Inventory material guarantee evaluation index dynamic weight calculation method and device

Abstract

The invention discloses an inventory material guarantee evaluation index dynamic weight calculation method and device. The method comprises the steps of obtaining a task set, a capability set and an index set of inventory material guarantee; the task set comprises N pieces of task information; the capability set comprises M pieces of capability information; the index set comprises T pieces of index information; m, N and T are positive integers; performing fusion processing on the task set, the capability set and the index set to obtain a semantic vector information set; and analyzing and processing the semantic vector information set to obtain inventory material guarantee capability evaluation index weight information. Visibly, the method is beneficial to associating different inventory material guarantee capabilities and indexes according to different tasks, so that the index weight distribution of the inventory material guarantee evaluation index system is more reasonable, and the difference of different guarantee tasks in the inventory material guarantee capability evaluation process can be dynamically reflected.

Description

Technical Field

[0001] The present invention relates to the technical field of inventory material security assessment, and in particular to a method and device for calculating dynamic weights of inventory material security assessment indicators. Background Art

[0002] The evaluation of inventory material support is essentially a decision-making problem of an indicator system composed of multiple indicators. The reasonable allocation of evaluation indicator weights is the basis and premise for the accurate evaluation of the indicator system. In the process of constructing the inventory material support evaluation indicator system, it is often necessary to select evaluation indicators from multiple perspectives. Due to the different perspectives of considering the support tasks, the weights of each evaluation indicator will also vary with the different support tasks. The size of the weight is directly related to the accuracy and authenticity of the inventory material support capability evaluation.

[0003] For a long time, inventory material support assessment has relied on various decision-making methods to select a single indicator or a few indicators, set static indicator weights, and form an indicator system with fixed indicator weights to evaluate inventory material support. The traditional inventory material support assessment method has some problems: (1) As the number of task types increases, different tasks may have different priorities, urgency, and resource requirements. The levels and dimensions of the inventory material support assessment indicator system also become redundant. The traditional simple tree structure can no longer show the complex relationship between tasks, capabilities, and indicators. (2) The dynamic changes of the weights of each indicator under different inventory material support tasks are not considered, and the indicator weights are set to be static. As a result, in the process of evaluating inventory material support capabilities, it is difficult to conduct targeted and dynamic reflection or evaluation based on the differences in specific support tasks. For example, in the daily support state, inventory material support is more inclined to maintain the stability and sustainability of inventory, while in the emergency support state, inventory material support is more inclined to rapid response and coordination of supply capabilities. (3) When constructing an inventory material support assessment system, if only the method of one-to-one correspondence between upper and lower indicators is adopted, the complex cross-correlation relationship between upper and lower indicators may be ignored. For example, some secondary indicators are actually related to multiple primary indicators to varying degrees, but in the weight calculation, they may only be associated with a relatively more important primary indicator. Although this simplified processing method improves the convenience of operation, it may also cause limitations in the evaluation results and be incomplete and inaccurate. Therefore, in order to more comprehensively reflect the multi-dimensionality and complexity of the evaluation object, the calculation of indicator weights should consider and handle these cross-correlations more carefully. According to the relationship between different support tasks, capabilities, and indicators in the inventory material support process, it is particularly important to dynamically obtain the weights of various indicators based on multiple tasks to reflect the differences in different support tasks. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method and device for calculating the dynamic weights of inventory material support assessment indicators, which is conducive to associating different inventory material support capabilities and indicators according to different tasks, so that the indicator weight distribution of the inventory material support assessment indicator system is more reasonable, and can dynamically reflect the differences of different support tasks in the inventory material support capability assessment process, and solve the problem of static invariance of the inventory material support assessment indicator weights under different tasks.

[0005] In order to solve the above technical problems, the first aspect of the embodiment of the present invention discloses a method for calculating the dynamic weight of inventory material guarantee evaluation indicators, the method comprising:

[0006] S1, obtaining a task set, a capability set and an indicator set for inventory material support; the task set includes N task information; the capability set includes M capability information; the indicator set includes T indicator information; M, N and T are all positive integers;

[0007] S2, fusing the task set, the capability set and the indicator set to obtain a semantic vector information set;

[0008] S3, analyzing and processing the semantic vector information set to obtain weight information of inventory material support capability assessment indicators.

[0009] As an optional implementation, in the first aspect of the embodiment of the present invention, the fusion processing of the task set, the capability set and the indicator set to obtain a semantic vector information set includes:

[0010] S21, extracting characteristic words from the task set, the capability set, and the indicator set to obtain a task characteristic word set, a capability characteristic word set, and an indicator characteristic word set;

[0011] S22, performing feature vector analysis processing on the task feature word set, the capability feature word set, and the indicator feature word set to obtain a task feature word vector set, a capability feature word vector set, and an indicator feature word vector set;

[0012] S23, fusing the task feature word vector set, the capability feature word vector set and the indicator feature word vector set to obtain a semantic vector information set.

[0013] As an optional implementation, in the first aspect of the embodiment of the present invention, the fusion processing of the task feature word vector set, the capability feature word vector set and the indicator feature word vector set to obtain a semantic vector information set includes:

[0014] S231, using the inventory material support semantic calculation model, calculating and processing the task feature word vector set, the capability feature word vector set, and the indicator feature word vector set to obtain task semantic vector information, capability semantic vector information, and indicator semantic vector information;

[0015] Among them, the inventory material guarantee semantic computing model is:

[0016]

[0017] Where V(r), V(c), and V(z) are the task semantic vector information, the capability semantic vector information, and the indicator semantic vector information. i1 ) is the semantic vector corresponding to the i1th task information in the task semantic vector information, V(c i2 ) is the semantic vector corresponding to the i2th capability information in the capability semantic vector information, V(z i3 ) is the semantic vector corresponding to the i3th indicator information in the indicator semantic vector information, is the l1th feature word vector of the i1th task information in the task feature word vector set, is the l2th feature word vector of the i2th capability information in the capability feature word vector set, is the l3th feature word vector of the i3th indicator information in the indicator feature word vector set, a i1 is the number of feature words corresponding to the i1th task information in the task feature word vector set, b i2 is the number of feature words corresponding to the i2th capability information in the capability feature word vector set, c i3 is the number of feature words corresponding to the i3th indicator information in the indicator feature word vector set, N, M and T are respectively the number of task information in the task feature word vector set, the number of capability information in the capability feature word vector set, and the number of indicator information in the indicator feature word vector set, δ1, δ3 and δ3 are respectively the first weight parameter, the second weight parameter and the third weight parameter.

[0018] S232, merging the task semantic vector information, the capability semantic vector information and the indicator semantic vector information to obtain a semantic vector information set.

[0019] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the analyzing and processing of the semantic vector information set to obtain the inventory material guarantee capability evaluation index weight information includes:

[0020] S31, using a first similarity calculation model, calculating and processing the semantic vector information set to obtain capability similarity information and indicator similarity information;

[0021] Among them, the first similarity calculation model:

[0022]

[0023] In the formula, S(V(c j1 ),V(c j2 )) is the similarity value between the j1th capability information and the j2th capability information in the capability similarity information, S(V(z j3 ),V(z j4 )) is the similarity value between the j3th indicator information and the j4th indicator information in the indicator similarity information, V(c j1 ) and V(c j2 ) respectively represent the semantic vectors corresponding to the j1th and j2th capability information of the capability semantic vector information in the semantic vector information set, V(z j3 ),V(z j4 ) respectively represent the semantic vectors corresponding to the j1th and j2th indicator information of the indicator semantic vector information in the semantic vector information set, M and T respectively represent the number of capability information in the capability semantic vector information and the number of indicator information in the indicator semantic vector information, θ1 and θ2 respectively represent the first similarity value offset constant and the second similarity value offset constant, · represents the vector inner product, and || represents the modulus of the vector;

[0024] S32, constructing a capability judgment matrix and an indicator judgment matrix by using the capability similarity information and the indicator similarity information;

[0025] S33, analyzing and processing the capability judgment matrix and the indicator judgment matrix to obtain capability weight coefficient information and indicator weight coefficient information;

[0026] S34, analyzing and processing the semantic vector information set to obtain first-layer edge weight coefficient information and second-layer edge weight coefficient information;

[0027] S35, calculating and processing the capability weight coefficient information, the indicator weight coefficient information, the first layer edge weight coefficient information and the second layer edge weight coefficient information to obtain inventory material support capability assessment indicator weight information.

[0028] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the analyzing and processing the capability judgment matrix and the indicator judgment matrix to obtain capability weight coefficient information and indicator weight coefficient information includes:

[0029] S331, performing eigenvector solving on the capability judgment matrix and the indicator judgment matrix to obtain capability eigenvector information and indicator eigenvector information;

[0030] S332, using a feature vector normalization model, respectively normalizing the capability feature vector information and the indicator feature vector information to obtain capability weight coefficient information and indicator weight coefficient information;

[0031] Wherein, the feature vector normalization model is:

[0032]

[0033] In the formula, ρ ′c and ρ ′z respectively represent the capability weight coefficient information and the indicator weight coefficient information, is the weight coefficient corresponding to the k1th capability information in the capability weight coefficient information, is the weight coefficient corresponding to the k2th indicator information in the indicator weight coefficient information, is the feature vector corresponding to the k1th capability information in the capability feature vector information, is the feature vector corresponding to the k2th indicator information in the indicator feature vector information, M is the number of capability information in the capability feature vector information, and T is the number of indicator information in the indicator feature vector information.

[0034] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the analyzing and processing the semantic vector information set to obtain the first layer edge weight coefficient information and the second layer edge weight coefficient information includes:

[0035] S341, performing calculation processing on the semantic vector information set to obtain first-layer edge similarity information and second-layer edge similarity information;

[0036] S342, preset s=1;

[0037] S343, determining whether the sth similarity value in the first layer edge similarity information is less than a preset first similarity threshold, and obtaining a first determination result;

[0038] When the first judgment result is yes, the similarity value is updated to 0, the updated first-layer edge similarity information is obtained, and the updated first-layer edge similarity information is determined to be the third-layer edge similarity information, and S344 is executed;

[0039] When the first judgment result is no, executing S344;

[0040] S344, determining whether s is equal to the number of similarity values ​​in the first layer edge similarity information, and obtaining a second determination result;

[0041] When the second judgment result is yes, execute S345;

[0042] When the second judgment result is no, executing S343;

[0043] S345, calculating and processing the second layer edge similarity information and a preset second similarity threshold to obtain a fourth layer edge similarity information;

[0044] S346 , normalizing the third layer edge similarity information and the fourth layer edge similarity information respectively to obtain first layer edge weight coefficient information and second layer edge weight coefficient information.

[0045] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the capability weight coefficient information, the indicator weight coefficient information, the first layer edge weight coefficient information and the second layer edge weight coefficient information are calculated and processed to obtain the inventory material guarantee capability evaluation indicator weight information, including:

[0046] Using the inventory material support weight calculation model, the capability weight coefficient information, the indicator weight coefficient information, the first layer edge weight coefficient information and the second layer edge weight coefficient information are calculated and processed to obtain the inventory material support capability evaluation indicator weight information;

[0047] Among them, the inventory material guarantee weight calculation model is:

[0048]

[0049] 1≤d1≤N;

[0050] 1≤d2≤T;

[0051] 1≤d3≤M;

[0052] Where P is the weight information of the inventory material support capability evaluation index, P d1,d2 is the weight of the d2th indicator information of the d1th task information in the inventory material support capability evaluation indicator weight information, ρ(V(r d1 ),V(c d3 )) is the similarity value between the d1th task information and the d3th capability information in the first layer edge weight coefficient information, ρ(V(c d3 ),V(z d2 )) is the similarity value between the d3th capability information and the d2th indicator information in the second layer edge weight coefficient information, is the weight coefficient corresponding to the d3th capability information in the capability weight coefficient information, is the weight coefficient corresponding to the d2th indicator information in the indicator weight coefficient information, N is the number of task information in the first layer edge weight coefficient information, T is the number of indicator information in the second layer edge weight coefficient information, M is the number of indicator information in the first layer edge weight coefficient information, ∈ d1 is the constant coefficient of the d1th task information.

[0053] A second aspect of an embodiment of the present invention discloses a device for calculating dynamic weights of inventory material security assessment indicators, the device comprising:

[0054] An acquisition module is used to acquire a task set, a capability set and an indicator set for inventory material support; the task set includes N task information; the capability set includes M capability information; the indicator set includes T indicator information; M, N and T are all positive integers;

[0055] A first computing module is used to perform fusion processing on the task set, the capability set and the indicator set to obtain a semantic vector information set;

[0056] The second calculation module is used to analyze and process the semantic vector information set to obtain the weight information of the inventory material guarantee capability evaluation index.

[0057] A third aspect of an embodiment of the present invention discloses another device for calculating dynamic weights of inventory material security assessment indicators, the device comprising:

[0058] processor;

[0059] a memory coupled to the processor and storing executable program code;

[0060] The processor calls the executable program code stored in the memory to execute part or all of the steps of the method for calculating the dynamic weight of inventory material security assessment indicators disclosed in the first aspect of an embodiment of the present invention.

[0061] A computer-readable storage medium stores computer instructions, which, when called, are used to execute part or all of the steps of the method for calculating dynamic weights of inventory material security assessment indicators disclosed in the first aspect of an embodiment of the present invention.

[0062] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0063] In an embodiment of the present invention, a task set, a capability set, and an indicator set for inventory material support are obtained; the task set includes N task information; the capability set includes M capability information; the indicator set includes T indicator information; M, N, and T are all positive integers; the task set, the capability set, and the indicator set are fused to obtain a semantic vector information set; the semantic vector information set is analyzed to obtain the weight information of the inventory material support capability evaluation indicator. It can be seen that the present application is conducive to associating different inventory material support capabilities and indicators according to different tasks, so that the indicator weight distribution of the inventory material support evaluation indicator system is more reasonable, and can dynamically reflect the differences of different support tasks in the inventory material support capability evaluation process, and solve the problem of static invariance of the inventory material support evaluation indicator weights under different tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] 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.

[0065] Figure 1 A schematic diagram of a flow chart of a method for calculating dynamic weights of inventory material security evaluation indicators disclosed in an embodiment of the present invention;

[0066] Figure 2 A schematic diagram of the structure of a dynamic weight calculation device for inventory material security evaluation indicators disclosed in an embodiment of the present invention;

[0067] Figure 3 It is a structural schematic diagram of another device for calculating dynamic weights of inventory material security evaluation indicators disclosed in an embodiment of the present invention;

[0068] Figure 4 This is a schematic diagram of the “task-capability-indicator” hypernetwork for inventory material support assessment disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0069] 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.

[0070] 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.

[0071] 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.

[0072] It should be noted that since the method of the embodiment of the present application is executed in a computer device, the processing objects of each computer device exist in the form of data or information. For example, capability is actually the data information corresponding to the capability. It can be understood that if tasks, indicators, weights, coefficients, etc. are mentioned in subsequent embodiments, they are all corresponding data and information for processing by the computer device, and the details will not be repeated here.

[0073] The present invention discloses a method and device for calculating the dynamic weight of inventory material support evaluation indicators, which is conducive to associating different inventory material support capabilities and indicators according to different tasks, so that the indicator weight distribution of the inventory material support evaluation indicator system is more reasonable. The following are detailed descriptions.

[0074] Embodiment 1

[0075] See also Figure 1 , Figure 1 This is a flow chart of a method for calculating the dynamic weight of inventory material security evaluation indicators disclosed in an embodiment of the present invention. Figure 1 The described method for calculating the dynamic weight of inventory material security assessment indicators is applied to an inventory material security assessment indicator dynamic weight calculation device, such as a local server or cloud server for optimizing the management of the dynamic weight calculation of inventory material security assessment indicators, and the embodiments of the present invention are not limited thereto. Figure 1 As shown, the dynamic weight calculation method of the inventory material guarantee evaluation index may include the following operations:

[0076] S1, obtaining a task set, a capability set and an indicator set for inventory material support; the task set includes N task information; the capability set includes M capability information; the indicator set includes T indicator information; M, N and T are all positive integers;

[0077] It should be noted that the task set, capability set and indicator set are obtained by analyzing and evaluating the support capabilities and indicators required for the support tasks received during the inventory material management support process. The task set, capability set and indicator set are as follows:

[0078] Task set R = {r i}i=1,2,3,4,…,N;

[0079] Capability set C = {c j}j=1,2,3,4,…,M;

[0080] Index set Z = {z k}k=1,2,3,4,…,T;

[0081] For example, inventory material support includes two task information: daily support task r1 and emergency support task r2. The capability set involves three capability information, namely capability c1, capability c2, and capability c3. The indicator set includes six indicator information, respectively represented as indicator z1, indicator z2, indicator z3, indicator z4, indicator z5, and indicator z6. Each set is as follows:

[0082] Task set R = {r1, r2};

[0083] Capability set C = {c1, c2, c3};

[0084] Index set Z = {z1,z2,z3,z4,z5,z6};

[0085] S2, fusing the task set, the capability set and the indicator set to obtain a semantic vector information set;

[0086] S3, analyzing and processing the semantic vector information set to obtain weight information of inventory material support capability assessment indicators.

[0087] It can be seen that the implementation of the dynamic weight calculation method for inventory material support evaluation indicators described in the embodiment of the present invention is conducive to associating different inventory material support capabilities and indicators according to different tasks, making the indicator weight distribution of the inventory material support evaluation indicator system more reasonable.

[0088] In an optional embodiment, the fusing process of the task set, the capability set and the indicator set to obtain a semantic vector information set includes:

[0089] S21, extracting characteristic words from the task set, the capability set, and the indicator set to obtain a task characteristic word set, a capability characteristic word set, and an indicator characteristic word set;

[0090] It should be noted that the above-mentioned extraction of feature words from the task set, capability set and indicator set is the extraction of feature words of the task description corresponding to the task information in the task set, the extraction of feature words of the capability description corresponding to the capability information in the capability set, and the extraction of feature words of the indicator description corresponding to the indicator information in the indicator set. For example, if the task information is "daily support tasks", the corresponding description is "formulate and implement replenishment plans based on demand forecasts and inventory conditions, and ensure that the warehouse environment is suitable, materials are stored in an orderly manner, and are easy to access"; the capability information is "emergency response capability", and the corresponding description is "in emergency situations, procurement and distribution decisions can be made quickly"; the indicator information is "emergency response time", and the corresponding description is "measurement of the time required from the emergence of sudden demand to the completion of material distribution."

[0091] It should be noted that the above-mentioned feature word extraction is performed by using Chinese word segmentation tools (such as: jieba, HANLP) to segment the task descriptions corresponding to the task information in the task set, the capability descriptions corresponding to the capability information in the capability set, and the indicator descriptions corresponding to the indicator information in the indicator set, and uses the TF-IDF algorithm to extract the core descriptive feature words of the task information, capability information and indicator information.

[0092] Exemplarily, the obtained task feature word set, capability feature word set and indicator feature word set are as follows:

[0093]

[0094] The feature word set of the sixth indicator z6:

[0095] S22, performing feature vector analysis processing on the task feature word set, the capability feature word set, and the indicator feature word set to obtain a task feature word vector set, a capability feature word vector set, and an indicator feature word vector set;

[0096] It should be noted that the above analysis of feature vectors is obtained by extracting feature word vectors using the BERT algorithm.

[0097] Exemplarily, the feature word vectors in the task feature word vector set, capability feature word vector set, and indicator feature word vector set are obtained as follows:

[0098] The feature word vectors of the first task r1 are:

[0099] The feature word vectors of the second task r2 are:

[0100] The feature word vectors of the first capability c1 are:

[0101] The feature word vectors of the second capability c2 are:

[0102] The feature word vectors of the third capability c3 are:

[0103] The feature word vectors of the first index z1 are:

[0104] The feature word vectors of the second index z2 are:

[0105] The feature word vectors of the third indicator z3 are:

[0106] The feature word vectors of the fourth indicator z4 are:

[0107] The feature word vectors of the fifth indicator z5 are:

[0108] The feature word vectors of the sixth indicator z6 are:

[0109] S23, fusing the task feature word vector set, the capability feature word vector set and the indicator feature word vector set to obtain a semantic vector information set.

[0110] It can be seen that the implementation of the dynamic weight calculation method for inventory material support evaluation indicators described in the embodiment of the present invention is conducive to associating different inventory material support capabilities and indicators according to different tasks, making the indicator weight distribution of the inventory material support evaluation indicator system more reasonable.

[0111] In another optional embodiment, the fusion processing of the task feature word vector set, the capability feature word vector set and the indicator feature word vector set to obtain a semantic vector information set includes:

[0112] S231, using the inventory material support semantic calculation model, calculating and processing the task feature word vector set, the capability feature word vector set, and the indicator feature word vector set to obtain task semantic vector information, capability semantic vector information, and indicator semantic vector information;

[0113] Among them, the inventory material guarantee semantic computing model is:

[0114]

[0115] Where V(r), V(c), and V(z) are the task semantic vector information, the capability semantic vector information, and the indicator semantic vector information. i1 ) is the semantic vector corresponding to the i1th task information in the task semantic vector information, V(c i2 ) is the semantic vector corresponding to the i2th capability information in the capability semantic vector information, V(z i3 ) is the semantic vector corresponding to the i3th indicator information in the indicator semantic vector information, is the l1th feature word vector of the i1th task information in the task feature word vector set, is the l2th feature word vector of the i2th capability information in the capability feature word vector set, is the l3th feature word vector of the i3th indicator information in the indicator feature word vector set, a i1 is the number of feature words corresponding to the i1th task information in the task feature word vector set, b i2 is the number of feature words corresponding to the i2th capability information in the capability feature word vector set, c i3 is the number of feature words corresponding to the i3th indicator information in the indicator feature word vector set, N, M and T are respectively the number of task information in the task feature word vector set, the number of capability information in the capability feature word vector set, and the number of indicator information in the indicator feature word vector set, δ1, δ3 and δ3 are respectively the first weight parameter, the second weight parameter and the third weight parameter.

[0116] It should be noted that the first weight parameter, the second weight parameter and the third weight parameter may be set by a user or may be obtained based on historical data, and the embodiment of the present invention does not specifically limit this.

[0117] S232, merging the task semantic vector information, the capability semantic vector information and the indicator semantic vector information to obtain a semantic vector information set.

[0118] It should be noted that the above merging process is to use the task semantic vector information, the capability semantic vector information and the indicator semantic vector information as subsets of the semantic vector information set. For example, if the task semantic vector information, the capability semantic vector information and the indicator semantic vector information are x1, x2 and x3 respectively, the merged semantic vector information set is {x1, x2, x3};

[0119] It should be noted that after obtaining the task semantic vector information, the capability semantic vector information and the indicator semantic vector information, an inventory material guarantee task, capability, indicator graph network and an inventory material guarantee "task-capability-indicator" super network will be constructed. Among them, task information, capability information and indicator information are used as nodes in their respective graph network structures. The "task" graph network uses task information as nodes, and there are only task nodes in the task graph network; the "capability" graph network uses capability information as nodes, and the lines between capability nodes represent the connection between different capabilities, which are called capability edges; the "indicator" graph network uses indicator information as nodes, and the lines between indicator nodes represent the connection between different indicators, which are called indicator edges. According to the semantic similarity value, the connection between the "task" node and the "capability" node in the "task" graph network and the "capability" graph network, and the connection between the "capability" node and the "indicator" node in the "capability" graph network and the "indicator" graph network are established respectively. This type of connection is called a layer edge, and "task node-layer edge-capability node-layer edge-indicator node" is called a link, and the "task-capability-indicator" super network is output. Figure 4 As shown, “task node-layer edge-capability node-layer edge-indicator node” is called a link.

[0120] It can be seen that the implementation of the dynamic weight calculation method for inventory material support evaluation indicators described in the embodiment of the present invention is conducive to associating different inventory material support capabilities and indicators according to different tasks, making the indicator weight distribution of the inventory material support evaluation indicator system more reasonable.

[0121] In yet another optional embodiment, the analyzing and processing the semantic vector information set to obtain the inventory material guarantee capability evaluation index weight information includes:

[0122] S31, using a first similarity calculation model, calculating and processing the semantic vector information set to obtain capability similarity information and indicator similarity information;

[0123] Among them, the first similarity calculation model:

[0124]

[0125] In the formula, S(V(c j1 ),V(c j2)) is the similarity value between the j1th capability information and the j2th capability information in the capability similarity information, S(V(z j3 ),V(z j4 )) is the similarity value between the j3th indicator information and the j4th indicator information in the indicator similarity information, V(c j1 ) and V(c j2 ) respectively represent the semantic vectors corresponding to the j1th and j2th capability information of the capability semantic vector information in the semantic vector information set, V(z j3 ),V(z j4 ) respectively represent the semantic vectors corresponding to the j1th and j2th indicator information of the indicator semantic vector information in the semantic vector information set, M and T respectively represent the number of capability information in the capability semantic vector information and the number of indicator information in the indicator semantic vector information, θ1 and θ2 respectively represent the first similarity value offset constant and the second similarity value offset constant, · represents the vector inner product, and || represents the modulus of the vector;

[0126] It should be noted that the first similarity value offset constant and the second similarity value offset constant may be set by a user, or may be obtained in other ways, and the embodiment of the present invention does not specifically limit them.

[0127] It should be noted that the similarity value mentioned above refers to the semantic similarity value.

[0128] Exemplarily, taking the semantic similarity value between the 1st indicator and the 2nd indicator, the semantic similarity value between the 1st indicator and the 3rd indicator, and the semantic similarity value between the 1st indicator and the 4th indicator as examples, assume that they are 0.8, 0.2, and 0.5, where the larger the value of the semantic similarity value S, the more similar the two semantic vectors are.

[0129] S32, constructing a capability judgment matrix and an indicator judgment matrix by using the capability similarity information and the indicator similarity information;

[0130] It should be noted that the above capability judgment matrix and indicator judgment matrix are as follows:

[0131]

[0132] In the formula, A c is the ability judgment matrix, A z is the indicator judgment matrix, M is the number of capability information, T is the number of indicator information, S(V(c M ),V(c M )) represents the similarity value between the Mth capability information and the Mth capability information, S(V(z T ),V(z T)) represents the similarity value between the Tth indicator information and the Tth indicator information.

[0133] S33, analyzing and processing the capability judgment matrix and the indicator judgment matrix to obtain capability weight coefficient information and indicator weight coefficient information;

[0134] S34, analyzing and processing the semantic vector information set to obtain first-layer edge weight coefficient information and second-layer edge weight coefficient information;

[0135] S35, calculating and processing the capability weight coefficient information, the indicator weight coefficient information, the first layer edge weight coefficient information and the second layer edge weight coefficient information to obtain inventory material support capability assessment indicator weight information.

[0136] It can be seen that the implementation of the dynamic weight calculation method for inventory material support evaluation indicators described in the embodiment of the present invention is conducive to associating different inventory material support capabilities and indicators according to different tasks, making the indicator weight distribution of the inventory material support evaluation indicator system more reasonable.

[0137] In an optional embodiment, the analyzing and processing the capability judgment matrix and the indicator judgment matrix to obtain capability weight coefficient information and indicator weight coefficient information includes:

[0138] S331, performing eigenvector solving on the capability judgment matrix and the indicator judgment matrix to obtain capability eigenvector information and indicator eigenvector information;

[0139] It should be noted that the above-mentioned capability weight coefficient information is the characteristic vector obtained by finding the maximum characteristic root of each row in the capability judgment matrix; the above-mentioned indicator weight coefficient information is the characteristic vector obtained by finding the maximum characteristic root of each row in the indicator judgment matrix. The obtained capability weight coefficient information and indicator weight coefficient information are as follows;

[0140]

[0141] In the above formula, ρ AC Represents capability feature vector information, ρ AZ represents the indicator feature vector information, M is the number of capability information, T is the number of indicator information, is the feature vector corresponding to the Mth capability information in the capability feature vector information, Represents the feature vector corresponding to the Tth indicator information in the indicator feature vector information.

[0142] S332, using a feature vector normalization model, respectively normalizing the capability feature vector information and the indicator feature vector information to obtain capability weight coefficient information and indicator weight coefficient information;

[0143] Wherein, the feature vector normalization model is:

[0144]

[0145] In the formula, ρ′ c and ρ′ z respectively represent the capability weight coefficient information and the indicator weight coefficient information, is the weight coefficient corresponding to the k1th capability information in the capability weight coefficient information, is the weight coefficient corresponding to the k2th indicator information in the indicator weight coefficient information, is the feature vector corresponding to the k1th capability information in the capability feature vector information, is the feature vector corresponding to the k2th indicator information in the indicator feature vector information, M is the number of capability information in the capability feature vector information, and T is the number of indicator information in the indicator feature vector information.

[0146] It can be seen that the implementation of the dynamic weight calculation method for inventory material support evaluation indicators described in the embodiment of the present invention is conducive to associating different inventory material support capabilities and indicators according to different tasks, making the indicator weight distribution of the inventory material support evaluation indicator system more reasonable.

[0147] In an optional embodiment, the analyzing and processing the semantic vector information set to obtain the first layer edge weight coefficient information and the second layer edge weight coefficient information includes:

[0148] S341, performing calculation processing on the semantic vector information set to obtain first-layer edge similarity information and second-layer edge similarity information;

[0149] It should be noted that the calculation and processing of the semantic vector information set to obtain the first layer edge similarity information and the second layer edge similarity information is obtained by using the second similarity calculation model;

[0150] The second similarity calculation model is:

[0151]

[0152] In the formula, S(V(r j5 ),V(c j6 )) is the similarity value between the j5th task information and the j6th capability information in the first layer edge similarity information, S(V(c j7),V(z j8 )) is the similarity value between the j7th capability information and the j8th indicator information in the second layer edge similarity information, V(r j5 ) and V(c j6 ) respectively represent the semantic vector corresponding to the j5th task information of the task semantic vector information and the semantic vector corresponding to the j6th capability information of the capability semantic vector information in the semantic vector information set, V(c j7 ) and V(z j8 ) respectively represent the semantic vector corresponding to the j7th capability information of the capability semantic vector information and the semantic vector corresponding to the j8th indicator information of the indicator semantic vector information in the semantic vector information set, N, M and T respectively represent the number of task information in the task semantic vector information, the number of capability information in the capability semantic vector information and the number of indicator information in the indicator semantic vector information, θ3 and θ4 respectively represent the third similarity value offset constant and the fourth similarity value offset constant, · represents the vector inner product, and || represents the modulus of the vector;

[0153] It should be noted that the third similarity value offset constant and the fourth similarity value offset constant may be set by a user, or may be obtained in other ways, and the embodiment of the present invention does not specifically limit them.

[0154] It should be noted that the similarity value mentioned above refers to the semantic similarity value.

[0155] S342, preset s=1;

[0156] S343, determining whether the sth similarity value in the first layer edge similarity information is less than a preset first similarity threshold, and obtaining a first determination result;

[0157] When the first judgment result is yes, the similarity value is updated to 0, the updated first-layer edge similarity information is obtained, and the updated first-layer edge similarity information is determined to be the third-layer edge similarity information, and S344 is executed;

[0158] When the first judgment result is no, executing S344;

[0159] S344, determining whether s is equal to the number of similarity values ​​in the first layer edge similarity information, and obtaining a second determination result;

[0160] When the second judgment result is yes, execute S345;

[0161] When the second judgment result is no, executing S343;

[0162] S345, calculating and processing the second layer edge similarity information and a preset second similarity threshold to obtain a fourth layer edge similarity information;

[0163] It should be noted that the calculation and processing of the second-layer edge similarity information and the preset second similarity threshold to obtain the fourth-layer edge similarity information is to judge the size of each similarity value in the second-layer edge similarity information and the preset second similarity threshold. When a similarity value in the second-layer edge similarity information is less than the preset second similarity threshold, the similarity value is updated to 0. After all judgments and updates are completed, the fourth-layer edge similarity information is obtained.

[0164] S346 , normalizing the third layer edge similarity information and the fourth layer edge similarity information respectively to obtain first layer edge weight coefficient information and second layer edge weight coefficient information.

[0165] It can be seen that the implementation of the dynamic weight calculation method for inventory material support evaluation indicators described in the embodiment of the present invention is conducive to associating different inventory material support capabilities and indicators according to different tasks, making the indicator weight distribution of the inventory material support evaluation indicator system more reasonable.

[0166] In an optional embodiment, the capability weight coefficient information, the indicator weight coefficient information, the first layer edge weight coefficient information, and the second layer edge weight coefficient information are calculated and processed to obtain the inventory material support capability evaluation indicator weight information, including:

[0167] Using the inventory material support weight calculation model, the capability weight coefficient information, the indicator weight coefficient information, the first layer edge weight coefficient information and the second layer edge weight coefficient information are calculated and processed to obtain the inventory material support capability evaluation indicator weight information;

[0168] Among them, the inventory material guarantee weight calculation model is:

[0169]

[0170] 1≤d1≤N;

[0171] 1≤d2≤T;

[0172] 1≤d3≤M;

[0173] Where P is the weight information of the inventory material support capability evaluation index, P d1,d2 is the weight of the d2th indicator information of the d1th task information in the inventory material support capability evaluation indicator weight information, ρ(V(r d1 ),V(c d3)) is the similarity value between the d1th task information and the d3th capability information in the first layer edge weight coefficient information, ρ(V(c d3 ),V(z d2 )) is the similarity value between the d3th capability information and the d2th indicator information in the second layer edge weight coefficient information, is the weight coefficient corresponding to the d3th capability information in the capability weight coefficient information, is the weight coefficient corresponding to the d2th indicator information in the indicator weight coefficient information, N is the number of task information in the first layer edge weight coefficient information, T is the number of indicator information in the second layer edge weight coefficient information, M is the number of indicator information in the first layer edge weight coefficient information, ∈ d1 is the constant coefficient of the d1th task information.

[0174] It should be noted that ∈ d1 It may be set by a user or obtained by other means, and is not specifically limited in the embodiment of the present invention, wherein 1≤d1≤N.

[0175] It can be seen that the implementation of the dynamic weight calculation method for inventory material support evaluation indicators described in the embodiment of the present invention is conducive to associating different inventory material support capabilities and indicators according to different tasks, making the indicator weight distribution of the inventory material support evaluation indicator system more reasonable.

[0176] Embodiment 2

[0177] See also Figure 2 , Figure 2 This is a schematic diagram of a dynamic weight calculation device for inventory material security evaluation indicators disclosed in an embodiment of the present invention. Figure 2 The described inventory material security assessment index dynamic weight calculation device is applied to an optimization system for inventory material security assessment index dynamic weight calculation, such as a local server or cloud server for inventory material security assessment index dynamic weight calculation, and the embodiment of the present invention is not limited. Figure 2 As shown, the inventory material guarantee evaluation index dynamic weight calculation device includes:

[0178] Acquisition module 201, used to acquire a task set, a capability set and an indicator set for inventory material support; the task set includes N task information; the capability set includes M capability information; the indicator set includes T indicator information; M, N and T are all positive integers;

[0179] A first calculation module 202 is used to perform fusion processing on the task set, the capability set and the indicator set to obtain a semantic vector information set;

[0180] The second calculation module 203 is used to analyze and process the semantic vector information set to obtain inventory material support capability assessment index weight information.

[0181] It can be seen that the implementation of the dynamic weight calculation device for inventory material support evaluation indicators described in the embodiment of the present invention is conducive to associating different inventory material support capabilities and indicators according to different tasks, making the indicator weight distribution of the inventory material support evaluation indicator system more reasonable.

[0182] Embodiment 3

[0183] See also Figure 3 , Figure 3 It is a schematic diagram of the structure of another dynamic weight calculation device for inventory material security evaluation index disclosed in an embodiment of the present invention. Figure 3 The described inventory material security assessment index dynamic weight calculation device is applied to an optimization system for inventory material security assessment index dynamic weight calculation, such as a local server or cloud server for inventory material security assessment index dynamic weight calculation, and the embodiment of the present invention is not limited. Figure 3 As shown, the inventory material guarantee evaluation index dynamic weight calculation device includes:

[0184] Processor 301;

[0185] A memory 302 coupled to the processor 301 and storing executable program codes;

[0186] The processor 301 calls the executable program code stored in the memory 302 to execute part or all of the steps of the method for calculating the dynamic weight of inventory material guarantee evaluation indicators described in Example 1.

[0187] It can be seen that the implementation of the dynamic weight calculation device for inventory material support evaluation indicators described in the embodiment of the present invention is conducive to associating different inventory material support capabilities and indicators according to different tasks, making the indicator weight distribution of the inventory material support evaluation indicator system more reasonable.

[0188] Embodiment 4

[0189] An embodiment 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 steps of the method for calculating the dynamic weight of inventory material guarantee evaluation indicators described in Example 1.

[0190] Embodiment 5

[0191] 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 part or all of the steps in the method for calculating the dynamic weights of inventory material security assessment indicators described in Example 1.

[0192] The system 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 labor.

[0193] 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.

[0194] Finally, it should be noted that the method and device for calculating the dynamic weights of inventory material security assessment indicators disclosed in the embodiments of the present invention only disclose the preferred embodiments of the present invention, which are only used to illustrate the technical scheme of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical schemes described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical schemes from the spirit and scope of the technical schemes of the embodiments of the present invention.

Claims

1. A method for calculating dynamic weights of inventory material security assessment indicators, characterized in that: The method comprises: S1, obtaining a task set, a capability set and an indicator set for inventory material support; the task set includes N task information; the capability set includes M capability information; the indicator set includes T indicator information; M, N and T are all positive integers; S2, fusing the task set, the capability set and the indicator set to obtain a semantic vector information set; S3, analyzing and processing the semantic vector information set to obtain weight information of inventory material support capability assessment indicators.

2. The method for calculating dynamic weights of inventory material security assessment indicators according to claim 1 is characterized in that: The fusing process of the task set, the capability set and the indicator set to obtain a semantic vector information set includes: S21, extracting characteristic words from the task set, the capability set, and the indicator set to obtain a task characteristic word set, a capability characteristic word set, and an indicator characteristic word set; S22, performing feature vector analysis processing on the task feature word set, the capability feature word set, and the indicator feature word set to obtain a task feature word vector set, a capability feature word vector set, and an indicator feature word vector set; S23, fusing the task feature word vector set, the capability feature word vector set and the indicator feature word vector set to obtain a semantic vector information set.

3. The method for calculating dynamic weights of inventory material security assessment indicators according to claim 2 is characterized in that: The fusing process of the task feature word vector set, the capability feature word vector set and the indicator feature word vector set to obtain a semantic vector information set includes: S231, using the inventory material support semantic calculation model, calculating and processing the task feature word vector set, the capability feature word vector set, and the indicator feature word vector set to obtain task semantic vector information, capability semantic vector information, and indicator semantic vector information; Among them, the inventory material guarantee semantic computing model is: Where V(r), V(c), and V(z) are the task semantic vector information, the capability semantic vector information, and the indicator semantic vector information. i1 ) is the semantic vector corresponding to the i1th task information in the task semantic vector information, V(c i2 ) is the semantic vector corresponding to the i2th capability information in the capability semantic vector information, V(z i3 ) is the semantic vector corresponding to the i3th indicator information in the indicator semantic vector information, is the l1th feature word vector of the i1th task information in the task feature word vector set, is the l2th feature word vector of the i2th capability information in the capability feature word vector set, is the l3th feature word vector of the i3th indicator information in the indicator feature word vector set, a i1 is the number of feature words corresponding to the i1th task information in the task feature word vector set, b i2 is the number of feature words corresponding to the i2th capability information in the capability feature word vector set, c i3 is the number of feature words corresponding to the i3th indicator information in the indicator feature word vector set, N, M and T are respectively the number of task information in the task feature word vector set, the number of capability information in the capability feature word vector set, and the number of indicator information in the indicator feature word vector set, δ1, δ3 and δ4 are respectively the first weight parameter, the second weight parameter and the third weight parameter; S232, merging the task semantic vector information, the capability semantic vector information and the indicator semantic vector information to obtain a semantic vector information set.

4. The method for calculating dynamic weights of inventory material security assessment indicators according to claim 1 is characterized in that: The analyzing and processing of the semantic vector information set to obtain the inventory material guarantee capability evaluation index weight information includes: S31, using a first similarity calculation model, calculating and processing the semantic vector information set to obtain capability similarity information and indicator similarity information; Among them, the first similarity calculation model: In the formula, S(V(c j1 ),V(c j2 )) is the similarity value between the j1th capability information and the j2th capability information in the capability similarity information, S(V(z j3 ),V(z j4 )) is the similarity value between the j3th indicator information and the j4th indicator information in the indicator similarity information, V(c j1 ) and V(c j2 ) respectively represent the semantic vectors corresponding to the j1th and j2th capability information of the capability semantic vector information in the semantic vector information set, V(z j3 ),V(z j4 ) respectively represent the semantic vectors corresponding to the j1th and j2th indicator information of the indicator semantic vector information in the semantic vector information set, M and T respectively represent the number of capability information in the capability semantic vector information and the number of indicator information in the indicator semantic vector information, θ1 and θ2 respectively represent the first similarity value offset constant and the second similarity value offset constant, · represents the vector inner product, and || represents the modulus of the vector; S32, constructing a capability judgment matrix and an indicator judgment matrix by using the capability similarity information and the indicator similarity information; S33, analyzing and processing the capability judgment matrix and the indicator judgment matrix to obtain capability weight coefficient information and indicator weight coefficient information; S34, analyzing and processing the semantic vector information set to obtain first-layer edge weight coefficient information and second-layer edge weight coefficient information; S35, calculating and processing the capability weight coefficient information, the indicator weight coefficient information, the first layer edge weight coefficient information and the second layer edge weight coefficient information to obtain inventory material support capability assessment indicator weight information.

5. The method for calculating dynamic weights of inventory material security assessment indicators according to claim 4 is characterized in that: The analyzing and processing the capability judgment matrix and the indicator judgment matrix to obtain capability weight coefficient information and indicator weight coefficient information includes: S331, performing eigenvector solving on the capability judgment matrix and the indicator judgment matrix to obtain capability eigenvector information and indicator eigenvector information; S332, using a feature vector normalization model, respectively normalizing the capability feature vector information and the indicator feature vector information to obtain capability weight coefficient information and indicator weight coefficient information; Wherein, the feature vector normalization model is: In the formula, ρ′ c and ρ′ z respectively represent the capability weight coefficient information and the indicator weight coefficient information, is the weight coefficient corresponding to the k1th capability information in the capability weight coefficient information, is the weight coefficient corresponding to the k2th indicator information in the indicator weight coefficient information, is the feature vector corresponding to the k1th capability information in the capability feature vector information, is the feature vector corresponding to the k2th indicator information in the indicator feature vector information, M is the number of capability information in the capability feature vector information, and T is the number of indicator information in the indicator feature vector information.

6. The method for calculating dynamic weights of inventory material security assessment indicators according to claim 4 is characterized in that: The analyzing and processing of the semantic vector information set to obtain first-layer edge weight coefficient information and second-layer edge weight coefficient information includes: S341, performing calculation processing on the semantic vector information set to obtain first-layer edge similarity information and second-layer edge similarity information; S342, preset s=1; S343, determining whether the sth similarity value in the first layer edge similarity information is less than a preset first similarity threshold, and obtaining a first determination result; When the first judgment result is yes, the similarity value is updated to 0, the updated first-layer edge similarity information is obtained, and the updated first-layer edge similarity information is determined to be the third-layer edge similarity information, and S344 is executed; When the first judgment result is no, executing S344; S344, determining whether s is equal to the number of similarity values ​​in the first layer edge similarity information, and obtaining a second determination result; When the second judgment result is yes, execute S345; When the second judgment result is no, executing S343; S345, calculating and processing the second layer edge similarity information and a preset second similarity threshold to obtain a fourth layer edge similarity information; S346 , normalizing the third layer edge similarity information and the fourth layer edge similarity information respectively to obtain first layer edge weight coefficient information and second layer edge weight coefficient information.

7. The method for calculating dynamic weights of inventory material security assessment indicators according to claim 4 is characterized in that: The calculating and processing the capability weight coefficient information, the indicator weight coefficient information, the first layer edge weight coefficient information and the second layer edge weight coefficient information to obtain the inventory material guarantee capability evaluation indicator weight information includes: Using the inventory material support weight calculation model, the capability weight coefficient information, the indicator weight coefficient information, the first layer edge weight coefficient information and the second layer edge weight coefficient information are calculated and processed to obtain the inventory material support capability evaluation indicator weight information; Among them, the inventory material guarantee weight calculation model is: 1≤d1≤N; 1≤d2≤T; 1≤d3≤M; Where P is the weight information of the inventory material support capability evaluation index, P d1,d2 is the weight of the d2th indicator information of the d1th task information in the inventory material support capability evaluation indicator weight information, ρ(V(r d1 ),V(c d3 )) is the similarity value between the d1th task information and the d3th capability information in the first layer edge weight coefficient information, ρ(V(c d3 ),V(z d2 )) is the similarity value between the d3th capability information and the d2th indicator information in the second layer edge weight coefficient information, is the weight coefficient corresponding to the d3th capability information in the capability weight coefficient information, is the weight coefficient corresponding to the d2th indicator information in the indicator weight coefficient information, N is the number of task information in the first layer edge weight coefficient information, T is the number of indicator information in the second layer edge weight coefficient information, M is the number of indicator information in the first layer edge weight coefficient information, ∈ d1 is the constant coefficient of the d1th task information.

8. A dynamic weight calculation device for inventory material security evaluation indicators, characterized in that: The device comprises: An acquisition module is used to acquire a task set, a capability set and an indicator set for inventory material support; the task set includes N task information; the capability set includes M capability information; the indicator set includes T indicator information; M, N and T are all positive integers; A first computing module is used to perform fusion processing on the task set, the capability set and the indicator set to obtain a semantic vector information set; The second calculation module is used to analyze and process the semantic vector information set to obtain the weight information of the inventory material guarantee capability evaluation index.

9. A dynamic weight calculation device for inventory material security assessment indicators, characterized in that: The device comprises: processor; a memory coupled to the processor and storing executable program code; The processor calls the executable program code stored in the memory to execute the dynamic weight calculation method for inventory material guarantee evaluation indicators as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, which, when called, are used to execute the dynamic weight calculation method for inventory material security assessment indicators as described in any one of claims 1 to 7.

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