A multi-data-dimension flow crane equipment utilization calculation method

Abstract

The application discloses a kind of multi-data dimension flow hoisting machinery equipment utilization rate calculation method, comprising the following steps: S1, hoisting machinery equipment, internet of things data collector starts;S2, internet of things platform obtains internet of things data collector data;S3, internet of things platform judges whether the off-car speed of hoisting machinery equipment is greater than set threshold value, if meet, hoisting machinery equipment is in mobile state, and count into effective working time;S4, continue to judge whether the engine speed of hoisting machinery equipment is greater than set threshold value, if not meet, then count into parking waiting time;S5, continue to judge whether the main arm angle and actual weight of hoisting machinery equipment are greater than respective set threshold value, if meet, then count into effective working time;If not meet, then count into parking waiting time;S6, calculate the utilization rate of hoisting machinery equipment.The application creates a kind of flow machine utilization rate algorithm in combination with the actual use scene and each type working condition mode of flow hoisting machinery equipment.

Description

Technical Field

[0001] The present invention relates to a method for calculating the utilization rate of mobile mechanical equipment, and more specifically, to a method for calculating the utilization rate of mobile lifting mechanical equipment in multiple data dimensions. Background Art

[0002] Existing methods for calculating the utilization rate of mobile cranes typically involve manual reporting or using conventional algorithm models to collect parameters such as equipment power-on and power-off times and wheel speeds to determine whether the equipment is in use. However, these methods cannot accurately and reliably reflect all real-world utilization conditions and also have the following problems and shortcomings:

[0003] 1) Low data accuracy. The human factors in the conventional manual data filling may lead to omissions, errors or exaggeration of the actual utilization rate, resulting in a large discrepancy between the statistical utilization rate and the actual equipment usage;

[0004] 2) Data is not real-time, usually manually filled in afterwards, and the recorded data feedback is delayed and timeliness is poor;

[0005] 3) There is a lot of human subjectivity, and reporting personnel often deviate from the actual equipment utilization data in order to cope with assessments;

[0006] 4) Poor vehicle compatibility, unable to meet the data collection requirements of new and old equipment of different brands and models;

[0007] 5) Low scenario coverage. Conventional algorithm models have insufficient recognition coverage for effective working conditions in all real scenarios. They ignore the combination of dynamic factors such as "get-off speed", "engine speed", "oil pressure", "main arm angle", "torque percentage", "actual weight", and the logical judgment of various working conditions that are coupled and verified with each other. They are unable to meet the needs of identifying and counting the working conditions used in all real scenarios of the acquisition equipment, which is not conducive to improving the value chain lean management to maximize the utilization of equipment assets. Summary of the Invention

[0008] In response to the defects in the existing technology, the purpose of the present invention is to provide a multi-data dimension mobile lifting machinery utilization calculation method, which adopts the Internet of Things (IOT) technology, combines the actual usage scenarios and various types of working modes of mobile lifting machinery, and combines the actual production conditions at the base to create an algorithm for mobile machine utilization.

[0009] To achieve the above object, the present invention adopts the following technical solutions:

[0010] A multi-data-dimensional method for calculating the utilization rate of mobile lifting machinery and equipment includes the following steps:

[0011] S1, the lifting machinery equipment is started, and the IoT data collector is also started simultaneously;

[0012] S2, the Internet of Things platform obtains data from the Internet of Things data collector;

[0013] S3, the IoT platform determines whether the dismounting speed of the lifting equipment is greater than a set threshold. If so, the lifting equipment is in a moving state, and the engine is in a warm-up state and the effective working time is counted. If not, the process proceeds to step S4;

[0014] S4, continue to determine whether the engine speed of the lifting machinery equipment is greater than the set threshold, and at the same time use the threshold corresponding to the oil pressure for mutual redundancy check. If it does not meet the requirements, the engine is in the engine off state and the parking waiting time is calculated; if it meets the requirements, proceed to step S5;

[0015] S5, continue to determine whether the main arm angle and actual weight of the lifting machinery equipment are greater than the respective set thresholds, or determine whether the torque percentage is greater than the set threshold. If both are met, the engine is in the warm-up state and the effective working time is counted; if neither is met, the parking waiting time is counted;

[0016] S6, calculating the utilization rate of the lifting machinery equipment.

[0017] Preferably, the Internet of Things data collector establishes data communication with the lifting machinery equipment through a CAN protocol bus.

[0018] Preferably, the IoT data collector transmits data to the IoT platform via a 4G network using the JT808 protocol, with an upload frequency of 10 Hz.

[0019] Preferably, the calculation of step S6 is as follows:

[0020] The utilization rate of the lifting machinery equipment uses the accumulation of the effective working time of the lifting machinery equipment as the numerator of the utilization rate statistics, and the natural time within the statistical interval as the denominator.

[0021] Preferably, the utilization rate calculation formula of the lifting machinery equipment is as follows:

[0022] Daily utilization rate during working hours = Σeffective working time per hour during working period / 1h*100%;

[0023] Effective working time per hour = Σeffective working time within 5 minutes per hour;

[0024] Effective working time within 5 minutes = Σeffective working time within 10 seconds within every 5 minutes;

[0025] The monthly and annual utilization rates are calculated based on the daily utilization rate using the average function.

[0026] Preferably, the waiting rate of the lifting machinery equipment uses the accumulation of the waiting time of the lifting machinery equipment in the engine hot state and the engine off state as the numerator of the waiting rate statistics, and the natural time within the statistical interval as the denominator.

[0027] The multi-data-dimensional mobile lifting equipment utilization calculation method provided by the present invention has the following beneficial effects:

[0028] 1) The data obtained by the present invention is real-time and objective, avoiding errors, omissions and delays. The data of all channels of the calculation model are automatically collected and used in real time, without manual data entry. The data is authentic and reliable, avoiding factors affected by human intervention;

[0029] 2) The algorithm model of the present invention comprehensively considers various real-time data of mobile lifting machinery and can truly reflect the actual load and usage status of the equipment. The key data mainly collected and used by the algorithm model include: the ACC status of "crane power start", "get-off speed", "engine speed", "oil pressure", "main arm angle", "torque percentage", and "actual weight". Through the coupling verification analysis of key data and the judgment of the working conditions of various utilization scenarios, it is possible to identify and count whether the mobile lifting machinery is in an effective working state and calculate it within the utilization rate, thereby ensuring the authenticity and effectiveness of the equipment utilization rate;

[0030] 3) The present invention integrates multi-sensor data to dynamically reflect the true multi-dimensional utilization of equipment, such as real-time / daily / monthly / yearly utilization, enterprise-wide or base-by-base utilization, and utilization of mobile lifting machinery of different brands and models at each base. The granularity can be adjusted as needed. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a flow chart of the method for calculating the utilization rate of mobile lifting machinery and equipment with multiple data dimensions of the present invention. DETAILED DESCRIPTION

[0032] In order to better understand the above technical solutions of the present invention, the technical solutions of the present invention are further described below with reference to the accompanying drawings and embodiments.

[0033] Combine Figure 1 As shown, the present invention provides a multi-data dimension mobile lifting equipment utilization calculation method, comprising the following steps:

[0034] S1, the lifting machinery equipment is started, and the IoT data collector is also started synchronously. The IoT data collector establishes data communication with the lifting machinery equipment through the CAN protocol bus, and collects key data such as the ACC status of "lifting equipment power start", "getting off speed", "engine speed", "oil pressure", "main arm angle", "torque percentage", and "actual weight".

[0035] S2, the IoT data collector transmits data to the IoT platform via the 4G network using the JT808 (2011) protocol with an upload frequency of 10 Hz.

[0036] In S3, the IoT platform analyzes the operating conditions of various scenarios by coupling and verifying key data, identifying and calculating the utilization rate of mobile cranes. First, the ACC status of "Crane Power On" is used to determine whether the vehicle is powered on and all sensors are operating. Next, the platform determines whether the crane's disembarkation speed exceeds a set threshold. If so, the crane is in motion, indicating that the engine is warming up and effective operating time is counted. If not, the platform proceeds to step S4.

[0037] S4, continue to determine whether the engine speed of the lifting machinery equipment is greater than the set threshold, and at the same time use the threshold corresponding to the oil pressure for mutual redundancy check. If it does not meet the requirements, the engine is in the off state and the parking waiting time is included; if it meets the requirements, enter step S5.

[0038] S5, continue to judge whether the main arm angle and actual weight of the lifting machinery equipment are greater than the respective set thresholds, or judge whether the torque percentage is greater than the set threshold. If both are met, it is in the engine hot state and is included in the effective working time; if both are not met, it is included in the parking waiting time.

[0039] S6, calculate the utilization rate of lifting machinery and equipment.

[0040] The utilization rate of lifting machinery and equipment takes the accumulation of effective working time of lifting machinery and equipment as the numerator of utilization rate statistics, and the natural time within the statistical interval as the denominator.

[0041] The waiting rate of lifting machinery and equipment is calculated by taking the cumulative waiting time of the lifting machinery and equipment in the engine hot state and the engine off state as the numerator of the waiting rate statistics, and the natural time within the statistical interval as the denominator.

[0042] In the multi-dimensional mobile crane utilization calculation method of this invention, the IoT platform uses real-time collected data to construct a calculation model, comprehensively considering the real-time data from various sensors to calculate the real-time utilization of mobile cranes. The data collector collects data every 10 seconds and accumulates it to calculate the utilization rate every 5 minutes. The numerator is the utilization time determined by the algorithm within each 10-second period of 5 minutes, and the denominator is 5 minutes. The utilization calculation formula for crane equipment is as follows:

[0043] Daily utilization rate during working hours = Σeffective working time per hour during working period / 1h*100%;

[0044] Effective working time per hour = Σeffective working time within 5 minutes per hour;

[0045] Effective working time within 5 minutes = Σeffective working time within 10 seconds within every 5 minutes;

[0046] The algorithm for calculating monthly and annual utilization rates is based on daily utilization using the average function. The algorithm for calculating waiting rates for hot and off engine states is similar. Equipment utilization outside of working hours (based on individual unit schedules) is included in overtime statistics but is not counted in utilization and waiting rates. This algorithm uses a scheduler to perform periodic calculations, collecting data every 10 seconds and calculating every 5 minutes. The results are stored in a real-time database.

[0047] Mobile hoisting equipment is generally divided into two parts: loading and unloading. Different operating conditions require different utilization status identification. The utilization algorithm in the multi-data-dimensional mobile hoisting equipment utilization calculation method of the present invention not only considers the ACC state (when the hoisting equipment is powered on), but also comprehensively considers the loading and unloading states. If the unloading state is moving (unloading speed > 5 km / h), it is also considered an effective working state. If the unloading state is stationary, it is necessary to perform a coupled verification based on real-time data such as "engine speed," "oil pressure," and "main arm angle" in conjunction with the loading state to comprehensively identify all operating conditions and compile statistical utilization statistics.

[0048] Those skilled in the art should recognize that the above embodiments are merely intended to illustrate the present invention and are not intended to limit the present invention. As long as they are within the spirit of the present invention, any changes or modifications to the above embodiments will fall within the scope of the claims of the present invention.

Claims

1. A multi-data dimension mobile lifting equipment utilization calculation method, characterized in that: The following steps are involved: S1, the lifting machinery equipment is started, and the IoT data collector is also started simultaneously; S2, the Internet of Things platform obtains data from the Internet of Things data collector; S3, the IoT platform determines whether the dismounting speed of the lifting equipment is greater than a set threshold. If so, the lifting equipment is in a moving state, and the engine is in a warm-up state and the effective working time is counted. If not, the process proceeds to step S4; S4, continue to determine whether the engine speed of the lifting machinery equipment is greater than the set threshold, and at the same time use the threshold corresponding to the oil pressure for mutual redundancy check. If it does not meet the requirements, the engine is in the engine off state and the parking waiting time is calculated; if it meets the requirements, proceed to step S5; S5, continue to determine whether the main arm angle and actual weight of the lifting machinery equipment are greater than the respective set thresholds, or determine whether the torque percentage is greater than the set threshold. If both are met, the engine is in the warm-up state and the effective working time is counted; if neither is met, the parking waiting time is counted; S6, calculating the utilization rate of the lifting machinery equipment.

2. The multi-data-dimensional mobile hoisting equipment utilization calculation method according to claim 1, characterized in that: The Internet of Things data collector establishes data communication with the lifting machinery equipment through the CAN protocol bus.

3. The multi-data-dimensional mobile hoisting equipment utilization calculation method according to claim 1, characterized in that: The IoT data collector transmits data to the IoT platform via the 4G network using the JT808 protocol, with an upload frequency of 10 Hz.

4. The multi-data dimension mobile lifting equipment utilization calculation method according to claim 1 is characterized in that: The calculation of step S6 is as follows: The utilization rate of the lifting machinery equipment uses the accumulation of the effective working time of the lifting machinery equipment as the numerator of the utilization rate statistics, and the natural time within the statistical interval as the denominator.

5. The multi-data dimension mobile lifting equipment utilization calculation method according to claim 4 is characterized in that: The calculation formula for the utilization rate of the lifting machinery equipment is as follows: Daily utilization rate during working hours = Σeffective working time per hour during working period / 1h*100%; Effective working time per hour = Σeffective working time within 5 minutes per hour; Effective working time within 5 minutes = Σeffective working time within 10 seconds within every 5 minutes; The monthly and annual utilization rates are calculated based on the daily utilization rate using the average function.

6. The multi-data-dimensional mobile hoisting equipment utilization calculation method according to claim 4, characterized in that: The waiting rate of the lifting machinery equipment is calculated by taking the accumulated waiting time of the lifting machinery equipment in the engine hot state and the engine off state as the numerator of the waiting rate statistics, and the natural time within the statistical interval as the denominator.

Images