A mobile robot system early warning method, electronic equipment and storage medium

Abstract

The application discloses a mobile robot system early warning method and electronic equipment and a storage medium. The method is applied to a mobile robot system. The method comprises the following steps: acquiring current index values corresponding to a plurality of evaluation indexes of the mobile robot system, wherein the plurality of evaluation indexes belong to at least two dimensions of the following: equipment failure condition, platform service running condition, task execution condition and congestion condition; determining a system score of the mobile robot system based on the current index values corresponding to the plurality of evaluation indexes; and early warning the mobile robot system based on the system score. The above scheme can improve the reliability of the operation of the mobile robot system.

Description

Technical Field

[0001] This application relates to the field of mobile robot technology, and in particular to a mobile robot system early warning method, electronic device, and storage medium. Background Technology

[0002] Current methods for evaluating the operational efficiency of Autonomous Mobile Robot (AMR) systems have significant limitations. Specifically, the indicators and definitions used in current evaluation systems are too simplistic, focusing primarily on isolated metrics such as hardware performance or task completion rate. They fail to adequately integrate multi-dimensional factors such as equipment operational capabilities, platform software stability, and overall system coordination, resulting in evaluation results that cannot comprehensively reflect the true operational status of the AMR system. Furthermore, existing methods lack the ability to dynamically identify potential system risks, making it difficult to accurately assess the existence of current operational risks or predict future operational trends. This single-dimensional evaluation model not only limits the comprehensive optimization of AMR system performance but may also lead to the accumulation of potential risks, ultimately affecting the system's reliability and stability. Summary of the Invention

[0003] This application provides at least one early warning method, electronic device, and storage medium for a mobile robot system, which can improve the reliability of the mobile robot system operation.

[0004] The first aspect of this application provides a method for early warning of a mobile robot system. The method is applied to a mobile robot system and includes: obtaining the current index values ​​corresponding to several evaluation indicators of the mobile robot system, wherein the several evaluation indicators belong to at least two of the following dimensions: equipment failure status, platform service operation status, task execution status, and congestion status; determining the system score of the mobile robot system based on the current index values ​​corresponding to the several evaluation indicators; and issuing an early warning for the mobile robot system based on the system score.

[0005] The evaluation indicators include at least one of the following: average number of device failures, number of platform service operation anomalies, task failure rate, task delay rate, and congestion anomaly rate. The average number of device failures represents the ratio of the total number of failures reported by all devices in the mobile robot system per unit time to the total number of all running devices. The number of platform service operation anomalies represents the sum of the number of anomalies in the platform software layer of the mobile robot system per unit time. The task failure rate represents the ratio of the number of failed tasks in the mobile robot system per unit time to the total number of executed tasks. The task delay rate represents the ratio of the number of tasks that could not be completed within the specified time in the mobile robot system per unit time to the total number of executed tasks. The congestion anomaly rate represents the ratio of the number of times equipment in the mobile robot system experienced abnormal congestion per unit time to the total number of congestion events.

[0006] The system score includes at least one of the system operation score and the risk quantification score. The system score of the mobile robot system is determined based on the current value of each of the several evaluation indicators, including: determining the actual indicator score of each evaluation indicator based on its current value; weighting the actual indicator scores of the several evaluation indicators to obtain the system operation score; calculating the indicator change rate and indicator statistical value for each evaluation indicator based on its current and historical values; and calculating the risk quantification score based on the indicator change rate and indicator statistical value for each evaluation indicator.

[0007] Specifically, the actual score of each evaluation indicator is determined based on its current value, including: for each evaluation indicator, determining the value range of the current value of the evaluation indicator, and obtaining the score that matches the value range of the current value as the actual score of the evaluation indicator; and / or, the evaluation indicators include at least one first-type evaluation indicator and at least one second-type evaluation indicator; the actual score of the evaluation indicators is weighted to obtain the system operation score, including: using the weights of each first-type evaluation indicator, the actual score of each first-type evaluation indicator is weighted and summed to obtain a first weighted score; and using the weights of each second-type evaluation indicator, the actual score of each second-type evaluation indicator is weighted and summed to obtain a second weighted score; and using the weights of the first weighted score and the second weighted score respectively, the first weighted score and the second weighted score are weighted and summed to obtain the system operation score.

[0008] The first category of evaluation indicators includes at least one of the following: average number of equipment failures, number of platform service operation anomalies, and task failure rate; the second category of evaluation indicators includes at least one of the following: task untimeliness rate and congestion anomaly rate; and / or, the sum of the weights of each first category of evaluation indicator, the sum of the weights of each second category of evaluation indicator, and the sum of the weights of the first weighted score and the second weighted score are all preset values.

[0009] The calculation of the indicator change rate and statistical value for each evaluation indicator, based on the current and historical values ​​of each indicator, includes at least one of the following steps: For each evaluation indicator, the difference between the current and historical values ​​is used as the first indicator difference, and the ratio of the first indicator difference to the historical values ​​is used as the indicator change rate; for each evaluation indicator, a preset multiple of the ratio of the sum of the indicator values ​​over multiple statistical periods to the total statistical period is used as the indicator statistical value, wherein the current indicator value is the current... The indicators corresponding to previous statistical periods, historical indicators include indicators corresponding to at least one previous statistical period, and the total statistical period is the sum of multiple statistical periods; and / or, based on the indicator change rate and indicator statistical value corresponding to each evaluation indicator, a risk quantification score is calculated, including: for each evaluation indicator, determining the indicator change rate score based on the change rate interval of the indicator change rate, and determining the indicator threshold score based on the statistical value interval of the indicator statistical value; and fusing the indicator change rate score and indicator threshold score corresponding to each evaluation indicator to obtain the risk quantification score.

[0010] Specifically, the risk quantification score is obtained by fusing the indicator change rate score and indicator threshold score corresponding to each evaluation indicator. This includes: obtaining the fused score of the indicator change rate score and indicator threshold score corresponding to each evaluation indicator for each evaluation indicator; and weighting the fused scores corresponding to each evaluation indicator to obtain the risk quantification score.

[0011] The weighted processing result is a weighted sum; and / or, obtaining the fusion score of the indicator change rate score and the indicator threshold score corresponding to the evaluation indicator, including: taking the sum of the indicator change rate score and the indicator threshold score corresponding to the evaluation indicator as the fusion score corresponding to the evaluation indicator; and / or, using the fusion scores corresponding to each evaluation indicator for weighted processing to obtain the risk quantification score, including: taking the preset ratio of the weighted processing result as the risk quantification score, and the sum of the weights of the fusion scores corresponding to each evaluation indicator is the reciprocal of the preset ratio.

[0012] The system score includes at least one of a system operation score and a risk quantification score. Based on the system score, an early warning is issued for the mobile robot system, including: in response to the system score including the system operation score, if the system operation score does not meet the preset operation score requirements, a problem evaluation indicator is selected from several evaluation indicators based on the actual indicator scores of each evaluation indicator to serve as the main problem point of the mobile robot system, wherein the actual indicator score is determined based on the current indicator value of the evaluation indicator; in response to the system score including the risk quantification score, if the risk quantification score does not meet the preset risk score requirements, a risk evaluation indicator is selected from several evaluation indicators based on at least one risk-related score corresponding to each evaluation indicator to serve as the risk point of the mobile robot system, wherein the several risk-related scores are determined based on the current indicator value and historical indicator value of the evaluation indicator.

[0013] Specifically, based on the actual scores of each evaluation indicator, problem evaluation indicators are selected from several evaluation indicators to serve as the main problem points of the mobile robot system. This includes: selecting the problem evaluation indicator with the lowest actual score from several evaluation indicators to serve as the main problem point of the mobile robot system; and / or, risk-related scores include at least one of the indicator change rate score and the indicator threshold score; and / or, based on at least one risk-related score corresponding to each evaluation indicator, risk evaluation indicators are selected from several evaluation indicators to serve as risk points of the mobile robot system. This includes: selecting at least one risk evaluation indicator with the lowest sum of risk-related scores from several evaluation indicators to serve as the risk point of the mobile robot system.

[0014] The second aspect of this application provides an electronic device including a memory and a processor coupled to each other, the processor being used to execute program instructions stored in the memory to implement the mobile robot system early warning method of the first aspect described above.

[0015] A third aspect of this application provides a computer-readable storage medium having program instructions stored thereon, which, when executed by a processor, implement the mobile robot system early warning method described in the first aspect above.

[0016] The above solution first selects evaluation indicators from at least two of four dimensions: equipment failure, platform service operation, task execution, and congestion, and obtains the current indicator values ​​for each indicator in real time. Then, using these current indicator values, the system score of the mobile robot system is calculated. Based on this system score, the mobile robot system can automatically trigger an early warning mechanism to achieve risk warning and location. Through comprehensive evaluation of multi-dimensional indicators, the operating status of the mobile robot system can be fully reflected, avoiding the limitations of single-indicator evaluation. Furthermore, the obtained system score enables dynamic monitoring and timely early warning, providing accurate basis for operation and maintenance decisions, thereby improving the operational reliability of the mobile robot system.

[0017] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this application. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with this application and, together with the specification, serve to explain the technical solutions of this application.

[0019] Figure 1 This is a flowchart illustrating an embodiment of the early warning method for a mobile robot system according to this application;

[0020] Figure 2 This is a flowchart illustrating an embodiment of step S120 in the mobile robot system early warning method of this application;

[0021] Figure 3 This is a flowchart illustrating another embodiment of the early warning method for the mobile robot system of this application;

[0022] Figure 4 This is a schematic diagram of the structure of an embodiment of the early warning device for a mobile robot system according to this application;

[0023] Figure 5 This is a schematic diagram of the framework of an embodiment of the electronic device of this application;

[0024] Figure 6 This is a schematic diagram of a framework of an embodiment of the computer-readable storage medium of this application. Detailed Implementation

[0025] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0026] In the following description, specific details such as particular system architectures, interfaces, and technologies are presented for illustrative purposes rather than for limiting purposes, in order to provide a thorough understanding of this application.

[0027] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship. Furthermore, "many" in this document means two or more. Moreover, the term "at least one" in this document means any combination of at least two of any one or more of a plurality of objects. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0028] In numerous fields such as industrial automation, logistics warehousing, and intelligent services, mobile robot systems have become key technological equipment for improving production efficiency, reducing labor costs, and enhancing service quality. With the widespread application of mobile robot systems, their operational reliability has become a major concern. Traditional evaluation methods often have limitations, making it difficult to comprehensively and in real-time reflect the system's operational status.

[0029] Existing technologies have some shortcomings in the evaluation of mobile robot systems. For example, single-index evaluation methods cannot comprehensively reflect the system's operational status, easily leading to biased evaluation results. In addition, traditional evaluation methods often lack real-time and dynamic capabilities, failing to capture changes and potential risks during system operation in a timely manner.

[0030] This solution innovatively proposes an early warning method for mobile robot systems. This method utilizes multi-dimensional evaluation indicators to calculate a system score. Based on the system score, the mobile robot system can automatically trigger an early warning mechanism to achieve risk warning and location.

[0031] Please see Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the early warning method for a mobile robot system according to this application. Specifically, it may include the following steps:

[0032] Step S110: Obtain the current index values ​​corresponding to several evaluation indicators of the mobile robot system.

[0033] Among them, several evaluation indicators fall into at least two of the following dimensions: equipment failure status, platform service operation status, task execution status, and congestion status.

[0034] In some implementations, sensor detection can be used to obtain the current index values ​​corresponding to several evaluation indicators of the mobile robot system.

[0035] Specifically, sensors, such as temperature sensors, current sensors, and vibration sensors, can be installed on key components and equipment of a mobile robot system to monitor the operating status of the equipment in real time. For example, when the temperature of a motor in the robot system is too high, the current is abnormal, or the vibration is too large, the sensors can detect these changes in a timely manner and transmit the data to the control system, thereby obtaining the current indicator value of the equipment failure status.

[0036] In addition, LiDAR, infrared sensors, and other sensors can be deployed in the operating environment of mobile robots to monitor congestion in passageways and work areas in real time. For example, LiDAR can scan the surrounding environment and determine whether there are obstacles or crowds based on the scan results, thereby determining the level of congestion.

[0037] In other embodiments, data analysis and processing methods can be used to obtain the current index values ​​corresponding to several evaluation indicators of the mobile robot system.

[0038] Specifically, relevant data on the mobile robot system platform service is collected, such as server response time, data transmission rate, and system resource utilization. By analyzing this data, the operational quality of the platform service can be evaluated, thereby obtaining current indicator values ​​related to the platform service's operational status. For example, the average and fluctuation range of server response time can be calculated. If the response time is too long or fluctuates significantly, it indicates that there may be an anomaly in the platform service, thus yielding corresponding indicator values.

[0039] Analyze historical data of mobile robots performing tasks, including task completion time, task success rate, and number of task interruptions. For example, by statistically analyzing the average task completion time over a certain period and comparing it with a set standard time, an indicator of task execution efficiency can be obtained; or the task success rate can be calculated, which is the proportion of successfully completed tasks out of the total number of tasks, as an important indicator of task performance.

[0040] Furthermore, mobile robot systems typically record operational logs, which include equipment fault information, maintenance records, and more. Therefore, by analyzing these system logs, the current values ​​of several evaluation metrics can be obtained. For example, by analyzing and organizing these logs, historical data on equipment fault conditions can be acquired, and combined with the current operating status, the metric values ​​for equipment fault conditions can be updated.

[0041] The platform service's operation logs reflect the system's service startup and shutdown times, error messages, user access records, and more. Analyzing these logs helps understand the stability and reliability of the platform service, thereby determining its current operational metrics.

[0042] In some embodiments, several evaluation metrics include at least one of the following: average number of device failures, number of platform service malfunctions, task failure rate, task untimeliness rate, and congestion anomaly rate.

[0043] The average number of equipment failures (AFM) represents the ratio of the total number of failures reported by all devices in a mobile robot system per unit time to the total number of all operating devices.

[0044] Average number of equipment failures = Total number of failures / Total number of equipment, unit: failures / unit. Equipment may include AMR and third-party equipment in the system, such as equipment load, AMR charging pile, floor cleaning robot, etc.

[0045] The number of platform service operation anomalies represents the sum of the number of anomalies in the platform software layer of the mobile robot system per unit of time. The platform software layer is mainly responsible for task processing and distribution, device interaction and management, etc.

[0046] The task failure rate is the ratio of the number of failed tasks to the total number of executed tasks in a mobile robot system per unit of time.

[0047] Task failure rate = (Number of failed tasks / Total number of executed tasks) * 100%.

[0048] Task delay rate represents the ratio of the number of tasks that cannot be completed within the specified time in a mobile robot system per unit of time to the total number of tasks executed.

[0049] Task delay rate = (Number of tasks that cannot be completed within the specified time / Total number of tasks executed) * 100%, where the total number of tasks executed refers to the total number of tasks completed within a unit of time (day).

[0050] The congestion anomaly rate represents the ratio of the number of abnormal congestion events occurring in a mobile robot system per unit time to the total number of congestion events.

[0051] Congestion anomaly rate = (Number of congestion anomalies / Total number of congestion events) * 100%.

[0052] Among them, the average number of equipment failures, the number of times the platform service operates abnormally, and the task failure rate are three evaluation indicators that can be combined to form an indicator reflecting the abnormality of the AMR system, while the task untimely rate and the congestion anomaly rate are two indicators that can be combined to form an indicator reflecting the efficiency of the AMR system.

[0053] In addition to the above, evaluation indicators may also include: overall equipment efficiency, mean time between failures (MTBF), task execution accuracy, and task result consistency. Among these, overall equipment efficiency is the product of time utilization rate, performance utilization rate, and pass rate, comprehensively reflecting equipment efficiency; mean time between failures (MTBF) is the ratio of total fault-free operation time to the number of failures in a statistical base period, reflecting the health status of the equipment; task execution accuracy is the proportion of task execution results that meet expected requirements; and task result consistency is the degree of consistency in results obtained when performing the same task at different times or on different equipment. Furthermore, overall equipment efficiency and MTBF are evaluation indicators used to reflect equipment performance and reliability, while task execution accuracy and task result consistency are evaluation indicators used to reflect task quality.

[0054] Step S120: Determine the system score of the mobile robot system based on the current index values ​​corresponding to several evaluation indicators.

[0055] In some embodiments, before calculating the system score, an evaluation system needs to be determined. This system assigns corresponding weights to each evaluation indicator based on the specific application scenario and requirements, reflecting its importance in the overall evaluation. Then, the system score of the mobile robot is obtained by multiplying the scores of all dimensions of the evaluation indicators by their total weights and summing the results. In this embodiment, the system score includes at least one of a system operation score and a risk quantification score. The system operation score is used to evaluate the operational capability of the mobile robot system, and the risk quantification score is used to reflect potential hazards in the mobile robot system. For details, please refer to [link to relevant documentation]. Figure 2 , Figure 2 This is a flowchart illustrating an embodiment of step S120 in the early warning method for a mobile robot system of this application. Specifically, it may include the following steps:

[0056] Step S121: Determine the actual index score of each evaluation index based on the current index value corresponding to each evaluation index.

[0057] In some embodiments, after obtaining the current index value corresponding to each evaluation index, for each evaluation index, the index value range in which the current index value of the evaluation index is located is determined, and the score that matches the index value range in which the current index value is located is obtained as the actual index score of the evaluation index.

[0058] Specifically, x can be used i Let S represent the current value of the i-th evaluation indicator, then the actual score S of the five evaluation indicators is... i The calculation is as follows:

[0059] 1. x1 represents the current value of the first evaluation indicator, namely the current value of the average number of equipment failures. The actual score S1 of the average number of equipment failures is calculated as follows:

[0060]

[0061] 2. x2 represents the current value of the second evaluation indicator, namely the current value of the number of times the platform service malfunctions. The actual score S2 for the number of times the platform service malfunctions is calculated as follows:

[0062]

[0063] 3. x3 represents the current value of the third evaluation indicator, i.e., the current value of the task failure rate. The actual score S3 of the task failure rate is calculated as follows:

[0064]

[0065] 4. x4 represents the current value of the fourth evaluation indicator, namely the current value of the task untimeliness rate. The actual score S4 of the task untimeliness rate is calculated as follows:

[0066]

[0067] 5. x5 represents the current value of the fifth evaluation indicator, namely the current value of the congestion anomaly rate. The actual score S5 of the congestion anomaly rate is calculated as follows:

[0068]

[0069] As can be seen from the above, the actual scores of some evaluation indicators are calculated in the same way, while the actual scores of other evaluation indicators are calculated in different ways.

[0070] Step S122: Weight the actual scores of several evaluation indicators to obtain the system operation score.

[0071] In some embodiments, the actual scores of several evaluation indicators can be directly weighted to obtain the system performance score. For example, S 总 = a1×S1+a2×S2+a3×S3+a4×S4+a5×S5, where a1+a2+a3+a4+a5=1.S 总 The score is given for system performance.

[0072] In other embodiments, considering that different categories of evaluation indicators may have different dimensions and magnitudes, directly performing weighted summation may lead to some evaluation indicators having an excessively large or small impact on the total score. Therefore, a classification method is first used to categorize evaluation indicators of different natures, and then the system operation score is calculated using the categorized evaluation indicators. Specifically, several evaluation indicators include at least one first-category evaluation indicator and at least one second-category evaluation indicator. Then, using the weights of each first-category evaluation indicator, the actual indicator scores of each first-category evaluation indicator are weighted and summed to obtain a first weighted score. Similarly, using the weights of each second-category evaluation indicator, the actual indicator scores of each second-category evaluation indicator are weighted and summed to obtain a second weighted score. Finally, using the weights corresponding to the first weighted score and the second weighted score respectively, the first weighted score and the second weighted score are weighted and summed to obtain the system operation score.

[0073] For example, the first type of evaluation indicators includes at least one of the following: average number of equipment failures, number of platform service operation anomalies, and task failure rate. The second type of evaluation indicators includes at least one of the following: task untimeliness rate and congestion anomaly rate. Furthermore, the sum of the weights of each first type of evaluation indicator, the sum of the weights of each second type of evaluation indicator, and the sum of the weights of the first weighted score and the second weighted score are all preset values. In this embodiment, the preset value is 1; therefore, the system operation score is calculated as follows:

[0074]

[0075] in,

[0076] Step S123: Based on the current and historical values ​​of each evaluation indicator, calculate the rate of change and statistical value of each evaluation indicator.

[0077] In some embodiments, for each evaluation indicator, to obtain the rate of change of each evaluation indicator, the difference between the current indicator value and the historical indicator value can be used as the first indicator difference of the evaluation indicator, and then the ratio between the first indicator difference and the historical indicator value of the evaluation indicator can be used as the rate of change of the evaluation indicator. Specifically, this can be combined with formula (7):

[0078]

[0079] Among them, R i Let x represent the rate of change of the i-th evaluation indicator. i|t Let x represent the current value of the i-th evaluation indicator within the current time t. i|t-1 Let x represent the historical value of the i-th evaluation indicator within the previous time period t-1. i|t-x i|t-1 The first indicator difference.

[0080] For each evaluation indicator, to obtain the corresponding statistical value, a preset multiple of the ratio of the sum of the indicator values ​​corresponding to multiple statistical times to the total statistical time is used as the statistical value. Here, the current indicator value is the indicator value corresponding to the current statistical time, historical indicator values ​​include the indicator values ​​corresponding to at least one previous statistical time, and the total statistical time is the sum of multiple statistical times. Specifically, this can be combined with formula (8):

[0081]

[0082] Among them, T i Let x represent the statistical value of the i-th evaluation indicator. i|j Let represent the value of the i-th evaluation indicator at time j, t be the total statistical time, and m be a preset multiple. In this embodiment, m = 2.

[0083] Step S124: Calculate the risk quantification score based on the rate of change and statistical value of each evaluation indicator.

[0084] In some embodiments, for each evaluation indicator, the indicator change rate score is determined based on the change rate interval of the indicator, and the indicator threshold score is determined based on the statistical value interval of the indicator. The indicator change rate score focuses on the trend of the evaluation indicator over time, helping to identify risks that are gradually worsening; the indicator threshold score focuses on whether the evaluation indicator exceeds the normal range, enabling timely detection of sudden anomalies. By integrating the two, the potential risks of the mobile robot system in different aspects can be more comprehensively assessed. Then, the indicator change rate score and indicator threshold score corresponding to each evaluation indicator are integrated to obtain a risk quantification score. Specifically, see formulas (9) and (10):

[0085]

[0086]

[0087] in, This represents the score for the rate of change of the i-th evaluation indicator. This represents the threshold score of the i-th evaluation indicator.

[0088] For each evaluation indicator, before fusing the indicator change rate score and indicator threshold score for each indicator, a fused score of the indicator change rate score and indicator threshold score for each indicator can be obtained first. The fused scores for each evaluation indicator are then weighted to obtain the risk quantification score. Specifically, the sum of the indicator change rate score and indicator threshold score for each evaluation indicator is first used as the fused score for that indicator. Then, a preset ratio of the weighted results is used as the risk quantification score, with the sum of the weights of the fused scores for each evaluation indicator being the reciprocal of this preset ratio. A weighted approach is used in calculating the risk quantification score, assigning different weights to each evaluation indicator based on its importance in the system risk. This method highlights the impact of key indicators on the risk quantification score, making the assessment results more consistent with reality. For example, for a mobile robot operating in a complex environment, the accuracy of the perception system may be more critical than the execution time of a single task. By reasonably allocating weights, it can be ensured that the risk quantification score better reflects the core risk of the system.

[0089] The result of the weighted processing is the weighted sum. See formula (11):

[0090]

[0091] Among them, S 风 This represents the risk quantification score. It's important to know that W in formula (11)... i With W in formula (6) i Similarly, 1 / 2 is the reciprocal of the preset ratio.

[0092] Step S130: Based on the system score, issue an early warning for the mobile robot system.

[0093] In some embodiments, the mobile robot system can be given an early warning based on its system score. For example, if the system score includes a system operation score, and the system operation score does not meet the preset operation score requirements, a problem evaluation indicator is selected from several evaluation indicators based on the actual indicator scores of each evaluation indicator. This problem indicator is then identified as the main problem point of the mobile robot system. The actual indicator score is determined based on the current value of the evaluation indicator. To accurately pinpoint the main problem point, an intuitive and effective method can be used: select the problem evaluation indicator with the lowest actual indicator score from several evaluation indicators as the main problem point of the mobile robot system. This approach quickly and accurately identifies the most urgent areas for improvement in the mobile robot system, providing clear guidance for subsequent optimization and upgrades. This improves the overall system performance and ensures the robot operates stably and efficiently in various complex and changing task scenarios.

[0094] For another example, if the system score includes a risk quantification score, when the risk quantification score does not meet the preset risk score requirement, at least one risk-related score corresponding to each evaluation index is used to select risk evaluation indexes from a number of evaluation indexes as the risk points of the mobile robot system. Among them, the number of risk-related scores is determined based on the current index value and historical index value of the evaluation index. The risk-related score includes at least one of the index change rate score and the index threshold score. The index change rate score reflects the change trend of the evaluation index over time, which can help the system identify those situations where the index value fluctuates abnormally or the change rate is too fast, and these situations often indicate potential risks. The index threshold score is determined according to whether the evaluation index exceeds the preset normal range. When the index value approaches or exceeds the threshold, the risk of the system will increase accordingly. By comprehensively considering these risk-related scores, the risk degree contained in each evaluation index can be comprehensively evaluated.

[0095] To accurately find the risk points in the mobile robot system, at least one risk evaluation index with the lowest sum of risk-related scores can be selected from a number of evaluation indexes as the risk point of the mobile robot system. The advantage of this method is that it not only considers the influence of a single risk factor, but also comprehensively considers the combined effect of multiple risk factors, making the risk assessment more comprehensive and accurate. In this way, the link with the highest risk degree in the system can be quickly and accurately identified, providing a clear direction for subsequent risk management and system optimization. For example, if the sum of the risk-related scores of a certain evaluation index is significantly lower than other indexes, it indicates that there are relatively high risks in the system functions or performances represented by this index, and improvement and optimization are required first.

[0096] Please refer to Figure 3 , Figure 3 which is a schematic flowchart of another embodiment of the warning method for the mobile robot system of the present application. Specifically, it may include the following steps:

[0097] Step S310: Obtain the current index value and historical index value corresponding to each of a number of evaluation indexes of the mobile robot system.

[0098] In some embodiments, the current index value corresponding to the evaluation index can be directly obtained through a sensor, or the current index value corresponding to the evaluation index can be obtained through data analysis or other means. The historical index value corresponding to the evaluation index can be called from a database. Specifically, the corresponding data in the database can be called according to the unique identifier corresponding to the evaluation index. Or the historical index value corresponding to the evaluation index can be obtained from the local storage space of the mobile robot system.

[0099] Step S320: Determine the system score of the mobile robot system based on the current index value corresponding to each of the number of evaluation indexes.

[0100] This step is the same as step S120 above, and will not be repeated here.

[0101] Step S330: Based on the historical index values ​​corresponding to several evaluation indicators, determine the changing trend of each evaluation indicator within a preset time period.

[0102] In some embodiments, historical data of evaluation indicators are analyzed to observe their trends and predict potential future risks. If an evaluation indicator shows a continuous upward or downward trend and approaches or exceeds a preset warning line, the system issues an early warning. For example, analyzing historical data of the task failure rate reveals a continuous increase over the past month. Although the current value has not yet exceeded the threshold, the upward trend indicates a potential problem, requiring early warning and intervention. The preset time period can be one month, one week, one day, etc.

[0103] The trends of multiple related indicators can also be analyzed together to more comprehensively determine whether there are risks in the system. For example, if the average number of device failures and the number of platform service malfunctions both show an upward trend, even if the individual indicator has not exceeded the threshold, it may be a signal of a decline in the overall performance of the system, requiring an early warning.

[0104] Furthermore, in this embodiment, the trend of change can be represented in binary form. For example, 00 indicates a normal trend, 01 indicates a continuously rising trend, 10 indicates a continuously falling trend, and 11 indicates a sudden change in data (which can indicate a sudden rise or fall in data) requiring an immediate alarm.

[0105] Step S340: Based on the system score and the trend of change, issue an early warning for the mobile robot system.

[0106] By combining the system score and the changing trends of various evaluation indicators, the problems of the mobile robot system can be quickly and accurately located, and warnings can be given about potential risks in the future so that preparations can be made in advance.

[0107] Based on the severity of system scores and indicator trends, risk warnings are categorized into different levels, such as low risk, medium risk, and high risk. Different levels of risk warnings correspond to different response measures and processing priorities.

[0108] In some embodiments, historical index values ​​corresponding to evaluation indicators can be used to train a machine learning model, allowing the model to learn the complex relationships between various evaluation indicators and system risk. For example, algorithms such as decision trees and neural networks can be used to train a risk prediction model based on historical index values ​​and corresponding risk events. During system operation, real-time collected evaluation indicator data is input into the trained machine learning model, which can automatically predict the current risk status of the system and trigger corresponding warnings based on the prediction results. This method can improve the accuracy and intelligence of risk warnings, enabling timely detection of potential risk problems.

[0109] This application comprehensively considers multiple dimensions in the AMR operating system, including equipment, platform services, task efficiency, and traffic congestion, resulting in more comprehensive and universal indicators that are not limited by the AMR system's operating scenarios. Furthermore, it proposes a risk warning method, defining a calculation method for system operational risk. Risk scores are quantified by combining two dimensions: the rate of change of indicators and the statistical values ​​of indicators. By quantifying risk, the current risk points in system operation can be more clearly identified, enabling timely warning measures to improve the reliability and stability of system operation. In addition, this application defines a separate scoring calculation method for each evaluation indicator, ensuring the independence of each indicator. When the system score is low, the scores of various indicators can be combined to quickly pinpoint the optimizable points in the current system.

[0110] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0111] Please see Figure 4 , Figure 4 This is a schematic diagram of an embodiment of the mobile robot system early warning device 400 of this application. The system early warning device 400 includes an acquisition module 410, a statistics module 420, and an early warning module 430. The acquisition module 410 acquires the current index values ​​corresponding to several evaluation indicators of the mobile robot system, wherein the several evaluation indicators belong to at least two of the following dimensions: equipment failure status, platform service operation status, task execution status, and congestion status. The statistics module 420 determines the system score of the mobile robot system based on the current index values ​​corresponding to the several evaluation indicators. The early warning module 430 issues an early warning to the mobile robot system based on the system score.

[0112] In some embodiments, several evaluation metrics include at least one of the following: average number of device failures, number of platform service operation anomalies, task failure rate, task delay rate, and congestion anomaly rate. The average number of device failures represents the ratio of the total number of failures reported by all devices in the mobile robot system per unit time to the total number of all running devices. The number of platform service operation anomalies represents the sum of anomalies in the platform software layer of the mobile robot system per unit time. The task failure rate represents the ratio of the number of failed tasks to the total number of executed tasks in the mobile robot system per unit time. The task delay rate represents the ratio of the number of tasks that could not be completed within the specified time to the total number of executed tasks in the mobile robot system per unit time. The congestion anomaly rate represents the ratio of the number of times device congestion occurred in the mobile robot system per unit time to the total number of congestion events.

[0113] In some embodiments, the statistics module 420 executes the system score, which includes at least one of the system operation score and the risk quantification score; and determines the system score of the mobile robot system based on the current index values ​​corresponding to several evaluation indicators, including: determining the actual index score of each evaluation indicator based on the current index value corresponding to each evaluation indicator; weighting the actual index scores of several evaluation indicators to obtain the system operation score; calculating the index change rate and index statistical value corresponding to each evaluation indicator based on the current index value and historical index value corresponding to each evaluation indicator; and calculating the risk quantification score based on the index change rate and index statistical value corresponding to each evaluation indicator.

[0114] In some embodiments, the statistics module 420 performs the following steps: determining the actual index score of each evaluation index based on the current index value corresponding to each evaluation index; for each evaluation index, determining the index value range in which the current index value of the evaluation index is located, and obtaining the score that matches the index value range in which the current index value is located as the actual index score of the evaluation index; and / or, the plurality of evaluation indicators include at least one first type of evaluation index and at least one second type of evaluation index; weighting the actual index scores of the plurality of evaluation indicators to obtain the system operation score; including: using the weights of each first type of evaluation index, weighting and summing the actual index scores of each first type of evaluation index to obtain a first weighted score; and using the weights of each second type of evaluation index, weighting and summing the actual index scores of each second type of evaluation index to obtain a second weighted score; and using the weights of the first weighted score and the second weighted score respectively, weighting and summing the first weighted score and the second weighted score to obtain the system operation score.

[0115] In some embodiments, the statistics module 420 executes at least one of the following first-class evaluation indicators: average number of equipment failures, number of platform service operation anomalies, and task failure rate; and at least one of the following second-class evaluation indicators: task untimeliness rate and congestion anomaly rate; and / or, the sum of the weights of each first-class evaluation indicator, the sum of the weights of each second-class evaluation indicator, and the sum of the weights of the first weighted score and the second weighted score are all preset values.

[0116] In some embodiments, the statistics module 420 performs calculations based on the current and historical values ​​of each evaluation indicator to determine the rate of change and statistical value of each indicator, including at least one of the following steps: For each evaluation indicator, the difference between the current and historical values ​​is used as the first indicator difference, and the ratio of the first indicator difference to the historical values ​​is used as the rate of change; for each evaluation indicator, a preset multiple of the ratio of the sum of the indicator values ​​over multiple statistical periods to the total statistical period is used as the statistical value. The current indicator value is the indicator value corresponding to the current statistical time. Historical indicator values ​​include the indicator values ​​corresponding to at least one previous statistical time. The total statistical time is the sum of multiple statistical times. And / or, based on the indicator change rate and indicator statistical value corresponding to each evaluation indicator, a risk quantification score is calculated, including: for each evaluation indicator, determining the indicator change rate score based on the change rate interval of the indicator change rate, and determining the indicator threshold score based on the statistical value interval of the indicator statistical value; and fusing the indicator change rate score and indicator threshold score corresponding to each evaluation indicator to obtain the risk quantification score.

[0117] In some embodiments, the statistics module 420 performs a fusion of the indicator change rate score and indicator threshold score corresponding to each evaluation indicator to obtain a risk quantification score, including: for each evaluation indicator, obtaining the fusion score of the indicator change rate score and indicator threshold score corresponding to the evaluation indicator; and performing weighted processing using the fusion scores corresponding to each evaluation indicator to obtain a risk quantification score.

[0118] In some embodiments, the result of the weighted processing performed by the statistics module 420 is a weighted summation result; and / or, obtaining the fusion score of the indicator change rate score and the indicator threshold score corresponding to the evaluation indicator, including: using the sum of the indicator change rate score and the indicator threshold score corresponding to the evaluation indicator as the fusion score corresponding to the evaluation indicator; and / or, using the fusion scores corresponding to each evaluation indicator for weighted processing to obtain a risk quantification score, including: using a preset ratio of the weighted processing result as the risk quantification score, where the sum of the weights of the fusion scores corresponding to each evaluation indicator is the reciprocal of the preset ratio.

[0119] In some embodiments, the early warning module 430 executes a system score including at least one of a system operation score and a risk quantification score; based on the system score, it issues an early warning for the mobile robot system, including: in response to the system score including the system operation score, if the system operation score does not meet the preset operation score requirements, selecting a problem evaluation indicator from several evaluation indicators based on the actual indicator scores of each evaluation indicator as the main problem point of the mobile robot system, wherein the actual indicator score is determined based on the current indicator value of the evaluation indicator; in response to the system score including the risk quantification score, if the risk quantification score does not meet the preset risk score requirements, selecting a risk evaluation indicator from several evaluation indicators based on at least one risk-related score corresponding to each evaluation indicator as the risk point of the mobile robot system, wherein the several risk-related scores are determined based on the current indicator value and historical indicator value of the evaluation indicator.

[0120] In some embodiments, the early warning module 430 performs a process of selecting a problem evaluation indicator from several evaluation indicators based on the actual indicator scores of each evaluation indicator, as the main problem point of the mobile robot system. This includes: selecting the problem evaluation indicator with the lowest actual indicator score from several evaluation indicators as the main problem point of the mobile robot system; and / or, the risk-related score includes at least one of the indicator change rate score and the indicator threshold score; and / or, based on at least one risk-related score corresponding to each evaluation indicator, selecting a risk evaluation indicator from several evaluation indicators as the risk point of the mobile robot system. This includes: selecting the risk evaluation indicator with the lowest sum of at least one risk-related score from several evaluation indicators as the risk point of the mobile robot system.

[0121] Please see Figure 5 , Figure 5 This is a schematic diagram of the framework of an embodiment of the electronic device 50 of this application. The electronic device 50 includes a memory 51 and a processor 52 coupled to each other. The processor 52 is used to execute program instructions stored in the memory 51 to implement the steps in any of the above embodiments of the mobile robot system early warning method. In a specific implementation scenario, the electronic device 50 may include, but is not limited to, a microcomputer or a server. In addition, the electronic device 50 may also include mobile devices such as laptops and tablets, which are not limited here.

[0122] Specifically, processor 52 controls itself and memory 51 to implement the steps in any of the above-described embodiments of the mobile robot system early warning method. Processor 52 can also be referred to as a CPU (Central Processing Unit). Processor 52 may be an integrated circuit chip with signal processing capabilities. Processor 52 can also be a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor can be a microprocessor or any conventional processor. Furthermore, processor 52 can be implemented using integrated circuit chips.

[0123] Please see Figure 6 , Figure 6 This is a schematic diagram of a framework of an embodiment of the computer-readable storage medium 60 of this application. The computer-readable storage medium 60 stores program instructions 601 that can be executed by a processor. The program instructions 601 are used to implement the steps in any of the above embodiments of the mobile robot system early warning method.

[0124] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0125] The description of the various embodiments above tends to emphasize the differences between the various embodiments. The similarities or similarities between them can be referred to, and for the sake of brevity, they will not be repeated here.

[0126] In the several embodiments provided in this application, it should be understood that the disclosed methods and apparatus can be implemented in other ways. For example, the apparatus implementations described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0127] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0128] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for early warning of a mobile robot system, characterized in that, The method is applied to a mobile robot system, and the method includes: Obtain the current index values ​​corresponding to several evaluation indicators of the mobile robot system, wherein the several evaluation indicators belong to at least two of the following dimensions: equipment failure status, platform service operation status, task execution status, and congestion status; Based on the current index values ​​corresponding to the aforementioned evaluation indicators, the system score of the mobile robot system is determined, wherein the system score includes a system operation score and a risk quantification score. Based on the system score, an early warning is issued for the mobile robot system; The step of determining the system score of the mobile robot system based on the current index values ​​corresponding to the plurality of evaluation indicators includes: Based on the current index value corresponding to each of the evaluation indicators, the actual index score of each of the evaluation indicators is determined. The actual scores of the aforementioned evaluation indicators are weighted to obtain the system performance score. Based on the current and historical values ​​of each evaluation indicator, the rate of change and statistical value of each evaluation indicator are calculated. The risk quantification score is calculated based on the rate of change and statistical value of each evaluation indicator.

2. The method according to claim 1, characterized in that, The evaluation metrics include at least one of the following: average number of device failures, number of platform service operation anomalies, task failure rate, task delay rate, and congestion anomaly rate. The average number of device failures represents the ratio of the total number of failures reported by all devices in the mobile robot system per unit time to the total number of all running devices. The number of platform service operation anomalies represents the sum of anomalies in the platform software layer of the mobile robot system per unit time. The task failure rate represents the ratio of the number of failed tasks to the total number of executed tasks in the mobile robot system per unit time. The task delay rate represents the ratio of the number of tasks that could not be completed within the specified time to the total number of executed tasks in the mobile robot system per unit time. The congestion anomaly rate represents the ratio of the number of times device congestion occurred in the mobile robot system per unit time to the total number of congestion events.

3. The method according to claim 1, characterized in that, The step of determining the actual score of each evaluation indicator based on the current indicator value corresponding to each evaluation indicator includes: For each of the evaluation indicators, determine the range of indicator values ​​in which the current indicator value of the evaluation indicator is located, and obtain the score that matches the range of indicator values ​​in which the current indicator value is located, as the actual indicator score of the evaluation indicator. And / or, the plurality of evaluation indicators includes at least one first-type evaluation indicator and at least one second-type evaluation indicator; the weighted processing of the actual indicator scores of the plurality of evaluation indicators to obtain the system operation score includes: Using the weights of each of the first type of evaluation indicators, the actual scores of each of the first type of evaluation indicators are weighted and summed to obtain a first weighted score; and, Using the weights of each of the second-category evaluation indicators, the actual scores of each of the second-category evaluation indicators are weighted and summed to obtain the second weighted score; The system performance score is obtained by weighting and summing the first weighted score and the second weighted score using their respective weights.

4. The method according to claim 3, characterized in that, The first category of evaluation indicators includes at least one of the following: average number of equipment failures, number of platform service operation anomalies, and task failure rate. The second category of evaluation indicators includes at least one of the following: task untimeliness rate and congestion anomaly rate. And / or, the sum of the weights of each of the first type of evaluation indicators, the sum of the weights of each of the second type of evaluation indicators, and the sum of the weights of the first weighted score and the second weighted score are all preset values.

5. The method according to claim 1, characterized in that, The step of calculating the rate of change and statistical value of each evaluation indicator based on the current and historical values ​​of each evaluation indicator includes at least one of the following steps: For each of the evaluation indicators, the difference between the current indicator value and the historical indicator value corresponding to the evaluation indicator is taken as the first indicator difference of the evaluation indicator, and the ratio between the first indicator difference of the evaluation indicator and the historical indicator value of the evaluation indicator is taken as the indicator change rate corresponding to the evaluation indicator. For each of the evaluation indicators, a preset multiple of the ratio of the sum of the indicator values ​​corresponding to multiple statistical times to the total statistical time is used as the statistical value of the indicator. The current indicator value is the indicator value corresponding to the current statistical time, the historical indicator value includes the indicator value corresponding to at least one previous statistical time, and the total statistical time is the sum of multiple statistical times. And / or, the calculation of the risk quantification score based on the rate of change and statistical value of each of the evaluation indicators includes: For each of the evaluation indicators, the indicator change rate score is determined based on the change rate interval of the indicator change rate, and the indicator threshold score is determined based on the statistical value interval of the indicator statistical value. The risk quantification score is obtained by fusing the rate of change score and the threshold score corresponding to each of the evaluation indicators.

6. The method according to claim 5, characterized in that, The risk quantification score is obtained by fusing the indicator change rate score and indicator threshold score corresponding to each of the evaluation indicators, including: For each of the evaluation indicators, obtain the fusion score of the indicator change rate score and the indicator threshold score corresponding to the evaluation indicator; The risk quantification score is obtained by weighting the fusion scores corresponding to each of the evaluation indicators.

7. The method according to claim 6, characterized in that, The result of the weighted processing is the result of weighted summation; And / or, obtaining the fused score of the indicator change rate score and the indicator threshold score corresponding to the evaluation indicator includes: The sum of the rate of change score and the threshold score corresponding to the evaluation indicator is taken as the fusion score corresponding to the evaluation indicator. And / or, the step of weighting the fusion scores corresponding to each of the evaluation indicators to obtain the risk quantification score includes: The preset ratio of the weighted processing result is used as the risk quantification score, and the sum of the weights of the fusion scores corresponding to each of the evaluation indicators is the reciprocal of the preset ratio.

8. The method according to claim 1, characterized in that, The system score includes at least one of the system operation score and the risk quantification score; The step of issuing an early warning to the mobile robot system based on the system score includes: In response to the system score including the system operation score, if the system operation score does not meet the preset operation score requirements, based on the actual index scores of each of the evaluation indicators, problem evaluation indicators are selected from the plurality of evaluation indicators as the main problem points of the mobile robot system, wherein the actual index scores are determined based on the current index values ​​of the evaluation indicators; In response to the system score including the risk quantification score, if the risk quantification score does not meet the preset risk score requirements, a risk evaluation index is selected from the plurality of evaluation indicators based on at least one risk-related score corresponding to each of the evaluation indicators, and is used as the risk point of the mobile robot system. The plurality of risk-related scores are determined based on the current index value and historical index value of the evaluation indicators.

9. The method according to claim 8, characterized in that, The step of selecting problem evaluation indicators from the plurality of evaluation indicators based on the actual index scores of each of the evaluation indicators, as the main problem points of the mobile robot system, includes: From the aforementioned evaluation indicators, the problem evaluation indicator with the lowest actual indicator score is selected as the main problem point of the mobile robot system. And / or, the risk-related score includes at least one of the indicator change rate score and the indicator threshold score; And / or, the step of selecting a risk assessment indicator from the plurality of evaluation indicators based on at least one risk-related score corresponding to each of the evaluation indicators, as a risk point of the mobile robot system, includes: From the aforementioned evaluation indicators, the risk evaluation indicator with the lowest sum of at least one risk-related scores is selected as the risk point of the mobile robot system.

10. An electronic device, characterized in that, The system includes a memory and a processor coupled to each other, the processor being used to execute program instructions stored in the memory to implement the system early warning method according to any one of claims 1 to 9.

11. A computer-readable storage medium having program instructions stored thereon, characterized in that, When the program instructions are executed by the processor, they implement the system early warning method according to any one of claims 1 to 9.

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