Urban community embedded service facility operation management method and system based on deep learning

Abstract

The invention relates to the technical field of intelligent monitoring and maintenance, in particular to an urban community embedded service facility operation management method and system based on deep learning, and the method comprises the steps: dynamically collecting equipment state data, analyzing the state data through a deep learning algorithm, and generating an equipment health index and a fault risk assessment value. And finally outputting fault early warning data. Through the intelligent monitoring and prediction capability, real-time monitoring and fault early warning can be realized, the maintenance decision can be optimized, the fault occurrence rate can be reduced, the efficient and safe operation of equipment can be ensured, and the intelligent development of urban communities can be promoted.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring and maintenance technology, and in particular to an urban community embedded service facility operation and management method and system based on deep learning. Background Art

[0002] As the global urbanization process continues to accelerate, the number of service facilities in urban communities has increased rapidly, such as water supply, power supply, traffic signals, public safety monitoring, etc. The effective operation of these facilities is directly related to the quality of life of urban residents and the overall efficiency of urban management. Therefore, how to achieve intelligent management of service facilities, improve their operating efficiency, and reduce the risk of failure has become an urgent problem to be solved.

[0003] Existing technologies generally rely on traditional monitoring and maintenance methods, such as regular manual inspections, empirical judgments based on historical data, and alarm mechanisms with preset thresholds. These methods have several significant shortcomings: First, manual inspections often have long time intervals and insufficient coverage, which leads to delayed recognition of early fault signals, thus affecting the normal operation of the equipment; second, traditional methods lack intelligence and cannot effectively integrate data from multiple sensors, resulting in an in-depth understanding of the overall operating status of the equipment. In addition, fault diagnosis that relies on rules often cannot adapt to the complex and changeable equipment operating environment, and is prone to misjudgment or missed judgment, which in turn increases the maintenance cost and downtime of the equipment. Summary of the invention

[0004] In view of the many problems existing in the above-mentioned prior art, the present invention provides an urban community embedded service facility operation management method and system based on deep learning. The present invention analyzes the equipment status data through a deep learning model and generates a fault risk assessment in combination with an integrated learning algorithm. This process can not only monitor the health status of the equipment in real time, but also predict faults based on historical data and real-time data, thereby generating detailed fault warnings and processing suggestions. Through the present invention, the intelligent and automated equipment management is realized, which significantly improves the operational efficiency and safety of the equipment.

[0005] A method for operating and managing urban community embedded service facilities based on deep learning, comprising the following steps: Dynamically collect device status data, where the device status data is obtained through multi-dimensional IoT sensors deployed on key devices to form first device status monitoring data; Build a digital twin model of the equipment based on the first equipment status monitoring data, use deep learning technology and multimodal data fusion technology to generate fused equipment status data, analyze the health status of the equipment in real time, and generate targeted optimization suggestion data; Execute intelligent equipment inspections. The inspection robot conducts autonomous inspections based on the generated optimization suggestion data, collects inspection equipment status data in real time, and forms second equipment status monitoring data. The second equipment status monitoring data is linked with the digital twin model for deep learning to generate analysis results of the equipment operation status. Based on the second equipment status monitoring data and its analysis results, deep learning algorithms and integrated learning algorithms are applied to identify the failure risk of equipment and generate failure warning data.

[0006] Preferably, the multidimensional Internet of Things sensor includes a temperature sensor, a humidity sensor, a vibration sensor and a sound sensor, and the first device status monitoring data includes the following parameters: a real-time temperature value collected by the temperature sensor, a real-time humidity value collected by the humidity sensor, a real-time vibration frequency and amplitude value collected by the vibration sensor, and a real-time sound intensity collected by the sound sensor.

[0007] Preferably, the construction of the digital twin model includes using the first device status monitoring data for deep learning training, specifically including: inputting the first device status monitoring data into the deep learning model, optimizing the model parameters, and realizing the prediction of the future state of the equipment through regression analysis.

[0008] Preferably, the fused device status data is composed of a weighted average of temperature data, humidity data, vibration data and sound data, and the weighting is dynamically adjusted by the historical accuracy and current reliability of each sensor, including: setting a weighting coefficient for each sensor, and adjusting its influence in the fusion calculation based on historical fault records and environmental changes.

[0009] Preferably, the optimization suggestion data includes adjustment suggestions for equipment operating parameters and a maintenance plan, and the maintenance plan includes: a maintenance schedule, a list of required materials and operating steps.

[0010] Preferably, when the inspection robot performs intelligent equipment inspection, it analyzes the inspection equipment status data collected in real time through a deep learning algorithm to achieve the evaluation and identification of the equipment operation status. The deep learning algorithm generates an equipment health index through classification and regression analysis of the inspection equipment status data. The equipment health index calculation expression is as follows:

[0011] in, Indicates the device health index; Indicates the number of monitored parameters; Indicates The weight of a parameter reflects the importance of the parameter in the calculation of the health index; Indicates The standardized value of each monitoring parameter.

[0012] Preferably, the analysis result includes an equipment health index and a potential fault risk level, wherein the equipment health index is obtained by weighted calculation of equipment status parameters, and the potential fault risk level is obtained by abnormal detection and calculation of equipment status data, and the potential fault risk level is calculated as follows:

[0013] in, Indicates the potential failure risk level; Indicates the device health index; Indicates environmental factors.

[0014] Preferably, the fault warning data includes the fault type, identification of the affected equipment and processing suggestions, wherein the processing suggestions include the time when the fault occurred, analysis of the cause of the fault and specific processing steps for the fault.

[0015] Preferably, the deep learning algorithm is used to classify the failure risks of the equipment, including: inputting the equipment status data into the deep learning model, learning different failure modes through a multi-layer neural network, and using an integrated learning algorithm to comprehensively evaluate the priority of the failure risk, thereby forming a comprehensive risk assessment report. The risk assessment calculation expression is as follows:

[0016] in, represents the fault risk assessment value; represents the number of risk factors; Indicates The weight of each risk factor; Indicates risk factor scores.

[0017] A system for implementing the deep learning-based urban community embedded service facility operation management method, comprising: A multi-dimensional IoT sensor module is used to be deployed on key equipment to dynamically collect equipment status data and form first equipment status monitoring data; A digital twin model building module is used to build a digital twin model of the device based on the first device status monitoring data, generate fused device status data using deep learning technology and multimodal data fusion technology, and analyze the health status of the device in real time to generate targeted optimization suggestion data; An intelligent inspection module is used to perform intelligent equipment inspections. The inspection robot of the intelligent inspection module performs autonomous inspections based on the generated optimization suggestion data, collects inspection equipment status data in real time, and forms second equipment status monitoring data. The second equipment status monitoring data is linked with the digital twin model for deep learning to generate analysis results of the equipment operation status. The fault identification and warning module is used to identify the failure risk of the equipment based on the second equipment status monitoring data and its analysis results, and generate fault warning data by applying deep learning algorithms and integrated learning algorithms.

[0018] Compared with the prior art, the advantages and beneficial effects of the present invention are: Through deep learning and integrated learning technology, accurate identification and real-time warning of equipment failure risks are achieved, significantly improving the reliability of equipment operation and maintenance efficiency. Compared with the existing technology, the present invention can not only collect multi-dimensional equipment status data in real time, but also comprehensively analyze environmental factors to form a comprehensive fault assessment report. This intelligent monitoring and prediction capability effectively overcomes the shortcomings of traditional methods and ensures the efficient and safe operation of equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the process of the present invention; Figure 2 It is a schematic diagram of the fusion device status data structure in the present invention; Figure 3 This is a schematic diagram of the fault warning data structure in the present invention; Figure 4 It is a schematic diagram of the fault risk assessment process in the present invention; Figure 5 It is a structural block diagram of the system of the present invention. DETAILED DESCRIPTION

[0020] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is obvious that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0021] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0022] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0023] like Figure 1 As shown, a method for operating and managing urban community embedded service facilities based on deep learning includes the following steps: Dynamically collect device status data, where the device status data is obtained through multi-dimensional IoT sensors deployed on key devices to form first device status monitoring data; Preferably, the multidimensional Internet of Things sensor includes a temperature sensor, a humidity sensor, a vibration sensor and a sound sensor, and the first device status monitoring data includes the following parameters: a real-time temperature value collected by the temperature sensor, a real-time humidity value collected by the humidity sensor, a real-time vibration frequency and amplitude value collected by the vibration sensor, and a real-time sound intensity collected by the sound sensor.

[0024] In the present invention, the process of dynamically collecting device status data is the core of realizing device monitoring and management. This process is carried out through multi-dimensional IoT sensors deployed on key devices to form first device status monitoring data. These sensors include temperature sensors, humidity sensors, vibration sensors and sound sensors, each of which is responsible for monitoring specific environmental and device status parameters.

[0025] Temperature sensors collect the temperature value of the equipment in real time, which is crucial for monitoring the working status of the equipment. An abnormal increase in temperature may indicate that the equipment is overloaded or malfunctioning, resulting in damage to the equipment. Humidity sensors are used to monitor the humidity of the surrounding environment, especially in air-conditioning and refrigeration equipment. Changes in humidity can affect the efficiency and comfort of the equipment. Vibration sensors evaluate the mechanical properties of the equipment by detecting the vibration frequency and amplitude of the equipment. Frequent or abnormal vibrations usually indicate mechanical failure or maintenance needs. Sound sensors are used to capture the sound intensity when the equipment is running. Abnormal sounds may indicate that the equipment is not operating properly or has malfunctioned.

[0026] The first equipment status monitoring data formed contains the real-time collection values ​​of these sensors. Through data fusion technology, the data of multiple sensors are integrated into a comprehensive equipment status description. This process not only improves the accuracy of the data, but also provides a comprehensive equipment operation status, laying the foundation for subsequent analysis.

[0027] In a specific implementation, the first device status monitoring data can be expressed in the following vector form:

[0028] in, represents a first device status monitoring data vector; Indicates the real-time temperature value collected by the temperature sensor; Indicates the real-time humidity value collected by the humidity sensor; Indicates the real-time vibration frequency collected by the vibration sensor; Indicates the real-time vibration amplitude collected by the vibration sensor; Indicates the real-time sound intensity collected by the sound sensor.

[0029] After data collection is completed, this information will be passed as input to the deep learning model for subsequent health status analysis and fault prediction. Through real-time analysis of the first equipment status monitoring data, the abnormal status of the equipment can be quickly identified, thereby realizing intelligent management of the equipment.

[0030] The effect of dynamically collecting equipment status data is to provide real-time and comprehensive equipment monitoring capabilities, which can not only effectively improve the operating efficiency of equipment and reduce the occurrence of failures, but also provide timely decision-making support for managers to achieve accurate maintenance and optimize resource allocation. For example, when the system detects that the temperature and vibration data exceed the preset thresholds at the same time, it can automatically trigger a fault warning and recommend managers to immediately inspect and maintain the equipment, thereby avoiding possible equipment damage and downtime, and ensuring the efficient operation of urban community service facilities.

[0031] Build a digital twin model of the equipment based on the first equipment status monitoring data, use deep learning technology and multimodal data fusion technology to generate fused equipment status data, analyze the health status of the equipment in real time, and generate targeted optimization suggestion data; In the present invention, building a digital twin model of the equipment is a key step in realizing intelligent monitoring and management. First, a digital twin model is built based on the first equipment status monitoring data. This process uses real-time data collected from multi-dimensional intelligent IoT sensors, including parameters such as temperature, humidity, vibration, and sound. These data reflect the current operating status of the equipment and provide the necessary input for the digital twin model.

[0032] The digital twin model is essentially a real-time mapping of physical equipment in virtual space, using deep learning technology for model construction and optimization. In the specific implementation, the first device status monitoring data is first input into a deep learning model, such as a long short-term memory network (LSTM) or a convolutional neural network (CNN), which can process time series data or multi-dimensional data to learn the operating mode and potential fault characteristics of the equipment. During the model training process, historical equipment status data is used to adjust the model parameters to improve the model's ability to predict future states.

[0033] Combined with multimodal data fusion technology, the model can comprehensively utilize data from different sensors to generate fused device status data. This process involves weighted integration of data from each sensor to ensure that each data reflects its importance in the final output. Weighted fusion can be performed using the following calculation formula:

[0034] in, Indicates the fusion device status data; Indicates The weight of each sensor; Indicates Data collected by sensors; Indicates the total number of sensors.

[0035] After the fusion equipment status data is generated, the model analyzes the health status of the equipment in real time, identifying equipment operating anomalies, potential failures, and efficiency losses. Through real-time monitoring and analysis, the system can generate targeted optimization suggestion data, including adjustment of equipment operating parameters, maintenance suggestions, and timing of component replacement. These optimization suggestions not only improve the operating efficiency of the equipment, but also reduce the probability of failure.

[0036] For example, during a certain operation, the system detected that both temperature and vibration data were outside the normal range. The digital twin model analyzed the data through a deep learning algorithm and found that this combination usually indicates that the equipment is about to fail. The system then generated optimization suggestions, suggesting that managers inspect and maintain the equipment, thereby avoiding potential failures and equipment downtime.

[0037] Preferably, the construction of the digital twin model includes using the first device status monitoring data for deep learning training, specifically including: inputting the first device status monitoring data into the deep learning model, optimizing the model parameters, and realizing the prediction of the future state of the equipment through regression analysis.

[0038] In the present invention, the construction of the digital twin model is an important part of achieving efficient equipment management. The process uses the first equipment status monitoring data for deep learning training, with the goal of accurately predicting the future status of the equipment through machine learning algorithms to improve operational efficiency and reduce failure rates.

[0039] The first step in building a digital twin model is to input the first device status monitoring data into the deep learning model. At this stage, the deep learning model (such as the long short-term memory network LSTM or the convolutional neural network CNN) processes the real-time data from multi-dimensional IoT sensors. The data provided by these sensors include parameters such as temperature, humidity, vibration, and sound. This information provides the model with a comprehensive description of the device status. The selection of the deep learning model is based on the time series characteristics of the device status data. LSTM is particularly suitable for processing sequence data and can effectively capture the temporal relationship of device status changes.

[0040] Next, the model parameters are optimized. This process usually uses the back-propagation algorithm to evaluate the model's predictive ability by calculating a loss function (such as mean square error). The optimization goal is to minimize the difference between the predicted value and the true value. During the training process, the model automatically adjusts the weights and biases to improve the accuracy of the prediction of the future state of the device.

[0041] Through regression analysis, the model can predict the future state of the device. For example, assuming that the health state of the device is composed of multiple parameters, the future health state can be expressed by the following formula:

[0042] in, Indicates the predicted health status of the device at the next time point; Indicates the health status of the device at the current time point; Represents the weights optimized during model training.

[0043] On this basis, the health status of the device can be concretized into multiple dimensions, such as a weighted combination of temperature, vibration, and sound. This comprehensive index helps to quickly assess the overall health of the device.

[0044] The construction of digital twin models not only provides basic data for real-time monitoring, but can also be used to implement predictive maintenance. By accurately predicting the future state of the equipment, managers can take action before problems occur and perform necessary maintenance or adjustments, thereby reducing downtime caused by equipment failure. For example, if the model predicts that the equipment may fail in the next few days, the system will automatically generate a maintenance notification and recommend that managers perform preventive maintenance.

[0045] Preferably, Figure 2 As shown, the fused device status data is composed of a weighted average of temperature data, humidity data, vibration data and sound data, and the weighting is dynamically adjusted by the historical accuracy and current reliability of each sensor, including: setting a weighting coefficient for each sensor, and adjusting its influence in the fusion calculation based on historical fault records and environmental changes.

[0046] In the present invention, the process of fusing equipment status data is a key link to improve equipment monitoring accuracy and decision support capabilities. The fused equipment status data consists of a weighted average of temperature data, humidity data, vibration data, and sound data. This method provides a comprehensive overview of equipment status by combining multi-dimensional data from different sensors.

[0047] The fusion process first requires setting the weighting coefficients for each sensor, which reflect the relative importance of each sensor in the data fusion process. The weighting coefficients are not set in stone, but are adjusted dynamically based on the historical accuracy and current reliability of each sensor. For example, in long-term operation, if a sensor performs poorly under fault conditions, its weighting coefficient will be appropriately reduced to reduce its impact on the fusion result. Conversely, sensors that perform well will receive higher weights.

[0048] The specific weighted average calculation can be expressed as:

[0049] in, Indicates the fusion device status data; Indicates The weighting coefficients of the sensors; Indicates Status data of each sensor; Indicates the total number of sensors.

[0050] In this formula, the device status data is integrated is the status data of all sensors and its corresponding weighting coefficient In this way, the system can effectively integrate the input of each sensor, eliminate the noise impact caused by fluctuations in single sensor data, and improve the reliability of equipment status.

[0051] For example, in actual applications, if the temperature sensor causes data anomalies due to environmental interference, its weighting coefficient can be dynamically adjusted through historical fault record analysis to reduce the impact of the sensor in the fusion calculation. Assuming that the historical accuracy score of the temperature sensor is 0.7, the humidity sensor is 0.9, the vibration sensor is 0.8, and the sound sensor is 0.6, after adjustment, the weighting coefficient of the temperature sensor will decrease, and the weighting coefficient of the humidity sensor It will increase, so that in the fusion calculation, the influence of humidity data will be enhanced, effectively improving the accuracy of the fusion equipment status data.

[0052] By implementing the above-mentioned dynamic weighted fusion technology, the system can not only reflect the comprehensive operating status of the equipment in real time, but also adapt to environmental changes and fluctuations in sensor performance. This efficient data fusion method enables subsequent deep learning models to obtain more accurate training data, thereby further improving the ability to analyze the health status of equipment and predict faults, and achieving more accurate operation management. Ultimately, this equipment status monitoring method based on dynamic weighted fusion can reduce the risk of equipment failure, improve the operating efficiency of service facilities, and provide strong support for the intelligent management of urban communities.

[0053] Preferably, the optimization suggestion data includes adjustment suggestions for equipment operating parameters and a maintenance plan, and the maintenance plan includes: a maintenance schedule, a list of required materials and operating steps.

[0054] In the present invention, the generation of optimization suggestion data is an important link to improve equipment operation efficiency and reduce the occurrence of failures. The optimization suggestion data specifically includes adjustment suggestions for equipment operation parameters and maintenance plans. The content of the maintenance plan covers the maintenance schedule, the list of required materials and the operation steps to ensure that managers can complete tasks in an orderly and efficient manner when performing equipment maintenance.

[0055] In principle, the generation of optimization recommendation data depends on the equipment status data collected in the early stage and the analysis results of the deep learning model. The digital twin model built by the deep learning model based on the first equipment status monitoring data can evaluate the health status of the equipment in real time and predict potential failures. These models will learn the performance of the equipment under different operating conditions and identify which parameter adjustments can significantly improve the performance of the equipment.

[0056] In the specific implementation, first, the system will automatically generate suggestions for adjusting the equipment operating parameters based on the current operating status of the equipment and the predicted future status. For example, if the temperature sensor data continues to be high, the system will recommend adjusting the operating mode of the cooling system or increasing the cooling frequency. This suggestion is obtained by calculating the relationship between the equipment operating efficiency and the current parameters, and the formula is:

[0057] in, Indicates the equipment operating efficiency; Indicates the various operating parameters of the equipment (such as temperature, humidity, vibration, etc.).

[0058] In terms of generating maintenance plans, the system will combine the health index of the equipment and the fault prediction results to develop a specific maintenance schedule. This maintenance schedule will be dynamically adjusted based on the frequency of equipment use, historical fault records and environmental factors. For example, the system recommends that maintenance be performed during the off-peak period after the peak use period of the equipment to minimize the impact of service interruptions. In addition, the maintenance plan will include a list of required materials, such as specific accessories and tools required to replace parts, to ensure that maintenance work is carried out efficiently.

[0059] Operational procedures are an important part of the maintenance plan and include: Preparation stage: Gather the necessary materials and make sure all tools and accessories are available.

[0060] Implementation steps: List each step in the maintenance process in detail, such as: shutting down the equipment, removing the old components, installing the new components, and performing system testing.

[0061] Post-inspection: Verify the performance of the equipment after maintenance to ensure that all parameters are within the normal range.

[0062] For example, in actual application, if the deep learning model predicts that a certain device will fail next week, the system will automatically generate a targeted maintenance plan, clearly listing the maintenance schedule for the weekend, the required replacement parts and their models, the tools required for maintenance, and provide step-by-step operation guides to ensure that maintenance personnel can complete maintenance tasks quickly and accurately.

[0063] Execute intelligent equipment inspections. The inspection robot conducts autonomous inspections based on the generated optimization suggestion data, collects inspection equipment status data in real time, and forms second equipment status monitoring data. The second equipment status monitoring data is linked with the digital twin model for deep learning to generate analysis results of the equipment operation status. In the present invention, the process of performing intelligent equipment inspection is crucial. It is performed autonomously by the inspection robot, aiming to collect equipment status data in real time and form second equipment status monitoring data. The core of this process is that the inspection robot uses the generated optimization suggestion data to make intelligent decisions and autonomous inspections to ensure efficient operation and timely maintenance of the equipment.

[0064] When conducting autonomous inspections, the inspection robot first automatically plans its inspection path based on the guidance in the optimization recommendation data, such as preset inspection routes and key monitoring parameters. This path planning usually combines the historical operation data and real-time status of the equipment to optimize the inspection efficiency and ensure that key equipment receives priority attention. The inspection robot is equipped with a variety of sensors, including temperature, humidity, vibration and sound sensors, which can collect the status data of the inspection equipment in real time during the inspection process. The real-time data feedback from these sensors provides the basis for forming the second equipment status monitoring data.

[0065] The formed second equipment status monitoring data can be expressed as:

[0066] in, represents a second device state monitoring data vector; Indicates the real-time temperature value collected during the inspection process; Indicates the real-time humidity value collected during the inspection process; Indicates the real-time vibration frequency collected during the inspection process; Indicates the real-time vibration amplitude collected during the inspection; Indicates the real-time sound intensity collected during the inspection process.

[0067] The data collected by the patrol robot in real time will be linked with the digital twin model for deep learning. The real-time analysis capability of the digital twin model enables the second equipment status monitoring data to be combined with the model's prediction results in a dynamic environment, thereby generating more accurate equipment operation status analysis results. By comparing the patrol data with the predicted status in the digital twin model, the system can identify anomalies or potential failures in equipment operation.

[0068] For example, suppose that during an inspection, the inspection robot detects temperature data that is significantly higher than the predicted value of the digital twin model, indicating that the equipment is overloaded. The system generates the following calculation expression to evaluate the temperature deviation:

[0069] in, Indicates temperature deviation; Indicates the real-time temperature value collected during the inspection process; Represents the temperature value predicted by the digital twin model.

[0070] if If the set threshold is exceeded, the system will issue an alarm and generate targeted maintenance recommendations, such as immediately reducing the equipment load or performing cooling treatment.

[0071] Through this mode of intelligent patrol and deep learning linkage, the operation and management of embedded service facilities in urban communities can achieve higher intelligence and automation, which not only improves the monitoring accuracy of equipment, but also reduces the probability of failure. In addition, the combination of patrol robot data collection capabilities and digital twin model analysis capabilities makes equipment maintenance and management more scientific and efficient, ultimately providing a strong guarantee for the sustainable development of the community.

[0072] Preferably, when the inspection robot performs intelligent equipment inspection, it analyzes the inspection equipment status data collected in real time through a deep learning algorithm to achieve the evaluation and identification of the equipment operation status. The deep learning algorithm generates an equipment health index through classification and regression analysis of the inspection equipment status data. The equipment health index calculation expression is as follows:

[0073] in, Indicates the device health index; Indicates the number of monitored parameters; Indicates The weight of a parameter reflects the importance of the parameter in the calculation of the health index; Indicates The standardized value of each monitoring parameter.

[0074] In this invention, when the inspection robot performs intelligent equipment inspection, it analyzes the real-time collected inspection equipment status data through deep learning algorithms, aiming to achieve accurate evaluation and identification of the equipment operation status. The core of this process is to convert multi-dimensional sensor data into actionable health indexes to provide decision support for managers.

[0075] During the inspection process, the inspection robot collects real-time equipment status data, including temperature, humidity, vibration, sound and other parameters. Through deep learning algorithms, especially classification and regression analysis models, the system can automatically identify patterns and anomalies in equipment status data. Classification analysis can classify data as normal or abnormal, while regression analysis is used to predict the health trend and future status of the equipment. This dual analysis capability ensures a comprehensive understanding of the equipment's operating status and can detect potential problems in a timely manner.

[0076] Equipment Health Index It is calculated by averaging the standardized values ​​of all monitored parameters. This process ensures that different types of data have a balanced impact on the final result. For example, if the temperature, humidity, and vibration parameters of a device are different standardized values, the calculated health index can comprehensively reflect the overall health of the device and help managers make targeted adjustments.

[0077] For example, suppose that during an inspection, the parameters collected by the inspection robot and their standardized values ​​are as follows: Temperature normalized value

[0078] Humidity normalized value

[0079] Vibration normalized value

[0080] If the number of monitoring parameters of the device , then the device health index The calculation is as follows:

[0081] In this example, the health index If the health index is 0.7, it means that the equipment is in good condition, but there is still room for improvement. The system can compare this health index with the preset threshold. If the health index is lower than a certain standard value, the system will trigger a fault warning and recommend further inspection or maintenance.

[0082] Through this deep learning algorithm-driven analysis method, the inspection robot can efficiently and accurately evaluate the operating status of the equipment, and the generated health index provides a scientific basis for managers to optimize maintenance decisions and improve equipment operating efficiency. This method not only enhances the monitoring capabilities of the equipment, but also improves the intelligence level of the entire urban community service facilities, providing strong support for achieving continuous safety and efficient operations.

[0083] Preferably, the analysis result includes an equipment health index and a potential fault risk level, wherein the equipment health index is obtained by weighted calculation of equipment status parameters, and the potential fault risk level is obtained by abnormal detection and calculation of equipment status data, and the potential fault risk level is calculated as follows:

[0084] in, Indicates the potential failure risk level; Indicates the device health index; Indicates environmental factors.

[0085] In the present invention, the generation of analysis results is a key link in realizing equipment monitoring and fault warning. These analysis results include equipment health index and potential fault risk level. The equipment health index is obtained by weighted calculation of equipment status parameters, while the potential fault risk level is calculated by abnormal detection of equipment status data, which further promotes the formulation of intelligent management and maintenance decisions.

[0086] Equipment Health Index It is calculated by comprehensively considering the weighted values ​​of various state parameters of the equipment. The calculation expression is: , reflecting the overall health of the equipment. These status parameters include temperature, humidity, vibration frequency, and sound intensity.

[0087] Potential failure risk level The calculation of the equipment health index and environmental factors Environmental factors include external temperature, humidity, etc., which may affect the performance of the device.

[0088] By combining the device health index with environmental factors, the system can identify the possible failure risk of the device. For example, if the device health index is low and the external humidity is high, the system will generate a higher failure risk level. , prompting managers to take timely measures. The risk level is calculated using a machine learning model trained to combine historical fault data and current status to achieve accurate assessment.

[0089] For example, suppose the device health index of a device is , and the current ambient humidity If the function obtained by the trained model is The following calculation results are produced: It can be concluded that: In this case, the potential failure risk level Indicates that the equipment is in a medium risk state. The management personnel should pay attention to this situation and take appropriate maintenance measures.

[0090] Based on the second equipment status monitoring data and its analysis results, deep learning algorithms and integrated learning algorithms are applied to identify the failure risk of equipment and generate failure warning data.

[0091] In the present invention, based on the second equipment status monitoring data and its analysis results, deep learning algorithms and integrated learning algorithms are used to identify equipment failure risks and generate failure warning data, which is the core link to ensure the efficient operation of the equipment. This process uses multiple algorithms to evaluate the health status of the equipment in real time, thereby optimizing maintenance decisions.

[0092] Second, equipment condition monitoring data is collected in real time by patrol robots, including multi-dimensional information such as temperature, humidity, vibration and sound. After standardization, these data are input into the deep learning model for analysis. Deep learning algorithms, such as convolutional neural networks (CNN) and long short-term memory networks (LSTM), can effectively identify complex features in the data and thus discover potential failure modes. Ensemble learning algorithms, such as random forests and gradient boosting trees, enhance the accuracy of the model and provide a more robust evaluation by combining the prediction results of multiple models.

[0093] Preferably, Figure 3 As shown, the fault warning data includes the fault type, the identification of the affected equipment and processing suggestions, wherein the processing suggestions include the time when the fault occurred, the analysis of the cause of the fault and the specific processing steps for the fault.

[0094] In principle, the generation of fault warning data relies on intelligent analysis of real-time device status data. Device status data (such as temperature, humidity, vibration, etc.) collected by multi-dimensional IoT sensors are input into deep learning models for fault risk assessment. These models are trained with a large amount of historical data and can identify abnormal patterns and potential faults in device operation. For example, when a sensor data exceeds the normal range, the system will immediately generate the corresponding fault type, indicating the specific identification of the affected device.

[0095] In terms of handling suggestions, the system records the time when the fault occurred, helping managers track the problem and assess the impact of the fault. In addition, fault cause analysis is based on the reasoning ability of the model, combining the current equipment status and historical fault records to analyze the possible factors that caused the fault. This information can be the aging of equipment components, the impact of environmental factors, or improper operation.

[0096] The system provides a series of operational suggestions for specific troubleshooting steps, such as checking power connections, replacing faulty components, or recalibrating equipment. These suggestions are generated based on data analysis results, ensuring that managers can respond quickly, reduce equipment downtime, and improve maintenance efficiency.

[0097] For example, suppose the fault warning data of an air conditioning device is generated as follows: Fault type: Electrical fault Affected equipment ID: Air conditioning equipment ID: AC-001 Failure time: August 13, 2024 14:00 Cause analysis: The device frequently experiences poor power connection problems at high temperatures.

[0098] Handling suggestions: 1. Check and reconnect the power cord; 2. Replace the damaged power adapter; 3. Appropriately reduce the equipment load in a high temperature environment.

[0099] Through this series of detailed fault warning data, managers can quickly locate problems and take measures to effectively reduce the impact of faults. Overall, the generation of fault warning data not only improves the level of intelligent equipment management, but also provides important support for the sustainable development of urban communities.

[0100] Preferably, Figure 4 As shown, the deep learning algorithm is used to classify the failure risks of equipment, including: inputting equipment status data into the deep learning model, learning different failure modes through a multi-layer neural network, and using an integrated learning algorithm to comprehensively evaluate the priority of failure risks, thereby forming a comprehensive risk assessment report. The risk assessment calculation expression is as follows:

[0101] in, represents the fault risk assessment value; represents the number of risk factors; Indicates The weight of each risk factor; Indicates risk factor scores.

[0102] In this invention, the application of deep learning algorithms in equipment failure risk assessment is the key to achieving intelligent monitoring and decision-making. By inputting equipment status data into the deep learning model, the system can learn different failure modes and combine the integrated learning algorithm to comprehensively evaluate the priority of failure risks, and finally generate a comprehensive risk assessment report.

[0103] First, the equipment status data, including parameters such as temperature, humidity, vibration and sound, is trained through a multi-layer neural network (such as a deep feedforward neural network or a convolutional neural network). During the training process, the model automatically learns and extracts data features of the equipment in normal and abnormal states, and identifies potential failure modes. This capability enables the system to accurately classify different fault types and improve the accuracy of fault warnings.

[0104] In the fault risk assessment phase, ensemble learning algorithms (such as random forests or gradient boosting trees) are used to comprehensively assess the priority of each risk factor. These algorithms enhance the stability and accuracy of fault risk assessment by combining the prediction results of multiple models, ensuring that the final report reflects the actual equipment status.

[0105] The calculation of the fault risk assessment value is based on the above formula. Through this formula, the system can combine the weight of each risk factor with its score to calculate a comprehensive risk assessment value. For example, suppose a device involves three risk factors, and their corresponding weights and scores are as follows: Temperature factors: ,

[0106] Humidity Factors: ,

[0107] Vibration factors: ,

[0108] The fault risk assessment value The calculation of is as follows:

[0109] In this example, the calculated risk assessment value is It means that the equipment is in a medium-risk state. Management personnel should pay attention to this and take timely measures to maintain it.

[0110] Through the combination of deep learning and integrated learning, the system can accurately classify and evaluate the failure risks of equipment and generate detailed risk assessment reports. These reports not only provide specific failure risk levels, but also provide managers with targeted treatment suggestions to ensure the efficient and safe operation of equipment and provide strong support for the intelligent management of urban community service facilities.

[0111] like Figure 5 As shown, a system for implementing the urban community embedded service facility operation management method based on deep learning includes: The multi-dimensional IoT sensor module is used to be deployed on key equipment to dynamically collect equipment status data and form the first equipment status monitoring data; the module consists of a variety of sensors (such as temperature, humidity, vibration and sound sensors), which are deployed on key equipment and are responsible for dynamically collecting equipment status data. These sensors monitor the operation of the equipment in real time and generate the first equipment status monitoring data. These data provide basic information for subsequent analysis to ensure that the system accurately grasps the equipment status.

[0112] The digital twin model construction module is used to construct a digital twin model of the device based on the first device status monitoring data, use deep learning technology and multimodal data fusion technology to generate fused device status data, and analyze the health status of the device in real time to generate targeted optimization suggestion data; this module constructs a digital twin model of the device based on the first device status monitoring data. Digital twin technology creates a virtual model of the device so that it reflects the status of the real device in a simulated environment. Combining deep learning technology and multimodal data fusion technology, the system can generate fused device status data in real time and analyze the health status of the device. This process not only improves the accuracy of fault prediction, but also generates targeted optimization suggestions to help managers formulate maintenance strategies.

[0113] The intelligent inspection module is used to perform intelligent equipment inspections. The inspection robot of the intelligent inspection module performs autonomous inspections based on the generated optimization suggestion data, collects the inspection equipment status data in real time, and forms the second equipment status monitoring data. The second equipment status monitoring data is linked with the digital twin model for deep learning to generate the analysis results of the equipment operation status. The intelligent inspection module performs autonomous inspections of the equipment through the inspection robot. The robot acts according to the optimization suggestion data generated above, collects the inspection equipment status data in real time, and forms the second equipment status monitoring data. These data are linked with the digital twin model for deep learning to generate the analysis results of the equipment operation status, ensuring the efficiency and accuracy of the inspection process.

[0114] The fault identification and early warning module is used to identify the fault risk of the equipment based on the second equipment status monitoring data and its analysis results, and to generate fault early warning data by applying deep learning algorithms and integrated learning algorithms. This module is based on the second equipment status monitoring data and its analysis results, and uses deep learning algorithms and integrated learning algorithms to identify the fault risk of the equipment. By comprehensively evaluating the equipment status and environmental factors, the system generates fault early warning data, promptly reminds managers to deal with potential problems, and reduce the risk of equipment failure.

[0115] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.

[0116] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for operating and managing urban community embedded service facilities based on deep learning, characterized in that: The following steps are involved: Dynamically collect device status data, where the device status data is obtained through multi-dimensional IoT sensors deployed on key devices to form first device status monitoring data; Build a digital twin model of the equipment based on the first equipment status monitoring data, use deep learning technology and multimodal data fusion technology to generate fused equipment status data, analyze the health status of the equipment in real time, and generate targeted optimization suggestion data; Execute intelligent equipment inspections. The inspection robot conducts autonomous inspections based on the generated optimization suggestion data, collects inspection equipment status data in real time, and forms second equipment status monitoring data. The second equipment status monitoring data is linked with the digital twin model for deep learning to generate analysis results of the equipment operation status. Based on the second equipment status monitoring data and its analysis results, deep learning algorithms and integrated learning algorithms are applied to identify the failure risk of equipment and generate failure warning data.

2. The method according to claim 1, characterized in that The multidimensional Internet of Things sensor includes a temperature sensor, a humidity sensor, a vibration sensor and a sound sensor, and the first device status monitoring data includes the following parameters: a real-time temperature value collected by the temperature sensor, a real-time humidity value collected by the humidity sensor, a real-time vibration frequency and amplitude value collected by the vibration sensor, and a real-time sound intensity collected by the sound sensor.

3. The method according to claim 1, characterized in that The construction of the digital twin model includes using the first device status monitoring data for deep learning training, specifically including: inputting the first device status monitoring data into the deep learning model, optimizing the model parameters, and realizing the prediction of the future status of the equipment through regression analysis.

4. The method according to claim 1, characterized in that: The fused device status data is composed of a weighted average of temperature data, humidity data, vibration data and sound data, and the weighting is dynamically adjusted by the historical accuracy and current reliability of each sensor, including: setting a weighting coefficient for each sensor, and adjusting its influence in the fusion calculation based on historical fault records and environmental changes.

5. The method according to claim 1, characterized in that The optimization suggestion data includes adjustment suggestions for equipment operating parameters and a maintenance plan, and the maintenance plan includes: a maintenance schedule, a list of required materials and operating steps.

6. The method according to claim 1, characterized in that When the inspection robot performs intelligent equipment inspection, it analyzes the inspection equipment status data collected in real time through a deep learning algorithm to evaluate and identify the equipment operation status. The deep learning algorithm generates an equipment health index through classification and regression analysis of the inspection equipment status data. The equipment health index calculation expression is as follows: in, Indicates the device health index; Indicates the number of monitored parameters; Indicates The weight of a parameter reflects the importance of the parameter in the calculation of the health index; Indicates The standardized value of each monitoring parameter.

7. The method according to claim 6, characterized in that The analysis results include the equipment health index and the potential failure risk level, wherein the equipment health index is obtained by weighted calculation of equipment status parameters, and the potential failure risk level is calculated by abnormal detection of equipment status data. The potential failure risk level is calculated as follows: in, Indicates the potential failure risk level; Indicates the device health index; Indicates environmental factors.

8. The method according to claim 1, characterized in that The fault warning data includes the fault type, identification of the affected equipment and processing suggestions, wherein the processing suggestions include the time when the fault occurred, analysis of the cause of the fault and specific processing steps for the fault.

9. The method according to claim 1, characterized in that: The deep learning algorithm is used to classify the failure risks of the equipment, including: inputting the equipment status data into the deep learning model, learning different failure modes through a multi-layer neural network, and using an integrated learning algorithm to comprehensively evaluate the priority of the failure risk, thereby forming a comprehensive risk assessment report. The risk assessment calculation expression is as follows: in, represents the fault risk assessment value; represents the number of risk factors; Indicates The weight of each risk factor; Indicates risk factor scores.

10. A system for implementing the urban community embedded service facility operation management method based on deep learning according to any one of claims 1 to 9, characterized in that: include: A multi-dimensional IoT sensor module is used to be deployed on key equipment to dynamically collect equipment status data and form first equipment status monitoring data; A digital twin model building module is used to build a digital twin model of the device based on the first device status monitoring data, generate fused device status data using deep learning technology and multimodal data fusion technology, and analyze the health status of the device in real time to generate targeted optimization suggestion data; An intelligent inspection module is used to perform intelligent equipment inspections. The inspection robot of the intelligent inspection module performs autonomous inspections based on the generated optimization suggestion data, collects inspection equipment status data in real time, and forms second equipment status monitoring data. The second equipment status monitoring data is linked with the digital twin model for deep learning to generate analysis results of the equipment operation status. The fault identification and warning module is used to identify the failure risk of the equipment based on the second equipment status monitoring data and its analysis results, and generate fault warning data by applying deep learning algorithms and integrated learning algorithms.

Images