A simplified method and device for evaluating inventory material support capability

Abstract

The present invention discloses a method and device for simplifying inventory material support capability evaluation indicators. The method comprises: obtaining evaluation indicator element network information; the evaluation indicator element network information comprises M indicator node information and N element node information; performing feature extraction processing on the evaluation indicator element network information to obtain target indicator node vector information; the target indicator node vector information comprises M target indicator node vectors arranged in sequence; performing calculation and analysis processing on the target indicator node vector information to obtain a target simplified evaluation threshold; the target simplified evaluation threshold is used to indicate a simplified inventory material support capability evaluation indicator.

Description

Technical Field

[0001] The present invention relates to the field of evaluation technology, and in particular to a simplified method and device for evaluating inventory material support capability. Background Art

[0002] The evaluation of inventory support capability is essentially a decision-making problem of an information system composed of multiple indicators. The correct selection of evaluation indicators is the basis and premise for accurate decision-making. In the process of constructing the inventory support capability evaluation indicator system, due to the emphasis on the comprehensiveness of the indicators, the selected indicators need to fully characterize the system from multiple angles, which often leads to the phenomenon of "seeking completeness but not precision", resulting in a certain degree of information redundancy in the inventory support capability evaluation indicator system. Therefore, indicator simplification is the process of indicators from "completeness" to "precision", and it is also an indispensable part of inventory support capability evaluation. However, there are some problems with the current simplification of inventory support capability evaluation indicators: (1) The actual indicator measurement workload has not been reduced. For example, the principal component analysis method must be based on the original indicator measurement value for feature extraction, which does not reduce the actual workload. (2) The method is subjective. For example, there is no fixed theoretical basis for setting the number of simplified indicators in the maximum uncorrelated method. It is generally set according to the subjective experience and needs of the evaluator. Similarly, the setting of the simplification threshold in the grey correlation analysis method is also subjective. (3) The method has limitations. For example, the index simplification algorithm based on rough set theory is mostly applicable to decision-making systems with cost control of decision index sets; the grey correlation analysis method is only applicable to decision-making systems and cannot be applied to information systems that lack decision indicators. The selection of index simplification methods often affects the results of inventory material support capability assessment. Therefore, the problems existing in the above-mentioned index simplification result in poor results when the index is simplified. Therefore, a method and device for simplifying inventory material support capability assessment indicators are provided to simplify the inventory material support capability assessment indicator simplification process, improve the efficiency and reliability of simplified inventory material support capability assessment indicators, and provide support for evaluating inventory material support capabilities and optimizing inventory material support assessment plans. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method and device for simplifying inventory material support capability assessment indicators, which is conducive to simplifying the simplification process of inventory material support capability assessment indicators, improving the efficiency and reliability of the simplified inventory material support capability assessment indicators, and further providing support for evaluating inventory material support capabilities and optimizing inventory material support assessment plans.

[0004] In order to solve the above technical problems, the first aspect of the embodiment of the present invention discloses a simplified method for evaluating inventory material guarantee capability, the method comprising:

[0005] Acquire evaluation index element network information; the evaluation index element network information includes M index node information and N element node information;

[0006] Performing feature extraction processing on the evaluation index element network information to obtain target index node vector information; the target index node vector information includes the M target index node vectors arranged in sequence;

[0007] The target indicator node vector information is calculated and analyzed to obtain a target simplicity evaluation threshold; the target simplicity evaluation threshold is used to indicate a simplified inventory material support capability evaluation indicator.

[0008] A second aspect of an embodiment of the present invention discloses a simplified device for evaluating inventory material guarantee capability, the device comprising:

[0009] An acquisition module is used to acquire evaluation index element network information; the evaluation index element network information includes M index node information and N element node information;

[0010] A first processing module is used to perform feature extraction processing on the evaluation index element network information to obtain target index node vector information; the target index node vector information includes the M target index node vectors arranged in sequence;

[0011] The second processing module is used to calculate and analyze the target indicator node vector information to obtain a target simplicity evaluation threshold; the target simplicity evaluation threshold is used to indicate a simplified inventory material guarantee capability evaluation indicator.

[0012] The third aspect of the present invention discloses another simplified device for evaluating inventory material guarantee capability, the device comprising:

[0013] A memory storing executable program code;

[0014] a processor coupled to the memory;

[0015] The processor calls the executable program code stored in the memory to execute part or all of the steps in the simplified method for evaluating inventory material support capability disclosed in the first aspect of the embodiment of the present invention.

[0016] The fourth aspect of the present invention discloses a computer-readable storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the simplified method for inventory material support capability assessment indicators disclosed in the first aspect of an embodiment of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 It is a flow chart of a simplified method for evaluating inventory material guarantee capability disclosed in an embodiment of the present invention;

[0019] Figure 2 It is a structural schematic diagram of a simplified device for evaluating inventory material guarantee capability disclosed in an embodiment of the present invention;

[0020] Figure 3 It is a structural schematic diagram of another simplified device for evaluating inventory material guarantee capability disclosed in an embodiment of the present invention;

[0021] Figure 4 It is a schematic diagram of an evaluation index element network disclosed in an embodiment of the present invention;

[0022] Figure 5 It is a schematic diagram of a node sequence disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0025] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0026] The present invention discloses a method and device for simplifying the evaluation index of inventory material guarantee capability, which is conducive to simplifying the evaluation index simplification process of inventory material guarantee capability, improving the efficiency and reliability of the simplified evaluation index of inventory material guarantee capability, and further providing support for evaluating the inventory material guarantee capability and optimizing the inventory material guarantee evaluation plan. The following are detailed descriptions.

[0027] Embodiment 1

[0028] See also Figure 1 , Figure 1 This is a flow chart of a simplified method for evaluating inventory material support capability disclosed in an embodiment of the present invention. Figure 1 The described simplified method for evaluating the inventory material guarantee capability index is applied to a management system, such as a local server or a cloud server for management, and the embodiments of the present invention are not limited thereto. Figure 1 As shown, the simplified method for evaluating the inventory material support capability can include the following operations:

[0029] 101. Obtain network information of evaluation index elements.

[0030] In the embodiment of the present invention, the evaluation index element network information includes M index node information and N element node information.

[0031] 102. Perform feature extraction processing on the evaluation index element network information to obtain the target index node vector information.

[0032] In the embodiment of the present invention, the target indicator node vector information includes M target indicator node vectors arranged in sequence.

[0033] 103. Calculate and analyze the target indicator node vector information to obtain the target simplicity evaluation threshold.

[0034] In the embodiment of the present invention, the target simplified evaluation threshold is used to indicate a simplified inventory material guarantee capability evaluation index.

[0035] like Figure 4As shown, the evaluation index element network includes M index nodes and N element nodes. Among them, the index node is represented by d, the element node is represented by e, and the corresponding subscripts are the index number and the element number respectively. Each index node corresponds to an index node information, and each element node corresponds to an element node information. Furthermore, each index node is connected to at least one element node, that is, the connection between the index node and the element node represents the formation relationship between the index node information and the element node information.

[0036] It should be noted that after obtaining the target simplified evaluation threshold, the indicators corresponding to the indicator nodes are merged to obtain a simplified inventory material support capability evaluation indicator set. Furthermore, the inventory material support capability evaluation indicator simplification method of the present application is not only applicable to the simplification of inventory material support capability evaluation indicators, but also applicable to the simplification of other evaluation indicators, and the use of this method is not limited.

[0037] It should be noted that the above-mentioned indicator d may be composed of multiple elements, and an element e may be a necessary factor for multiple indicators.

[0038] It should be noted that the inventory material support capability evaluation index simplification method of the present application extracts indicator features through the Deepwalk algorithm and the Skip-Gram model, and vectorizes the indicators, and then calculates the vector correlation values ​​between indicator nodes to achieve indicator simplification. It does not need to calculate the original indicator measurement value, thereby reducing the actual indicator measurement workload of subsequent evaluation work, which is conducive to improving the efficiency of performance evaluation work.

[0039] It can be seen that the implementation of the inventory material support capability evaluation index simplification method described in the embodiment of the present invention is conducive to simplifying the inventory material support capability evaluation index simplification process, improving the efficiency and reliability of the simplified inventory material support capability evaluation index, and further providing support for evaluating inventory material support capabilities and optimizing inventory material support evaluation plans.

[0040] In an optional embodiment, the above-mentioned calculation and analysis of the target indicator node vector information to obtain the target simplicity evaluation threshold includes:

[0041] Based on the target indicator node vector information, basic parsimony evaluation threshold information is determined; the basic parsimony evaluation threshold information includes a number of basic parsimony evaluation thresholds;

[0042] The minimum basic parsimony evaluation threshold is selected from the basic parsimony evaluation threshold information as the target parsimony evaluation threshold.

[0043] It can be seen that the implementation of the inventory material support capability evaluation index simplification method described in the embodiment of the present invention is conducive to simplifying the inventory material support capability evaluation index simplification process, improving the efficiency and reliability of the simplified inventory material support capability evaluation index, and further providing support for evaluating inventory material support capabilities and optimizing inventory material support evaluation plans.

[0044] In another optional embodiment, based on the target indicator node vector information, basic simplified evaluation threshold information is determined, including:

[0045] Obtaining initial indicator parameter information; the initial indicator parameter information includes an initial parsimony evaluation threshold and a threshold adjustment parameter;

[0046] Based on the initial indicator parameter information and the target indicator node vector information, determine the analysis and evaluation threshold;

[0047] Determine whether the analysis evaluation threshold is 0, and obtain a zero judgment result;

[0048] When the null judgment result is no, the analytical evaluation threshold is determined to be a basic parsimony evaluation threshold;

[0049] When the zero judgment result is yes, the number of zero judgments is obtained;

[0050] Determine whether the number of zero judgments is equal to the judgment threshold, and obtain the threshold judgment result;

[0051] When the threshold judgment result is negative, the trigger is executed to obtain the initial indicator parameter information;

[0052] When the threshold judgment result is yes, the trigger is to select the minimum basic parsimony evaluation threshold from the basic parsimony evaluation threshold information as the target parsimony evaluation threshold.

[0053] It should be noted that the above judgment threshold may be input by the user or a default value given by the system. Preferably, the above judgment threshold is 10.

[0054] In this optional implementation example, as an optional implementation mode, the above-mentioned obtaining of initial indicator parameter information includes:

[0055] A value between [0.5-0.6] is randomly generated as the initial parsimony assessment threshold;

[0056] A value is randomly generated between [-0.5, 0.5] as the threshold adjustment parameter.

[0057] It should be noted that the above-mentioned randomly generated different initial simplicity evaluation thresholds can produce different indicator simplification effects. A reasonable basic simplicity evaluation threshold can not only reduce the amount of indicator calculation but also ensure the "precision" of the indicator system when realizing indicator simplification.

[0058] It can be seen that the implementation of the inventory material support capability evaluation index simplification method described in the embodiment of the present invention is conducive to simplifying the inventory material support capability evaluation index simplification process, improving the efficiency and reliability of the simplified inventory material support capability evaluation index, and further providing support for evaluating inventory material support capabilities and optimizing inventory material support evaluation plans.

[0059] In yet another optional embodiment, determining the analysis and evaluation threshold based on the initial indicator parameter information and the target indicator node vector information includes:

[0060] Based on the initial simplified evaluation threshold and the target indicator node vector information in the initial indicator parameter information, the optimized indicator node vector information is determined; the optimized indicator node vector information includes K optimized indicator node vectors; K is a positive integer not greater than M;

[0061] Determine whether the optimization index node vector information meets the index simplicity condition and obtain the conditional judgment result;

[0062] When the conditional judgment result is yes, the initial parsimony evaluation threshold is used as the analysis evaluation threshold;

[0063] When the conditional judgment result is no, judging whether the threshold adjustment parameter in the initial indicator parameter information has been used, and obtaining a use judgment result;

[0064] When the judgment result is yes, 0 is used as the analysis evaluation threshold;

[0065] When the judgment result is no, the sum of the threshold adjustment parameter and the initial simplicity evaluation threshold is used as the new initial simplicity evaluation threshold, and the execution is triggered based on the initial simplicity evaluation threshold and the target indicator node vector information in the initial indicator parameter information to determine the optimized indicator node vector information.

[0066] It should be noted that the above-mentioned indicator simplicity condition represents that the merged indicators can cover the indicator system dimensions, that is, each indicator dimension must contain at least one indicator. Furthermore, the above-mentioned indicator system dimensions include three indicator dimensions, namely, the mobilization and use capability dimension, the material allocation capability dimension, and the comprehensive processing capability dimension. Furthermore, each indicator dimension corresponds to an indicator simplified serial number interval. For example, when the mobilization and use capability dimension includes indicators d1, d2, d3, and d4, the first indicator simplified serial number interval corresponding to the mobilization and use capability dimension is [1,4]; the material allocation capability dimension includes indicators d5, d6, d7, d8, and d9, the second indicator simplified serial number interval corresponding to the material allocation capability dimension is [5,9]; the comprehensive processing capability dimension includes indicators d 10 ,d 11 ,d 12, then the simplified serial number interval of the third indicator corresponding to the comprehensive processing capability dimension is [10,12].

[0067] It can be seen that the implementation of the inventory material support capability evaluation index simplification method described in the embodiment of the present invention is conducive to simplifying the inventory material support capability evaluation index simplification process, improving the efficiency and reliability of the simplified inventory material support capability evaluation index, and further providing support for evaluating inventory material support capabilities and optimizing inventory material support evaluation plans.

[0068] In yet another optional embodiment, based on the initial simplified evaluation threshold and the target indicator node vector information in the initial indicator parameter information, the optimization indicator node vector information is determined, including:

[0069] The target indicator node vector ranked first in the target indicator node vector information is used as the standby indicator node vector;

[0070] Determine the standby indicator node vector as an optimized indicator node vector;

[0071] Determine whether the standby indicator node vector is the last target indicator node vector in the target indicator node vector information, and obtain a first vector determination result;

[0072] When the first vector judgment result is yes, trigger the execution to judge whether the optimization index node vector information meets the index simplicity condition, and obtain the condition judgment result;

[0073] When the judgment result of the first vector is no, determining the next target indicator node vector corresponding to the standby indicator node vector as the candidate indicator node vector;

[0074] The association calculation model is used to calculate the indicator node vector to be used and the candidate indicator node vector to obtain the vector association value;

[0075] Among them, the associated calculation model is:

[0076]

[0077] In the formula, GLZ is the vector association value; A is the indicator node vector to be used; B is the candidate indicator node vector;

[0078] Determine whether the vector association value is greater than the initial simplicity evaluation threshold in the initial indicator parameter information, and obtain a second vector determination result;

[0079] When the second vector judgment result is negative, the candidate indicator node vector is determined as a new standby indicator node vector, and triggering execution to determine the standby indicator node vector as an optimized indicator node vector;

[0080] When the second vector judgment result is yes, it is judged whether the candidate indicator node vector is the last target indicator node vector in the target indicator node vector information to obtain a third vector judgment result;

[0081] When the third vector judgment result is yes, trigger the execution to judge whether the optimization indicator node vector information meets the indicator simplicity condition, and obtain the condition judgment result;

[0082] When the third vector judgment result is no, the next target indicator node vector corresponding to the candidate indicator node vector is determined as a new candidate indicator node vector, and the associated calculation model is triggered to calculate the candidate indicator node vector and the candidate indicator node vector to obtain a vector association value.

[0083] It should be noted that by calculating the vector correlation value between two indicator nodes, the indicator nodes with high correlation can be merged, that is, two similar indicators are merged, and the indicator with a smaller indicator number is used as the representative, thereby simplifying the indicator system. Furthermore, due to the use of a quantifiable calculation method, the influence of subjective factors in indicator simplification is reduced, making the inventory material support capability assessment indicator simplification process more objective and reliable.

[0084] It can be seen that the implementation of the inventory material support capability evaluation index simplification method described in the embodiment of the present invention is conducive to simplifying the inventory material support capability evaluation index simplification process, improving the efficiency and reliability of the simplified inventory material support capability evaluation index, and further providing support for evaluating inventory material support capabilities and optimizing inventory material support evaluation plans.

[0085] In an optional embodiment, the above-mentioned determination of whether the optimization index node vector information satisfies the index simplicity condition and obtaining the condition determination result includes:

[0086] Determine whether there is an index number in the first index simplified number interval among the index numbers of the index node information corresponding to the optimization index node vector in the optimization index node vector information, and obtain a first interval determination result;

[0087] When the first interval judgment result is no, determining the condition judgment result is no;

[0088] When the first interval judgment result is yes, it is judged whether there is an indicator number located in the second indicator simplified serial number interval in the indicator serial number of the indicator node information corresponding to the optimization indicator node vector in the optimization indicator node vector information, and a second interval judgment result is obtained; the lower limit serial number of the second indicator simplified serial number interval is greater than the upper limit serial number of the first indicator simplified serial number interval;

[0089] When the second interval judgment result is no, determining the condition judgment result is no;

[0090] When the second interval judgment result is yes, it is judged whether there is an indicator number located in the third indicator simplified serial number interval in the indicator serial number of the indicator node information corresponding to the optimization indicator node vector in the optimization indicator node vector information, and a third interval judgment result is obtained; the lower limit serial number of the third indicator simplified serial number interval is greater than the upper limit serial number of the second indicator simplified serial number interval;

[0091] When the third interval judgment result is no, the determination condition judgment result is no;

[0092] When the third interval judgment result is yes, the determination condition judgment result is yes.

[0093] It should be noted that the above indicator serial number is the subscript of the corresponding indicator node information. For example, when the mobilization capability dimension includes indicators d1, d2, d3, and d4, the first indicator simplified serial number interval corresponding to the mobilization capability dimension is [1, 4], and the indicator serial number corresponding to the indicator node information (d1) is 1. Furthermore, the lower limit serial number and upper limit serial number of the first indicator simplified serial number interval are 1 and 4 respectively.

[0094] It can be seen that the implementation of the inventory material support capability evaluation index simplification method described in the embodiment of the present invention is conducive to simplifying the inventory material support capability evaluation index simplification process, improving the efficiency and reliability of the simplified inventory material support capability evaluation index, and further providing support for evaluating inventory material support capabilities and optimizing inventory material support evaluation plans.

[0095] In another optional embodiment, feature extraction processing is performed on the evaluation index element network information to obtain target index node vector information, including:

[0096] Perform node extraction processing on the evaluation index element network information to obtain node sequence information; the node sequence information includes L node sequences; the node sequence includes staggered index node information and element node information; L is a positive integer greater than the sum of M and N;

[0097] The node sequence information is vectorized to obtain the target indicator node vector information.

[0098] It should be noted that the above node extraction process for the evaluation index element network information is based on the Deepwalk algorithm. Figure 5 As shown in the figure, we randomly select an indicator node as the starting point, use the Deepwalk algorithm, and use the random walk method to alternately walk the indicator node and the element node on the evaluation indicator element network for 6 steps to form an "indicator-element" trajectory for inventory material support capability evaluation, that is, to extract an "indicator-element" node sequence s. Repeat the operation to extract 100 "indicator-element" node sequences for inventory material support capability evaluation, that is, the sequence set S = {s1, s2, ..., s100}.

[0099] It should be noted that the above-mentioned vectorization of node sequence information to obtain the target indicator node vector information is based on the Skip-Gram model, that is, by using the target node to predict the context node in the current context, the input is a specific node vector and the output is the context corresponding to the node vector, that is, the log value of the probability of other nodes appearing in the sequence when the node appears, and the vector representation of the node is updated with the help of the stochastic gradient descent algorithm. For example, in Figure 4 and Figure 5 The total number of indicator nodes in the evaluation indicator element network shown is 12, and the total number of element nodes is 9, so there are 21 nodes in the evaluation indicator element network. The weights of the output layer and the hidden layer of the Skip-Gram model are represented as a matrix W. Each row in W is a 100-dimensional vector, and the "indicator-element" node set is:

[0100] V={d1,d2,…,d 12 ,e1,e2,…,e9}={w1,w2,…,w 21};

[0101] The i-th node w in the “indicator-element” node set i The corresponding representation in W is v wi , assuming that the input layer input x∈R V , where x k =1 (indicates that the input node is w k ), and x k' =0, k≠k', then the hidden layer can be expressed as:

[0102]

[0103] The output layer has a total of 4 100-dimensional vectors, and the j-th node of the i-th vector is represented by w i,j Indicates that the linear sum of the jth unit from the hidden layer to the i-th vector of the output layer is u i,j Indicates that the probability value after the softmax function is processed by y i,j If it means:

[0104]

[0105] Since the hidden layer to the output layer share the same weight W', we have:

[0106]

[0107] Similarly, Represents the “indicator-element” node w j The "output vector" of , which is also the j-th column of W'.

[0108] At this time, the objective function E can be defined as follows:

[0109]

[0110] Then, by solving the minimum value of the objective function, the target indicator node vector information can be obtained.

[0111] It can be seen that the implementation of the inventory material support capability evaluation index simplification method described in the embodiment of the present invention is conducive to simplifying the inventory material support capability evaluation index simplification process, improving the efficiency and reliability of the simplified inventory material support capability evaluation index, and further providing support for evaluating inventory material support capabilities and optimizing inventory material support evaluation plans.

[0112] Embodiment 2

[0113] See also Figure 2 , Figure 2 This is a schematic diagram of a simplified device for evaluating inventory material support capability disclosed in an embodiment of the present invention. Figure 2 The described device can be applied to a warehouse management system, such as a local server or cloud server for simple management of warehouse logistics inventory material guarantee capability evaluation indicators, and the embodiments of the present invention are not limited thereto. Figure 2 As shown, the device may include:

[0114] The acquisition module 201 is used to acquire the evaluation index element network information; the evaluation index element network information includes M index node information and N element node information;

[0115] The first processing module 202 is used to perform feature extraction processing on the evaluation index element network information to obtain target index node vector information; the target index node vector information includes M target index node vectors arranged in sequence;

[0116] The second processing module 203 is used to calculate and analyze the target indicator node vector information to obtain a target simplicity evaluation threshold; the target simplicity evaluation threshold is used to indicate a simple inventory material guarantee capability evaluation indicator.

[0117] It can be seen that the implementation Figure 2 The described simplified device for inventory material support capability assessment index is conducive to simplifying the simplified process of inventory material support capability assessment index, improving the efficiency and reliability of the simplified inventory material support capability assessment index, and further providing support for assessing inventory material support capability and optimizing inventory material support assessment plans.

[0118] In another optional embodiment, Figure 2As shown, the second processing module 203 performs calculation and analysis on the target indicator node vector information to obtain the target simplicity evaluation threshold, including:

[0119] Based on the target indicator node vector information, basic parsimony evaluation threshold information is determined; the basic parsimony evaluation threshold information includes a number of basic parsimony evaluation thresholds;

[0120] The minimum basic parsimony evaluation threshold is selected from the basic parsimony evaluation threshold information as the target parsimony evaluation threshold.

[0121] It can be seen that the implementation Figure 2 The described simplified device for inventory material support capability assessment index is conducive to simplifying the simplified process of inventory material support capability assessment index, improving the efficiency and reliability of the simplified inventory material support capability assessment index, and further providing support for assessing inventory material support capability and optimizing inventory material support assessment plans.

[0122] In yet another optional embodiment, Figure 2 As shown, the second processing module 203 determines basic simplified evaluation threshold information based on the target indicator node vector information, including:

[0123] Obtaining initial indicator parameter information; the initial indicator parameter information includes an initial parsimony evaluation threshold and a threshold adjustment parameter;

[0124] Based on the initial indicator parameter information and the target indicator node vector information, determine the analysis and evaluation threshold;

[0125] Determine whether the analysis evaluation threshold is 0, and obtain a zero judgment result;

[0126] When the null judgment result is no, the analytical evaluation threshold is determined to be a basic parsimony evaluation threshold;

[0127] When the zero judgment result is yes, the number of zero judgments is obtained;

[0128] Determine whether the number of zero judgments is equal to the judgment threshold, and obtain the threshold judgment result;

[0129] When the threshold judgment result is negative, the trigger is executed to obtain the initial indicator parameter information;

[0130] When the threshold judgment result is yes, the trigger is to select the minimum basic parsimony evaluation threshold from the basic parsimony evaluation threshold information as the target parsimony evaluation threshold.

[0131] It can be seen that the implementation Figure 2The described simplified device for inventory material support capability assessment index is conducive to simplifying the simplified process of inventory material support capability assessment index, improving the efficiency and reliability of the simplified inventory material support capability assessment index, and further providing support for assessing inventory material support capability and optimizing inventory material support assessment plans.

[0132] In yet another optional embodiment, Figure 2 As shown, the second processing module 203 determines the analysis and evaluation threshold based on the initial indicator parameter information and the target indicator node vector information, including:

[0133] Based on the initial simplified evaluation threshold and the target indicator node vector information in the initial indicator parameter information, the optimized indicator node vector information is determined; the optimized indicator node vector information includes K optimized indicator node vectors; K is a positive integer not greater than M;

[0134] Determine whether the optimization index node vector information meets the index simplicity condition and obtain the conditional judgment result;

[0135] When the conditional judgment result is yes, the initial parsimony evaluation threshold is used as the analysis evaluation threshold;

[0136] When the conditional judgment result is no, judging whether the threshold adjustment parameter in the initial indicator parameter information has been used, and obtaining a use judgment result;

[0137] When the judgment result is yes, 0 is used as the analysis evaluation threshold;

[0138] When the judgment result is no, the sum of the threshold adjustment parameter and the initial simplicity evaluation threshold is used as the new initial simplicity evaluation threshold, and the execution is triggered based on the initial simplicity evaluation threshold and the target indicator node vector information in the initial indicator parameter information to determine the optimized indicator node vector information.

[0139] It can be seen that the implementation Figure 2 The described simplified device for inventory material support capability assessment index is conducive to simplifying the simplified process of inventory material support capability assessment index, improving the efficiency and reliability of the simplified inventory material support capability assessment index, and further providing support for assessing inventory material support capability and optimizing inventory material support assessment plans.

[0140] In yet another optional embodiment, Figure 2 As shown, the second processing module 203 determines the optimization indicator node vector information based on the initial simplified evaluation threshold and the target indicator node vector information in the initial indicator parameter information, including:

[0141] The target indicator node vector ranked first in the target indicator node vector information is used as the standby indicator node vector;

[0142] Determine the standby indicator node vector as an optimized indicator node vector;

[0143] Determine whether the standby indicator node vector is the last target indicator node vector in the target indicator node vector information, and obtain a first vector determination result;

[0144] When the first vector judgment result is yes, trigger the execution to judge whether the optimization index node vector information meets the index simplicity condition, and obtain the condition judgment result;

[0145] When the judgment result of the first vector is no, determining the next target indicator node vector corresponding to the standby indicator node vector as the candidate indicator node vector;

[0146] The association calculation model is used to calculate the indicator node vector to be used and the candidate indicator node vector to obtain the vector association value;

[0147] Among them, the associated calculation model is:

[0148]

[0149] In the formula, GLz is the vector association value; A is the indicator node vector to be used; B is the candidate indicator node vector;

[0150] Determine whether the vector association value is greater than the initial simplicity evaluation threshold in the initial indicator parameter information, and obtain a second vector determination result;

[0151] When the second vector judgment result is negative, the candidate indicator node vector is determined as a new standby indicator node vector, and triggering execution to determine the standby indicator node vector as an optimized indicator node vector;

[0152] When the second vector judgment result is yes, it is judged whether the candidate indicator node vector is the last target indicator node vector in the target indicator node vector information to obtain a third vector judgment result;

[0153] When the third vector judgment result is yes, trigger the execution to judge whether the optimization indicator node vector information meets the indicator simplicity condition, and obtain the condition judgment result;

[0154] When the third vector judgment result is no, the next target indicator node vector corresponding to the candidate indicator node vector is determined as a new candidate indicator node vector, and the associated calculation model is triggered to calculate the candidate indicator node vector and the candidate indicator node vector to obtain a vector association value.

[0155] It can be seen that the implementation Figure 2The described simplified device for inventory material support capability assessment index is conducive to simplifying the simplified process of inventory material support capability assessment index, improving the efficiency and reliability of the simplified inventory material support capability assessment index, and further providing support for assessing inventory material support capability and optimizing inventory material support assessment plans.

[0156] In yet another optional embodiment, Figure 2 As shown, the second processing module 203 determines whether the optimization index node vector information meets the index simplicity condition, and obtains the condition judgment result, including:

[0157] Determine whether there is an index number in the first index simplified number interval among the index numbers of the index node information corresponding to the optimization index node vector in the optimization index node vector information, and obtain a first interval determination result;

[0158] When the first interval judgment result is no, determining the condition judgment result is no;

[0159] When the first interval judgment result is yes, it is judged whether there is an indicator number located in the second indicator simplified serial number interval in the indicator serial number of the indicator node information corresponding to the optimization indicator node vector in the optimization indicator node vector information, and a second interval judgment result is obtained; the lower limit serial number of the second indicator simplified serial number interval is greater than the upper limit serial number of the first indicator simplified serial number interval;

[0160] When the second interval judgment result is no, determining the condition judgment result is no;

[0161] When the second interval judgment result is yes, it is judged whether there is an indicator number located in the third indicator simplified serial number interval in the indicator serial number of the indicator node information corresponding to the optimization indicator node vector in the optimization indicator node vector information, and a third interval judgment result is obtained; the lower limit serial number of the third indicator simplified serial number interval is greater than the upper limit serial number of the second indicator simplified serial number interval;

[0162] When the third interval judgment result is no, the determination condition judgment result is no;

[0163] When the third interval judgment result is yes, the determination condition judgment result is yes.

[0164] It can be seen that the implementation Figure 2 The described simplified device for inventory material support capability assessment index is conducive to simplifying the simplified process of inventory material support capability assessment index, improving the efficiency and reliability of the simplified inventory material support capability assessment index, and further providing support for assessing inventory material support capability and optimizing inventory material support assessment plans.

[0165] In yet another optional embodiment, Figure 2As shown, the first processing module 202 performs feature extraction processing on the evaluation index element network information to obtain target index node vector information, including:

[0166] Perform node extraction processing on the evaluation index element network information to obtain node sequence information; the node sequence information includes L node sequences; the node sequence includes staggered index node information and element node information; L is a positive integer greater than the sum of M and N;

[0167] The node sequence information is vectorized to obtain the target indicator node vector information.

[0168] It can be seen that the implementation Figure 2 The described simplified device for inventory material support capability assessment index is conducive to simplifying the simplified process of inventory material support capability assessment index, improving the efficiency and reliability of the simplified inventory material support capability assessment index, and further providing support for assessing inventory material support capability and optimizing inventory material support assessment plans.

[0169] Embodiment 3

[0170] See also Figure 3 , Figure 3 This is a schematic diagram of the structure of another simplified device for evaluating the inventory material guarantee capability disclosed in an embodiment of the present invention. Figure 3 The described device can be applied to a warehouse management system, such as a local server or cloud server for simple management of warehouse logistics inventory material guarantee capability evaluation indicators, and the embodiments of the present invention are not limited thereto. Figure 3 As shown, the device may include:

[0171] A memory 301 storing executable program codes;

[0172] a processor 302 coupled to the memory 301;

[0173] The processor 302 calls the executable program code stored in the memory 301 to execute the steps in the simplified method for evaluating the inventory material guarantee capability described in the first embodiment.

[0174] Embodiment 4

[0175] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the simplified method for evaluating inventory material support capability described in the first embodiment.

[0176] Embodiment 5

[0177] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the simplified method for evaluating inventory material support capability indicators described in the first embodiment.

[0178] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0179] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0180] Finally, it should be noted that the simplified method and device for evaluating inventory material support capability disclosed in the embodiment of the present invention discloses only the preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A simplified method for evaluating inventory material support capability, characterized in that: The method comprises: Acquire evaluation index element network information; the evaluation index element network information includes M index node information and N element node information; Performing feature extraction processing on the evaluation index element network information to obtain target index node vector information; the target index node vector information includes M target index node vectors arranged in sequence; Calculating and analyzing the target indicator node vector information to obtain a target simplicity evaluation threshold; the target simplicity evaluation threshold is used to indicate a simple inventory material guarantee capability evaluation indicator; The step of calculating and analyzing the target indicator node vector information to obtain the target simplicity evaluation threshold value includes: Based on the target indicator node vector information, basic parsimony evaluation threshold information is determined; the basic parsimony evaluation threshold information includes a plurality of basic parsimony evaluation thresholds; Selecting the minimum basic parsimony evaluation threshold from the basic parsimony evaluation threshold information as the target parsimony evaluation threshold; Wherein, determining basic simplified evaluation threshold information based on the target indicator node vector information includes: Acquire initial indicator parameter information; the initial indicator parameter information includes an initial parsimony evaluation threshold and a threshold adjustment parameter; Determining an analysis and evaluation threshold based on the initial indicator parameter information and the target indicator node vector information; Determine whether the analysis and evaluation threshold is 0, and obtain a zero determination result; When the zero judgment result is no, determining the analysis evaluation threshold as one of the basic parsimony evaluation thresholds; When the zero judgment result is yes, obtaining the number of zero judgments; Determine whether the zero determination number is equal to a determination threshold, and obtain a threshold determination result; When the threshold judgment result is no, triggering the execution of obtaining the initial indicator parameter information; When the threshold judgment result is yes, triggering the execution of selecting the minimum basic parsimony evaluation threshold from the basic parsimony evaluation threshold information as the target parsimony evaluation threshold; Wherein, determining the analysis and evaluation threshold based on the initial indicator parameter information and the target indicator node vector information includes: Based on the initial simplified evaluation threshold value and the target indicator node vector information in the initial indicator parameter information, the optimized indicator node vector information is determined; the optimized indicator node vector information includes K optimized indicator node vectors; K is a positive integer not greater than M; Determine whether the optimization index node vector information meets the index simplicity condition, and obtain a conditional judgment result; When the conditional judgment result is yes, using the initial parsimony evaluation threshold as the analysis evaluation threshold; When the conditional judgment result is no, judging whether the threshold adjustment parameter in the initial indicator parameter information has been used, and obtaining a use judgment result; When the use judgment result is yes, 0 is used as the analysis evaluation threshold; When the use judgment result is no, the sum of the threshold adjustment parameter and the initial simplicity evaluation threshold is used as a new initial simplicity evaluation threshold, and the initial simplicity evaluation threshold and the target indicator node vector information in the initial indicator parameter information are triggered to determine the optimization indicator node vector information; The feature extraction process of the evaluation index element network information to obtain the target index node vector information includes: Performing node extraction processing on the evaluation index element network information to obtain node sequence information; the node sequence information includes L node sequences; the node sequence includes the index node information and element node information distributed in staggered order; L is a positive integer greater than the sum of M and N; The node sequence information is vectorized to obtain target indicator node vector information.

2. The simplified method for evaluating inventory material support capability according to claim 1 is characterized in that: The step of determining the optimization indicator node vector information based on the initial simplified evaluation threshold and the target indicator node vector information in the initial indicator parameter information includes: The target indicator node vector ranked first in the target indicator node vector information is used as a standby indicator node vector; Determine the standby indicator node vector as one of the optimized indicator node vectors; Determine whether the standby indicator node vector is the last target indicator node vector in the target indicator node vector information, and obtain a first vector determination result; When the first vector judgment result is yes, triggering the execution of judging whether the optimization index node vector information meets the index simplicity condition, and obtaining the condition judgment result; When the judgment result of the first vector is no, determining the next target indicator node vector corresponding to the standby indicator node vector as the candidate indicator node vector; Calculating the standby indicator node vector and the candidate indicator node vector using an association calculation model to obtain a vector association value; Wherein, the association calculation model is: Wherein, GLZ is the vector association value; A is the standby indicator node vector; B is the candidate indicator node vector; Determine whether the vector association value is greater than the initial simplicity evaluation threshold in the initial indicator parameter information to obtain a second vector determination result; When the judgment result of the second vector is no, the candidate indicator node vector is determined as a new standby indicator node vector, and the step of determining the standby indicator node vector as one of the optimized indicator node vectors is triggered; When the second vector judgment result is yes, judging whether the candidate indicator node vector is the last target indicator node vector in the target indicator node vector information, and obtaining a third vector judgment result; When the third vector judgment result is yes, triggering the execution of judging whether the optimization index node vector information meets the index simplicity condition, and obtaining the condition judgment result; When the judgment result of the third vector is no, the next target indicator node vector corresponding to the candidate indicator node vector is determined to be a new candidate indicator node vector, and the calculation of the standby indicator node vector and the candidate indicator node vector using the association calculation model is triggered to obtain a vector association value.

3. The simplified method for evaluating inventory material support capability according to claim 1 is characterized in that: The step of judging whether the optimization index node vector information satisfies the index simplicity condition and obtaining the condition judgment result includes: Determine whether there is the indicator sequence number located in the first indicator simplified sequence number interval in the indicator sequence number of the indicator node information corresponding to the optimization indicator node vector in the optimization indicator node vector information, and obtain a first interval determination result; When the first interval judgment result is no, determining the condition judgment result is no; When the first interval judgment result is yes, determine whether the indicator sequence number of the indicator node information corresponding to the optimization indicator node vector in the optimization indicator node vector information is located in the second indicator simplified sequence number interval, and obtain the second interval judgment result; the lower limit sequence number of the second indicator simplified sequence number interval is greater than the upper limit sequence number of the first indicator simplified sequence number interval; When the second interval judgment result is no, determining that the condition judgment result is no; When the second interval judgment result is yes, determine whether the indicator sequence number of the indicator node information corresponding to the optimization indicator node vector in the optimization indicator node vector information is located in the third indicator simplified sequence number interval, and obtain a third interval judgment result; the lower limit sequence number of the third indicator simplified sequence number interval is greater than the upper limit sequence number of the second indicator simplified sequence number interval; When the third interval judgment result is no, determining that the condition judgment result is no; When the third interval judgment result is yes, it is determined that the condition judgment result is yes.

4. A simplified device for evaluating inventory material support capability, 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 simplified method for evaluating inventory material support capability as described in any one of claims 1 to 3.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, which, when called, are used to execute the inventory material support capability evaluation index simplification method as described in any one of claims 1 to 3.

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