Association analysis and evaluation method and equipment for cultural heritage protection and utilization and medium

Abstract

The invention provides a correlation analysis evaluation method and device for cultural heritage protection and utilization and a medium. The method comprises the following steps: constructing a multi-layer structure model; sequentially determining the value of each level of index from bottom to top, and finally determining an evaluation result; wherein the step of determining the value of the index comprises the sub-steps of determining an incidence matrix of the index of the next level; calculating moduli and eigenvalues of the correlation vectors; determining the size sequence of the modules corresponding to the indexes, and determining the weights of the indexes according to the feature values with the same sequence in the size sequence of the feature values; and determining the value of the corresponding index of the upper layer according to the value and the weight of the index. The equipment comprises a processor and a memory in which instructions are stored, and the instructions are used for executing the method. The medium stores instructions. The cultural heritage protection and utilization can be accurately evaluated; and under the conditions that the importance degrees of the indexes for cultural heritage protection and utilization are difficult to distinguish and the interdependence degree is relatively high, the method is more applicable, and the obtained result is more practical.

Description

Technical Field

[0001] The present invention relates to the field of cultural heritage evaluation, and in particular to an association analysis evaluation method, a computer device and a computer-readable storage medium for the protection and utilization of cultural heritage. Background Art

[0002] From a psychological perspective, evaluation refers to the value judgment or choice made by an individual or group on things based on certain standards. Specifically, evaluation is the behavior of determining the attributes of the object being evaluated according to a predetermined purpose and converting these attributes into objective quantitative values ​​and subjective utilities (Qin Shoukang, 2003). Evaluation has subjective characteristics because it is the subjective response of the subject to value facts. The content reflected by the evaluation is not the object itself, but the meaning of the object to the subject and the value needs of the subject. The evaluation system requires the evaluation to reflect the needs of the subject with the help of certain standards and norms. The evaluation results not only reflect the current status of the object, but more importantly, the prescriptive meaning of the nature, size, change and possibility of value, which serves the subject's choice and behavior direction. Therefore, evaluation is the subjective judgment of the subject on the value relationship between the object and the subject's needs.

[0003] With the mutual penetration and influence between related disciplines, the evaluation methods applied to heritage protection are also quite rich. By summarizing and summarizing the evaluation research at home and abroad, the following are the commonly used comprehensive evaluation methods in recent years: fuzzy comprehensive evaluation, principal component analysis, hierarchical analysis, entropy method, Delphi method and conditional value method.

[0004] At present, most evaluation methods are aimed at independent evaluation factors, without considering the relationship between the evaluation factors. In the comprehensive evaluation process, the issue of indicator weights is inevitably involved. Most methods determine the weights based on the importance of the indicators, but the importance of indicators is often difficult to determine and has a certain degree of subjectivity. Some methods determine the weights through certain technical transformations, level quantification and other means, but they still cannot get rid of human subjective factors. Summary of the invention

[0005] The purpose of the present invention is to solve at least one of the above-mentioned deficiencies in the prior art.

[0006] On the one hand, the present invention provides a correlation analysis and evaluation method for the protection and utilization of cultural heritage.

[0007] The method comprises: constructing a structural model with multiple levels according to the situation of protection and utilization of cultural heritage, and determining the value of the lowest level indicator; determining the value of each indicator of the upper level in order from bottom to top, and finally determining the evaluation result; wherein, determining the value of each indicator of the upper level comprises: for each indicator of the upper level, determining the association matrix of the indicator corresponding to several indicators of the lower level; calculating the modulus of the association vector and the eigenvalue of the association matrix, wherein the association vector is the row vector of the association matrix; determining the weights of several indicators of the lower level according to the eigenvalue of the association vector and the order of the eigenvalue size; and determining the value of the corresponding indicator of the upper level according to the value and weight of several indicators of the lower level.

[0008] Optionally, the structural model is a factor set hierarchy structure, and includes a target layer, a main factor layer and a factor layer.

[0009] Optionally, the next level of indicators include a1~a n ; The next level of association matrix is:

[0010]

[0011] Among them, a ij For indicator a i and indicator a j interdependence;

[0012] Among them, b ij for a i to a j The dependence of ji for a j to a i , and the dependence is between

[01] .

[0013] Optionally, the a ij To satisfy the following formula:

[0014] Optionally, the association vector is represented by r1, r2…r n ; The modulus of the association vector is:

[0015]

[0016] Optionally, the weights of the next level indicators are determined using the following formula:

[0017] β vu =λ v / n; where β vu For indicator a u The weight, λ vis the eigenvalue of the association matrix ranked vth from largest to smallest.

[0018] Optionally, for the protection and utilization of cultural heritage in post-disaster reconstruction, the main factor layers include the cultural heritage value of traditional villages, the post-disaster status of traditional villages, the social capital of traditional villages, post-disaster reconstruction policies and guarantees, natural geography and environmental conditions, and socio-economic benefits.

[0019] Optionally, the factors corresponding to the cultural heritage value of the traditional village include at least two of the following factors: the protection level of the traditional village, the connection with major historical events and famous figures, the importance of national culture, the particularity of national and local characteristics, the representativeness of the regional architectural style and spatial pattern of the traditional village, the uniqueness of local materials and craftsmanship, and the presence of national cultural inheritors.

[0020] Optionally, the factor layer corresponding to the post-disaster status of the traditional village includes indicators: at least two of the post-disaster integrity of traditional buildings and historical environment, the authenticity of the village itself, the size of the village and the post-disaster integrity of public facilities.

[0021] Optionally, the factor layer corresponding to the social capital of traditional villages includes indicators of at least two of the following: mutual assistance and altruistic behavior in the villagers' own relationship network, the appeal and influence of village grassroots organizations, the influence of village elites, the trust and emotional stability of villagers, the execution ability of non-governmental organizations, and the support for raising private capital.

[0022] Optionally, the post-disaster reconstruction policy and guarantee corresponding factor layer includes indicators: at least two of the following: the social impact of the disaster, policy support and guarantee for post-disaster reconstruction, strategic rationality, post-disaster execution of government departments, support for government reconstruction funds, and trust of disaster-stricken people in the government.

[0023] Optionally, the natural geography and environmental conditions corresponding factor layer includes indicators: geological disaster environmental permissibility, traffic convenience, land resources required for reconstruction and village ecological environment value.

[0024] Optionally, the factor layer corresponding to the social and economic benefits includes indicators of: a substantial improvement in the living standards of villagers, a substantial increase in the happiness index of villagers, influence on the surrounding areas, embodiment of heritage protection value, and at least two of the village's cultural tourism and future development value.

[0025] Another aspect of the present invention provides a computer device.

[0026] The computer device comprises: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, and the program instructions comprise: instructions for executing the method as described above.

[0027] According to another aspect of the present invention, a computer-readable storage medium is provided.

[0028] The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the method described above is implemented.

[0029] Another aspect of the present invention provides an application of an association analysis evaluation method in comprehensive evaluation of a target field.

[0030] Among them, the target area is the field of protection and utilization of non-cultural heritage. The comprehensive evaluation of the target area involves indicators at multiple levels. The indicators at adjacent levels are interrelated, and the weights of the indicators need to be judged.

[0031] Compared with the prior art, the beneficial effects of the present invention include at least one of the following:

[0032] (1) The method of the present invention is simple and can accurately evaluate the protection and utilization of cultural heritage.

[0033] (2) When the importance of indicators related to cultural heritage protection and utilization is difficult to distinguish and the indicators are highly interdependent, the present invention is more applicable and the results obtained are more realistic. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 The diagram of constructing the index system based on the association analysis evaluation method is shown. DETAILED DESCRIPTION

[0035] Hereinafter, the present invention will be described in detail with reference to exemplary embodiments.

[0036] Exemplary Embodiment 1

[0037] This exemplary embodiment provides a method for evaluating association analysis for the protection and utilization of cultural heritage. The method can generally be performed according to the following steps:

[0038] S1. Establish different hierarchical structures of factor sets;

[0039] S2, determine the correlation matrix of each level;

[0040] S3, calculating the modulus of the correlation vector and the eigenvalues ​​of the correlation matrix;

[0041] S4, determining the weight according to the magnitude of the correlation vector modulus;

[0042] S5. Calculate the evaluation results.

[0043] In this embodiment, in step S1, the present invention constructs a structural model with different levels. This step analyzes the problem based on a deep understanding of the object to be evaluated, organizes and hierarchizes the problem, and decomposes the complex problem into components of various factors. Each factor (also called an indicator) forms several levels according to its type and relationship, such as Figure 1 The factors in the previous layer serve as the goals and criteria for the related factors in the next layer, forming a hierarchical hierarchy from top to bottom.

[0044] As an example, the structural model may include the target layer, the main factor layer and the factor layer. Among them, the target layer: This layer is the predetermined goal and ideal result of the problem decision, usually with only one element. The main factor layer: This layer can be composed of several factors, involving many large factors to achieve the target layer, as well as relevant criteria that need to be considered. The factor layer: This layer is composed of various decision factors and measures that are required and optional for the decision goals and results.

[0045] The number of levels in the structural model is related to the complexity of the decision-making problem. The factors in each level should generally not exceed 8, that is, the number of factors in the next level included by each factor in the previous level should not exceed 8. If there are too many factors, it will be difficult to judge the correlation between the factors.

[0046] Taking an indicator that includes multiple indicators belonging to the lowest level and itself belongs to the second-to-last level as an example, steps S2, S3 and S4 are further explained.

[0047] (1) Regarding step S2:

[0048] The hierarchical structure reflects the relationship between the indicators, but the correlation ratios between the factors are not necessarily the same. To compare the influence of each factor on a certain factor, a correlation matrix can be established by the degree of dependence between the two indicators. Step S2 may specifically include:

[0049] Suppose that an indicator of the previous level includes n indicators a1, a1, ..., a2 belonging to the next level. n a1~a n The various indicators of are not completely independent, they have a certain degree of dependence on each other. The size of the dependence is defined between [0 1]. According to the connotation of the indicator, if the indicator a i 10% of the contribution comes from a j , then define a i to a j The dependence is b ij =0.1. Thus we get the matrix:

[0050]

[0051] Usuallyij Not necessarily equal to b ji .

[0052] Take index a i With indicator a j The interdependence is a ij ,in,

[0053]

[0054] b ij Convert to a ij It can be guaranteed that the following association matrix A is a symmetric matrix.

[0055] Construct the matrix:

[0056]

[0057] Because matrix A fully reflects the relationship between various indicators, A can be called a correlation matrix.

[0058] The dependence of an indicator on itself is 1, and the sum of its dependence on other indicators should be less than 1. Otherwise, from the perspective of evaluation or decision-making, if the sum of the dependence of an indicator on other indicators is greater than 1, it means that all the information represented by this indicator is already contained in other indicators, so this indicator is redundant, or the determination of the dependence is unreasonable. Therefore, it can be further required that:

[0059]

[0060] Therefore, A is a real symmetric matrix with all diagonal elements being 1 and other elements being between [0 1], and is strictly diagonally dominant. Thus, A is a positive definite matrix, and all its eigenvalues ​​are greater than zero.

[0061] (2) Regarding step S3: Calculate the modulus of the correlation vector and the eigenvalues ​​of the correlation matrix.

[0062]

[0063] The row vectors of matrix A are r1, r2…r n , we call them correlation vectors. The modulus of the correlation vector is

[0064] Since A is a positive definite matrix, its eigenvalues ​​are real numbers and are all greater than zero. Use Matlab to calculate the eigenvalues ​​of the correlation matrix A.

[0065] Suppose the eigenvalues ​​of the correlation matrix A are sorted from large to small as follows: λ1, λ2, …, λ n .

[0066] (3) Regarding step S4: determining the weight according to the magnitude of the associated vector modulus. Specifically, the magnitude order of the modulus corresponding to each indicator may be determined, and the weight of the indicator may be determined according to the eigenvalues ​​having the same magnitude order in the eigenvalue magnitude sorting.

[0067] Set the magnitude of the associated vector ||r1||,||r2||,…||r n ||Sort by largest to smallest: r i1 ≥r j2 ≥…r kn , that is, ||r i ||max,||r j ||Second largest,||r k || minimum. That is, r uv The first subscript indicates the corresponding index a u , the second subscript corresponds to ||r u The size of || ranks in vth place.

[0068] r uv The corresponding indicator a u The weight is taken as β vu , u=1,2,…n.

[0069]

[0070] That is, the larger the modulus of the associated vector corresponding to an indicator, the greater the corresponding weight.

[0071] (4) Regarding step S5: Calculate the evaluation result.

[0072] Calculate the total evaluation value of a factor:

[0073] Y=β v1 a1+β v2 a2+…+β vn a n +ε.

[0074] Where Y is the evaluation result of the factor, and a1, a2, ... a n Represents the score of each indicator. ε is a control variable. The control variable is introduced to consider the impact of certain principles, policies and some special abnormal indicators on the evaluation results, and also to deal with the occurrence of certain special situations. For example, after a certain type of disaster occurs, the government wants to relocate all residents around a scenic spot for replanning, but the local residents do not agree to the relocation, and the relocation cannot be implemented in the end.

[0075] Similarly, the evaluation results of other factors that belong to the same level as the factor and correspond to the same upper-level factor can be obtained. Then, according to the same method, the evaluation results of the upper-level factors can be obtained, and so on, and finally the final evaluation results can be obtained.

[0076] In order to better understand the above exemplary embodiments of the present invention, they are further described below with reference to specific examples.

[0077] Example 1

[0078] This example takes the selection of the post-disaster recovery and reconstruction mode of Xieluobaozi as an example to illustrate the association analysis and evaluation method for cultural heritage protection and utilization of the present invention, as follows:

[0079] By recording and understanding the current status of the disaster, the value of the material and intangible cultural heritage of the village, the natural geography and environmental conditions, etc. during the on-site survey, the research team randomly learned about the villagers' wishes during the investigation, held discussions with relevant management departments, and reviewed the post-disaster reconstruction policies and related documents, analyzed and prospected the social capital and social and economic benefits of traditional villages, and combined with the rules of previous post-disaster reconstruction and the requirements of scientific restoration of traditional village heritage resources, the reconstruction mode is divided into three types: ① Restoring traditional villages on the original site, keeping the original pattern as much as possible (referred to as original site restoration); ② Relocating traditional villages to other places, creating village tourism areas on the original site (referred to as relocation and restoration); ③ Organically evacuating the core of the village, taking into account the wishes of the indigenous people (referred to as organic evacuation and multi-party consideration). Then, combined with the evaluation factors and scoring standards, on the basis of improving the original evaluation data, 6 professional and technical personnel (including 2 engaged in heritage protection, 2 engaged in architectural design, and 2 engaged in urban planning) were invited to rate the support of the three modes respectively based on the indirect influencing factors of village heritage attributes and post-disaster reconstruction. The scoring principle is that the higher the protection level of the village (A1), the greater the support for on-site restoration and the less support for relocation; the more serious the post-disaster integrity of traditional buildings and historical environment (B1) is, and the authenticity of the village itself (B2) is lost, the greater the support for off-site reconstruction. Based on the scoring results of 6 professional technicians, the support for three models of post-disaster restoration and reconstruction in Xieluobaozi is obtained: "on-site restoration", "off-site relocation, on-site restoration" and "organic evacuation, multi-faceted consideration".

[0080] Table 1 Support for the selection of the post-disaster recovery and reconstruction model of Xieluobaozi

[0081]

[0082]

[0083] The last column in Table 1 is the support rating of professional and technical personnel for the three models of “repair on the original site”, “relocation to another location and restoration on the original site” and “organic evacuation and consideration of multiple parties”.

[0084] Through the analysis of the index system, it can be found that the importance of indicators is difficult to determine, such as the importance of national culture, the particularity of national and local characteristics, whether there are national cultural inheritors, how to compare their importance? If the entropy method is used to determine the weight, there will be many difficulties in quantifying the information entropy of each indicator. However, many indicators are interdependent and interrelated. For example: the protection level of traditional villages is related to the importance of their national culture, the particularity of national and local characteristics. And the regional architectural style of traditional villages, etc. There is a certain connection. Therefore, it is more applicable to use the method of the present invention when evaluating such an index system.

[0085] The following takes the original site restoration model as an example to further illustrate the association analysis and evaluation method for cultural heritage protection and utilization of the present invention.

[0086] (1) Calculate the support of each first-level indicator.

[0087] Taking the cultural heritage value of traditional villages (A) as an example, according to the connotation of the indicators, the dependence between the secondary indicators of the cultural heritage value of traditional villages (A) is determined as shown in the following table.

[0088] Table 2 Dependence between the secondary indicators of the indicator Traditional village cultural heritage value (A)

[0089]

[0090] In the first-level indicator (A), the dependence of the second-level indicator of the particularity of ethnic and local characteristics (A4) on the importance of ethnic culture (A3) is 0.1; the dependence of the importance of ethnic culture (A3) on the particularity of ethnic and local characteristics (A4) is 0.3, and the correlation between the importance of ethnic culture (A3) and the particularity of ethnic and local characteristics (A4) is 0.2. Therefore, the correlation matrix of each second-level indicator in the first-level indicator of the value of traditional village cultural heritage (A) is determined as follows:

[0091]

[0092] Compute the magnitude of the correlation vector:

[0093] ||r3||=1.0440, ||r4||=1.05, ||r5||=1.0296, ||r6||=1.044, ||r7||=1.0259.

[0094] The order of the size of the modules is: r 41 、r 32 、r 63 、r 14 、r 55 、r 76 、r 27,Thus, we obtain that the indicators are sorted as a4, a3, a6, a1, a5, a7, and a2 according to the degree of association.

[0095] Determine the weights:

[0096] The eigenvalues ​​of the correlation matrix A are calculated using Matlab. The eigenvalues ​​of matrix A are, from large to small: λ1=1.5023, λ2=1.2009, λ3=1.0554, λ4=0.9177, λ5=0.8291, λ6=0.8000, and λ7=0.6946.

[0097] The weights of the secondary indicators are calculated as follows:

[0098] β 23 =0.1716, β 36 =0.1508, β 41 =0.1311, β 55 =0.1184, β 67 =0.1143, β 72 =0.0992;

[0099] Calculate the support of the first-level indicator traditional village cultural heritage value (A):

[0100] Y1=β 14 a4+β 23 a3+β 36 a6+β 41 a1+β 55 a5+β 67 a7+β 72 a2

[0101] =0.2145×5+0.1716×5+0.1508×4+0.1311×5+0.1184×4+0.1143×5+0.0992×4=4.6311

[0102] The other secondary indicator correlation matrices are:

[0103]

[0104] Calculated using the same method, we obtain: Y2=3.4958, Y3=3.334, Y4=3.6053, Y5=3.3235, Y6=3.938.

[0105] (2) Calculate the total support.

[0106] The heritage value of traditional villages, the degree of damage, and the social and economic benefits of traditional villages affect the direction of policies, and the formulation of policies needs to fully consider the needs of the people. The trust and support of the people's relationship network have a positive impact on the restoration of the social and economic order in the disaster area. Together with the control variable, that is, the strong will of the people, it ultimately determines the choice of the restoration and reconstruction model. Based on the relationship and influence between these indicators, the correlation dependence of the first-level indicators is:

[0107] Table 3 Correlation and dependence of first-level indicators

[0108]

[0109] The correlation matrix of the first-level indicators is calculated as:

[0110]

[0111] The magnitude of the correlation vector:

[0112]

[0113] The order of the modules is:

[0114] r 31 =1.1057,r 12 =1.0747,r 43 =1.0747,r 64 =1.0560,r 55 =1.0428,r 26 =1.0271.

[0115] It can be concluded that the indicators are ranked as a3, a1, a4, a6, a5, and a2 according to the degree of correlation, and the indicator scores are Y3, Y1, Y4, Y6, Y5, and Y2 respectively.

[0116] Determine the weight. Specifically, use Matlap to calculate the eigenvalues ​​of G from large to small for the first-level index layer:

[0117] λ1=1.6994, λ2=1.0572, λ3=0.9513, λ4=0.8700, λ5=0.7860, λ6=0.6360.

[0118] β 21 =0.1762, β 34 =0.1586, β 46 =0.145, β 55 =0.131, β 62 =0.106;

[0119] Calculate the total support for selecting the in-situ repair mode:

[0120] Y=β 13 Y3+β 21 Y1+β 34 Y4+β 46 Y6+β 55 Y5+β 62 Y

[0121] =0.2832×3.334+0.1762×4.6311+0.1586×3.6053+0.145×3.938

[0122] +0.131×3.3235+0.106×3.4958=3.7089

[0123] The same method was used to calculate the support for the "relocation and restoration on the original site" and "organic evacuation and multi-faceted consideration" models. The support for the three models is shown in the following table:

[0124] Table 4 Support for the three models of post-disaster reconstruction in Xieluobaozi

[0125]

[0126]

[0127] According to the above calculation results, "repair on the original site" has the highest support and ranks first, followed by "organic evacuation and multi-faceted consideration", and "relocation to a different place and restoration on the original site" ranks last.

[0128] According to the association analysis and evaluation method for the protection and utilization of cultural heritage of the present invention, it can be programmed as a computer program and the corresponding program code or instructions can be stored in a computer-readable storage medium. When the program code or instructions are executed by the processor, the processor executes the above method. The following processor and memory can be included in the computer device.

[0129] Exemplary Embodiment 2

[0130] This exemplary embodiment provides a computer device, including:

[0131] at least one processor;

[0132] A memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, and the program instructions include instructions for executing the association analysis and evaluation method for protection and utilization of cultural heritage according to exemplary embodiment 1.

[0133] Exemplary Embodiment 3

[0134] The present exemplary embodiment provides a computer-readable storage medium.

[0135] The storage medium stores a computer program, and when the computer program instructions are executed by the processor, the association analysis and evaluation method for the protection and utilization of cultural heritage as described in exemplary embodiment 1 is implemented.

[0136] The computer-readable storage medium may be any data storage device that stores data that can be read by a computer system. For example, examples of computer-readable storage media may include: read-only memory, random access memory, read-only optical disk, magnetic tape, floppy disk, optical data storage device, and carrier wave (such as data transmission via the Internet via a wired or wireless transmission path).

[0137] Example 4

[0138] This exemplary embodiment provides an application of the method in exemplary embodiment 1, that is, the method can be extended to applications in other similar fields that require comprehensive evaluation. There are multiple levels of indicators, and there are correlations between indicators at adjacent levels. For example, each indicator at the previous level corresponds to several indicators at the next level. In addition, the weight of the indicators needs to be determined.

[0139] Although the present invention has been described above in conjunction with the exemplary embodiments and the accompanying drawings, it should be apparent to those skilled in the art that various modifications may be made to the above-described embodiments without departing from the spirit and scope of the claims.

Claims

1. A correlation analysis and evaluation method for the protection and utilization of cultural heritage, characterized in that: The method comprises: According to the situation of cultural heritage protection and utilization, a structural model with multiple levels is constructed, and the value of the lowest level indicators is determined; Determine the value of each indicator at the previous level from bottom to top, and finally determine the evaluation results; The determining of the value of each indicator of the previous level includes: for each indicator of the previous level, determining the correlation matrix of the indicator corresponding to a plurality of indicators of the next level; calculating the modulus of the correlation vector and the eigenvalue of the correlation matrix, wherein the correlation vector is the row vector of the correlation matrix; determining the order of the corresponding moduli of the plurality of indicators of the next level, and determining the weight of each indicator according to the eigenvalues ​​having the same order in the eigenvalue order; According to the values ​​and weights of several indicators at the next level, the values ​​of corresponding indicators at the previous level are determined.

2. The method according to claim 1, characterized in that The indicator corresponds to several indicators at the next level, including a1~a n ; The association matrix is: Among them, a ij For indicator a i and indicator a j interdependence; Among them, b ij for a i to a j The dependence of ji for a j to a i , and the dependence is between [0 1].

3. The method according to claim 2, characterized in that The a ij Satisfy the following formula:

4. The method according to claim 2, characterized in that: The association vector is represented by r1, r2…r n ; The modulus of the association vector is:

5. The method according to claim 4, characterized in that The weight of the next level indicator is determined using the following formula: b vu =λ v n; Among them, β vu For indicator a u The weight, λ v is the eigenvalue of the association matrix ranked vth from largest to smallest.

6. The method according to claim 2, characterized in that For the protection and utilization of cultural heritage in post-disaster reconstruction, the structural model is a factor set hierarchy, and includes a target layer, a main factor layer, and a factor layer; The main factor layers include the cultural heritage value of traditional villages, the post-disaster status of traditional villages, the social capital of traditional villages, post-disaster reconstruction policies and guarantees, natural geography and environmental conditions, and socio-economic benefits.

7. The method according to claim 6, characterized in that The factor layer corresponding to the cultural heritage value of traditional villages includes at least two of the following indicators: The protection level of traditional villages, their relevance to major historical events and famous figures, the importance of national culture, the particularity of national and local characteristics, the representativeness of the regional architectural style and spatial pattern of traditional villages, the uniqueness of local materials and craftsmanship, and the existence of national cultural inheritors; The factor layer corresponding to the post-disaster status of the traditional villages includes indicators: at least two of the post-disaster integrity of traditional buildings and historical environment, the authenticity of the village itself, the size of the village, and the post-disaster integrity of public facilities; The corresponding factor layer of the traditional village social capital includes indicators of at least two of the following: mutual assistance and altruistic behavior of villagers’ own relationship networks, the appeal and influence of village grassroots organizations, the influence of village elites, the trust and emotional stability of villagers, the executive power of non-governmental organizations, and the support for raising private capital; The post-disaster reconstruction policy and guarantee corresponding factor layer includes indicators: at least two of the following: the social impact of the disaster, policy support and guarantee for post-disaster reconstruction, rationality of strategy, post-disaster execution of government departments, support of government reconstruction funds, and trust of disaster-affected people in the government; The corresponding factor layer of the physical geography and environmental conditions includes indicators: environmental permissibility of geological disasters, transportation convenience, land resources required for reconstruction, and ecological environmental value of villages; The social and economic benefit corresponding factor layer includes indicators: the villagers' living standards will be greatly improved, the villagers' happiness index will be greatly increased, the influence on the surrounding areas, the embodiment of heritage protection value, and at least two of the village's cultural tourism and future development value.

8. A computer device, characterized in that: include: at least one processor; A memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, and the program instructions include: instructions for executing the method according to any one of claims 1-7.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. Application of the association analysis evaluation method described in any one of claims 1 to 7 in the comprehensive evaluation of a target field, wherein the target field is the field of protection and utilization of non-cultural heritage, and the comprehensive evaluation of the target field involves indicators at multiple levels, and indicators at adjacent levels are interrelated, and the weights of the indicators need to be judged.

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