Material storage information processing method and device

Abstract

The invention discloses a material storage information processing method and device. The method comprises the following steps: obtaining storage distribution historical information and a material storage historical information set; preprocessing the storage demand information set and the material resource description information set to obtain a storage demand matrix and a material resource description matrix; and carrying out identification processing on the storage distribution historical information, the storage demand matrix and the material resource description matrix to obtain a material storage model. According to the invention, the historical text data of task deployment is effectively utilized, and the rule of warehouse-out material warehouse deployment is summarized and extracted from the historical text data, so that the efficiency and accuracy of warehouse deployment are improved.

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 processing material storage information. Background Art

[0002] Warehousing plays a vital role in the logistics system and is the focus of current research and planning in the logistics field. Efficient and reasonable warehousing can help speed up the flow of materials, reduce costs, and ensure the smooth progress of various types of support operations.

[0003] In modern society, the degree of informatization is increasing day by day, and all walks of life cannot do without a large amount of text information processing. Especially in the field of material storage management, material storage tables are a common text form used to record the deployment information of various types of materials, such as warehouse distribution, material warehouses, etc. However, most material storage tables are still in the form of paper or Word documents, resulting in the modification, search, and maintenance of deployment information mainly relying on manual completion, which is inefficient and prone to problems such as missing records and difficulty in management. At the same time, there are also a lot of errors and noise in the material storage table, which affects the accuracy and efficiency of material storage.

[0004] At the same time, a large amount of historical data on material warehousing is a successful case of material warehousing, but there is also a large amount of irrelevant data and noise in the material warehousing data. How to effectively mine the material warehousing information contained in the historical data and obtain successful methods for material warehousing is also a problem that needs to be solved urgently. How to effectively use the historical text data of task deployment, summarize and extract the rules of material warehousing deployment, and thus improve the efficiency and accuracy of material warehousing deployment is a problem that needs to be solved urgently. Summary of the invention

[0005] The present invention mainly solves the problem of how to effectively utilize the historical text data of task deployment, summarize and extract the rules of material storage deployment, thereby improving the efficiency and accuracy of material storage deployment. The present invention discloses a method and device for processing material storage information.

[0006] In a first aspect of the embodiment of the present application, a method for processing material storage information is disclosed, comprising:

[0007] S1, obtaining storage allocation history information and material storage history information set; the material storage history information set includes a storage demand information set and a material resource description information set; the storage demand information set includes storage demand information; the material resource description information set includes material resource description information; the storage demand information includes the warehouse's demand information for each material capacity indicator; the material resource description information includes the capacity indicator information of each material;

[0008] S2, preprocessing the storage demand information set and the material resource description information set to obtain a storage demand matrix and a material resource description matrix;

[0009] S3, identifying and processing the storage allocation history information, storage demand matrix and material resource description matrix to obtain a material storage model.

[0010] The preprocessing of the storage demand information set and the material resource description information set to obtain a storage demand matrix and a material resource description matrix includes:

[0011] S21, performing text recognition processing on the storage demand information set and the material resource description information set to obtain natural language text format information;

[0012] S22, preprocessing the natural language text format information to obtain a natural language text set;

[0013] S23, performing text encoding conversion processing on the natural language text set to obtain a storage demand matrix and a material resource description matrix.

[0014] The text encoding conversion process is performed on the natural language text set to obtain a storage demand matrix and a material resource description matrix, including:

[0015] S231, performing text encoding conversion processing on each storage demand information in the natural language text set to obtain a corresponding storage demand vector;

[0016] S232, using each storage demand vector as a row vector, constructing a storage demand matrix; the row dimension and column dimension of the storage demand matrix are M and N respectively;

[0017] S233, performing text encoding conversion processing on each material resource description information in the natural language text set to obtain a corresponding material resource description vector;

[0018] S234, using each material resource description vector as a row vector, constructing a material resource description matrix; the row dimension and column dimension of the material resource description matrix are P and N respectively.

[0019] The identification and processing of the storage allocation history information, the storage demand matrix and the material resource description matrix to obtain the material storage model includes:

[0020] S41, the storage allocation history information is represented as a vector V, the i-th element v of the vector V i Indicates the number of materials stored in the i-th warehouse;

[0021] S42, classifying the storage demand matrix to obtain a storage category information set; the storage category information set includes storage category information of each warehouse;

[0022] S43, classifying the material resource description matrix to obtain a material resource category information set; the material resource category information set includes capability range category information of each type of material;

[0023] S44, using the vector V, establishing a matching correspondence between each element of the storage category information set and each element of the material resource category information set;

[0024] S45, based on the matching correspondence, mapping, identifying and calculating the storage category information set and the material resource category information set to obtain a material storage model.

[0025] The warehousing demand matrix is ​​classified to obtain a warehousing category information set, including:

[0026] S421, obtaining a storage category description matrix; the row vector of the storage category description matrix is ​​a standard row vector for each storage category;

[0027] S422, performing a first distance calculation process on each row vector of the storage demand matrix and the storage category description matrix to obtain a first distance vector corresponding to the row vector;

[0028] The expression of the first distance calculation process is:

[0029]

[0030] Among them, a ij is the element in the i-th row and j-th column of the storage demand matrix, A1 kj is the element in the kth row and jth column of the storage category description matrix, σ is the preset adjustment factor, and d ik represents the kth element of the first distance vector corresponding to the i-th row vector of the storage demand matrix;

[0031] S423, determining the sequence number value of the minimum element in the first distance vector as the storage category value of the row vector;

[0032] S424, merging the storage category values ​​of all row vectors of the storage demand matrix to obtain a storage category information set.

[0033] The material resource description matrix is ​​classified to obtain a material resource category information set, including:

[0034] S431, obtaining a material resource category description matrix; the row vector of the material resource category description matrix is ​​a standard row vector for each material resource category;

[0035] S432, performing a second distance calculation process on each row vector of the material resource description matrix and the material resource category description matrix to obtain a second distance vector;

[0036] S433, determining the sequence number value of the maximum element in the second distance vector as the material resource category value of the row vector;

[0037] S434, combining the material resource category values ​​of all row vectors of the material resource description matrix to obtain a material resource category information set.

[0038] The expression of the second distance calculation process is:

[0039]

[0040] Among them, b ij is the element in the i-th row and j-th column of the material resource description matrix, A2 kj is the element in the kth row and jth column of the material resource category description matrix, μ 1 and μ 2 is the preset weighting coefficient, c is the preset bias coefficient, e ik Represents the kth element of the second distance vector corresponding to the i-th row vector of the material resource description matrix.

[0041] In a second aspect of an embodiment of the present invention, a device for processing material storage information is disclosed, the device comprising:

[0042] A memory storing executable program code;

[0043] a processor coupled to the memory;

[0044] The processor calls the executable program code stored in the memory to execute the material storage information processing method.

[0045] 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 material storage information processing method.

[0046] 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 material storage information processing method.

[0047] The beneficial effects of the present invention are:

[0048] The present invention effectively suppresses the influence of various noise interferences in the data collection process by performing text recognition and preprocessing on the information set before performing recognition processing, thereby ensuring the accuracy of the acquired data.

[0049] The present invention effectively utilizes the historical text data of task deployment, summarizes and extracts the rules of warehouse material storage deployment, thereby improving the efficiency and accuracy of storage deployment.

[0050] The present invention mines historical material deployment data to effectively mine the material deployment information contained in the historical data, successfully obtains a warehouse material deployment model, and improves the efficiency of material deployment. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flow chart for implementing the method of the present invention. DETAILED DESCRIPTION

[0052] In order to better understand the content of the present invention, an embodiment is given here.

[0053] Figure 1 It is a flow chart for implementing the method of the present invention.

[0054] In a first aspect of the embodiment of the present application, a method for processing material storage information is disclosed, comprising:

[0055] S1, obtaining storage allocation history information and material storage history information set; the material storage history information set includes a storage demand information set and a material resource description information set; the storage demand information set includes storage demand information; the material resource description information set includes material resource description information; the storage demand information includes the warehouse's demand information for each material capacity indicator; the material resource description information includes the capacity indicator information of each material;

[0056] S2, preprocessing the storage demand information set and the material resource description information set to obtain a storage demand matrix and a material resource description matrix;

[0057] S3, identifying and processing the storage allocation history information, storage demand matrix and material resource description matrix to obtain a material storage model.

[0058] The preprocessing of the storage demand information set and the material resource description information set to obtain a storage demand matrix and a material resource description matrix includes:

[0059] S21, performing text recognition processing on the storage demand information set and the material resource description information set to obtain natural language text format information;

[0060] S22, preprocessing the natural language text format information to obtain a natural language text set;

[0061] S23, performing text encoding conversion processing on the natural language text set to obtain a storage demand matrix and a material resource description matrix.

[0062] The text encoding conversion process is performed on the natural language text set to obtain a storage demand matrix and a material resource description matrix, including:

[0063] S231, performing text encoding conversion processing on each storage demand information in the natural language text set to obtain a corresponding storage demand vector;

[0064] S232, using each storage demand vector as a row vector, constructing a storage demand matrix; the row dimension and column dimension of the storage demand matrix are M and N respectively;

[0065] S233, performing text encoding conversion processing on each material resource description information in the natural language text set to obtain a corresponding material resource description vector;

[0066] S234, using each material resource description vector as a row vector, constructing a material resource description matrix; the row dimension and column dimension of the material resource description matrix are P and N respectively.

[0067] The identification and processing of the storage allocation history information, the storage demand matrix and the material resource description matrix to obtain the material storage model includes:

[0068] S41, the storage allocation history information is represented as a vector V, the i-th element v of the vector V i Indicates the number of materials stored in the i-th warehouse;

[0069] S42, classifying the storage demand matrix to obtain a storage category information set; the storage category information set includes storage category information of each warehouse;

[0070] S43, classifying the material resource description matrix to obtain a material resource category information set; the material resource category information set includes capability range category information of each type of material;

[0071] S44, using the vector V, establishing a matching correspondence between each element of the storage category information set and each element of the material resource category information set;

[0072] S45, based on the matching correspondence, mapping, identifying and calculating the storage category information set and the material resource category information set to obtain a material storage model.

[0073] The warehousing demand matrix is ​​classified to obtain a warehousing category information set, including:

[0074] S421, obtaining a storage category description matrix; the row vector of the storage category description matrix is ​​a standard row vector for each storage category;

[0075] S422, performing a first distance calculation process on each row vector of the storage demand matrix and the storage category description matrix to obtain a first distance vector corresponding to the row vector;

[0076] The expression of the first distance calculation process is:

[0077]

[0078] Among them, a ij is the element in the i-th row and j-th column of the storage demand matrix, A1 kj is the element in the kth row and jth column of the storage category description matrix, σ is the preset adjustment factor, and d ik represents the kth element of the first distance vector corresponding to the i-th row vector of the storage demand matrix;

[0079] S423, determining the sequence number value of the minimum element in the first distance vector as the storage category value of the row vector;

[0080] S424, merging the storage category values ​​of all row vectors of the storage demand matrix to obtain a storage category information set.

[0081] The material resource description matrix is ​​classified to obtain a material resource category information set, including:

[0082] S431, obtaining a material resource category description matrix; the row vector of the material resource category description matrix is ​​a standard row vector for each material resource category;

[0083] S432, performing a second distance calculation process on each row vector of the material resource description matrix and the material resource category description matrix to obtain a second distance vector;

[0084] S433, determining the sequence number value of the maximum element in the second distance vector as the material resource category value of the row vector;

[0085] S434, combining the material resource category values ​​of all row vectors of the material resource description matrix to obtain a material resource category information set.

[0086] The expression of the second distance calculation process is:

[0087]

[0088] Among them, b ij is the element in the i-th row and j-th column of the material resource description matrix, A2 kj is the element in the kth row and jth column of the material resource category description matrix, μ 1 and μ 2 is the preset weighting coefficient, c is the preset bias coefficient, e ik Represents the kth element of the second distance vector corresponding to the i-th row vector of the material resource description matrix.

[0089] The text recognition processing is performed on the storage demand information set and the material resource description information set to obtain natural language text format information, including:

[0090] Each information in the storage demand information set and the material resource description information set is subjected to text recognition processing to obtain the information after text recognition; all the information after text recognition is integrated to obtain the natural language text format information. The natural language text format information includes the storage demand information set and the material resource description information set after text recognition processing.

[0091] The preprocessing of the natural language text format information to obtain a natural language text set includes:

[0092] Performing boundary check processing on the storage demand information set and the material resource description information set in the natural language text format information respectively, to obtain the storage demand information set and the material resource description information set after boundary check;

[0093] The storage demand information set and the material resource description information set after the boundary check are respectively subjected to data cleaning processing to obtain a natural language text set; the natural language text set includes the storage demand information set and the material resource description information set after data cleaning.

[0094] The text encoding conversion may be to set corresponding values, such as 8, 6, 4, and 2, for strong, medium, general, and weak in the demand information, or to set corresponding values, such as 10, 8, 6, 4, and 2, for leading, excellent, available, general, and unqualified in the capability evaluation values;

[0095] The text recognition process can be implemented using text recognition software or program.

[0096] The row vector of the material resource description matrix is ​​a material resource description vector;

[0097] The text vector conversion process can be implemented using the word2vector model;

[0098] The use of the vector V to establish a matching correspondence between each element of the storage category information set and each element of the material resource category information set includes: taking the serial number value of each element in the vector V as the first variable, taking the value of the element as the second variable, taking the element with the serial number of the first variable from the storage category information set, taking the element with the serial number of the second variable from the material resource category information set, establishing a corresponding relationship between the two elements, repeating the above operation for each element in the vector V, and establishing a matching correspondence between each element of the storage category information set and each element of the material resource category information set.

[0099] The element with the serial number of the first variable is taken out from the storage category information set, and the element with the serial number of the second variable is taken out from the material resource category information set, and the corresponding relationship between the two elements is established, which can be achieved by establishing a mapping function.

[0100] The material storage model is obtained by mapping, identifying and calculating the storage category information set and the material resource category information set based on the matching correspondence relationship, taking the storage category information set as the dependent variable y and the material resource category information set as the independent variable x, using the matching correspondence relationship to establish a pairwise matching relationship between the elements of x and y, performing function fitting processing on the matching relationship, and obtaining a fitting function as the material storage model; the input variable of the material storage model is the material resource category value, and the output variable of the material storage model is the storage category value. The function fitting can adopt linear fitting or nonlinear fitting.

[0101] The mapping recognition calculation process may be to use a function approximation method to perform curve fitting on x and y, and may use a best consistent linear approximation method. The best consistent approximation polynomial f(x) is expressed as:

[0102]

[0103] Where P1 is the order of the best consistent approximation polynomial f(x), α0, α1, α2, …, α P1 are the coefficients of the best consistent approximation polynomial f(x);

[0104] The step of performing boundary check processing on the storage demand information set and the material resource description information set respectively to obtain the storage demand information set and the material resource description information set after boundary check includes:

[0105] 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;

[0106] Performing fusion processing on all boundary-checked data of the information set to obtain a boundary-checked information set;

[0107] 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:

[0108] f(Ix)=α P1 (Ix) P1 +α P1-1 (Ix) P1-1 +…+α 2 (Ix) 2 +α 1 (Ix)+α 0 ,

[0109] 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);

[0110] The step of performing boundary check processing on the storage demand information set and the material resource description information set respectively to obtain the storage demand information set and the material resource description information set after boundary check includes:

[0111] 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;

[0112] 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;

[0113] S2203, executing S2201 and S2202 for all data in the information set to obtain the information set after boundary checking;

[0114] 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.

[0115] 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.

[0116] In a second aspect of an embodiment of the present invention, a device for processing material storage information is disclosed, the device comprising:

[0117] A memory storing executable program code;

[0118] a processor coupled to the memory;

[0119] The processor calls the executable program code stored in the memory to execute the material storage information processing method.

[0120] 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 material storage information processing method.

[0121] 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 material storage information processing method.

[0122] 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 processing material storage information, characterized in that: include: S1, obtaining storage allocation history information and material storage history information set; the material storage history information set includes a storage demand information set and a material resource description information set; the storage demand information set includes storage demand information; the material resource description information set includes material resource description information; the storage demand information includes the warehouse's demand information for each material capacity indicator; the material resource description information includes the capacity indicator information of each material; S2, preprocessing the storage demand information set and the material resource description information set to obtain a storage demand matrix and a material resource description matrix; S3, identifying and processing the storage allocation history information, storage demand matrix and material resource description matrix to obtain a material storage model.

2. The material storage information processing method according to claim 1, characterized in that: The preprocessing of the storage demand information set and the material resource description information set to obtain a storage demand matrix and a material resource description matrix includes: S21, performing text recognition processing on the storage demand information set and the material resource description information set to obtain natural language text format information; S22, preprocessing the natural language text format information to obtain a natural language text set; S23, performing text encoding conversion processing on the natural language text set to obtain a storage demand matrix and a material resource description matrix.

3. The material storage information processing method according to claim 2, characterized in that: The text encoding conversion process is performed on the natural language text set to obtain a storage demand matrix and a material resource description matrix, including: S231, performing text encoding conversion processing on each storage demand information in the natural language text set to obtain a corresponding storage demand vector; S232, using each storage demand vector as a row vector, constructing a storage demand matrix; the row dimension and column dimension of the storage demand matrix are M and N respectively; S233, performing text encoding conversion processing on each material resource description information in the natural language text set to obtain a corresponding material resource description vector; S234, using each material resource description vector as a row vector, constructing a material resource description matrix; the row dimension and column dimension of the material resource description matrix are P and N respectively.

4. The material storage information processing method according to claim 2, characterized in that: The identification and processing of the storage allocation history information, the storage demand matrix and the material resource description matrix to obtain the material storage model includes: S41, the storage allocation history information is represented as a vector V, the i-th element v of the vector V i Indicates the number of materials stored in the i-th warehouse; S42, classifying the storage demand matrix to obtain a storage category information set; the storage category information set includes storage category information of each warehouse; S43, classifying the material resource description matrix to obtain a material resource category information set; the material resource category information set includes capability range category information of each type of material; S44, using the vector V, establishing a matching correspondence between each element of the storage category information set and each element of the material resource category information set; S45, based on the matching correspondence, mapping, identifying and calculating the storage category information set and the material resource category information set to obtain a material storage model.

5. The material storage information processing method according to claim 4, characterized in that: The warehousing demand matrix is ​​classified to obtain a warehousing category information set, including: S421, obtaining a storage category description matrix; the row vector of the storage category description matrix is ​​a standard row vector for each storage category; S422, performing a first distance calculation process on each row vector of the storage demand matrix and the storage category description matrix to obtain a first distance vector corresponding to the row vector; The expression of the first distance calculation process is: Among them, a ij is the element in the i-th row and j-th column of the storage demand matrix, A1 kj is the element in the kth row and jth column of the storage category description matrix, σ is the preset adjustment factor, and d ik represents the kth element of the first distance vector corresponding to the i-th row vector of the storage demand matrix; S423, determining the sequence number value of the minimum element in the first distance vector as the storage category value of the row vector; S424, merging the storage category values ​​of all row vectors of the storage demand matrix to obtain a storage category information set.

6. The material storage information processing method according to claim 5, characterized in that: The material resource description matrix is ​​classified to obtain a material resource category information set, including: S431, obtaining a material resource category description matrix; the row vector of the material resource category description matrix is ​​a standard row vector for each material resource category; S432, performing a second distance calculation process on each row vector of the material resource description matrix and the material resource category description matrix to obtain a second distance vector; S433, determining the sequence number value of the maximum element in the second distance vector as the material resource category value of the row vector; S434, combining the material resource category values ​​of all row vectors of the material resource description matrix to obtain a material resource category information set.

7. The material storage information processing method according to claim 6, characterized in that: The expression of the second distance calculation process is: Among them, b ij is the element in the i-th row and j-th column of the material resource description matrix, A2 kj is the element in the kth row and jth column of the material resource category description matrix, μ1 and μ2 are the preset weighting coefficients, c is the preset bias coefficient, and e ik Represents the kth element of the second distance vector corresponding to the i-th row vector of the material resource description matrix.

8. A material storage information processing device, 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 material storage information processing method according to any one of claims 1 to 7.

9. 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 material storage information processing method according to any one of claims 1 to 7.

10. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the material storage information processing method according to any one of claims 1 to 7.

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