Automatic detection and control system and method for intelligent integrated equipment

Abstract

The invention relates to the technical field of equipment detection and control, and discloses an intelligent integrated equipment automatic detection and control system and method, and the method comprises the steps: obtaining the operation data of equipment in real time through a state detection module through a data obtaining strategy by means of a sensor network; the fault identification module judges the abnormity of the equipment according to the operation data of the equipment, the dependence on manual inspection is reduced through automatic fault identification detection, and the speed and accuracy of equipment problem discovery are improved; the analysis and diagnosis module analyzes and diagnoses the abnormal type of the equipment, so that technicians can quickly understand problems and take targeted treatment measures, the operation efficiency of the self-detection and control system is improved through quick and accurate fault diagnosis and treatment, and the problems of traffic jam and the like caused by faults are reduced; and the self-reset module executes a reset strategy according to the abnormal type of the equipment and optimizes the reset strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment detection and control, and in particular to an automatic detection and control system and method for intelligent integrated equipment. Background Art

[0002] At present, the research on intelligent integrated equipment is developing in the direction of multidisciplinary integration, including mechanical engineering, electronic engineering, computer science and artificial intelligence. By using advanced technologies such as machine learning and deep learning, intelligent integrated equipment can more accurately identify patterns, predict faults and respond through automatic detection and control, and can significantly improve production efficiency and operation efficiency, reduce manual intervention and reduce labor costs. However, most of them have not solved the problem of how to obtain the operation data of the equipment in real time to determine the abnormality of the equipment, diagnose the abnormal type of the equipment, and execute and optimize the reset strategy.

[0003] For example, the Chinese patent application with publication number CN117348426A discloses a device control method, apparatus, control device, device control system and medium, the method includes: when it is detected that both the home appliance and the media player are in working state, determine the noise value of the home appliance at the user's position, and determine the musical sound value of the media player at the user's position; determine whether the home appliance will affect the user's use of the media player according to the noise value and the musical sound value; if it will affect, formulate a control strategy for the home appliance and / or the media player according to the type of the home appliance; control the home appliance and / or the media player according to the control strategy to reduce the impact of the home appliance on the user's use of the media player. It can not only reduce the impact of the home appliance on the user's use of the media player, but also try to ensure the normal operation of some home appliances, thereby improving the user's experience of using the home appliance and the media player.

[0004] For example, a Chinese patent with authorization announcement number CN116880438B discloses a fault detection method and system for an annealing equipment control system. By relying on the template annealing equipment control data of the detection data, the network convergence optimization of the annealing equipment fault detection network can be achieved, and the target attribute of the fault detection target of the fault location root cause of the detection template annealing equipment control data is introduced, so that the aggregated coding vector set used for network convergence optimization has the characteristics of both the template annealing equipment control data and the fault detection target; for example, based on the fault detection target, the network convergence optimization of the detection data of the fault location root cause of the template annealing equipment control data is performed, which can simulate the detection effect of the fault detection personnel on the fault location root cause of the template annealing equipment control data, ensure that the annealing equipment fault detection network can well fit the fault detection status of the fault detection personnel on the annealing equipment control data, and improve the reliability of annealing equipment fault detection.

[0005] The above prior art has the problem raised by the background technology: the above prior art does not solve the problem of how to obtain the operation data of the device in real time to judge the abnormality of the device, diagnose the abnormal type of the device, and execute and optimize the reset strategy. To solve this problem, the present invention proposes an automatic detection and control system and method for smart integrated devices. Summary of the invention

[0006] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0007] In view of the above-mentioned problems existing in the existing automatic detection and control system and method of intelligent integrated equipment, the present invention is proposed.

[0008] Therefore, the object of the present invention is to provide a system and method for automatic detection and control of intelligent integrated equipment.

[0009] In order to solve the above technical problems, the present invention provides an intelligent integrated equipment automatic detection and control system, including: a state detection module, a fault identification module, an analysis and diagnosis module and a self-reset module;

[0010] The state detection module is used to obtain the operation data of the equipment in real time through the data acquisition strategy with the help of the sensor network;

[0011] The fault identification module is used to determine the abnormality of the device according to the operation data of the device, and the determination of the abnormality of the device includes respectively determining the abnormality of the operation data according to the operation data of the device, and determining the abnormality of the device according to the abnormality of the operation data, and the determination logic of the abnormality of the operation data includes determining the temperature abnormality and the position abnormality according to the temperature change degree and the position change degree of the device;

[0012] The analysis and diagnosis module is used to analyze and diagnose the abnormal type of the equipment. The strategy of analyzing and diagnosing the abnormal type of the equipment includes judging the collaborative abnormal result of the equipment according to the correlation between the operation data, and comprehensively diagnosing the abnormal type of the equipment in combination with the operation data;

[0013] The self-reset module is used to execute a reset strategy according to the abnormal type of the device and optimize the reset strategy.

[0014] As a preferred solution of the automatic detection and control system of a smart integrated device described in the present invention, the strategy for determining the abnormality of the device includes:

[0015] According to the operation data of the equipment, the abnormality of the operation data is judged respectively, and the vibration threshold Δv is configured. The vibration value v(t) of the equipment at time t is compared with the vibration threshold to obtain the vibration abnormality. If v(t)≤Δv, the equipment shows that there is no abnormal vibration.

[0016] If v(t)>Δv, the device shows abnormal vibration;

[0017] The temperature values ​​of the device in adjacent time periods are subtracted, that is, the temperature value T(t) of the device at time t is subtracted from the temperature value T(t-1) of the device at time t-1 to obtain the temperature difference value |T(t)-T(t-1)| of the device. A temperature change threshold ΔT is configured, and the temperature difference value of the device is compared with the temperature change threshold to obtain temperature anomaly. If |T(t)-T(t-1)|≤ΔT, the device displays no temperature anomaly.

[0018] If |T(t)-T(t-1)|>ΔT, the device displays abnormal temperature.

[0019] As a preferred solution of the automatic detection and control system of a smart integrated device described in the present invention, the strategy for determining the abnormality of the device also includes:

[0020] Configure the standard position value p of the device at time t, perform subtraction processing on the position value p(t) of the device at time t and the standard position value of the device at time t to obtain the position difference value |p(t)-p| of the device, configure the position change threshold Δp, compare the position difference value of the device with the position change threshold to obtain the position abnormality, if p(t)-p|≤Δp, then the device display position is normal;

[0021] If |p(t)-p|>Δp, the device displays an abnormal position;

[0022] The torque values ​​of the device in adjacent time periods are subtracted, that is, the torque value m(t) of the device at time t is subtracted from the torque value m(t-1) of the device at time t-1 to obtain the torque difference value m(t)-m(t-1)| of the device, and the torque change threshold Δm is configured. The torque difference value of the device is compared with the torque change threshold to obtain the torque abnormality. If |m(t)-m(t-1)|≤Δm, the device displays no torque abnormality.

[0023] If |m(t)-m(t-1)|>Δm, the device displays abnormal torque;

[0024] The abnormality of the equipment is judged according to the abnormality of the operating data. If the displayed operating data is normal, then the equipment is normal.

[0025] If any operating data is displayed as abnormal, the equipment is abnormal.

[0026] As a preferred solution of the automatic detection and control system of a smart integrated device described in the present invention, when the device is abnormal, the strategy for analyzing and diagnosing the abnormal type of the device includes:

[0027] Record the abnormality of the operating data. If a single operating data is abnormal, the abnormality type of the diagnostic equipment is a sensor failure.

[0028] If more than one operating data is abnormal, the correlation between the operating data is calculated to obtain the correlation r of the current time operating data. XY , configure standard dependencies for run data The correlation of the current time operation data is subtracted from the standard correlation of the operation data to obtain the correlation difference Δr of the operation data. XY , configure the relevant difference threshold The correlation difference of the operating data is compared with the correlation difference threshold to obtain the coordinated abnormality result of the equipment. The abnormality type of the equipment is comprehensively diagnosed according to the coordinated abnormality result and operating data of the equipment through the coordinated abnormality logic.

[0029] As a preferred solution of the automatic detection and control system of a smart integrated device described in the present invention, the function expression of the correlation difference of the operating data is as follows:

[0030]

[0031] In the formula, Δr XY Represents the correlation difference of the running data, r XY Indicates the relevance of running data at the current time, Represents the standard correlation of the running data.

[0032] As a preferred solution of the automatic detection and control system of a smart integrated device described in the present invention, the collaborative abnormality logic includes:

[0033] If X is the vibration value of the equipment, Y is the torque value of the equipment, and Then the abnormal type of the equipment is diagnosed as mechanical failure;

[0034] If X is the vibration value of the equipment, Y is the temperature value of the equipment, and The abnormal type of the equipment is diagnosed as a temperature control failure;

[0035] If X is the torque value of the device, Y is the position value of the device, and The abnormality type of the device is diagnosed as a system failure.

[0036] As a preferred solution of the automatic detection and control system of a smart integrated device described in the present invention, a reset strategy is executed according to the abnormal type of the device, and the reset strategy includes:

[0037] If the abnormal type of the device is sensor failure, the sensor network is initialized and calibrated;

[0038] If the abnormality type of the equipment is a mechanical failure, the mechanical parts are detected and repaired;

[0039] If the abnormality type of the equipment is a temperature control failure, check and repair the temperature control system;

[0040] If the abnormality type of the device is system failure, check and restart the control system.

[0041] As a preferred solution of the automatic detection and control system of a smart integrated device described in the present invention, the data acquisition strategy includes:

[0042] The sensor network includes vibration sensors, temperature sensors, position sensors and torque sensors. The vibration sensor is used to obtain the vibration frequency and vibration amplitude of the device in real time during operation to obtain the vibration value of the device. The temperature sensor is used to obtain the temperature of the device in real time during operation to obtain the temperature value of the device. The position sensor is used to obtain the position of the device in real time during operation to obtain the position value of the device. The torque sensor is used to obtain the torque of the device in real time during operation to obtain the torque value of the device.

[0043] A method for automatic detection and control of a smart integrated device, comprising:

[0044] S1, using data acquisition strategies to obtain equipment operation data in real time with the help of sensor networks;

[0045] S2. Determine the abnormality of the equipment based on the equipment's operating data;

[0046] S3. Analyze and diagnose the abnormal type of the equipment;

[0047] S4. Execute the reset strategy according to the abnormal type of the device and optimize the reset strategy.

[0048] The beneficial effects of the present invention are as follows: the present invention uses a state detection module to obtain the operating data of the equipment in real time with the help of a sensor network through a data acquisition strategy, and provides continuous operating status feedback for the self-detection and control system, so that any deviation from the normal range can be discovered in time; the fault identification module determines the abnormality of the equipment based on the operating data of the equipment, and the automated fault identification and detection reduces the reliance on manual inspection, and improves the speed and accuracy of equipment problem discovery; the analysis and diagnosis module analyzes and diagnoses the abnormal type of the equipment, which helps technicians to quickly understand the problem and take targeted treatment measures. Fast and accurate fault diagnosis and treatment improves the operating efficiency of the self-detection and control system, and reduces problems such as traffic congestion caused by faults; the self-reset module executes the reset strategy according to the abnormal type of the equipment, and optimizes the reset strategy. The reset strategy is continuously optimized according to the operating data of the equipment, and the self-detection and control system's adaptability and reliability are improved, which can significantly improve the stability and reliability of the equipment and reduce downtime and maintenance costs caused by faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0050] Figure 1 This is a system structure diagram of an automatic detection and control system for a smart integrated device of the present invention;

[0051] Figure 2 This is a flow chart of position abnormality judgment of an intelligent integrated device automatic detection and control system of the present invention;

[0052] Figure 3 This is a flow chart of the abnormal type diagnosis strategy of the barrier machine of the intelligent integrated device automatic detection and control system of the present invention;

[0053] Figure 4 This is a method flow chart of an automatic detection and control method for a smart all-in-one device of the present invention. DETAILED DESCRIPTION

[0054] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0055] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0056] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0057] Example 1

[0058] In this embodiment, a system structure diagram of an automatic detection and control system of a smart integrated device is provided, such as Figure 1 As shown, a smart integrated device automatic detection and control system includes a state detection module, a fault identification module, an analysis and diagnosis module and a self-reset module.

[0059] The intelligent integrated equipment here includes industrial robots and barrier machines, etc., and the barrier machine is specifically used as an example for illustration.

[0060] The state detection module is used to obtain the operation data of the barrier machine in real time through the data acquisition strategy with the help of the sensor network.

[0061] Data acquisition strategies include:

[0062] The sensor network includes a vibration sensor, a temperature sensor, a position sensor and a torque sensor. The vibration frequency and vibration amplitude of the barrier machine during operation are obtained in real time through the vibration sensor to obtain the vibration value of the barrier machine. The temperature of the barrier machine during operation is obtained in real time through the temperature sensor to obtain the temperature value of the barrier machine. The position of the barrier machine during operation is obtained in real time through the position sensor to obtain the position value of the barrier machine. The torque of the barrier machine during operation is obtained in real time through the torque sensor to obtain the torque value of the barrier machine.

[0063] The function expression of the vibration value of the barrier machine is as follows:

[0064] v(t)=A×sin(w×t+φ);

[0065] Where v(t) represents the vibration value of the barrier machine at time t, A represents the vibration amplitude of the barrier machine, w represents the vibration frequency of the barrier machine, and φ represents the initial phase of the barrier machine.

[0066] It needs to be explained that: the vibration value v(t) of the barrier machine refers to the vibration response value measured by the vibration sensor, that is, the acceleration response of the railing in the barrier machine; the vibration amplitude A of the barrier machine refers to the intensity of the railing vibration or the maximum distance of deviation from the equilibrium position, which determines the maximum acceleration of the railing vibration. The value range of the vibration amplitude A of the barrier machine is non-negative, which usually depends on the vibration range of the railing; the vibration frequency w of the barrier machine refers to the number of vibration cycles completed per unit time, and the value range of the vibration frequency w of the barrier machine is a positive number; the initial phase φ of the barrier machine refers to the starting angle of the railing vibration at time 0, and the value range of the initial phase φ of the barrier machine is any real value, but usually it is between 0 and 2π.

[0067] The function expression of the temperature value of the barrier machine is as follows:

[0068] T(t)=T 0 +α×I(t);

[0069] Where, T(t) represents the temperature value of the barrier machine at time t, T 0 represents the initial temperature of the barrier machine, α represents the response coefficient of temperature to current, and I(t) represents the current of the barrier machine at time t.

[0070] What needs to be explained is: the initial temperature T of the barrier machine 0 It refers to the starting temperature of the barrier machine at time 0, which is usually the temperature when there is no external current or load, that is, room temperature; the temperature response coefficient α to current refers to the sensitivity of temperature to current or load changes, which describes the magnitude of temperature changes caused by unit current or load changes. The temperature response coefficient α to current is a positive constant; the current I(t) of the barrier machine at time t refers to the current or load at time t, which is usually a positive value.

[0071] The function expression of the position value of the barrier machine is as follows:

[0072] p(t)=p 0 +v×t;

[0073] Where p(t) represents the position of the barrier at time t, p 0 It represents the initial position of the railing, and v represents the running speed of the railing in the barrier machine.

[0074] It should be explained that the position value p(t) of the barrier at time t refers to the displacement or position of the barrier at time t, which refers to the trajectory of the barrier movement. The value is an arbitrary real number and varies according to the starting position and direction of the barrier movement. The initial position p of the barrier is 0It refers to the initial position of the railing at time 0, which is a fixed constant when the railing is in a horizontal position. The running speed v of the railing in the railing machine refers to the displacement of the railing per unit time. In practical applications, the movement of the railing is uniform, which is a constant value.

[0075] The function expression of the moment value of the barrier machine is as follows:

[0076] m(t) = F × r;

[0077] Where m(t) represents the torque value of the barrier machine at time t, F represents the force applied to the railing, and r represents the length of the railing.

[0078] What needs to be explained is that the torque value m(t) of the barrier machine at time t refers to the torque generated on the railing due to the force at time t, and its value can be any real number. The direction of the torque is determined by the movement direction and direction of the railing; the force F applied to the railing refers to the force applied to the railing, which is measured by the torque sensor and its value can be any real number; the value range of the railing length r is 0 to a positive value. The longer the railing length, the greater the torque generated.

[0079] In specific applications, the sensor network is installed in the specific structure and working environment of the barrier machine and connected to the control system to ensure that all functions work normally and the data is accurate. The sensor network is maintained regularly to ensure long-term stable operation. The operating data of the barrier machine is obtained in real time with the help of the sensor network, that is, the vibration frequency, vibration amplitude, temperature, position and torque of the barrier machine during operation are obtained through vibration sensors, temperature sensors, position sensors and torque sensors respectively, so as to obtain the vibration value, temperature value, position value and torque value of the barrier machine. Among them, the vibration value of the barrier machine can help evaluate the stability of the barrier machine and possible mechanical failures. The temperature value of the barrier machine helps to understand whether the barrier machine is overheated during operation, thereby preventing potential safety problems. The position value of the barrier machine is crucial to ensure that the railing moves according to the predetermined trajectory. The torque value of the barrier machine helps to analyze the load situation of the railing to ensure the safety of the result. The above strategy content can effectively realize the status detection of the barrier machine.

[0080] The fault identification module is used to judge the abnormality of the barrier machine based on the operating data of the barrier machine. Judging the abnormality of the barrier machine includes judging the abnormality of the operating data separately according to the operating data of the barrier machine, and judging the abnormality of the barrier machine based on the abnormality of the operating data. The judgment logic of the abnormality of the operating data includes judging temperature abnormality and position abnormality according to the degree of temperature change and position change of the barrier machine.

[0081] Strategies for determining the abnormality of the barrier include:

[0082] According to the operation data of the barrier machine, the abnormality of the operation data is judged respectively, and the vibration threshold Δv is configured. The vibration value of the barrier machine is compared with the vibration threshold to obtain the vibration abnormality. If v(t)≤Δv, the barrier machine shows that there is no abnormality in vibration;

[0083] If v(t)>Δv, the barrier machine shows abnormal vibration.

[0084] The temperature values ​​of the barrier machine in adjacent time are processed by difference to obtain the temperature difference value T(t)-T(t-1)| of the barrier machine, and the temperature change threshold ΔT is configured. The temperature difference value of the barrier machine is compared with the temperature change threshold to obtain the temperature abnormality. If |T(t)-T(t-1)|≤ΔT, the barrier machine displays no abnormal temperature, where the temperature difference value of the barrier machine is the degree of temperature change of the barrier machine.

[0085] If |T(t)-T(t-1)|>ΔT, the barrier machine displays abnormal temperature.

[0086] like Figure 2 As shown, configure the standard position value p of the barrier machine at time t, perform difference processing on the position value of the barrier machine at time t and the standard position value of the barrier machine at time t, and obtain the position difference value p(t)-p| of the barrier machine, configure the position change threshold Δp, and compare the position difference value of the barrier machine with the position change threshold value to obtain the position abnormality. If |p(t)-p|≤Δp, the barrier machine displays no abnormality in the position, wherein the position difference value of the barrier machine is the degree of position change of the barrier machine;

[0087] If |p(t)-p|>Δp, the barrier machine displays an abnormal position.

[0088] The torque values ​​of the barrier machine in adjacent time are processed by difference to obtain the torque difference value m(t)-m(t-1)| of the barrier machine, and the torque change threshold Δm is configured. The torque difference value of the barrier machine is compared with the torque change threshold to obtain the torque abnormality. If |m(t)-m(t-1)|≤Δm, the barrier machine shows that there is no abnormality in the torque, and the torque difference value of the barrier machine is the degree of torque change of the barrier machine.

[0089] If |m(t)-m(t-1)|>Δm, the barrier machine will show abnormal torque.

[0090] The abnormality of the barrier machine is judged according to the abnormality of the operating data. If the displayed operating data is normal, then the barrier machine is normal;

[0091] If any operating data displayed is abnormal, the barrier machine is abnormal.

[0092] In specific applications, the sensor network is initialized to ensure that all sensors can work normally, the acquired operating data of the barrier machine is processed, the temperature difference, position difference and torque difference are calculated, and the above strategies are used to determine whether the operating data are abnormal. If any operating data abnormality is detected, it means that the barrier machine is abnormal, and it is necessary to analyze and diagnose the abnormal type of the barrier machine for subsequent maintenance operations. At the same time, it is necessary to record abnormal events and related operating data for subsequent analysis and improvement. The above strategies can effectively realize the abnormality judgment of the barrier machine, timely discover and deal with potential problems, and ensure the normal operation and safe use of the barrier machine.

[0093] The analysis and diagnosis module is used to analyze and diagnose the abnormal type of the barrier machine. The strategy for analyzing and diagnosing the abnormal type of the barrier machine includes judging the coordinated abnormal results of the barrier machine based on the correlation between the operating data, and comprehensively diagnosing the abnormal type of the barrier machine in combination with the operating data.

[0094] When the barrier machine is abnormal, the strategy for analyzing and diagnosing the abnormal type of the barrier machine is as follows Figure 3 As shown, specifically including:

[0095] Record the abnormality of the operating data. If a single operating data is abnormal, the abnormality type of the barrier machine is diagnosed as sensor failure;

[0096] If more than one operating data is abnormal, the correlation between the operating data is calculated to obtain the correlation r of the current time operating data. XY , configure standard dependencies for run data The correlation of the current time operation data is subtracted from the standard correlation of the operation data to obtain the correlation difference Δr of the operation data. XY , configure the relevant difference threshold The correlation difference of the operating data is compared with the correlation difference threshold to obtain the coordinated abnormal result of the barrier machine. The abnormal type of the barrier machine is comprehensively diagnosed based on the coordinated abnormal result and operating data of the barrier machine through the coordinated abnormal logic.

[0097] The function expression for the correlation between running data is as follows:

[0098]

[0099] In the formula, r XY Indicates the correlation of the running data at the current time, X i represents the running data of the i-th running data pair, represents the mean of the running data, Y i represents the other running data of the i-th running data pair, represents the mean of another running data, and n represents the total number of running data pairs obtained at each time.

[0100] What needs to be explained is: the correlation of the current time running data r XY It is used to measure the linear correlation between two running data. It is a standardized value that represents the strength and direction of the linear relationship between the two running data. The correlation of the running data at the current time r XY The value range is between -1 and 1. XY =1 means there is a perfect positive correlation between the two running data, r XY =-1 means that there is a complete negative correlation between the two running data, r XY =0 means there is no linear correlation between the two running data, r XY Other values ​​indicate positive or negative correlation to varying degrees; the total number n of operating data pairs acquired at each time refers to the total number of operating data pairs acquired before the current time (an operating data pair is composed of two operating data for which correlation is to be calculated).

[0101] The function expression of the correlation difference of the running data is as follows:

[0102]

[0103] In the formula, Δr XY Represents the correlation difference of the running data, r XY Indicates the relevance of running data at the current time, Represents the standard correlation of the running data.

[0104] What needs to be explained: Standard correlation of running data Refers to the correlation between X and Y when the barrier machine is in normal operation. Normal operation generally refers to the correlation between two operating data without abnormality or failure. The standard correlation of operating data The value range is between -1 and 1.

[0105] The collaborative exception logic includes:

[0106] If X is the vibration value of the barrier machine, Y is the torque value of the barrier machine, and Then the abnormal type of the barrier machine is diagnosed as a mechanical failure;

[0107] If X is the vibration value of the barrier machine, Y is the temperature value of the barrier machine, and Then the abnormal type of the barrier machine is diagnosed as a temperature control failure;

[0108] If X is the moment value of the barrier machine, Y is the position value of the barrier machine, and The abnormal type of the barrier machine is diagnosed as a system failure.

[0109] In specific applications, mechanical failure refers to a failure of the moving parts of the barrier machine (such as motors, transmission devices, bearings and chains, etc.), including loosening, wear, breakage and jamming; temperature control failure refers to the excessive temperature of the motor and control system of the barrier machine, which may cause the barrier machine to overheat and affect normal operation; system failure refers to the loss of signal from the electronic control board or sensor network or problems with the control algorithm, which may cause the barrier machine to fail to start or stop normally; sensor failure refers to an abnormality in the operating data obtained by only a single sensor network, while the operating data obtained by other sensor networks are normal, which may be due to a failure of the sensor network itself.

[0110] There are expected coordinated changes between the operating data of the barrier machine. Under the normal operating conditions of the barrier machine, the vibration value and torque value of the barrier machine should have a strong positive correlation (for example, close to 1), because the change of torque usually affects the mechanical vibration. If within a certain period of time, the correlation between the vibration value and the torque value of the barrier machine deviates from the normal range, and the degree of deviation from the normal range is high, it means that a mechanical failure has occurred in the barrier machine. For example, if the correlation between the vibration value and the torque value of the barrier machine is significantly reduced, a mechanical failure may have occurred. Under the normal operating conditions of the barrier machine, the correlation between the vibration value and the temperature value of the barrier machine is weak (for example, close to 0), because vibration is usually related to factors such as mechanical friction and load, while temperature is mainly affected by heat sources and environment, but when the correlation between the vibration value and the temperature value of the barrier machine is When the value of the barrier machine deviates from the normal range and the degree of deviation from the normal range is high, it means that the temperature rise may cause the change of vibration, so as to obtain the temperature control failure of the barrier machine. For example, the correlation between the vibration value and the temperature value of the barrier machine suddenly increases, which may be due to abnormal vibration caused by overheating of the barrier machine. Under the normal operation of the barrier machine, the torque value and the position value of the barrier machine should maintain a certain degree of correlation (for example, between 0.5 and 0.8). When the correlation between the torque value and the position value of the barrier machine changes significantly, it may be a signal of a control system failure. For example, the correlation between the torque value and the position value of the barrier machine decreases significantly, which may be a signal of a control system failure. According to the above strategy content, the analysis and diagnosis of the abnormal type of the barrier machine can be realized, so as to accurately locate the abnormal type and better select the reset strategy.

[0111] The self-reset module is used to execute the reset strategy according to the abnormal type of the barrier machine and optimize the reset strategy.

[0112] The reset strategy is executed according to the abnormal type of the barrier machine. The reset strategy includes:

[0113] If the abnormal type of the barrier machine is sensor failure, the sensor network is initialized and calibrated;

[0114] If the abnormality type of the barrier machine is a mechanical failure, then detect and repair the mechanical parts;

[0115] If the abnormality type of the barrier machine is a temperature control failure, check and repair the temperature control system;

[0116] If the abnormal type of the barrier machine is a system failure, check and restart the control system.

[0117] In specific applications, sensor failure may be caused by signal loss or data abnormality. It is necessary to stop all operations of the current barrier machine, reinitialize the sensor network and perform self-test, calibrate the abnormal sensor network, ensure normal acquisition of operating data, and then restart the barrier machine; mechanical failure may be due to abnormal changes in torque and vibration. It is necessary to stop the operation of the barrier machine to ensure safety, check whether the mechanical parts are loose, worn or otherwise physically damaged, repair or replace the mechanical parts, ensure that all mechanical parts are working properly, and then restart the barrier machine to observe whether it resumes normal operation; temperature control failure is usually due to abnormal temperature sensors or temperature control systems, and it is necessary to stop the operation of the barrier machine. Check whether the temperature control system is working properly, including checking the radiator and coolant flow, etc., and check the function of the temperature sensor to ensure the accuracy of the temperature sensor. If the temperature control system fails, it needs to be repaired or the relevant parts replaced, and then restart the barrier machine to check whether it returns to normal; system failure is usually due to abnormal changes in torque and position. It is necessary to stop the operation of the barrier machine, check the electrical connection of the control system, the communication status between the sensor network and the control system, and check whether the data transmission between the position sensor and the torque sensor is normal. If the control system fails, check the system configuration, restart or reset the software if necessary, and then restart the barrier machine to observe whether it returns to normal.

[0118] After the reset strategy is executed, the operation data of the barrier machine is recorded, and the barrier machine is judged whether there is still an abnormality according to the above-mentioned strategy for judging the abnormality of the barrier machine. If the barrier machine still has an abnormality, the reset strategy is optimized. The logic of the optimized reset strategy includes:

[0119] Add a second reset attempt, increase the time interval when the reset fails, or use a different reset operation (for example, restart the control system first, then check the mechanical parts). The increase in the time interval is to avoid affecting the stability of the barrier machine due to too frequent operations.

[0120] According to the abnormal type of the barrier machine, the correlation difference and the correlation difference threshold of the operating data are adjusted to better compare the abnormality of the barrier machine after resetting, and at the same time reflect the adaptability under different working environments or loads.

[0121] The function expression for adjusting the correlation difference of running data is as follows:

[0122]

[0123] In the formula, Represents the correlation difference of the adjusted running data, r XY Indicates the relevance of running data at the current time, represents the standard correlation of the running data, var(r 0 Y ) represents the variance of the standard correlation of the running data.

[0124] What needs to be explained is: the variance of the standard correlation of the running data var(r 0 Y ) is used to measure the fluctuation range of the correlation between two operating data of the barrier machine under normal operating conditions, and the variance of the standard correlation of the operating data The value of is always non-negative; the correlation difference of the adjusted running data Indicates the difference between the correlation between the two operating data after reset and the correlation between the two operating data under normal operation. After normalization, in order to better compare the abnormal situation of the barrier machine after reset, the correlation difference of the adjusted operating data The value of is also non-negative, and the greater the difference, the greater the correlation difference of the adjusted running data. The larger the value of .

[0125] The function expression for adjusting the relevant difference threshold is as follows:

[0126]

[0127] In the formula, represents the adjusted correlation difference threshold, represents the correlation difference threshold, λ represents the adjustment factor, and b represents the trend factor.

[0128] What needs to be explained is: the adjusted correlation difference threshold Determines the maximum correlation difference that the control system can tolerate at the current time. It is adjusted according to the real-time changes of the operating data obtained by the sensor network, reflecting the adaptability under different working environments or loads. The adjusted correlation difference threshold The value of is usually non-negative; the relevant difference threshold It is usually a fixed value set by the control system when it starts running, indicating the maximum correlation difference allowed by the control system in the absence of any data change trend. The value of is usually non-negative; the adjustment factor λ is used to control the influence of the trend factor on the adjustment of the relevant difference threshold. The value of the adjustment factor λ is usually a positive number and a small value to avoid excessive fluctuations in the relevant difference threshold; the trend factor b refers to the changing trend of the operating data obtained by the sensor network over time. The value of the trend factor b is positive, negative or 0.

[0129] In specific applications, after executing the reset strategy, the operating data of the barrier machine after reset is recorded to determine whether the barrier machine still has abnormalities. If the barrier machine still has abnormalities, it is necessary to enter the reset strategy optimization step, including making a second reset attempt or adjusting the correlation difference and correlation difference threshold of the barrier machine to adapt to the current working environment or load, and adjusting and optimizing the reset strategy and related parameters according to the actual operating conditions to ensure the normal operation and safe use of the barrier machine, while reducing misjudgments and missed judgments; it is necessary to use machine learning algorithms to analyze the fluctuation trend of the operating data obtained by the sensor network to gradually adjust the correlation difference and correlation difference threshold used for abnormal type classification. After the reset strategy is optimized, its effect still needs to be verified. It is necessary to record the short-term and long-term operating status of the barrier machine after reset to verify the applicability and stability of the optimized reset strategy in various situations.

[0130] Example 2

[0131] In this embodiment, a method flow chart of a method for automatic detection and control of a smart integrated device is provided, such as Figure 4 As shown, a method for automatic detection and control of a smart integrated device includes:

[0132] S1, using data acquisition strategy to obtain the operation data of the barrier machine in real time with the help of sensor network;

[0133] S2. Determine the abnormality of the barrier machine according to the operating data of the barrier machine;

[0134] S3. Analyze and diagnose the abnormal type of the barrier machine;

[0135] S4. Execute the reset strategy according to the abnormal type of the barrier machine and optimize the reset strategy.

[0136] For the specific content of an automatic detection and control method for an intelligent integrated device, please refer to an automatic detection and control system for an intelligent integrated device, which will not be repeated here.

[0137] Example 3

[0138] In this embodiment, a computer device is provided, including a memory and a processor, the memory is used to store instructions, and the processor is used to execute the instructions, so that the computer device executes the steps of implementing the above-mentioned method for automatic detection and control of a smart all-in-one device.

[0139] Example 4

[0140] In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed, the steps of the above-mentioned method for automatic detection and control of a smart all-in-one device are implemented.

[0141] The computer-readable storage medium includes: a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk, and other media for storing program codes.

[0142] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A smart integrated equipment automatic detection and control system, characterized by: include: Status detection module, fault identification module, analysis and diagnosis module and self-reset module; The state detection module is used to obtain the operation data of the equipment in real time through the data acquisition strategy with the help of the sensor network; The fault identification module is used to determine the abnormality of the device according to the operation data of the device, and the determination of the abnormality of the device includes respectively determining the abnormality of the operation data according to the operation data of the device, and determining the abnormality of the device according to the abnormality of the operation data, and the determination logic of the abnormality of the operation data includes determining the temperature abnormality and the position abnormality according to the temperature change degree and the position change degree of the device; The analysis and diagnosis module is used to analyze and diagnose the abnormal type of the equipment. The strategy of analyzing and diagnosing the abnormal type of the equipment includes judging the collaborative abnormal result of the equipment according to the correlation between the operation data, and comprehensively diagnosing the abnormal type of the equipment in combination with the operation data; The self-reset module is used to execute a reset strategy according to the abnormal type of the device and optimize the reset strategy.

2. The automatic detection and control system for intelligent integrated equipment according to claim 1, characterized in that: The strategy for determining the abnormality of the device includes: According to the operation data of the equipment, the abnormality of the operation data is judged respectively, and the vibration threshold Δv is configured. The vibration value v(t) of the equipment at time t is compared with the vibration threshold to obtain the vibration abnormality. If v(t)≤Δv, the equipment shows that there is no abnormal vibration. If v(t)>Δv, the device shows abnormal vibration; The temperature values ​​of the device in adjacent time periods are subtracted, that is, the temperature value T(t) of the device at time t is subtracted from the temperature value T(t-1) of the device at time t-1 to obtain the temperature difference value |T(t)-T(t-1)| of the device. A temperature change threshold ΔT is configured, and the temperature difference value of the device is compared with the temperature change threshold to obtain temperature anomaly. If |T(t)-T(t-1)|≤ΔT, the device displays no temperature anomaly. If |T(t)-T(t-1)|>ΔT, the device displays abnormal temperature.

3. The automatic detection and control system for intelligent integrated equipment according to claim 2, characterized in that: The strategy for determining the abnormality of the device also includes: Configure the standard position value p of the device at time t, perform subtraction processing on the position value p(t) of the device at time t and the standard position value of the device at time t to obtain the position difference value |p(t)-p| of the device, configure the position change threshold Δp, compare the position difference value of the device with the position change threshold to obtain the position abnormality, if p(t)-p|≤Δp, then the device display position is normal; If |p(t)-p|>Δp, the device displays an abnormal position; The torque value of the device in adjacent time is processed by difference, that is, the torque value m(t) of the device at time t is processed by difference with the torque value m(t-1) of the device at time t-1, and the torque difference value of the device is obtained. | m(t)-m(t-1) | , configure the torque change threshold Δm, compare the torque difference of the equipment with the torque change threshold to obtain the torque abnormality. | m(t)-m(t-1) | ≤Δm, the equipment displays no abnormal torque; like | m(t)-m(t-1) | >Δm, the equipment displays abnormal torque; The abnormality of the equipment is judged according to the abnormality of the operating data. If the displayed operating data is normal, then the equipment is normal. If any operating data is displayed as abnormal, the equipment is abnormal.

4. The automatic detection and control system for intelligent integrated equipment according to claim 3, characterized in that: When the device is abnormal, the strategy for analyzing and diagnosing the abnormal type of the device includes: Record the abnormality of the operating data. If a single operating data is abnormal, the abnormality type of the diagnostic equipment is a sensor failure. If more than one operating data is abnormal, the correlation between the operating data is calculated to obtain the correlation r of the current time operating data. XY , configure standard dependencies for run data The correlation of the current time operation data is subtracted from the standard correlation of the operation data to obtain the correlation difference Δr of the operation data. XY , configure the relevant difference threshold The correlation difference of the operating data is compared with the correlation difference threshold to obtain the coordinated abnormality result of the equipment. The abnormality type of the equipment is comprehensively diagnosed according to the coordinated abnormality result and operating data of the equipment through the coordinated abnormality logic.

5. The automatic detection and control system for intelligent integrated equipment according to claim 4, characterized in that: The functional expression of the correlation difference of the running data is as follows: In the formula, Δr XY Represents the correlation difference of the running data, r XY Indicates the relevance of running data at the current time, Represents the standard correlation of the running data.

6. The automatic detection and control system for intelligent integrated equipment according to claim 5, characterized in that: The collaborative exception logic includes: If X is the vibration value of the equipment, Y is the torque value of the equipment, and Then the abnormal type of the equipment is diagnosed as mechanical failure; If X is the vibration value of the equipment, Y is the temperature value of the equipment, and The abnormal type of the equipment is diagnosed as a temperature control failure; If X is the torque value of the device, Y is the position value of the device, and The abnormality type of the device is diagnosed as a system failure.

7. The automatic detection and control system for intelligent integrated equipment according to claim 6, characterized in that: A reset strategy is executed according to the abnormal type of the device, and the reset strategy includes: If the abnormal type of the device is sensor failure, the sensor network is initialized and calibrated; If the abnormality type of the equipment is a mechanical failure, the mechanical parts are detected and repaired; If the abnormality type of the equipment is a temperature control failure, check and repair the temperature control system; If the abnormality type of the device is system failure, check and restart the control system.

8. The automatic detection and control system for intelligent integrated equipment according to claim 7, characterized in that: The data acquisition strategy includes: The sensor network includes vibration sensors, temperature sensors, position sensors and torque sensors. The vibration sensor is used to obtain the vibration frequency and vibration amplitude of the device in real time during operation to obtain the vibration value of the device. The temperature sensor is used to obtain the temperature of the device in real time during operation to obtain the temperature value of the device. The position sensor is used to obtain the position of the device in real time during operation to obtain the position value of the device. The torque sensor is used to obtain the torque of the device in real time during operation to obtain the torque value of the device.

9. A method for automatic detection and control of a smart integrated device, implemented based on an automatic detection and control system for a smart integrated device according to any one of claims 1 to 8, characterized in that: include: S1, using data acquisition strategies to obtain equipment operation data in real time with the help of sensor networks; S2. Determine the abnormality of the equipment based on the equipment's operating data; S3. Analyze and diagnose the abnormal type of the equipment; S4. Execute the reset strategy according to the abnormal type of the device and optimize the reset strategy.

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