Unmanned operation monitoring and autonomous regulation and control system and method for intelligent equipment

Abstract

The invention relates to an unmanned operation monitoring and autonomous regulation and control system and method for intelligent equipment. The system comprises a multi-dimensional sensing module, a data preprocessing module, an operation state evaluation module, an autonomous regulation and control decision module, an execution module, a closed-loop feedback module and a remote monitoring and interaction module. The multi-dimensional sensing module is used for collecting operation state parameters, environment influence parameters and equipment attitude parameters of the intelligent equipment; the data preprocessing module is in communication connection with the multi-dimensional sensing module and is used for carrying out noise reduction, abnormal value elimination and standardization processing on the original collected data; according to the invention, unmanned intervention of the whole process from data acquisition to regulation and control execution is realized, and dynamic optimization and closed-loop feedback are combined, so that the regulation and control precision and reliability are improved; remote parameter configuration and strategy library updating are supported, and unmanned operation requirements of different types of intelligent equipment are met; and full-process data is visually displayed, so that remote supervision and troubleshooting of operation and maintenance personnel are facilitated, and the operation and maintenance cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control and equipment monitoring, and particularly relates to an intelligent equipment unmanned operation monitoring and autonomous regulation and control system and method. BACKGROUND

[0002] With the development of Internet of Things and artificial intelligence technology, intelligent equipment gradually upgrades to unmanned operation. However, there are still many defects in the operation management of existing intelligent equipment. Firstly, the monitoring method is single, and most of the monitoring is only for the core operation parameters (such as voltage and current) of the equipment, ignoring the environmental factors (such as temperature and humidity, dust) and the equipment posture (such as vibration and inclination) that affect the stability of the equipment operation, resulting in incomplete monitoring and delayed fault warning. Secondly, the regulation and control mechanism is passive, and most of the equipment needs manual intervention to complete parameter adjustment or fault disposal, and cannot generate regulation and control strategies autonomously according to real-time operation states, which cannot meet the autonomous needs of unmanned operation. Thirdly, there is a lack of closed-loop feedback mechanism, and the operation state of the equipment after regulation and control cannot be timely fed back to correct the regulation and control strategy, resulting in low regulation and control precision and limited equipment operation efficiency.

[0003] Therefore, the present application provides an intelligent equipment unmanned operation monitoring and autonomous regulation and control system and method to solve the problems in the background. SUMMARY

[0004] The present application aims to provide an intelligent equipment unmanned operation monitoring and autonomous regulation and control system and method to solve the problems in the background.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solutions.

[0006] An intelligent equipment unmanned operation monitoring and autonomous regulation and control system comprises a multi-dimensional perception module, a data preprocessing module, an operation state evaluation module, an autonomous regulation and control decision module, an execution module, a closed-loop feedback module, and a remote monitoring and interaction module.

[0007] As a further solution of the present application, the multi-dimensional perception module comprises a sensor group and a data acquisition interface, the sensor group at least comprises a voltage sensor, a current sensor, a temperature sensor, a temperature and humidity sensor, a vibration sensor, and an inclination sensor, and the acquisition frequency is dynamically configured within the range of 1-50 Hz; the multi-dimensional perception module is adapted to the body of the intelligent equipment and the operation environment; and is used to acquire the operation state parameters, the environmental influence parameters, and the equipment posture parameters of the intelligent equipment; the operation state parameters comprise voltage, current, power, rotating speed, and temperature; the environmental influence parameters comprise environmental temperature and humidity, dust concentration, and air pressure; and the equipment posture parameters comprise vibration frequency and inclination angle; the module is internally provided with the sensor group and the data acquisition interface, supports synchronous acquisition of multiple parameters, and the acquisition frequency can be dynamically configured (1-50 Hz).

[0008] As a further scheme of the present application: the data preprocessing module is in communication connection with the multi-dimensional perception module, and is used for carrying out noise reduction, abnormal value elimination and standardization processing on the original collected data; the data preprocessing module adopts a sliding average filtering algorithm to realize noise reduction, eliminates abnormal values through a 3σ criterion, maps the data to an interval [0, 1] through min-max standardization, and outputs the standardized data to a subsequent module.

[0009] As a further scheme of the present application: the running state evaluation module is in communication connection with the data preprocessing module, and is internally provided with a device running standard threshold value library and a lightweight state evaluation model; the standardized data is compared with the standard threshold value library, and the device running state is analyzed in combination with the state evaluation model (obtained based on a decision tree algorithm) to output a state evaluation result; the running state evaluation module is used for outputting the device running state in combination with the standard threshold value library and the state evaluation model; the lightweight state evaluation model internally provided in the running state evaluation module is obtained based on a decision tree algorithm, and the output device running state includes three types of normal operation, sub-health early warning and fault alarm.

[0010] As a further scheme of the present application: the autonomous regulation and control decision module is in communication connection with the running state evaluation module, the autonomous regulation and control decision module includes a regulation and control strategy library and a dynamic optimization unit, the regulation and control strategy library pre-stores basic regulation and control schemes corresponding to different device types and different running states, the dynamic optimization unit optimizes parameters of the basic regulation and control schemes in combination with historical running data of the device in the past 24 hours to generate optimal regulation and control instructions; the regulation and control strategy library pre-stores regulation and control parameter ranges and operation logics corresponding to different device types and different running states.

[0011] As a further scheme of the present application: the execution module is in communication connection with the autonomous regulation and control decision module; the execution module includes a driving unit and an execution element, the execution element at least includes a frequency converter, a relay, a servo motor and an electromagnetic valve, can execute parameter adjustment, device start-stop and running mode switching operations, and is used for executing regulation and control operations; receives the optimal regulation and control instructions, and drives the execution element to complete parameter adjustment (such as speed adjustment and voltage adjustment), device start-stop or running mode switching operations and the like.

[0012] As a further scheme of the present application: the closed-loop feedback module is in communication connection with the execution module, the multi-dimensional perception module and the running state evaluation module respectively; after the execution module completes the regulation and control operation, the multi-dimensional perception module is triggered to collect device running data after the regulation and control, the data is preliminarily screened and then transmitted to the running state evaluation module, and the regulation and control effect is verified; if the device after the regulation and control is still not in a normal running state, the autonomous regulation and control decision module is triggered to generate regulation and control instructions again, and a closed-loop regulation and control is formed; the closed-loop feedback module is used for triggering data collection and re-evaluation after the regulation and control, and forming a closed loop.

[0013] As a further scheme of the present application: the remote monitoring and interaction module includes a remote terminal / platform, which is in communication connection with the running state evaluation module and the autonomous regulation and control decision module; is used for real-time visual display of the device running state, the regulation and control process, and fault information, supports remote configuration of the acquisition frequency, updating of the standard threshold library, and updating of the regulation and control strategy library, and can issue manual intervention instructions and autonomous regulation and control instructions in an emergency; and is used for visual display and remote configuration.

[0014] The method of the intelligent device unmanned operation monitoring and autonomous regulation and control system comprises the following steps:

[0015] S1: parameter acquisition configuration: the acquisition parameters (acquisition frequency, parameter type to be acquired) of the multi-dimensional perception module are configured through the remote monitoring and interaction module, and data acquisition is started;

[0016] S2: multi-dimensional data acquisition: the multi-dimensional perception module acquires the running state parameters, environmental influence parameters, and device posture parameters of the intelligent device in real time, forms an original data sequence, and transmits the original data sequence to the data preprocessing module;

[0017] S3: data preprocessing: the data preprocessing module adopts a sliding average filtering algorithm to denoise the original data, removes abnormal values through a 3σ criterion, and obtains standardized data through min-max standardization processing;

[0018] The implementation mode of the 3σ criterion is that the mean μ and the standard deviation σ of the preprocessed data are calculated, and if the absolute value of the difference between the data value and the mean is greater than 3σ, the data value is determined to be an abnormal value and is removed;

[0019] S4: running state evaluation: the running state evaluation module calls the device running standard threshold library, compares the standardized data with the corresponding threshold, analyzes in combination with a lightweight state evaluation model, and outputs the device running state (normal operation, sub-health early warning, and fault alarm);

[0020] S5: regulation and control decision generation: if the state evaluation result is normal operation, regulation and control is not needed, and the step S2 is returned to continue data acquisition; if the state evaluation result is sub-health early warning or fault alarm, the autonomous regulation and control decision module matches a basic regulation and control scheme from the regulation and control strategy library, and optimizes to obtain an optimal regulation and control instruction in combination with historical running data;

[0021] If the evaluation result is normal operation, a regulation and control instruction does not need to be generated, and the step S2 is returned to continue data acquisition; if the evaluation result is sub-health early warning or fault alarm, matching and optimization of the regulation and control scheme are performed;

[0022] S6: regulation and control execution: the execution module receives the optimal regulation and control instruction, and drives an execution element to complete a corresponding regulation and control operation;

[0023] S7: Closed-loop verification: the closed-loop feedback module triggers the multi-dimensional perception module to collect the running data of the device after regulation and control, and transmits the data to the running state evaluation module for reevaluation; if the evaluation result is normal running, the closed loop ends; if it is still a sub-health early warning or a fault alarm, return to S5 to generate a regulation and control instruction again until the device returns to normal operation;

[0024] S8: Remote monitoring: the remote monitoring and interaction module displays the whole process data (acquisition data, preprocessing results, state evaluation results, regulation and control instructions, and execution results) in real time, and supports remote configuration and emergency intervention.

[0025] Compared with the prior art, the beneficial effects of the present application are:

[0026] 1. The present application synchronously collects the running state parameters, environmental influence parameters and device posture parameters of the intelligent device through the multi-dimensional perception module, breaking the limitation of traditional single parameter monitoring; in combination with the sliding average filter denoising and 3 sigma criterion outlier rejection of the data preprocessing module, invalid interference data is effectively filtered, the data quality of the input evaluation module is ensured, thereby eliminating the monitoring blind area, shortening the fault early warning response time, and improving the fault identification accuracy.

[0027] 2. The present application supports remote adjustment of the acquisition frequency, parameter type and other acquisition configurations through the remote monitoring and interaction module, and online update of the standard threshold library and the regulation and control strategy library, without the need to change the hardware on site; the multi-dimensional perception module adopts a modular sensor group design, which can flexibly expand or replace the sensor types according to the needs of different intelligent devices (industrial automation devices, smart homes, etc.), realize the adaptation to various unmanned running intelligent devices, and greatly improve the universality and scene coverage of the system.

[0028] 3. The present application realizes the whole process unmanned intervention from data acquisition to regulation and control execution, improves the regulation and control precision and reliability in combination with dynamic optimization and closed-loop feedback, supports remote configuration of parameters and update of strategy library, adapts to the unmanned running needs of different types of intelligent devices, and visually displays the whole process data, which is convenient for remote supervision and fault troubleshooting of the operation and maintenance personnel, and reduces the operation and maintenance cost. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 It is a structural schematic view of an intelligent device unmanned running monitoring and self-regulation and control system and method. DETAILED DESCRIPTION

[0030] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be clearly and completely described below, obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work belong to the scope of protection of the present application.

[0031] Please refer to Figure 1 In the embodiments of the present application, an intelligent device unmanned operation monitoring and autonomous control system comprises a multi-dimensional perception module, a data preprocessing module, an operation state evaluation module, an autonomous control decision module, an execution module, a closed-loop feedback module and a remote monitoring and interaction module.

[0032] The multi-dimensional perception module comprises a sensor group and a data acquisition interface, the sensor group at least comprises a voltage sensor, a current sensor, a temperature sensor, a temperature and humidity sensor, a vibration sensor and an inclination sensor, and the acquisition frequency is dynamically configured in the range of 1-50 Hz; the multi-dimensional perception module is adapted to the intelligent device body and the operation environment; and the module is used for collecting the operation state parameters, the environmental influence parameters and the device posture parameters of the intelligent device; the operation state parameters comprise voltage, current, power, rotating speed and temperature, the environmental influence parameters comprise environmental temperature and humidity, dust concentration and air pressure, and the device posture parameters comprise vibration frequency and inclination angle; the module is provided with the sensor group and the data acquisition interface, supports multi-parameter synchronous acquisition, and the acquisition frequency can be dynamically configured (1-50 Hz);

[0033] The data preprocessing module is in communication connection with the multi-dimensional perception module, and is used for carrying out noise reduction, abnormal value elimination and standardization processing on the original collected data; the data preprocessing module adopts a sliding average filtering algorithm to realize noise reduction, eliminates abnormal values through a 3σ criterion, maps the data to the [0, 1] interval through min-max standardization, and outputs the standardized data to the subsequent module;

[0034] The operation state evaluation module is in communication connection with the data preprocessing module, and is provided with an equipment operation standard threshold library and a lightweight state evaluation model; the standardized data is compared with the standard threshold library, the device operation state is analyzed in combination with the state evaluation model (obtained based on a decision tree algorithm), and the state evaluation result is output; the operation state evaluation module is used for outputting the device operation state in combination with the standard threshold library and the state evaluation model; the lightweight state evaluation model built in the operation state evaluation module is obtained based on a decision tree algorithm, and the output device operation state comprises three types of normal operation, sub-health early warning and fault alarm;

[0035] The autonomous regulation decision module is in communication connection with the running state evaluation module, the autonomous regulation decision module comprises a regulation strategy library and a dynamic optimization unit, the regulation strategy library pre-stores basic regulation schemes corresponding to different equipment types and different running states, the dynamic optimization unit optimizes parameters of the basic regulation schemes in combination with historical running data of the equipment in the past 24 hours, and is used for generating optimal regulation instructions; the regulation strategy library pre-stores regulation parameter ranges and operation logics corresponding to different equipment types and different running states;

[0036] The execution module is in communication connection with the autonomous regulation decision module; the execution module comprises a driving unit and an execution element, the execution element at least comprises a frequency converter, a relay, a servo motor and an electromagnetic valve, can execute parameter adjustment, equipment start-stop and running mode switching operations, and is used for executing regulation operations; the optimal regulation instructions are received, and the execution element is driven to complete parameter adjustment (such as speed adjustment and voltage adjustment), equipment start-stop or running mode switching and the like.

[0037] The closed-loop feedback module is in communication connection with the execution module, the multi-dimensional perception module and the running state evaluation module respectively; after the execution module completes the regulation operation, the multi-dimensional perception module is triggered to collect running data of the equipment after the regulation, the data is preliminarily screened and then transmitted to the running state evaluation module, and the regulation effect is verified; if the equipment after the regulation is still not in a normal running state, the autonomous regulation decision module is triggered to generate regulation instructions again, and closed-loop regulation is formed; data collection after the regulation and re-evaluation are triggered, and a closed loop is formed.

[0038] The remote monitoring and interaction module comprises a remote terminal / platform, and is in communication connection with the running state evaluation module and the autonomous regulation decision module; is used for visually displaying the running state of the equipment, the regulation process and fault information in real time, supporting remote configuration of a collection frequency, updating of a standard threshold library and a regulation strategy library, and issuing of manual intervention instructions in an emergency; is used for visual display and remote configuration.

[0039] The method of the intelligent equipment unmanned running monitoring and autonomous regulation system comprises the following steps:

[0040] S1: parameter collection configuration: the collection parameters (collection frequency, types of parameters to be collected) of the multi-dimensional perception module are configured through the remote monitoring and interaction module, and data collection is started;

[0041] S2: multi-dimensional data collection: the multi-dimensional perception module collects running state parameters, environmental influence parameters and equipment posture parameters of the intelligent equipment in real time, forms an original data sequence, and transmits the original data sequence to the data preprocessing module;

[0042] S3: Data preprocessing: The data preprocessing module adopts a sliding average filtering algorithm to denoise the original data, removes outliers through the 3 sigma criterion, and then obtains standardized data through min-max standardization processing;

[0043] The implementation method of the 3 sigma criterion is as follows: the mean mu and the standard deviation sigma of the preprocessed data are calculated, if the absolute value of the difference between the data value and the mean is greater than 3 sigma, it is determined that the data value is an outlier and is removed;

[0044] S4: Running state evaluation: The running state evaluation module calls the device running standard threshold library, compares the standardized data with the corresponding threshold, combines the lightweight state evaluation model analysis, and outputs the device running state (normal operation, sub-health warning, fault alarm);

[0045] S5: Control decision generation: If the state evaluation result is normal operation, no control is needed, and the process returns to S2 to continue collecting; if it is a sub-health warning or a fault alarm, the autonomous control decision module matches the basic control scheme from the control strategy library, and optimizes the optimal control instruction based on the historical operation data;

[0046] If the evaluation result is normal operation, no control instruction needs to be generated, and the process returns to step S2 to continue collecting data; if it is a sub-health warning or a fault alarm, the matching and optimization of the control scheme are performed;

[0047] S6: Control execution: The execution module receives the optimal control instruction and drives the execution element to complete the corresponding control operation;

[0048] S7: Closed loop verification: The closed loop feedback module triggers the multi-dimensional perception module to collect the device running data after the control, and transmits it to the running state evaluation module for re-evaluation; if the evaluation result is normal operation, the closed loop ends; if it is still a sub-health warning or a fault alarm, the process returns to S5 to generate a control instruction again until the device returns to normal operation;

[0049] S8: Remote monitoring: The remote monitoring and interaction module displays the whole process data (collection data, preprocessing result, state evaluation result, control instruction, execution result) in real time, supports remote configuration and emergency intervention.

[0050] The above is only the preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can make equivalent replacement or change according to the technical solution and the inventive concept of the present application within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. An intelligent device unmanned operation monitoring and autonomous control system, characterized in that, It includes a multi-dimensional perception module, a data preprocessing module, an operational status assessment module, an autonomous control and decision-making module, an execution module, a closed-loop feedback module, and a remote monitoring and interaction module; The multi-dimensional sensing module is used to collect the operating status parameters, environmental impact parameters, and device posture parameters of the intelligent device. The data preprocessing module is communicatively connected to the multi-dimensional perception module and is used to perform noise reduction, outlier removal and standardization on the raw collected data. The operation status assessment module is communicatively connected to the data preprocessing module and is used to output the device operation status by combining the standard threshold library and the status assessment model. The autonomous control decision-making module is communicatively connected to the operation status evaluation module and is used to generate optimal control instructions; The execution module is communicatively connected to the autonomous control decision module and is used to perform control operations; The closed-loop feedback module is used to trigger data acquisition and re-evaluation after regulation, forming a closed loop; The remote monitoring and interaction module is used for visualization and remote configuration.

2. The intelligent device unmanned operation monitoring and autonomous control system according to claim 1, characterized in that, The multi-dimensional sensing module includes a sensor group and a data acquisition interface. The sensor group includes at least a voltage sensor, a current sensor, a temperature sensor, a humidity sensor, a vibration sensor, and a tilt sensor, and the acquisition frequency is dynamically configured in the range of 1~50Hz.

3. The intelligent device unmanned operation monitoring and autonomous control system according to claim 1, characterized in that, The data preprocessing module uses a moving average filtering algorithm to reduce noise, removes outliers using the 3σ criterion, and maps the data to the [0,1] interval using min-max standardization.

4. The intelligent device unmanned operation monitoring and autonomous control system according to claim 1, characterized in that, The lightweight status assessment model built into the operation status assessment module is trained based on the decision tree algorithm, and the output device operation status includes three categories: normal operation, sub-health warning, and fault alarm.

5. The intelligent device unmanned operation monitoring and autonomous control system according to claim 1, characterized in that, The autonomous control decision-making module includes a control strategy library and a dynamic optimization unit. The control strategy library pre-stores basic control schemes corresponding to different equipment types and different operating states. The dynamic optimization unit optimizes the parameters of the basic control schemes by combining the equipment's historical operating data of the past 24 hours.

6. The intelligent device unmanned operation monitoring and autonomous control system according to claim 1, characterized in that, The execution module includes a drive unit and execution elements. The execution elements include at least a frequency converter, a relay, a servo motor, and a solenoid valve, and can perform parameter adjustment, equipment start-up and shutdown, and operation mode switching operations.

7. The intelligent device unmanned operation monitoring and autonomous control system according to claim 1, characterized in that, After the execution module completes the control operation, the closed-loop feedback module triggers the multi-dimensional sensing module to collect the control data. If the equipment is still not operating normally, the autonomous control decision module is triggered to regenerate the control command.

8. The method for unmanned operation monitoring and autonomous control system of intelligent equipment according to any one of claims 1-7, characterized in that, Includes the following steps: S1: Parameter acquisition configuration: Configure the acquisition parameters (acquisition frequency, parameter types to be acquired) of the multi-dimensional sensing module through the remote monitoring and interaction module, and start data acquisition; S2: Multi-dimensional data acquisition: The multi-dimensional sensing module collects the operating status parameters, environmental impact parameters and device posture parameters of the intelligent device in real time, forms the raw data sequence and transmits it to the data preprocessing module; S3: Data Preprocessing: The data preprocessing module uses a moving average filtering algorithm to reduce noise in the raw data, removes outliers using the 3σ criterion, and then obtains standardized data through min-max standardization. S4: Operational Status Assessment: The operational status assessment module calls the equipment operation standard threshold library, compares the standardized data with the corresponding thresholds, and analyzes the data using a lightweight status assessment model to output the equipment operation status (normal operation, sub-health warning, fault alarm). S5: Control Decision Generation: If the status assessment result is normal operation, no control is required, and return to S2 to continue data collection; If it is a sub-health warning or a fault alarm, the autonomous control decision module matches the basic control scheme from the control strategy library and optimizes it by combining historical operation data to obtain the optimal control instruction. S6: Control Execution: The execution module receives the optimal control instruction and drives the execution element to complete the corresponding control operation; S7: Closed-loop verification: The closed-loop feedback module triggers the multi-dimensional perception module to collect the equipment operation data after regulation and transmits it to the operation status evaluation module for re-evaluation; if the evaluation result is normal operation, the closed loop ends; if it is still a sub-health warning or fault alarm, return to S5 to regenerate the regulation command until the equipment returns to normal operation. S8: Remote Monitoring: The remote monitoring and interaction module displays the entire process data in real time (collected data, preprocessing results, status assessment results, control instructions, and execution results), and supports remote configuration and emergency intervention.

9. The method for unmanned operation monitoring and autonomous control system of intelligent equipment according to claim 8, characterized in that, In step S3, the 3σ criterion is implemented as follows: calculate the mean μ and standard deviation σ of the preprocessed data. If the absolute value of the difference between the data value and the mean is greater than 3σ, it is judged as an outlier and removed.

10. The method for unmanned operation monitoring and autonomous control system of intelligent equipment according to claim 8, characterized in that, In step S5, if the evaluation result is normal operation, there is no need to generate control instructions, and the process returns to step S2 to continue collecting data. If a sub-health warning or fault alarm is detected, the control plan will be matched and optimized.

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