A method and device for evaluating storage capacity of warehouse materials

Abstract

The present invention discloses a method and device for evaluating the storage capacity of warehouse materials, the method comprising: obtaining a warehouse material storage capacity information set; the warehouse material storage capacity information set includes warehouse material storage capacity information; the warehouse material storage capacity information includes sampling values ​​of all sampling moments of each warehouse material storage capacity; preprocessing the warehouse material storage capacity information set to obtain a preprocessed warehouse material storage capacity information set; evaluating the preprocessed warehouse material storage capacity information set to obtain a warehouse material storage capacity evaluation value. The present invention effectively screens and cleans the performance test data generated by the warehouse in various logistics scenarios during the material storage, circulation, and test application process, thereby ensuring the accuracy of the original data and providing an effective premise for the development of capacity evaluation.

Description

Technical Field

[0001] The present invention relates to the fields of logistics and information processing, and in particular to a method and device for evaluating the storage capacity of warehouse materials. Background Art

[0002] Warehousing is the inventory control center in logistics and supply chain, an important part of modern logistics, plays a vital role in the logistics system, and is the focus of current research and planning in the logistics field. In the process of material storage, circulation, and testing and application, warehouses will generate a large amount of performance test data in various logistics scenarios. These performance test data generally have multi-source heterogeneous characteristics such as complex formats, inconsistent standards, and a lot of data. How to effectively aggregate, process and evaluate these complex multi-source heterogeneous performance test data, so as to accurately judge the storage efficiency of warehouses and correctly extract performance characteristics, and provide support for effectively improving the storage capacity of warehouses, is a key issue that needs to be solved in the current warehousing and logistics field. Summary of the invention

[0003] The present invention mainly solves the problem of how to effectively aggregate, process and evaluate complex multi-source heterogeneous performance test data, so as to accurately judge the storage efficiency of the warehouse and correctly extract the performance characteristics, and provide support for effectively improving the storage capacity of the warehouse. The present invention discloses a method and device for evaluating the storage capacity of warehouse materials.

[0004] In a first aspect of an embodiment of the present application, a method for evaluating the storage capacity of warehouse materials is disclosed, comprising: S1, obtaining a warehouse material storage capacity information set; the warehouse material storage capacity information set includes warehouse material storage capacity information; the warehouse material storage capacity information includes sampling values ​​of all sampling moments of each warehouse material storage capacity;

[0005] S2, preprocessing the warehouse material storage capacity information set to obtain a preprocessed warehouse material storage capacity information set;

[0006] S3, evaluating the preprocessed warehouse material storage capacity information set to obtain a warehouse material storage capacity evaluation value.

[0007] The preprocessing of the warehouse material storage capacity information set to obtain the preprocessed warehouse material storage capacity information set includes:

[0008] S21, checking and processing the warehouse material storage capacity information set to obtain the warehouse material storage capacity information set after boundary checking;

[0009] S22, performing data cleaning processing on the warehouse material storage capacity information set after the boundary check to obtain a pre-processed warehouse material storage capacity information set.

[0010] The checking and processing of the warehouse material storage capacity information set to obtain the checked warehouse material storage capacity information set includes:

[0011] S211, for each type of data attribute of the warehouse material storage capacity information set, using the data collection information of the data as an independent variable and the data value of the data as a dependent variable, perform autoregression-sliding average modeling to obtain regression models of the data attributes of the type;

[0012] S212, using the regression model to perform calculation processing on the independent variable to obtain regression data values;

[0013] S213, determining whether the absolute value of the difference between the regression data value and the corresponding dependent variable value is greater than a set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, deleting the data from the warehouse material storage capacity information set; if it is less than or equal to the first regression discrimination threshold, not processing the data;

[0014] S214, fusing all data after executing S211 to S213 of the warehouse material storage capacity information set to obtain a checked warehouse material storage capacity information set.

[0015] The step of evaluating the preprocessed warehouse material storage capacity information set to obtain a warehouse material storage capacity evaluation value includes:

[0016] S31, representing the preprocessed warehouse material storage capacity information set as a matrix W; the i-th row vector of the matrix W represents the sampling value of the i-th capacity type of the warehouse at all sampling times;

[0017] S32, performing time-frequency transformation on each row vector of the matrix W to obtain a corresponding time-frequency vector; using all the time-frequency vectors, constructing a time-frequency matrix P;

[0018] S33, performing classification extreme value extraction processing on each column vector of the time-frequency matrix P to obtain corresponding first solution information and second solution information;

[0019] S34, performing characteristic distance calculations on each column vector of the time-frequency matrix P and the corresponding first solution information and second solution information, respectively, to obtain a first characteristic distance vector and a second characteristic distance vector;

[0020] S35, decomposing the time-frequency matrix P to obtain a feature matrix Y;

[0021] The decomposition process is calculated as follows:

[0022] P=UYV,

[0023] Among them, U is the left decomposition matrix, Y is the characteristic matrix, V is the right decomposition matrix, and both U and V are orthogonal matrices;

[0024] S36, extracting the diagonal elements of the characteristic matrix, and constructing a diagonal vector using all the extracted diagonal elements;

[0025] S37, performing fusion and weighted processing on the first characteristic distance vector, the second characteristic distance vector and the diagonal vector to obtain a warehouse material storage capacity evaluation value.

[0026] The expression of the classification extreme value extraction process is:

[0027]

[0028] in, is the first solution information obtained using the i-th row vector of the time-frequency matrix P, p i ′ is the second solution information obtained using the i-th row vector of the time-frequency matrix P; i∈J * indicates that the i-th capability type belongs to the benefit-type indicator, i∈J′ indicates that the i-th capability type belongs to the cost-type indicator, and J * is a benefit indicator, J' is a cost indicator; the category number of the capability type is determined according to the row number of the matrix W; m and n are the row dimension and column dimension of the time-frequency matrix P respectively.

[0029] The expression for calculating the feature distance is:

[0030]

[0031] in, is the i-th element of the first feature distance vector, S′ i is the i-th element of the second feature distance vector.

[0032] The calculation expression of the fusion weighted processing is:

[0033]

[0034] Where v is the storage capacity assessment value of warehouse materials, i = 1, 2, ..., m, u i is the i-th element of the diagonal vector.

[0035] In a second aspect of an embodiment of the present invention, a device for evaluating the storage capacity of warehouse materials is disclosed, the device comprising:

[0036] A memory storing executable program code;

[0037] a processor coupled to the memory;

[0038] The processor calls the executable program code stored in the memory to execute the method for evaluating the warehouse material storage capacity.

[0039] According to a third aspect of an embodiment of the present invention, a computer storable medium is disclosed, wherein the computer storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the method for evaluating the warehouse material storage capacity.

[0040] According to a fourth aspect of the embodiments of the present invention, an information data processing terminal is disclosed, and the information data processing terminal is used to implement the method for evaluating the warehouse material storage capacity.

[0041] The beneficial effects of the present invention are:

[0042] The present invention carries out effective data screening and cleaning processing on the performance test data of warehouses in various logistics scenarios generated during material storage, circulation, and testing and application, thereby ensuring the accuracy of the original data and providing an effective premise for the development of capability assessment.

[0043] In the process of capability evaluation, the present invention ensures the effective integration of various types of indicators by adopting different classification extreme value extraction processing methods for cost-type indicators and performance-type indicators.

[0044] The present invention proposes a feature distance calculation method and a fusion weighted calculation method, which realizes the effective extraction and fusion of indicator features, suppresses the irrelevant amount of indicators, realizes the accurate judgment of warehouse storage efficiency and the correct extraction of performance characteristics, and completes the accurate evaluation of warehouse storage capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a flow chart for implementing the method of the present invention. DETAILED DESCRIPTION

[0046] In order to better understand the content of the present invention, an embodiment is given here.

[0047] Figure 1 It is a flow chart for implementing the method of the present invention.

[0048] In a first aspect of the embodiment of the present application, a method for evaluating the storage capacity of warehouse materials is disclosed, comprising:

[0049] In a first aspect of an embodiment of the present application, a method for evaluating the storage capacity of warehouse materials is disclosed, comprising: S1, obtaining a warehouse material storage capacity information set; the warehouse material storage capacity information set includes warehouse material storage capacity information; the warehouse material storage capacity information includes sampling values ​​of all sampling moments of each warehouse material storage capacity;

[0050] S2, preprocessing the warehouse material storage capacity information set to obtain a preprocessed warehouse material storage capacity information set;

[0051] S3, evaluating the preprocessed warehouse material storage capacity information set to obtain a warehouse material storage capacity evaluation value.

[0052] The preprocessing of the warehouse material storage capacity information set to obtain the preprocessed warehouse material storage capacity information set includes:

[0053] S21, checking and processing the warehouse material storage capacity information set to obtain the warehouse material storage capacity information set after boundary checking;

[0054] S22, performing data cleaning processing on the warehouse material storage capacity information set after the boundary check to obtain a pre-processed warehouse material storage capacity information set.

[0055] The checking and processing of the warehouse material storage capacity information set to obtain the checked warehouse material storage capacity information set includes:

[0056] S211, for each type of data attribute of the warehouse material storage capacity information set, using the data collection information of the data as an independent variable and the data value of the data as a dependent variable, perform autoregression-sliding average modeling to obtain regression models of the data attributes of the type;

[0057] S212, using the regression model to perform calculation processing on the independent variable to obtain regression data values;

[0058] S213, determining whether the absolute value of the difference between the regression data value and the corresponding dependent variable value is greater than a set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, deleting the data from the warehouse material storage capacity information set; if it is less than or equal to the first regression discrimination threshold, not processing the data;

[0059] S214, fusing all data after executing S211 to S213 of the warehouse material storage capacity information set to obtain a checked warehouse material storage capacity information set.

[0060] The step of evaluating the preprocessed warehouse material storage capacity information set to obtain a warehouse material storage capacity evaluation value includes:

[0061] S31, representing the preprocessed warehouse material storage capacity information set as a matrix W; the i-th row vector of the matrix W represents the sampling value of the i-th capacity type of the warehouse at all sampling times;

[0062] S32, performing time-frequency transformation on each row vector of the matrix W to obtain a corresponding time-frequency vector; using all the time-frequency vectors, constructing a time-frequency matrix P;

[0063] S33, performing classification extreme value extraction processing on each column vector of the time-frequency matrix P to obtain corresponding first solution information and second solution information;

[0064] S34, performing characteristic distance calculations on each column vector of the time-frequency matrix P and the corresponding first solution information and second solution information, respectively, to obtain a first characteristic distance vector and a second characteristic distance vector;

[0065] S35, decomposing the time-frequency matrix P to obtain a feature matrix Y;

[0066] The decomposition process is calculated as follows:

[0067] P=UYV,

[0068] Among them, U is the left decomposition matrix, Y is the characteristic matrix, V is the right decomposition matrix, and both U and V are orthogonal matrices;

[0069] S36, extracting the diagonal elements of the characteristic matrix, and constructing a diagonal vector using all the extracted diagonal elements;

[0070] S37, performing fusion and weighted processing on the first characteristic distance vector, the second characteristic distance vector and the diagonal vector to obtain a warehouse material storage capacity evaluation value.

[0071] The expression of the classification extreme value extraction process is:

[0072]

[0073] in, is the first solution information obtained using the i-th row vector of the time-frequency matrix P, p i ′ is the second solution information obtained using the i-th row vector of the time-frequency matrix P; i∈J * indicates that the i-th capability type belongs to the benefit-type indicator, i∈J′ indicates that the i-th capability type belongs to the cost-type indicator, and J *is a benefit indicator, J' is a cost indicator; the category number of the capability type is determined according to the row number of the matrix W; m and n are the row dimension and column dimension of the time-frequency matrix P respectively.

[0074] The expression for calculating the feature distance is:

[0075]

[0076] in, is the i-th element of the first feature distance vector, S i ′ is the i-th element of the second feature distance vector.

[0077] The calculation expression of the fusion weighted processing is:

[0078]

[0079] Where v is the storage capacity assessment value of warehouse materials, i = 1, 2, ..., m, u i is the i-th element of the diagonal vector.

[0080] The warehouse material storage capacity information includes warehouse storage capacity, power consumption per unit material storage, material storage time per unit, material retrieval time per unit, and maximum fault-free operation time of the warehouse. Among them, power consumption per unit material storage, material storage time per unit, and material retrieval time per unit are cost indicators, and the other indicators are performance indicators.

[0081] The autoregressive-moving average modeling can be implemented using the ARMA method.

[0082] The data cleaning process includes filling missing values, smoothing noise data, and smoothing or deleting outliers; the smoothed noise data is obtained by first determining the noise data, and then smoothing the noise data according to the data before and after the noise data; the noise data is a value whose value is less than the detection sensitivity of the sensor of the observed data, or greater than the measurement upper limit of the sensor of the observed data. The outlier point can be determined by the Kalman filter method. The filling value of the missing value can be determined by averaging the measured values ​​within a certain sampling interval before and after the missing value.

[0083] The checking and processing of the warehouse material storage capacity information set to obtain the checked warehouse material storage capacity information set includes:

[0084] For each type of data attribute of the information set, the data collection information of the data is used as a known independent variable, and the data value of the data is used as a known dependent variable. The known independent variable and the known dependent variable are used to construct a curve to be approximated, and the function approximation method is used to perform curve fitting on the curve to be approximated to obtain the best consistent approximation polynomial f(Ix) of the data attribute; the best consistent approximation polynomial f(Ix) is used to calculate and process the known independent variables to obtain an approximate dependent variable; it is determined whether the absolute value of the difference between the approximate dependent variable and the corresponding known dependent variable is greater than a set second regression discrimination threshold; if it is greater than the second regression discrimination threshold, the data is deleted from the information set; if it is less than or equal to the second regression discrimination threshold, the data is not processed;

[0085] Performing fusion processing on all data of the information set that performs boundary check to obtain a checked information set;

[0086] The curve fitting of the curve to be approximated by the function approximation method may be performed by using the best consistent linear approximation method. The best consistent approximation polynomial f(Ix) is expressed as:

[0087] f(Ix)=α P1 (Ix) P1 +α P1-1 (Ix) P1-1 +…+α2(Ix) 2 +α1(Ix)+α0,

[0088] Where P1 is the order of the best consistent approximation polynomial f(Ix), α0, α1, α2, …, α P1 are the coefficients of the optimal consistent approximation polynomial f(Ix);

[0089] The warehouse material storage capacity information set is checked and processed respectively to obtain the checked warehouse material storage capacity information set, including:

[0090] S2201, for each data attribute of the information set, taking the data collection information of the data as the independent variable and the data value of the data as the dependent variable, perform cluster analysis processing to obtain clustering result information of the data attribute of the class; the clustering result information includes the cluster category to which all data of the data attribute of the class belongs;

[0091] S2202, determining the number of data included in each cluster category; setting a data volume threshold; deleting the data included in the cluster category whose data volume is less than the data volume threshold from the information set;

[0092] S2203, executing S2201 and S2202 for all data in the information set to obtain the checked information set;

[0093] The data collection information includes time information, space information or capability type information corresponding to the data collection; when performing data fitting, any of the above information can be used as an independent variable, and the corresponding data value can be used as a dependent variable.

[0094] In a second aspect of an embodiment of the present invention, a device for evaluating the storage capacity of warehouse materials is disclosed, the device comprising:

[0095] A memory storing executable program code;

[0096] a processor coupled to the memory;

[0097] The processor calls the executable program code stored in the memory to execute the method for evaluating the warehouse material storage capacity.

[0098] According to a third aspect of an embodiment of the present invention, a computer storable medium is disclosed, wherein the computer storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the method for evaluating the warehouse material storage capacity.

[0099] According to a fourth aspect of the embodiments of the present invention, an information data processing terminal is disclosed, and the information data processing terminal is used to implement the method for evaluating the warehouse material storage capacity.

[0100] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for evaluating the storage capacity of warehouse materials, characterized in that: include: S1, obtain the warehouse material storage capacity information set; The warehouse material storage capacity information set includes warehouse material storage capacity information; The warehouse material storage capacity information includes the sampling values ​​of all sampling moments of each warehouse material storage capacity; the types of the warehouse material storage capacity information include warehouse storage capacity, power consumption per unit material storage in the warehouse, unit material storage time, unit material retrieval time, and maximum trouble-free operation time of the warehouse; S2, preprocessing the warehouse material storage capacity information set to obtain a preprocessed warehouse material storage capacity information set; S3, evaluating the preprocessed warehouse material storage capacity information set to obtain a warehouse material storage capacity evaluation value; The step of evaluating the preprocessed warehouse material storage capacity information set to obtain a warehouse material storage capacity evaluation value includes: S31, representing the preprocessed warehouse material storage capacity information set as a matrix W; the i-th row vector of the matrix W represents the sampling value of the i-th capacity type of the warehouse at all sampling times; S32, performing time-frequency transformation on each row vector of the matrix W to obtain a corresponding time-frequency vector; using all the time-frequency vectors, constructing a time-frequency matrix P; S33, performing classification extreme value extraction processing on each column vector of the time-frequency matrix P to obtain corresponding first solution information and second solution information; S34, performing characteristic distance calculations on each column vector of the time-frequency matrix P and the corresponding first solution information and second solution information, respectively, to obtain a first characteristic distance vector and a second characteristic distance vector; S35, decomposing the time-frequency matrix P to obtain a feature matrix Y; The decomposition process is calculated as follows: P=UYV, Among them, U is the left decomposition matrix, Y is the characteristic matrix, V is the right decomposition matrix, and both U and V are orthogonal matrices; S36, extracting the diagonal elements of the characteristic matrix, and constructing a diagonal vector using all the extracted diagonal elements; S37, performing fusion and weighted processing on the first characteristic distance vector, the second characteristic distance vector and the diagonal vector to obtain a warehouse material storage capacity evaluation value.

2. The method for evaluating the storage capacity of warehouse materials according to claim 1, characterized in that: The preprocessing of the warehouse material storage capacity information set to obtain the preprocessed warehouse material storage capacity information set includes: S21, checking and processing the warehouse material storage capacity information set to obtain the warehouse material storage capacity information set after boundary checking; S22, performing data cleaning processing on the warehouse material storage capacity information set after the boundary check to obtain a pre-processed warehouse material storage capacity information set.

3. The method for evaluating the storage capacity of warehouse materials as claimed in claim 2, characterized in that: The checking and processing of the warehouse material storage capacity information set to obtain the checked warehouse material storage capacity information set includes: S211, for each type of data attribute of the warehouse material storage capacity information set, using the data collection information of the data as an independent variable and the data value of the data as a dependent variable, perform autoregression-sliding average modeling to obtain regression models of the data attributes of the type; S212, using the regression model to perform calculation processing on the independent variable to obtain regression data values; S213, determining whether the absolute value of the difference between the regression data value and the corresponding dependent variable value is greater than a set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, deleting the data from the warehouse material storage capacity information set; if it is less than or equal to the first regression discrimination threshold, not processing the data; S214, fusing all data after executing S211 to S213 of the warehouse material storage capacity information set to obtain a checked warehouse material storage capacity information set.

4. The method for evaluating the storage capacity of warehouse materials according to claim 1, characterized in that: The expression of the classification extreme value extraction process is: in, is the first solution information obtained using the i-th row vector of the time-frequency matrix P, p ij represents the element of the i-th row and j-th column of the time-frequency matrix P, p i ′ is the second solution information obtained using the i-th row vector of the time-frequency matrix P; i∈J * indicates that the i-th capability type belongs to the benefit-type indicator, i∈J′ indicates that the i-th capability type belongs to the cost-type indicator, and J * is a benefit indicator, J' is a cost indicator; the category number of the capability type is determined according to the row number of the matrix W; m and n are the row dimension and column dimension of the time-frequency matrix P respectively.

5. The method for evaluating the storage capacity of warehouse materials according to claim 4, characterized in that: The expression for calculating the feature distance is: in, is the i-th element of the first feature distance vector, S i ′ is the i-th element of the second feature distance vector.

6. The method for evaluating the storage capacity of warehouse materials according to claim 5, characterized in that: The calculation expression of the fusion weighted processing is: Where v is the storage capacity assessment value of warehouse materials, i = 1, 2, ..., m, u i is the i-th element of the diagonal vector.

7. A device for evaluating the storage capacity of warehouse materials, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the method for evaluating the warehouse material storage capacity according to any one of claims 1 to 6.

8. A computer storable medium, characterized in that: The computer storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the method for evaluating the warehouse material storage capacity according to any one of claims 1 to 6.

9. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the method for evaluating the warehouse material storage capacity as described in any one of claims 1 to 6.

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