CN117078117B

CN117078117B - Fork truck workload determination method based on Internet of things, internet of things server and medium

Info

Publication number: CN117078117B
Application number: CN202311340557.5A
Authority: CN
Inventors: 陶关平; 王严严; 赵党斌
Original assignee: Shenzhen Da Shang Technology Co ltd; Guangdong Dachang Internet Of Things Technology Co ltd
Current assignee: Shenzhen Da Shang Technology Co ltd; Guangdong Dachang Internet Of Things Technology Co ltd
Priority date: 2023-10-17
Filing date: 2023-10-17
Publication date: 2024-02-23
Anticipated expiration: 2043-10-17
Also published as: CN117078117A

Abstract

The invention relates to the technical field of data processing of the Internet of things, in particular to a forklift workload determination method based on the Internet of things, an Internet of things server and a medium. The time spent in the process of switching the load state of the forklift from the first state to the second state is obtained and is used as the interval time to determine the data sent by the sensor on the forklift in the interval time, so that the single cargo carrying time length, the driving mileage and the cargo carrying quantity of the forklift are determined according to the data, and the workload of a driver is comprehensively considered according to the single cargo carrying time length, the driving mileage and/or the cargo carrying quantity. The work load of the forklift driver in the actual working process is reflected more accurately by adopting a multidimensional work load statistics mode, and the aim of improving the effect of the internet of things on the work load statistics precision of the forklift driver is achieved.

Description

Fork truck workload determination method based on Internet of things, internet of things server and medium

Technical Field

The invention relates to the technical field of data processing of the Internet of things, in particular to a forklift workload determination method based on the Internet of things, an Internet of things server and a medium.

Background

In the traditional forklift management field, when the workload of a forklift driver is counted, a common counting mode is to count in a manual mode, but the defects of insufficient counting accuracy and difficult supervision of a background are easily caused in the manual counting mode, and in order to better manage the forklift work flow, the internet of things is introduced into the logistics companies to supervise the forklift transportation process.

In the related scheme of the work load statistics of the forklift in the internet of things, the work load of a forklift driver is generally calculated by recording the cargo load of each time and accumulating the cargo load of each time.

However, the statistical method of the workload has the defect of insufficient statistical precision because the statistical dimension is single and the workload of a forklift driver in the actual working process can not be reflected objectively.

The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.

Disclosure of Invention

The invention mainly aims to provide a forklift workload determination method based on the Internet of things, and aims to solve the problem of how to improve the statistical accuracy of the workload of a forklift driver by the Internet of things.

In order to achieve the above object, the invention provides a forklift workload determining method based on the internet of things, which comprises the following steps:

Acquiring corresponding interval time in the process of switching the load state of the forklift from the first state to the second state;

determining the corresponding pulse signal jump times of a pulse sensor on a forklift tire in the interval time, and determining the corresponding pressure change value of a pressure sensor on a goods shelf of the forklift in the interval time;

determining a single cargo carrying time length of the forklift according to the interval time, determining a corresponding driving mileage of the forklift in the single cargo carrying time length according to the pulse signal jump times and the tire circumference of the forklift, and determining a corresponding cargo carrying amount of the forklift in the single cargo carrying time length according to a pressure change value;

and determining the workload of a forklift driver according to the single cargo time length, the driving mileage and/or the cargo quantity counted in the preset period.

Optionally, the first state is a carrying state, the second state is an empty state, and before the step of obtaining the corresponding interval time in the process of switching the loading state of the forklift from the first state to the second state, the method further includes:

determining a corresponding load state as the carrying state when the forklift is positioned at the loading position and the pressure value on the pressure sensor is larger than a preset pressure threshold value, and determining a corresponding moment when the forklift leaves the loading position as an initial moment;

Determining a load state corresponding to the pressure value on the pressure sensor when the pressure value is switched to the empty state as the empty state, and determining a moment corresponding to the determined empty state as an ending moment;

and determining the interval time according to the time difference between the ending time and the initial time.

Optionally, the first state is an empty state, the second state is a carrying state, and before the step of obtaining the corresponding interval time in the process of switching the loading state of the forklift from the first state to the second state, the method further includes:

the forklift is located at the unloading position, a loading state corresponding to the pressure value on the pressure sensor when the pressure value is empty is determined to be the empty state, and a moment corresponding to the forklift leaving the unloading position is determined to be an initial moment;

the forklift is located at a loading position, a corresponding loading state is determined to be the loading state when the pressure change value on the pressure sensor is larger than the pressure change threshold value, and a corresponding moment is determined to be the loading state and is determined to be the ending moment;

Optionally, the forklift workload determining method based on the internet of things further includes:

when receiving the wireless radio frequency signals sent by the first radio frequency identification sensor for odd number, determining that the forklift is positioned at the loading position, and when receiving the wireless radio frequency signals sent by the first radio frequency identification sensor for even number, determining that the forklift is away from the loading position;

when the wireless radio frequency signals sent by the second radio frequency identification sensor for odd number are received, determining that the forklift is positioned at the unloading position, and when the wireless radio frequency signals sent by the second radio frequency identification sensor for even number are received, determining that the forklift is away from the unloading position;

the first radio frequency identification sensor is arranged in the loading area, and the second radio frequency identification sensor is arranged in the unloading area.

Optionally, the step of determining the workload of the forklift driver according to the driving mileage, the freight volume and/or the single freight duration counted in the preset period includes:

acquiring a first statistical weight corresponding to the driving mileage, a second statistical weight corresponding to the freight volume and/or a third statistical weight corresponding to the single freight duration;

And determining the workload according to the driving mileage and the first statistical weight counted in the preset period, the freight volume and the second statistical weight counted in the preset period, and/or the average freight duration, freight trip number and the third statistical weight counted in the preset period of the single freight duration.

Optionally, the step of determining the single cargo carrying duration of the forklift according to the interval time, determining a driving range corresponding to the forklift in the single cargo carrying duration according to the pulse signal jump times and the tire circumference of the forklift, and determining the cargo carrying quantity corresponding to the forklift in the single cargo carrying duration according to the pressure change value further includes:

acquiring the jump times of pulse signals generated by rotation of a motor in a period corresponding to a preset movement distance of the forklift;

determining the number of turns of the motor according to the jump times;

according to the rotation number of the motor and the transmission parameters, determining the rotation number of the hub in the process of moving the forklift by the preset distance;

and determining the circumference of the tire corresponding to the forklift according to the preset distance and the rotation number of the hub.

Optionally, after the step of determining the tire circumference corresponding to the forklift according to the preset distance and the rotation number of the hub, the method further includes:

determining a numerical difference between the tire circumference and a standard tire circumference of the forklift;

calculating the abrasion coefficient of the forklift based on the numerical value difference;

and calibrating the workload according to the wear coefficient.

Optionally, the step of calculating the wear coefficient of the forklift based on the numerical difference includes:

determining a driving behavior evaluation value associated with the forklift in a preset history time, and determining the abrasion coefficient according to the driving behavior evaluation value and the numerical value difference; and/or the number of the groups of groups,

and determining a tire state evaluation value of the forklift in the current state, and determining the abrasion coefficient according to the tire state evaluation value and the numerical value difference.

In addition, in order to achieve the above object, the present invention further provides an internet of things server, which includes: the forklift workload determination method based on the Internet of things comprises a memory, a processor and a forklift workload determination program based on the Internet of things, wherein the forklift workload determination program based on the Internet of things is stored in the memory and can run on the processor, and the steps of the forklift workload determination method based on the Internet of things are realized when the forklift workload determination program based on the Internet of things is executed by the processor.

In addition, in order to achieve the above object, the present invention further provides a computer readable storage medium, where a forklift workload determination program based on the internet of things is stored on the computer readable storage medium, and the steps of the forklift workload determination method based on the internet of things are implemented when the forklift workload determination program based on the internet of things is executed by a processor.

The embodiment of the invention provides a forklift workload determination method based on the Internet of things, an Internet of things server and a medium, wherein the time spent in the process of switching a loading state of a forklift from a first state to a second state is obtained and is used as interval time to determine data sent by a sensor on the forklift in the interval time, so that single cargo carrying duration, driving mileage and cargo carrying capacity of the forklift are determined according to the data, and the workload of a driver is comprehensively considered according to the single cargo carrying duration, the driving mileage and/or the cargo carrying capacity. The multi-dimensional workload statistics mode is adopted, so that the workload of a forklift driver in the actual working process is reflected more accurately, and the effect of improving the workload statistics precision of the internet of things on the forklift driver is achieved.

Drawings

Fig. 1 is a schematic architecture diagram of a hardware running environment of an internet of things server according to an embodiment of the present invention;

Fig. 2 is a schematic flow chart of a first embodiment of the forklift workload determination method based on the internet of things according to the present invention;

fig. 3 is a schematic flow chart of a second embodiment of the forklift workload determination method based on the internet of things according to the present invention;

fig. 4 is a schematic flow chart of a third embodiment of the forklift workload determination method based on the internet of things according to the present invention;

fig. 5 is a flowchart of a fourth embodiment of the forklift workload determination method based on the internet of things according to the present invention.

The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

According to the method and the device, the time spent in the process of switching the load state of the forklift from the first state to the second state is taken as the interval time, the data sent by the sensor on the forklift in the interval time is determined, the single cargo carrying duration, the driving mileage and the cargo carrying quantity of the forklift are determined, and the workload of a driver is comprehensively considered according to the single cargo carrying duration, the driving mileage and/or the cargo carrying quantity. The multi-dimensional workload statistics mode is adopted, so that the workload of a forklift driver in the actual working process is reflected more accurately, and the effect of improving the workload statistics precision of the internet of things on the forklift driver is achieved.

In order to better understand the above technical solution, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

As an implementation scheme, fig. 1 is a schematic architecture diagram of a hardware running environment of an internet of things server according to an embodiment of the present invention.

As shown in fig. 1, the server of the internet of things may include: a processor 1001, such as a CPU, memory 1005, user interface 1003, network interface 1004, communication bus 1002. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.

Those skilled in the art will appreciate that the internet of things server architecture shown in fig. 1 is not limiting of the internet of things server and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.

As shown in fig. 1, an operating system, a network communication module, a user interface module, and a forklift workload determination program based on the internet of things may be included in the memory 1005 as one storage medium. The operating system is a program for managing and controlling hardware and software resources of the Internet of things server, and determines programs and other software or program operations based on forklift workload of the Internet of things.

In the forklift workload determination based on the internet of things shown in fig. 1, the user interface 1003 is mainly used for connecting a terminal and performing data communication with the terminal; the network interface 1004 is mainly used for a background server and is in data communication with the background server; the processor 1001 may be configured to invoke the forklift workload determination program based on the internet of things stored in the memory 1005.

In this embodiment, the server of the internet of things includes: memory 1005, processor 1001 and stored on the memory and executable on the processor fork truck workload determination program based on the internet of things, wherein:

When the processor 1001 invokes the forklift workload determination program based on the internet of things stored in the memory 1005, the following operations are performed:

determining the number of turns of the motor according to the jump times;

and calibrating the workload according to the wear coefficient.

Based on the hardware architecture of the Internet of things server based on the Internet of things data processing technology, the embodiment of the forklift workload determination method based on the Internet of things is provided.

Referring to fig. 2, in a first embodiment, the forklift workload determination method based on the internet of things includes the following steps:

step S10, acquiring corresponding interval time in the process of switching the load state of the forklift from the first state to the second state;

in this embodiment, be provided with load detection device on the fork truck, load detection device can detect the current load state that is in of fork truck, and the load state includes first state and second state.

In this embodiment, when the forklift enters the first state, the load detection device records the current time of the forklift and sends the current time to the internet of things server as the start time of the interval time, when the forklift enters the second state, the load detection device records the current time of the forklift and sends the current time of the interval time as the end time of the interval time to the internet of things server, and the server determines the interval time corresponding to the process of switching the load state of the forklift from the first state to the second state according to the time difference between the current time and the end time.

Alternatively, the first state may be a carrying state, the carrying state being characterized as a state when the forklift is loaded with cargo for transportation on a pallet of the forklift, and the second state being an empty state, the empty state being characterized as a state when the forklift is not loaded with cargo for transportation. When the first state is a carrying state and the second state is an empty state, the process of switching the forklift from the first state to the second state is a process of unloading after the forklift takes goods from the goods taking area, and the loaded goods are transported to the unloading area. The corresponding interval time is the time spent by the forklift from taking to unloading.

Alternatively, the first state may be an empty state, and the second state may be a carrying state. When the first state is an empty state and the second state is a carrying state, the process of switching the forklift from the first state to the second state is a process of returning to the goods taking area to take goods after the forklift is unloaded from the goods taking area. The corresponding interval time is the time spent by the forklift from unloading to taking.

Step S20, determining the corresponding pulse signal jump times of a pulse sensor on a forklift tire in the interval time, and determining the corresponding pressure change value of a pressure sensor on a goods shelf of the forklift in the interval time;

Step S30, determining a single cargo carrying time length of the forklift according to the interval time, determining a corresponding driving distance of the forklift in the single cargo carrying time length according to the pulse signal jump times and the tire circumference of the forklift, and determining a corresponding cargo carrying amount of the forklift in the single cargo carrying time length according to a pressure change value;

in this embodiment, the interval time is defined as the shipping time of the forklift in the shipping process, that is, the current shipping time.

In this embodiment, a pulse sensor is disposed on the forklift tire, and the pulse sensor hops to generate a pulse signal once every time the tire rotates, where the hopping frequency of the pulse signal is the number of turns of the tire.

Further, the jump times of the pulse signals in the interval time are the number of turns of the forklift tyre in the process of transporting the forklift, and the driving mileage of the forklift in the process of transporting the forklift can be determined according to the number of turns and the circumference of the tyre.

In this embodiment, be provided with pressure sensor on fork truck's the goods shelves, when fork truck driver operated fork truck got goods, the goods were piled and are pressed on the goods shelves, and pressure sensor's pressure value can increase, and when fork truck driver operated fork truck put goods, pressure sensor's pressure value can reduce.

Further, the amount of the forklift in the process of this shipment is determined according to the pressure change value of the pressure sensor in the interval time, generally, the pressure value on the pressure sensor is fixed after the forklift picks up the goods, and when the pressure value changes, the forklift is meant to load/unload the goods. And determining the weight of the goods, namely the cargo capacity of the forklift according to the variation of the pressure value.

Step S40, determining the workload of a forklift driver according to the single cargo time length, the driving mileage and/or the cargo amount counted in the preset period;

in this embodiment, after obtaining a single cargo time length, and a driving distance and a cargo amount corresponding to the single cargo time length, the driving distance and/or the cargo amount in a preset period are counted, so as to calculate the workload of the forklift driver.

Optionally, the preset period may be a natural day, and the daily workload corresponding to the forklift driver in the day of the statistical workload is calculated; the preset period can be a natural month, and the statistical workload is the month workload corresponding to the forklift driver in the current month.

It should be noted that, compared to the conventional workload calculation scheme, the workload calculated in this embodiment counts the amount of goods that are loaded and unloaded by the forklift driver each time (because the greater the amount of goods is, the more effort is required by the forklift driver in taking/unloading), the length of time of the goods that are loaded each time (the longer the length of time of the goods is, the higher the cost of a single trip of the forklift driver is), and/or the corresponding driving mileage in each time of the goods that are loaded each time (the farther the driving mileage is, the higher the cost of a single trip of the forklift driver is).

In this embodiment, the single shipment duration, the driving mileage, the shipment volume, and the workload are all in a proportional relationship.

In the technical scheme provided by the embodiment, the time spent in the process of switching the load state of the forklift from the first state to the second state is taken as the interval time, the data sent by the sensor on the forklift in the interval time is determined to determine the single cargo carrying time length, the driving mileage and the cargo carrying quantity of the forklift, and the workload of the driver is comprehensively considered according to the single cargo carrying time length, the driving mileage and/or the cargo carrying quantity. The multi-dimensional workload statistics mode is adopted, so that the workload of a forklift driver in the actual working process is reflected more accurately, and the effect of improving the workload statistics precision of the internet of things on the forklift driver is achieved.

Further, in this embodiment, before the step S10, the method further includes:

step S501, determining a corresponding loading state as the loading state when the forklift is located at a loading position and the pressure value on the pressure sensor is greater than a preset pressure threshold, and determining a corresponding moment when the forklift leaves the loading position as an initial moment;

step S601, determining a load state corresponding to when the pressure value on the pressure sensor is switched to a space as the idle state, and determining a time corresponding to when the load state is determined as the idle state as an end time;

Step S701, determining the interval time according to a time difference between the ending time and the initial time.

Alternatively, in this embodiment, if the unloading position and the loading position of the forklift are located at different positions, and the forklift is specified to be only unloaded at the unloading position and only loaded at the loading position. Let the first state be the carrying state and the second state be the no-load state, then this procedure is: the forklift starts to load and fetch goods from the loading site, and the whole process is finished after the forklift is transported to the unloading site to be unloaded. How to determine what state the current loading state of the forklift is in and the start-stop time of the interval time are defined below.

In this embodiment, when the server detects that the forklift is located at a preset loading position and detects that the pressure value on the pressure sensor is greater than a preset pressure threshold, it is determined that the forklift is loaded with the load, and the load detection module determines that the load state at this time is a carrying state (i.e., a first state). When the server detects that the forklift leaves the loading position, the corresponding moment of the forklift, which is judged to leave the loading position, is determined to be the initial moment.

In this embodiment, when the server detects that the forklift is located at a preset unloading position and detects that the pressure sensor is zeroed (i.e., the pressure value is switched to be empty), it is determined that the forklift is unloading the goods, and the load detection module determines that the load state at this time is an empty state (i.e., the second state). When the server detects that the forklift leaves the unloading position, the corresponding moment of the forklift, which is judged to leave the unloading position, is determined to be the end moment.

According to the time difference between the end time and the initial time, the corresponding interval time in the process of switching the loading state of the forklift from the first state to the second state, namely the time spent by the forklift from loading to unloading, can be determined.

Further, in this embodiment, before the step S10, the method further includes:

step S502, determining a loading state corresponding to the situation that the forklift is located at a unloading position and the pressure value on the pressure sensor is empty as the empty state, and determining a moment corresponding to the situation that the forklift leaves the unloading position as an initial moment;

step S602, determining a corresponding loading state as the carrying state when the forklift is positioned at the loading position and the pressure change value of the pressure sensor is larger than the pressure change threshold value, and determining a corresponding moment as the ending moment when the loading state is determined;

step S702, determining the interval time according to the time difference between the ending time and the initial time.

Alternatively, in this embodiment, if the unloading position and the loading position of the forklift are located at different positions, and the forklift is specified to be only unloaded at the unloading position and only loaded at the loading position. If the first state is an empty state and the second state is a carrying state, the process is: the whole process from the unloading of the forklift to the transportation to the loading place for loading is finished, and how to determine what state the current loading state of the forklift is in and the starting and stopping moments of the interval time are defined below.

In this embodiment, when the server detects that the forklift is located at a preset unloading position and detects that the pressure sensor is zeroed (i.e., the pressure value is switched to be empty), it is determined that the forklift is unloading the goods, and the load detection module determines that the load state at this time is an empty state (i.e., the first state). When the server detects that the forklift leaves the unloading position, the corresponding moment of the forklift, which is judged to leave the unloading position, is determined as the starting moment.

In this embodiment, when the server detects that the forklift is located at a preset pickup position and detects that the pressure value on the pressure sensor is greater than a preset pressure threshold, it is determined that the forklift is loaded with the goods, and the load detection module determines that the load state at this time is a carrying state (i.e., a second state). When the server detects that the forklift leaves the loading position, the corresponding moment of the forklift, which is judged to leave the loading position, is determined to be the ending moment.

According to the time difference between the end time and the initial time, the corresponding interval time in the process of switching the loading state of the forklift from the first state to the second state, namely the time spent by the forklift from unloading to loading in the whole process, can be determined.

Further, in this embodiment, the forklift workload determining method based on the internet of things further includes:

step S80, when receiving the radio frequency signals sent by the first radio frequency identification sensor for odd number, determining that the forklift is positioned at the loading position, and when receiving the radio frequency signals sent by the first radio frequency identification sensor for even number, determining that the forklift is away from the loading position;

step S90, when receiving the radio frequency signals sent by the second radio frequency identification sensor for odd number of times, determining that the forklift is located at the unloading position, and when receiving the radio frequency signals sent by the second radio frequency identification sensor for even number of times, determining that the forklift is away from the unloading position.

Optionally, it is determined how the forklift is in/out of the loading position, and in/out of the unloading position. In this embodiment, a radio frequency identification sensor is disposed in a loading area of a forklift as a first radio frequency identification sensor; and a radio frequency identification sensor is arranged in the unloading area of the forklift and is used as a second radio frequency identification sensor.

Each time the forklift passes through the radio frequency identification sensor, the radio frequency identification sensor sends a wireless radio frequency signal to the internet of things server, and when the wireless radio frequency signal received by the internet of things server is a signal sent by the radio frequency identification sensor for an odd number of times, the forklift is judged to enter the position; when the wireless radio frequency signals received by the Internet of things server are signals sent by the radio frequency identification sensor for an even number of times, the forklift is judged to leave the position.

It will be appreciated that in this embodiment, the starting position of the forklift is not in the loading position or the unloading position, the signal sent by the rfid sensor is an odd number of signals when the forklift enters the loading/unloading position for the first time, and then the signal sent by the rfid sensor is an even number of signals when the forklift is driven out of the loading/unloading position.

In addition, optionally, in order to avoid frequent movement of the forklift at the cargo/unloading position, which leads to incorrect judgment of the forklift state by the internet of things server, a signal transmission interval period may be further provided on the radio frequency identification sensor, and the radio frequency identification sensor performs a signal transmission operation every interval of the signal transmission interval period.

Taking the forklift entering and exiting from the loading position as an example, when the forklift enters the loading position for the first time, the radio frequency identification sensor sends out a radio frequency signal to the internet of things server, which is an odd number of radio frequency signals, the internet of things server judges that the forklift is located at the loading position, after a certain time interval (10 minutes are assumed), the radio frequency identification sensor sends out a radio frequency signal to the internet of things server, which is an even number of radio frequency signals, and the internet of things server judges that the forklift leaves the loading position.

In the technical scheme provided by the embodiment, the radio frequency identification sensor is arranged in the loading/unloading area, and the server judges whether the forklift is at/away from the current position according to the odd-number/even-number wireless radio frequency signals sent by the radio frequency identification sensor. In consideration of the positioning accuracy provided by the GPS when the forklift works in a warehouse under an indoor environment, whether the forklift is in a loading/unloading position cannot be accurately judged, so that whether the forklift passes through the current position is sensed by adopting the radio frequency identification sensor, whether the forklift is in the current position can be accurately and timely judged, the workload of a forklift driver in the actual working process is accurately reflected, and the effect of improving the statistical accuracy of the workload of the forklift driver by the Internet of things is further achieved.

Referring to fig. 3, in the second embodiment, based on any one of the embodiments, the step S40 includes:

step S41, obtaining a first statistical weight corresponding to the driving mileage, a second statistical weight corresponding to the freight volume and/or a third statistical weight corresponding to the single freight duration;

step S42, determining the workload according to the driving mileage and the first statistical weight counted in the preset period, the shipment amount counted in the preset period, the second statistical weight, and/or the average shipment duration counted in the preset period during the single shipment duration, the shipment trip number, and the third statistical weight.

As an optional embodiment, in this embodiment, a statistical weight is set for each workload statistical data, and after each workload statistical data is converted into a corresponding workload value, the workload values are multiplied by the corresponding statistical weights and added to determine the current workload of the forklift.

For example, assuming that the preset period is a working day, the working condition recorded on a forklift is: the driving mileage is 100 kilometers in total, the freight quantity is 100 tons in total, the freight trip number is 36, and the average freight duration is as follows: 15 minutes.

Assuming that the first statistical weight of the driving distance is 0.4, the second statistical weight of the cargo quantity is 0.3, and the third statistical weight of the single cargo duration is 0.3.

Then, the calculation workload is: 500 0.4+100×0.3+ (15×36) ×0.3=392

That is, the workload of the forklift in this working day is 392.

Further, in this embodiment, after calculating the workload of the forklift, the platform may set performance and/or deduction corresponding to the workload to calculate salary according to the workload of the forklift driver.

In the technical scheme provided by the embodiment, each piece of work load statistical data is provided with a statistical weight, after each piece of work load statistical data is converted into a corresponding work load value, the corresponding statistical weights are multiplied by the work load values and added to determine the current work load of the forklift, and the work load of a forklift driver in the actual working process is reflected more accurately by adopting a multi-dimensional work load statistical mode, so that the effect of improving the work load statistical precision of the forklift driver by the Internet of things is achieved.

Referring to fig. 4, in a third embodiment, before step S30, based on any embodiment, the method further includes:

step S100, acquiring the jump times of pulse signals generated by rotation of a motor in a period corresponding to a preset movement distance of a forklift;

step S110, determining the rotation turns of the motor according to the jump times;

step S120, determining the rotation number of the hub in the process of moving the forklift for the preset distance according to the rotation number of the motor and the transmission parameters;

and step S130, determining the circumference of the tire corresponding to the forklift according to the preset distance and the rotation number of the hub.

As an alternative embodiment, in order to further improve the statistical accuracy of the workload, since the driving mileage in the workload is calculated based on the tire circumference, and the surface of the tire is easily worn due to the heavy weight of the load on the forklift during the driving of the forklift, so as to reduce the tire radius, at this time, if the tire circumference parameter when the tire leaves the factory is used to calculate the workload, an error occurs, and therefore, in this embodiment, a method for the internet of things server to obtain the current tire circumference of the forklift is further provided.

In this embodiment, the server can monitor the position change of the forklift, and the pulse signal generated when the internal motor of the forklift rotates in the position change process of the forklift, and can record the jump frequency of the pulse signal.

In this embodiment, the pulse signal generated by the rotation of the motor is a signal generated by one rotation of the motor, that is, the pulse signal jumps once every time the motor rotates one rotation.

In this embodiment, the server correspondingly confirms the number of turns of the motor according to the number of pulse signals by the pulse signals output when the synchronous motor rotates.

As an alternative implementation, a sensor module capable of communicating with a server is arranged at the motor of the forklift, and the server acquires pulse signals generated by the motor of the forklift through the sensor module. The sensor module can convert pulse signals of the motor into digital signals or analog signals, then the signals are transmitted to the server through a communication protocol, and the server acquires pulse signals generated by rotation of the motor of the forklift through analyzing the signals transmitted by the sensor module.

In this embodiment, the server uses the number of hops of the pulse signal generated by the rotation of the motor as the number of revolutions of the motor during the movement of the forklift by the preset distance in a period corresponding to the movement of the forklift by the preset distance.

In this embodiment, after determining the number of turns of the motor, according to the number of turns of the motor and the transmission parameters, the number of turns of the hub corresponding to the forklift in the process of moving the preset distance is determined.

It should be noted that, because the motor of the forklift continuously rotates during the moving process, the ratio between the rotation number of the forklift and the rotation number of the wheel hub of the tire is a constant value, that is, the transmission ratio between the rotation number of the motor and the rotation number of the wheel hub can be measured in advance. And the hub is used as a cylindrical metal part of which the inner profile of the tire supports the tire and is arranged on the shaft in the center, so that the hub is not worn and deformed during normal running of the forklift. Therefore, in this embodiment, based on a certain transmission ratio, the number of wheel hub rotations corresponding to the number of motor rotations when the forklift moves by a preset distance is determined.

In this embodiment, after determining the number of turns of the hub corresponding to the preset distance for the forklift to move, the circumference of the tire can be determined according to the number of turns of the hub and the preset distance.

As an alternative embodiment, the preset distance is set to be H, the rotation number of the hub is set to be N, and the circumference of the tire to be calculated is set to be L

The entire flow is exemplarily described below:

Assuming that the preset distance is 2M, assuming that the forklift starts to travel 2M from rest, taking the initial moment T0 when the forklift starts to generate speed, taking the final moment T1 after the forklift moves 2M distance, acquiring pulse signal jump times of the motor in the period of [ T0, T1] to be 500 turns by the server, determining that the number of turns of the motor is 50 turns in the process, and assuming that the transmission ratio between the number of turns of the motor and the number of turns of the hub is 10:1, namely, the motor rotates 10 turns, the hub rotates one turn, the number of turns of the hub is determined to be 5 turns, and the calculated circumference of the tire is 0.4m.

In the technical scheme provided by the embodiment, the number of motor rotation turns is determined through the pulse signal jump times generated by the rotation of the motor after the forklift moves a known preset distance, the wheel hub rotation turns corresponding to the preset distance are determined according to the motor rotation turns, and the circumference of the tire is calculated according to the preset distance and the wheel hub rotation turns. The tire perimeter of the forklift can be calculated only by collecting the jump times of the motor pulse signals on the forklift, and related data of the forklift is not required to be collected in the field or corresponding measuring fields are not required to be set, so that the internet of things server serving as a background can acquire the tire perimeter of the forklift supervised under the internet of things server at any time and at any place, and the driving mileage in the workload can be calculated more accurately according to the tire perimeter of the current forklift.

Referring to fig. 5, in the fourth embodiment, after step S130, based on any embodiment, the method further includes:

step S140, determining a numerical difference between the tire circumference and a standard tire circumference of the forklift;

step S150, calculating the abrasion coefficient of the forklift based on the numerical value difference;

step S160, calibrating the workload according to the abrasion coefficient.

As an alternative embodiment, in order to further improve the calculation accuracy of the workload, in this embodiment, the internet of things server calculates the wear coefficient of the forklift according to the obtained numerical difference between the tire circumference and the standard tire circumference of the forklift, and calibrates the workload according to the wear coefficient.

In this embodiment, the wear severity of the forklift in the working process is reflected according to the numerical difference between the current tire circumference of the forklift and the standard tire circumference of the forklift. When fork truck's coefficient of wear is great, mean that fork truck's degree of wear is great, then fork truck is when the driver is driving the fork truck, the fork truck work load that calculates can be less than its actual work load, and fork truck that degree of wear is big also can bring the increase of certain degree of difficulty when driving, consider this aspect, propose to calibrate the work load according to fork truck's coefficient of wear in this embodiment, when fork truck's coefficient of wear is great, the work load that obtains according to the appropriate increase of certain proportion, when fork truck's coefficient of wear is less, also can suitably reduce the work load, in order to balance the work load that calculates between each fork truck.

In the technical scheme provided by the embodiment, in order to further improve the calculation accuracy of the workload, in the embodiment, the internet of things server calculates the abrasion coefficient of the forklift according to the obtained numerical difference between the circumference of the tire and the circumference of the standard tire of the forklift, then calibrates the workload according to the abrasion coefficient of the forklift, properly increases the obtained workload according to a certain proportion when the abrasion coefficient of the forklift is larger, and properly reduces the workload when the abrasion coefficient of the forklift is smaller so as to balance the calculated workload among the forklifts, thereby calculating the driving mileage in the workload according to the circumference of the tire of the current forklift more accurately and improving the statistical accuracy of the workload.

Further, in this embodiment, the step S150 includes:

step S151, determining a driving behavior evaluation value associated with the forklift within a preset history time, and determining the abrasion coefficient according to the driving behavior evaluation value and the numerical value difference; and/or the number of the groups of groups,

step S152, determining a tire state evaluation value of the forklift in the current state, and determining the wear coefficient according to the tire state evaluation value and the numerical difference.

Optionally, it is further proposed in the present embodiment how to calculate the wear coefficient of the tire, and three ways of calculating the wear coefficient are further provided in the present embodiment:

1. the wear coefficient is calculated based on the operating driving dimension of the driver, i.e. the wear coefficient is calculated from the driving behavior evaluation value and the numerical difference. The driving behavior evaluation value is characterized as an evaluation value which is comprehensively determined after the server evaluates and scores various driving behaviors in the driving process of the driver.

As an alternative implementation scheme, the driving behavior evaluation value is full initially, the identity information of a driver needs to be input when the driver drives the forklift, the server determines the identity of the driver according to the identity information, monitors the behavior of the driver in the forklift driving process, and when the driver makes some illegal behaviors judged by the server in the driving process, the server deducts the corresponding scores of the illegal behaviors.

The lower the driving behavior evaluation value is, the higher the driving risk coefficient of the driver in the driving process of the forklift is, when the abrasion coefficient is calculated, the obtained abrasion coefficient is higher than the abrasion coefficient which is the same in numerical value difference but higher in driving behavior evaluation value, so that the situation that the forklift tire can bear the illegal behaviors occurring in the driving process of the driver is ensured.

Optionally, the act for evaluating the driver includes: the vehicle steering system comprises the following components of vehicle overweight times, vehicle overweight amplitude, vehicle emergency brake times, vehicle overspeed duration, vehicle overspeed proportion and vehicle emergency steering times, wherein the vehicle emergency brake times are characterized by times that the instantaneous acceleration of the vehicle is larger than an acceleration threshold value with a negative value, and the vehicle emergency steering times are characterized by times that the steering wheel steering exceeds a preset steering threshold value when the speed of the vehicle is larger than a preset speed threshold value.

Exemplary acceleration threshold is-15 m/s ² The preset speed threshold is 20km/h and the preset steering threshold is 90 degrees, which can be adjusted according to the actual requirements, which is only exemplary data here.

In the present embodiment, the calculation of the driving behavior evaluation value may include the steps of:

the method comprises the steps of obtaining historical overweight times, historical overweight amplitude, historical sudden braking times, historical vehicle overspeed duration, historical vehicle overspeed proportion and historical vehicle sudden steering times of a driver in a preset historical duration, and obtaining a first weight value corresponding to the historical overweight times, a second weight value corresponding to the historical overweight amplitude, a third weight value corresponding to the historical sudden braking times, a fourth weight value corresponding to the historical vehicle overspeed duration, a fifth weight value corresponding to the historical vehicle overspeed proportion and a sixth weight value corresponding to the historical vehicle sudden steering times.

And calculating the driving behavior evaluation value according to the historical overweight times, the first weight value, the historical overweight amplitude, the second weight value, the historical sudden braking times, the third weight value, the historical vehicle overspeed duration, the fourth weight value, the historical vehicle overspeed proportion, the fifth weight value, the historical vehicle sudden steering times and/or the sixth weight value.

In addition, the method for evaluating the behavior of the driver can further comprise some illegal behaviors, which can cause potential safety hazards, of the driver, wherein the illegal behaviors are acquired by the shooting device arranged on the driving position on the forklift, and the illegal behaviors comprise but are not limited to: smoking, both hands leaving the steering wheel for more than a preset period of time (2 seconds), etc.

2. The wear coefficient is calculated from the tire condition evaluation value and the numerical difference. The tire state evaluation value server calculates the generated evaluation value representing the tire wear state by inputting the tire state parameters into the tire wear model.

As an alternative embodiment, the parameters for evaluating the tire condition include static parameters and/or dynamic parameters.

In the present embodiment, the static parameters include at least one of tire pressure, tire usage time, tire mileage, tire temperature, tire tread depth, and tire load, characterized as parameters acquired while the tire is stationary.

The evaluation of the static parameters may comprise the steps of:

firstly, determining at least one of the tire pressure difference between the tire pressure and the tire pressure threshold value, the time difference between the tire service time and the tire departure time, whether the tire temperature is in a temperature interval, the similarity between the tire pattern depth and the pattern depth when the tire is at the departure time, and the load difference between the tire load and the load threshold value.

If the tire pressure difference is larger than the tire pressure difference threshold value, the time difference is larger than the time threshold value, the tire temperature is not in the temperature interval, the similarity is smaller than the similarity threshold value, the load difference is larger than any one condition in the load difference threshold value, and the corresponding tire state evaluation value is deducted.

In this embodiment, the dynamic parameters include: at least one of a positive peak and/or a negative peak appearing in the circumferential acceleration waveform obtained by differentiating the time-series waveform of the circumferential acceleration of the tire, a ratio of a tire rest radius when the forklift tire is stationary to a tire movement radius when the tire is running, and a sliding distance when the forklift running speed is greater than a preset speed is characterized as a parameter obtained when the tire is running.

The evaluation of the dynamic parameter may comprise at least one of the following steps:

And determining a tire state evaluation value according to the number of positive peaks and/or the number of negative peaks appearing in the circumferential acceleration waveform, wherein the more the number of positive peaks and/or the more the number of negative peaks is, the more serious the deformation of the tire during acceleration is, and the lower the tire state evaluation value is.

And determining a tire state evaluation value according to the ratio of the radiuses, wherein the smaller the ratio of the radiuses is, the greater the degree of shrinkage of the tire during the operation of the forklift is, the more the risk of tire burst is likely to occur, and the lower the tire state evaluation value is.

And determining a tire state evaluation value according to the sliding distance, wherein the farther the sliding distance is, the lower the tire friction force is, the more serious the abrasion is, and the lower the tire state evaluation value is.

It is to be understood that, if the above-described steps of the plurality of dynamic parameters are selected to calculate the tire condition evaluation value, the tire condition evaluation values obtained in the respective steps are given corresponding weight values, and the tire condition evaluation values in the respective steps are multiplied by the weight values and added to obtain the integrated tire condition evaluation value.

It can be understood that if one or both of the dynamic parameter and the static parameter are selected to calculate the tire condition evaluation value, a weight value corresponding to each of the different tire condition evaluation values is also given, and each of the tire condition evaluation values is multiplied by the weight value and added to obtain the integrated tire condition evaluation value.

The lower the tire condition evaluation value, the higher the risk of the tire, and the higher the wear coefficient is calculated, the higher the wear coefficient obtained is than the wear coefficient having the same numerical value difference but the lower the tire condition evaluation value is, so as to reduce the risk of the tire being worn out when the tire is used for a long time.

Finally, the wear coefficient is calculated from the driving behavior evaluation value and the numerical difference between the tire circumference and the standard tire circumference, and/or the numerical difference between the tire circumference and the standard tire circumference.

Alternatively, the determination of the wear coefficient may include the following:

wear coefficient=numerical difference (ratio of driving behavior evaluation value to driving behavior evaluation sample value);

wear coefficient=numerical difference (ratio of tire state evaluation value to tire state evaluation sample value);

wear coefficient=numerical difference (ratio of tire state evaluation value to tire state evaluation sample value) ×tire state weight coefficient+numerical difference (ratio of driving behavior evaluation value to driving behavior evaluation sample value) ×driving behavior weight coefficient.

In the technical scheme provided by the embodiment, besides the numerical difference between the circumference of the tire and the circumference of the standard tire, the driving behavior evaluation value and/or the tire state evaluation value are introduced to calculate the abrasion coefficient of the tire, so that the workload of the forklift is verified according to the abrasion coefficient, and the workload statistical precision is further improved.

Furthermore, it will be appreciated by those of ordinary skill in the art that implementing all or part of the processes in the methods of the above embodiments may be accomplished by computer programs to instruct related hardware. The computer program comprises program instructions, and the computer program may be stored in a storage medium, which is a computer readable storage medium. The program instructions are executed by at least one processor in the internet of things server to implement the flow steps of the embodiments of the method described above.

Therefore, the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a forklift workload determination program based on the internet of things, and the forklift workload determination program based on the internet of things realizes the steps of the forklift workload determination method based on the internet of things according to the embodiment when being executed by a processor.

The computer readable storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk, etc. which may store the program code.

It should be noted that, because the storage medium provided in the embodiments of the present application is a storage medium used to implement the method in the embodiments of the present application, based on the method described in the embodiments of the present application, a person skilled in the art can understand the specific structure and the modification of the storage medium, and therefore, the description thereof is omitted herein. All storage media used in the methods of the embodiments of the present application are within the scope of protection intended in the present application.

It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names.

While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.

It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims

1. The forklift workload determining method based on the Internet of things is characterized by being applied to an Internet of things server and comprises the following steps of:

Acquiring the jump times of pulse signals generated by rotation of a motor in a period corresponding to a preset movement distance of the forklift; determining the number of turns of the motor according to the jump times; according to the rotation number of the motor and the transmission parameters, determining the rotation number of the hub in the process of moving the forklift by the preset distance;

determining the circumference of the tire corresponding to the forklift according to the preset distance and the rotation number of the hub; determining a numerical difference between the tire circumference and a standard tire circumference of the forklift;

the method comprises the steps of obtaining historical overweight times, historical overweight amplitude, historical sudden braking times, historical vehicle overspeed duration, historical vehicle overspeed proportion and historical vehicle sudden steering times of a driver in preset historical duration, and a first weight value corresponding to the historical overweight times, a second weight value corresponding to the historical overweight amplitude, a third weight value corresponding to the historical sudden braking times, a fourth weight value corresponding to the historical vehicle overspeed duration, a fifth weight value corresponding to the historical vehicle overspeed proportion and a sixth weight value corresponding to the historical vehicle sudden steering times;

according to the historical overweight times, the first weight value, the historical overweight amplitude, the second weight value, the historical emergency brake times, the third weight value, the historical vehicle overspeed duration, the fourth weight value, the historical vehicle overspeed proportion, the fifth weight value, the historical vehicle emergency steering times and/or the sixth weight value, calculating a driving behavior evaluation value associated with the forklift within a preset historical duration, wherein the initial value of the driving behavior evaluation value is full, and when a driver makes an illegal action in the driving process, the driving behavior evaluation value is withheld according to branches corresponding to the illegal action;

Determining a tire state evaluation value of the forklift in a current state, and determining a wear coefficient according to the tire state evaluation value and the numerical value difference, and the driving behavior evaluation value and the numerical value difference, wherein a calculation formula of the wear coefficient comprises: wear coefficient=numerical difference (ratio of tire state evaluation value to tire state evaluation sample value) ×tire state weight coefficient+numerical difference (ratio of driving behavior evaluation value to driving behavior evaluation sample value) ×driving behavior weight coefficient;

according to the abrasion coefficient, the workload of a forklift driver is increased or reduced so as to balance the calculated workload among all forklifts and improve the statistical accuracy of the workload;

determining a single cargo carrying time length of the forklift according to the interval time, determining a corresponding driving mileage of the forklift in the single cargo carrying time length according to the pulse signal jump times and the tire circumference of the forklift, and determining a corresponding cargo carrying amount of the forklift in the single cargo carrying time length according to a pressure change value, wherein the interval time is the single cargo carrying time length;

acquiring a first statistical weight corresponding to the driving mileage, a second statistical weight corresponding to the freight volume and a third statistical weight corresponding to the single freight duration;

And determining the workload of the forklift driver according to the travel mileage and the first statistical weight counted in the preset period, the freight volume and the second statistical weight counted in the preset period, and the average freight duration, freight trip number and the third statistical weight counted in the preset period of the single freight duration.

2. The method for determining the workload of a forklift based on the internet of things according to claim 1, wherein the first state is a carrying state, the second state is an empty state, and the step of acquiring the corresponding interval time in the process of switching the loading state of the forklift from the first state to the second state further comprises:

3. The method for determining workload of a forklift based on the internet of things according to claim 1 or 2, wherein the first state is an empty state, the second state is a carrying state, and the step of acquiring a corresponding interval time in the process of switching the loading state of the forklift from the first state to the second state further comprises:

4. The forklift workload determination method based on the internet of things of claim 3, further comprising:

5. The utility model provides an thing networking server which characterized in that, thing networking server includes: the method for determining the forklift workload based on the internet of things comprises a memory, a processor and a forklift workload determining program based on the internet of things, wherein the forklift workload determining program based on the internet of things is stored in the memory and can run on the processor, and the steps of the forklift workload determining method based on the internet of things according to any one of claims 1 to 4 are realized when the forklift workload determining program based on the internet of things is executed by the processor.

6. A computer-readable storage medium, wherein the computer-readable storage medium stores a forklift workload determination program based on the internet of things, and the forklift workload determination program based on the internet of things realizes the steps of the forklift workload determination method based on the internet of things according to any one of claims 1 to 4 when being executed by a processor.