CN111289277A

CN111289277A - Load weight detection method and device, computer equipment and storage medium

Info

Publication number: CN111289277A
Application number: CN202010081468.3A
Authority: CN
Inventors: 吴邦春; 魏勇超; 周航宇; 魏勇豪; 李少兵
Original assignee: SHENZHEN CELIJIA CONTROL TECHNOLOGY CO LTD
Current assignee: SHENZHEN CELIJIA CONTROL TECHNOLOGY CO LTD
Priority date: 2020-02-06
Filing date: 2020-02-06
Publication date: 2020-06-16
Anticipated expiration: 2040-02-06
Also published as: CN111289277B

Abstract

The application relates to a load weight detection method, a load weight detection device, computer equipment and a storage medium. The method comprises the following steps: acquiring the average angular speed of operation of the crane in a preset time length and a target unit distance energy consumption value; determining a corresponding unit distance energy consumption value set according to the workload statistical model parameter set and the average angular speed of the crane; the method comprises the steps that a workload statistical model parameter set is obtained by debugging according to the load weight and the angular speed of a crane, and each group of parameter values in the workload statistical model parameter set is used for the relation between unit distance energy consumption and average angular speed; fitting each unit distance energy consumption value in the unit distance energy consumption value set and the debugging weight corresponding to the unit distance energy consumption value, and determining a target parameter value, wherein the target parameter value is a parameter value used for representing the relation between the load weight and the unit distance energy consumption; and calculating the current load weight of the crane according to the target parameter value and the target unit distance energy consumption. By adopting the method, the accuracy of load weight detection can be improved.

Description

Load weight detection method and device, computer equipment and storage medium

Technical Field

The present application relates to the field of crane technologies, and in particular, to a method and an apparatus for detecting a load weight, a computer device, and a storage medium.

Background

The crane is widely applied in social production activities, in part of application scenes, the accurate and reliable statistics on the workload of the crane in the operation process is needed, and the workload statistical data is used as an important reference for operation management, goods settlement and equipment maintenance. The crane workload statistics refers to the comprehensive statistics of the load weight of each operation of the crane in the crane operation process.

At present, the crane workload statistics mainly depends on sensing equipment arranged on a crane to detect load weight signals in the operation process, the workload is calculated and counted according to the signal change of the sensing equipment in the operation process, and a force sensor measurement method and a motor signal detection method are commonly used. The direct measuring method of the force sensor adopts a strain type pressure and tension sensor, is arranged at a stress position of the crane together with a matched mechanical structure, monitors the load weight, and counts the workload according to the change of a sensor signal in the operation process of the crane. The motor signal detection method is a method for calculating the load weight by detecting the current and power signals of the crane motor.

However, the current method for counting the workload of the crane has the problem of large detection error of the load weight.

Disclosure of Invention

In view of the above, it is necessary to provide a load weight detection method, apparatus, computer device, and storage medium capable of load weight detection accuracy.

A method of load weight detection, the method comprising:

acquiring the average angular speed of operation of the crane in a preset time length and a target unit distance energy consumption value;

determining a corresponding unit distance energy consumption value set according to the workload statistical model parameter set and the average angular speed of the crane; the method comprises the steps that a workload statistical model parameter set is obtained by debugging according to the load weight and the angular speed of a crane, and each group of parameter values in the workload statistical model parameter set is used for the relation between unit distance energy consumption and average angular speed;

fitting each unit distance energy consumption value in the unit distance energy consumption value set and the debugging weight corresponding to the unit distance energy consumption value, and determining a target parameter value, wherein the target parameter value is a parameter value used for representing the relation between the load weight and the unit distance energy consumption;

and calculating the current load weight of the crane according to the target parameter value and the target unit distance energy consumption.

In one embodiment, acquiring the average angular speed and the target energy consumption per unit distance of the crane working in the preset time length comprises the following steps:

acquiring real-time angular speed and electric quantity detection data of the crane within a preset time length;

determining the average angular speed of the crane in the preset time length by calculating the integral value of the real-time angular speed in the preset time length;

and determining the unit distance energy consumption of the crane in the preset time length according to the real-time angular speed and the electric quantity detection data.

In one embodiment, the charge detection data includes a real-time voltage and a real-time current;

according to real-time angular velocity and electric quantity detection data, confirm the unit distance energy consumption of hoist in the length of time of predetermineeing, include:

determining the total distance energy consumption of the crane in a preset time length according to the real-time voltage and the real-time current;

determining the working distance of the crane in a preset time length according to the real-time angular speed;

and determining the unit distance energy consumption of the crane in the preset time length according to the total distance energy consumption and the working distance.

In one embodiment, the working capacity statistical model parameter set is obtained by debugging the load weight and the angular speed of the crane, and comprises the following steps:

acquiring a debugging real-time angular velocity set and a debugging electric quantity detection data set corresponding to each load weight of operation of the crane in a preset debugging time length;

acquiring a functional relation between the real-time angular velocity and the electric quantity detection data;

substituting each real-time angular velocity in the debugging real-time angular velocity set and each electric quantity detection data in the debugging electric quantity detection data set into a functional relation between the real-time angular velocity and the electric quantity detection data, and determining a debugging unit distance energy consumption set of the crane operating in a debugging time period;

and determining a workload statistical model parameter set according to the debugging real-time angular speed and the debugging unit distance energy consumption.

In one embodiment, determining a workload statistic model parameter set according to the debugging real-time angular velocity and the debugging unit distance energy consumption comprises:

determining the debugging average angular speed of the crane in the debugging time length by calculating the integral value of each debugging real-time angular speed in the debugging real-time angular speed set in the debugging time length;

and fitting the debugging average angular velocity and the debugging unit distance energy consumption to determine a workload statistical model parameter set.

In one embodiment, determining a unit distance energy consumption set corresponding to a debugging weight set according to a workload statistical model parameter set and an average angular velocity of a crane comprises:

acquiring a function relation of unit distance energy consumption and average angular speed, wherein the energy consumption function relation comprises an energy consumption coefficient;

and assigning each group of parameter values in the workload statistical model parameter value set to the energy consumption coefficient in the energy consumption function relational expression, substituting the average angular velocity into the energy consumption functional relational expression, and determining unit distance energy consumption corresponding to each debugging weight to obtain a unit distance energy consumption set.

In one embodiment, calculating the current operating weight of the crane according to the target parameter and the target energy consumption per unit distance comprises:

acquiring a load function relation between load weight and unit distance energy consumption, wherein the load function relation comprises load parameters;

and assigning the target parameter value to the load parameter in the load function relation, substituting the target unit distance energy consumption into the function relation of the load weight and the unit distance energy consumption, and calculating to obtain the current load weight of the crane.

A load weight detecting device, the device comprising:

the acquiring module is used for acquiring the average angular speed of the operation of the crane in the preset time length and the target unit distance energy consumption value;

the determining module is used for determining a corresponding unit distance energy consumption value set according to the workload statistical model parameter set and the average angular speed of the crane; the method comprises the steps that a workload statistical model parameter set is obtained by debugging according to the load weight and the angular speed of a crane, and each group of parameter values in the workload statistical model parameter set is used for the relation between unit distance energy consumption and average angular speed;

the fitting module is used for fitting each unit distance energy consumption value in the unit distance energy consumption value set and the debugging weight corresponding to the unit distance energy consumption value to determine a target parameter value, wherein the target parameter value is a parameter value used for representing the relation between the load weight and the unit distance energy consumption;

and the calculation module is used for calculating the current load weight of the crane according to the target parameter value and the target unit distance energy consumption.

A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:

A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:

According to the load weight detection method, the load weight detection device, the computer equipment and the storage medium, the average angular speed of the crane in operation in the preset time length and the target unit distance energy consumption value are obtained; determining a corresponding unit distance energy consumption value set according to the workload statistical model parameter set and the average angular speed of the crane; the method comprises the steps that a workload statistical model parameter set is obtained by debugging according to the load weight and the angular speed of a crane, and each group of parameter values in the workload statistical model parameter set is used for the relation between unit distance energy consumption and average angular speed; fitting each unit distance energy consumption value in the unit distance energy consumption value set and the debugging weight corresponding to the unit distance energy consumption value, and determining a target parameter value, wherein the target parameter value is a parameter value used for representing the relation between the load weight and the unit distance energy consumption; and calculating the current load weight of the crane according to the target parameter value and the target unit distance energy consumption. And determining a target parameter value through the working capacity statistical model parameter value set and the average angular speed, calculating the current load weight of the crane according to the target parameter value and the target unit distance energy consumption value, reducing the operation process mode of the crane and the calculation error of a motor model in the crane on the current load weight, and improving the precision of load weight detection.

Drawings

FIG. 1 is a diagram illustrating an internal structure of a computer device according to an embodiment;

FIG. 2 is a schematic flow chart of a load weight detection method according to an embodiment;

FIG. 3 is a flowchart illustrating a method for calculating a parameter value set of a workload statistical model according to an embodiment;

FIG. 4 is a schematic flow chart of a load weight detection method according to another embodiment;

FIG. 5 is a system diagram of a method for detecting the load weight of a crane according to an embodiment;

FIG. 6 is a block diagram showing the structure of a load weight detecting apparatus according to an embodiment;

fig. 7 is a block diagram showing the structure of the load weight detecting apparatus in one embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 1. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a load weight detection method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.

Those skilled in the art will appreciate that the architecture shown in fig. 1 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

In an embodiment, as shown in fig. 2, a method for detecting a load weight is provided, and this embodiment is illustrated by applying the method to a terminal, and it is to be understood that the method may also be applied to a server, and may also be applied to a system including a terminal and a server, and is implemented by interaction between the terminal and the server. In this embodiment, the method includes the steps of:

step 202, acquiring the average angular speed of the crane in the preset time and the target unit distance energy consumption value.

The preset duration refers to the running duration of the crane dragging the load weight to work. The unit distance energy consumption refers to the total active energy consumption of the motor of the distance that the crane runs in the preset time length when dragging the load weight, and the total active energy consumption is divided by the distance. The average angular velocity may be calculated by dividing an integrated value of the real-time angular velocity of the motor during the operation of the crane over a preset time period by the preset time period, for example,

wherein the content of the first and second substances,

the running average angular speed (or the running average rotating speed) of a motor for dragging the load weight of the crane by a certain distance is shown as omega, and the running real-time angular speed of the motor is shown as omega; t may be the operating time of the crane pulling the load weight for a distance. The real-time angular speed of the motor can be measured by installing a rotating speed sensor, and can be calculated by combining a sensorless rotating speed algorithm according to real-time voltage and real-time current measured by an electric quantity detection sensor.

Specifically, the terminal obtains a real-time angular velocity in the running process of the crane through a rotating speed sensor, and calculates an average angular velocity of operation in a preset duration according to the angular velocity; and acquiring electric quantity detection data in the running process of the crane through an electric quantity detection sensor, and determining corresponding target unit distance energy consumption according to the electric quantity detection data and the real-time angular speed.

204, determining a corresponding unit distance energy consumption value set according to the workload statistical model parameter value set and the average angular speed of the crane; the parameter value set of the workload statistical model is obtained by debugging according to the load weight and the angular speed of the crane, and each group of parameter values in the parameter value set of the workload statistical model is used for representing the relation between unit distance energy consumption and average angular speed.

The parameter value set of the workload statistical model is obtained by debugging according to the load weight and the angular speed of the crane, namely the load weight and the real-time angular speed of the crane are input into the well-established workload statistical algorithm model and output after calculationThe obtained value. For example, the set of parameter values of the workload statistical model is M ═ { M ═ M₁,M₂........,M_n},(n≥1)，M_n＝{a_n,b_n,c_nIs a workload statistic model parameter in the set of workload statistic model parameter values. The load weight may be a commissioning weight of the crane for field commissioning, e.g. in crane field commissioning, n (n ≧ 3) load weights m are used₁、m₂......m_n. The angular velocity may be a real-time angular velocity of the motor for each load weight of the crane during commissioning.

Specifically, according to each workload statistical model parameter value and the average angular velocity in the workload statistical model parameter value set of the crane, a corresponding unit distance energy consumption value is calculated through a functional relation between the workload statistical model parameter value and the average angular velocity, and a unit distance energy consumption value set is obtained through each unit distance energy consumption value. Alternatively, the functional relationship between the values of the statistical model of the workload parameter and the average angular velocity may be:

wherein e is the unit distance energy consumption of the crane when dragging the load weight; { a, b and c } are a group of parameter values in the workload statistical model parameter value set, and represent the unit distance energy consumption e of the crane and the running average rotating speed of the motor in a job running process

And (4) the functional relation of the quadratic function, wherein a is a quadratic term coefficient in the functional relation, b is a first order term coefficient, and c is a constant term coefficient.

Step 206, fitting each unit distance energy consumption value in the unit distance energy consumption value set and the load weight corresponding to the unit distance energy consumption value to determine a target parameter value; the target parameter value is a parameter value for characterizing the relationship between the load weight and the energy consumption per unit distance.

Specifically, fitting is performed on each energy consumption value per unit distance in the calculated energy consumption value set per unit distance and the load weight corresponding to each energy consumption value per unit distance in the calculated energy consumption value set per unit distance by a least square method, and a target parameter value of a relation between the load weight and the energy consumption per unit distance is obtained through fitting.

And 208, calculating the current load weight of the crane according to the target parameter value and the target unit distance energy consumption.

Specifically, the target parameters and the target energy consumption per unit distance are substituted into a load function relation between the load weight and the energy consumption per unit distance, and the current load weight of the crane is obtained through calculation. Optionally, the load function relationship between load weight and energy consumption per unit distance is:

m＝a’e²+b’e+c’

wherein, the load function relation m ═ a' e²+ b 'e + c' is used for describing the relation between the load weight and the energy consumption per unit distance when the real-time angular velocity is read as a fixed value; m is the load weight of the crane in the operation process, e is the unit distance energy consumption, { a ', b', c '} is the load parameter, a' is the quadratic term coefficient of the load function relational expression, the load parameter b 'is the first order term coefficient, and the load parameter c' is the constant term coefficient. For example, the target parameter value is obtained as { a₀’，b₀’，c₀' } and energy consumption per unit distance e₀Substitution m ═ a' e²+ b 'e + c', the current load weight to the crane is calculated.

In the load weight detection method, the average angular speed of the operation of the crane in a preset time length and the target energy consumption value per unit distance are obtained; determining a corresponding unit distance energy consumption value set according to the workload statistical model parameter set and the average angular speed of the crane; the method comprises the steps that a workload statistical model parameter set is obtained by debugging according to the load weight and the angular speed of a crane, and each group of parameter values in the workload statistical model parameter set is used for the relation between unit distance energy consumption and average angular speed; fitting each unit distance energy consumption value in the unit distance energy consumption value set and the debugging weight corresponding to the unit distance energy consumption value, and determining a target parameter value, wherein the target parameter value is a parameter value used for representing the relation between the load weight and the unit distance energy consumption; and calculating the current load weight of the crane according to the target parameters and the target unit distance energy consumption. The target parameters are determined through the working capacity statistical model parameter value set and the average angular speed, the current load weight of the crane is calculated according to the target parameters and the target unit distance energy consumption value, the operation process mode of the crane and the calculation error of the motor model in the crane on the current load weight are reduced, the precision of load weight detection is improved, and the service life of the crane is prolonged.

In an embodiment, as shown in fig. 3, a method for calculating a parameter value set of a statistical model of a workload is provided, and this embodiment is exemplified by applying the method to a terminal, and the method includes the following steps:

step 302, obtaining a debugging real-time angular velocity set and a debugging electric quantity detection data set corresponding to each load weight of the crane operation in a preset debugging time length.

The preset debugging time length refers to the running time length for dragging the load weight operation in the preset crane debugging process. The debugging real-time angular velocity set comprises at least one debugging real-time angular velocity, wherein the debugging real-time angular velocity refers to that different real-time angular velocities are set by dragging each load weight in the debugging process of the crane, and corresponding debugging electric quantity detection data exist in each debugging real-time angular velocity.

Specifically, the crane can set a plurality of different load weights in the debugging process, and the preset debugging time t of the crane is obtained₀Dragging a debugging real-time angular velocity set of a motor for each load weight operation, wherein the debugging real-time angular velocity set at least comprises one debugging real-time angular velocity; when the crane drags the load weight at a debugging real-time angular speed, corresponding electric quantity detection data can be obtained through the measurement of the electric quantity detection sensor. For example, in crane field commissioning, n (n ≧ 3) load weights m may be used₁、m₂......m_nLoad weight m₁Corresponding debugging real-time angular velocity concentrations include debugging real-time angular velocity

Load weight m_nCorresponding debugging real-time angular velocity concentrations include debugging real-time angular velocity

And step 304, acquiring a functional relation between the real-time angular velocity and the electric quantity detection data.

Optionally, the functional relation between the obtained real-time angular velocity and the electric quantity detection data may be:

the P is real-time active power of the crane motor in operation and can be obtained by calculating the real-time voltage and the real-time current of the motor detected according to the electric quantity; omega is the real-time angular speed of the motor operation, and can be obtained by combining the motor real-time voltage and real-time current data detected by electric quantity with a motor rotating speed algorithm without a rotating speed sensor; the speed can be obtained by installing a rotating speed sensor; and e is the unit distance energy consumption of the crane when the weight of the load is dragged.

And step 306, substituting each real-time angular velocity in the debugging real-time angular velocity set and each debugging electric quantity detection data in the debugging electric quantity detection data set into a functional relation between the real-time angular velocity and the electric quantity detection data, and determining a debugging unit distance energy consumption set of the crane operating in the debugging time length.

Optionally, m is obtained₁、m₂......m_nThe corresponding set of commissioning real-time angular velocities for each load weight is

Substituting each real-time angular velocity in the debugging real-time angular velocity set and each debugging electric quantity detection data in the debugging electric quantity detection data set into a functional relation between the real-time angular velocity and the electric quantity detection data, and calculating to obtain a debugging unit distance energy consumption set for determining the operation of the crane in the debugging time length

And 308, determining a workload statistical model parameter set according to the debugging real-time angular velocity and the debugging unit distance energy consumption.

Specifically, calculating an integral value of each debugging real-time angular speed in the debugging time length in each debugging real-time angular speed set of each load weight, and dividing the calculated integral value by the debugging time length to obtain a value as a debugging average angular speed of the crane in the debugging time length; and fitting the debugging average angular velocity of each load weight and the energy consumption of the debugging unit distance to obtain a workload statistical model parameter set.

determining the debugging average angular speed of the crane in the debugging time length by calculating the integral value of each debugging real-time angular speed in the debugging real-time angular speed set in the debugging time length; and fitting the debugging average angular velocity and the debugging unit distance energy consumption to determine a workload statistical model parameter set.

Specifically, during the commissioning process, for each load weight m₁、m₂…m_nThe crane of each debugging data drags the following data of the load weight operation running process to carry out statistics, and the energy consumption e of each debugging running process in the debugging unit distance is calculated according to the real-time active power P and the real-time rotating speed omega of the motor_1,1、

Real-time rotating speed according to debugging of motor

Calculate to obtain eachAverage speed of work operation

According to the data in Table 1, a load weight m is arbitrarily selected_i(i is more than or equal to 1 and less than or equal to n), and the average angular speed of the motor in the debugging operation process of the load weight dragged by the crane each time

Respectively corresponding to unit distance active energy consumption

When the crane drags a fixed load weight m to operate, at average angular velocity

Energy consumption e per unit distance and average rotating speed of motor in different operation processes

Is a quadratic function relation, so that the least square method can be used for averaging the rotating speed of the motor

And energy consumption per unit distance

Performing quadratic fitting to obtain the energy consumption coefficient p of the energy consumption functional relation between the average angular velocity and the unit distance energy consumption_i＝{a_i,b_i,c_iAnd the energy consumption functional relation can be a quadratic functional relation, and the energy consumption parameter can be a quadratic functional parameter.

Repeating the above steps for each load weight to obtain a loadCarrying capacity m₁、m₂…m_nQuadratic function parameter p of corresponding energy consumption function relation₁、p₂…p_nAs shown in table 1.

Table 1:

in the method for calculating the parameter value set of the workload statistical model, a debugging real-time angular velocity set and a debugging electric quantity detection data set corresponding to each load weight of the operation of the crane in a preset debugging time length are obtained; acquiring a functional relation between the real-time angular velocity and the electric quantity detection data; substituting each real-time angular velocity in the debugging real-time angular velocity set and each debugging electric quantity detection data in the debugging electric quantity detection data set into a functional relation between the real-time angular velocity and the electric quantity detection data, and determining a debugging unit distance energy consumption set of the crane operating in a debugging time period; and determining a workload statistical model parameter set according to the debugging real-time angular speed and the debugging unit distance energy consumption. In the debugging process of the crane, the workload statistic model parameters are calculated by setting a plurality of groups of test data, so that the test data error is reduced, and the accuracy and reliability of the workload statistic model parameters are improved.

In another embodiment, as shown in fig. 4, a method for detecting a load weight is provided, which is exemplified by applying the method to a terminal, and the method includes the following steps:

and 402, acquiring real-time angular speed and electric quantity detection data of the crane within a preset time length.

Specifically, the terminal acquires the real-time angular speed of a motor of the crane within a preset time length through a rotating speed sensor and acquires corresponding electric quantity detection data through an electric quantity detection sensor; the charge detection data may include real-time voltage and real-time current.

And step 404, determining the average angular speed of the crane in the preset time length by calculating the integral value of the real-time angular speed in the preset time length.

The calculation mode for calculating the integral value of the real-time angular speed in the preset time length is as follows:

wherein the content of the first and second substances,

the running average angular speed (or the running average rotating speed) of the motor for dragging the load weight of the crane for a certain distance is shown as omega, the running real-time angular speed of the motor is shown as t, and the running preset time length for dragging the load weight of the crane for a certain distance is shown as t.

And 406, determining the energy consumption value of the crane in unit distance in the preset time length according to the real-time angular speed and the electric quantity detection data.

Specifically, real-time angular speed and electric quantity detection data of the crane within a preset time length are obtained, and real-time active power of the crane within the preset time length is calculated according to real-time voltage and real-time current in the electric quantity detection data; through a functional relation between the real-time angular velocity and the electric quantity detection data:

(k is a constant), and determining the energy consumption value of the crane in the preset time length t.

In one embodiment, the charge detection data includes a real-time voltage and a real-time current; according to real-time angular velocity and electric quantity detection data, confirm the unit distance energy consumption of hoist in the length of time of predetermineeing, include:

determining the total distance energy consumption of the crane in a preset time length according to the real-time voltage and the real-time current; determining the working distance of the crane in a preset time length according to the real-time angular speed; and determining the unit distance energy consumption of the crane in the preset time length according to the total distance energy consumption and the working distance.

The calculation method for determining the total distance energy consumption of the crane in the preset time length by the real-time voltage and the real-time current is as follows:

E＝∫Pdt

and E is the total active energy consumption of the crane for dragging the heavy object for a distance within the preset time, the E is equal to the integral of the real-time active power P of the motor operation within the time to the preset time t, and the P is equal to the product of the real-time voltage and the real-time current of the crane within the preset time.

The calculation formula of the real-time angular speed and the working distance is as follows:

ω＝kv

h is the distance of the crane dragging the load weight within a preset time t, and is equal to the integral of t by the real-time speed v of the crane dragging the load weight to operate within the time; the motor operation real-time rotating speed omega and the crane dragging load weight operation real-time speed v are in a proportional relation, the proportionality coefficient is k, therefore, the crane dragging weight distance h and the motor operation real-time angular speed omega are in direct proportion to the integral of time t, and the proportionality coefficient is 1/k.

According to the total distance energy consumption and the working distance, determining a calculation formula of the unit distance energy consumption of the crane in a preset time length as follows:

and the unit distance energy consumption E of the crane for dragging the heavy object is equal to the total active energy consumption E of the motor for the distance of the crane to run divided by the distance h. Alternatively, the value of the scaling factor k may be 1.

Optionally, calculating real-time active power of the crane in a preset time length according to the real-time voltage and the real-time current, and obtaining total distance energy consumption by integrating the real-time active power with the preset time length; calculating a real-time speed according to the real-time angular speed, and determining the working distance of the crane in the preset time length by calculating the integral of the real-time speed to the preset time length; and determining the unit distance energy consumption of the crane in the preset time length according to the total distance energy consumption and the working distance. According to the integral value of the real-time active power in the preset time length and the integral value of the real-time speed in the preset time length, the unit distance energy consumption of the crane in the preset time length is determined, the calculation error of the unit distance energy consumption is reduced, and the reliability of data is improved.

Step 408, determining a corresponding energy consumption value set of unit distance according to the workload statistical model parameter set and the average angular speed of the crane; the parameter value set of the workload statistical model is obtained by debugging according to the load weight and the angular speed of the crane, and each group of parameter values in the parameter value set of the workload statistical model is used for the relation between unit distance energy consumption and average angular speed.

Specifically, according to each workload statistical model parameter value and the average angular velocity in the workload statistical model parameter value set of the crane, a corresponding unit distance energy consumption value is calculated through a functional relation between the workload statistical model parameter value and the average angular velocity, and a unit distance energy consumption value set is obtained through each unit distance energy consumption value.

In one embodiment, determining a unit distance energy consumption set corresponding to the debugging weight set according to the workload statistical model parameter set and the average angular speed of the crane comprises:

acquiring an energy consumption function relation of unit distance energy consumption and average angular speed, wherein the energy consumption function relation comprises an energy consumption coefficient; and assigning each group of parameter values in the workload statistical model parameter value set to the energy consumption coefficient in the energy consumption function relational expression, substituting the average angular velocity into the energy consumption functional relational expression, and determining unit distance energy consumption corresponding to each debugging weight to obtain a unit distance energy consumption set.

The obtained functional relation between the energy consumption per unit distance and the average angular velocity may be:

wherein, the relational expression

The method is used for describing the relationship between the energy consumption per unit distance and the average angular speed when the load weight is a fixed value; e is the unit distance energy consumption of the crane when dragging the load weight; a. b and c are units of one operation processDistance energy consumption e and motor running average rotating speed

And the energy consumption coefficient of the quadratic function, wherein a is a quadratic term coefficient, b is a first order term coefficient, and c is a constant term coefficient.

Optionally, the average speed of the operation process is adjusted

And the weight of each load m in Table 1₁、m₂…m_nCorresponding work quantity statistical model parameter value p₁、p₂…p_nSubstituting into the functional relation

Calculating each debugging weight m one by one₁、m₂…m_nThe average rotation speed

Corresponding set of energy consumptions per unit distance { e₁，e₂…e_n}。

And step 410, fitting each unit distance energy consumption value in the unit distance energy consumption value set and the debugging weight corresponding to the unit distance energy consumption value, and determining a target parameter value, wherein the target parameter value is a parameter value used for representing the relation between the load weight and the unit distance energy consumption.

Optionally, the average speed of the operation process is adjusted

Corresponding set of energy consumptions per unit distance, { e₁，e₂…e_nAnd performing quadratic fitting by using a least square method to obtain a target parameter value of a functional relation between the load weight and the energy consumption at a unit distance.

Step 412, a load function relation between the load weight and the energy consumption per unit distance is obtained, wherein the load function relation includes a load parameter.

Optionally, the load function relation between the load weight and the energy consumption per unit distance may be obtained as follows:

m＝a’e²+b’e+c’

wherein m is the load weight of the crane in the operation process, e is the unit distance energy consumption, the load parameters comprise a ', b' and c ', a' is a quadratic term coefficient, b 'is a primary term coefficient, and c' is a constant term coefficient.

And 414, assigning the target parameter value to the load parameter in the load function relation, substituting the target unit distance energy consumption into the function relation between the load weight and the unit distance energy consumption, and calculating to obtain the current load weight of the crane. In the load weight detection method, real-time angular speed and electric quantity detection data of the crane within a preset time length are obtained; the method comprises the steps of determining the average angular speed of the crane in the preset duration by calculating the integral value of the real-time angular speed in the preset duration, and solving the average angular speed data through the integral, so that the accuracy of the data is ensured; determining the energy consumption value of the crane in unit distance in preset time according to the real-time angular speed and the electric quantity detection data; determining a corresponding unit distance energy consumption value set according to the workload statistical model parameter set and the average angular speed of the crane; the method comprises the steps that a workload statistical model parameter set is obtained by debugging according to the load weight and the angular speed of a crane, and each group of parameter values in the workload statistical model parameter set is used for the relation between unit distance energy consumption and average angular speed; fitting each unit distance energy consumption value in the unit distance energy consumption value set and the debugging weight corresponding to the unit distance energy consumption value, and determining a target parameter value, wherein the target parameter value is a parameter value used for representing the relation between the load weight and the unit distance energy consumption; and acquiring a load function relation between the load weight and the energy consumption in unit distance, wherein the load function relation comprises load parameters. The electric quantity detection data are acquired through the electric quantity detection sensor, and the sensor is high in anti-interference capacity; the accuracy of load weight detection is improved through the real-time angular speed, the electric quantity detection data and the workload statistical model parameter set, and the reliability of the data is improved.

In one embodiment, as shown in fig. 5, a system structure diagram of a crane load weight detection method is provided. Wherein, the motor M is an asynchronous three-phase motor. The crane drags a load weight through a transmission shaft, a speed reducer, a pulley, a steel wire rope drum and a steel wire rope, real-time voltage U and real-time current I in the operation process are measured and obtained through an electric quantity detection sensor arranged on a crane driving motor, and real-time active power P of the crane can be calculated according to the obtained real-time voltage and real-time current; the real-time angular velocity omega of the motor can be obtained through the rotating speed sensor, and the average angular velocity in the operation process is obtained according to the integral of the real-time angular velocity to the operation time in the operation process.

Calculating a target unit distance energy consumption value of the crane operating in a preset time length through a functional relation between the real-time angular speed and the electric quantity detection data; acquiring a workload statistical model parameter set corresponding to the crane workload statistical algorithm model, and determining a corresponding unit distance energy consumption value set according to the workload statistical model parameter set and the average angular speed; the parameter value set of the workload statistical model is obtained by debugging according to the load weight and the angular speed of the crane, and each group of parameter values in the parameter value set of the workload statistical model is used for representing the relation between unit distance energy consumption and average angular speed. Fitting each unit distance energy consumption value in the unit distance energy consumption value set and the load weight corresponding to the unit distance energy consumption value to determine a target parameter value; the target parameter value is a parameter value for characterizing a relationship between the load weight and the energy consumption per unit distance; and calculating the current load weight m of the crane according to the target parameters and the target unit distance energy consumption.

It should be understood that although the various steps in the flow charts of fig. 2-4 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-4 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.

In one embodiment, as shown in fig. 6, there is provided a load weight detecting device 600 including: a first obtaining module 602, a determining module 604, a fitting module 606, and a calculating module 608, wherein:

the first obtaining module 602 is configured to obtain an average angular velocity and a target energy consumption per unit distance value of a crane operating in a preset time period.

A determining module 604, configured to determine a corresponding energy consumption value set per unit distance according to the workload statistical model parameter set and the average angular velocity of the crane; the parameter value set of the workload statistical model is obtained by debugging according to the load weight and the angular speed of the crane, and each group of parameter values in the parameter value set of the workload statistical model is used for the relation between unit distance energy consumption and average angular speed.

A fitting module 606, configured to fit each unit distance energy consumption value in the unit distance energy consumption value set and the debugging weight corresponding to the unit distance energy consumption value, and determine a target parameter value, where the target parameter value is a parameter value used to represent a relationship between the load weight and the unit distance energy consumption.

And the calculating module 608 is configured to calculate the current load weight of the crane according to the target parameter value and the target energy consumption per unit distance.

In the load capacity detection device, the average angular speed of the operation of the crane in a preset time length and the target unit distance energy consumption value are obtained; determining a corresponding unit distance energy consumption value set according to the workload statistical model parameter set and the average angular speed of the crane; the method comprises the steps that a workload statistical model parameter set is obtained by debugging according to the load weight and the angular speed of a crane, and each group of parameter values in the workload statistical model parameter set is used for the relation between unit distance energy consumption and average angular speed; fitting each unit distance energy consumption value in the unit distance energy consumption value set and the debugging weight corresponding to the unit distance energy consumption value, and determining a target parameter value, wherein the target parameter value is a parameter value used for representing the relation between the load weight and the unit distance energy consumption; and calculating the current load weight of the crane according to the target parameters and the target unit distance energy consumption. Target parameters are determined through the working capacity statistical model parameter value set and the average angular speed, the current load weight of the crane is calculated according to the target parameter values and the target unit distance energy consumption values, the operation process mode of the crane and the calculation error of a motor model in the crane on the current load weight are reduced, and the precision of load weight detection is improved.

In another embodiment, as shown in fig. 7, there is provided a load weight detecting apparatus 600, which comprises, in addition to the first obtaining module 602, the determining module 604, the fitting module 606 and the calculating module 608: wherein:

the second obtaining module 610 is configured to obtain a functional relation between the real-time angular velocity and the power detection data.

In one embodiment, the second obtaining module 610 is further configured to obtain an energy consumption function relation between energy consumption per unit distance and an average angular velocity, where the energy consumption function relation includes an energy consumption coefficient.

In one embodiment, the second obtaining module 610 is further configured to obtain a load function relation between the load weight and the energy consumption per unit distance, where the load function relation includes a load parameter.

In one embodiment, the first obtaining module 602 is further configured to obtain real-time angular velocity and electric quantity detection data of the crane within a preset time period.

In one embodiment, the first obtaining module 602 is further configured to obtain a debugging real-time angular velocity set and a debugging electric quantity detection data set corresponding to each load weight of the crane during a preset debugging time period.

In one embodiment, the determining module 604 is further configured to substitute each real-time angular velocity in the debug real-time angular velocity set and each power detection data in the debug power detection data set into a functional relationship between the real-time angular velocity and the power detection data, and determine a debug unit distance energy consumption set of the crane operating in the debug duration.

In one embodiment, the determining module 604 is further configured to determine the set of workload statistical model parameters according to the debugging real-time angular velocity and the debugging unit distance energy consumption.

In an embodiment, the determining module 604 is further configured to assign each group of parameter values in the workload statistical model parameter value set to an energy consumption coefficient in the energy consumption functional relation, and substitute the average angular velocity into the energy consumption functional relation to determine the unit distance energy consumption corresponding to each debugging weight, so as to obtain a unit distance energy consumption set.

In one embodiment, the determining module 604 is further configured to determine an average angular velocity of the crane over a preset time period by calculating an integral value of the real-time angular velocity over the preset time period; and determining the unit distance energy consumption of the crane in the preset time length according to the real-time angular speed and the electric quantity detection data.

In one embodiment, the determining module 604 is further configured to determine the total distance energy consumption of the crane in a preset time period according to the real-time voltage and the real-time current; determining the working distance of the crane in a preset time length according to the real-time angular speed; and determining the unit distance energy consumption of the crane in the preset time length according to the total distance energy consumption and the working distance.

In one embodiment, the fitting module 606 is further configured to fit the debugging average angular velocity and the debugging unit distance energy consumption to determine a set of workload statistical model parameters.

In one embodiment, the calculation module 608 is further configured to determine the debugging average angular velocity of the crane in the debugging time period by calculating an integral value of each debugging real-time angular velocity in the debugging real-time angular velocity set within the debugging time period.

In one embodiment, the calculating module 608 is further configured to assign the target parameter value to a load parameter in the load functional relation, and substitute the target energy consumption per unit distance into the functional relation between the load weight and the energy consumption per unit distance to calculate the current load weight of the crane.

In one embodiment, the terminal acquires real-time angular speed and electric quantity detection data of the crane within a preset time length; the method comprises the steps of determining the average angular speed of the crane in the preset duration by calculating the integral value of the real-time angular speed in the preset duration, and solving the average angular speed data through the integral, so that the accuracy of the data is ensured; determining the energy consumption value of the crane in unit distance in preset time according to the real-time angular speed and the electric quantity detection data; determining a corresponding unit distance energy consumption value set according to the workload statistical model parameter set and the average angular speed of the crane; the method comprises the steps that a workload statistical model parameter set is obtained by debugging according to the load weight and the angular speed of a crane, and each group of parameter values in the workload statistical model parameter set is used for the relation between unit distance energy consumption and average angular speed; fitting each unit distance energy consumption value in the unit distance energy consumption value set and the debugging weight corresponding to the unit distance energy consumption value, and determining a target parameter value, wherein the target parameter value is a parameter value used for representing the relation between the load weight and the unit distance energy consumption; and acquiring a load function relation between the load weight and the energy consumption in unit distance, wherein the load function relation comprises load parameters.

The electric quantity detection data are acquired through the electric quantity detection sensor, and the sensor is high in anti-interference capacity; the accuracy of load weight detection is improved through the real-time angular speed, the electric quantity detection data and the workload statistical model parameter set, and the reliability of the data is improved. Meanwhile, the load weight in the operation process of the crane can be accurately obtained by improving the accuracy of load weight detection, and the safety factor of the crane is improved.

For specific limitations of the load weight detection device, reference may be made to the above limitations of the load weight detection method, which are not described herein again. The modules in the above-mentioned load weight detecting device can be realized by software, hardware or their combination in whole or in part. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:

In one embodiment, the processor, when executing the computer program, further performs the steps of:

the electric quantity detection data comprises real-time voltage and real-time current;

acquiring an energy consumption function relation of unit distance energy consumption and average angular speed, wherein the energy consumption function relation comprises an energy consumption coefficient;

In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:

In one embodiment, the computer program when executed by the processor further performs the steps of:

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims

1. A method of load weight detection, the method comprising:

determining a corresponding unit distance energy consumption value set according to the workload statistical model parameter set and the average angular speed of the crane; the parameter value set of the workload statistical model is obtained by debugging according to the load weight and the angular speed of the crane, and each group of parameter values in the parameter value set of the workload statistical model is used for the relation between unit distance energy consumption and average angular speed;

fitting each unit distance energy consumption value in the unit distance energy consumption value set and the debugging weight corresponding to the unit distance energy consumption value to determine a target parameter value, wherein the target parameter value is a parameter value used for representing the relation between the load weight and the unit distance energy consumption;

2. The method of claim 1, wherein the obtaining the average angular velocity and the target energy consumption per unit distance for the crane operating in the preset time period comprises:

determining the average angular speed of the crane in the preset time period by calculating the integral value of the real-time angular speed in the preset time period;

and determining the energy consumption of the crane in the unit distance in the preset time according to the real-time angular speed and the electric quantity detection data.

3. The method of claim 2, wherein the charge detection data comprises real-time voltage and real-time current;

the determining the energy consumption of the crane in the unit distance in the preset time according to the real-time angular speed and the electric quantity detection data comprises the following steps:

determining the working distance of the crane in the preset time length according to the real-time angular speed;

4. The method of claim 1, wherein the set of workload statistical model parameter values are derived from a load weight and an angular velocity commissioning of the crane, comprising:

acquiring a debugging real-time angular velocity set and a debugging electric quantity detection data set corresponding to each load weight of the crane in the preset debugging time;

substituting each real-time angular velocity in the debugging real-time angular velocity set and each electric quantity detection data in the debugging electric quantity detection data set into a functional relation between the real-time angular velocity and the electric quantity detection data, and determining a debugging unit distance energy consumption set of the crane operating in the debugging time length;

and determining a workload statistical model parameter set according to the debugging real-time angular velocity and the debugging unit distance energy consumption.

5. The method of claim 4, wherein determining a set of workload statistical model parameters based on the commissioning real-time angular velocity and the commissioning unit distance energy consumption comprises:

determining a debugging average angular velocity of the crane in the debugging time length by calculating an integral value of each debugging real-time angular velocity in the debugging real-time angular velocity set in the debugging time length;

fitting the debugging average angular velocity and the debugging unit distance energy consumption, and determining a workload statistical model parameter set.

6. The method according to claim 4, wherein the determining a set of energy consumption per unit distance corresponding to the set of commissioning weights according to the set of statistical model parameters of the workload of the crane and the average angular velocity comprises:

and assigning each group of parameter values in the workload statistical model parameter value set to an energy consumption coefficient in the energy consumption function relational expression, substituting the average angular velocity into the energy consumption functional relational expression, and determining unit distance energy consumption corresponding to each debugging weight to obtain a unit distance energy consumption set.

7. The method of claim 1, wherein said calculating a current work weight of said crane based on said target parameter value and said target energy consumption per unit distance comprises:

8. A load weight detecting device, characterized in that the device comprises:

the determining module is used for determining a corresponding unit distance energy consumption value set according to the workload statistical model parameter set and the average angular speed of the crane; the parameter value set of the workload statistical model is obtained by debugging according to the load weight and the angular speed of the crane, and each group of parameter values in the parameter value set of the workload statistical model is used for the relation between unit distance energy consumption and average angular speed;

a fitting module, configured to fit each unit distance energy consumption value in the unit distance energy consumption value set and a debugging weight corresponding to the unit distance energy consumption value, and determine a target parameter value, where the target parameter value is a parameter value used to represent a relationship between a load weight and unit distance energy consumption;

9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.