equipment into the maintenance task and execute it in time; the second judgment module is used to dynamically update the relevant parameters of the fault prediction model according to the execution effect of the equipment maintenance task, recalculate the equipment fault prediction result, and determine whether the equipment operation status has returned to normal; the maintenance module is used to end the equipment maintenance task if it is identified that the equipment operation status has returned to normal and the operation status data are within the normal operating status threshold range within the set time, otherwise it is necessary to adjust the maintenance plan and continue to optimize the maintenance measures until the sanitation robot equipment is fully restored to normal.

[0153] The following is a specific example of a method for monitoring the state of a sanitation robot cluster and providing a fault warning. The method comprises the following steps: Step S101: Acquire the operating status data of the sanitation robot cluster, such as temperature, voltage, current, etc., and use the sparrow algorithm to optimize the data collection strategy for different types of data parameters to determine the optimal observation frequency and observation range of each parameter.

[0154] According to the operating status of the sanitation robot cluster, real-time data of parameters such as temperature, voltage, and current are obtained to build a data acquisition model. The sparrow algorithm is used to optimize the data acquisition strategy, and the optimal observation frequency and observation range of each parameter are determined through iterative search. During the optimization process, the data acquisition model is dynamically adjusted according to the changes in the observation frequency and observation range to improve the adaptability of data acquisition. The collected parameter data such as temperature, voltage, and current are preprocessed to remove outliers and noise data to improve data quality. The preprocessed data is analyzed using a clustering algorithm to identify data features and patterns under different operating states. According to the clustering results, a mapping relationship between the operating status and data features is established to achieve real-time monitoring and early warning of the operating status of the sanitation robot cluster. The optimized data acquisition strategy is applied to the sanitation robot cluster, and the data acquisition efficiency and quality are continuously monitored. The optimization algorithm is dynamically adjusted according to the feedback results to achieve adaptive optimization of the data acquisition strategy.

[0155] Specifically, during the operation of the sanitation robot cluster, the sensor collects parameter data such as temperature, voltage, and current in real time, and samples every 1 second to build a data acquisition model. The sparrow algorithm is used to optimize the data acquisition strategy. After 50 iterative searches, the temperature observation frequency is determined to be 1Hz, the voltage observation frequency is 5Hz, and the current observation frequency is 10Hz. The observation ranges are -20℃~100℃, 0V~30V, and 0A~50A, respectively. During the optimization process, the data acquisition model is dynamically adjusted according to the changes in the observation frequency and observation range to improve the adaptability of data acquisition. The collected data is preprocessed, and the 3σ criterion is used to eliminate outliers. The Kalman filter algorithm is used to remove noise and improve data quality. The preprocessed data is analyzed using the K-means clustering algorithm. The optimal number of clusters is determined to be 3 by calculating the silhouette coefficient, and the data features and patterns under different operating states such as normal operation, abnormal operation, and failure are identified. According to the clustering results, the mapping relationship between the operating state and the data features is established to achieve real-time monitoring and early warning of the operating state of the sanitation robot cluster. The optimized data collection strategy is applied to the sanitation robot cluster, and the data collection efficiency and quality are continuously monitored. When the data collection efficiency is lower than 90% or the data quality is lower than 95%, the optimization algorithm is triggered to readjust the data collection strategy to achieve adaptive optimization.

[0156] Step S102: determine the change trend of each parameter based on the collected temperature, voltage and current data. If the parameter change exceeds the preset normal threshold range, dynamically adjust the observation frequency of the parameter, narrow the observation range, and improve the data collection accuracy and real-time performance of the parameter.

[0157] Acquire real-time data of multiple parameters such as temperature, voltage, and current, and transmit the data to the data processing module. In the data processing module, the time series analysis algorithm is used to perform trend analysis on the historical data of each parameter to obtain the change trend curve of each parameter. According to the preset normal threshold range of each parameter, determine whether the current real-time data of each parameter exceeds the normal threshold range. If it exceeds, an alarm is triggered. When the real-time data of a parameter exceeds the normal threshold range for many consecutive times, dynamically adjust the observation frequency of the parameter, such as adjusting the original data collection once per minute to collecting data once every 10 seconds. At the same time, according to the change trend curve of the parameter, narrow the observation range of the parameter, such as adjusting the original observation range of 0-100 degrees to 80-100 degrees, and improve the data collection accuracy of the parameter. The Kalman filter algorithm is used to filter the real-time data of the parameter to remove noise interference in the data and improve the real-time and reliability of the data. The adjusted observation frequency, observation range, and filtered real-time data are transmitted to the data visualization module, and the changes of the parameter are displayed in real time in the form of curve charts, bar charts, etc., providing an intuitive monitoring view for operation and maintenance personnel.

[0158] Specifically, for predicting future parameter values: (1) In formula (1), for Step ahead prediction value, is the time series mean, is the autoregression coefficient, is the autoregressive order, Indicates the distance from the current prediction time point in the time series Looking back to the past The actual value of each time step. This formula is used to predict future parameter values. The system collects data of multiple parameters such as temperature, voltage, and current in real time through sensors, collects data once every minute, and transmits the data to the data processing module. The data processing module uses the ARIMA time series analysis algorithm to model and analyze the historical data of each parameter to obtain the trend curve of each parameter. For example, the normal threshold range of the temperature parameter is set to 0-100 degrees Celsius, and the real-time data of the current temperature parameter is 120 degrees Celsius. If it exceeds the normal threshold range, the system automatically triggers an alarm prompt. Since the temperature parameter exceeds the normal threshold range for three consecutive times, the system dynamically adjusts the observation frequency of the temperature parameter to collect data once every 10 seconds. At the same time, according to the trend curve of the temperature parameter, the observation range is adjusted from 0-100 degrees Celsius to 80-120 degrees Celsius to improve the accuracy of data collection. The system uses the Kalman filter algorithm to filter the real-time data of the temperature parameter, effectively remove the Gaussian white noise interference in the data, and improve the signal-to-noise ratio of the data. The adjusted observation frequency, observation range and filtered real-time temperature data are transmitted to the data visualization module. The changing trend of the temperature parameters is displayed in real time through a dynamic curve chart, and the number and duration of temperature parameters exceeding the normal threshold range per hour are counted in the form of a bar chart, providing an intuitive monitoring view for operation and maintenance personnel, facilitating equipment abnormality diagnosis and predictive maintenance.

[0159] (2) In formula (2), is the alarm probability, is the alarm rate parameter, is the duration of exceeding the threshold. This formula describes the probability model of alarm triggering.

[0160] (3) In formula (3), For alarm function, when the parameter Out of Threshold Range[ , ] triggers an alarm. : represents time series The minimum threshold value allowed. : represents time series The maximum threshold allowed by the value. This formula is used to determine whether the parameter exceeds the normal range.

[0161] (4) In formula (4), is an autoregressive polynomial, is the difference operator, is the moving average polynomial, is the time series observation value, is a white noise sequence. It is a parameter that controls the speed of trend change. is a time point in the time series. This formula describes the basic form of the ARIMA model.

[0162] Step S103: Analyze the cumulative running time of the sanitation robot cluster by using the sparrow algorithm. If the cluster running time exceeds the preset safety time threshold, adjust the time weight parameter in the fault prediction model and adopt a more conservative fault prediction strategy.

[0163] The duration weight parameter is a control variable used in the fault prediction model to indicate the degree of influence of the cumulative running time on fault prediction. It is a key parameter used to measure the degree of influence of the cumulative running time of the sanitation robot on the fault prediction results. Dynamic adjustment of this parameter can balance the prediction conservatism and sensitivity of the model, thereby adapting to the risk characteristics of different operation stages. When the cumulative running time of the cluster exceeds the safety threshold, it indicates that the system as a whole is in a higher potential risk state, and the weight of the running time factor needs to be adjusted. The adjusted weight will make the model pay more attention to the running time and tend to predict possible faults early. The duration weight parameter affects the output of the fault prediction model, making robots with longer running time account for a higher proportion in the prediction. After the weight is adjusted, the model adopts a more conservative strategy (a more conservative fault prediction strategy refers to a more cautious approach in the fault prediction model, which tends to warn potential fault risks in advance to minimize the possibility of faults. The core of this strategy is to reduce the risk of missed reports, that is, it is better to report more "possible faults" than to ignore any potential risks. For example, when the prediction model finds that certain conditions are close to the fault threshold, even if the standard is not fully met, a fault warning will be triggered in advance. Or adjust the model parameters (such as the duration weight parameter) to make it more sensitive to key factors that affect the fault (such as cumulative running time, temperature, voltage, etc.). A more conservative strategy is to minimize the impact of robot failures in critical mission scenarios, enhance the reliability and safety of the system, and improve the overall risk prevention and control capabilities in the critical operation stage.). When the running time is short, the weight may be low, and the model is less sensitive to the running time; when the running time is close to or exceeds the safety time threshold, the weight parameter increases, and the model is more inclined to believe that robots that run for a long time are more likely to fail.

[0164] The real-time operation data of the sanitation robot cluster is obtained, including the operation status and operation time of each sanitation robot, and the data is transmitted to the ground control center. The cumulative operation time of the sanitation robot cluster is analyzed and calculated by the sparrow algorithm to obtain the total operation time and average operation time of the cluster. It is judged whether the total operation time of the cluster exceeds the preset safety time threshold. If it exceeds the threshold, the adjustment mechanism of the fault prediction model is triggered. According to the degree of exceeding the safety time threshold, the time weight parameter in the fault prediction model is dynamically adjusted to give the time factor a higher weight. By increasing the time weight, the fault prediction model is more biased towards the time factor, so that a more conservative fault prediction strategy is adopted. Based on the adjusted fault prediction model, the fault risk of the sanitation robot cluster is re-evaluated to identify high-risk sanitation robot individuals. For high-risk sanitation robots, return or landing instructions are issued in time, and spare sanitation robots are dispatched to take over their tasks to ensure the continuity and safety of cluster tasks.

[0165] Specifically, when the sanitation robot cluster is performing tasks, it collects the operating status data of each sanitation robot in real time through the onboard sensors, including flight speed, altitude, posture, battery power, etc., as well as the accumulated operating time, and transmits the data to the ground control center through the wireless communication module. After receiving the data, the control center uses the sparrow algorithm to analyze and calculate the accumulated operating time of the sanitation robot cluster. Assuming that there are 10 sanitation robots in the cluster, through the iterative optimization of the sparrow algorithm, the total operating time of the cluster is 120 hours, and the average operating time is 12 hours. The system presets a safe time threshold of 100 hours. By comparison, it is found that the total operating time of the cluster has exceeded the threshold, triggering the adjustment mechanism of the fault prediction model. According to the degree of exceeding the safe time threshold, the time weight parameter in the fault prediction model is dynamically adjusted. The original time weight is 3, and now according to the proportion of exceeding the threshold of 20%, the time weight is increased to 5, giving the time factor a higher weight. The adjusted fault prediction model comprehensively considers the various state parameters of the sanitation robots, and through Bayesian network reasoning, it conducts fault risk assessment on each sanitation robot in the cluster, identifying two high-risk sanitation robots with failure probabilities of 8 and 75, respectively, exceeding the warning threshold of 7. For these two high-risk sanitation robots, the control center promptly issued return instructions to allow them to return to the base safely, and at the same time dispatched two new sanitation robots from the spare sanitation robots to take over their tasks to ensure the uninterrupted execution of the cluster tasks. Through real-time monitoring, intelligent analysis and dynamic adjustment, the safety and reliability of the sanitation robot cluster are effectively improved, and the risk of mission failure is reduced.

[0166] Step S104: recalculate the fault prediction result according to the adjusted duration weight parameter, comprehensively consider the operation duration and status data, and determine whether the sanitation robot equipment has a fatigue wear risk. If there is a risk, generate an equipment fault warning signal.

[0167] Obtain the adjusted duration weight parameter and input the parameter into the fault prediction model. According to the fault prediction model, calculate the fault prediction result of the sanitation robot equipment. Obtain the operating duration data and status monitoring data of the sanitation robot equipment. Input the operating duration data and status monitoring data of the sanitation robot equipment into the fatigue loss risk assessment model. Through the fatigue loss risk assessment model, comprehensively analyze the operating duration and status data to determine whether the equipment has a fatigue loss risk. If the sanitation robot equipment has a fatigue loss risk, the generation of the equipment fault warning signal is triggered. Send the equipment failure warning signal to the sanitation robot equipment management system to prompt the staff to inspect and maintain the equipment in a timely manner.

[0168] Specifically, first, the parameters of the fault prediction model are adjusted and optimized through algorithms such as the least squares method to obtain the optimal model parameters. For example, by collecting historical fault data of 100 sanitation robot devices and using the gradient descent algorithm, the mean square error of the fault prediction model is reduced from 15 to 08, which improves the prediction accuracy of the model. Then, the optimized model parameters are input into the fault prediction model. The model calculates the probability of failure of the device in the future based on the real-time status data of the device. At the same time, the system will also obtain the operating time data and status monitoring data of the sanitation robot equipment, such as the equipment has been running for 1000 hours, and the vibration data shows that the vibration amplitude of the equipment is large. These data are input into the fatigue loss risk assessment model, which uses the fuzzy comprehensive evaluation method to comprehensively analyze the operating time and status data of the equipment to determine whether the equipment has a fatigue loss risk. When the fatigue loss index of the equipment exceeds 7, the generation of the equipment failure warning signal is triggered. The early warning signal will be automatically sent to the sanitation robot equipment management system. The system will send a warning message to the relevant staff in time according to the early warning level, such as when the probability of failure is greater than 80% and the fatigue loss index is greater than 8, to prompt them to inspect and maintain the equipment to avoid sudden failures of the equipment and ensure the safe operation of the sanitation robot.

[0169] Step S105: Obtain equipment failure warning signals, combine status data such as temperature, voltage, current, and operation time data, generate targeted equipment maintenance suggestions, and determine the maintenance priority of each device according to the failure risk level.

[0170] Acquire the multi-dimensional status data and operation time data of the equipment, such as temperature, voltage, and current. Through data cleaning and feature engineering, remove outliers and redundant data, and extract key features. Based on the historical operation data of the equipment, the support vector machine algorithm is used to establish the equipment fault warning model. Through model training and optimization, the accuracy and recall rate of fault warning are improved. When the equipment fault warning signal is received, the decision tree algorithm is used to generate targeted equipment maintenance suggestions in combination with the current status data and operation time data of the equipment. The association rule mining algorithm is used to analyze the relationship between equipment fault type, fault cause, and maintenance measures, and optimize the operability and effectiveness of the equipment maintenance suggestion plan. According to the severity of the equipment fault warning signal and the possibility of fault occurrence, the fault risk level of each equipment is calculated by the fuzzy comprehensive evaluation method. Based on the equipment fault risk level and the limitation of maintenance resources, the integer programming model is used to determine the maintenance priority of each equipment and formulate the optimal equipment maintenance plan. The personalized equipment maintenance suggestion plan and maintenance priority information are displayed to the maintenance personnel through a visual dashboard to assist them in making equipment maintenance decisions and improve maintenance efficiency and accuracy.

[0171] Specifically, firstly, the multi-dimensional status data of the equipment, such as temperature, voltage, current, etc., are collected through sensors, such as the temperature data range is 0-100℃, the voltage data range is 0-380V, and the current data range is 0-100A, and the operation time data of the equipment is recorded at the same time. Then, the collected data is cleaned and feature-engineered, outliers are removed by the 3σ principle, and redundant data are removed by principal component analysis (PCA), and key features of the data such as mean, variance, kurtosis, etc. are extracted. Then, according to the historical operation data of the equipment, the support vector machine (SVM) algorithm is used to establish a fault warning model, and the model parameters are optimized by grid search and cross-validation methods, so that the accuracy and recall rate of fault warning reach more than 95%. When the equipment fault warning signal is received, the C5 decision tree algorithm is used to generate targeted equipment maintenance recommendations in combination with the current status data and operation time data of the equipment. The Apriori association rule mining algorithm is used to analyze the correlation between equipment fault type, fault cause, and maintenance measures, and obtain association rules with a confidence level greater than 80%, which are used to optimize the equipment maintenance recommendation plan. According to the severity of the equipment failure warning signal and the possibility of failure, the failure risk level of each device is calculated by fuzzy comprehensive evaluation method, and the risk level is divided into three levels: high, medium and low. Based on the equipment failure risk level and maintenance resource limitations, the 0-1 integer programming model is used to determine the maintenance priority of each device. The objective function is to minimize the equipment failure risk, and the constraint condition is the upper limit of maintenance resources. Finally, the personalized equipment maintenance recommendation plan and maintenance priority information are presented to maintenance personnel through a visual dashboard. A pie chart is used to show the distribution of fault types, a bar chart is used to show the trend of the number of faults, and a radar chart is used to show the equipment health index, to assist maintenance personnel in making equipment maintenance decisions.

[0172] Step S106: Use the sparrow algorithm to optimize the execution order of maintenance tasks for multiple devices, and give priority to devices with high fault risk levels and high maintenance priorities in the maintenance tasks and execute them in a timely manner.

[0173] Obtain historical fault data and maintenance records of multiple devices, and calculate the fault risk level and maintenance priority value of each device through big data analysis. According to the fault risk level and maintenance priority value of the device, use the sparrow algorithm to optimize the execution order of the equipment maintenance tasks to obtain the optimal equipment maintenance task execution sequence. Generate an equipment maintenance plan based on the optimized equipment maintenance task execution sequence, and give priority to the equipment with high fault risk level and high maintenance priority in the maintenance plan. Obtain the real-time operation status data of the equipment. If the key operation parameters of the equipment exceed the preset safety threshold range, the early warning maintenance task of the equipment is triggered, and the maintenance task priority of the equipment is set to the highest. According to the equipment maintenance plan, real-time scheduling of maintenance personnel to perform equipment maintenance tasks. If an early warning maintenance task occurs, personnel are prioritized to handle the early warning maintenance task. Real-time collection and analysis of equipment operation data through the Internet of Things platform, continuous optimization of equipment failure prediction model using machine learning algorithms, and dynamic update of equipment failure risk level. According to the latest equipment failure risk level, regularly re-execute the sparrow algorithm to optimize the execution order of equipment maintenance tasks, generate a new equipment maintenance plan, and form a dynamic optimization closed loop of equipment maintenance tasks.

[0174] Specifically, by analyzing the failure data and maintenance records of 1,000 devices in the past five years, the association rule mining algorithm is used to calculate the failure risk level of each device, with a value range of 1-5. The larger the value, the higher the failure risk. At the same time, the hierarchical analysis method is used to comprehensively evaluate the maintenance priority of the equipment from five dimensions, such as equipment importance, failure frequency, and failure impact, to obtain the maintenance priority value of each device, with a value range of 0-1. On this basis, the sparrow search algorithm is used to optimize the execution order of equipment maintenance tasks. By setting the fitness function, the weighted sum of the equipment failure risk level and the maintenance priority value is maximized as the optimization target. After 500 iterative searches, the optimal equipment maintenance task execution sequence is obtained. The equipment maintenance plan is generated according to the optimized sequence, and the equipment with a failure risk level of 4 or above and a maintenance priority value greater than 8 is included in the plan first. By installing sensors on the equipment to collect key operating parameters such as temperature, vibration, and current of the equipment in real time, when the temperature of a certain equipment is monitored to exceed the preset safety threshold of 80℃ for 5 consecutive minutes, the system automatically issues a warning maintenance task and sets the maintenance task priority of the equipment to the highest. The system automatically dispatches maintenance personnel to handle maintenance tasks every hour. When encountering an early warning task, the system immediately notifies the nearest maintenance personnel to handle it. By deploying the Internet of Things platform, the equipment operation data is collected every 30 seconds, and the equipment failure prediction model is trained and optimized using the long short-term memory neural network algorithm, and the equipment failure risk level is updated once a day. When the failure risk level of a certain equipment increases for three consecutive days, the system automatically triggers the sparrow algorithm to re-optimize the execution order of the equipment maintenance tasks, generate the latest equipment maintenance plan, and form a closed-loop management of dynamic optimization of equipment maintenance tasks.

[0175] Step S107: According to the execution effect of the equipment maintenance task, the relevant parameters of the fault prediction model are dynamically updated, the equipment fault prediction result is recalculated, and it is determined whether the equipment operation status has returned to normal.

[0176] Obtain the execution effect data of the equipment maintenance task, including equipment operating parameters, maintenance operation records, etc.; dynamically adjust the relevant parameters of the fault prediction model according to the obtained execution effect data, including model weights, thresholds, etc.; use the adjusted fault prediction model to recalculate the failure probability of the equipment to obtain updated equipment fault prediction results; compare the updated equipment fault prediction results with the preset normal operation threshold to determine whether the equipment operation status has returned to normal; if the equipment operation status has returned to normal, determine that the equipment maintenance task execution is effective and maintain the current maintenance strategy; if the equipment operation status has not returned to normal, determine that the equipment maintenance task execution is invalid and the maintenance strategy needs to be further optimized; according to the judgment result of whether the equipment operation status has returned to normal, dynamically adjust the execution frequency and maintenance content of the equipment maintenance task to form a closed-loop optimization.

[0177] Specifically, when obtaining the data on the performance of equipment maintenance tasks, the operating parameters of the equipment, such as temperature, pressure, vibration, etc., can be collected in real time through the sensors installed on the equipment. The data is collected every 5 minutes and uploaded to the database. At the same time, after completing the equipment maintenance operation, the maintenance personnel need to enter the content, time, personnel and other information of the maintenance operation into the system to form a complete maintenance operation record. According to the acquired equipment operating parameters and maintenance operation record data, the relevant parameters of the fault prediction model are dynamically adjusted using machine learning algorithms such as support vector machines (SVM). Through grid search and cross-validation methods, the hyperparameters of the model, such as the penalty coefficient C and the kernel function type, are optimized so that the model can better fit the data and improve the accuracy of fault prediction. The adjusted fault prediction model can recalculate the failure probability of the equipment through the logistic regression algorithm to obtain the updated equipment fault prediction result. The updated equipment fault prediction result is compared with the preset normal operation threshold (such as the failure probability is less than 1). If the failure probability of the equipment is lower than the threshold, it is judged that the equipment operation state has returned to normal, the maintenance task execution is effective, and the current maintenance strategy can be maintained. If the failure probability of the equipment is higher than the threshold, it is judged that the equipment operation status has not returned to normal, the maintenance task execution is invalid, and the maintenance strategy needs to be further optimized, such as shortening the maintenance cycle, increasing the maintenance content, etc. According to the judgment result of whether the equipment operation status has returned to normal, the execution frequency and maintenance content of the equipment maintenance tasks are dynamically adjusted, such as changing the original monthly scheduled maintenance to once every half month, and adding the replacement of equipment parts to the original routine maintenance content. Through continuous feedback optimization, a closed-loop management of equipment maintenance is formed to continuously improve the reliability and stability of the equipment.

[0178] Step S108: If the equipment operation status returns to normal, key parameters such as temperature, voltage, and current are within the safety threshold range, and there are no signs of repeated failures in the subsequent period of time, the maintenance task of the equipment can be completed. Otherwise, the maintenance plan needs to be adjusted and the maintenance measures continue to be optimized until the equipment is fully restored to normal.

[0179] The real-time operation status data of the equipment is obtained, including key parameters such as temperature, voltage, and current, and the operation status of the equipment is judged to be restored to normal according to the preset safety threshold range. If the operation status of the equipment is restored to normal, the operation data of the equipment is continuously monitored, and the big data analysis technology is used to judge whether the equipment has signs of failure in the subsequent period of time. If the equipment has no signs of failure in the subsequent period of time, it can be determined that the equipment has completely recovered to normal, and the end instruction of the equipment maintenance task is generated to terminate the maintenance task of the equipment. If the operation status of the equipment has not recovered to normal, or there are signs of failure in the subsequent period of time, the adjustment mechanism of the maintenance plan is triggered, and the maintenance measures of the equipment are automatically optimized through the machine learning algorithm. In the process of adjusting the maintenance plan, the decision tree algorithm is used to comprehensively consider the historical operation data, current operation status, and maintenance cost of the equipment to obtain the optimal combination of maintenance measures. The optimized maintenance measures are sent to the equipment, and the equipment is remotely controlled to perform the corresponding maintenance operations through the Internet of Things technology, and the changes in the operation status of the equipment are monitored in real time. According to the changes in the operation status of the equipment, the above steps are executed repeatedly until the equipment is completely restored to normal, the conditions for ending the maintenance task are met, and the completion signal of the equipment maintenance task is output.

[0180] Specifically, by installing sensors on the equipment, key parameters such as temperature, voltage, and current of the equipment are collected in real time. For example, the temperature sensor collects the temperature of the equipment as 65°C, the voltage sensor collects the working voltage of the equipment as 220V, and the current sensor collects the working current of the equipment as 2A. The collected data is compared with the preset safety threshold range, with the temperature threshold set to 0°C~100°C, the voltage threshold set to 200V~240V, and the current threshold set to 0A~5A. By comparison, it can be determined whether the current equipment operation status is normal. If the equipment operation status returns to normal, the equipment operation data is continuously monitored, and the historical operation data of the equipment is analyzed through big data analysis technology. The time series analysis algorithm is used to predict the operation trend of the equipment in the future period of time to determine whether there may be signs of failure. If the prediction results show that the equipment has no failure risk in the next month, it can be determined that the equipment has fully returned to normal, and the end instruction of the equipment maintenance task is generated to terminate the equipment maintenance task. If the equipment operation status has not returned to normal, or the prediction results show that the equipment has a greater failure risk in the next month, the adjustment mechanism of the maintenance plan is triggered. Through the reinforcement learning technology in the machine learning algorithm, combined with the historical operation and maintenance data of the equipment, the maintenance measures of the equipment are automatically learned and optimized to obtain the best combination of maintenance measures. For example, by analyzing historical data, it is found that when the temperature of the equipment exceeds 80°C, the equipment needs to be cooled down, and when the voltage of the equipment is lower than 210V, the power supply of the equipment needs to be checked. Using the decision tree algorithm, the historical operation data, current operation status and maintenance cost of the equipment are comprehensively considered to generate a decision tree for maintenance measures. According to the branch conditions of the decision tree, the optimal combination of maintenance measures under different circumstances is obtained and sent to the equipment. Through the Internet of Things technology, the equipment is remotely controlled to perform corresponding maintenance operations, such as starting the cooling system of the equipment, adjusting the power supply voltage of the equipment, etc., and the changes in the operation status of the equipment are monitored in real time. According to the changes in the operation status of the equipment, the above steps are executed repeatedly to continuously optimize the maintenance plan until the key parameters such as temperature, voltage, and current of the equipment are restored to the normal range, the conditions for ending the maintenance task are met, and the completion signal of the equipment maintenance task is output.

[0181] Step S109: Add status monitoring and fault warning of key components of the sanitation robot.

[0182] In order to further improve the reliability and cluster operation efficiency of the sanitation robot cluster, the present invention adds a state monitoring and fault warning mechanism for key components of the sanitation robot, specifically including: Battery: By real-time monitoring of parameters such as battery power and number of charge and discharge cycles, an algorithm is used to evaluate the battery health status, timely warning of risks such as low battery power and excessive number of cycles, and notifying users in advance to replace or recharge.

[0183] Sweeping disc: monitors the sweeping disc’s rotation speed, degree of wear, cleaning efficiency and other indicators. If the cleaning efficiency is reduced or the wear is excessive, the system will issue a fault warning in advance and recommend users to clean or replace it.

[0184] Fan: By monitoring parameters such as fan speed, noise and vibration, early signs of fan failure, such as abnormal speed and excessive vibration, can be detected in time, and maintenance or replacement can be carried out in advance.

[0185] Suction nozzle: Monitor the suction power and blockage degree of the suction nozzle. If the suction power decreases or the blockage is serious, the system will alarm in advance and suggest the user to clean or replace it.

[0186] Filter: Monitor the status of the filter, including filtration efficiency, degree of blockage, etc. If the filtration efficiency is reduced or the blockage is serious, the system will issue an early warning and recommend cleaning or replacement.

[0187] LiDAR: Real-time monitoring of the working status of LiDAR, including laser emission power, received signal strength, etc. Once a signal abnormality or power drop is detected, the system will issue a warning.

[0188] Camera: Monitors the camera's image quality, focal length, etc. If the image quality deteriorates or the focal length is distorted, the system will notify the user in advance to clean or repair it.

[0189] Ultrasonic: Monitors the signal quality and response time of ultrasonic sensors. If the signal quality deteriorates or the response time is abnormal, the system will issue a fault warning in advance and recommend inspection or replacement of the sensor.

[0190] By adding status monitoring and early warning mechanisms to the above key components, the system can identify potential failures in advance and avoid cluster task interruptions caused by component failures, thereby improving the overall operating efficiency and reliability of the sanitation robot cluster.

[0191] In order to further improve the reliability and cluster operation efficiency of the sanitation robot cluster, the present invention adds a state monitoring and fault warning mechanism for key components of the sanitation robot, specifically including: Battery: By real-time monitoring of battery power, charge and discharge cycle times and other parameters, the following formula is used to comprehensively evaluate the battery health: (5) In formula (5), For battery health, is the number of times the battery is charged and discharged, is the depth of charge, The maximum capacity of the battery. When the battery is below a certain threshold, the system will remind the user to replace or recharge it. Sweeper disc: By monitoring the changes in the sweeping length and ground contact pressure of the sweeper disc, the following formula is used to calculate the degree of wear of the sweeper disc: (6) In formula (6), The degree of wear of the sweeping disc. is the current sweeping length value, is the initial length of the sweeping hair, is the current ground contact pressure value, is the maximum permissible ground contact pressure value. When the preset threshold is reached, the system will issue a fault warning to remind the user to clean or replace it.

[0192] Fan: Monitor the fan flow and negative pressure, and calculate the fan performance using the following formula: (7) In formula (7), is the fan power, is the fan flow rate, is the negative pressure of the fan, is the fan speed, is the fan efficiency. When it exceeds the normal range, the system will issue an early warning to remind the user to check and maintain the fan.

[0193] Nozzle: By monitoring the wear and suction force of the nozzle, the nozzle performance can be judged using the following formula: (8) In formula (8), is the suction attenuation degree of the nozzle, is the initial suction force, is the current suction force. When a certain value is reached, the system will issue a warning that the nozzle needs maintenance.

[0194] Filters: By monitoring the filter usage time and water flow rate, the filter effectiveness is evaluated using the following formula: (9) In formula (9), is the filter efficiency, For usage time, For the maximum usage time, is the current water flow, is the maximum water flow. When the filter pressure is less than the preset threshold, the system will remind you to clean or replace the filter.

[0195] LiDAR: Monitor the monitoring accuracy of the LiDAR and use the following formula to evaluate the radar performance: (10) In formula (10), is the radar accuracy, is the maximum detection distance of the radar, is the minimum detection distance. When it exceeds the allowable range, the system will trigger a fault warning.

[0196] Camera: Monitor the camera’s resolution and use the following formula to calculate the camera image quality: (11) In formula (11), is the image quality of the camera, and are the actual resolution height and width of the camera, and are the maximum resolution height and width. When it falls below the threshold, the system will issue a camera failure warning.

[0197] Ultrasonic: Monitor the sensing accuracy of the ultrasonic sensor and use the following formula to evaluate the sensing effect: (12) In formula (12), is the ultrasonic sensing accuracy, and are the maximum and minimum detection distances respectively. Below a certain threshold, the system warns of reduced sensing accuracy.

[0198] By adding status monitoring and early warning mechanisms to the above key components, the system can identify potential failures in advance and avoid cluster task interruptions caused by component failures, thereby improving the overall operating efficiency and reliability of the sanitation robot cluster.

[0199] S110. Comprehensively analyze the fault warning mechanism of the key components of the sanitation robot combined with the status parameters and sensor data.

[0200] In order to more accurately evaluate the operating status of the sanitation robot cluster, the present invention proposes to comprehensively analyze the status parameters of each key component and the sensor data to achieve more accurate fault warning. The specific steps are as follows: 1. Comprehensive parameter analysis: The battery health, sweeper wear, fan flow, nozzle wear, filter status, lidar monitoring accuracy, camera resolution, ultrasonic sensing accuracy and other parameters are integrated with the temperature, voltage, current, vibration and other data collected by the sanitation robot sensors to establish a comprehensive evaluation model. Through the weighted average method, each parameter is comprehensively scored according to its weight influencing the operation of the equipment.

[0201] 2. Comprehensive evaluation model: Assume that the influence of the state parameter of each component is: , , , , , , , , corresponding to the weight coefficients of battery, fan, sweeper, nozzle, filter, lidar, camera, and ultrasound. Comprehensive health score It can be expressed as: (13) In formula (13), For battery health, The degree of wear of the sweeping disc. is the fan power, is the degree of suction attenuation of the nozzle. is the filter efficiency, is the laser radar accuracy, is the camera image quality, Ultrasonic sensing accuracy.

[0202] The overall rating After combining the sensor data, the fault warning model is used to make a judgment. If it falls below a preset safety threshold, the system will sound an alarm and recommend maintenance.

[0203] 3. Fault warning mechanism combined with sensor data: During the data collection process, the sanitation robot monitors the equipment status in real time through sensors such as temperature, voltage, current, vibration, etc. Time series analysis models (such as ARIMA or LSTM) are used to model historical data and predict the future health status of the equipment.

[0204] The sensor data (such as temperature, voltage, current, etc.) is combined with the above comprehensive score to form a composite scoring model. The model is continuously updated and optimized through machine learning methods (such as support vector machine SVM, decision tree, etc.) to make fault warning more accurate.

[0205] When the composite scoring model assesses that a sanitation robot has a high risk of failure, the system will generate a corresponding maintenance strategy based on the specific failure type and risk level, and prioritize high-risk equipment for inspection and maintenance.

[0206] 4. Comprehensive analysis and real-time monitoring: During the operation of the sanitation robot cluster, the system monitors the status of each device in real time, including battery power, sweeper wear, fan flow, etc., and combines sensor data to automatically calculate the comprehensive score of each device. When the comprehensive score of a device is lower than the preset threshold for multiple consecutive times, the system will issue a warning signal and optimize the maintenance plan for the device.

[0207] For example, when the flow and negative pressure parameters of the fan are abnormal, combined with the battery health and camera resolution data, the system can determine the overall operation of the equipment and take timely measures to avoid overall mission failure due to the failure of a single component.

[0208] 5. Adjustment and optimization: The system continuously collects and analyzes the real-time data of the equipment, combines the above comprehensive scores, and optimizes the execution order of maintenance tasks for each equipment through the sparrow algorithm. For equipment with low scores and high failure risks, priority is given to maintenance to reduce the risk of cluster operation interruption.

[0209] Through comprehensive analysis of the above key component status parameters and sensor data, the present invention can more accurately assess the failure risk of equipment, provide early warning and take effective maintenance measures, thereby improving the operating efficiency of the sanitation robot cluster and the long-term reliability of the equipment.

[0210] Although preferred embodiments of the present invention have been described, additional changes and modifications may be made to these embodiments by those skilled in the art once the basic inventive concepts are known. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for monitoring the status and early warning of sanitation robot clusters, characterized in that: The following steps are involved: Obtaining operating status data of the sanitation robot cluster; the operating status data includes temperature, voltage and current data; Compare the acquired operating status data with a preset normal operating status threshold, and if the operating status data exceeds the normal operating status threshold, dynamically adjust the observation frequency of the operating status data to narrow the observation range; Obtain the cumulative running time of the sanitation robot cluster, and if the cumulative running time exceeds a preset safety time threshold, adjust the time weight parameter in the fault prediction model; According to the adjusted duration weight parameter, the fault prediction result is recalculated, and the accumulated operation duration and operation status data are comprehensively considered to determine whether each sanitation robot equipment has a fatigue loss risk. If there is a fatigue loss risk, an equipment failure warning signal is generated; Obtain the equipment failure warning signal, combine the operation status data and the accumulated operation time, generate a targeted equipment maintenance suggestion plan, and determine the maintenance priority of each sanitation robot equipment according to the failure risk level; Optimize the execution order of maintenance tasks for each sanitation robot device, and give priority to the equipment with high failure risk level and high maintenance priority among the sanitation robot devices and execute them in time; According to the execution effect of the equipment maintenance task, the relevant parameters of the fault prediction model are dynamically updated, the equipment fault prediction results are recalculated, and it is determined whether the equipment operation status has returned to normal; If it is identified that the equipment operating status has returned to normal and the operating status data are within the normal operating status threshold within the set time, the equipment maintenance task is terminated. Otherwise, the maintenance plan needs to be adjusted and the maintenance measures continue to be optimized until the sanitation robot equipment is fully restored to normal.

2. The sanitation robot cluster status monitoring and fault warning method according to claim 1, characterized in that: The step of obtaining the running status data of the sanitation robot cluster includes: According to the operating status of the sanitation robot cluster, obtain real-time data of the operating status data and build a data collection model; The sparrow algorithm is used to optimize the data collection strategy, and the optimal observation frequency and observation range of each parameter in the operating status data are determined through iterative search; During the optimization process, the data acquisition model is dynamically adjusted according to changes in the observation frequency and the observation range; Preprocessing the operating status data to remove abnormal values ​​and noise data in the operating status data; Use clustering algorithms to analyze the preprocessed operating status data and identify data features and patterns under different operating states; According to the clustering results, a mapping relationship between the operating status and data features is established to achieve real-time monitoring and early warning of the operating status of the sanitation robot cluster; The optimized data collection strategy is applied to the sanitation robot cluster, and the data collection efficiency and quality are continuously monitored. The optimization algorithm is dynamically adjusted according to the feedback results to achieve adaptive optimization of the data collection strategy.

3. The sanitation robot cluster status monitoring and fault warning method according to claim 1, characterized in that: The step of comparing the acquired operating status data with a preset normal operating status threshold, and if the operating status data exceeds the normal operating status threshold, dynamically adjusting the observation frequency of the operating status data to narrow the observation range includes: Transmitting the operating status data to a data processing module; The data processing module uses a time series analysis algorithm to perform trend analysis on the historical data of each parameter in the operating status data to obtain a change trend curve of each parameter; According to the preset normal threshold range of each parameter, determine whether the real-time data of each parameter in the operating status data exceeds the normal threshold range, and if the real-time data of each parameter exceeds the normal threshold range, trigger an alarm; If it is identified that the real-time data of a certain parameter exceeds the normal threshold range for multiple times in a row, the observation frequency of the current parameter is dynamically adjusted; At the same time, according to the change trend curve, the observation range of the current parameter is narrowed; The Kalman filter algorithm is used to filter the real-time data of the current parameters to remove noise interference in the operating status data; The adjusted observation frequency, observation range, and real-time data after filtering are transmitted to the data visualization module, and the changes of the current parameters are displayed in real time via the data visualization module.

4. The sanitation robot cluster status monitoring and fault warning method according to claim 1, characterized in that: The step of obtaining the cumulative running time of the sanitation robot cluster and adjusting the time weight parameter in the fault prediction model if the cumulative running time exceeds a preset safety time threshold comprises: Acquire the real-time operation data of the sanitation robot cluster and transmit the real-time operation data to the ground control center; the real-time operation data includes the operation status and operation time information of each sanitation robot; The sparrow algorithm is used to analyze and calculate the cumulative running time of the sanitation robot cluster, and the total running time and average running time of the cluster are obtained; Determine whether the total operation time of the cluster exceeds the preset safety time threshold. If the total operation time of the cluster exceeds the safety time threshold, the adjustment mechanism of the fault prediction model is triggered; According to the degree of exceeding the safety time threshold, the time weight parameter in the fault prediction model is dynamically adjusted to give the time factor a higher weight; By increasing the duration weight, the fault prediction model is more biased towards the duration factor; Based on the adjusted fault prediction model, the failure risk of the sanitation robot cluster is re-evaluated, and high-risk sanitation robot individuals are identified; For high-risk sanitation robots, return instructions or landing instructions are issued in a timely manner, and spare sanitation robots are dispatched to take over the work tasks of the high-risk sanitation robots.

5. The sanitation robot cluster status monitoring and fault warning method according to claim 1, characterized in that: The step of recalculating the fault prediction result according to the adjusted duration weight parameter, comprehensively considering the accumulated operation duration and operation status data, and judging whether each sanitation robot equipment has a fatigue loss risk, and if there is a fatigue loss risk, generating an equipment fault warning signal includes: Obtaining an adjusted duration weight parameter, and inputting the adjusted duration weight parameter into a fault prediction model; According to the fault prediction model, a fault prediction result of the sanitation robot equipment is calculated; Obtain operating time data and status monitoring data of sanitation robot equipment; Input the operating time data and condition monitoring data of the sanitation robot equipment into the fatigue loss risk assessment model; The fatigue loss risk assessment model comprehensively analyzes the operating time data and the status monitoring data to determine whether the sanitation robot equipment has a fatigue loss risk; If it is identified that the sanitation robot equipment has a risk of fatigue wear, a warning signal for equipment failure is generated; The fault warning signal is sent to the sanitation robot equipment management system.

6. The sanitation robot cluster status monitoring and fault warning method according to claim 1, characterized in that: The steps of obtaining the equipment failure warning signal, combining the operation status data and the accumulated operation time, generating a targeted equipment maintenance suggestion plan, and determining the maintenance priority of each sanitation robot equipment according to the failure risk level include: Performing data cleaning and feature engineering on the running status data and the accumulated running time, removing outliers and redundant data, and extracting key features; Based on the historical operation data of the equipment, a fault warning model is established using a support vector machine algorithm, and the fault warning model is trained and optimized; When the equipment failure warning signal is received, a targeted equipment maintenance suggestion plan is generated through a decision tree algorithm in combination with the current status data and operation time data of the equipment; Use association rule mining algorithm to analyze the relationship between equipment failure type, failure cause and maintenance measures; According to the severity of the equipment fault warning signal and the possibility of the fault occurring, the fault risk level of each sanitation robot equipment is calculated by a fuzzy comprehensive evaluation method; Based on the failure risk level of each sanitation robot equipment and the limitation of maintenance resources, an integer programming model is used to determine the maintenance priority information of each sanitation robot equipment; The equipment maintenance recommendation plan and the maintenance priority information are displayed through a visual dashboard.

7. The sanitation robot cluster status monitoring and fault warning method according to claim 1, characterized in that: The step of optimizing the execution order of maintenance tasks of each sanitation robot device, and prioritizing the devices with high fault risk level and high maintenance priority among the sanitation robot devices to be included in the maintenance tasks and executed in a timely manner comprises: Obtain historical fault data and maintenance records of multiple sanitation robot devices, and calculate the fault risk level and maintenance priority value of each sanitation robot device through big data analysis; According to the fault risk level and maintenance priority value, the sparrow algorithm is used to optimize the execution order of the maintenance tasks of the sanitation robot equipment to obtain the optimal equipment maintenance task execution sequence; Generate an equipment maintenance plan based on the optimized equipment maintenance task execution sequence, and prioritize sanitation robot equipment with high failure risk level and high maintenance priority into the maintenance plan; Acquire the real-time operating status data of the sanitation robot equipment. If it is identified that the key operating parameters of the sanitation robot equipment exceed the preset safety threshold range, the early warning maintenance task of the equipment is triggered, and the maintenance task priority of the current equipment is set to the highest; According to the equipment maintenance plan, maintenance personnel are dispatched in real time to perform equipment maintenance tasks; If a warning maintenance task is identified, personnel will be dispatched to handle the warning maintenance task first; Collect and analyze equipment operation data in real time through the Internet of Things platform, use machine learning algorithms to continuously optimize the fault prediction model, and dynamically update the fault risk level of the equipment; According to the latest equipment failure risk level, the sparrow algorithm is re-executed regularly to optimize the execution sequence of equipment maintenance tasks, generate a new equipment maintenance plan, and form a dynamic optimization closed loop of equipment maintenance tasks.

8. The sanitation robot cluster status monitoring and fault warning method according to claim 1, characterized in that: If it is identified that the equipment operation status has returned to normal, and the operation status data are within the normal operation status threshold range within the set time, the equipment maintenance task is terminated, otherwise the maintenance plan needs to be adjusted and the maintenance measures continue to be optimized until the sanitation robot equipment is completely restored to normal. The steps include: Obtaining execution effect data of equipment maintenance tasks; the execution effect data includes equipment operating parameters and maintenance operation records; Dynamically adjusting relevant parameters of a fault prediction model according to the acquired execution effect data; the relevant parameters of the fault prediction model include model weights and thresholds; The adjusted fault prediction model is used to recalculate the failure probability of the sanitation robot equipment and obtain updated equipment failure prediction results; Compare the updated equipment fault prediction results with the preset normal operation threshold to determine whether the equipment operation status has returned to normal; If the equipment operation status returns to normal, it is determined that the equipment maintenance task is executed effectively and the current maintenance strategy is maintained; If the equipment operation status does not return to normal, it is determined that the equipment maintenance task is invalid and the maintenance strategy needs to be further optimized; Based on the judgment result of whether the equipment operating status has returned to normal, the execution frequency and maintenance content of the equipment maintenance tasks are dynamically adjusted to form a closed-loop optimization.

9. The sanitation robot cluster status monitoring and fault warning method according to claim 1, characterized in that: The steps of dynamically updating relevant parameters of the fault prediction model according to the execution effect of the equipment maintenance task, recalculating the equipment fault prediction result, and judging whether the equipment operation status has returned to normal include: Obtain real-time operating status data of sanitation robot equipment and determine whether the equipment operating status has returned to normal based on the preset safety threshold range; If it is identified that the equipment operation status has returned to normal, the equipment operation data will be continuously monitored, and through big data analysis technology, it will be determined whether the sanitation robot equipment has any signs of failure within the subsequent set time; If it is identified that the sanitation robot equipment has no signs of failure within the subsequent set time, it is determined that the sanitation robot equipment has completely recovered to normal, and an end instruction of the equipment maintenance task is generated to terminate the equipment maintenance task; If it is identified that the equipment's operating status has not returned to normal, or if there are signs of failure within a period of time, the maintenance plan adjustment mechanism will be triggered, and the equipment's maintenance measures will be automatically optimized through machine learning algorithms; In the process of adjusting the maintenance plan, the decision tree algorithm is used to comprehensively consider the historical operation data and current operation status of the equipment as well as the maintenance cost factors to obtain the optimal combination of maintenance measures; The optimized maintenance measures are sent to the sanitation robot equipment. Through the Internet of Things technology, the equipment is remotely controlled to perform corresponding maintenance operations and the operating status changes of the equipment are monitored in real time; According to the changes in the equipment operating status, the above steps are executed repeatedly until the equipment is completely restored to normal and the conditions for ending the maintenance task are met, and a completion signal of the equipment maintenance task is output.

10. A sanitation robot cluster state monitoring and fault warning system, applied to the sanitation robot cluster state monitoring and fault warning method according to any one of claims 1 to 9, characterized in that: The sanitation robot cluster status monitoring and fault warning system includes: An acquisition module is used to acquire the operating status data of the sanitation robot cluster; the operating status data includes temperature, voltage and current data; A comparison module, used to compare the acquired operation status data with a preset normal operation status threshold, and if the operation status data exceeds the normal operation status threshold, dynamically adjust the observation frequency of the operation status data to narrow the observation range; An adjustment module is used to obtain the cumulative running time of the sanitation robot cluster, and if the cumulative running time exceeds a preset safety time threshold, adjust the time weight parameter in the fault prediction model; The first judgment module is used to recalculate the fault prediction result according to the adjusted duration weight parameter, comprehensively consider the accumulated operation time and operation status data, and judge whether each sanitation robot equipment has a fatigue loss risk. If there is a fatigue loss risk, an equipment failure warning signal is generated; A determination module is used to obtain the equipment failure warning signal, generate a targeted equipment maintenance suggestion plan in combination with the operation status data and the accumulated operation time, and determine the maintenance priority of each sanitation robot equipment according to the failure risk level; An optimization module is used to optimize the execution order of maintenance tasks of each sanitation robot device, and give priority to the equipment with high fault risk level and high maintenance priority among the sanitation robot devices and execute them in time; The second judgment module is used to dynamically update the relevant parameters of the fault prediction model according to the execution effect of the equipment maintenance task, recalculate the equipment fault prediction result, and judge whether the equipment operation status has returned to normal; The maintenance module is used to end the equipment maintenance task if it is identified that the equipment operating status has returned to normal and the operating status data are within the normal operating status threshold within the set time. Otherwise, the maintenance plan needs to be adjusted and the maintenance measures continue to be optimized until the sanitation robot equipment is fully restored to normal.

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