Intelligent evaluation method and system for operation of special equipment based on laser sensor

Abstract

The invention discloses a special equipment operation intelligent evaluation method and system based on a laser sensor, and the method comprises the steps: obtaining the motion information of a hoisting machine during the operation through the laser sensor, the movement information comprises the actual position of the hoisting machinery, the position of an obstacle, the acceleration of the hoisting machinery during object hoisting movement and the speed of the hoisting machinery during object hoisting movement; the method comprises the following steps of: setting a hoisting machinery hoisting object motion trail fitting degree evaluation model, calculating a motion trail fitting degree evaluation value, setting a dynamic collision risk evaluation model between a hoisting machinery hoisting object and an obstacle, calculating a collision risk probability between the hoisting machinery hoisting object and the obstacle when the hoisting machinery hoisting object is hoisted, and setting a hoisting machinery hoisting object motion process stability evaluation model. Calculating a motion stability evaluation value; and checking the operation of the hoisting machinery according to the motion trail fitting degree evaluation value, the collision risk probability of the hoisting machinery and the obstacle when the hoisting machinery is used for hoisting the object and the motion stability evaluation value.

Description

Technical Field

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

[0002] As a type of special equipment, lifting machinery is widely used in current production. The safe use of this equipment relies on skilled operators. Therefore, assessing the skill level of relevant operators is a crucial means of ensuring production safety. Traditional lifting machinery inspections are primarily conducted manually, with authorized examiners determining whether operators pass the exam. This model suffers from high subjectivity, low efficiency, inconsistent assessment standards, and difficulty in management. Therefore, a technical solution is urgently needed that can implement intelligent, automated, and digital inspection methods to improve the efficiency, accuracy, and safety of inspections. 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 laser sensors, comprising: Acquiring motion information of the crane during operation by means of a laser sensor, wherein the motion information includes: the actual position of the crane, the position of obstacles, the acceleration of the crane's load during movement, and the speed of the crane's load during movement; Set up a crane hoisting object motion trajectory fitting evaluation model, calculate the motion trajectory fitting evaluation value, which is used to evaluate the fitting degree of the crane hoisting object motion trajectory with the assessment trajectory; set up a crane hoisting object and obstacle dynamic collision risk assessment model, calculate the collision risk probability of the crane hoisting object with the obstacle; and set up a crane hoisting object motion process stability assessment model, calculate the motion stability evaluation value; The operation of the lifting machinery is assessed by 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.

[0004] Furthermore, the hoisting machinery hoisting object motion trajectory fitting evaluation model includes: , in, is the motion trajectory fitting evaluation value, 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 acceleration, is the total number of time points, For lifting machinery The actual location at a point in time, For lifting machinery The assessment position at a certain time point, For time point, is the weight of the cutoff rate.

[0005] Furthermore, the dynamic collision risk assessment model between the hoisting object and the obstacle includes:

[0006] in, is the collision risk probability of the lifting machinery with obstacles when lifting objects, is the current distance between the lifting machinery and the obstacle when lifting objects, It is the minimum safe distance limit allowed between the lifting machinery and obstacles when lifting objects. is the standard deviation of the motion error of the lifting machinery when lifting objects.

[0007] Furthermore, the stability assessment model for the hoisting object movement process of the lifting machinery includes: , in, is the motion stability evaluation value, For the The movement of the lifting object of the crane at a certain time point Axis acceleration, for Axis average acceleration, For the The movement of the lifting object of the crane at a certain time point Axis acceleration, for Axis average acceleration, For the The movement of the lifting object of the crane at a certain time point Axis acceleration, for Axis average acceleration, is the total number of time points.

[0008] Further, The movement of the lifting object of the crane at a certain time point Axis acceleration , No. The movement of the lifting object of the crane at a certain time point Axis acceleration Hedi The movement of the lifting object of the crane at a certain time point Axis acceleration include: , , , in, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point Axis speed.

[0009] Furthermore, a fitting threshold corresponding to the motion trajectory fitting evaluation value is set; Setting a collision threshold corresponding to the collision risk probability of the lifting machinery colliding with an obstacle when lifting an object; A stability threshold corresponding to the motion stability evaluation value is set.

[0010] Furthermore, when the motion trajectory fit evaluation value exceeds the fit threshold, the collision risk probability of the lifting machinery with an obstacle when hoisting an object 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 fitting evaluation value exceeds the fitting 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.

[0011] The present invention also proposes a system for determining the operation of a lifting machinery using a laser sensor, comprising: 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 is in motion, and the speed of the lifting machinery when the lifting object is in motion; Setting up a model module, used to set up a crane hoisting object motion trajectory fitting evaluation model, calculate the motion trajectory fitting evaluation value, used to evaluate the fitting degree of the crane motion trajectory with the assessment trajectory, set up a crane hoisting object and obstacle dynamic collision risk evaluation model, calculate the probability of collision risk between the crane hoisting object and the obstacle, and set up a crane hoisting object motion process stability evaluation model, calculate the motion stability evaluation value; 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.

[0012] Furthermore, the hoisting machinery hoisting object motion trajectory fitting evaluation model includes: , in, is the motion trajectory fitting evaluation value, 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 acceleration, is the total number of time points, For lifting machinery The actual location at a point in time, For lifting machinery The assessment position at a certain time point, For time point, is the weight of the cutoff rate.

[0013] Furthermore, the dynamic collision risk assessment model between the hoisting object and the obstacle includes: , in, is the collision risk probability of the lifting machinery with obstacles when lifting objects, is the current distance between the lifting machinery and the obstacle when lifting objects, It is the minimum safe distance limit allowed between the lifting machinery and obstacles when lifting objects. is the standard deviation of the motion error of the lifting machinery when lifting objects.

[0014] Furthermore, the stability assessment model for the hoisting object movement process of the lifting machinery includes: , in, is the motion stability evaluation value, For the The movement of the lifting object of the crane at a certain time point Axis acceleration, for Axis average acceleration, For the The movement of the lifting object of the crane at a certain time point Axis acceleration, for Axis average acceleration, For the The movement of the lifting object of the crane at a certain time point Axis acceleration, for Axis average acceleration, is the total number of time points.

[0015] Further, The movement of the lifting object of the crane at a certain time point Axis acceleration , No. The movement of the lifting object of the crane at a certain time point Axis acceleration Hedi The movement of the lifting object of the crane at a certain time point Axis acceleration include: , , , in, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point Axis speed.

[0016] Compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects: The present invention obtains motion information of the crane during operation through a laser sensor, wherein the motion information includes: the actual position of the crane, the position of the obstacle, the acceleration of the crane when the crane is hoisting, and the speed of the crane when the crane is hoisting; a crane hoisting motion trajectory fitting evaluation model is set to calculate a motion trajectory fitting evaluation value for evaluating the fitting degree between the crane motion trajectory and the assessment trajectory; a crane hoisting dynamic collision risk evaluation model is set to calculate the collision risk probability of the crane hoisting with the obstacle; and a crane hoisting motion process stability evaluation model is set to calculate the motion stability evaluation value; the crane operation is assessed by the motion trajectory fitting evaluation value, the collision risk probability of the crane hoisting with the obstacle, and the motion stability evaluation value. According to the above technical solution, the present invention can realize automatic judgment of the crane operation test, improve the test efficiency, and reduce the labor cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of the method of embodiment 1 of the present invention; Figure 2 This is a system structure diagram of Example 2 of the present invention. DETAILED DESCRIPTION

[0018] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0019] The method provided by the present invention can be implemented in the following terminal environment, wherein the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, which is loaded and executed by the processor to implement the method described in the following embodiments.

[0020] A processor can include one or more processing cores. It connects various components within the terminal using various interfaces and circuits. It executes instructions, programs, code sets, or instruction sets stored in storage media, and accesses data stored in storage media to perform various terminal functions and process data.

[0021] The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets, or instructions.

[0022] The display is used to show the interactive sections of each application.

[0023] All subscripts in the formulas of the present invention are only used to distinguish parameters and have no actual meaning.

[0024] In addition, those skilled in the art will appreciate that the structure of the terminal described above does not limit the terminal. The terminal may include more or fewer components, or a combination of certain components, or a different arrangement of components. For example, the terminal may also include a radio frequency circuit, an input unit, a sensor, an audio circuit, a power supply, and other components, which will not be described in detail here.

[0025] Example 1 like Figure 1 As shown, an embodiment of the present invention provides a method for intelligently evaluating special equipment operation based on a laser sensor, comprising: Step 101: acquiring motion information of a lifting machine during operation by means of a laser sensor, wherein the motion information includes: an actual position of the lifting machine, a position of an obstacle, an acceleration of a load being hoisted by the lifting machine during movement, and a speed of the load being hoisted by the lifting machine during movement; Step 102: Setting a hoisting object motion trajectory fit evaluation model, calculating a motion trajectory fit evaluation value, which is used to evaluate the fit between the hoisting object motion trajectory and the assessment trajectory; setting a hoisting object dynamic collision risk evaluation model and an obstacle collision risk evaluation model, calculating the probability of collision between the hoisting object and the obstacle; and setting a hoisting object motion process stability evaluation model and calculating a motion stability evaluation value. Specifically, the hoisting machinery hoisting object motion trajectory fitting evaluation model includes: , in, is the motion trajectory fitting evaluation value, 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 acceleration, is the total number of time points, For lifting machinery The actual location at a point in time, For lifting machinery The assessment position at a certain time point, For time point, is the weight of the cutoff rate.

[0026] Specifically, the dynamic collision risk assessment model between the hoisting object and the obstacle includes: , in, is the collision risk probability of the lifting machinery with obstacles when lifting objects, is the current distance between the lifting machinery and the obstacle when lifting objects, It is the minimum safe distance limit allowed between the lifting machinery and obstacles when lifting objects. is the standard deviation of the motion error of the lifting machinery when lifting objects.

[0027] Specifically, the stability assessment model for the hoisting object movement process of the lifting machinery includes: , in, is the motion stability evaluation value, For the The movement of the lifting object of the crane at a certain time point Axis acceleration, for Axis average acceleration, For the The movement of the lifting object of the crane at a certain time point Axis acceleration, for Axis average acceleration, For the The movement of the lifting object of the crane at a certain time point Axis acceleration, for Axis average acceleration, is the total number of time points.

[0028] Specifically, The movement of the lifting object of the crane at a certain time point Axis acceleration , No. The movement of the lifting object of the crane at a certain time point Axis acceleration Hedi The movement of the lifting object of the crane at a certain time point Axis acceleration include: , , , in, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point Axis speed.

[0029] Step 103 , assessing the operation of the crane using the motion trajectory fitting evaluation value, the collision risk probability of the crane with an obstacle when lifting objects, and the motion stability evaluation value.

[0030] Specifically, setting a fitting threshold corresponding to the motion trajectory fitting evaluation value; Setting a collision threshold corresponding to the collision risk probability of the lifting machinery colliding with an obstacle when lifting an object; A stability threshold corresponding to the motion stability evaluation value is set.

[0031] Specifically, if the motion trajectory fit evaluation value exceeds the fit threshold, the collision risk probability of the lifting machinery with an obstacle when hoisting an object 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 fitting evaluation value exceeds the fitting 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.

[0032] Example 2 like Figure 2 As shown, the embodiment of the present invention further proposes a system for determining a lifting machinery operation test using a laser sensor, comprising: 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 is in motion, and the speed of the lifting machinery when the lifting object is in motion; Setting up a model module, used to set up a crane hoisting object motion trajectory fitting evaluation model, calculate the motion trajectory fitting evaluation value, used to evaluate the fitting degree of the crane motion trajectory with the assessment trajectory, set up a crane hoisting object and obstacle dynamic collision risk evaluation model, calculate the probability of collision risk between the crane hoisting object and the obstacle, and set up a crane hoisting object motion process stability evaluation model, calculate the motion stability evaluation value; Specifically, the hoisting machinery hoisting object motion trajectory fitting evaluation model includes: , in, is the motion trajectory fitting evaluation value, 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 acceleration, is the total number of time points, For lifting machinery The actual location at a point in time, For lifting machinery The assessment position at a certain time point, For time point, is the weight of the cutoff rate.

[0033] Specifically, the dynamic collision risk assessment model between the hoisting object and the obstacle includes: , in, is the collision risk probability of the lifting machinery with obstacles when lifting objects, is the current distance between the lifting machinery and the obstacle when lifting objects, It is the minimum safe distance limit allowed between the lifting machinery and obstacles when lifting objects. is the standard deviation of the motion error of the lifting machinery when lifting objects.

[0034] Specifically, the stability assessment model for the hoisting object movement process of the lifting machinery includes: , in, is the motion stability evaluation value, For the The movement of the lifting object of the crane at a certain time point Axis acceleration, for Axis average acceleration, For the The movement of the lifting object of the crane at a certain time point Axis acceleration, for Axis average acceleration, For the The movement of the lifting object of the crane at a certain time point Axis acceleration, for Axis average acceleration, is the total number of time points.

[0035] Specifically, The movement of the lifting object of the crane at a certain time point Axis acceleration , No. The movement of the lifting object of the crane at a certain time point Axis acceleration Hedi The movement of the lifting object of the crane at a certain time point Axis acceleration include: , , , in, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point Axis speed.

[0036] 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.

[0037] Specifically, setting a fitting threshold corresponding to the motion trajectory fitting evaluation value; Setting a collision threshold corresponding to the collision risk probability of the lifting machinery colliding with an obstacle when lifting an object; A stability threshold corresponding to the motion stability evaluation value is set.

[0038] Specifically, if the motion trajectory fit evaluation value exceeds the fit threshold, the collision risk probability of the lifting machinery with an obstacle when hoisting an object 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 fitting evaluation value exceeds the fitting 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.

[0039] Example 3 An embodiment of the present invention further provides a storage medium storing a plurality of instructions, wherein the instructions are used to implement the intelligent evaluation method for special equipment operation based on a laser sensor.

[0040] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0041] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: Step 101, obtaining motion information of a lifting machine during operation by a laser sensor, wherein the motion information includes: an actual position of the lifting machine, a position of an obstacle, an acceleration of a load of the lifting machine during movement, and a speed of the load of the lifting machine during movement; Step 102: Setting a hoisting object motion trajectory fit evaluation model, calculating a motion trajectory fit evaluation value, which is used to evaluate the fit between the hoisting object motion trajectory and the assessment trajectory; setting a hoisting object dynamic collision risk evaluation model and an obstacle collision risk evaluation model, calculating the probability of collision between the hoisting object and the obstacle; and setting a hoisting object motion process stability evaluation model and calculating a motion stability evaluation value. Specifically, the hoisting machinery hoisting object motion trajectory fitting evaluation model includes: , in, is the motion trajectory fitting evaluation value, 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 acceleration, is the total number of time points, For lifting machinery The actual location at a point in time, For lifting machinery The assessment position at a certain time point, For time point, is the weight of the cutoff rate.

[0042] Specifically, the dynamic collision risk assessment model between the hoisting object and the obstacle includes: , in, is the collision risk probability of the lifting machinery with obstacles when lifting objects, is the current distance between the lifting machinery and the obstacle when lifting objects, It is the minimum safe distance limit allowed between the lifting machinery and obstacles when lifting objects. is the standard deviation of the motion error of the lifting machinery when lifting objects.

[0043] Specifically, the stability assessment model for the hoisting object movement process of the lifting machinery includes: , in, is the motion stability evaluation value, For the The movement of the lifting object of the crane at a certain time point Axis acceleration, for Axis average acceleration, For the The movement of the lifting object of the crane at a certain time point Axis acceleration, for Axis average acceleration, For the The movement of the lifting object of the crane at a certain time point Axis acceleration, for Axis average acceleration, is the total number of time points.

[0044] Specifically, The movement of the lifting object of the crane at a certain time point Axis acceleration , No. The movement of the lifting object of the crane at a certain time point Axis acceleration Hedi The movement of the lifting object of the crane at a certain time point Axis acceleration include: , , , in, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point Axis speed.

[0045] Step 103 , assessing the operation of the crane using the motion trajectory fitting evaluation value, the collision risk probability of the crane with an obstacle when lifting objects, and the motion stability evaluation value.

[0046] Specifically, setting a fitting threshold corresponding to the motion trajectory fitting evaluation value; Setting a collision threshold corresponding to the collision risk probability of the lifting machinery colliding with an obstacle when lifting an object; A stability threshold corresponding to the motion stability evaluation value is set.

[0047] Specifically, if the motion trajectory fit evaluation value exceeds the fit threshold, the collision risk probability of the lifting machinery with an obstacle when hoisting an object 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 fitting evaluation value exceeds the fitting 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.

[0048] Example 4 An embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor, wherein the storage medium stores multiple instructions, which can be loaded and executed by the processor to enable the processor to execute an intelligent evaluation method for special equipment operation based on laser sensors.

[0049] Specifically, the electronic device of this embodiment may be a computer terminal, which may include: one or more processors and a storage medium.

[0050] Among them, the storage medium can be used to store software programs and modules, such as the corresponding program instructions / modules of a special equipment operation intelligent evaluation method based on a laser sensor in an embodiment of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizing the above-mentioned special equipment operation intelligent evaluation method based on a laser sensor. The storage medium may include high-speed random access storage media, and may also include non-volatile storage media, 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 storage media remotely located relative to the processor, and these remote storage media may be connected to the terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, corporate intranet, local area network, mobile communication network, and combinations thereof.

[0051] The processor can call the information and application stored in the storage medium through the transmission system to execute the following steps: Step 101, obtaining 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 the obstacle, the acceleration of the lifting machinery when the lifting object is in motion, and the speed of the lifting machinery when the lifting object is in motion; Step 102: Setting a hoisting object motion trajectory fit evaluation model, calculating a motion trajectory fit evaluation value, which is used to evaluate the fit between the hoisting object motion trajectory and the assessment trajectory; setting a hoisting object dynamic collision risk evaluation model and an obstacle collision risk evaluation model, calculating the probability of collision between the hoisting object and the obstacle; and setting a hoisting object motion process stability evaluation model and calculating a motion stability evaluation value. Specifically, the hoisting machinery hoisting object motion trajectory fitting evaluation model includes: , in, is the motion trajectory fitting evaluation value, 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 acceleration, is the total number of time points, For lifting machinery The actual location at a point in time, For lifting machinery The assessment position at a certain time point, For time point, is the weight of the cutoff rate.

[0052] Specifically, the dynamic collision risk assessment model between the hoisting object and the obstacle includes: , in, is the collision risk probability of the lifting machinery with obstacles when lifting objects, is the current distance between the lifting machinery and the obstacle when lifting objects, It is the minimum safe distance limit allowed between the lifting machinery and obstacles when lifting objects. is the standard deviation of the motion error of the lifting machinery when lifting objects.

[0053] Specifically, the stability assessment model for the hoisting object movement process of the lifting machinery includes: , in, is the motion stability evaluation value, For the The movement of the lifting object of the crane at a certain time point Axis acceleration, for Axis average acceleration, For the The movement of the lifting object of the crane at a certain time point Axis acceleration, for Axis average acceleration, For the The movement of the lifting object of the crane at a certain time point Axis acceleration, for Axis average acceleration, is the total number of time points.

[0054] Specifically, The movement of the lifting object of the crane at a certain time point Axis acceleration , No. The movement of the lifting object of the crane at a certain time point Axis acceleration Hedi The movement of the lifting object of the crane at a certain time point Axis acceleration include: , , , in, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point axis speed, For the The movement of the lifting object of the crane at a certain time point Axis speed.

[0055] Step 103 , assessing the operation of the crane using the motion trajectory fitting evaluation value, the collision risk probability of the crane with an obstacle when lifting objects, and the motion stability evaluation value.

[0056] Specifically, setting a fitting threshold corresponding to the motion trajectory fitting evaluation value; Setting a collision threshold corresponding to the collision risk probability of the lifting machinery colliding with an obstacle when lifting an object; A stability threshold corresponding to the motion stability evaluation value is set.

[0057] Specifically, if the motion trajectory fit evaluation value exceeds the fit threshold, the collision risk probability of the lifting machinery with an obstacle when hoisting an object 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 fitting evaluation value exceeds the fitting 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.

[0058] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0059] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0060] 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 merely 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. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, and can be electrical or other forms.

[0061] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0062] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0063] If the 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, 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, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only storage medium (ROM, Read-Only Memory), random access storage medium (RAM, RandomAccess Memory), mobile hard disk, magnetic disk or optical disk, and other media that can store program code.

[0064] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection 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; 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 hoisting machinery hoisting object motion trajectory fitting evaluation model includes: ， in, is the motion trajectory fitting evaluation value, 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 acceleration, is the total number of time points, For lifting machinery The actual location at a point in time, For lifting machinery The assessment position at a time point, For time point, is the weight of the cutoff rate.

3. The method for intelligent evaluation of special equipment operation based on laser sensor according to claim 1, characterized in that: The dynamic collision risk assessment model between the hoisting object and the obstacle includes: ， in, is the collision risk probability of the lifting machinery with obstacles when lifting objects, is the current distance between the lifting machinery and the obstacle when lifting objects, It is the minimum safe distance limit allowed between the lifting machinery and obstacles when lifting objects. It is the standard deviation of the motion error of the lifting machinery when hoisting objects.

4. The method for intelligent evaluation of special equipment operation based on laser sensor according to claim 1, characterized in that: The stability assessment model for the lifting machinery hoisting object movement process includes: ， in, is the motion stability evaluation value, For the The movement of the lifting machinery at a certain time point Axis acceleration, for The average acceleration of the axis, For the The movement of the lifting machinery at a certain time point Axis acceleration, for The average acceleration of the axis, For the The movement of the lifting machinery at a certain time point Axis acceleration, for The average acceleration of the axis, is the total number of time points.

5. The method for intelligent evaluation of special equipment operation based on laser sensor according to claim 4, characterized in that: No. The movement of the lifting machinery at a certain time point Axis acceleration , No. The movement of the lifting machinery at a certain time point Axis acceleration and The movement of the lifting machinery at a certain time point Axis acceleration include: ， ， ， in, For the The movement of the lifting machinery at a certain time point Axis speed, For the The movement of the lifting machinery at a certain time point Axis speed, For the The movement of the lifting machinery at a certain time point Axis speed, For the The movement of the lifting machinery at a certain time point Axis speed, For the The movement of the lifting machinery at a certain time point Axis speed, For the The movement of the lifting machinery at a certain time point Axis speed.

6. 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.

7. The method for intelligent evaluation of special equipment operation based on laser sensor according to claim 6, 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.

8. 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; 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.

9. A determination system for lifting machinery operation test using a laser sensor as claimed in claim 8, characterized in that: The hoisting machinery hoisting object motion trajectory fitting evaluation model includes: ， in, is the motion trajectory fitting evaluation value, 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 acceleration, is the total number of time points, For lifting machinery The actual location at a point in time, For lifting machinery The assessment position at a time point, For time point, is the weight of the cutoff rate.

10. A determination system for lifting machinery operation test using a laser sensor as claimed in claim 8, characterized in that: The dynamic collision risk assessment model between the hoisting object and the obstacle includes: ， in, is the collision risk probability of the lifting machinery with obstacles when lifting objects, is the current distance between the lifting machinery and the obstacle when lifting objects, It is the minimum safe distance limit allowed between the lifting machinery and obstacles when lifting objects. It is the standard deviation of the motion error of the lifting machinery when hoisting objects.

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