Trust Evidence Reasoning Method, Apparatus and Device for Multi-Source Data Fusion

Abstract

The present application relates to a credible evidence reasoning method, apparatus and equipment for multi-source data fusion, which constructs a multi-source decision information system. In this multi-source decision information system, a support matrix is designed to quantify the correlation between multi-source data attributes, and the concepts of similarity and distance are combined to evaluate the relationship. First, multi-source data of different scales are converted into confidence distributions and given probabilistic meanings, so that the generated evidence can be applied to multi-scale data fusion. Then, a credible evidence reasoning rule is constructed, in which the credibility of the evidence is determined by its weight and reliability. By integrating multi-source data samples of different scales, the confidence distribution of the results is derived. This solves the problem of difficulty in determining the confidence distribution of evidence.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device and equipment for credible evidence reasoning based on multi-source data fusion. Background Art

[0002] In contemporary artificial intelligence (AI) and data science, the integration of multi-source information is a key technology for improving analytical depth and accuracy, thereby enhancing the precision of AI processing tasks. The proliferation of data-generating devices, platforms, and systems has spurred the development of powerful multi-source data fusion (MSDF) methods, which aim to integrate disparate data streams into a coherent and information-rich whole. Despite significant progress in this field, ensuring the trustworthiness of the fused data remains a critical issue.

[0003] Based on methodological requirements and the uncertainty of the results, MSDF techniques are generally categorized into model-based and statistical methods. The former relies on mathematical structures to integrate information from diverse sources, thereby improving data authenticity and decision quality. Such methods include Kalman filtering, extended Kalman filtering, and maximum likelihood estimation. The latter focuses on applying statistical principles to integrate multi-source data, including principal component analysis, Bayesian inference, and cluster analysis. Furthermore, the combination of model-based and statistical methods has driven the development of artificial intelligence, including fuzzy logic, rough sets, Dempster-Shafer theory (DST), neural networks, and machine learning. The emergence of machine learning and data mining has accelerated the evolution of MSDF, enabling it to encompass more complex and automated data integration strategies. However, the "black box" nature of these models complicates the reliability of the integration process and the accuracy of the resulting data. Furthermore, these models require large amounts of labeled data, which can be scarce or biased.

[0004] Information fusion methods based on DST are a prominent intelligent framework that allows for the representation of uncertainty and the combination of evidence from independent sources without the need for precise probability assignments. DST has been widely used in fields such as information fusion, multi-attribute decision-making, and pattern recognition, and has been further developed into evidential reasoning (ER). Zhang et al. designed an ER rule-based structural health assessment method (CL Zhang, ZJ Zhou, GY Hu, LH Yang, and SWTang, “Health assessment of the wharf based on evidential reasoning rule considering optimal sensor placement,” Measurement, vol. 186, pp. 110-184, 2021). By considering the optimal placement of sensors, they improved the accuracy of multi-sensor data fusion. Ren et al. developed a data fusion technique based on the ER rule (ML Ren, P. He, and JJ Zhou, “Decision fusion of two sensors object classification based on the evidential reasoning rule,” Expert Syst. Appl. vol. 210, pp. 118620, 2022), which takes into account differences in classification decisions and validates it on the Nuscenes and Waymo datasets. Liu et al. proposed an ozone prediction model based on the ER method (XQ Liu, YJ Zhang, JP Wang, H. Huang, and H. Yin, “Multi-source and multivariate ozone prediction based on fuzzy cognitive maps and evidential reasoning theory,” Applied Soft Comput. vol. 119, pp. 108600, 2022.), which improves the prediction accuracy of multi-source and multivariate time series data.Wu et al. proposed a tunnel collapse risk assessment method based on the ER method (B. Wu, WX Qiu, GW Meng, Y. Nong, and JSHuang, “A multi-source information fusion evaluation method for the tunnelingcollapse disaster based on the artificial intelligence deformationprediction,” Arabian J. Sci. Eng. vol. 47, no. 4, pp. 5053-5071, 2022.). Huang et al. proposed a multi-source transfer learning method based on the ER method (LQ Huang, JF Fan, WBZhao, and Y. You, “A new multi-source transfer learning method based on two-stage weighted fusion,” Knowl. Based Syst. vol. 262, pp. 110233, 2023.), which significantly improved classification accuracy. Sun and Guo proposed a dynamic fire danger level assessment technique based on particle swarm optimization (B. Sun, and T. Guo, “Evidential reasoning and lightweight multi-source heterogeneous data fusion-driven fire danger level dynamic assessment technique,” Process Safety. Environ. Protect., vol. 185, pp. 350-366, 2024.), which can process multi-source heterogeneous data.

[0005] Although existing research has significantly advanced the development of evidence theory, it still has significant flaws, such as "counter-intuitive" problems and the combinatorial explosion problem in large-scale data fusion. In particular, when ER is applied to multi-source and multi-scale data fusion, it faces three challenges: (1) the difficulty of determining the confidence distribution of evidence; (2) the dilemma of achieving credible reasoning; and (3) the bottleneck of determining the reliability of evidence. Summary of the Invention

[0006] Based on this, it is necessary to provide a credible evidence reasoning method, device and equipment for multi-source data fusion to address the above technical problems.

[0007] A credible evidence reasoning method for multi-source data fusion, the method comprising:

[0008] Constructing a multi-source decision information system; the multi-source decision information system is composed of multiple independent single-source decision information systems, the single-source decision information systems generate data for artificial intelligence training, and the multi-source decision information system is used to integrate the data used for artificial intelligence training; the single-source decision information system is composed of data samples, attributes, data matrices, mapping functions, and decision feature sets; the mapping functions establish mapping relationships between data samples, attributes, and data matrices;

[0009] Dividing the multi-source decision information system into a plurality of data matrices according to attributes, and determining the association relationship between the single-source decision information systems based on the decision feature set, and calculating the total similarity between the elements in the data matrix and other similar elements when an association relationship exists, and calculating the total distance between the elements in the data matrix and other dissimilar elements when no association exists;

[0010] Determining the support of the elements in the data matrix to the data matrix based on the total similarity and the total distance, and calculating the first confidence of the data sample in terms of attributes relative to the single-source decision information system based on the support;

[0011] generating evidence for a single-source decision information system based on the first confidence level;

[0012] Establishing an identification framework based on the evidence, and calculating the credibility of the evidence based on the weight and reliability of the evidence, and obtaining a second confidence level and a basic probability mass distribution of the evidence based on the credibility;

[0013] A combined confidence level between any two pieces of evidence is established based on the basic probability mass distribution and the second confidence level, and a confidence distribution of multiple pieces of evidence in a multi-source decision information system is obtained based on the combined confidence level.

[0014] In one embodiment, the method further includes dividing the multi-source decision information system into a plurality of data matrices according to attributes, wherein the data matrices are represented as follows:

[0015] ;

[0016] in, Representation attributes The corresponding data matrix, Each row of represents a sample, Each column of represents a single-source decision information system;

[0017] The elements in the data matrix are normalized and expressed as:

[0018] ;

[0019] The normalized elements are used to represent the data matrix to obtain a normalized data matrix.

[0020] In one embodiment, the further step further includes: determining, based on the decision feature set, the association relationship between the single-source decision information systems:

[0021] ;

[0022] in, represents a data sample, represents the decision features in the decision feature set, , ;

[0023] When there is an association relationship, the total similarity between the element in the data matrix and other similar elements is calculated as:

[0024] ;

[0025] When there is no correlation, the total distance between an element in the data matrix and other dissimilar elements is calculated as:

[0026] .

[0027] In one embodiment, the further step includes: determining, based on the total similarity and the total distance, the support degree of the elements in the data matrix to the data matrix:

[0028] ;

[0029] The first confidence of the data sample in terms of attributes relative to the single-source decision information system is calculated based on the support:

[0030] .

[0031] In one embodiment, the further step includes: generating evidence of a single-source decision information system based on the first confidence level as follows:

[0032] ;

[0033] in, Represents data samples In the properties The evidence provided above and .

[0034] In one embodiment, the invention further includes: establishing an identification framework based on the evidence ; Represents a single-source decision information system;

[0035] Based on the weight and reliability of the evidence, the credibility of the evidence is calculated as:

[0036] ;

[0037] in, and Evidence The weight and reliability of For evidence credibility;

[0038] According to the confidence level, the second confidence level is calculated as:

[0039] ;

[0040] Among them, evidence The basic probability distribution is:

[0041] ;

[0042] ;

[0043] in, and Evidence Assigned to and The probability mass of is a power set, represented by:

[0044] ;

[0045] According to the basic probability, the evidence The basic probability mass distribution of is:

[0046] ;

[0047] in, .

[0048] In one embodiment, the method further includes: establishing a combined confidence level between any two pieces of evidence based on the basic probability mass distribution and the second confidence level:

[0049] ;

[0050] in, , , , ; and Represent evidence separately Assigned to and The probability mass of and Assigned to and The unnormalized combined probability mass of and Assigned to and Normalized combined probability mass, After the combination is assigned to confidence level;

[0051] According to the combined confidence, the confidence distribution of multiple pieces of evidence in the multi-source decision information system is obtained as follows:

[0052] ;

[0053] is the joint credibility of all evidence.

[0054] A credible evidence reasoning device for multi-source data fusion, comprising:

[0055] A multi-source decision system construction module is used to construct a multi-source decision information system; the multi-source decision information system is composed of multiple independent single-source decision information systems, the single-source decision information systems generate data for artificial intelligence training, and the multi-source decision information system is used to integrate the data used for artificial intelligence training; the single-source decision information system is composed of data samples, attributes, data matrices, mapping functions, and decision feature sets; the mapping functions establish mapping relationships between data samples, attributes, and data matrices;

[0056] An evidence generation module is used to divide the multi-source decision information system into multiple data matrices according to attributes, and to determine the association relationship between the single-source decision information systems based on the decision feature set; when an association relationship exists, calculate the total similarity between the elements in the data matrix and other similar elements; when no association exists, calculate the total distance between the elements in the data matrix and other dissimilar elements; based on the total similarity and the total distance, determine the support of the elements in the data matrix for the data matrix; based on the support, calculate a first confidence level of the data sample relative to the single-source decision information system in terms of attributes; and generate evidence of the single-source decision information system based on the first confidence level;

[0057] a basic probability mass distribution calculation module, configured to establish an identification framework based on the evidence, calculate the credibility of the evidence based on the weight and reliability of the evidence, and obtain a second confidence level of the evidence and a basic probability mass distribution based on the credibility;

[0058] A fusion module is used to establish a combined confidence between any two pieces of evidence based on the basic probability mass distribution and the second confidence level, and obtain the confidence distribution of multiple pieces of evidence in the multi-source decision information system based on the combined confidence level.

[0059] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0060] Constructing a multi-source decision information system; the multi-source decision information system is composed of multiple independent single-source decision information systems, the single-source decision information systems generate data for artificial intelligence training, and the multi-source decision information system is used to integrate the data used for artificial intelligence training; the single-source decision information system is composed of data samples, attributes, data matrices, mapping functions, and decision feature sets; the mapping functions establish mapping relationships between data samples, attributes, and data matrices;

[0061] Dividing the multi-source decision information system into a plurality of data matrices according to attributes, and determining the association relationship between the single-source decision information systems based on the decision feature set, and calculating the total similarity between the elements in the data matrix and other similar elements when an association relationship exists, and calculating the total distance between the elements in the data matrix and other dissimilar elements when no association exists;

[0062] Determining the support of the elements in the data matrix to the data matrix based on the total similarity and the total distance, and calculating the first confidence of the data sample in terms of attributes relative to the single-source decision information system based on the support;

[0063] generating evidence for a single-source decision information system based on the first confidence level;

[0064] Establishing an identification framework based on the evidence, and calculating the credibility of the evidence based on the weight and reliability of the evidence, and obtaining a second confidence level and a basic probability mass distribution of the evidence based on the credibility;

[0065] A combined confidence level between any two pieces of evidence is established based on the basic probability mass distribution and the second confidence level, and a confidence distribution of multiple pieces of evidence in a multi-source decision information system is obtained based on the combined confidence level.

[0066] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0067] Constructing a multi-source decision information system; the multi-source decision information system is composed of multiple independent single-source decision information systems, the single-source decision information systems generate data for artificial intelligence training, and the multi-source decision information system is used to integrate the data used for artificial intelligence training; the single-source decision information system is composed of data samples, attributes, data matrices, mapping functions, and decision feature sets; the mapping functions establish mapping relationships between data samples, attributes, and data matrices;

[0068] Dividing the multi-source decision information system into a plurality of data matrices according to attributes, and determining the association relationship between the single-source decision information systems based on the decision feature set, and calculating the total similarity between the elements in the data matrix and other similar elements when an association relationship exists, and calculating the total distance between the elements in the data matrix and other dissimilar elements when no association exists;

[0069] Determining the support of the elements in the data matrix to the data matrix based on the total similarity and the total distance, and calculating the first confidence of the data sample in terms of attributes relative to the single-source decision information system based on the support;

[0070] generating evidence for a single-source decision information system based on the first confidence level;

[0071] Establishing an identification framework based on the evidence, and calculating the credibility of the evidence based on the weight and reliability of the evidence, and obtaining a second confidence level and a basic probability mass distribution of the evidence based on the credibility;

[0072] A combined confidence level between any two pieces of evidence is established based on the basic probability mass distribution and the second confidence level, and a confidence distribution of multiple pieces of evidence in a multi-source decision information system is obtained based on the combined confidence level.

[0073] The above-mentioned credible evidence reasoning method, apparatus, and device for multi-source data fusion builds a multi-source decision-making information system. Within this system, a support matrix is designed to quantify the correlation between attributes in multi-source data, combining the concepts of similarity and distance to evaluate relationships. First, multi-source data at different scales is converted into a confidence distribution, imbued with probabilistic meaning, enabling the generated evidence to be applied to multi-scale data fusion. Then, a credible evidence reasoning rule is constructed in which the credibility of evidence is determined by its weight and reliability. By integrating multi-source data samples at different scales, the resulting confidence distribution is derived. This solves the difficulty of determining the confidence distribution of evidence. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 1 is a flow chart of a credible evidence reasoning method for multi-source data fusion in one embodiment;

[0075] Figure 2 1. A structural block diagram of a credible evidence reasoning device for multi-source data fusion in one embodiment;

[0076] Figure 3 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0077] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0078] In one embodiment, Figure 1 As shown, a credible evidence reasoning method for multi-source data fusion is provided, which includes the following steps:

[0079] Step 102: Build a multi-source decision-making information system.

[0080] In this step, the multi-source decision information system is composed of multiple independent single-source decision information systems. The single-source decision information system generates data for artificial intelligence training, and the multi-source decision information system is used to integrate the data used for artificial intelligence training; the single-source decision information system is composed of data samples, attributes, data matrices, mapping functions and decision feature sets; the mapping function establishes the mapping relationship between data samples, attributes and data matrices.

[0081] Step 104, divide the multi-source decision information system into multiple data matrices according to attributes, and judge the association relationship between single-source decision information systems based on the decision feature set. When there is an association relationship, calculate the total similarity between the elements in the data matrix and other similar elements. When there is no association, calculate the total distance between the elements in the data matrix and other dissimilar elements.

[0082] In this step, a support matrix is designed to quantify the correlation between attributes of multi-source data, combining the concepts of similarity and distance to evaluate the relationship. First, the multi-source data of different scales are converted into confidence distributions to give them probabilistic meaning.

[0083] Step 106 , judging the support of the elements in the data matrix to the data matrix based on the total similarity and the total distance, and calculating the first confidence of the data sample in terms of attributes relative to the single-source decision information system based on the support.

[0084] Step 108: Generate evidence of a single-source decision information system based on the confidence level.

[0085] In step 110 , an identification framework is established based on the evidence, and the credibility of the evidence is calculated based on the weight and reliability of the evidence. Based on the credibility, a second confidence level and a basic probability mass distribution of the evidence are obtained.

[0086] Step 112: establishing a combined confidence between any two pieces of evidence based on the basic probability mass distribution and the second confidence level, and obtaining the confidence distribution of multiple pieces of evidence in the multi-source decision information system based on the combined confidence level.

[0087] The aforementioned credible evidence reasoning method for multi-source data fusion constructs a multi-source decision-making information system. Within this system, a support matrix is designed to quantify the correlations between attributes in multi-source data, integrating the concepts of similarity and distance to evaluate these relationships. First, multi-source data at different scales is converted into a confidence distribution, imbued with probabilistic meaning. This allows the generated evidence to be applied to multi-scale data fusion. Next, a credible evidence reasoning rule is constructed, in which the credibility of evidence is determined by its weight and reliability. By integrating multi-source data samples at different scales, the resulting confidence distribution is derived. This overcomes the difficulty of determining the confidence distribution of evidence.

[0088] In one embodiment, the multi-source decision information system is composed of multiple independent single-source decision information systems and can be described as follows:

[0089] ;

[0090] in, Represents a single-source decision information system; Include data samples, ; Include properties, ; is the data matrix consisting of all data samples, ; express middle No. A data matrix, . Is a mapping function that establishes , and The relationship between them. is the decision feature set, .

[0091] Table 1 shows a general MSDIS. Data samples sharing the same attributes between different data sources may be considered to be at different scales. From the attribute perspective, Can also be divided into A data matrix.

[0092] Table 1 A general MSDIS

[0093]

[0094] In one embodiment, the multi-source decision information system is divided into multiple data matrices according to attributes. , data matrix It can be expressed as , the dimension is , in , the data matrix is represented as:

[0095] ;

[0096] in, Representation attributes The corresponding data matrix, Each row of represents a sample, Each column represents a single-source decision information system; the elements in the data matrix are normalized to be expressed as:

[0097] ;

[0098] The normalized data matrix is obtained by using the normalized elements to represent the data matrix .

[0099] Table 1 shows the association between different data samples and different decision features. In order to quantify the relationship between different data sources, in one embodiment, the association between single-source decision information systems is determined based on the decision feature set:

[0100] ;

[0101] in, represents a data sample, represents the decision features in the decision feature set, , .

[0102] When there is an association relationship, the total similarity between the element in the data matrix and other similar elements is calculated as:

[0103] ;

[0104] When there is no correlation, the total distance between an element in the data matrix and other dissimilar elements is calculated as:

[0105] .

[0106] In another embodiment, the data matrix Elements in The support for an information system is defined as the sum of the total similarity between the element and similar elements and the total distance between the element and dissimilar elements. The specific description is as follows:

[0107] ;

[0108] Through normalization, the support matrix can be obtained , which contains each element in Support in By normalization For each row of In the properties Relative to the data source The first confidence level of is:

[0109] .

[0110] In one embodiment, based on the first confidence level, the evidence for generating a single-source decision information system is:

[0111] ;

[0112] in, Represents a data sample In the properties The evidence provided above and It is worth noting that confidence is only assigned to a single data source, and mixed data sources are not discussed in this application.

[0113] In one embodiment, the ER rule was initially proposed based on the Bayesian rule, the DS rule, and the ER algorithm. It is built on the so-called Framework of Discrimination (FoD), which is described as follows:

[0114] ;

[0115] in, It is A proposition, and , in the context of MSDIS, Equivalent to a single-source decision information system.

[0116] Once evidence is obtained from different data sources, whether and to what extent to trust this evidence remains an open question. Therefore, before making inferences, some consensus needs to be reached:

[0117] 1) Evidence To a certain extent, it is credible, which can be measured by the credibility index ( ) to measure.

[0118] 2) Evidence Weight contribute to its unreliability; in particular, It can characterize the degree to which evidence provides accurate judgments or assertions.

[0119] 3) Evidence Reliability Contributes to its unreliability; here, Defined as the degree to which evidence accurately reflects valid conclusions, results, or assessments.

[0120] In one embodiment, the credibility of the evidence is calculated based on the weight and reliability of the evidence:

[0121] .

[0122] The basic principle of the credibility formula is that when the reliability of evidence reaches its maximum value, the evidence is considered to be completely credible. On the contrary, when the reliability of evidence is lower than 1, its credibility increases with the weight of evidence. increased with the increase of .

[0123] Therefore, the first confidence can be rewritten as the second confidence as:

[0124] ;

[0125] in, and Evidence Assigned to and The probability mass of . is a power set, containing of The subsets are as follows:

[0126] ;

[0127] In the above analysis, the evidence The associated untrustworthiness is assigned to the power set and is calculated as ( ), the basic principle behind it is that Reflects global uncertainty or unknown factors, which can indirectly reveal The degree of doubt about the proposition.

[0128] Therefore, the evidence The basic probability mass distribution of can be expressed as:

[0129] ;

[0130] in, .

[0131] In one embodiment, based on the description of the identification framework above, there are two pieces of evidence and , whose weight and reliability are and . and The combination follows the following process:

[0132] ;

[0133] ;

[0134] ;

[0135] ;

[0136] ;

[0137] In the above formula, and Represent evidence separately Assigned to and The probability mass of . and Assigned to and The unnormalized combined probability mass of is generated by the orthogonal sum operator. and Assigned to and Normalized combined probability mass. After the combination is assigned to The confidence level, , obviously, .

[0138] If there is The confidence distribution of the combined result can be generated by iteratively applying the above calculation process, as follows:

[0139] ;

[0140] in, is the joint credibility of all evidence. It's essentially a new piece of evidence.

[0141] property The optimal data source or data scale can be determined by the following formula:

[0142] .

[0143] Data samples from the optimal data source for each attribute can be selected from the MSDIS to construct the final integrated system. In theory, by integrating different data sources and data scales and combining them with the selection of high-quality data, this system ensures a more comprehensive consideration of the unique characteristics of each data source and improves the credibility and reliability of the information system.

[0144] The following example illustrates the application of the invented method, using the communication support capability assessment of a communication support drone. The drone's communication coverage radius (km), maneuvering speed (km / h), and payload capacity (kg) were selected as evaluation metrics. Data was collected using three different sensors: one for each sensor. When establishing a multi-source decision-making information system, the single-source decision-making information system generated sample data from a single sensor. The processed simulation data is shown in Table 2.

[0145] Table 2 UAV communication capability evaluation test data

[0146]

[0147] In Table 2, the three attributes Corresponding to three evaluation indicators: communication coverage radius, maneuvering speed and load capacity, three data sources Corresponding to three sensors with three different precisions, the three data samples are recorded as , the three decision features {1, 2, 3} represent the data accuracy as “high”, “medium” and “low” respectively. For example, the data matrix is as follows:

[0148] ;

[0149] This gives the normalized data matrix As shown below:

[0150] ;

[0151] As can be seen from Table 2, the three groups of samples are all different samples. Therefore, The total similarity and total distance can be calculated as follows:

[0152] ;

[0153] ;

[0154] therefore, The support degree of is 1.67; by analogy, the support matrix can be obtained as follows:

[0155] ;

[0156] This allows you to determine the properties The confidence level of each data source relative to all data samples. For example:

[0157] ;

[0158] Therefore, the basic credibility matrix can be obtained:

[0159] ;

[0160] In the above formula, Each row of reflects the evidence for a data sample. Assume 、 and The weights are Then, the reliability of each piece of evidence can be calculated as follows:

[0161] ;

[0162] ;

[0163] Therefore, the basic probability distribution is as follows:

[0164] ;

[0165] The above formula shows that the credibility of each piece of evidence is as high as 0.5456, 0.5452, and 0.5456 respectively. By using the evidence reasoning rules, the fusion results can be described as follows:

[0166] ;

[0167] It is clear that the credibility of the fusion result is significantly improved compared to any single initial evidence. This also shows that ER rules can effectively improve the credibility of inference results by integrating evidence from different data sources.

[0168] Similarly, for the attribute and , the fusion results are as follows:

[0169] ;

[0170] According to the above results, 、 and The optimal data sources are 、 and Therefore, the final fusion is shown in Table 3.

[0171] Table 3 Final fusion system

[0172]

[0173] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0174] In one embodiment, Figure 2 As shown, a credible evidence reasoning device for multi-source data fusion is provided, comprising: a multi-source decision system construction module 202, an evidence generation module 204, a basic probability mass distribution calculation module 206 and a fusion module 208, wherein:

[0175] A multi-source decision system construction module 202 is used to construct a multi-source decision information system; the multi-source decision information system is composed of multiple independent single-source decision information systems, the single-source decision information systems generate data for artificial intelligence training, and the multi-source decision information system is used to integrate the data used for artificial intelligence training; the single-source decision information system is composed of data samples, attributes, data matrices, mapping functions, and decision feature sets; the mapping functions establish mapping relationships between data samples, attributes, and data matrices;

[0176] Evidence generation module 204 is used to divide the multi-source decision information system into multiple data matrices according to attributes, and determine the association relationship between the single-source decision information systems based on the decision feature set. When an association relationship exists, the total similarity between the elements in the data matrix and other similar elements is calculated; when no association exists, the total distance between the elements in the data matrix and other dissimilar elements is calculated; based on the total similarity and the total distance, the support of the elements in the data matrix to the data matrix is determined; based on the support, a first confidence level of the data sample relative to the single-source decision information system in terms of attributes is calculated; and based on the first confidence level, evidence of the single-source decision information system is generated;

[0177] A basic probability mass distribution calculation module 206 is configured to establish an identification framework based on the evidence, calculate the credibility of the evidence based on the weight and reliability of the evidence, and obtain a second confidence level and a basic probability mass distribution of the evidence based on the credibility;

[0178] The fusion module 208 is configured to establish a combined confidence between any two pieces of evidence based on the basic probability mass distribution and the second confidence level, and obtain a confidence distribution of multiple pieces of evidence in the multi-source decision information system based on the combined confidence level.

[0179] The specific definition of the trusted evidence reasoning device for multi-source data fusion can be found in the definition of the trusted evidence reasoning method for multi-source data fusion above, which will not be repeated here. The various modules in the above-mentioned trusted evidence reasoning device for multi-source data fusion can be implemented in whole or in part through software, hardware, and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0180] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, memory, network interface, display screen and input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a credible evidence reasoning method for multi-source data fusion is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0181] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0182] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the method in the above embodiment when executing the computer program.

[0183] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method in the above embodiment are implemented.

[0184] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0185] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0186] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and such modifications and improvements are intended to fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A credible evidence reasoning method based on multi-source data fusion, characterized by: The method comprises: Construct a multi-source decision information system; the multi-source decision information system is composed of multiple independent single-source decision information systems, the single-source decision information system generates data for artificial intelligence training, and the multi-source decision information system is used to integrate the data used for artificial intelligence training; the single-source decision information system is composed of data samples, attributes, data matrices, mapping functions and decision feature sets; the mapping function establishes a mapping relationship between data samples, attributes and data matrices; the multi-source decision information system is a communication guarantee capability evaluation system for communication guarantee UAVs, the single-source decision information systems are respectively the communication coverage radius, maneuvering speed and load capacity of the UAV, each single-source decision information system generates sample data by sensors, and the sensors include three types of precision sensors to generate data samples, and the decision characteristics of each data sample are: high, medium and low; the attributes correspond to evaluation indicators, and the evaluation indicators include: communication coverage radius, maneuvering speed and load capacity; Dividing the multi-source decision information system into a plurality of data matrices according to attributes, and determining the association relationship between the single-source decision information systems based on the decision feature set, and calculating the total similarity between the elements in the data matrix and other similar elements when an association relationship exists, and calculating the total distance between the elements in the data matrix and other dissimilar elements when no association exists; Determining the support of the elements in the data matrix to the data matrix based on the total similarity and the total distance, and calculating the first confidence of the data sample in terms of attributes relative to the single-source decision information system based on the support; generating evidence for a single-source decision information system based on the first confidence level; Establishing an identification framework based on the evidence, and calculating the credibility of the evidence based on the weight and reliability of the evidence, and obtaining a second confidence level and a basic probability mass distribution of the evidence based on the credibility; A combined confidence level between any two pieces of evidence is established based on the basic probability mass distribution and the second confidence level, and a confidence distribution of multiple pieces of evidence in a multi-source decision information system is obtained based on the combined confidence level.

2. The method according to claim 1, characterized in that The multi-source decision information system is divided into multiple data matrices according to attributes, including: The multi-source decision information system is divided into multiple data matrices according to attributes, and the data matrices are expressed as: Among them, the dimension of the data matrix is , Representation attributes The corresponding data matrix, Each row of represents a sample, Each column of represents a single-source decision information system; The elements in the data matrix are normalized and expressed as: The normalized elements are used to represent the data matrix to obtain a normalized data matrix.

3. The method according to claim 2, characterized in that The association relationship between the single-source decision information systems is determined based on the decision feature set. When an association relationship exists, the total similarity between the element in the data matrix and other similar elements is calculated. When no association relationship exists, the total distance between the element in the data matrix and other dissimilar elements is calculated, including: According to the decision feature set, the association relationship between the single-source decision information systems is determined as follows: in, represents a data sample, represents the decision features in the decision feature set, , ; When there is an association relationship, the total similarity between the element in the data matrix and other similar elements is calculated as: When there is no correlation, the total distance between an element in the data matrix and other dissimilar elements is calculated as: 。 4. The method according to claim 3, characterized in that Determining the support of the elements in the data matrix to the data matrix based on the total similarity and the total distance, and calculating the first confidence of the data sample in terms of attributes relative to the single-source decision information system based on the support, including: According to the total similarity and the total distance, the support degree of the elements in the data matrix to the data matrix is determined as: The first confidence of the data sample in terms of attributes relative to the single-source decision information system is calculated based on the support: 。 5. The method according to claim 4, characterized in that Generate evidence of a single-source decision-making information system based on the first confidence level, including: According to the first confidence level, the evidence for generating a single-source decision information system is: in, Represents a data sample In the properties The evidence provided above and , Indicates the number of attributes.

6. The method according to claim 5, characterized in that Based on the evidence, an identification framework is established, and the credibility of the evidence is calculated based on the weight and reliability of the evidence. Based on the credibility, a second confidence level of the evidence and a basic probability mass distribution are obtained, including: Based on the evidence, establish an identification framework ; Represents a single-source decision information system; Based on the weight and reliability of the evidence, the credibility of the evidence is calculated as: in, and Evidence The weight and reliability of For evidence credibility; According to the confidence level, the second confidence level is calculated as: Among them, evidence The basic probability distribution is: in, and Evidence Assigned to and The probability mass of is a power set, represented by: According to the basic probability, the evidence The basic probability mass distribution of is: in, .

7. The method according to claim 6, characterized in that Establishing a combined confidence between any two pieces of evidence based on the basic probability mass distribution and the second confidence level, and obtaining a confidence distribution of multiple pieces of evidence in a multi-source decision information system based on the combined confidence level, including: The combined confidence between any two pieces of evidence established according to the basic probability mass distribution and the second confidence level is: in, , , , ; and Represent evidence separately Assigned to and The probability mass of and Assigned to and The unnormalized combined probability mass of and Assigned to and Normalized combined probability mass, After the combination is assigned to confidence level; According to the combined confidence, the confidence distribution of multiple pieces of evidence in the multi-source decision information system is obtained as follows: is the joint credibility of all evidence, L Indicates the amount of evidence.

8. A credible evidence reasoning device based on multi-source data fusion, characterized in that: The device comprises: A multi-source decision system construction module is used to construct a multi-source decision information system; the multi-source decision information system is composed of multiple independent single-source decision information systems, the single-source decision information system generates data for artificial intelligence training, and the multi-source decision information system is used to integrate the data used for artificial intelligence training; the single-source decision information system is composed of data samples, attributes, data matrices, mapping functions and decision feature sets; the mapping function establishes a mapping relationship between data samples, attributes and data matrices; the multi-source decision information system is a communication guarantee capability evaluation system for communication guarantee UAVs, the single-source decision information systems are respectively the communication coverage radius, maneuvering speed and load capacity of the UAV, each single-source decision information system generates sample data by sensors, and the sensors include three types of precision sensors to generate data samples, and the decision characteristics of each data sample are: high, medium and low; the attributes correspond to evaluation indicators, and the evaluation indicators include: communication coverage radius, maneuvering speed and load capacity; An evidence generation module is used to divide the multi-source decision information system into multiple data matrices according to attributes, and to determine the association relationship between the single-source decision information systems based on the decision feature set; when an association relationship exists, calculate the total similarity between the elements in the data matrix and other similar elements; when no association exists, calculate the total distance between the elements in the data matrix and other dissimilar elements; based on the total similarity and the total distance, determine the support of the elements in the data matrix for the data matrix; based on the support, calculate a first confidence level of the data sample relative to the single-source decision information system in terms of attributes; and generate evidence of the single-source decision information system based on the first confidence level; a basic probability mass distribution calculation module, configured to establish an identification framework based on the evidence, calculate the credibility of the evidence based on the weight and reliability of the evidence, and obtain a second confidence level of the evidence and a basic probability mass distribution based on the credibility; A fusion module is used to establish a combined confidence between any two pieces of evidence based on the basic probability mass distribution and the second confidence level, and obtain the confidence distribution of multiple pieces of evidence in the multi-source decision information system based on the combined confidence level.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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