Auxiliary decision-making method and device

Abstract

The invention discloses an auxiliary decision-making method and device. The method comprises the following steps: acquiring information of a to-be-decided scheme; the to-be-decided scheme information comprises a plurality of pieces of initial scheme information, and the to-be-decided scheme information is calculated and processed to obtain target decision weight information; the target decision weight information comprises a plurality of scheme weight information; the scheme weight information comprises a plurality of pieces of decision weight information; the decision weight information comprises first decision weight information and second decision weight information; the quantity of the decision weight information is consistent with the quantity of the scheme evaluation information; performing weighted calculation and screening processing on the target decision weight information to obtain target decision scheme information; the target decision scheme information comprises at least one target decision scheme; the target decision scheme information is used for assisting the user in accurate decision making. Therefore, the scheme identification precision can be improved, and the decision-making efficiency and precision can be improved.

Description

Technical Field

[0001] The present invention relates to the field of information processing technology, and in particular to a decision-making assistance method and device. Background Art

[0002] At present, multi-scheme decision-making is a common decision-making problem encountered in the fields of engineering, military, etc. Since multi-scheme decision-making is usually carried out by weighted scoring of schemes, the decision-making efficiency is relatively low and the accuracy is not high. Therefore, an auxiliary decision-making method and device are provided to improve the recognition accuracy of schemes, improve decision-making efficiency and accuracy. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a decision-making assistance method and device that is conducive to improving the recognition accuracy of solutions and improving decision-making efficiency and accuracy.

[0004] In order to solve the above technical problems, a first aspect of an embodiment of the present invention discloses a decision support method, the method comprising:

[0005] Acquire information of a solution to be decided; the solution to be decided includes a number of initial solution information; the initial solution information includes a number of solution evaluation information; the solution evaluation information includes solution attribute information and attribute weight information; any two decision solutions corresponding to the initial solution information are inconsistent; the solution evaluation information represents the evaluation of the same decision solution by different users;

[0006] The information of the scheme to be decided is calculated and processed to obtain target decision weight information; the target decision weight information includes a plurality of scheme weight information; the scheme weight information includes a plurality of decision weight information; the decision weight information includes first decision weight information and second decision weight information; the number of the decision weight information is consistent with the number of the scheme evaluation information;

[0007] The target decision weight information is weighted calculated and screened to obtain target decision solution information; the target decision solution information includes at least one target decision solution; the target decision solution information is used to assist the user in making accurate decisions.

[0008] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the calculating and processing the information of the solution to be decided to obtain the target decision weight information includes:

[0009] For any of the scheme evaluation information, normalizing and calculating the scheme evaluation information to obtain normalized attribute information and target weight information;

[0010] The scheme identity information, the normalized attribute information and the target weight information corresponding to the scheme evaluation information are processed to obtain the decision weight information corresponding to the scheme evaluation information.

[0011] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the normalizing and calculating the scheme evaluation information to obtain normalized attribute information and target weight information includes:

[0012] Using a normalization model set to normalize the scheme attribute information corresponding to the scheme evaluation information to obtain normalized attribute information; the normalized attribute information includes a plurality of normalized attribute values;

[0013] The normalized attribute information and the attribute weight information corresponding to the scheme evaluation information are optimized using a weight optimization model to obtain target weight information; the target weight information includes a plurality of target weight vectors; the attribute weight information includes a plurality of attribute weight vectors;

[0014] Wherein, the weight optimization model is:

[0015]

[0016] Where W p is the pth target weight in the target weight vector; w p is the pth attribute weight in the attribute weight vector; x ip is the normalized attribute value of the p-th scheme attribute in the normalized attribute information corresponding to the i-th scheme evaluation information; jp is the normalized attribute value of the pth scheme attribute in the normalized attribute information corresponding to the jth scheme evaluation information; m is the number of the scheme evaluation information; and n is the number of scheme attributes in the scheme attribute information.

[0017] As an optional implementation, in the first aspect of the embodiment of the present invention, the normalization model set includes a first normalization model and a second normalization model;

[0018] The method of using the normalization model set to normalize the solution attribute information corresponding to the solution evaluation information to obtain normalized attribute information includes:

[0019] For any of the scheme attributes in the scheme attribute information corresponding to the scheme evaluation information, determine whether the attribute value of the scheme attribute is greater than 0, and obtain an attribute determination result;

[0020] When the attribute judgment result is yes, the scheme attribute is normalized using the first normalization model to obtain a normalized attribute value corresponding to the scheme attribute;

[0021] Wherein, the first normalized model is:

[0022]

[0023] In the formula, s rt is the normalized attribute value of the t-th scheme attribute in the normalized attribute information corresponding to the r-th scheme evaluation information; X rt is the solution attribute value of the t-th solution attribute in the solution attribute information corresponding to the r-th solution evaluation information;

[0024] When the attribute judgment result is no, the scheme attribute is normalized using a second normalization model to obtain a normalized attribute value corresponding to the scheme attribute;

[0025] Wherein, the second normalized model is:

[0026]

[0027] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the scheme identity information, the normalized attribute information and the target weight information corresponding to the scheme evaluation information are processed to obtain the decision weight information corresponding to the scheme evaluation information, including:

[0028] Calculating and associating the solution identity information, the normalized attribute information, and the target weight information corresponding to the solution evaluation information to obtain first decision weight information;

[0029] The solution identity information corresponding to the solution evaluation information and the normalized attribute information are calculated and associated to obtain second decision weight information.

[0030] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the scheme identity information corresponding to the scheme evaluation information, the normalized attribute information and the target weight information are calculated and associated to obtain the first decision weight information, including:

[0031] Using a first weighted model to perform weighted calculation processing on the target weight information to obtain first weighted value information;

[0032] Wherein, the first weighted model is:

[0033]

[0034] In the formula, J aa is the aath first weighted value in the first weighted value information; W aa is the aath target weight in the target weight vector; n is the number of scheme attributes in the scheme attribute information;

[0035] Using a second weighting model to calculate and process the first weighted value information to obtain second weighted value information;

[0036] Wherein, the second weighted model is:

[0037]

[0038] In the formula, K bb is the bb-th second weighted value in the second weighted value information; J bb is the bb-th first weighted value in the first weighted value information;

[0039] Using a third weighting model to calculate and process the second weighted value information to obtain third weighted value information;

[0040] Wherein, the third weighted model is:

[0041]

[0042] Where, L cc is the ccth third weighted value in the third weighted value information; K cc is the cc-th second weighted value in the second weighted value information;

[0043] Using an optimized weighted model to calculate and process the normalized attribute information, the target weight information, and the third weighted value information to obtain optimized weighted value information;

[0044] Wherein, the optimization weighted model is:

[0045]

[0046] Where M uv is the uvth optimized weighted value in the optimized weighted value information; L uv is the uvth third weighted value in the third weighted value information; s uv is the uvth normalized attribute value in the normalized attribute information corresponding to the scheme evaluation information; W uv The uvth normalized attribute value in the target weight vector corresponding to the scheme evaluation information;

[0047] The scheme identity information corresponding to the scheme evaluation information and the optimization weight information are associated with each other to obtain first decision weight information.

[0048] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the weighted calculation and screening processing of the target decision weight information to obtain the target decision solution information includes:

[0049] For any of the decision weight information, the first decision weight information and the second decision weight information matching the scheme identity information corresponding to the decision weight information are screened out from all the first decision weight information and all the second decision weight information as the first target decision weight information and the second target decision weight information;

[0050] Performing vector product calculation on the first target decision weight information and the second target decision weight information to obtain a first target decision value corresponding to the decision weight information;

[0051] For any of the scheme weight information, first target decision values ​​corresponding to all decision weight information in the scheme weight information are cumulatively summed to obtain a second target decision value corresponding to the scheme weight information;

[0052] Sorting all the second target decision values ​​in descending order to obtain a target decision value sequence;

[0053] Get the target solution quantity value;

[0054] sequentially determining the second target decision value in the target decision value sequence as the target decision value;

[0055] Determine whether the sequence number value corresponding to the target decision value is equal to the target solution quantity value, and obtain a quantity determination result;

[0056] When the result of the quantity judgment is no, determining that the decision scheme corresponding to the target decision value is one of the target decision schemes, and triggering the execution of determining the second target decision value in the target decision value sequence as the target decision value;

[0057] When the quantity determination result is yes, the process is terminated.

[0058] A second aspect of an embodiment of the present invention discloses a decision support device, the device comprising:

[0059] The acquisition module is used to acquire the information of the solution to be decided; the solution to be decided includes a number of initial solution information; the initial solution information includes a number of solution evaluation information; the solution evaluation information includes solution attribute information and attribute weight information; the decision solutions corresponding to any two of the initial solution information are inconsistent; the solution evaluation information represents the evaluation of different users on the same decision solution;

[0060] A first processing module is used to calculate and process the information of the solution to be decided to obtain target decision weight information; the target decision weight information includes a plurality of solution weight information; the solution weight information includes a plurality of decision weight information; the decision weight information includes a first decision weight information and a second decision weight information; the number of the decision weight information is consistent with the number of the solution evaluation information;

[0061] The second processing module is used to perform weighted calculation and screening processing on the target decision weight information to obtain target decision plan information; the target decision plan information includes at least one target decision plan; the target decision plan information is used to assist the user in making accurate decisions.

[0062] The third aspect of the present invention discloses another auxiliary decision-making device, the device comprising:

[0063] A memory storing executable program code;

[0064] a processor coupled to the memory;

[0065] The processor calls the executable program code stored in the memory to execute part or all of the steps in the auxiliary decision-making method disclosed in the first aspect of the embodiment of the present invention.

[0066] 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 part or all of the steps in the auxiliary decision-making method disclosed in the first aspect of an embodiment of the present invention.

[0067] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0068] In the embodiment of the present invention, the information of the scheme to be decided is obtained; the information of the scheme to be decided includes several initial scheme information; the initial scheme information includes several scheme evaluation information; the scheme evaluation information includes scheme attribute information and attribute weight information; the decision schemes corresponding to any two initial scheme information are inconsistent; the scheme evaluation information represents the evaluation of the same decision scheme by different users; the information of the decision scheme is calculated and processed to obtain the target decision weight information; the target decision weight information includes several scheme weight information; the scheme weight information includes several decision weight information; the decision weight information includes the first decision weight information and the second decision weight information; the number of decision weight information and the number of scheme evaluation information are consistent; the target decision weight information is weighted and screened to obtain the target decision scheme information; the target decision scheme information includes at least one target decision scheme; the target decision scheme information is used to assist users in making accurate decisions. It can be seen that the present invention is conducive to improving the recognition accuracy of the scheme, improving the decision efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] 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.

[0070] Figure 1 It is a flowchart of a decision-making assistance method disclosed in an embodiment of the present invention;

[0071] Figure 2 It is a structural schematic diagram of a decision support device disclosed in an embodiment of the present invention;

[0072] Figure 3 It is a structural schematic diagram of another auxiliary decision-making device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0073] 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.

[0074] 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.

[0075] 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.

[0076] The present invention discloses a decision-making assistance method and device, which are beneficial to improving the recognition accuracy of solutions, and improving the decision-making efficiency and accuracy. The following are detailed descriptions.

[0077] Embodiment 1

[0078] See also Figure 1 , Figure 1 : is a flowchart of a decision support method disclosed in an embodiment of the present invention. Figure 1 The described decision support method is applied to a solution management system, such as a local server or a cloud server for decision support management, and the embodiments of the present invention do not limit this. Figure 1 As shown, the auxiliary decision-making method may include the following operations:

[0079] 101. Obtain information on plans to be decided.

[0080] In the embodiment of the present invention, the to-be-decided solution information includes a plurality of initial solution information.

[0081] In the embodiment of the present invention, the initial solution information includes a plurality of solution evaluation information.

[0082] In the embodiment of the present invention, the solution evaluation information includes solution attribute information and attribute weight information.

[0083] In the embodiment of the present invention, the decision solutions corresponding to any two pieces of initial solution information are inconsistent.

[0084] In the embodiment of the present invention, the solution evaluation information represents the evaluation of the same decision solution by different users.

[0085] 102. Calculate and process the decision plan information to obtain target decision weight information.

[0086] In the embodiment of the present invention, the target decision weight information includes a plurality of scheme weight information.

[0087] In the embodiment of the present invention, the scheme weight information includes a plurality of decision weight information.

[0088] In the embodiment of the present invention, the decision weight information includes first decision weight information and second decision weight information.

[0089] In the embodiment of the present invention, the amount of the decision weight information is consistent with the amount of the solution evaluation information.

[0090] 103. Perform weighted calculation and screening on the target decision weight information to obtain the target decision plan information.

[0091] In the embodiment of the present invention, the target decision scheme information includes at least one target decision scheme.

[0092] In the embodiment of the present invention, the target decision solution information is used to assist the user in making accurate decisions.

[0093] It should be noted that the use of the auxiliary decision-making method of the present application can avoid the influence of excessively high single weights, reduce the probability of decision-making errors, and thus improve the ability to make accurate decisions.

[0094] It can be seen that implementing the auxiliary decision-making method described in the embodiment of the present invention is conducive to improving the recognition accuracy of the solution and improving the decision-making efficiency and accuracy.

[0095] In an optional embodiment, the above-mentioned calculation and processing of the decision solution information to obtain the target decision weight information includes:

[0096] For any scheme evaluation information, the scheme evaluation information is normalized and calculated to obtain normalized attribute information and target weight information;

[0097] The scheme identity information, normalized attribute information and target weight information corresponding to the scheme evaluation information are processed to obtain the decision weight information corresponding to the scheme evaluation information.

[0098] It should be noted that the above-mentioned scheme identity information may be a number, a code or a code corresponding to the scheme evaluation information, which is not limited in the embodiment of the present invention.

[0099] It can be seen that implementing the auxiliary decision-making method described in the embodiment of the present invention is conducive to improving the recognition accuracy of the solution and improving the decision-making efficiency and accuracy.

[0100] In another optional embodiment, the scheme evaluation information is normalized and calculated to obtain normalized attribute information and target weight information, including:

[0101] Using a normalization model set to normalize the scheme attribute information corresponding to the scheme evaluation information, to obtain normalized attribute information; the normalized attribute information includes a plurality of normalized attribute values;

[0102] The weight optimization model is used to optimize the attribute weight information corresponding to the normalized attribute information and the scheme evaluation information to obtain the target weight information; the target weight information includes a plurality of target weight vectors; the attribute weight information includes a plurality of attribute weight vectors;

[0103] Among them, the weight optimization model is:

[0104]

[0105] Where W p is the pth target weight in the target weight vector; w pis the pth attribute weight in the attribute weight vector; x ip is the normalized attribute value of the p-th scheme attribute in the normalized attribute information corresponding to the i-th scheme evaluation information; jp is the normalized attribute value of the pth scheme attribute in the normalized attribute information corresponding to the jth scheme evaluation information; m is the number of scheme evaluation information; and n is the number of scheme attributes in the scheme attribute information.

[0106] It should be noted that the above normalization of weights can reduce the subjective influence of a single evaluation factor, improve the accuracy of decision-making scheme evaluation, and thus improve the ability to optimize schemes.

[0107] It can be seen that implementing the auxiliary decision-making method described in the embodiment of the present invention is conducive to improving the recognition accuracy of the solution and improving the decision-making efficiency and accuracy.

[0108] In yet another optional embodiment, the normalized model set includes a first normalized model and a second normalized model;

[0109] The scheme attribute information corresponding to the scheme evaluation information is normalized using the normalization model set to obtain normalized attribute information, including:

[0110] For any scheme attribute in the scheme attribute information corresponding to the scheme evaluation information, determine whether the attribute value of the scheme attribute is greater than 0, and obtain an attribute determination result;

[0111] When the attribute judgment result is yes, the scheme attribute is normalized using the first normalization model to obtain a normalized attribute value corresponding to the scheme attribute;

[0112] Among them, the first normalized model is:

[0113]

[0114] In the formula, s rt is the normalized attribute value of the t-th scheme attribute in the normalized attribute information corresponding to the r-th scheme evaluation information; X rt is the solution attribute value of the t-th solution attribute in the solution attribute information corresponding to the r-th solution evaluation information;

[0115] When the attribute judgment result is no, the second normalization model is used to normalize the scheme attribute to obtain a normalized attribute value corresponding to the scheme attribute;

[0116] Among them, the second normalized model is:

[0117]

[0118] It should be noted that each solution attribute corresponds to an attribute value. When the attribute value is greater than 0, the evaluation of the decision solution corresponding to the solution attribute is positive.

[0119] It can be seen that implementing the auxiliary decision-making method described in the embodiment of the present invention is conducive to improving the recognition accuracy of the solution and improving the decision-making efficiency and accuracy.

[0120] In yet another optional embodiment, the solution identity information, normalized attribute information, and target weight information corresponding to the solution evaluation information are processed to obtain decision weight information corresponding to the solution evaluation information, including:

[0121] Calculating and associating the scheme identity information, normalized attribute information, and target weight information corresponding to the scheme evaluation information to obtain first decision weight information;

[0122] The scheme identity information and normalized attribute information corresponding to the scheme evaluation information are calculated and associated to obtain second decision weight information.

[0123] It should be noted that the association processing is to establish a connection relationship between two pieces of data information, and any association processing method known in the prior art may be used.

[0124] In this optional embodiment, as an optional implementation mode, the scheme identity information and normalized attribute information corresponding to the scheme evaluation information are calculated and associated to obtain the second decision weight information, including:

[0125] For any normalized attribute value, a normalized attribute value matching the decision solution corresponding to the solution identity information corresponding to the normalized attribute value is selected from all normalized attribute information as the target normalized attribute value to obtain the target normalized attribute value information;

[0126] Using the first attribute model to calculate and process the normalized attribute value and the target normalized attribute value information to obtain first attribute value information;

[0127] Among them, the first attribute model is:

[0128]

[0129] Where D1 ee is the ee-th first attribute value in the first attribute value information; s ee is the ee-th target normalized attribute value in the target normalized attribute value information; tt is the number of target normalized attribute values;

[0130] Using the second attribute model to calculate the first attribute value information, to obtain the second attribute value information;

[0131] Among them, the second attribute model is:

[0132]

[0133] Where D2 ff D1 is the ffth second attribute value in the second attribute value information; ff is the ffth first attribute value in the first attribute value information;

[0134] Perform product calculation processing on the first attribute value information and the second attribute value information to obtain third attribute value information;

[0135] The third attribute value information and the solution identity information corresponding to the solution evaluation information are associated with each other to obtain second decision weight information.

[0136] It should be noted that the number of the above-mentioned target normalized attribute values ​​is consistent with the number of scheme evaluation information.

[0137] It should be noted that the scheme identity information of the normalized attribute value obtained based on the normalization processing of the scheme attribute information is consistent with the scheme identity information of the scheme evaluation information. Furthermore, the scheme identity information includes the corresponding decision scheme situation. Furthermore, the same decision scheme corresponds to multiple scheme evaluation information due to different evaluation situations. Therefore, the normalized attribute values ​​belonging to the same decision scheme in different scheme evaluation information can be determined by matching the decision schemes.

[0138] It can be seen that implementing the auxiliary decision-making method described in the embodiment of the present invention is conducive to improving the recognition accuracy of the solution and improving the decision-making efficiency and accuracy.

[0139] In another optional embodiment, the scheme identity information, normalized attribute information, and target weight information corresponding to the scheme evaluation information are calculated and associated to obtain first decision weight information, including:

[0140] Using a first weighted model to perform weighted calculation processing on the target weight information to obtain first weighted value information;

[0141] Among them, the first weighted model is:

[0142]

[0143] In the formula, J aa is the aath first weighted value in the first weighted value information; W aa is the aath target weight in the target weight vector; n is the number of scheme attributes in the scheme attribute information;

[0144] Using a second weighted model to calculate and process the first weighted value information to obtain second weighted value information;

[0145] Among them, the second weighted model is:

[0146]

[0147] In the formula, K bb is the bb-th second weighted value in the second weighted value information; J bb is the bb-th first weighted value in the first weighted value information;

[0148] Using a third weighted model to calculate and process the second weighted value information to obtain third weighted value information;

[0149] Among them, the third weighted model is:

[0150]

[0151] Where, L cc is the ccth third weighted value in the third weighted value information; K cc is the cc-th second weighted value in the second weighted value information;

[0152] The optimized weighted model is used to calculate and process the normalized attribute information, the target weight information and the third weighted value information to obtain the optimized weighted value information;

[0153] Among them, the optimized weighted model is:

[0154]

[0155] Where M uv is the uvth optimized weighted value in the optimized weighted value information; L uv is the uvth third weighted value in the third weighted value information; s uv is the uvth normalized attribute value in the normalized attribute information corresponding to the scheme evaluation information; W uv The uvth normalized attribute value in the target weight vector corresponding to the scheme evaluation information;

[0156] The scheme identity information and the optimization weight value information corresponding to the scheme evaluation information are associated with each other to obtain first decision weight value information.

[0157] It should be noted that each of the above scheme evaluation information corresponds to 1 target weight vector and 1 normalized attribute information.

[0158] It can be seen that implementing the auxiliary decision-making method described in the embodiment of the present invention is conducive to improving the recognition accuracy of the solution and improving the decision-making efficiency and accuracy.

[0159] In an optional embodiment, the target decision weight information is weighted calculated and screened to obtain target decision solution information, including:

[0160] For any decision weight information, the first decision weight information and the second decision weight information matching the scheme identity information corresponding to the decision weight information are screened out from all the first decision weight information and all the second decision weight information as the first target decision weight information and the second target decision weight information;

[0161] Perform vector product calculation on the first target decision weight information and the second target decision weight information to obtain the first target decision value corresponding to the decision weight information;

[0162] For any scheme weight information, the first target decision values ​​corresponding to all decision weight information in the scheme weight information are cumulatively summed to obtain the second target decision value corresponding to the scheme weight information;

[0163] Sort all second target decision values ​​from large to small to obtain a target decision value sequence;

[0164] Get the target solution quantity value;

[0165] sequentially determining the second target decision value in the target decision value sequence as the target decision value;

[0166] Determine whether the sequence number value corresponding to the target decision value is equal to the target solution quantity value, and obtain the quantity judgment result;

[0167] When the result of the quantity judgment is no, determining the decision scheme corresponding to the target decision value as a target decision scheme, and triggering execution to sequentially determine the second target decision value in the target decision value sequence as the target decision value;

[0168] When the quantity judgment result is yes, the process is terminated.

[0169] It should be noted that the matching of the above scheme identity information refers to the scheme evaluation information that the decision weight information, the first decision weight information and the second decision weight information correspond to the same decision scheme.

[0170] It should be noted that the above-mentioned determination of the second target decision value in the target decision value sequence as the target decision value is determined one by one from the front to the back according to the serial number value of the second target decision value in the target decision value sequence, that is, each time the second target decision value is selected, the serial number value is increased by 1.

[0171] It should be noted that the above target number of solutions is to select the required number of solutions from all decision solutions.

[0172] It can be seen that implementing the auxiliary decision-making method described in the embodiment of the present invention is conducive to improving the recognition accuracy of the solution and improving the decision-making efficiency and accuracy.

[0173] Embodiment 2

[0174] See also Figure 2 , Figure 2 : is a schematic diagram of a structure of a decision support device disclosed in an embodiment of the present invention. Figure 2 The described device can be applied to a solution management system, such as a local server or a cloud server for assisting decision management, and the embodiments of the present invention are not limited thereto. Figure 2 As shown, the device may include:

[0175] The acquisition module is used to acquire the information of the solution to be decided; the information of the solution to be decided includes a number of initial solution information; the initial solution information includes a number of solution evaluation information; the solution evaluation information includes solution attribute information and attribute weight information; the decision solutions corresponding to any two initial solution information are inconsistent; the solution evaluation information represents the evaluation of the same decision solution by different users;

[0176] The first processing module is used to calculate and process the decision scheme information to obtain target decision weight information; the target decision weight information includes a plurality of scheme weight information; the scheme weight information includes a plurality of decision weight information; the decision weight information includes a first decision weight information and a second decision weight information; the number of decision weight information and the number of scheme evaluation information are consistent;

[0177] The second processing module is used to perform weighted calculation and screening processing on the target decision weight information to obtain target decision plan information; the target decision plan information includes at least one target decision plan; the target decision plan information is used to assist users in making accurate decisions.

[0178] It can be seen that the implementation Figure 2 The described auxiliary decision-making device is beneficial to improving the recognition accuracy of solutions and improving decision-making efficiency and accuracy.

[0179] In another optional embodiment, Figure 2 As shown, the first processing module calculates and processes the decision scheme information to obtain the target decision weight information, including:

[0180] For any scheme evaluation information, the scheme evaluation information is normalized and calculated to obtain normalized attribute information and target weight information;

[0181] The scheme identity information, normalized attribute information and target weight information corresponding to the scheme evaluation information are processed to obtain the decision weight information corresponding to the scheme evaluation information.

[0182] It can be seen that the implementation Figure 2 The described auxiliary decision-making device is beneficial to improving the recognition accuracy of solutions and improving decision-making efficiency and accuracy.

[0183] In yet another optional embodiment, Figure 2 As shown, the first processing module normalizes and calculates the scheme evaluation information to obtain normalized attribute information and target weight information, including:

[0184] Using a normalization model set to normalize the scheme attribute information corresponding to the scheme evaluation information, to obtain normalized attribute information; the normalized attribute information includes a plurality of normalized attribute values;

[0185] The weight optimization model is used to optimize the attribute weight information corresponding to the normalized attribute information and the scheme evaluation information to obtain the target weight information; the target weight information includes a plurality of target weight vectors; the attribute weight information includes a plurality of attribute weight vectors;

[0186] Among them, the weight optimization model is:

[0187]

[0188] Where W p is the pth target weight in the target weight vector; w p is the pth attribute weight in the attribute weight vector; x ip is the normalized attribute value of the p-th scheme attribute in the normalized attribute information corresponding to the i-th scheme evaluation information; jp is the normalized attribute value of the pth scheme attribute in the normalized attribute information corresponding to the jth scheme evaluation information; m is the number of scheme evaluation information; and n is the number of scheme attributes in the scheme attribute information.

[0189] It can be seen that the implementation Figure 2 The described auxiliary decision-making device is beneficial to improving the recognition accuracy of solutions and improving decision-making efficiency and accuracy.

[0190] In yet another optional embodiment, Figure 2 As shown, the normalized model set includes a first normalized model and a second normalized model;

[0191] The first processing module uses a normalization model set to normalize the solution attribute information corresponding to the solution evaluation information to obtain normalized attribute information, including:

[0192] For any scheme attribute in the scheme attribute information corresponding to the scheme evaluation information, determine whether the attribute value of the scheme attribute is greater than 0, and obtain an attribute determination result;

[0193] When the attribute judgment result is yes, the scheme attribute is normalized using the first normalization model to obtain a normalized attribute value corresponding to the scheme attribute;

[0194] Among them, the first normalized model is:

[0195]

[0196] In the formula, s rt is the normalized attribute value of the t-th scheme attribute in the normalized attribute information corresponding to the r-th scheme evaluation information; X rt is the solution attribute value of the t-th solution attribute in the solution attribute information corresponding to the r-th solution evaluation information;

[0197] When the attribute judgment result is no, the second normalization model is used to normalize the scheme attribute to obtain a normalized attribute value corresponding to the scheme attribute;

[0198] Among them, the second normalized model is:

[0199]

[0200] It can be seen that the implementation Figure 2 The described auxiliary decision-making device is beneficial to improving the recognition accuracy of solutions and improving decision-making efficiency and accuracy.

[0201] In yet another optional embodiment, Figure 2 As shown, the first processing module processes the scheme identity information, normalized attribute information and target weight information corresponding to the scheme evaluation information to obtain the decision weight information corresponding to the scheme evaluation information, including:

[0202] Calculating and associating the scheme identity information, normalized attribute information, and target weight information corresponding to the scheme evaluation information to obtain first decision weight information;

[0203] The scheme identity information and normalized attribute information corresponding to the scheme evaluation information are calculated and associated to obtain second decision weight information.

[0204] It can be seen that the implementation Figure 2 The described auxiliary decision-making device is beneficial to improving the recognition accuracy of solutions and improving decision-making efficiency and accuracy.

[0205] In yet another optional embodiment, Figure 2 As shown, the first processing module calculates and associates the solution identity information, normalized attribute information, and target weight information corresponding to the solution evaluation information to obtain first decision weight information, including:

[0206] Using a first weighted model to perform weighted calculation processing on the target weight information to obtain first weighted value information;

[0207] Among them, the first weighted model is:

[0208]

[0209] In the formula, J aa is the aath first weighted value in the first weighted value information; W aa is the aath target weight in the target weight vector; n is the number of scheme attributes in the scheme attribute information;

[0210] Using a second weighted model to calculate and process the first weighted value information to obtain second weighted value information;

[0211] Among them, the second weighted model is:

[0212]

[0213] In the formula, K bb is the bb-th second weighted value in the second weighted value information; J bb is the bb-th first weighted value in the first weighted value information;

[0214] Using a third weighted model to calculate and process the second weighted value information to obtain third weighted value information;

[0215] Among them, the third weighted model is:

[0216]

[0217] Where, L cc is the ccth third weighted value in the third weighted value information; K cc is the cc-th second weighted value in the second weighted value information;

[0218] The optimized weighted model is used to calculate and process the normalized attribute information, the target weight information and the third weighted value information to obtain the optimized weighted value information;

[0219] Among them, the optimized weighted model is:

[0220]

[0221] Where M uv is the uvth optimized weighted value in the optimized weighted value information; L uv is the uvth third weighted value in the third weighted value information; s uv is the uvth normalized attribute value in the normalized attribute information corresponding to the scheme evaluation information; W uvThe uvth normalized attribute value in the target weight vector corresponding to the scheme evaluation information;

[0222] The scheme identity information and the optimization weight value information corresponding to the scheme evaluation information are associated with each other to obtain first decision weight value information.

[0223] It can be seen that the implementation Figure 2 The described auxiliary decision-making device is beneficial to improving the recognition accuracy of solutions and improving decision-making efficiency and accuracy.

[0224] In yet another optional embodiment, Figure 2 As shown, the second processing module performs weighted calculation and screening processing on the target decision weight information to obtain the target decision solution information, including:

[0225] For any decision weight information, the first decision weight information and the second decision weight information matching the scheme identity information corresponding to the decision weight information are screened out from all the first decision weight information and all the second decision weight information as the first target decision weight information and the second target decision weight information;

[0226] Perform vector product calculation on the first target decision weight information and the second target decision weight information to obtain the first target decision value corresponding to the decision weight information;

[0227] For any scheme weight information, the first target decision values ​​corresponding to all decision weight information in the scheme weight information are cumulatively summed to obtain the second target decision value corresponding to the scheme weight information;

[0228] Sort all second target decision values ​​from large to small to obtain a target decision value sequence;

[0229] Get the target solution quantity value;

[0230] sequentially determining the second target decision value in the target decision value sequence as the target decision value;

[0231] Determine whether the sequence number value corresponding to the target decision value is equal to the target solution quantity value, and obtain the quantity judgment result;

[0232] When the result of the quantity judgment is no, determining the decision scheme corresponding to the target decision value as a target decision scheme, and triggering execution to sequentially determine the second target decision value in the target decision value sequence as the target decision value;

[0233] When the quantity judgment result is yes, the process is terminated.

[0234] It can be seen that the implementation Figure 2 The described auxiliary decision-making device is beneficial to improving the recognition accuracy of solutions and improving decision-making efficiency and accuracy.

[0235] Embodiment 3

[0236] See also Figure 3 , Figure 3 is a schematic diagram of the structure of another auxiliary decision-making device disclosed in an embodiment of the present invention. Figure 3 The described device can be applied to a solution management system, such as a local server or a cloud server for assisting decision management, and the embodiments of the present invention are not limited thereto. Figure 3 As shown, the device may include:

[0237] A memory 301 storing executable program codes;

[0238] a processor 302 coupled to the memory 301;

[0239] The processor 302 calls the executable program code stored in the memory 301 to execute the steps in the auxiliary decision-making method described in the first embodiment.

[0240] Embodiment 4

[0241] An embodiment of the present invention discloses a computer-readable storage medium, which stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the auxiliary decision-making method described in the first embodiment.

[0242] Embodiment 5

[0243] 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 auxiliary decision-making method described in the first embodiment.

[0244] 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.

[0245] 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.

[0246] Finally, it should be noted that the auxiliary decision-making method and device disclosed in the embodiments of the present invention disclose only the preferred embodiments of the present invention, which are 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, those skilled in the art should understand 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 embodiments of the present invention.

Claims

1. A decision support method, characterized in that: The method comprises: Acquire information of a solution to be decided; the solution to be decided includes a number of initial solution information; the initial solution information includes a number of solution evaluation information; the solution evaluation information includes solution attribute information and attribute weight information; any two decision solutions corresponding to the initial solution information are inconsistent; the solution evaluation information represents the evaluation of the same decision solution by different users; The information of the scheme to be decided is calculated and processed to obtain target decision weight information; the target decision weight information includes a plurality of scheme weight information; the scheme weight information includes a plurality of decision weight information; the decision weight information includes first decision weight information and second decision weight information; the number of the decision weight information is consistent with the number of the scheme evaluation information; The target decision weight information is weighted calculated and screened to obtain target decision solution information; the target decision solution information includes at least one target decision solution; the target decision solution information is used to assist the user in making accurate decisions.

2. The decision support method according to claim 1, characterized in that: The step of calculating and processing the information of the solution to be decided to obtain target decision weight information includes: For any of the scheme evaluation information, normalizing and calculating the scheme evaluation information to obtain normalized attribute information and target weight information; The scheme identity information, the normalized attribute information and the target weight information corresponding to the scheme evaluation information are processed to obtain the decision weight information corresponding to the scheme evaluation information.

3. The decision support method according to claim 2, characterized in that: The scheme evaluation information is normalized and calculated to obtain normalized attribute information and target weight information, including: Using a normalization model set to normalize the scheme attribute information corresponding to the scheme evaluation information to obtain normalized attribute information; the normalized attribute information includes a plurality of normalized attribute values; The normalized attribute information and the attribute weight information corresponding to the scheme evaluation information are optimized using a weight optimization model to obtain target weight information; the target weight information includes a plurality of target weight vectors; the attribute weight information includes a plurality of attribute weight vectors; Wherein, the weight optimization model is: Where W p is the pth target weight in the target weight vector; w p is the pth attribute weight in the attribute weight vector; x ip is the normalized attribute value of the p-th scheme attribute in the normalized attribute information corresponding to the i-th scheme evaluation information; jp is the normalized attribute value of the pth scheme attribute in the normalized attribute information corresponding to the jth scheme evaluation information; m is the number of the scheme evaluation information; and n is the number of scheme attributes in the scheme attribute information.

4. The decision support method according to claim 3, characterized in that: The normalized model set includes a first normalized model and a second normalized model; The method of using the normalization model set to normalize the solution attribute information corresponding to the solution evaluation information to obtain normalized attribute information includes: For any of the scheme attributes in the scheme attribute information corresponding to the scheme evaluation information, determine whether the attribute value of the scheme attribute is greater than 0, and obtain an attribute determination result; When the attribute judgment result is yes, the scheme attribute is normalized using the first normalization model to obtain a normalized attribute value corresponding to the scheme attribute; Wherein, the first normalized model is: In the formula, s rt is the normalized attribute value of the t-th scheme attribute in the normalized attribute information corresponding to the r-th scheme evaluation information; X rt is the solution attribute value of the t-th solution attribute in the solution attribute information corresponding to the r-th solution evaluation information; When the attribute judgment result is no, the scheme attribute is normalized using a second normalization model to obtain a normalized attribute value corresponding to the scheme attribute; Wherein, the second normalized model is:

5. The decision support method according to claim 2, characterized in that: The processing of the scheme identity information, the normalized attribute information and the target weight information corresponding to the scheme evaluation information to obtain the decision weight information corresponding to the scheme evaluation information includes: Calculating and associating the solution identity information, the normalized attribute information, and the target weight information corresponding to the solution evaluation information to obtain first decision weight information; The solution identity information corresponding to the solution evaluation information and the normalized attribute information are calculated and associated to obtain second decision weight information.

6. The decision support method according to claim 5, characterized in that: The scheme identity information corresponding to the scheme evaluation information, the normalized attribute information and the target weight information are calculated and associated to obtain the first decision weight information, including: Using a first weighted model to perform weighted calculation processing on the target weight information to obtain first weighted value information; Wherein, the first weighted model is: In the formula, J aa is the aath first weighted value in the first weighted value information; W aa is the aath target weight in the target weight vector; n is the number of scheme attributes in the scheme attribute information; Using a second weighting model to calculate and process the first weighted value information to obtain second weighted value information; Wherein, the second weighted model is: In the formula, K bb is the bb-th second weighted value in the second weighted value information; J bb is the bb-th first weighted value in the first weighted value information; Using a third weighting model to calculate and process the second weighted value information to obtain third weighted value information; Wherein, the third weighted model is: Where, L cc is the ccth third weighted value in the third weighted value information; K cc is the cc-th second weighted value in the second weighted value information; Using an optimized weighted model to calculate and process the normalized attribute information, the target weight information, and the third weighted value information to obtain optimized weighted value information; Wherein, the optimization weighted model is: Where M uv is the uvth optimized weighted value in the optimized weighted value information; L uv is the uvth third weighted value in the third weighted value information; s uv is the uvth normalized attribute value in the normalized attribute information corresponding to the scheme evaluation information; W uv The uvth normalized attribute value in the target weight vector corresponding to the scheme evaluation information; The scheme identity information corresponding to the scheme evaluation information and the optimization weight information are associated with each other to obtain first decision weight information.

7. The decision support method according to claim 1, characterized in that: The target decision weight information is weighted calculated and screened to obtain target decision solution information, including: For any of the decision weight information, the first decision weight information and the second decision weight information matching the scheme identity information corresponding to the decision weight information are screened out from all the first decision weight information and all the second decision weight information as the first target decision weight information and the second target decision weight information; Performing vector product calculation on the first target decision weight information and the second target decision weight information to obtain a first target decision value corresponding to the decision weight information; For any of the scheme weight information, first target decision values ​​corresponding to all decision weight information in the scheme weight information are cumulatively summed to obtain a second target decision value corresponding to the scheme weight information; Sorting all the second target decision values ​​in descending order to obtain a target decision value sequence; Get the target solution quantity value; sequentially determining the second target decision value in the target decision value sequence as the target decision value; Determine whether the sequence number value corresponding to the target decision value is equal to the target solution quantity value, and obtain a quantity determination result; When the result of the quantity judgment is no, determining that the decision scheme corresponding to the target decision value is one of the target decision schemes, and triggering the execution of determining the second target decision value in the target decision value sequence as the target decision value; When the quantity determination result is yes, the process is terminated.

8. A decision support device, characterized in that: The device comprises: The acquisition module is used to acquire the information of the solution to be decided; the solution to be decided includes a number of initial solution information; the initial solution information includes a number of solution evaluation information; the solution evaluation information includes solution attribute information and attribute weight information; the decision solutions corresponding to any two of the initial solution information are inconsistent; the solution evaluation information represents the evaluation of different users on the same decision solution; A first processing module is used to calculate and process the information of the solution to be decided to obtain target decision weight information; the target decision weight information includes a plurality of solution weight information; the solution weight information includes a plurality of decision weight information; the decision weight information includes a first decision weight information and a second decision weight information; the number of the decision weight information is consistent with the number of the solution evaluation information; The second processing module is used to perform weighted calculation and screening processing on the target decision weight information to obtain target decision plan information; the target decision plan information includes at least one target decision plan; the target decision plan information is used to assist the user in making accurate decisions.

9. A decision support device, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the auxiliary decision-making method 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 auxiliary decision-making method as described in any one of claims 1-7.

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