A method for identifying the operating state of an electric forklift in an artificially awakened state

Abstract

The present invention belongs to the field of electric forklifts, and specifically relates to a method for identifying the operating state of an electric forklift in an artificially awakened state, comprising the following steps: Step 1, the main control module collects power supply data and dynamically determines the current distinction threshold of the dynamic and static working modes; Step 2, the main control module performs dynamic and static mode judgment. The present invention can automatically analyze the characteristics of the device in different working modes, and calculate a reasonable device working mode distinction threshold, which can meet the working mode distinction requirements of different models of equipment, or the same model of equipment in different usage states and life cycles, has the characteristics of automatic learning analysis, and has strong applicability.

Description

Technical Field

[0001] The invention belongs to the field of electric forklifts, and in particular relates to a method for identifying the operating state of an electric forklift in an artificially awakened state. Background Art

[0002] The discharge operation state of an electric forklift can be divided into dynamic and static. Dynamic means that the electric forklift is in working state. At this time, the forklift needs to load heavy objects and move, so there is a large current in the working circuit; static means that the electric forklift is in a stationary or power-off sleep state. At this time, the working current is very small or the current is zero. The usefulness of judging the dynamic and static state of the forklift is that in dynamic and static modes, the operating state and operating parameters of the forklift are quite different. In order to better control the operation of the forklift, it is necessary to effectively and accurately judge the dynamic and static modes of the forklift. At the same time, obtaining the operating mode of the forklift can provide a reference for the design of the control strategy of the forklift and the battery, and design different control logics for dynamic and static modes to better meet the control requirements under different modes.

[0003] The conventional way to distinguish the dynamic and static working modes of a forklift is to set a fixed current threshold. When the current passing through the working circuit is detected to be greater than the preset current threshold, it is considered to be in a dynamic state; when the current passing through the working circuit is detected to be less than the preset current threshold, it is considered to be in a static state.

[0004] However, in actual applications, it is difficult to distinguish the working modes of different models of electric forklifts with a fixed current threshold due to the different power and static power consumption of different models of electric forklifts. At the same time, for the same model, aging or failure of equipment and lines will also cause changes in the current threshold for distinguishing between dynamic and static working modes.

[0005] Based on the above problems, the applicant proposes a method for identifying the operating status of an electric forklift in an artificially awakened state. Summary of the invention

[0006] In order to make up for the deficiencies of the prior art, the present invention provides a technical solution for a method for identifying the operating state of an electric forklift in an artificially awakened state.

[0007] A method for identifying the operating state of an electric forklift in an artificially awakened state comprises the following steps:

[0008] Step 1, the main control module collects power supply data and dynamically determines the current distinction threshold of the dynamic and static working modes;

[0009] Step 2: The main control module determines the dynamic and static modes.

[0010] Furthermore, the step 1 comprises:

[0011] Step 1.1, the main control module collects the current I and voltage V of the power supply;

[0012] Step 1.2: The main control module distinguishes the current data continuously according to the preset initial current threshold I0. Divided into two data sets: dynamic current data set and quiescent current data sets The expression is divided into:

[0013]

[0014]

[0015] Step 1.3, when the dataset The length m and the dataset When the length n of each of the two datasets exceeds the preset data length threshold L, the most recently obtained L data in the two datasets are intercepted, and the header data beyond the L length is discarded to obtain the two datasets again:

[0016]

[0017]

[0018] Step 1.4, calculate the current data set separately The mean μ1 and current data set The mean μ2 of the current data set The standard deviation σ1 and the current data set The standard deviation σ2 is expressed as:

[0019]

[0020]

[0021]

[0022]

[0023] Step 1.5: Update the dynamic and static current distinction threshold I0 based on the calculated mean values ​​μ1 and μ2. The expression is:

[0024]

[0025] Furthermore, the step 1 further comprises:

[0026] As the electric forklift is used, the main control module continuously collects new current data, and the new current data is divided into dynamic current data sets according to the dynamic and static distinction threshold I0 that is updated in a rolling manner. or a static current data set At the same time, the current data set and Always keep the number of current data at L. or When a new data is obtained, it will remove the oldest current data from the data set, thereby realizing a rolling update of the data set elements. After the data set is updated, its mean and standard deviation are also calculated and updated.

[0027] Furthermore, the step 2 comprises:

[0028] When Q current data [I1, I2, I3…I i ,…I Q ], and Q current data meet the following conditions:

[0029] μ1-σ1≤I i ≤μ1+σ1

[0030] It is determined that the electric forklift enters the dynamic mode;

[0031] When Q current data [I1, I2, I3…I i ,…I Q ], and Q current data satisfy:

[0032] μ2-σ2≤I i ≤μ2+σ2

[0033] It is determined that the electric forklift enters the static mode.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention can automatically analyze the characteristics of the device in different working modes, actively collect relevant device parameters, and calculate reasonable thresholds for distinguishing the working modes of the device through statistical analysis, which can meet the needs of different models of devices for intelligently distinguishing working modes. At the same time, the present invention can roll over the data set to automatically and intelligently distinguish the working modes of the device when the device is in different usage states and life cycles. The present invention can automatically and accurately distinguish the working modes of the device, providing an effective reference for the design of the device control strategy, so that the strategy developers can design different control strategies for different working modes to achieve the purpose of accurately controlling the device. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a flow chart of the present invention;

[0037] Figure 2 is a flow chart of step 1 in the present invention;

[0038] Figure 3 The figure is a schematic diagram of the circuit relationship of the battery control system of the electric forklift of the present invention. DETAILED DESCRIPTION

[0039] In the description of the present invention, it is necessary to understand that the terms "one end", "the other end", "outside", "upper", "inside", "horizontal", "coaxial", "center", "end", "length", "outer end" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0040] The present invention will be further described below in conjunction with the accompanying drawings.

[0041] like Figure 1-3 As shown, a method for identifying the operating state of an electric forklift in an artificially awakened state comprises the following steps:

[0042] Step 1: The main control module collects power supply data and dynamically determines the current distinction threshold of the dynamic and static working modes.

[0043] Step 1 includes:

[0044] Step 1.1, the main control module collects the current I and voltage V of the power supply;

[0045] Step 1.2: The main control module distinguishes the current data continuously according to the preset initial current threshold I0. Divided into two data sets: dynamic current data set and quiescent current data sets The expression is:

[0046]

[0047]

[0048] Step 1.3, when the dataset and When the lengths m and n of both exceed the preset data length threshold L, the most recently obtained L data in the two data sets are intercepted, and the header data beyond the L length is discarded; at this point, the two data sets are re-obtained, and the expression is:

[0049]

[0050]

[0051] Step 1.4, calculate the current data set separately and The mean μ1 and μ2, and the standard deviation σ1 and σ2 are expressed as:

[0052]

[0053]

[0054]

[0055]

[0056] Step 1.5, based on the calculated mean values ​​μ1 and μ2, update the dynamic and static current distinction threshold I0, expressed as:

[0057]

[0058] As the electric forklift is used, the main control module continuously collects new current data, and the new current data is divided into dynamic current data sets according to the dynamic and static distinction threshold I0 that is updated in a rolling manner. or a static current data set At the same time, the current data set and Always keep the number of current data as L, so whenever or When a new data is obtained, it will remove the oldest current data from the data set, thereby realizing a rolling update of the data set elements. After the data set is updated, its mean and standard deviation are also calculated and updated.

[0059] Step 2: The main control module determines the dynamic and static modes.

[0060] Step 2 includes:

[0061] When the device is in the automatic wake-up state, it is directly considered that the device is in the static working mode;

[0062] When the device is in manual wake-up state, dynamic and static distinction is made according to the following conditions:

[0063] When Q current data [I1, I2, I3…I i ,…I Q ], and Q current data meet the following conditions:

[0064] μ1-σ1≤I i ≤μ1+σ1

[0065] It is determined that the electric forklift enters the dynamic mode;

[0066] When Q current data [I1, I2, I3…I i,…I Q ], and Q current data satisfy:

[0067] μ2-σ2≤I i ≤μ2+σ2

[0068] It is determined that the electric forklift enters the static mode;

[0069] The other cases will remain the same as the last working mode judgment.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying the operating state of an electric forklift in an artificially awakened state, characterized in that: The steps include: Step 1: The main control module collects power supply data and dynamically determines the current distinction threshold of the dynamic and static working modes, including: Step 1.1, the main control module collects the current I and voltage V of the power supply; Step 1.2: The main control module distinguishes the threshold according to the preset initial current , the continuously collected current data Divided into two data sets: dynamic current data set and quiescent current dataset , the expression is divided into: ； ； Step 1.3, when the dataset Length and dataset Length All exceed the preset data length threshold Then, the most recently obtained data and discard Header data other than length, re-obtain two data sets: ； ； Step 1.4, calculate the current data set separately The mean and current data sets The mean , and the current dataset Standard Deviation and current data sets Standard Deviation , the expression is: ； ； ； ； Step 1.5: Based on the calculated mean and , update the dynamic and static current distinction threshold , the expression is: ； Step 2: The main control module determines the dynamic and static modes, including: When continuously collecting Current data ,and When the current data meets the following conditions: ； It is determined that the electric forklift enters the dynamic mode; When continuously collecting Current data ,and The current data meets the following requirements: ； It is determined that the electric forklift enters the static mode.

2. The method for identifying the operating state of an electric forklift in an artificially awakened state according to claim 1, characterized in that: The step 1 also includes: As the electric forklift is used, the main control module continuously collects new current data, and the new current data is updated according to the dynamic and static distinction thresholds. The dynamic current data set or a static current data set At the same time, the current data set and Always keep the number of current data as ,whenever or When a new data is obtained, it will remove the oldest current data from the data set, thereby realizing a rolling update of the data set elements. After the data set is updated, its mean and standard deviation are also calculated and updated.

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