Online analysis method and system for multi-dimensional operation data of electric forklift

Abstract

The invention discloses an online analysis method and system for multi-dimensional operation data of an electric forklift, and the method comprises the steps: firstly obtaining original multi-dimensional operation data of the electric forklift, carrying out the preprocessing of the original multi-dimensional operation data, calculating the working efficiency based on the preprocessed multi-dimensional operation data of the electric forklift through the cargo quality, the carrying stroke and the carrying time, and carrying out the online analysis of the multi-dimensional operation data of the electric forklift; walking energy consumption, lifting energy consumption and other energy consumption are calculated through integration of instantaneous voltage and current of each component on time, and endurance duration is estimated through historical workload, working efficiency and battery SOC. And finally, taking the acceleration, the energy consumption ratio, the working saturation and the residual working duration of the electric forklift as online analysis results of the multi-dimensional operation data of the electric forklift, and carrying out visualization. The real-time performance of electric forklift operation data analysis is improved, and the dimensionality of electric forklift operation data acquisition is improved.

Description

Technical Field

[0001] The present invention belongs to the field of data processing, and in particular relates to an online analysis method and system for multi-dimensional operation data of an electric forklift. Background Art

[0002] The real-time operating status, working efficiency, energy consumption and endurance of electric forklifts determine the efficiency and cost of the company's production activities, and also reflect the performance indicators of forklifts. It is one of the factors that enterprises and R&D personnel are most concerned about. The operating status of electric forklifts includes: vehicle speed, acceleration, direction of travel, cargo mass, travel motor speed, travel motor torque, travel motor voltage, travel motor current, travel motor temperature, oil pump motor speed, oil pump motor torque, oil pump motor voltage, oil pump motor current, oil pump motor temperature, battery voltage, battery current, battery temperature and other information. The working efficiency of electric forklifts can be expressed as: how much cargo is carried per unit time and how far. The energy consumption of electric forklifts can be divided into travel energy consumption, lifting energy consumption and other energy consumption. Travel energy consumption is used to drive the forklift forward or backward, which can be expressed as how much energy is consumed per unit distance; lifting energy consumption is used to increase or decrease the cargo, which can be expressed as how much energy is consumed per unit mass of cargo to lift once; other energy consumption is used for steering assistance, fork posture adjustment, lights, horns, etc. The battery life of an electric forklift is the length of time it can continue to operate with the current remaining battery capacity.

[0003] Traditional methods of analyzing the operation data of electric forklifts are mostly offline analysis, that is, after reading the operation data of the electric forklift through the data acquisition equipment, the operation data is imported into the local computer for data analysis, which cannot be achieved in real time. In other words, data collection and data analysis are carried out separately. Enterprises and users' analysis of the operation data of electric forklifts always lags behind the time when the data is generated, and they cannot obtain the data analysis results in the first time and intervene in production activities in time.

[0004] Most electric forklift transparent transmission gateway cloud platforms only have data storage functions, but no data analysis functions; the data collected by most transparent transmission gateway cloud platforms have low dimensions and fail to include all the operating conditions of electric forklifts; the data collected by most transparent transmission gateway cloud platforms have low sampling frequencies, usually once every 1 to 30 seconds, usually less than 1Hz, or even up to 1 / 30Hz. The low sampling frequency makes it impossible to conduct effective data analysis on the collected data. The main purpose of the existing online analysis methods for electric forklifts is to monitor whether the electric forklift is online, whether there are faults, whether the operation is safe, etc. The main functions are only the statistics of the online rate of electric forklifts, fault diagnosis, and cargo detection, etc., and do not involve the calculation and analysis of work efficiency, energy consumption, and battery life. In addition, since the calculation of the work efficiency and energy consumption of electric forklifts requires comprehensive and high-sampling rate raw data, data with low dimensions and low sampling frequencies make it impossible to calculate work efficiency and energy consumption. Summary of the invention

[0005] The purpose of the present invention is to solve the problems existing in the prior art and to provide an online analysis method and system for multi-dimensional operation data of an electric forklift.

[0006] In order to achieve the above-mentioned invention object, the present invention specifically adopts the following technical solutions:

[0007] In a first aspect, the present invention provides an online analysis method for multi-dimensional operation data of an electric forklift, which comprises the following steps:

[0008] S1. Obtaining original multi-dimensional operation data of an electric forklift and preprocessing the data to obtain preprocessed multi-dimensional operation data of an electric forklift;

[0009] S2. For the pre-processed multi-dimensional operation data of the electric forklift, the acceleration of the electric forklift is calculated according to the timestamp and vehicle speed data, the travel motor power is calculated according to the travel motor voltage and the travel motor current, the oil pump motor power is calculated according to the oil pump motor voltage and the oil pump motor current, and the battery power is calculated according to the battery voltage and the battery current;

[0010] S3, identifying the start timestamp and end timestamp of each lifting process in the pre-processed multi-dimensional operation data of the electric forklift according to the lifting segment division algorithm, taking a segment between a start timestamp and an end timestamp in the lifting process as a lifting segment, and obtaining the mass of the goods lifted in each lifting segment by the average speed and average torque of the oil pump motor in each lifting segment through a table lookup method;

[0011] S4, identifying the start timestamp and end timestamp of each walking process in the preprocessed electric forklift multidimensional operation data according to the walking segment division algorithm, taking a segment between a start timestamp and an end timestamp in the walking process as a walking segment, and calculating the mileage and driving duration of each walking segment based on the timestamp in each walking segment and the speed of the electric forklift;

[0012] S5. Calculate the walking energy consumption corresponding to each walking segment according to the walking motor power, calculate the lifting energy consumption corresponding to each lifting segment according to the oil pump motor power, calculate the total battery energy consumption corresponding to each pre-processed electric forklift multi-dimensional operation data according to the battery power, add the walking energy consumption of all walking segments corresponding to each pre-processed electric forklift multi-dimensional operation data as the total walking energy consumption, add the lifting energy consumption of all lifting segments corresponding to each pre-processed electric forklift multi-dimensional operation data as the total lifting energy consumption, remove the total walking energy consumption and the total lifting energy consumption from the total battery energy consumption as other energy consumption, and calculate the energy consumption proportion of the total walking energy consumption, the total lifting energy consumption and other energy consumption in the total battery energy consumption;

[0013] S6. Calculate the theoretical working efficiency and actual working efficiency of the electric forklift within the preset time period according to the cargo mass, handling distance and handling time within the preset time period, and use the ratio of the actual working efficiency to the theoretical working efficiency as the working saturation of the electric forklift;

[0014] S7, calculating the remaining working time under the current battery state of charge according to the relationship between the actual working efficiency and the decrease in the battery state of charge;

[0015] S8. The acceleration of the electric forklift calculated by S2, the energy consumption ratio calculated by S5, the working saturation of the electric forklift calculated by S6, and the remaining working time calculated by S7 are used as online analysis results of the multi-dimensional operation data of the electric forklift and visualized.

[0016] Based on the above scheme, each step can be implemented in the following preferred specific manner.

[0017] As a preferred embodiment of the first aspect, in step S1, a vehicle networking intelligent transparent transmission gateway with a sampling frequency higher than 50 Hz is used to collect the original electric forklift multi-dimensional operation data in real time, and the original electric forklift multi-dimensional operation data is screened, and the timestamp, electric forklift speed, accelerator pedal opening, brake pedal opening, shift switch, reverse gear switch, accelerator pedal switch, brake pedal switch, lifting signal, VCU requested travel motor speed, travel motor actual speed, travel motor temperature, travel motor voltage, travel motor current, travel motor torque, VCU requested oil pump motor speed, oil pump motor actual speed, oil pump motor temperature, oil pump motor voltage, oil pump motor current, oil pump motor torque, battery state of charge, battery voltage and battery current in the multi-dimensional operation data of the electric forklift are retained to obtain the screened electric forklift multi-dimensional operation data, and the screened electric forklift multi-dimensional operation data is obtained. The data is converted into a decimal number, and outlier detection is performed on the converted multi-dimensional operation data of the electric forklift. Then, data denoising is performed on the multi-dimensional operation data of the electric forklift after outlier detection. The data of one dimension in the multi-dimensional operation data of the electric forklift after denoising is selected as the reference data, the timestamp of the reference data is obtained and used as the reference timestamp, and the interpolation method is used to calculate the values ​​of the data of the remaining dimensions in the multi-dimensional operation data of the electric forklift after denoising at the reference timestamp, so that the interpolated data of the remaining dimensions are consistent with the length of the reference data, and the multi-dimensional operation data of the electric forklift with a unified timestamp is obtained. The intelligent transparent transmission gateway of the Internet of Vehicles wirelessly transmits the multi-dimensional operation data of the electric forklift with a unified timestamp to the transparent transmission gateway cloud platform. The transparent transmission gateway cloud platform classifies the received data according to the vehicle number and the collection time and packages and stores the classified data to obtain the pre-processed multi-dimensional operation data of the electric forklift.

[0018] As a preferred embodiment of the first aspect, in step S3, the specific process of obtaining the lifting segment according to the lifting segment division algorithm is as follows: in the process of lifting segment division, for the lifting signal in the pre-processed multi-dimensional operation data of the electric forklift, if the timestamp of the current traversal exceeds the data length of the lifting signal, the traversal is stopped and the divided lifting segment is output; if the timestamp of the current traversal does not exceed the data length of the lifting signal, two lifting signals with consecutive timestamps are searched backward from the timestamp of the current traversal, and when the lifting signal of the timestamp of the current moment is 0 and the lifting signal of the timestamp of the next moment is 1, the timestamp of the next moment is marked as is the start timestamp of the lifting segment, the timestamp of the first lifting signal being 0 after the start timestamp is searched and the timestamp before the timestamp is used as the end timestamp of the lifting segment, if the difference between the end timestamp and the start timestamp of the lifting segment exceeds the preset lifting segment threshold, a lifting segment is formed by the preprocessed multi-dimensional operation data of the electric forklift between the end timestamp and the start timestamp, if the difference between the end timestamp and the start timestamp of the lifting segment does not exceed the preset lifting segment threshold, two lifting signals with consecutive timestamps are searched again after the end timestamp until a lifting segment is formed.

[0019] As a preferred embodiment of the first aspect, in step S4, the specific process of obtaining the walking segment according to the walking segment division algorithm is as follows: in the process of walking segment division, for the electric forklift speed data in the pre-processed electric forklift multi-dimensional operation data, if the current traversal timestamp exceeds the data length of the electric forklift speed, the traversal is stopped and the divided walking segment is output; if the current traversal timestamp does not exceed the data length of the electric forklift speed, the electric forklift speed with two consecutive timestamps is searched backward from the current traversal timestamp, and when the electric forklift speed at the current timestamp is 0 and the electric forklift speed at the next timestamp is 1, the electric forklift speed at the next timestamp is output. The timestamp is marked as the starting timestamp of the walking segment. The first timestamp after the starting timestamp with the electric forklift speed being 0 is found and the timestamp before the timestamp is used as the ending timestamp of the walking segment. If the difference between the ending timestamp and the starting timestamp of the walking segment exceeds the preset walking segment threshold, the pre-processed electric forklift multi-dimensional operation data between the ending timestamp and the starting timestamp constitutes a walking segment. If the difference between the ending timestamp and the starting timestamp of the walking segment does not exceed the preset walking segment threshold, the electric forklift speeds with two consecutive timestamps are searched again after the ending timestamp until a walking segment is formed.

[0020] As a preferred embodiment of the first aspect, in step S4, the specific method of calculating the mileage and driving duration of each walking segment is as follows:

[0021] T=t end -t start

[0022]

[0023] Where: T is the driving time of the walking segment; t start and t end are the start and end timestamps of the walking segment respectively; S is the mileage of the walking segment; N t is the data length of each walking segment; v i and v i+1 are the speeds of the electric forklift at the i-th moment and the i+1-th moment, t i and t i+1 It is the timestamp of the i-th moment and the i+1-th moment.

[0024] As a preferred embodiment of the first aspect, in step S5, the walking energy consumption corresponding to the jth walking segment is Lifting energy consumption corresponding to the kth lifting segment The total battery energy consumption EC corresponding to each pre-processed electric forklift multi-dimensional operation data b And other energy consumption EC other The calculation method is as follows:

[0025]

[0026] Where: P ti , P pi , P bi are the instantaneous power of the travel motor, the instantaneous power of the oil pump motor, and the instantaneous power of the battery at the i-th moment; N p N is the length of data for each lifting segment; N is the length of multi-dimensional operation data of each pre-processed electric forklift; M t is the number of walking segments contained in each preprocessed multi-dimensional operation data of electric forklift; M p The number of lifting segments contained in the pre-processed multi-dimensional operation data of each electric forklift.

[0027] As a preferred embodiment of the first aspect, in step S6, the specific calculation method of the theoretical work efficiency and the actual work efficiency is:

[0028]

[0029] Where: m j , S j and T j are the mass of cargo lifted, mileage and driving time in the jth walking segment respectively; T set The length of the preset time period.

[0030] As a preferred embodiment of the above-mentioned first aspect, the specific method of obtaining the mass of the cargo lifted in the j-th walking segment is: starting from the starting timestamp of the j-th walking segment, look forward to the first lifting segment and use it as a reference lifting segment, and use the mass of the cargo lifted in the reference lifting segment as the mass of the cargo lifted in the j-th walking segment.

[0031] As a preferred embodiment of the first aspect, the remaining working time T r The specific calculation method is as follows:

[0032]

[0033] Where: W2 is the battery state of charge, which is the current battery state of charge value X% used to the minimum battery state of charge value X min % is the remaining workload of the electric forklift; W1 is the cumulative workload of the electric forklift when the battery state of charge is 100% and the current battery state of charge value is X%; γ is the discount coefficient.

[0034] In a second aspect, the present invention provides an online analysis system for multi-dimensional operation data of an electric forklift, comprising:

[0035] A result acquisition module, used to obtain online analysis results of multi-dimensional operation data of an electric forklift according to the online analysis method of multi-dimensional operation data of an electric forklift described in any one of the solutions of the first aspect above;

[0036] The result output module is used to feed back the online analysis results obtained by the result acquisition module to the electric forklift manufacturer or the electric forklift driver.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The online analysis method for multi-dimensional operation data of electric forklifts proposed in the present invention can obtain the operation status, work efficiency, energy consumption and battery life of the electric forklifts in real time, help enterprises and users grasp the key information of production activities in real time to improve production efficiency, and help R&D personnel obtain the working characteristics of the product in real time to improve product performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a schematic diagram of the online analysis process of multi-dimensional operation data of an electric forklift of the present invention;

[0040] Figure 2 Schematic diagram of outlier detection results in an embodiment of the present invention;

[0041] Figure 3 This is a schematic diagram of data denoising results in an embodiment of the present invention;

[0042] Figure 4 It is a schematic diagram of the lifting segment division method;

[0043] Figure 5 This is a schematic diagram of the lifting segment division result;

[0044] Figure 6 Schematic diagram of the walking segment division method;

[0045] Figure 7 This is a schematic diagram of the walking segment division results;

[0046] Figure 8 This is a schematic diagram of the total energy consumption for walking, total energy consumption for lifting, and the proportion of other energy consumption;

[0047] Fig. 9 This is a schematic diagram of work efficiency calculation results;

[0048] Fig.10 Schematic diagram of the steps for calculating the remaining working time;

[0049] Fig.11 This is a schematic diagram of the remaining working time estimation result;

[0050] Fig.12 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0051] In order to make the above-mentioned purpose, features and advantages of the present invention more obvious and easy to understand, the specific implementation mode of the present invention is described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below. The technical features in each embodiment of the present invention can be combined accordingly without conflicting with each other.

[0052] In the description of the present invention, it should be understood that the terms "first" and "second" are only used for the purpose of distinguishing descriptions, and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features.

[0053] The industrial vehicle industry is transforming towards intelligence and networking. Forklifts, as the core part of industrial vehicles, are widely used in many key sectors of the national economy, such as ports, stations, airports, freight yards, factory workshops, warehouses, circulation centers and distribution centers. Electric forklifts are widely used due to their advantages such as environmental protection and energy saving, and their market penetration rate is rising year by year. The intelligence of electric forklifts allows the operation data of each component to be read in real time without additional sensors, and networking allows the operation data to be uploaded to the cloud in real time for storage and analysis by the cloud platform. In order to facilitate enterprises and R&D personnel to analyze the operation status, work efficiency, energy consumption, battery life and driver's operating habits of electric forklifts, the present invention proposes an online analysis method and system for multi-dimensional operation data of electric forklifts. In the present invention, the multi-dimensional operation data of the electric forklift is uploaded to the cloud after local data preprocessing, the transparent transmission gateway cloud platform groups and classifies the data for storage, and the data analysis software reads the data and performs data analysis, including electric forklift operation status analysis, electric forklift work efficiency analysis, electric forklift energy consumption analysis, electric forklift battery life analysis and driver operation habit analysis functions. Taking an electric forklift with a rated load of 5t as an example, an online analysis system for the multi-dimensional operation data of the electric forklift was designed, and the analysis results were demonstrated.

[0054] like Figure 1 As shown, in a preferred implementation of the present invention, the above-mentioned online analysis method for multi-dimensional operation data of an electric forklift includes the following steps S1 to S8. The specific implementation process is described in detail below.

[0055] S1. Acquire original multi-dimensional operation data of an electric forklift and pre-process the data to obtain pre-processed multi-dimensional operation data of the electric forklift.

[0056] It should be noted that in step S1 of the present invention, the multi-dimensional operation data of the electric forklift is collected in real time from the CAN port of the electric forklift through the intelligent transparent transmission gateway of the Internet of Vehicles with a sampling frequency of at least 50Hz, and the built-in microprocessor performs data preprocessing to reduce the memory space occupied by the data. The data is uploaded to the transparent transmission gateway cloud platform via 4G / 5G / Wi-Fi signals and read in real time to realize the function of analyzing the operation status, work efficiency, energy consumption and endurance of the electric forklift. Specifically, Figure 1As shown, the intelligent transparent transmission gateway of the Internet of Vehicles with a sampling frequency higher than 50Hz is used to collect the original multi-dimensional operation data of the electric forklift in real time. The intelligent transparent transmission gateway of the Internet of Vehicles supports simultaneous transparent transmission of serial ports, network ports and CAN ports, and supports connecting cloud gateways in different regions to the cloud platform through the Internet to realize functions such as remote data monitoring, remote program download and remote configuration. It also supports the recording and storage of CAN data and the remote extraction of recorded files. After the data collection is completed, the microprocessor built into the intelligent transparent transmission gateway of the Internet of Vehicles pre-processes the original multi-dimensional operation data of the electric forklift. The first is the data screening process, which retains the data items involved in the subsequent analysis, cleans the data items that are not related to the subsequent analysis, and obtains the screened multi-dimensional operation data of the electric forklift. In this embodiment, the microprocessor built into the intelligent transparent transmission gateway of the Internet of Vehicles selectively retains the data items in Table 1. Next is the data conversion process. The microprocessor built into the intelligent transparent transmission gateway of the Internet of Vehicles converts the screened multi-dimensional operation data of the electric forklift into a decimal number according to the CAN communication protocol provided by the forklift manufacturer. Then comes the outlier detection process. The microprocessor built into the IoV intelligent transparent transmission gateway performs outlier detection on each data item and replaces the outlier with the valid value of the nearest neighbor. The outlier detection method used here can be based on moving median, moving average, percentile, etc., which is not limited in the present invention. In this embodiment, the outlier detection result is as follows: Figure 2 Then comes the data denoising process. The microprocessor built into the IoV intelligent transparent transmission gateway performs data denoising on all non-switching data (only 0 and 1). The data denoising method can be moving median, moving average, Gaussian filter, etc., which are not limited in the present invention. In this embodiment, the data denoising result is as follows: Figure 3As shown. Next is the unified timestamp process. Based on the data of one dimension in the multi-dimensional operation data of the electric forklift after noise reduction (i.e., the data of one item in Table 1) as the reference data, the timestamp of the reference data is obtained, and the interpolation method is used to calculate the values ​​of the data of the remaining dimensions in the multi-dimensional operation data of the electric forklift after noise reduction at the reference timestamp, so that the data after interpolation of the remaining dimensions is consistent with the length of the reference data, and the multi-dimensional operation data of the electric forklift with unified timestamps is obtained, and all the dimensional data items therein are stored in the format of a "timestamp-value" two-dimensional table. In this embodiment, since each data item in the CAN bus has its own timestamp, the timestamps of each data item are not synchronized, which is not convenient for subsequent analysis, and the timestamps of all data items need to be unified. Taking the timestamp of a certain data item as the benchmark, for other data items, the interpolation method is used to obtain the values ​​of other data items at the benchmark timestamp to achieve the unification of the timestamps of all data. The unified result can be stored in a two-dimensional table, with the timestamp represented vertically and the values ​​of each data item horizontally. The interpolation method can be linear interpolation, nearest neighbor interpolation, polynomial interpolation, etc. Then comes the data sending process, where the intelligent transparent transmission gateway of the Internet of Vehicles sends the processed two-dimensional data table (i.e., the multi-dimensional operating data of the electric forklift with a unified timestamp) to the transparent transmission gateway cloud platform via wireless transmission (4G / 5G / Wi-Fi signal). Next comes the data classification process, where the transparent transmission gateway cloud platform classifies the received data according to the vehicle number and collection time. After the data classification is completed, the transparent transmission gateway cloud platform packages and stores the classified data to obtain the pre-processed multi-dimensional operating data of the electric forklift. The last step is the data reading process, which is to read the packaged data (pre-processed multi-dimensional operating data of the electric forklift) of the transparent transmission gateway cloud platform. Thus, the pre-processing process of the entire step S1 is completed.

[0057] It should also be noted that the transparent transmission mentioned in the above process refers to transparent transmission, that is, during the transmission process, it is transparent to the outside world, regardless of the content and data protocol form of the transmission, and no processing is done on the data to be transmitted. The CAN (Controller Area Network) bus mentioned in the above process is a multi-host serial communication protocol for vehicles, which allows multiple electronic control units (ECUs) to communicate with each other. The CAN bus was developed by Bosch in Germany in the early 1980s and was mainly used in the automotive industry, but later it was also widely used in industrial automation, ships, medical equipment and other fields. Its specific implementation was not repeated at the time.

[0058] Table 1. Multidimensional data items collected for electric forklifts

[0059]

[0060]

[0061] S2. For the pre-processed multi-dimensional operation data of the electric forklift, the acceleration of the electric forklift is calculated according to the timestamp (in seconds, s) and vehicle speed (in kilometers per hour, km / h) data therein, the travel motor power is calculated according to the travel motor voltage (in volts, V) and the travel motor current (in amperes, A), the oil pump motor power is calculated according to the oil pump motor voltage (in volts, V) and the oil pump motor current (in amperes, A), and the battery power is calculated according to the battery voltage (in volts, V) and the battery current (in amperes, A).

[0062] It should be noted that, in step S2 of the present invention, Figure 1 As shown, the first step is acceleration calculation. The data analysis software calculates the acceleration a of the electric forklift at the i-th moment according to the following formula i (Unit: meter per second squared, m / s 2 ):

[0063]

[0064] Where: v i and v i+1 are the speed of the electric forklift at the i-th moment and the i+1-th moment (in kilometers per hour, km / h), t i and t i+1 is the timestamp (in seconds, s) at the i-th moment and the i+1-th moment, and N is the length of the multi-dimensional operation data of the electric forklift after preprocessing.

[0065] After calculating the acceleration, the power calculation is performed. The data analysis software calculates the travel motor power, oil pump motor power and battery power based on the travel motor voltage (in volts, V), travel motor current (in amperes, A), oil pump motor voltage (in volts, V), oil pump motor current (in amperes, A), battery voltage (in volts, V) and battery current (in amperes, A). The calculation formula is as follows:

[0066]

[0067] Where: P ti , P pi , P bi are the instantaneous power of the travel motor, the instantaneous power of the oil pump motor, and the instantaneous power of the battery at the i-th moment (in watts, W); U ti , U pi , U bi are the instantaneous voltage of the travel motor, the instantaneous voltage of the oil pump motor, and the instantaneous voltage of the battery at the i-th moment; I ti ,I pi ,I biThey are respectively the instantaneous current of the travel motor, the instantaneous current of the oil pump motor, and the instantaneous current of the battery at the i-th moment.

[0068] S3. According to the lifting segment division algorithm, the start timestamp and the end timestamp of each lifting process in the pre-processed electric forklift multi-dimensional operation data are identified, and the segment between the start timestamp and the end timestamp in the lifting process is taken as a lifting segment. The mass of the goods lifted in each lifting segment is obtained by the average speed and average torque of the oil pump motor in each lifting segment through a table lookup method.

[0069] It should be noted that, in the present invention, after the power calculation is completed, the lifting segment division is performed. In the embodiment of the present invention, the flowchart of the lifting segment division algorithm is as follows: Figure 4 As shown in Figure 2, the lifting segment division results are as follows: Figure 5 shown.

[0070] Specifically, the process of obtaining the lifting segment according to the lifting segment division algorithm is as follows: in the process of lifting segment division, for the lifting signal in the pre-processed multi-dimensional operation data of the electric forklift, if the current traversal timestamp exceeds the data length of the lifting signal, the traversal is stopped and the divided lifting segment is output; if the current traversal timestamp does not exceed the data length of the lifting signal, two lifting signals with consecutive timestamps are searched backward from the current traversal timestamp, and when the lifting signal of the current timestamp is 0 and the lifting signal of the next timestamp is 1, the next timestamp is marked as the starting timestamp of the lifting segment (i.e. Figure 4 The starting point in the figure), find the timestamp after the start timestamp where the first lift signal is 0 and use the timestamp before the timestamp as the end timestamp of the lift segment (i.e. Figure 4 ), if the difference between the end timestamp and the start timestamp of the lifting segment exceeds the preset lifting segment threshold, the preprocessed electric forklift multi-dimensional operation data between the end timestamp and the start timestamp constitutes a lifting segment, if the difference between the end timestamp and the start timestamp of the lifting segment does not exceed the preset lifting segment threshold, then two lifting signals with consecutive timestamps are searched again after the end timestamp until a lifting segment is formed.

[0071] It should be noted that in the present invention, after the lifting segment division is completed, the cargo quality identification can be performed. According to the lifting segments divided in step S4, the cargo quality lifted in each lifting segment is obtained by the average speed and average torque of the oil pump motor in each lifting segment through the table lookup method. In an embodiment of the present invention, when the electric forklift lifts or lowers the cargo, the oil pump motor usually works at the rated power. The higher the cargo quality, the greater the torque required by the oil pump motor. Since the power is constant, the speed of the oil pump motor is lower. Different cargo quality sizes correspond to different oil pump motor torque sizes. In addition, each forklift manufacturer will formulate a table of the corresponding relationship between the average speed and average torque of the oil pump motor and the mass of the lifted cargo. Therefore, according to the lifting segments divided in step S4, the average speed and average torque of the oil pump motor in each segment can be calculated, and the corresponding cargo quality can be obtained by the table lookup method. Therefore, the specific process will not be repeated.

[0072] S4. According to the walking segment division algorithm, the start timestamp and the end timestamp of each walking process in the preprocessed electric forklift multi-dimensional operation data are identified, and a segment between a start timestamp and an end timestamp in the walking process is taken as a walking segment. The mileage and driving duration of each walking segment are calculated based on the timestamp in each walking segment and the speed of the electric forklift.

[0073] It should be noted that, in the present invention, after completing the lifting segment division, the walking segment division is performed. In the embodiment of the present invention, the flowchart of the walking segment division algorithm is as follows: Figure 6 As shown in Figure 2, the walking segment division results are as follows: Figure 7 As shown, different colors represent different walking segments.

[0074] Specifically, the process of obtaining walking segments according to the walking segment division algorithm is as follows: in the process of walking segment division, for the electric forklift speed data in the preprocessed electric forklift multi-dimensional operation data, if the current traversal timestamp exceeds the data length of the electric forklift speed, the traversal is stopped and the divided walking segments are output; if the current traversal timestamp does not exceed the data length of the electric forklift speed, then starting from the current traversal timestamp, two consecutive electric forklift speeds are searched backwards, and when the electric forklift speed at the current timestamp is 0 and the electric forklift speed at the next timestamp is 1, the next timestamp is marked as the walking segment. The starting timestamp of the walking segment is used to find the first timestamp after the starting timestamp where the speed of the electric forklift is 0 and use the timestamp before the timestamp as the ending timestamp of the walking segment. If the difference between the ending timestamp and the starting timestamp of the walking segment exceeds the preset walking segment threshold, the pre-processed electric forklift multi-dimensional operation data between the ending timestamp and the starting timestamp constitutes a walking segment. If the difference between the ending timestamp and the starting timestamp of the walking segment does not exceed the preset walking segment threshold, the electric forklift speeds with two consecutive timestamps are searched again after the ending timestamp until a walking segment is formed.

[0075] It should be noted that in the present invention, after completing the division of walking segments, the travel distance and duration calculation can be performed. According to the walking segments divided in step S4, the mileage and driving duration of each walking segment are calculated by the timestamp (in seconds, s) and the speed of the electric forklift (in kilometers per hour, km / h) in each walking segment. The calculation formula is as follows:

[0076] T=t end -t start

[0077]

[0078] Where: T is the driving time of the walking segment (in seconds, s); t start and t end are the start and end timestamps of the walking segment respectively; S is the mileage of the walking segment (in meters, m); N t The data length for each walking segment.

[0079] S5. Calculate the walking energy consumption corresponding to each walking segment according to the walking motor power, calculate the lifting energy consumption corresponding to each lifting segment according to the oil pump motor power, calculate the total battery energy consumption corresponding to each pre-processed electric forklift multi-dimensional operation data according to the battery power, add the walking energy consumption of all walking segments corresponding to each pre-processed electric forklift multi-dimensional operation data as the total walking energy consumption, add the lifting energy consumption of all lifting segments corresponding to each pre-processed electric forklift multi-dimensional operation data as the total lifting energy consumption, remove the total walking energy consumption and the total lifting energy consumption from the total battery energy consumption as other energy consumption, and calculate the energy consumption proportion of the total walking energy consumption, the total lifting energy consumption and other energy consumption in the total battery energy consumption.

[0080] It should be noted that, in the present invention, after the cargo mass identification and the travel distance and duration calculation are completed, the energy consumption calculation begins. ti , the oil pump motor power and the battery power, the lifting segments divided in step S3 and the walking segments divided in step S4, and calculate the total battery energy consumption EC corresponding to each pre-processed electric forklift multi-dimensional operation data b , calculate the walking energy consumption corresponding to the jth walking segment Lifting energy consumption corresponding to the kth lifting segment In this way, other energy consumption can be obtained and the proportion of the above energy consumption can be calculated.

[0081]

[0082] Where: N p The data length for each lifting segment; M t is the number of walking segments contained in each preprocessed multi-dimensional operation data of electric forklift; M p The number of lifting segments contained in the pre-processed multi-dimensional operation data of each electric forklift.

[0083] In the above formula, the number of lifting segments and the number of walking segments contained in each pre-processed electric forklift multi-dimensional operation data may not be the same. Therefore, the present invention calculates the lifting energy consumption of each lifting segment and the walking energy consumption of each walking segment respectively, and calculates the total lifting energy consumption and the total walking energy consumption in a cumulative manner for the lifting energy consumption and the walking energy consumption, and then obtains other energy consumptions. The units of the above various energy consumptions are all kilojoules (kJ). In addition, the proportion of total walking energy consumption, the proportion of total lifting energy consumption, and the proportion of other energy consumption can be obtained by dividing the corresponding energy consumption by the total battery energy consumption, so the specific implementation process will not be repeated. The change rules of each energy consumption proportion are as follows: Figure 8 shown.

[0084] S6. Calculate the theoretical work efficiency and actual work efficiency of the electric forklift within the preset time period according to the cargo mass, handling distance and handling time within the preset time period, and use the ratio of the actual work efficiency to the theoretical work efficiency as the work saturation of the electric forklift.

[0085] It should be noted that in the present invention, after calculating the energy consumption, the work efficiency is calculated. According to the mass of goods (in tons, t), the handling distance (in meters, m) and the handling time (in seconds, s) in a recent period of time, the work efficiency of the electric forklift in that period of time is calculated (in tons × kilometers per hour, t×km / h). In this embodiment, the length of the preset time period can be set by the user, such as nearly ten minutes, nearly one hour, nearly eight hours, nearly a week, etc. The calculated work efficiency is divided into theoretical work efficiency and actual work efficiency. Among them, the theoretical work efficiency only considers the work efficiency of the forklift in operation (during the walking or lifting segment), and does not include the time when the forklift is parked and rested; the actual work efficiency takes into account the time when the driver is not working due to rest or other reasons.

[0086] The above theoretical working efficiency η theo and actual work efficiency η real The specific calculation method is:

[0087]

[0088] Where: m j , S j and T j are the mass of cargo lifted, mileage and driving time in the jth walking segment respectively; T set The length of the preset time period.

[0089] In the above formula, the specific method for obtaining the mass of the cargo lifted in the j-th walking segment is: starting from the starting timestamp of the j-th walking segment, look forward to the first lifting segment and use it as the reference lifting segment, and use the mass of the cargo lifted in the reference lifting segment as the mass of the cargo lifted in the j-th walking segment.

[0090] In this embodiment, the calculation results of the theoretical work efficiency and the actual work efficiency for nearly one hour are as follows: Fig. 9 The theoretical working efficiency is about 10t km / h to 20t km / h. 10t km / h means that 10 tons of goods can be moved 1 kilometer per hour; the actual working efficiency is 4t km / h to 10t km / h. The maximum speed of this 5t electric forklift is 25km / h, and the maximum theoretical working efficiency is 125t km / h.

[0091] S7. Calculate the remaining working time under the current battery state of charge according to the relationship between the actual working efficiency and the decrease in the battery state of charge.

[0092] It should be noted that in the present invention, after calculating the working efficiency, the battery life is estimated. According to the relationship between the actual working efficiency calculated in step S6 and the decrease in the battery state of charge SOC (in %), the remaining working time T under the current battery state of charge is calculated. r In this embodiment, the calculation steps of the remaining working time (i.e., the endurance time, the unit of which is hours, h) are as follows: Fig.10 As shown, you can also refer to the following formula for calculation:

[0093]

[0094] Where: W2 is the battery state of charge, which is the current battery state of charge value X% used to the minimum battery state of charge value X min W1 is the remaining workload of the electric forklift when the battery state of charge is 100% and the current battery state of charge value is X% (in tons × kilometers, t×km); X min % is the minimum value of the battery state of charge, which can be set according to actual conditions, usually 10% to 25%; γ is the discount factor, which can be set to 0.90 to 0.99.

[0095] In this embodiment, the estimated result of the battery life is as follows: Fig.11 shown. Fig.11 In the calculation, the estimated value of the remaining working time starts from when the battery SOC drops to 90%, and then Fig.10 The remaining working time is calculated in real time in hours. When the battery SOC is as low as 23%, the electric forklift returns to the charging station for charging. Fig.11 The actual endurance time in the calculation is the actual time from the current time to when the battery SOC reaches 23%. Fig.11 The estimated driving time in the chart is close to the actual remaining working time, especially when the battery SOC is below 60%.

[0096] S8. The acceleration of the electric forklift calculated by S2, the energy consumption ratio calculated by S5, the working saturation of the electric forklift calculated by S6, and the remaining working time calculated by S7 are used as online analysis results of the multi-dimensional operation data of the electric forklift and visualized.

[0097] It should be noted that after the above process, the online analysis results of the multi-dimensional operation data of the electric forklift will eventually be obtained and displayed visually to the user. The user can then perform corresponding post-processing or data analysis based on the online analysis results. Here, the appropriate post-processing method or data analysis method can be selected according to actual needs, so the present invention is not limited.

[0098] Based on the above analysis, it can be seen that although the traditional offline analysis methods can achieve the purpose of offline analysis of electric forklift operation data, these methods are not real-time. Although the existing online intelligent monitoring system for electric forklifts can achieve the purpose of electric forklift online rate statistics, fault diagnosis, cargo detection, etc., it does not have the multiple data analysis functions of the present invention.

[0099] When improving the existing online analysis method or system for multi-dimensional operation data of electric forklifts, the present invention has the following technical features: 1. The operation data of electric forklifts has multiple dimensions. The vehicle speed, travel gear, lifting gear, accelerator pedal opening, brake pedal opening, vehicle control unit (VCU) request for travel motor speed, travel motor actual speed, travel motor torque, travel motor voltage, travel motor current, travel motor temperature, VCU request for oil pump motor speed, oil pump motor actual speed, oil pump motor torque, oil pump motor voltage, oil pump motor current, oil pump motor temperature, battery state of charge (State of Charge, SOC), battery voltage, battery current, battery temperature and other multi-dimensional operation data of electric forklifts are collected in real time by the intelligent transparent transmission gateway of the Internet of Vehicles through the CAN (Controller Area Network) bus. 2. The sampling frequency of the operation data of electric forklifts is high. The sampling frequency of the original data is 10Hz to 50Hz. 3. Preprocessing of the operation data of electric forklifts. The original data is preprocessed by the microprocessor built into the intelligent transparent transmission gateway of the Internet of Vehicles for data conversion, data cleaning and timestamp alignment and other data preprocessing operations. 4. High upload efficiency. The preprocessed data is uploaded to the transparent transmission gateway cloud platform via 4G / 5G / Wi-Fi signals. 5. Convenient data management. The cloud platform classifies and stores the data uploaded by each electric forklift according to the vehicle number and collection time. 6. Multiple analysis functions. By reading the operating data in the cloud platform and performing multiple data analysis tasks, the operating status of each component of the electric forklift is analyzed through data visualization and threshold detection; the work efficiency is calculated by the quality of the goods, the handling journey and the handling time; the walking energy consumption, lifting energy consumption and other energy consumption are calculated by integrating the instantaneous voltage and current of each component over time; the battery life is estimated by the historical workload, work efficiency and battery SOC.

[0100] In summary, compared with the prior art, the advantages of the present invention are: improving the real-time performance of the electric forklift operation data analysis, improving the dimension of the electric forklift operation data collection, reducing the storage space of the electric forklift CAN bus raw data, and being able to obtain the electric forklift's work efficiency, energy consumption and endurance time online in real time.

[0101] It should also be noted that the online analysis method for multi-dimensional operation data of electric forklifts in the above embodiment can essentially be executed by a computer program or module. Therefore, similarly, based on the same inventive concept, another preferred embodiment of the present invention also provides an online analysis system for multi-dimensional operation data of electric forklifts corresponding to the online analysis method for multi-dimensional operation data of electric forklifts provided in the above embodiment, such as Fig.12 As shown, it includes:

[0102] A result acquisition module, used for acquiring online analysis results of multi-dimensional operation data of an electric forklift according to the online analysis method of multi-dimensional operation data of an electric forklift described in the above embodiment;

[0103] The result output module is used to feed back the online analysis results obtained by the result acquisition module to the electric forklift manufacturer or the electric forklift driver.

[0104] It should also be noted that those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the system described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. In the various embodiments provided in this application, the division of steps or modules in the system and method is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules or steps can be combined or integrated together, and a module or step can also be split.

[0105] The above-described embodiment is only a preferred solution of the present invention, but it is not intended to limit the present invention. A person skilled in the relevant technical field may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present invention.

Claims

1. A method for online analysis of multi-dimensional operation data of an electric forklift, characterized in that: The following steps are involved: S1. Obtaining original multi-dimensional operation data of an electric forklift and preprocessing the data to obtain preprocessed multi-dimensional operation data of an electric forklift; S2. For the pre-processed multi-dimensional operation data of the electric forklift, the acceleration of the electric forklift is calculated according to the timestamp and vehicle speed data, the travel motor power is calculated according to the travel motor voltage and the travel motor current, the oil pump motor power is calculated according to the oil pump motor voltage and the oil pump motor current, and the battery power is calculated according to the battery voltage and the battery current; S3, identifying the start timestamp and end timestamp of each lifting process in the pre-processed multi-dimensional operation data of the electric forklift according to the lifting segment division algorithm, taking a segment between a start timestamp and an end timestamp in the lifting process as a lifting segment, and obtaining the mass of the goods lifted in each lifting segment by the average speed and average torque of the oil pump motor in each lifting segment through a table lookup method; S4, identifying the start timestamp and end timestamp of each walking process in the preprocessed electric forklift multidimensional operation data according to the walking segment division algorithm, taking a segment between a start timestamp and an end timestamp in the walking process as a walking segment, and calculating the mileage and driving duration of each walking segment based on the timestamp in each walking segment and the speed of the electric forklift; S5. Calculate the walking energy consumption corresponding to each walking segment according to the walking motor power, calculate the lifting energy consumption corresponding to each lifting segment according to the oil pump motor power, calculate the total battery energy consumption corresponding to each pre-processed electric forklift multi-dimensional operation data according to the battery power, add the walking energy consumption of all walking segments corresponding to each pre-processed electric forklift multi-dimensional operation data as the total walking energy consumption, add the lifting energy consumption of all lifting segments corresponding to each pre-processed electric forklift multi-dimensional operation data as the total lifting energy consumption, remove the total walking energy consumption and the total lifting energy consumption from the total battery energy consumption as other energy consumption, and calculate the energy consumption proportion of the total walking energy consumption, the total lifting energy consumption and other energy consumption in the total battery energy consumption; S6. Calculate the theoretical working efficiency and actual working efficiency of the electric forklift within the preset time period according to the cargo mass, handling distance and handling time within the preset time period, and use the ratio of the actual working efficiency to the theoretical working efficiency as the working saturation of the electric forklift; S7, calculating the remaining working time under the current battery state of charge according to the relationship between the actual working efficiency and the decrease in the battery state of charge; S8. The acceleration of the electric forklift calculated by S2, the energy consumption ratio calculated by S5, the working saturation of the electric forklift calculated by S6, and the remaining working time calculated by S7 are used as online analysis results of the multi-dimensional operation data of the electric forklift and visualized.

2. The method for online analysis of multi-dimensional operation data of an electric forklift according to claim 1, characterized in that: In step S1, the original multi-dimensional operation data of the electric forklift is collected in real time using the intelligent transparent transmission gateway of the Internet of Vehicles with a sampling frequency higher than 50Hz, and the original multi-dimensional operation data of the electric forklift is screened, and the timestamp, electric forklift speed, accelerator pedal opening, brake pedal opening, shift switch, reverse gear switch, accelerator pedal switch, brake pedal switch, lifting signal, VCU request travel motor speed, travel motor actual speed, travel motor temperature, travel motor voltage, travel motor current, travel motor torque, VCU request oil pump motor speed, oil pump motor actual speed, oil pump motor temperature, oil pump motor voltage, oil pump motor current, oil pump motor torque, battery charge state, battery voltage and battery current in the multi-dimensional operation data of the electric forklift are retained to obtain the screened multi-dimensional operation data of the electric forklift, and the screened multi-dimensional operation data of the electric forklift is converted into ten The converted electric forklift multidimensional operation data is processed by a binary number, and outlier detection is performed on the converted electric forklift multidimensional operation data. Then, data denoising is performed on the electric forklift multidimensional operation data after outlier detection. Data of one dimension in the denoised electric forklift multidimensional operation data is selected as reference data. The timestamp of the reference data is obtained and used as the reference timestamp. The interpolation method is used to calculate the values ​​of the data of the remaining dimensions in the denoised electric forklift multidimensional operation data at the reference timestamp, so that the interpolated data of the remaining dimensions are consistent with the length of the reference data, and the electric forklift multidimensional operation data with a unified timestamp is obtained. The intelligent transparent transmission gateway of the Internet of Vehicles wirelessly transmits the electric forklift multidimensional operation data with a unified timestamp to the transparent transmission gateway cloud platform. The transparent transmission gateway cloud platform classifies the received data according to the vehicle number and the collection time and packages and stores the classified data to obtain the preprocessed electric forklift multidimensional operation data.

3. The method for online analysis of multi-dimensional operation data of an electric forklift according to claim 2, characterized in that: In step S3, the specific process of obtaining the lifting segment according to the lifting segment division algorithm is as follows: in the process of lifting segment division, for the lifting signal in the pre-processed multi-dimensional operation data of the electric forklift, if the timestamp of the current traversal exceeds the data length of the lifting signal, the traversal is stopped and the divided lifting segment is output; If the currently traversed timestamp does not exceed the data length of the lifting signal, then two lifting signals with consecutive timestamps are searched backward from the currently traversed timestamp. When the lifting signal of the current timestamp is 0 and the lifting signal of the next timestamp is 1, the next timestamp is marked as the starting timestamp of the lifting segment. The first timestamp with a lifting signal of 0 after the starting timestamp is searched and the timestamp before the timestamp is used as the ending timestamp of the lifting segment. If the difference between the ending timestamp and the starting timestamp of the lifting segment exceeds the preset lifting segment threshold, a lifting segment is formed by the pre-processed electric forklift multi-dimensional operation data between the ending timestamp and the starting timestamp. If the difference between the ending timestamp and the starting timestamp of the lifting segment does not exceed the preset lifting segment threshold, two lifting signals with consecutive timestamps are searched again after the ending timestamp until a lifting segment is formed.

4. The method for online analysis of multi-dimensional operation data of an electric forklift according to claim 2, characterized in that: In step S4, the specific process of obtaining the walking segments according to the walking segment division algorithm is as follows: in the process of walking segment division, for the electric forklift speed data in the pre-processed electric forklift multi-dimensional operation data, if the current traversal timestamp exceeds the data length of the electric forklift speed, the traversal is stopped and the divided walking segments are output; If the currently traversed timestamp does not exceed the data length of the electric forklift speed, then start from the currently traversed timestamp and search backward for two consecutive timestamps of the electric forklift speed. When the electric forklift speed of the current timestamp is 0 and the electric forklift speed of the next timestamp is 1, mark the next timestamp as the starting timestamp of the walking segment, find the first timestamp after the starting timestamp where the electric forklift speed is 0 and use the timestamp before the timestamp as the ending timestamp of the walking segment. If the difference between the ending timestamp and the starting timestamp of the walking segment exceeds the preset walking segment threshold, a walking segment is formed by the preprocessed electric forklift multi-dimensional operation data between the ending timestamp and the starting timestamp. If the difference between the ending timestamp and the starting timestamp of the walking segment does not exceed the preset walking segment threshold, then search again for two consecutive timestamps of the electric forklift speed after the ending timestamp until a walking segment is formed.

5. The method for online analysis of multi-dimensional operation data of an electric forklift according to claim 1, characterized in that: In step S4, the specific method of calculating the mileage and driving duration of each walking segment is as follows: T=t end -t start Where: T is the driving time of the walking segment; t start and t end are the start and end timestamps of the walking segment respectively; S is the mileage of the walking segment; N t is the data length of each walking segment; v i and v i+1 are the speeds of the electric forklift at the i-th moment and the i+1-th moment, t i and t i+1 It is the timestamp of the i-th moment and the i+1-th moment.

6. The method for online analysis of multi-dimensional operation data of an electric forklift according to claim 5, characterized in that: In step S5, the walking energy consumption corresponding to the jth walking segment is Lifting energy consumption corresponding to the kth lifting segment The total battery energy consumption EC corresponding to each pre-processed electric forklift multi-dimensional operation data b And other energy consumption EC other The calculation method is as follows: Where: P ti , P pi , P bi are the instantaneous power of the travel motor, the instantaneous power of the oil pump motor, and the instantaneous power of the battery at the i-th moment; N p N is the length of data for each lifting segment; N is the length of multi-dimensional operation data of each pre-processed electric forklift; M t is the number of walking segments contained in each preprocessed multi-dimensional operation data of electric forklift; M p The number of lifting segments contained in the pre-processed multi-dimensional operation data of each electric forklift.

7. The method for online analysis of multi-dimensional operation data of an electric forklift according to claim 6, characterized in that: In step S6, the specific calculation method of the theoretical work efficiency and the actual work efficiency is: Where: m j , S j and T j are the mass of cargo lifted, mileage and driving time in the jth walking segment respectively; T set The length of the preset time period.

8. The method for online analysis of multi-dimensional operation data of an electric forklift according to claim 7, characterized in that: The specific method for obtaining the mass of the cargo lifted in the jth walking segment is: starting from the starting timestamp of the jth walking segment, look forward to the first lifting segment and use it as a reference lifting segment, and use the mass of the cargo lifted in the reference lifting segment as the mass of the cargo lifted in the jth walking segment.

9. The method for online analysis of multi-dimensional operation data of an electric forklift according to claim 7, characterized in that: The remaining working time T r The specific calculation method is as follows: Where: W2 is the battery state of charge, which is the current battery state of charge value X% used to the minimum battery state of charge value X min % is the remaining workload of the electric forklift; W1 is the cumulative workload of the electric forklift when the battery state of charge is 100% and the current battery state of charge value is X%; γ is the discount coefficient.

10. An online analysis system for multi-dimensional operation data of electric forklifts, characterized in that: include: A result acquisition module, used for acquiring online analysis results of multi-dimensional operation data of an electric forklift according to the online analysis method of multi-dimensional operation data of an electric forklift according to any one of claims 1 to 9; The result output module is used to feed back the online analysis results obtained by the result acquisition module to the electric forklift manufacturer or the electric forklift driver.

Images