A safety status monitoring method and system for industrial automation control equipment

Abstract

The invention discloses a safety status monitoring method and system for industrial automation control equipment, which relates to the field of industrial automation technology. The main scheme is: acquiring the basic operation data of the robot in real time, calculating the torque evaluation score Z, the output speed #imgabs0#, the load abnormality evaluation index #imgabs1# and the overall deviation value of the grasping force #imgabs2# in combination with the historical operation data, judging whether the robot joints, the motors in the drive system, the load conditions and the grasping stability are abnormal; and executing the corresponding alarm strategy based on the comparison result of the comprehensive evaluation score and the preset threshold value; finally, the system adaptively adjusts the operation parameters of the robot control equipment or provides decision support according to the monitoring results, and monitors the execution effect; this method realizes comprehensive and accurate operation status monitoring and evaluation of the industrial automation robot, ensures production safety, improves production efficiency, optimizes equipment performance, and enhances product quality stability and enterprise competitiveness.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automation, and in particular to a safety status monitoring method and system for industrial automation control equipment. Background Art

[0002] In today's highly industrialized and intelligent era, industrial automation control equipment plays a vital role in the field of production and manufacturing; with the continuous expansion of industrial production scale, the increasing complexity of production processes, and the continuous improvement of production efficiency and product quality requirements, ensuring the safe and stable operation of industrial automation control equipment has become the focus of enterprises; among them, robots, as the main equipment for automated production, have been widely used.

[0003] In modern industrial production, robots are often in high-speed, high-load operation, and their operating environment may be relatively harsh, which puts higher requirements on the reliability and stability of the equipment; once the equipment fails or malfunctions, it will not only affect the production progress and cause product quality to decline, but may also cause serious safety accidents, causing huge economic losses and casualties to the company.

[0004] However, the existing monitoring methods of industrial automation control equipment are single and inefficient, unable to fully and accurately reflect the operating status of the equipment and difficult to achieve intelligent early warning. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the deficiencies in the prior art, the present invention provides a method and system for monitoring the safety status of industrial automation control equipment. Relevant indicators are calculated based on the basic operating data and historical operating data of the robot to determine whether the robot's joints and other parts are abnormal. The comprehensive evaluation score S is calculated and compared with the threshold to determine the operating status and execute an alarm strategy, thereby solving the problems of the existing industrial automation control equipment monitoring methods being single, inefficient, unable to fully and accurately reflect the equipment's operating status, and difficult to achieve intelligent early warning.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for monitoring the safety status of industrial automation control equipment, comprising the following steps:

[0009] Obtain the basic operating data of the robot in real time and transmit the basic operating data to the data processing center;

[0010] The data processing center receives basic operation data and historical operation data; by analyzing and calculating the basic operation data and historical operation data, the torque evaluation score Z, output speed V, load abnormality evaluation index C and grasping force overall deviation value K are obtained; based on the obtained data, it is determined whether the robot joints are abnormal, whether the motors in the drive system are abnormal, whether the robot load is abnormal, and whether the robot grasping stability is abnormal;

[0011] When the robot has any abnormal situation, the comprehensive evaluation score S is calculated according to the torque evaluation score Z, output speed V, load abnormality evaluation index C and grasping force overall deviation value K, and the alarm evaluation threshold is preset; the comprehensive evaluation score S is compared with the preset alarm evaluation threshold, and the operation state of the robot is judged according to the comparison result, and different alarm strategies are executed according to the operation state;

[0012] Based on the comprehensive evaluation results and alarm feedback, the operating parameters or decision support of the industrial automation robot control equipment are adaptively adjusted, and the execution effect is monitored.

[0013] In the preferred embodiment of the safety status monitoring method of the above industrial automation control equipment: the basic operation data at least includes the real-time torque data M, the input-output speed ratio R, the change rate of the input speed Load bearing capacity F, function of load changing with time F(t), grasping data G, target grasping force G t and the grasping force deviation value ΔG;

[0014] The historical operation data at least includes the torque historical data M i , Motor input speed V in 、Load historical data F a , load data historical average value F g , historical data capture capability G x ;

[0015] Based on the torque history data M i Ability to calculate torque assessment score Z;

[0016] Based on the motor input speed V in and the rate of change of input speed Able to calculate output speed V;

[0017] Based on load history data F a , load data historical average value F g And the function F(t) of load variation over time can calculate the load anomaly evaluation index C;

[0018] Based on historical data, the grasping force G x , target grasping force G tAnd the grasping force deviation value ΔG can calculate the overall deviation value K of the grasping force.

[0019] In the preferred solution of the above safety status monitoring method for industrial automation control equipment: The method for judging whether a robot joint is abnormal is as follows:

[0020] Obtain the torque historical data M i , calculate the historical average torque value M N , and the calculation formula is:

[0021]

[0022] where, M i is the i-th historical torque value, and the value range of i is 1, 2, 3... N; N is the number of historical torque values and takes positive integer values;

[0023] According to the torque historical data M i and the historical average torque value M N calculate the torque standard deviation σ M , and the formula is as follows:

[0024]

[0025] According to the historical average torque value M N , the torque real-time data M and the torque standard deviation σ M calculate the torque evaluation score Z, and the formula is as follows:

[0026]

[0027] Preset a positive threshold Z1 and a negative threshold Z2. When the condition Z2 < Z < Z1 is satisfied, the current torque value is normal; when the condition Z > Z1 or Z < Z2 is satisfied, the current torque value is abnormal and the robot joint is abnormal.

[0028] In the preferred solution of the above safety status monitoring method for industrial automation control equipment: The method for judging whether a motor in the drive system is abnormal is as follows: Establish a motor dynamic model and obtain the input speed V of the motor in and the change rate of the input speed Calculate the output speed V of the motor, and its formula is:

[0029]

[0030] where, R represents the input-output speed ratio, and k is a damping coefficient;

[0031] Set the output speed evaluation threshold range as [V min , V max , when the condition V min ≤V≤Vmax When V <V min or V>V max , it is determined that the robot's motor speed is abnormal.

[0032] In the preferred embodiment of the above-mentioned method for monitoring the safety status of industrial automation control equipment: the load history data F a , load data historical average value F g and F(t) are functions of load changing with time. The load bearing capacity F of the robot is monitored and the load abnormality evaluation index C is calculated. The formula is:

[0033]

[0034] Among them, k1 and k2 are weight coefficients, which are used to adjust the relative importance of the sum of squared deviations and the integral of deviations, and k1+k2=1; F a is the a-th load history data, the value range of a is 1, 2, 3...m; m is the number of collected load data points, which is a positive integer; t0 and t a The start and end time of the evaluation period;

[0035] Preset load abnormality assessment threshold C th :If C>C th , it is judged that the load condition is abnormal; if C≤C th , the load condition is normal.

[0036] In the preferred embodiment of the safety status monitoring method of the above industrial automation control equipment: the method for determining whether the robot grasping stability is abnormal is:

[0037] Based on historical data, the grasping force G x , target grasping force G t The formula for calculating the overall deviation value K of the grasping force based on the grasping force deviation value ΔG is as follows:

[0038]

[0039] Where n is the number of times the grasping force is measured, which is a positive integer; G x is the grasping force sampled at the xth time, and the value range of x is 1, 2, 3...n; n is the number of grasping force data points collected, and the value is a positive integer; ΔG is the allowable grasping force deviation range value;

[0040] Set the grasping stability threshold K th , if K≤K th , the grasping force stability is evaluated as normal; if K>K th , it is judged that the grasping force stability assessment is abnormal.

[0041] In the preferred solution of the above-mentioned safety status monitoring method for industrial automation control equipment: The method for calculating the comprehensive evaluation score S is as follows:

[0042] S = W Z ×Z + W V ×V + W C ×C + W K ×K

[0043] Where: W Z is the weight coefficient of the torque evaluation score Z, and its value range is 0.25 - 0.3; W V is the weight coefficient of the output rotational speed V, and its value range is 0.2 - 0.35; W C is the weight coefficient of the load abnormality evaluation index C, and its value range is 0.2 - 0.3; W K is the weight coefficient of the overall deviation value K of the grasping force, and its value range is 0.2 - 0.3; and W Z + W V + W C + W K = 1.

[0044] In the preferred solution of the above-mentioned safety status monitoring method for industrial automation control equipment: The method for determining the execution of different alarm strategies is as follows:

[0045] The alarm evaluation threshold includes a first threshold I1 and a second threshold I2;

[0046] When S ≤ I2 < I1, it indicates that the robot is in a seriously unsafe state, and alarm strategy one is executed;

[0047] When I2 < S ≤ I1, it indicates that the robot is in a mildly unsafe state, and alarm strategy two is executed;

[0048] In the preferred solution of the above-mentioned safety status monitoring method for industrial automation control equipment: The specific alarm plan for executing alarm strategy two is as follows:

[0049] Visual alarm: Display the alarm information on the robot in a prominent color, accompanied by a flashing effect to attract the operator's attention;

[0050] The specific alarm plan for executing alarm strategy one is as follows:

[0051] Sound alarm: Sound an alarm through the built-in speaker of the system or connect to an external audio device, and the sound is set to different frequencies or tones to distinguish different levels of alarms;

[0052] Remote notification: Send an alarm notification to relevant personnel via email, text message, or instant messaging tool to ensure that even if the operator is not on-site, they can promptly receive the alarm information.

[0053] The present invention also discloses a safety status monitoring system for industrial automation control equipment, which is used to implement the above safety status monitoring method, including:

[0054] The data acquisition module is used to obtain the basic operation data of the robot in real time and transmit the basic operation data to the data processing center;

[0055] The data processing module is used for the data processing center to receive basic operation data and historical operation data; by analyzing and calculating the basic operation data and historical operation data, the torque evaluation score Z, the output speed V, the load abnormality evaluation index C and the overall deviation value K of the grasping force are obtained; according to the obtained data, it is judged whether the robot joint is abnormal, whether the motor in the drive system is abnormal, whether the robot load condition is abnormal, and whether the robot grasping stability is abnormal;

[0056] The early warning and response module is used to calculate the comprehensive evaluation score S according to the torque evaluation score Z, output speed V, load abnormality evaluation index C and grasping force overall deviation value K when the robot has any abnormal situation, and preset the alarm evaluation threshold; compare the comprehensive evaluation score S with the preset alarm evaluation threshold, judge the operation status of the robot according to the comparison result, and execute different alarm strategies according to the operation status;

[0057] The intelligent response and decision support module is used to adaptively adjust the operating parameters or decision support of industrial automation robot control equipment according to the comprehensive evaluation results and alarm feedback, and monitor the execution effect.

[0058] (III) Beneficial effects

[0059] The present invention provides a method and system for monitoring the safety status of industrial automation control equipment, which has the following beneficial effects:

[0060] (1) By acquiring the basic operating data of the robot in real time, the robot status is monitored immediately, which improves the real-time and accuracy of monitoring; this helps to respond quickly to any potential problems and reduce losses caused by delayed discovery;

[0061] (2) This method not only focuses on a single operating parameter, but also conducts a comprehensive evaluation from multiple dimensions such as torque evaluation, output speed, load abnormality, and grasping force stability. This multi-dimensional evaluation method can more comprehensively reflect the actual operating status of the robot and improve the accuracy and reliability of the evaluation.

[0062] (3) Through the analysis and calculation of basic operating data and historical data, it can automatically determine whether there are any abnormalities in the robot joints, motors in the drive system, load conditions, and grasping stability. Once an abnormality is found, further evaluation is immediately carried out, and the corresponding alarm strategy is executed based on the evaluation results, thus realizing intelligent diagnosis and early warning.

[0063] (4) Based on the comprehensive evaluation results and alarm feedback, the system can automatically determine the operating status of the robot and implement different alarm strategies. This hierarchical processing mechanism can ensure that emergency measures are taken in a timely manner in serious unsafe conditions, and can also provide early warnings in mild unsafe conditions, so that operators can intervene in time.

[0064] (5) Through real-time monitoring, accurate evaluation, intelligent alarm and adaptive adjustment, the safety and operating efficiency of industrial automation control equipment are effectively improved; it reduces the risk of production interruptions and safety accidents caused by equipment failure, and ensures the stable operation of the production line and the safety of personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A schematic diagram of the steps of the safety status monitoring method of industrial automation control equipment of the present invention; DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] See also Figure 1 The present invention provides a method for monitoring the safety status of industrial automation control equipment, comprising the following steps:

[0068] Step 1: Obtain the basic operation data of the robot in real time and transmit the basic operation data to the data processing center;

[0069] Step one includes the following steps:

[0070] Step 101: Obtain the basic operating data of the robot, which includes the real-time torque data M, the input-output speed ratio R, and the change rate of the input speed. Load bearing capacity F, function of load changing with time F(t), grasping data G, target grasping force G t and the grasping force deviation value ΔG;

[0071] Step 102: Configure a data transmission network and use the MQTT data transmission protocol to transmit data to a data transmission center;

[0072] It should be noted that when configuring the data acquisition module or software in the robot control system and setting the data acquisition frequency, the robot performance should not be affected by excessive data volume.

[0073] Step 2: The data processing center receives basic operation data and historical operation data; by analyzing and calculating the basic operation data and historical operation data, the torque evaluation score Z, output speed V, load abnormality evaluation index C and grasping force overall deviation value K are obtained; based on the obtained data, it is determined whether the robot joint is abnormal, whether the motor in the drive system is abnormal, whether the robot load is abnormal, and whether the robot grasping stability is abnormal;

[0074] Step 2 includes the following steps:

[0075] Step 201: Obtain historical operation data of the robot, which at least includes torque historical data M i , Motor input speed V in 、Load historical data F a , load data historical average value F g , historical data capture capability G x ;

[0076] Step 202: Based on the torque real-time data M and torque historical data M in the basic operation data i , use the predetermined evaluation algorithm to calculate the torque evaluation score Z to determine whether the robot's joints are abnormal, the method is:

[0077] Get all historical torque values ​​from the database of robot joint torques over the past period of time, denoted as M i , calculate the historical average torque value M N , the calculation formula is:

[0078]

[0079] Among them, M i is the i-th historical torque value, i ranges from 1, 2, 3...N; N is the number of historical torque values, which is a positive integer;

[0080] According to the torque history data M i Compared with the historical average torque value M N Calculate the torque standard deviation σ M , the formula is as follows:

[0081]

[0082] According to the historical average torque value M N , torque real-time data M and torque standard deviation σ M The torque evaluation score Z is calculated based on the following formula:

[0083]

[0084] According to the statistical characteristics of historical torque data and statistical principles, in a normal distribution, approximately 95% of the data will fall within the range of ±2σ, and the data outside this range has an abnormal possibility; therefore, the standard deviation multiple is used to determine the threshold range, and the positive threshold Z1 and negative threshold Z2 are preset, and the conditions to be satisfied are Z1 = 2σ M , Z2 = -2σ M ;

[0085] When the condition Z2 < Z < Z1 is satisfied, the current torque value is normal; when the condition Z > Z1 or Z < Z2 is satisfied, the current torque value is abnormal, and there is an abnormality in the robot joint.

[0086] It should be noted that a period of time can be adjusted according to the situation, such as set to one week or one month;

[0087] When the robot joint does not bear an external load or bears a very small load during task execution, the recorded historical torque value is the torque under the no-load state. The no-load torque data can be used to evaluate the basic performance of the robot joint, such as lubrication condition, mechanical wear degree, and the efficiency of the motor and transmission system;

[0088] When the robot joint bears a small external load during task execution, the recorded torque value is the torque under the light-load state. The light-load torque data helps to understand the performance of the robot when handling lighter tasks and the influence degree of load changes on the torque value;

[0089] When the robot joint bears a large external load during task execution, the recorded torque value is the torque under the heavy-load state. The heavy-load torque data is generally used to evaluate the working ability of the robot under extreme conditions;

[0090] When the robot joint starts to move, the torque value will suddenly increase to overcome static friction and inertia; while when it stops, the torque value gradually decreases until it becomes 0.

[0091] Step 203: Establish a motor dynamic model to determine whether the motor in the drive system is abnormal. The method is as follows:

[0092] According to the obtained input speed V of the motor in and the change rate of the input speed Calculate the output speed V of the motor. The formula is:

[0093]

[0094] where R represents the input-output speed ratio, k is a damping coefficient, representing the contribution coefficient of this variable to the input speed; dt represents the time differential, and the derivative of the input speed V in is taken to represent the input speed V inrate of change over time;

[0095] It should be noted that when the motor is operating normally, the output speed fluctuates within a certain range of the input speed; according to the robot's design parameters, manufacturing accuracy, working conditions and other factors, the output speed evaluation threshold range is set to [V min , V max ], when V min ≤V≤V max When V <V min or V>V max , it is determined that the robot's motor speed is abnormal.

[0096] Motor input speed V in The rate of change is very important for judging the dynamic performance and response characteristics of the motor; if the rate of change of the input speed of the motor during the startup phase If the input speed is larger, it means that the motor can quickly reach the operating speed and has better starting performance; on the contrary, if the input speed change rate is If it is smaller, it will start slower;

[0097] When the motor load suddenly increases, the rate of change of the input speed A rapid drop means that the motor cannot respond to changes in load in time, and there is a risk of overload or stall.

[0098] Step 204: Obtain load history data F a and load data historical average value F g , monitor the load bearing capacity F of the robot and calculate the load abnormality evaluation index C, the formula is:

[0099]

[0100] Among them, k1 and k2 are weight coefficients, which are used to adjust the relative importance of the sum of squared deviations and the integral of deviations, and k1+k2=1; is the sum of squared deviations, is the deviation integral;

[0101] F a is the a-th load history data, the value range of a is 1, 2, 3...m; m is the number of collected load data points, which is a positive integer; F(t) is the function of load changing with time; t0 and t a The start and end time of the evaluation period;

[0102] Based on historical data and the robot's design specifications, a load anomaly assessment threshold C is preset. th :If C>C th , it is judged that the load condition is abnormal; if C≤C th, the load condition is normal;

[0103] It should be noted that if the current load is significantly higher than the historical average of the load data, it indicates that the robot is bearing an abnormally heavy load and needs to be checked for faults or unreasonable work arrangements.

[0104] Step 205: When the robot performs a grasping task, grasping data G is collected and recorded in real time. The grasping data G at least includes historical data grasping force G x , target grasping force G t and the grasping force deviation value ΔG; set an evaluation cycle, record the number of grasping operations performed by the robot in the cycle, and ensure that n is a positive integer;

[0105] The overall deviation value K of the grasping force is calculated through these data to determine whether the robot's grasping stability is abnormal. The calculation formula is:

[0106]

[0107] Among them, G x is the grasping force sampled at the xth time, and the value range of x is 1, 2, 3...n; n is the number of grasping force data points collected, and the value is a positive integer; ΔG is the allowable grasping force deviation range value;

[0108] According to the robot performance, application scenarios and historical data statistical analysis, set a reasonable grasping stability threshold K th , if K≤K th , the grasping force stability is evaluated as normal; if K>K th , it is judged that the grasping force stability assessment is abnormal.

[0109] Step 3: When the robot has any abnormal situation, the comprehensive evaluation score S is calculated according to the torque evaluation score Z, the output speed V, the load abnormality evaluation index C and the overall deviation value K of the grasping force, and the alarm evaluation threshold is preset; the comprehensive evaluation score S is compared with the preset alarm evaluation threshold, and the operation state of the robot is judged according to the comparison result, and different alarm strategies are executed according to the operation state;

[0110] Step 301: Based on the torque evaluation score Z, the output speed V, the load abnormality evaluation index C and the overall deviation value K of the gripping force, a comprehensive evaluation score S is designed, and the calculation formula is:

[0111] S=W Z ×Z+W V ×V+W C ×C+W K ×K

[0112] Where: W Zis the weight coefficient of the torque evaluation score Z, and its value range is 0.25 to 0.3; W V is the weight coefficient of the output speed V, and its value range is 0.2 to 0.35; W C is the weight coefficient of the load abnormality evaluation index C, and its value range is 0.2 to 0.3; W K is the weight coefficient of the overall deviation value K of the grasping force, and its value range is 0.2 to 0.3; and W Z +W V +W C +W K = 1;

[0113] Assume that the torque evaluation score Z = 80, the output speed V = 1200, the load abnormality evaluation index C = 0.8, and the overall deviation value K of the grasping force = 15, W Z = 0.275, W V = 0.275, W C = 0.25, W K = 0.2, and the calculation gives:

[0114] S = 80×0.275 + 1200×0.275 + 0.8×0.25 + 15×0.2 = 355.2

[0115] Step 302: According to the operating state of the robot, the alarm evaluation threshold is set to two levels, including the first threshold I1 and the second threshold I2;

[0116] When S ≤ I2 < I1, it indicates that the robot is in a seriously unsafe state, and alarm strategy one is executed;

[0117] When I2 < S ≤ I1, it indicates that the robot is in a mildly unsafe state, and alarm strategy one is executed;

[0118] Among them, the first threshold I1 is usually set to a lower threshold, and the second threshold I2 is set to a higher value;

[0119] It should be noted that: The setting of the alarm evaluation thresholds I1 and I2 is comprehensively considered according to factors such as the specific model of the robot, working conditions, safety standards, and historical operation data, ensuring that the thresholds neither cause frequent false alarms nor fail to detect potential dangers in a timely manner.

[0120] The specific alarm plan for executing alarm strategy two is as follows:

[0121] Visual alarm: Display the alarm information in a prominent color on the robot, accompanied by a flashing effect to attract the operator's attention;

[0122] The specific alarm plan for executing alarm strategy one is as follows:

[0123] Sound alarm: Sound alarm is issued through the system's built-in speaker or connected to an external audio device. The sound is set to different frequencies or tones to distinguish different levels of alarms;

[0124] Remote notification: Send alarm notifications to relevant personnel via email, SMS or instant messaging tools to ensure that operators can receive alarm information in a timely manner even if they are not on site;

[0125] Alarm strategy one includes visual alarm, and alarm strategy two includes audible alarm and remote notification;

[0126] It should be noted that: in addition to this, other alarm methods may also be used, such as light alarm, mobile police car alarm, vibration or tactile alarm, and wireless signal alarm.

[0127] Step 4: Based on the comprehensive evaluation results and alarm feedback, adaptively adjust the operating parameters or decision support of the industrial automation robot control equipment, and monitor the execution effect.

[0128] It should be noted that: according to the preset rules or thresholds, when a parameter of the robot exceeds the normal range, the adjustment mechanism is automatically triggered, and the adjustment operation can be completed automatically;

[0129] The control system's monitoring interface displays the robot's operating status and parameter changes in real time. If the adjustment operation fails to achieve the expected effect or the robot encounters a new abnormality, the alarm system will immediately sound an alarm to alert the user and take appropriate measures.

[0130] On the other hand, the present invention also discloses a safety status monitoring system for industrial automation control equipment, which is used to implement the above safety status monitoring method, including:

[0131] The data acquisition module is used to obtain the basic operation data of the robot in real time and transmit the basic operation data to the data processing center;

[0132] Data processing module: The data processing center receives basic operation data and historical operation data; by analyzing and calculating the basic operation data and historical operation data, the torque evaluation score Z, output speed V, load abnormality evaluation index C and grasping force overall deviation value K are obtained; according to the obtained data, it is judged whether the robot joint is abnormal, whether the motor in the drive system is abnormal, whether the robot load is abnormal, and whether the robot grasping stability is abnormal;

[0133] The early warning and response module is used to calculate the comprehensive evaluation score S according to the torque evaluation score Z, output speed V, load abnormality evaluation index C and grasping force overall deviation value K when the robot has any abnormal situation; preset the alarm evaluation threshold; compare the comprehensive evaluation score S with the preset alarm evaluation threshold, judge the operation status of the robot according to the comparison result, and execute different alarm strategies according to the operation status;

[0134] The intelligent response and decision support module is used to adaptively adjust the operating parameters or decision support of the industrial automation robot control equipment according to the comprehensive evaluation results and alarm feedback, and monitor the execution effect. The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art can appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0135] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0136] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for monitoring the safety status of industrial automation control equipment, characterized in that: It includes the following steps: Obtain the basic operation data of the robot in real time and transmit the basic operation data to the data processing center; The data processing center receives basic operation data and historical operation data. The basic operation data at least includes torque real-time data M, input-output speed ratio R, input speed change rate Load bearing capacity F, function of load changing with time F(t), grasping data G, target grasping force G t and the gripping force deviation value ΔG; the historical operation data at least includes the torque historical data M i , Motor input speed V in , Load historical data F a , load data historical average value F g , historical data capture capability G x ; According to the torque history data M i , calculate the historical average torque value M N , according to the torque history data M i and the historical average torque value M N calculate the torque standard deviation σ M , according to the historical average torque value M N , the torque real-time data M and the torque standard deviation σ M calculate the torque evaluation score Z; preset a positive threshold Z1 and a negative threshold Z2. When the condition Z2 < Z < Z1 is satisfied, the current torque value is normal; when the condition Z > Z1 or Z < Z2 is satisfied, the current torque value is abnormal and there is an abnormality in the robot joint; Based on the motor input speed V in and the rate of change of input speed Able to calculate output speed V; Set the output speed evaluation threshold range to [V min , V max ], when V min ≤V≤V max When V <V min or V>V max When , it is determined that the motor speed of the robot is abnormal; Based on load history data F a , load data historical average value F g And the function F(t) of load variation over time can calculate the load anomaly evaluation index C; Preset load abnormality assessment threshold C th :If C>C th , it is judged that the load condition is abnormal; if C≤C th , the load condition is normal; Based on historical data, the grasping force G x , target grasping force G t And the grasping force deviation value ΔG can calculate the overall grasping force deviation value K; set the grasping stability threshold K th , if K≤K th , the grasping force stability is evaluated as normal; if K>K th , the grasping force stability assessment was judged to be abnormal; When any abnormal situation occurs to the robot, calculate the comprehensive evaluation score S according to the torque evaluation score Z, output rotational speed V, load abnormality evaluation index C, and overall deviation value K of the grasping force, and preset the alarm evaluation threshold; compare the comprehensive evaluation score S with the preset alarm evaluation threshold, judge the operation status of the robot according to the comparison result, and judge and execute different alarm strategies according to the operation status; According to the comprehensive evaluation result and alarm feedback, adaptively adjust the operation parameters or decision support of the industrial automation robot control device and monitor the execution effect.

2. The method for monitoring the safety status of industrial automation control equipment according to claim 1, characterized in that: Calculate the historical average torque value M N The formula is: Among them, M i is the i-th historical torque value, i ranges from 1, 2, 3...N; N is the number of historical torque values, which is a positive integer; Calculate the torque standard deviation σ M , the formula is as follows: Calculate the torque evaluation score Z, and the formula is as follows:

3. The method for monitoring the safety status of industrial automation control equipment according to claim 2, characterized in that: Establish a dynamic model of the motor and obtain the input speed V of the motor in and the rate of change of input speed Calculate the output speed V of the motor, the formula is: Wherein, R represents the input-output rotational speed ratio, and k is a damping coefficient.

4. The method for monitoring the safety status of industrial automation control equipment according to claim 3, characterized in that: The formula for calculating the load abnormality evaluation index C is: Among them, k1 and k2 are weight coefficients, which are used to adjust the relative importance of the sum of squared deviations and the integral of deviations, and k1+k2=1; F a is the a-th load history data, the value range of a is 1, 2, 3...m; m is the number of collected load data points, which is a positive integer; t0 and t a The start and end time of the evaluation period.

5. The method for monitoring the safety status of industrial automation control equipment according to claim 4, characterized in that: The formula for calculating the overall deviation value K of the grasping force is as follows: Where n is the number of times the grasping force is measured, which is a positive integer; G x is the grasping force sampled at the xth time, and the value range of x is 1, 2, 3...n; ΔG is the allowable grasping force deviation range.

6. The method for monitoring the safety status of industrial automation control equipment according to claim 5, characterized in that: The method for calculating the comprehensive evaluation score S is: S=W Z ×Z+W V ×V+W C ×C+W K ×K Where: W Z is the weight coefficient of the torque evaluation score Z, ranging from 0.25 to 0.3; W V is the weight coefficient of the output speed V, ranging from 0.2 to 0.35; W C is the weight coefficient of the load abnormality evaluation index C, ranging from 0.2 to 0.3; W K is the weight coefficient of the overall deviation value K of the grasping force, ranging from 0.2 to 0.3; and W Z +W V +W C +W K =1.

7. The method for monitoring the safety status of industrial automation control equipment according to claim 6, characterized in that: The method for judging and executing different alarm strategies is as follows: The alarm evaluation threshold includes a first threshold I1 and a second threshold I2; When S≤I2<I1, it indicates that the robot is in a seriously unsafe state, and alarm strategy one is executed; When I2<S≤I1, it indicates that the robot is in a slightly unsafe state, and alarm strategy two is executed.

8. The method for monitoring the safety status of industrial automation control equipment according to claim 7, characterized in that: The specific alarm plan for executing alarm strategy two is: Visual alarm: Display the alarm information on the robot in a prominent color, accompanied by a flashing effect to attract the operator's attention; The specific alarm plan for executing alarm strategy one is: Sound alarm: Sound an alarm through the built-in speaker of the system or connect to an external audio device, and set the sound to different frequencies or tones to distinguish different levels of alarms; Remote notification: Send an alarm notification to relevant personnel through email, text message or instant messaging tool to ensure that even if the operator is not on site, the alarm information can be obtained in time.

9. A safety status monitoring system for industrial automation control equipment, used to implement the safety status monitoring method of any one of claims 1 to 8, characterized in that: It includes: A data acquisition module, which is used to obtain the basic operation data of the robot in real time and transmit the basic operation data to the data processing center; A data processing module, which is used for the data processing center to receive the basic operation data and historical operation data; through analyzing and calculating the basic operation data and historical operation data, obtain the torque evaluation score Z, output rotational speed V, load abnormality evaluation index C, and overall deviation value K of the grasping force; respectively judge whether the robot joints are abnormal, whether the motors in the drive system are abnormal, whether the robot load condition is abnormal, and whether the robot grasping stability is abnormal according to the obtained data; A warning and response module, which is used to calculate the comprehensive evaluation score S according to the torque evaluation score Z, output rotational speed V, load abnormality evaluation index C, and overall deviation value K of the grasping force when any abnormal situation occurs to the robot, and preset the alarm evaluation threshold; compare the comprehensive evaluation score S with the preset alarm evaluation threshold, judge the operation status of the robot according to the comparison result, and judge and execute different alarm strategies according to the operation status; An intelligent response and decision support module, which is used to adaptively adjust the operation parameters or decision support of the industrial automation robot control device according to the comprehensive evaluation result and alarm feedback, and monitor the execution effect.

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