Intelligent Evaluation Method and System for Special Equipment Operation Based on Laser Sensors

Abstract

The present invention discloses an intelligent evaluation method and system for special equipment operation based on a laser sensor. The method includes: obtaining the motion information during the operation of a hoisting machine through a laser sensor, where the motion information includes: the actual position of the hoisting machine, the position of obstacles, the acceleration during the movement of the hoisted object of the hoisting machine, and the speed during the movement of the hoisted object of the hoisting machine; setting an evaluation model for the fitting degree of the movement trajectory of the hoisted object of the hoisting machine, calculating the evaluation value of the fitting degree of the movement trajectory, setting a dynamic collision risk evaluation model between the hoisted object of the hoisting machine and obstacles, calculating the collision risk probability between the hoisted object of the hoisting machine and obstacles, and setting an evaluation model for the stability of the movement process of the hoisted object of the hoisting machine, calculating the evaluation value of the movement stability; and evaluating the operation of the hoisting machine through the evaluation value of the fitting degree of the movement trajectory, the collision risk probability between the hoisted object of the hoisting machine and obstacles, and the evaluation value of the movement stability.

Description

Technical Field

[0001] The present invention belongs to the technical field of hoisting machinery operation examination determination, and more specifically, relates to an intelligent evaluation method and system for special equipment operation based on a laser sensor. Background Art

[0002] As a kind of special equipment, hoisting machinery is widely used in current social production. The safe use of such equipment is based on staff who master proficient operation skills. Therefore, the assessment of the skill level of relevant operators is an important means to ensure production safety. When conducting traditional hoisting machinery examinations, manual assessment methods are mainly used, and whether an operator passes the examination is determined by an authorized examiner. This mode has the disadvantages of strong subjectivity, low efficiency, inconsistent assessment standards, and difficulty in management. Therefore, there is an urgent need for a technical solution that can provide an intelligent, automated, and digital examination method to improve the efficiency, accuracy, and safety of the assessment. Summary of the Invention

[0003] To solve the above technical problems, the present invention proposes an intelligent evaluation method for special equipment operation based on a laser sensor, including:

[0004] Obtaining the motion information during the operation of the hoisting machinery through a laser sensor, where the motion information includes: the actual position of the hoisting machinery, the position of the obstacle, the acceleration when the hoisted object of the hoisting machinery moves, and the speed when the hoisted object of the hoisting machinery moves;

[0005] Setting an evaluation model for the fitting degree of the motion trajectory of the hoisted object of the hoisting machinery, calculating the evaluation value of the fitting degree of the motion trajectory, which is used to evaluate the fitting degree of the motion trajectory of the hoisting machinery and the assessment trajectory, setting a dynamic collision risk assessment model between the hoisted object of the hoisting machinery and the obstacle, calculating the collision risk probability between the hoisted object of the hoisting machinery and the obstacle, and setting an evaluation model for the stability of the motion process of the hoisted object of the hoisting machinery, calculating the evaluation value of the motion stability;

[0006] Evaluating the operation of the hoisting machinery through the evaluation value of the fitting degree of the motion trajectory, the collision risk probability between the hoisted object of the hoisting machinery and the obstacle, and the evaluation value of the motion stability.

[0007] Furthermore, the evaluation model for the fitting degree of the motion trajectory of the hoisted object of the hoisting machinery includes:

[0008] ,

[0009] Wherein, is the evaluation value of the fitting degree of the motion trajectory, is the weight of the root mean square error, is the root mean square error of the position of the hoisting machinery, is the weight of the mean absolute error, is the position mean absolute error of the lifting machinery, is the weight of the acceleration, is the total number of time points, is the actual position of the lifting machinery at the -th time point, is the assessment position of the lifting machinery at the -th time point, is the time point, is the weight of the intercept.

[0010] Furthermore, the dynamic collision risk assessment model between the lifted object of the lifting machinery and the obstacle includes:

[0011]

[0012] where, is the collision risk probability between the lifted object of the lifting machinery and the obstacle, is the current distance between the lifted object of the lifting machinery and the obstacle, is the limit value of the minimum safe distance allowed between the lifted object of the lifting machinery and the obstacle, is the standard deviation of the motion error when the lifting machinery is lifting an object.

[0013] Furthermore, the motion process stability assessment model of the lifted object of the lifting machinery includes:

[0014] ,

[0015] where, is the motion stability assessment value, is the -th time point of the acceleration of the lifted object of the lifting machinery in the axis, is axis average acceleration, is the -th time point of the acceleration of the lifted object of the lifting machinery in the axis, is axis average acceleration, is the -th time point of the acceleration of the lifted object of the lifting machinery in the axis, is axis average acceleration, is the total number of time points.

[0016] Furthermore, the -th time point of the acceleration of the lifted object of the lifting machinery in the axis 、the The axial acceleration at the time point during the movement of the lifting object of the lifting machinery axial acceleration include:

[0017] ,

[0018] ,

[0019] ,

[0020] wherein, is the axial velocity during the movement of the lifting object of the lifting machinery at the time point, is the axial velocity during the movement of the lifting object of the lifting machinery at the time point, is the axial velocity during the movement of the lifting object of the lifting machinery at the time point, is the axial velocity during the movement of the lifting object of the lifting machinery at the time point, is the axial velocity during the movement of the lifting object of the lifting machinery at the time point, is the axial velocity during the movement of the lifting object of the lifting machinery at the time point.

[0021] Furthermore, a fitting threshold corresponding to the evaluation value of the fitting degree of the movement trajectory is set;

[0022] A collision threshold corresponding to the collision risk probability between the lifting machinery and the obstacle when lifting the object is set;

[0023] A stability threshold corresponding to the evaluation value of the movement stability is set.

[0024] Furthermore, when the evaluation value of the fitting degree of the movement trajectory exceeds the fitting threshold, the collision risk probability between the lifting machinery and the obstacle when lifting the object is less than the collision threshold, or the evaluation value of the movement stability exceeds the stability threshold, the test result is qualified;

[0025] Or when the evaluation value of the fitting degree of the movement trajectory exceeds the fitting threshold, the collision risk probability between the lifting machinery and the obstacle when lifting the object is less than the collision threshold, and the evaluation value of the movement stability exceeds the stability threshold, the test result is qualified.

[0026] The present invention also provides a judgment system for lifting machinery operation examinations implemented by a laser sensor, including:

[0027] A data acquisition module, configured to acquire the motion information during the operation of the lifting machinery through the laser sensor, wherein the motion information includes: the actual position of the lifting machinery, the position of the obstacle, the acceleration during the movement of the lifted object of the lifting machinery, and the speed during the movement of the lifted object of the lifting machinery;

[0028] A model setting module, configured to set an evaluation model for the fitting degree of the movement trajectory of the lifted object of the lifting machinery, calculate the evaluation value of the fitting degree of the movement trajectory, for evaluating the fitting degree of the movement trajectory of the lifting machinery and the examination trajectory, set a dynamic collision risk evaluation model between the lifted object of the lifting machinery and the obstacle, calculate the collision risk probability between the lifted object of the lifting machinery and the obstacle, and set a movement stability evaluation model during the movement of the lifted object of the lifting machinery, calculate the movement stability evaluation value;

[0029] An examination module, configured to examine the operation of the lifting machinery through the evaluation value of the fitting degree of the movement trajectory, the collision risk probability between the lifted object of the lifting machinery and the obstacle, and the movement stability evaluation value.

[0030] Further, the evaluation model for the fitting degree of the movement trajectory of the lifted object of the lifting machinery includes:

[0031] ,

[0032] Wherein, is the evaluation value of the fitting degree of the movement trajectory, is the weight of the root mean square error, is the root mean square error of the position of the lifting machinery, is the weight of the mean absolute error, is the mean absolute error of the position of the lifting machinery, is the weight of the acceleration, is the total number of time points, is the actual position of the lifting machinery at the th time point, is the examination position of the lifting machinery at the th time point, is the time point, is the weight of the intercept.

[0033] Further, the dynamic collision risk evaluation model between the lifted object of the lifting machinery and the obstacle includes:

[0034] ,

[0035] Wherein, is the collision risk probability between the lifted object of the lifting machinery and the obstacle, The current distance between the lifted object of the lifting machinery and the obstacle The limit value of the minimum safe distance between the lifted object of the lifting machinery and the obstacle The standard deviation of the motion error when the lifting machinery is lifting an object

[0036] Furthermore, the stability evaluation model for the motion of the lifted object of the lifting machinery includes:

[0037] ,

[0038] wherein, is the motion stability evaluation value, is the -axis acceleration of the lifted object of the lifting machinery at the -th time point during the motion, is the -axis average acceleration, is the -axis acceleration of the lifted object of the lifting machinery at the -th time point during the motion, is the -axis average acceleration, is the -axis acceleration of the lifted object of the lifting machinery at the -th time point during the motion, is the -axis average acceleration, is the total number of time points.

[0039] Furthermore, the -axis acceleration of the lifted object of the lifting machinery at the -th time point during the motion , the -axis acceleration of the lifted object of the lifting machinery at the -th time point during the motion and the -axis acceleration of the lifted object of the lifting machinery at the -th time point during the motion include:

[0040] ,

[0041] ,

[0042] ,

[0043] wherein, is the -axis velocity of the lifted object of the lifting machinery at the -th time point during the motion, is the The shaft speed when the hoisted object of the lifting machinery moves at a certain time point is the shaft speed when the hoisted object of the lifting machinery moves at the th time point is the shaft speed when the hoisted object of the lifting machinery moves at the th time point is the shaft speed when the hoisted object of the lifting machinery moves at the th time point is the shaft speed when the hoisted object of the lifting machinery moves at the th time point is the shaft speed when the hoisted object of the lifting machinery moves at the th time point is the shaft speed when the hoisted object of the lifting machinery moves at the th time point is the shaft speed.

[0044] Compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects:

[0045] The present invention obtains the motion information during the operation of the lifting machinery through a laser sensor. Among them, the motion information includes: the actual position of the lifting machinery, the position of the obstacle, the acceleration when the hoisted object of the lifting machinery moves, and the speed when the hoisted object of the lifting machinery moves; a motion trajectory fitting degree evaluation model for the hoisted object of the lifting machinery is set to calculate the motion trajectory fitting degree evaluation value, which is used to evaluate the fitting degree of the motion trajectory of the lifting machinery and the assessment trajectory. A dynamic collision risk assessment model between the hoisted object of the lifting machinery and the obstacle is set to calculate the collision risk probability between the hoisted object of the lifting machinery and the obstacle, and a motion process stability assessment model for the hoisted object of the lifting machinery is set to calculate the motion stability evaluation value; the operation of the lifting machinery is evaluated through the motion trajectory fitting degree evaluation value, the collision risk probability between the hoisted object of the lifting machinery and the obstacle, and the motion stability evaluation value. According to the above technical solution, the present invention can realize the automatic determination of the lifting machinery operation examination, improve the examination efficiency, and reduce the labor cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is the flowchart of the method of Embodiment 1 of the present invention;

[0047] Figure 2 is the system structure diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0048] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific embodiments.

[0049] The method provided by the present invention can be implemented in the following terminal environment. The terminal may include one or more of the following components: a processor, a storage medium, and a display screen. Among them, at least one instruction is stored in the storage medium, and the instruction is loaded and executed by the processor to implement the method described in the following embodiments.

[0050] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts within the entire terminal, and by running or executing instructions, programs, code sets, or instruction sets stored in the storage medium, as well as calling data stored in the storage medium, it executes various functions of the terminal and processes data.

[0051] The storage medium may include a random access memory (RAM), or may also include a read-only memory (ROM). The storage medium can be used to store instructions, programs, code, code sets, or instructions.

[0052] The display screen is used to display the interaction cross-sections of various application programs.

[0053] All subscripts in the formula of the present invention are only for distinguishing parameters and have no actual meaning.

[0054] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal. The terminal may include more or fewer components, or combine certain components, or have different component arrangements. For example, the terminal may also include components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, a power supply, etc., which will not be elaborated here.

[0055] Embodiment 1

[0056] As Figure 1 shown, the embodiment of the present invention provides an intelligent evaluation method for special equipment operation based on a laser sensor, including:

[0057] Step 101, obtaining the motion information during the operation of the lifting machinery through a laser sensor, where the motion information includes: the actual position of the lifting machinery, the position of the obstacle, the acceleration during the movement of the lifted object of the lifting machinery, and the speed during the movement of the lifted object of the lifting machinery;

[0058] Step 102, setting an evaluation model for the fitting degree of the movement trajectory of the lifted object of the lifting machinery, calculating the evaluation value of the fitting degree of the movement trajectory, which is used to evaluate the fitting degree of the movement trajectory of the lifting machinery and the assessment trajectory, setting a dynamic collision risk assessment model between the lifted object of the lifting machinery and the obstacle, calculating the collision risk probability between the lifted object of the lifting machinery and the obstacle, and setting an evaluation model for the stability of the movement process of the lifted object of the lifting machinery, calculating the evaluation value of the movement stability;

[0059] Specifically, the evaluation model for the fitting degree of the lifting path of the lifting machinery includes:

[0060] ,

[0061] Among them, is the evaluation value of the fitting degree of the movement path, is the weight of the root mean square error, is the root mean square error of the position of the lifting machinery, is the weight of the mean absolute error, is the mean absolute error of the position of the lifting machinery, is the weight of the acceleration, is the total number of time points, is the lifting machinery at the th actual position at the time point, is the lifting machinery at the th assessment position at the time point, is the time point, is the weight of the intercept.

[0062] Specifically, the dynamic collision risk assessment model between the lifted object of the lifting machinery and the obstacle includes:

[0063] ,

[0064] Among them, is the collision risk probability between the lifted object of the lifting machinery and the obstacle, is the current distance between the lifted object of the lifting machinery and the obstacle, is the limit value of the minimum safe distance allowed between the lifted object of the lifting machinery and the obstacle, is the standard deviation of the movement error when the lifting machinery lifts an object.

[0065] Specifically, the evaluation model for the stability of the movement process of the lifted object of the lifting machinery includes:

[0066] ,

[0067] Among them, is the evaluation value of the movement stability, is the th axis acceleration when the lifted object of the lifting machinery moves at the time point, axis acceleration, is axis average acceleration, is the th axis acceleration when the lifted object of the lifting machinery moves at the time point, axis acceleration, is axis average acceleration, is the The axial acceleration during the movement of the hoisted object of the lifting machinery at a time point, is the average axial acceleration, and

[0068] is the total number of time points. Specifically, the axial acceleration during the movement of the hoisted object of the lifting machinery at the -th time point, the axial acceleration during the movement of the hoisted object of the lifting machinery at the -th time point, and the axial acceleration during the movement of the hoisted object of the lifting machinery at the -th time point include:

[0069] ,

[0070] ,

[0071] ,

[0072] wherein, is the axial velocity during the movement of the hoisted object of the lifting machinery at the -th time point, is the axial velocity during the movement of the hoisted object of the lifting machinery at the -th time point, is the axial velocity during the movement of the hoisted object of the lifting machinery at the -th time point, is the axial velocity during the movement of the hoisted object of the lifting machinery at the -th time point, is the axial velocity during the movement of the hoisted object of the lifting machinery at the -th time point, is the axial velocity during the movement of the hoisted object of the lifting machinery at the -th time point.

[0073] Step 103, evaluate the operation of the lifting machinery by using the motion trajectory fitting degree evaluation value, the collision risk probability between the hoisted object of the lifting machinery and the obstacle, and the motion stability evaluation value.

[0074] Specifically, set a fitting degree threshold corresponding to the motion trajectory fitting degree evaluation value;

[0075] Set a collision threshold corresponding to the probability of collision between the lifted object of the hoisting machinery and obstacles;

[0076] Set a stability threshold corresponding to the value of the motion stability assessment.

[0077] Specifically, when the evaluation value of the motion trajectory fitting degree exceeds the fitting degree threshold, the probability of collision between the lifted object of the hoisting machinery and obstacles is less than the collision threshold, or the evaluation value of the motion stability exceeds the stability threshold, the examination result is qualified;

[0078] Or when the evaluation value of the motion trajectory fitting degree exceeds the fitting degree threshold, the probability of collision between the lifted object of the hoisting machinery and obstacles is less than the collision threshold, and the evaluation value of the motion stability exceeds the stability threshold, the examination result is qualified.

[0079] Embodiment 2

[0080] As Figure 2 shown, the embodiment of the present invention also proposes a determination system for realizing the hoisting machinery operation examination by using a laser sensor, including:

[0081] A data acquisition module, configured to acquire motion information during the operation of the hoisting machinery through a laser sensor, where the motion information includes: the actual position of the hoisting machinery, the position of the obstacle, the acceleration during the movement of the lifted object of the hoisting machinery, and the speed during the movement of the lifted object of the hoisting machinery;

[0082] A model setting module, configured to set an evaluation model for the fitting degree of the motion trajectory of the lifted object of the hoisting machinery, calculate the evaluation value of the motion trajectory fitting degree for evaluating the fitting degree of the motion trajectory of the hoisting machinery and the assessment trajectory, set a dynamic collision risk assessment model between the lifted object of the hoisting machinery and the obstacle, calculate the probability of collision between the lifted object of the hoisting machinery and the obstacle, and set a stability assessment model for the movement process of the lifted object of the hoisting machinery, and calculate the evaluation value of the motion stability;

[0083] Specifically, the evaluation model for the fitting degree of the motion trajectory of the lifted object of the hoisting machinery includes:

[0084] ,

[0085] wherein, is the evaluation value of the motion trajectory fitting degree, is the weight of the root mean square error, is the root mean square error of the position of the hoisting machinery, is the weight of the mean absolute error, is the mean absolute error of the position of the hoisting machinery, is the weight of the acceleration, is the total number of time points, is the hoisting machinery at the The actual position at a time point is the assessment position of the lifting machinery at the th time point is the time point is the weight of the intercept rate

[0086] Specifically, the dynamic collision risk assessment model between the lifted object of the lifting machinery and the obstacle includes:

[0087] ,

[0088] wherein is the collision risk probability between the lifted object of the lifting machinery and the obstacle is the current distance between the lifted object of the lifting machinery and the obstacle is the limit value of the minimum safe distance allowed between the lifted object of the lifting machinery and the obstacle is the standard deviation of the motion error when the lifting machinery lifts an object

[0089] Specifically, the motion stability assessment model of the lifted object of the lifting machinery includes:

[0090] ,

[0091] wherein is the motion stability assessment value is the th time point, the axis acceleration when the lifted object of the lifting machinery moves is the axis average acceleration is the th time point, the axis acceleration when the lifted object of the lifting machinery moves is the axis average acceleration is the th time point, the axis acceleration when the lifted object of the lifting machinery moves is the axis average acceleration is the total number of time points

[0092] Specifically, the th time point, the axis acceleration when the lifted object of the lifting machinery moves , the th time point, the axis acceleration when the lifted object of the lifting machinery moves and the th time point, the axis acceleration when the lifted object of the lifting machinery moves including:

[0093] ,

[0094] ,

[0095] ,

[0096] wherein, is the axial velocity of the hoisted object of the crane during movement at the th time point, is the axial velocity of the hoisted object of the crane during movement at the th time point, is the axial velocity of the hoisted object of the crane during movement at the th time point, is the axial velocity of the hoisted object of the crane during movement at the th time point, is the axial velocity of the hoisted object of the crane during movement at the th time point, is the axial velocity of the hoisted object of the crane during movement at the th time point.

[0097] An assessment module for assessing the operation of the crane by means of the motion trajectory fitting degree evaluation value, the collision risk probability between the hoisted object of the crane and the obstacle, and the motion stability evaluation value.

[0098] Specifically, a fitting degree threshold corresponding to the motion trajectory fitting degree evaluation value is set;

[0099] A collision threshold corresponding to the collision risk probability between the hoisted object of the crane and the obstacle is set;

[0100] A stability threshold corresponding to the motion stability evaluation value is set.

[0101] Specifically, when the motion trajectory fitting degree evaluation value exceeds the fitting degree threshold, the collision risk probability between the hoisted object of the crane and the obstacle is less than the collision threshold, or the motion stability evaluation value exceeds the stability threshold, the test result is qualified;

[0102] Or when the motion trajectory fitting degree evaluation value exceeds the fitting degree threshold, the collision risk probability between the hoisted object of the crane and the obstacle is less than the collision threshold, and the motion stability evaluation value exceeds the stability threshold, the test result is qualified.

[0103] Example 3

[0104] An embodiment of the present invention further provides a storage medium storing a plurality of instructions for implementing the intelligent evaluation method for special equipment operation based on a laser sensor.

[0105] Optionally, in this embodiment, the above storage medium may be located in any one of the computer terminals in the computer terminal group in the computer network or in any one of the mobile terminals in the mobile terminal group.

[0106] Optionally, in this embodiment, the storage medium is set to store program code for performing the following steps: Step 101, obtaining the motion information during the operation of the hoisting machinery through a laser sensor, where the motion information includes: the actual position of the hoisting machinery, the position of the obstacle, the acceleration during the movement of the hoisted object of the hoisting machinery, and the speed during the movement of the hoisted object of the hoisting machinery;

[0107] Step 102, setting an evaluation model for the fitting degree of the movement trajectory of the hoisted object of the hoisting machinery, calculating the evaluation value of the fitting degree of the movement trajectory, which is used to evaluate the fitting degree of the movement trajectory of the hoisting machinery and the assessment trajectory, setting a dynamic collision risk assessment model between the hoisted object of the hoisting machinery and the obstacle, calculating the collision risk probability when the hoisted object of the hoisting machinery collides with the obstacle, and setting an evaluation model for the stability of the movement process of the hoisted object of the hoisting machinery, calculating the evaluation value of the movement stability;

[0108] Specifically, the evaluation model for the fitting degree of the movement trajectory of the hoisted object of the hoisting machinery includes:

[0109] ,

[0110] where is the evaluation value of the fitting degree of the movement trajectory, is the weight of the root mean square error, is the root mean square error of the position of the hoisting machinery, is the weight of the mean absolute error, is the mean absolute error of the position of the hoisting machinery, is the weight of the acceleration, is the total number of time points, is the hoisting machinery at the th time point of the actual position, is the hoisting machinery at the th time point of the assessment position, is the time point, is the weight of the intercept.

[0111] Specifically, the dynamic collision risk assessment model between the hoisted object of the hoisting machinery and the obstacle includes:

[0112] ,

[0113] wherein, is the collision risk probability between the lifted object of the hoisting machinery and the obstacle, is the current distance between the lifted object of the hoisting machinery and the obstacle, is the limit value of the minimum safe distance allowed between the lifted object of the hoisting machinery and the obstacle, is the standard deviation of the motion error when the hoisting machinery is lifting an object.

[0114] Specifically, the stability evaluation model for the motion of the lifted object of the hoisting machinery includes:

[0115] ,

[0116] wherein, is the motion stability evaluation value, is the -axis acceleration of the lifted object of the hoisting machinery at the -th time point during the motion, is the -axis average acceleration, is the -axis acceleration of the lifted object of the hoisting machinery at the -th time point during the motion, is the -axis average acceleration, is the -axis acceleration of the lifted object of the hoisting machinery at the -th time point during the motion, is the -axis average acceleration, is the total number of time points.

[0117] Specifically, the -axis acceleration of the lifted object of the hoisting machinery at the -th time point during the motion, the -axis acceleration of the lifted object of the hoisting machinery at the -th time point during the motion, and the -axis acceleration of the lifted object of the hoisting machinery at the -th time point during the motion include:

[0118] ,

[0119] ,

[0120] ,

[0121] wherein, is the The shaft speed during the movement of the hoisted object of the lifting machinery at a certain time point, is the shaft speed during the movement of the hoisted object of the lifting machinery at the nth time point, is the shaft speed during the movement of the hoisted object of the lifting machinery at the mth time point, is the shaft speed during the movement of the hoisted object of the lifting machinery at the pth time point, is the shaft speed during the movement of the hoisted object of the lifting machinery at the qth time point, is the shaft speed during the movement of the hoisted object of the lifting machinery at the rth time point.

[0122] Step 103, evaluate the operation of the lifting machinery through the motion trajectory fitting degree evaluation value, the collision risk probability between the hoisted object of the lifting machinery and the obstacle, and the motion stability evaluation value.

[0123] Specifically, set a fitting degree threshold corresponding to the motion trajectory fitting degree evaluation value;

[0124] Set a collision threshold corresponding to the collision risk probability between the hoisted object of the lifting machinery and the obstacle;

[0125] Set a stability threshold corresponding to the motion stability evaluation value.

[0126] Specifically, when the motion trajectory fitting degree evaluation value exceeds the fitting degree threshold, the collision risk probability between the hoisted object of the lifting machinery and the obstacle is less than the collision threshold, or the motion stability evaluation value exceeds the stability threshold, the test result is qualified;

[0127] Or when the motion trajectory fitting degree evaluation value exceeds the fitting degree threshold, the collision risk probability between the hoisted object of the lifting machinery and the obstacle is less than the collision threshold, and the motion stability evaluation value exceeds the stability threshold, the test result is qualified.

[0128] Embodiment 4

[0129] The embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor. The storage medium stores multiple instructions, and the instructions can be loaded and executed by the processor so that the processor can execute an intelligent evaluation method for special equipment operation based on a laser sensor.

[0130] Specifically, the electronic device in this embodiment may be a computer terminal, and the computer terminal may include: one or more processors and a storage medium.

[0131] Among them, the storage medium can be used to store software programs and modules, such as a special equipment operation intelligent evaluation method based on a laser sensor in the embodiment of the present invention, and the corresponding program instructions / modules. The processor runs the software programs and modules stored in the storage medium to execute various functional applications and data processing, that is, to implement the above-mentioned special equipment operation intelligent evaluation method based on a laser sensor. The storage medium may include a high-speed random storage medium, and may also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include a storage medium remotely set relative to the processor, and these remote storage media can be connected to the terminal through a network. Examples of the above network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network and their combinations.

[0132] The processor can call the information and application programs stored in the storage medium through the transmission system to execute the steps: Step 101, obtain the motion information during the operation of the crane through the laser sensor, where the motion information includes: the actual position of the crane, the position of the obstacle, the acceleration during the movement of the crane's lifted object, and the speed during the movement of the crane's lifted object;

[0133] Step 102, set an evaluation model for the fitting degree of the motion trajectory of the crane's lifted object, calculate the evaluation value of the fitting degree of the motion trajectory, which is used to evaluate the fitting degree of the motion trajectory of the crane and the assessment trajectory, set a dynamic collision risk assessment model between the crane's lifted object and the obstacle, calculate the collision risk probability when the crane's lifted object is lifted, and set an evaluation model for the stability of the movement process of the crane's lifted object, and calculate the evaluation value of the movement stability;

[0134] Specifically, the evaluation model for the fitting degree of the motion trajectory of the crane's lifted object includes:

[0135] ,

[0136] Among them, is the evaluation value of the fitting degree of the motion trajectory, is the weight of the root mean square error, is the root mean square error of the position of the crane, is the weight of the mean absolute error, is the mean absolute error of the position of the crane, is the weight of the acceleration, is the total number of time points, is the crane at the The actual position at a time point, is the assessment position of the lifting machinery at the th time point, is the time point, is the weight of the intercept rate.

[0137] Specifically, the dynamic collision risk assessment model between the lifted object of the lifting machinery and the obstacle includes:

[0138] ,

[0139] wherein, is the collision risk probability between the lifted object of the lifting machinery and the obstacle, is the current distance between the lifted object of the lifting machinery and the obstacle, is the limit value of the minimum safe distance allowed between the lifted object of the lifting machinery and the obstacle, is the standard deviation of the motion error when the lifting machinery lifts an object.

[0140] Specifically, the motion stability assessment model of the lifted object of the lifting machinery includes:

[0141] ,

[0142] wherein, is the motion stability assessment value, is the th time point when the lifted object of the lifting machinery is in motion, axis acceleration, is axis average acceleration, is the th time point when the lifted object of the lifting machinery is in motion, axis acceleration, is axis average acceleration, is the th time point when the lifted object of the lifting machinery is in motion, axis acceleration, is axis average acceleration, is the total number of time points.

[0143] Specifically, the th time point when the lifted object of the lifting machinery is in motion, axis acceleration , the th time point when the lifted object of the lifting machinery is in motion, axis acceleration and the th time point when the lifted object of the lifting machinery is in motion, axis acceleration Including:

[0144] ,

[0145] ,

[0146] ,

[0147] Wherein, is the th axial velocity of the hoisted object of the crane during movement at the th time point, is the th axial velocity of the hoisted object of the crane during movement at the th time point, is the th axial velocity of the hoisted object of the crane during movement at the th time point, is the th axial velocity of the hoisted object of the crane during movement at the th time point, is the th axial velocity of the hoisted object of the crane during movement at the th time point, is the th axial velocity of the hoisted object of the crane during movement at the th time point.

[0148] Step 103, assess the operation of the crane by the motion trajectory fitting degree evaluation value, the collision risk probability between the hoisted object of the crane and the obstacle, and the motion stability evaluation value.

[0149] Specifically, set a fitting degree threshold corresponding to the motion trajectory fitting degree evaluation value;

[0150] Set a collision threshold corresponding to the collision risk probability between the hoisted object of the crane and the obstacle;

[0151] Set a stability threshold corresponding to the motion stability evaluation value.

[0152] Specifically, when the motion trajectory fitting degree evaluation value exceeds the fitting degree threshold, the collision risk probability between the hoisted object of the crane and the obstacle is less than the collision threshold, or the motion stability evaluation value exceeds the stability threshold, the test result is qualified;

[0153] Or when the motion trajectory fitting degree evaluation value exceeds the fitting degree threshold, the collision risk probability between the hoisted object of the crane and the obstacle is less than the collision threshold, and the motion stability evaluation value exceeds the stability threshold, the test result is qualified.

[0154] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0155] In the above embodiments of the present invention, the descriptions of the various embodiments each have their own emphasis. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0156] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the units or modules can be in electrical or other forms.

[0157] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0158] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0159] If the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. And the foregoing storage medium includes: USB flash drives, read-only storage media (ROM, Read-Only Memory), random access storage media (RAM, Random Access Memory), mobile hard disks, magnetic disks or optical disks and other various media that can store program codes.

[0160] Obviously, the above embodiments are merely examples given for clear illustration and are not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all implementation manners here. And the obvious changes or modifications derived therefrom still fall within the protection scope of the present invention.

Claims

1. A laser sensor-based intelligent evaluation method for special equipment operation, characterized in that: include: Acquiring motion information of the lifting machinery during operation by means of a laser sensor, wherein the motion information includes: the actual position of the lifting machinery, the position of obstacles, the acceleration of the lifting machinery during the movement of the lifting object, and the speed of the lifting machinery during the movement of the lifting object; Set up a hoisting machinery hoisting object motion trajectory fitting evaluation model, calculate the motion trajectory fitting evaluation value, which is used to evaluate the fitting degree of the hoisting machinery motion trajectory and the assessment trajectory, set up a dynamic collision risk evaluation model between the hoisting machinery hoisting object and obstacles, calculate the collision risk probability of the hoisting machinery hoisting object and obstacles, and set up a hoisting machinery hoisting object motion process stability evaluation model to calculate the motion stability evaluation value; Specifically, the hoisting machinery hoisting object motion trajectory fitting evaluation model includes: Among them, J is the evaluation value of the motion trajectory fit, α is the weight of the root mean square error, RMSE is the root mean square error of the position of the lifting machinery, β is the weight of the mean absolute error, MAE is the mean absolute error of the position of the lifting machinery, γ is the weight of the acceleration, n is the total number of time points, y i is the actual position of the lifting machinery at the i-th time point, is the assessment position of the lifting machinery at the i-th time point, t is the time point, and λ is the weight of the intercept rate; The dynamic collision risk assessment model between the hoisting object and the obstacle includes: Where R is the collision risk probability of the lifting machinery and obstacles when lifting objects, d safe is the current distance between the lifting machinery and the obstacle when lifting objects, d limit is the minimum safe distance limit allowed between the lifting machinery and obstacles when lifting objects, and σ is the standard deviation of the motion error when lifting objects; The stability assessment model for the lifting machinery hoisting object movement process includes: Among them, SI is the motion stability evaluation value, a x,i is the x-axis acceleration of the lifting machinery when the object is moving at the i-th time point, is the average acceleration on the x-axis, a y,i is the y-axis acceleration of the lifting machinery when the object is moving at the i-th time point, is the average acceleration on the y-axis, a z,i is the z-axis acceleration of the hoisting machinery at the i-th time point, is the average acceleration on the z-axis, and n is the total number of time points; The operation of the lifting machinery is assessed by means of the motion trajectory fitting evaluation value, the collision risk probability of the lifting machinery with obstacles when lifting objects, and the motion stability evaluation value.

2. The method for intelligent evaluation of special equipment operation based on laser sensor according to claim 1, characterized in that: The x-axis acceleration a of the hoisting object during the movement of the hoisting machinery at the i-th time point x，i , the y-axis acceleration a of the hoisting object at the i-th time point y,i and the z-axis acceleration a of the hoisting object at the i-th time point z，i include: Among them, v x,i+1 is the x-axis speed of the lifting machinery when the object is moving at the i+1th time point, v x,i is the x-axis speed of the lifting machinery when the object is moving at the i-th time point, v y,i+1 is the y-axis speed of the lifting machinery when the object is moving at the i+1th time point, v y，i is the y-axis speed of the lifting machinery when the object is moving at the i-th time point, v z，i+1 is the z-axis speed of the lifting machinery when the object is moving at the i+1th time point, v z,i is the z-axis speed of the lifting machinery when the object is moving at the i-th time point.

3. The method for intelligent evaluation of special equipment operation based on laser sensor according to claim 1, characterized in that: Setting a fitting threshold value corresponding to the motion trajectory fitting evaluation value; Setting a collision threshold value corresponding to the collision risk probability of the lifting machinery and an obstacle when lifting an object; A stability threshold corresponding to the motion stability evaluation value is set.

4. The method for intelligent evaluation of special equipment operation based on laser sensor according to claim 3, characterized in that: When the motion trajectory fit evaluation value exceeds the fit threshold, the collision risk probability of the lifting machinery with obstacles when hoisting objects is less than the collision threshold, or the motion stability evaluation value exceeds the stability threshold, the test result is qualified; Or when the motion trajectory fit evaluation value exceeds the fit threshold, the collision risk probability of the lifting machinery with obstacles when hoisting objects is less than the collision threshold, and the motion stability evaluation value exceeds the stability threshold, the test result is qualified.

5. A determination system for lifting machinery operation test using a laser sensor, characterized in that: include: A data acquisition module is used to acquire motion information of the lifting machinery during operation through a laser sensor, wherein the motion information includes: the actual position of the lifting machinery, the position of obstacles, the acceleration of the lifting machinery when the lifting object moves, and the speed of the lifting machinery when the lifting object moves; Setting up a model module, which is used to set up a hoisting machinery hoisting object motion trajectory fitting evaluation model, calculate the motion trajectory fitting evaluation value, which is used to evaluate the fitting degree of the hoisting machinery motion trajectory with the assessment trajectory, set up a dynamic collision risk evaluation model between the hoisting machinery hoisting object and obstacles, calculate the collision risk probability of the hoisting machinery hoisting object with obstacles, and set up a hoisting machinery hoisting object motion process stability evaluation model, and calculate the motion stability evaluation value; Specifically, the hoisting machinery hoisting object motion trajectory fitting evaluation model includes: Among them, J is the evaluation value of the motion trajectory fit, α is the weight of the root mean square error, RMSE is the root mean square error of the position of the lifting machinery, β is the weight of the mean absolute error, MAE is the mean absolute error of the position of the lifting machinery, γ is the weight of the acceleration, n is the total number of time points, y i is the actual position of the lifting machinery at the i-th time point, is the assessment position of the lifting machinery at the i-th time point, t is the time point, and λ is the weight of the intercept rate; The dynamic collision risk assessment model between the hoisting object and the obstacle includes: Where R is the collision risk probability of the lifting machinery and obstacles when lifting objects, d safe is the current distance between the lifting machinery and the obstacle when lifting objects, d limit is the minimum safe distance limit allowed between the lifting machinery and obstacles when lifting objects, and σ is the standard deviation of the motion error when lifting objects; The stability assessment model for the lifting machinery hoisting object movement process includes: Among them, SI is the motion stability evaluation value, a x,i is the x-axis acceleration of the lifting machinery when the object is moving at the i-th time point, is the average acceleration on the x-axis, a y，i is the y-axis acceleration of the lifting machinery when the object is moving at the i-th time point, is the average acceleration on the y-axis, a z，i is the z-axis acceleration of the hoisting machinery at the i-th time point, is the average acceleration on the z-axis, and n is the total number of time points; The assessment module is used to assess the operation of the lifting machinery through the motion trajectory fitting evaluation value, the collision risk probability of the lifting machinery with obstacles when lifting objects, and the motion stability evaluation value.

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