Automatic storage equipment state evaluation and prediction method and device

Abstract

The embodiment of the invention provides an automatic storage equipment state evaluation and prediction method and device based on a grey prediction model, and the method comprises the steps: obtaining a health evaluation score data time sequence according to an equipment state evaluation result, taking the health evaluation score data time sequence as original data of state prediction, and carrying out the preprocessing of the original data; and establishing an original model equation based on the preprocessed original data, obtaining a parameter value of the original model equation, substituting the parameter value into a prediction formula of a one-time accumulation sequence, and performing cumulative subtraction on the calculated prediction sequence to obtain an original data prediction value. According to the embodiment of the invention, the required original data is less, the calculation is simple, generally, multi-factor data is not required, only the single-factor data of the object needs to be predicted, and the dependence on historical data is small.

Description

Technical Field

[0001] This document relates to the field of computer technology, and in particular to a method and device for evaluating and predicting the status of automated warehousing equipment based on a grey prediction model. Background Art

[0002] The reliability of the current power system metering material warehouse equipment is high, the fault information collected is limited, and the parameters of the fault information provided by the original manufacturer when it occurs are inconsistent with the parameters of the current warehouse operation, making it impossible to effectively predict equipment failures through machine learning methods.

[0003] Grey prediction method is to establish a grey differential prediction model through a small amount of incomplete information, so as to predict the future development trend of things and make a fuzzy long-term description of the law of development of things. Grey prediction method is suitable for prediction when the amount of historical data is small.

[0004] The prediction model is established by using the grey theory. First, the model established with the known sequence is used to determine the first prediction value. After the known sequence, the first known data is removed at the same time to make the constructed development sequence equal to the original sequence. After such step-by-step prediction, the prediction is supplemented in sequence until the prediction step is reached. The grey prediction model established in this way is called the grey number supplement dynamic prediction model.

[0005] For smart warehouse equipment, sometimes it is necessary to predict its status in a few days or even dozens of days, which must be achieved through multi-step prediction. However, as time goes by, some disturbance factors in the future will continue to affect the system. The farther the future moment is, the larger the gray interval of the predicted value is, and the prediction value that is truly meaningful and accurate is the most recent data. Therefore, the gray number supplementary prediction model has better predictability. This model not only has the advantages of high prediction accuracy, few samples required, simple calculation and easy verification of the basic gray prediction model, but also enables the constructed model to have the ability to dynamically update data to make the prediction more reasonable. Therefore, there is an urgent need for a technical solution for the evaluation and prediction of the status of automated storage equipment based on the gray prediction model to solve the problem that the operation and maintenance of the metering material warehouse of the power system is under the condition of unmanned operation and maintenance, and the automation equipment of the smart warehouse can only realize abnormal alarms and cannot predict the status of the equipment, resulting in the operation and maintenance work can only be operated and maintained after the fact and passively, which poses certain hidden dangers to safe production. Summary of the invention

[0006] The purpose of the present invention is to provide a method and device for evaluating and predicting the state of automated storage equipment based on a grey prediction model, aiming to solve the above-mentioned problems in the prior art.

[0007] The present invention provides a method for evaluating and predicting the state of automated storage equipment based on a grey prediction model, comprising:

[0008] According to the equipment status evaluation result, a time series of health evaluation score data is obtained and used as raw data for status prediction, and the raw data is preprocessed;

[0009] Based on the preprocessed original data, an original model equation is established, parameter values ​​of the original model equation are obtained, the parameter values ​​are substituted into a prediction formula of a cumulative series, and the calculated prediction series is cumulatively subtracted to obtain the original data prediction value.

[0010] The present invention provides an automated storage equipment status evaluation and prediction device based on a grey prediction model, comprising:

[0011] An acquisition preprocessing module is used to acquire a time series of health evaluation score data according to the equipment status evaluation result, and preprocess the raw data as raw data for status prediction;

[0012] A calculation module is established to establish an original model equation based on the preprocessed original data, obtain parameter values ​​of the original model equation, bring the parameter values ​​into a prediction formula of a cumulative series, and perform cumulative subtraction on the calculated prediction series to obtain the prediction value of the original data.

[0013] An embodiment of the present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the above-mentioned method for evaluating and predicting the status of automated warehousing equipment based on a grey prediction model are implemented.

[0014] An embodiment of the present invention further provides a computer-readable storage medium, on which a program for implementing information transmission is stored. When the program is executed by a processor, the steps of the above-mentioned method for evaluating and predicting the state of automated warehousing equipment based on a grey prediction model are implemented.

[0015] By adopting the embodiment of the present invention, less original data is needed, the calculation is simple, and generally, multi-factor data is not needed but only single-factor data of the prediction object itself is needed, and the dependence on historical data is relatively small. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0017] Figure 1It is a flow chart of an automated storage equipment status evaluation and prediction method based on a grey prediction model according to an embodiment of the present invention;

[0018] Figure 2 Schematic diagram of the grey prediction method modeling process of an embodiment of the present invention;

[0019] Figure 3 is a schematic diagram of an automated storage equipment state evaluation and prediction device based on a grey prediction model according to an embodiment of the present invention;

[0020] Figure 4 is a schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will be combined with the drawings in one or more embodiments of this specification to clearly and completely describe the technical solutions in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0022] Method Embodiment

[0023] According to an embodiment of the present invention, a method for evaluating and predicting the state of automated storage equipment based on a grey prediction model is provided. Figure 1 FIG. 1 is a flow chart of a method for evaluating and predicting the state of automated storage equipment based on a grey prediction model according to an embodiment of the present invention. Figure 1 As shown, the automated storage equipment status evaluation and prediction method based on the grey prediction model according to an embodiment of the present invention specifically includes:

[0024] Step S101, according to the equipment status evaluation result, obtain the health evaluation score data time series, and use it as the original data for status prediction, and pre-process the original data; specifically including:

[0025] According to the equipment status evaluation results, a health evaluation score data time series is established, and the health evaluation score data time series is used as the original data for status prediction;

[0026] Performing an accumulation operation on the original data to generate an accumulated data sequence;

[0027] The adjacent mean value is calculated for the original sequence to generate an adjacent mean value sequence.

[0028] Step S102, based on the pre-processed original data, establish the original model equation, obtain the parameter value of the original model equation, bring the parameter value into the prediction formula of the primary cumulative series, and perform cumulative subtraction on the calculated prediction series to obtain the original data prediction value. Specifically including:

[0029] The original model equation is constructed according to Formula 1:

[0030] x (0) (k)+az (1) (k) = b Formula 1;

[0031] Among them, x (0) (k) represents the time series of health evaluation score data, z (1) (k) represents the adjacent mean sequence, a and b are equation parameters, and k represents the equipment status evaluation order;

[0032] According to formula 2, the whitened differential equation is established through the original equation:

[0033]

[0034] Among them, X (1) Represents a cumulative data sequence;

[0035] The whitened differential equation is linearly transformed and the values ​​of parameters a and b are obtained by solving it using the least square method.

[0036] Substitute the parameter values ​​a and b into Formula 3, and perform cumulative subtraction on the calculated time response equation to restore it to the original data prediction value shown in Formula 4 and Formula 5:

[0037]

[0038]

[0039]

[0040] Among them, x (1) represents the cumulative data sequence, x (0) (k) represents the time series of health assessment score data, a and b are equation parameters, and k represents the order of equipment status evaluation.

[0041] The above technical solution of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings.

[0042] The modeling process of grey prediction method is as follows Figure 2 As shown, the model established by the grey system represents the order of the differential equation and also the number of variables. It is a time-continuous function model of the differential equation and can reveal the continuous and long process of the development of things.

[0043] The device status evaluation prediction implementation process according to the embodiment of the present invention specifically includes:

[0044] 1) Generate the original data sequence.

[0045] According to the equipment status evaluation results, continuous equipment status evaluation sequence data is obtained as the original data for status prediction.

[0046] For example, the actual status evaluation scores of the stacker in the past 8 days are shown in Table 1.

[0047] Table 1

[0048]

[0049] Establish a time series of health assessment score data based on the original data:

[0050] X (0) =(x (0) (1),x (0) (2),x (0) (3),x (0) (4)

[0051] =(88.70,89.00,90.00,90.00,90.10,89.80,90.00,92.00)

[0052] 2) Perform data processing

[0053] For the original data X (0) Perform accumulation operation to generate cumulative data sequence:

[0054] X (1) =(x (1) (1),x (1) (2),x (1) (3),x (1) (4),x (1) (5),x (1) (6),x (1) (7)

[0055] =(177.7,267.7,357.7,447.8,537.6,627.6,719.6)

[0056] For the original sequence X (0) Perform neighboring mean calculations to generate neighboring mean sequences:

[0057] Z (1) ={88.85,89.50,90.00,90.05,89.95}

[0058] 3) Establish the original model equation

[0059] a. Construct the initial equation of GM(1,1) model

[0060] x (0) (k)+az (1) (k) = b

[0061] b. Establish the whitened differential equation through the original equation

[0062]

[0063] c. Through linear transformation, the parameters a and b are solved by the least squares method to discretize the whitened differential equation and obtain:

[0064]

[0065] Shifting the phase of the discrete equation, we get:

[0066]

[0067] Let k = 1, 2, 3, 4, 5, 6, 7, 8, then

[0068]

[0069] make

[0070] have to

[0071] make

[0072] Using the least squares method, we can solve After matrix transformation, we can get:

[0073]

[0074] The solution is:

[0075] a=-0.0035

[0076] b=88.7208

[0077] 4) Substitute the prediction formula of the cumulative series and subtract the calculated prediction series to obtain the predicted value

[0078] Will Perform Laplace Transform

[0079] Solved

[0080]

[0081] so

[0082] The time response of the GM model is:

[0083] Substitute a and b, and subtract the calculated time response formula to restore it to the original data prediction value.

[0084]

[0085]

[0086] The model prediction value table is shown in Table 2:

[0087] Table 2

[0088]

[0089] In summary, with the help of the technical solution of the embodiment of the present invention, the model requires less original data, is simple to calculate, and generally does not require multi-factor data but only single-factor data of the prediction object itself, and has less dependence on historical data.

[0090] Device Example 1

[0091] According to an embodiment of the present invention, a device for evaluating and predicting the state of automated storage equipment based on a grey prediction model is provided. Figure 3 4 is a schematic diagram of an embodiment of the present invention, such as Figure 3 As shown, the automatic storage equipment status evaluation and prediction device based on the grey prediction model according to an embodiment of the present invention specifically includes:

[0092] The acquisition preprocessing module 30 is used to obtain the health evaluation score data time series according to the equipment status evaluation result, and preprocess the raw data as the raw data for status prediction; specifically, it is used to:

[0093] According to the equipment status evaluation results, a health evaluation score data time series is established, and the health evaluation score data time series is used as the original data for status prediction;

[0094] Performing an accumulation operation on the original data to generate an accumulated data sequence;

[0095] The adjacent mean value is calculated for the original sequence to generate an adjacent mean value sequence.

[0096] A calculation module 32 is established to establish an original model equation based on the preprocessed original data, obtain parameter values ​​of the original model equation, bring the parameter values ​​into the prediction formula of a cumulative series, and perform cumulative subtraction on the calculated prediction series to obtain the original data prediction value. Specifically used for:

[0097] The original model equation is constructed according to Formula 1:

[0098] x (0) (k)+az (1) (k) = b Formula 1;

[0099] Among them, x (0) (k) represents the time series of health evaluation score data, z (1) (k) represents the adjacent mean sequence, a and b are equation parameters, and k represents the equipment status evaluation order;

[0100] According to formula 2, the whitened differential equation is established through the original equation:

[0101]

[0102] Among them, X (1) Represents a cumulative data sequence;

[0103] The whitened differential equation is linearly transformed and the values ​​of parameters a and b are obtained by solving it using the least square method.

[0104] Substitute the parameter values ​​a and b into Formula 3, and perform cumulative subtraction on the calculated time response equation to restore it to the original data prediction value shown in Formula 4 and Formula 5:

[0105]

[0106]

[0107]

[0108] Among them, X (1) represents the cumulative data sequence, x (0) (k) represents the time series of health assessment score data, a and b are equation parameters, and k represents the order of equipment status evaluation.

[0109] The embodiment of the present invention is a device embodiment corresponding to the above method embodiment. The specific operations of each module can be understood by referring to the description of the method embodiment, which will not be repeated here.

[0110] Device Example 2

[0111] An embodiment of the present invention provides an electronic device, such as Figure 4 As shown, it includes: a memory 40, a processor 42, and a computer program stored in the memory 40 and executable on the processor 42. When the computer program is executed by the processor 42, the steps described in the method embodiment are implemented.

[0112] Device Example 3

[0113] An embodiment of the present invention provides a computer-readable storage medium, on which a program for implementing information transmission is stored. When the program is executed by the processor 42, the steps described in the method embodiment are implemented.

[0114] The computer-readable storage medium in this embodiment includes, but is not limited to, ROM, RAM, magnetic disk or optical disk, etc.

[0115] 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 evaluating and predicting the state of automated storage equipment based on a grey prediction model, characterized in that: include: According to the equipment status evaluation result, a time series of health evaluation score data is obtained and used as raw data for status prediction, and the raw data is preprocessed; Based on the preprocessed original data, an original model equation is established, parameter values ​​of the original model equation are obtained, the parameter values ​​are substituted into a prediction formula of a cumulative series, and the calculated prediction series is cumulatively subtracted to obtain the original data prediction value.

2. The method according to claim 1, characterized in that According to the equipment status evaluation results, a time series of health evaluation score data is obtained and used as raw data for status prediction. The raw data is preprocessed, specifically including: According to the equipment status evaluation results, a health evaluation score data time series is established, and the health evaluation score data time series is used as the original data for status prediction; Performing an accumulation operation on the original data to generate an accumulated data sequence; The adjacent mean value is calculated for the original sequence to generate an adjacent mean value sequence.

3. The method according to claim 2, characterized in that Based on the preprocessed original data, an original model equation is established, and obtaining parameter values ​​of the original model equation specifically includes: The original model equation is constructed according to Formula 1: x (0) (k)+az (1) (k) = b Formula 1; Among them, x (0) (k) represents the time series of health evaluation score data, z (1) (k) represents the adjacent mean sequence, a and b are equation parameters, and k represents the equipment status evaluation order; According to formula 2, the whitened differential equation is established through the original equation: Among them, X (1) Represents a cumulative data sequence; The whitened differential equation is linearly transformed and the values ​​of parameters a and b are obtained by solving it using the least square method.

4. The method according to claim 1, characterized in that Substituting the parameter value into the prediction formula of the cumulative series, and subtracting the calculated prediction series, to obtain the original data prediction value specifically includes: Substitute the parameter values ​​a and b into Formula 3, and perform cumulative subtraction on the calculated time response equation to restore it to the original data prediction value shown in Formula 4 and Formula 5: Among them, X (1) represents the cumulative data sequence, x (0) (k) represents the time series of health assessment score data, a and b are equation parameters, and k represents the order of equipment status evaluation.

5. An automated storage equipment status evaluation and prediction device based on a grey prediction model, characterized in that: include: An acquisition preprocessing module is used to acquire a time series of health evaluation score data according to the equipment status evaluation result, and preprocess the raw data as raw data for status prediction; A calculation module is established to establish an original model equation based on the preprocessed original data, obtain parameter values ​​of the original model equation, bring the parameter values ​​into a prediction formula of a cumulative series, and perform cumulative subtraction on the calculated prediction series to obtain the prediction value of the original data.

6. The device according to claim 5, characterized in that The acquisition preprocessing module is specifically used for: According to the equipment status evaluation results, a health evaluation score data time series is established, and the health evaluation score data time series is used as the original data for status prediction; Performing an accumulation operation on the original data to generate an accumulated data sequence; The adjacent mean value is calculated for the original sequence to generate an adjacent mean value sequence.

7. The device according to claim 6, characterized in that The establishment of the calculation module is specifically used for: The original model equation is constructed according to Formula 1: x (0) (k)+az (1) (k) = b Formula 1; Among them, x (0) (k) represents the time series of health evaluation score data, z (1) (k) represents the adjacent mean sequence, a and b are equation parameters, and k represents the equipment status evaluation order; According to formula 2, the whitened differential equation is established through the original equation: Among them, X (1) Represents a cumulative data sequence; The whitened differential equation is linearly transformed and the values ​​of parameters a and b are obtained by solving it using the least square method.

8. The device according to claim 5, characterized in that The establishment of the calculation module is specifically used for: Substitute the parameter values ​​a and b into Formula 3, and perform cumulative subtraction on the calculated time response equation to restore it to the original data prediction value shown in Formula 4 and Formula 5: Among them, X (1) represents the cumulative data sequence, x (0) (k) represents the time series of health assessment score data, a and b are equation parameters, and k represents the order of equipment status evaluation.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method for evaluating and predicting the state of automated warehousing equipment based on a grey prediction model as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an implementation program for information transmission, and when the program is executed by the processor, the steps of the automated storage equipment status evaluation and prediction method based on the grey prediction model as described in any one of claims 1 to 4 are implemented.

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