Road maintenance comprehensive evaluation method and system based on grey correlation coefficient

Abstract

The invention provides a road maintenance comprehensive evaluation method and system based on a grey correlation coefficient, and the method comprises the steps: constructing a comprehensive maintenance evaluation index system, and extracting corresponding road maintenance evaluation index data; according to different maintenance units and corresponding road maintenance evaluation index data, an initial matrix is constructed and standardized to obtain a standardized matrix, and then the weight of each evaluation index in the initial matrix is calculated by adopting an entropy weight method; calculating to obtain a weighted standardized evaluation matrix, and calculating to obtain a grey correlation coefficient matrix by combining the optimal ideal value in each evaluation index, thereby respectively constructing an improved positive ideal solution model and an improved negative ideal solution model, and calculating a first Euclidean distance and a second Euclidean distance; and calculating the close degree of each maintenance unit according to the first Euclidean distance and the second Euclidean distance, and finally sorting the close degrees of all the maintenance units according to the size to obtain a comprehensive evaluation result of all the maintenance units so as to complete the comprehensive evaluation of the road maintenance.

Description

Technical Field

[0001] The present invention relates to the technical fields of intelligent highways and smart cities, and particularly relates to a comprehensive evaluation method and system for road maintenance based on grey correlation coefficients. Background Art

[0003] To meet the requirements of the high-quality development of highway transportation, it is necessary to strengthen the supervision of the maintenance market, improve the maintenance bidding system, establish and improve the highway maintenance credit system, and strengthen the management of market access and order supervision for maintenance, so as to accelerate the construction of a unified, open, standardized and orderly maintenance market. China has been actively promoting highway maintenance work and has introduced a series of policies and regulations to ensure the safety and smoothness of highways. For example, the Highway Law of the People's Republic of China clearly stipulates the responsible entities and requirements for highway maintenance and emphasizes the importance of highway maintenance. In addition, the Ministry of Transport has also issued the Highway Maintenance Management Specification, which puts forward specific requirements for the quality, safety, environmental protection, etc. of highway maintenance. Therefore, in practice, the performance evaluation of highway maintenance usually conducts a comprehensive evaluation according to relevant policies and regulations and in combination with the actual situation. At the same time, the government and all sectors of society are also actively exploring more scientific and reasonable highway maintenance performance evaluation methods to improve the quality and effect of highway maintenance.

[0004] Currently, the evaluation of road maintenance mainly adopts two aspects of indicators. On the one hand, the highway technical condition evaluation indicators such as MQI and PQI stipulated in the Standard for Evaluation of Highway Technical Condition (JTG 5210—2018) are used to evaluate the grades of highways as excellent, good, medium and poor. On the other hand, there are management methods established by the transportation departments in some regions, mainly in the way of subjective scoring by management units after classifying management items. Such an evaluation system mainly has two problems: 1) The evaluation indicators are more targeted at roads and it is difficult to evaluate the maintenance work. According to the relevant provisions of the Standard for Evaluation of Highway Technical Condition (JTG 5210—2018), the highway technical condition evaluation indicators such as MQI and PQI are used to evaluate the grades of highways. However, it only expands the evaluation index level of highways, and there is no clear indication for the explanation of the changes in highway technical conditions. Therefore, it does not provide guidance for the evaluation of maintenance work. 2) The subjective evaluation is relatively cumbersome, and the comprehensive evaluation methods are stacked layer by layer and cannot present the objective situation. There are a large number of qualitative evaluation criteria in the maintenance management method, and at the same time, the assessment items for qualitative evaluation are also different in different places, resulting in difficulty in distinguishing the gaps between maintenance units in the assessment results. At the same time, there are also situations where the comprehensive evaluation method is multi-party assessment and the comprehensive scoring weight has no basis, etc. The result will be that the comprehensive scoring method cannot objectively display the performance of maintenance units.

[0005] Meanwhile, in addition to the problems existing in the existing evaluation methods, there are also problems with the current unreasonable evaluation methods. In actual work, there are also the following problems in maintenance evaluation: 1) There is a lack of a comprehensive evaluation index system and effective management methods established according to local conditions, making it difficult to ensure the implementation effectiveness. 2) It does not effectively combine objective quantitative data and the subjective scores of management personnel, and it is impossible to obtain a more realistic reflection of the maintenance results. 3) Only by simply setting key performance indicators, there are problems of difficult definition and strong subjectivity, resulting in overly detailed key performance indicators or actually ignoring important indicators. 4) The evaluation results cannot provide guidance and support for the marketization of maintenance work. Summary of the Invention

[0006] To solve the problems in the existing road maintenance evaluation process, such as difficult evaluation of maintenance work, cumbersome subjective evaluation, and inability to present objective situations, the present invention provides a comprehensive road maintenance evaluation method based on grey correlation coefficient, which can quantitatively evaluate the closeness between the performance of each maintenance unit and the ideal state, effectively improve the objectivity and authenticity of the evaluation, and can effectively conduct a comprehensive performance evaluation of the work of the maintenance unit, thereby providing support for promoting marketization. The present invention also relates to a comprehensive road maintenance evaluation system based on grey correlation coefficient.

[0007] The technical solution of the present invention is as follows:

[0008] A comprehensive road maintenance evaluation method based on grey correlation coefficient, characterized by comprising the following steps:

[0009] Index data acquisition and matrix construction step: Construct a multi-level comprehensive maintenance evaluation index system including business evaluation, management evaluation, financial evaluation, economic and social benefit indicators, then send instructions to different maintenance units, and then extract the road maintenance evaluation index data corresponding to the multi-level comprehensive maintenance evaluation index system of each maintenance unit. Then, construct an initial matrix according to different maintenance units and their corresponding road maintenance evaluation index data, and perform standardization processing on the initial matrix to obtain a standardized matrix;

[0010] Grey correlation coefficient calculation step: Use the entropy weight method to calculate the weight of each road maintenance evaluation index in the initial matrix, and calculate the weighted standardized evaluation matrix according to the standardized matrix and the weight; then use the grey correlation analysis method to calculate multiple grey correlation coefficients based on the weighted standardized evaluation matrix and the most ideal value in each evaluation index, and then construct a grey correlation coefficient matrix;

[0011] Steps for constructing the ideal solution model and calculating the closeness degree: On the basis of the TOPSIS model, a grey correlation coefficient matrix is introduced to construct an improved positive ideal solution model and a negative ideal solution model respectively. The first Euclidean distance between each maintenance unit and the positive ideal solution model is calculated according to the grey correlation coefficient and the improved positive ideal solution model, and the second Euclidean distance between each maintenance unit and the negative ideal solution model is calculated according to the grey correlation coefficient and the improved negative ideal solution model. The closeness degree of each maintenance unit is calculated based on the first Euclidean distance and the second Euclidean distance.

[0012] Steps for comprehensive evaluation of road maintenance: Sort the closeness degrees of each maintenance unit according to their magnitudes to obtain the comprehensive evaluation results of each maintenance unit, and complete the comprehensive evaluation of road maintenance.

[0013] Preferably, in the steps for calculating the grey correlation coefficient, calculating the weight of each evaluation index in the initial matrix by using the entropy weight method specifically includes: standardizing the evaluation indexes of each maintenance unit in the initial matrix by using the range variation method, then normalizing the standardized evaluation indexes of the maintenance unit, calculating the entropy value according to the normalized evaluation indexes of the maintenance unit, and calculating the weight of each evaluation index in the initial matrix according to the entropy value.

[0014] Preferably, in the steps for constructing the ideal solution model and calculating the closeness degree, introducing a grey correlation coefficient matrix on the basis of the TOPSIS model to construct an improved positive ideal solution model and a negative ideal solution model respectively includes: constructing an improved positive ideal solution model based on the maximum value of each column composed of multiple evaluation indexes in the grey correlation coefficient matrix; and constructing an improved negative ideal solution model based on the minimum value of each column composed of evaluation indexes in the grey correlation coefficient matrix.

[0015] Preferably, in the steps for obtaining index data and constructing the matrix, the constructed multi-level comprehensive maintenance evaluation index system includes two evaluation index levels: primary road maintenance evaluation indexes and secondary road maintenance evaluation indexes. The primary road maintenance evaluation indexes include business assessment, management assessment, financial assessment, and economic and social benefit indexes. The secondary road maintenance evaluation indexes include the road technical condition index, pavement condition index, international roughness index, number of demonstration roads, and bridge quantity change index covered under the business assessment index dimension; the pavement automatic acquisition coverage rate, popularization rate of scientific decision-making technology, real-time monitoring coverage rate, proportion of annual mileage of preventive maintenance implemented, and daily assessment score index covered under the management assessment index dimension; the fund implementation rate, fund utilization rate, and planned audit index covered under the financial assessment index dimension; and the road asset value change, complaint settlement rate, media exposure rate, and number of work safety accidents covered under the economic and social benefit index dimension.

[0016] Preferably, in the step of obtaining index data and constructing a matrix, the constructed multi-level comprehensive maintenance evaluation index system includes, in addition to the two evaluation index levels, an evaluation index type, which includes a positive evaluation index and a negative evaluation index. All the secondary road maintenance evaluation indicators covered under the dimensions of business assessment, management assessment, and financial assessment are positive evaluation indicators. Among the secondary road maintenance evaluation indicators covered under the dimension of economic and social benefits, both the change in road asset value and the complaint settlement rate are positive evaluation indicators. The media exposure rate and the number of work safety accidents are negative evaluation indicators;

[0017] In the step of calculating the grey correlation coefficient, the maximum value of the positive evaluation indicator or the minimum value of the negative evaluation indicator is used as the most ideal value for each corresponding evaluation indicator.

[0018] A road maintenance comprehensive evaluation system based on grey correlation coefficient, characterized by comprising an index data acquisition and matrix construction module, a grey correlation coefficient calculation module, an ideal solution model construction and closeness calculation module, and a road maintenance comprehensive evaluation module connected in sequence,

[0019] The index data acquisition and matrix construction module constructs a multi-level comprehensive maintenance evaluation index system including business assessment, management assessment, financial assessment, and economic and social benefit indicators, then issues instructions to different maintenance units, extracts the road maintenance evaluation index data corresponding to the multi-level comprehensive maintenance evaluation index system of each maintenance unit, constructs an initial matrix based on the different maintenance units and their corresponding road maintenance evaluation index data, and performs standardization processing on the initial matrix to obtain a standardized matrix;

[0020] The grey correlation coefficient calculation module calculates the weight of each road maintenance evaluation indicator in the initial matrix by using the entropy weight method, and calculates a weighted standardized evaluation matrix based on the standardized matrix and the weight; then calculates multiple grey correlation coefficients by using the grey correlation analysis method according to the weighted standardized evaluation matrix and the most ideal value in each evaluation indicator, and further constructs a grey correlation coefficient matrix;

[0021] The ideal solution model construction and closeness calculation module introduces the grey correlation coefficient matrix on the basis of the TOPSIS model to respectively construct an improved positive ideal solution model and a negative ideal solution model, calculates the first Euclidean distance between each maintenance unit and the positive ideal solution model according to the grey correlation coefficient and the improved positive ideal solution model, and calculates the second Euclidean distance between each maintenance unit and the negative ideal solution model according to the grey correlation coefficient and the improved negative ideal solution model; calculates the closeness of each maintenance unit according to the first Euclidean distance and the second Euclidean distance;

[0022] The comprehensive road maintenance evaluation module sorts the closeness degrees of each maintenance unit according to their magnitudes to obtain the comprehensive evaluation results of each maintenance unit, thereby completing the comprehensive evaluation of road maintenance.

[0023] Preferably, in the grey correlation coefficient calculation module, the entropy weight method is used to calculate the weight of each evaluation index in the initial matrix, which specifically includes: standardizing the evaluation indexes of each maintenance unit in the initial matrix by using the range variation method, then normalizing the standardized evaluation indexes of the maintenance unit, calculating the entropy value according to the normalized evaluation indexes of the maintenance unit, and then calculating the weight of each evaluation index in the initial matrix according to the entropy value.

[0024] Preferably, in the ideal solution model construction and closeness degree calculation module, on the basis of the TOPSIS model, a grey correlation coefficient matrix is introduced to construct an improved positive ideal solution model and a negative ideal solution model respectively, including: constructing an improved positive ideal solution model based on the maximum value of each column composed of multiple evaluation indexes in the grey correlation coefficient matrix; and constructing an improved negative ideal solution model based on the minimum value of each column composed of evaluation indexes in the grey correlation coefficient matrix.

[0025] Preferably, in the index data acquisition and matrix construction module, the constructed multi-level comprehensive maintenance evaluation index system includes two evaluation index levels: the first-level road maintenance evaluation index and the second-level road maintenance evaluation index. The first-level road maintenance evaluation index includes business assessment, management assessment, financial assessment, and economic and social benefit indexes. The second-level road maintenance evaluation index includes the road technical condition index, pavement condition index, international roughness index, number of demonstration roads, bridge quantity change index covered under the business assessment index dimension; the pavement automatic acquisition coverage rate, popularization rate of scientific decision-making technology, real-time monitoring coverage rate, proportion of annual mileage of preventive maintenance implemented, daily assessment score index covered under the management assessment index dimension; the fund implementation rate, fund utilization rate, planned audit index covered under the financial assessment index dimension; and the road asset value change, complaint settlement rate, media exposure rate, and number of work safety accidents indexes covered under the economic and social benefit index dimension.

[0026] Preferably, in the index data acquisition and matrix construction module, in addition to the two evaluation index levels, the constructed multi-level comprehensive maintenance evaluation index system also includes evaluation index types. The evaluation index types include positive evaluation indexes and negative evaluation indexes. Each second-level road maintenance evaluation index covered under the business assessment, management assessment, and financial assessment index dimensions is a positive evaluation index. Among the second-level road maintenance evaluation indexes covered under the economic and social benefit index dimension, the road asset value change and complaint settlement rate are positive evaluation indexes, and the media exposure rate and number of work safety accidents indexes are negative evaluation indexes;

[0027] In the grey correlation coefficient calculation module, the maximum value of the positive evaluation index or the minimum value of the negative evaluation index is used as the most ideal value of each corresponding evaluation index.

[0028] The beneficial effects of the present invention are as follows:

[0029] A comprehensive road maintenance evaluation method based on grey correlation coefficient provided by the present invention first constructs a multi-level comprehensive maintenance evaluation index system including business evaluation, management evaluation, financial evaluation, economic and social benefit indicators, then issues instructions to different maintenance units, and then extracts the road maintenance evaluation index data corresponding to the multi-level comprehensive maintenance evaluation index system of each maintenance unit. An initial matrix is constructed according to different maintenance units and their corresponding road maintenance evaluation index data, and the initial matrix is standardized to obtain a standardized matrix, which can adjust all data to the same scale, thus ensuring the fairness and accuracy of the subsequent analysis results and avoiding deviations caused by different units or magnitudes. Then, the entropy weight method is used to calculate the weight of each evaluation index in the initial matrix, and a weighted standardized evaluation matrix is calculated according to the standardized matrix and the weight. An evaluation index system based on the comprehensive performance of road maintenance is constructed, and the indicators and evaluation weights are adjusted, realizing an objective evaluation of the maintenance performance. It can evaluate the comprehensive performance level of maintenance management according to the index system, and can also evaluate the performance of different aspects according to the decomposed system, which helps to promote the update and improvement of the management system through performance evaluation and assessment. Then, according to the weighted standardized evaluation matrix and the most ideal value in each evaluation index, the grey correlation analysis method is used to calculate multiple grey correlation coefficients, and then a grey correlation coefficient matrix is constructed, which can quantitatively evaluate the closeness between the performance of each maintenance unit and the ideal state, effectively improving the objective authenticity of the evaluation. Finally, on the basis of the TOPSIS model, the grey correlation coefficient matrix is introduced to construct an improved positive ideal solution model and a negative ideal solution model respectively, and the first Euclidean distance and the second Euclidean distance are calculated according to the grey correlation coefficient and the positive and negative ideal solution models respectively, and then the closeness is calculated. The closeness of each maintenance unit is sorted according to the size to obtain the comprehensive evaluation results of each maintenance unit, completing the comprehensive evaluation of road maintenance, expanding the original evaluation method only for road technical conditions, and being able to effectively conduct a comprehensive performance evaluation of the work of maintenance units, thus providing support for promoting marketization.

[0030] The present invention can also be referred to as an improved entropy weight TOPSIS evaluation method introducing grey relational analysis, or a comprehensive road maintenance evaluation method based on an improved entropy weight TOPSIS model, which is used for the comprehensive road maintenance evaluation method. By adopting the entropy weight method and calculating the grey relational coefficient, various qualitative and quantitative evaluation indicators can be better integrated. Compared with the traditional entropy weight TOPSIS model, which directly weights the normalized matrix after normalizing and standardizing the original data, the normalized matrix of the improved model of the present invention is constructed by the grey relational coefficient, and the objective authenticity of the evaluation is further improved by introducing the grey relational coefficient.

[0031] The present invention also relates to a comprehensive road maintenance evaluation system based on the grey relational coefficient. This system corresponds to the above-mentioned comprehensive road maintenance evaluation method based on the grey relational coefficient, and can be understood as a system for implementing the above-mentioned comprehensive road maintenance evaluation method based on the grey relational coefficient, including an index data acquisition and matrix construction module, a grey relational coefficient calculation module, an ideal solution model construction and closeness calculation module, and a comprehensive road maintenance evaluation module connected in sequence. Each module works in cooperation with each other. From the perspective of performance evaluation, by constructing a comprehensive maintenance evaluation index system, the weight of each evaluation index in the initial matrix is calculated by the entropy weight method, and the grey relational coefficient and the closeness of each maintenance unit are calculated by a specific calculation method to evaluate the road maintenance work itself, expanding the original evaluation method only for the road technical condition, and being able to effectively conduct a comprehensive performance evaluation of the work of the maintenance unit, thus providing support for promoting marketization. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 FIG. is the working principle diagram of the comprehensive road maintenance evaluation method based on the grey relational coefficient of the present invention.

[0033] Figure 2 FIG. is the flowchart of the comprehensive road maintenance evaluation method based on the grey relational coefficient of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] The present invention will be described below with reference to the accompanying drawings.

[0035] The present invention relates to a comprehensive road maintenance evaluation method based on the grey relational coefficient, as Figure 1As shown in the working principle diagram, first, data acquisition: establish a comprehensive maintenance performance evaluation index system and obtain the index data therein. Then, perform data processing such as matrix construction, weight calculation, correlation degree calculation, and closeness degree calculation. Matrix construction: construct an initial matrix based on the data in the established evaluation index system; Weight calculation: determine the weights of each index based on the entropy weight method, perform standardization processing on the data, calculate the entropy value of the evaluation index, and then calculate the specific weights of each index; Correlation degree calculation: calculate the grey correlation coefficient based on the entropy weight standardized matrix, introduce the grey correlation degree for improvement, and enhance the relevance of the comprehensive evaluation of this method; Closeness degree calculation: construct an improved TOPSIS model based on the weights and measures determined above, construct the positive and negative ideal solutions and the distances between the evaluation objects and them, and calculate the closeness degree between the evaluation objects and the ideal solutions. Finally, comprehensive evaluation: sort the evaluation objects according to the results of data processing to obtain the results of comprehensive evaluation. When performing data processing such as weight calculation, correlation degree calculation, and closeness degree calculation, it involves the grey correlation coefficient calculation step, the construction of the ideal solution model, and the closeness degree calculation step. The grey correlation degree is introduced to improve the entropy weight TOPSIS evaluation method. The use of this method takes into account the objectivity, relevance, and flexibility of evaluation:

[0036] The entropy weight method assigns weights to different indicators from the perspective of information theory. By calculating the entropy value of each indicator, its dispersion degree can be judged. The greater the dispersion degree of the indicator, the greater the impact of the indicator on the comprehensive evaluation, that is, the higher the weight. In the present invention, the entropy weight method is used to eliminate the subjective judgment influence of the indicator weights. TOPSIS is a multi-dimensional decision analysis method that calculates the correction coefficient of the value ratio from the perspective of quantitative analysis, making the correction coefficient closer to the comprehensive situation represented by each indicator of the maintenance unit. Also known as the method for order preference by similarity to ideal solution, this model is a method for ranking according to the closeness degree between the evaluation object and the ideal target, and judges the advantages and disadvantages of each evaluation object by the distances between each evaluation object and the assumed positive and negative ideal solutions. In the present invention, a grey correlation coefficient matrix is introduced on the basis of the TOPSIS model to construct an improved positive ideal solution model and a negative ideal solution model respectively. Compared with the ordinary entropy weight TOPSIS model, the normalized matrix of the improved model is constructed by the grey correlation coefficient. The grey correlation analysis measures the correlation between the research object and each factor by the calculated correlation degree, and judges whether their connection is close by determining the closeness degree between the reference sequence and the set curves of several comparison sequences. Introducing the grey correlation coefficient matrix into this model further improves the objectivity of the evaluation.

[0037] The flow chart of the road maintenance comprehensive evaluation method based on grey correlation coefficient of the present invention is as Figure 2 shown, and successively includes the following steps:

[0038] I. Steps for obtaining index data and constructing a matrix: Construct a multi-level comprehensive maintenance evaluation index system that includes business assessment, management assessment, financial assessment, and economic and social benefit indicators. Subsequently, send instructions to different maintenance units, and then extract the road maintenance evaluation index data corresponding to the multi-level comprehensive maintenance evaluation index system for each maintenance unit. Then, construct an initial matrix based on different maintenance units and their corresponding road maintenance evaluation index data, and perform standardization processing on the initial matrix to obtain a standardized matrix.

[0039] Specifically, first construct a multi-level comprehensive maintenance evaluation index system that includes business assessment, management assessment, financial assessment, and economic and social benefit indicators. Subsequently, send instructions to different maintenance units, and then extract the road maintenance evaluation index data corresponding to the multi-level comprehensive maintenance evaluation index system for each maintenance unit from the annual report data and daily information systems of the management unit.

[0040] As shown in Table 1, the multi-level comprehensive maintenance evaluation index system includes two evaluation index levels and one evaluation index type. The two evaluation index levels are: primary road maintenance evaluation indicators and secondary road maintenance evaluation indicators. Among them, the primary road maintenance evaluation indicators include business assessment, management assessment, financial assessment, and economic and social benefit indicators. Preferably, the secondary road maintenance evaluation indicators include the road technical condition index MQI, pavement condition index PCI, international roughness index IRI, number of demonstration roads, and bridge quantity change indicators covered under the business assessment indicator dimension; the pavement automatic collection coverage rate, popularization rate of scientific decision-making technology, real-time monitoring coverage rate, proportion of annual mileage of preventive maintenance implemented, and daily assessment score indicators covered under the management assessment indicator dimension; the fund implementation rate, fund utilization rate, and planned audit indicators covered under the financial assessment indicator dimension; and the road asset value change, complaint settlement rate, media exposure rate, and number of work safety accidents indicators covered under the economic and social benefit indicator dimension.

[0041] The evaluation index type includes positive evaluation indicators and negative evaluation indicators. All secondary road maintenance evaluation indicators covered under the business assessment, management assessment, and financial assessment indicator dimensions are positive evaluation indicators. Among the secondary road maintenance evaluation indicators covered under the economic and social benefit indicator dimension, the road asset value change and complaint settlement rate are positive evaluation indicators, and the media exposure rate and number of work safety accidents indicators are negative evaluation indicators. And take the maximum value of the positive evaluation indicator or the minimum value of the negative evaluation indicator as the most ideal value of each corresponding evaluation indicator. Among them, the comprehensive maintenance evaluation index system is shown in Table 1.

[0042] Table 1

[0043]

[0044] Then, the initial matrix X = (x ij ) n×m , as shown below:

[0045]

[0046] In the above formula, x ij is the value of the jth road maintenance evaluation index of the ith maintenance unit, n is the number of maintenance units, and m is the number of road maintenance evaluation indicators.

[0047] The initial matrix is ​​normalized to obtain a normalized matrix, as shown in the following formula:

[0048]

[0049] 2. Grey correlation coefficient calculation steps: The entropy weight method is used to calculate the weight of each road maintenance evaluation index in the initial matrix, and the weighted standardized evaluation matrix is ​​obtained based on the standardized matrix and the weight calculation; then, the grey correlation analysis method is used to calculate multiple grey correlation coefficients based on the weighted standardized evaluation matrix and the optimal value of each evaluation index, and then the grey correlation coefficient matrix is ​​constructed.

[0050] Specifically, the range change method is first used to standardize the evaluation indicators of each maintenance unit in the initial matrix to eliminate the dimensional differences of different evaluation indicators.

[0051] The positive evaluation indicators in the evaluation indicators are standardized according to the following formula:

[0052]

[0053] The negative evaluation indicators in the evaluation indicators are standardized according to the following formula:

[0054]

[0055] Then the evaluation index of the maintenance unit after the standardized treatment is normalized, that is, the proportion of the evaluation index p is calculated. ij , calculated according to the following formula:

[0056]

[0057] Then calculate the entropy value of the evaluation index according to the proportion of the evaluation index, and calculate it according to the following formula:

[0058]

[0059] Among them, k=1 / lnn>0, satisfying e f ≥0.

[0060] Calculate the weight of each evaluation index in the initial matrix according to the entropy value, and calculate according to the following formula:

[0061]

[0062] Then multiply the standardized matrix by the weight to obtain the weighted standardized evaluation matrix y ij , as shown in the following formula:

[0063] y ij = ω j r ij , i = 1, 2, …, n; j = 1, 2, …, m (8)

[0064] According to the weighted standardized evaluation matrix y ij and the most ideal value y 0j in each evaluation index, and use the grey relational analysis method to calculate multiple grey relational coefficients (i.e., correlation degrees), and then construct a grey relational coefficient matrix, and calculate according to the following formula:

[0065]

[0066] Among them, ρ = 0.5.

[0067] III. Steps for constructing the ideal solution model and calculating the closeness degree: On the basis of the TOPSIS model, introduce the grey relational coefficient matrix to construct the improved positive ideal solution model and negative ideal solution model respectively, and calculate the first Euclidean distance between each maintenance unit and the positive ideal solution model according to the grey relational coefficient and the improved positive ideal solution model, and calculate the second Euclidean distance between each maintenance unit and the negative ideal solution model according to the grey relational coefficient and the improved negative ideal solution model; Calculate the closeness degree of each maintenance unit according to the first Euclidean distance and the second Euclidean distance.

[0068] Specifically, first introduce the grey relational coefficient matrix on the basis of the TOPSIS model to construct the improved positive ideal solution model and negative ideal solution model respectively, that is, construct the improved positive ideal solution model based on the maximum value of each column composed of multiple evaluation indexes in the grey relational coefficient matrix τ and construct the improved negative ideal solution model based on the minimum value of each column composed of evaluation indexes in the grey relational coefficient matrix as shown in the following formula:

[0069]

[0070]

[0071] Then, according to the grey correlation coefficient and the improved positive ideal solution model, the first Euclidean distance between each maintenance unit and the improved positive ideal solution model is calculated, and according to the grey correlation coefficient and the improved negative ideal solution model, the second Euclidean distance between each maintenance unit and the negative ideal solution model is calculated, as shown in the following formula:

[0072]

[0073] Finally, according to the first Euclidean distance and the second Euclidean distance, the closeness degree T of each maintenance unit is calculated i , as shown in the following formula:

[0074]

[0075] IV. Steps for comprehensive evaluation of road maintenance: Sort the closeness degrees of each maintenance unit from small to large. Among them, the closeness degree T i The result is between (0, 1). When T i is closer to 1, it indicates that the comprehensive evaluation result is more excellent, and the maintenance units with higher scores are ranked higher. Finally, the scores and rankings of the comprehensive evaluation results of each maintenance unit are obtained, thus completing the comprehensive evaluation of road maintenance.

[0076] The present invention also relates to a comprehensive road maintenance evaluation system based on grey correlation coefficient. This system corresponds to the above-mentioned comprehensive road maintenance evaluation method based on grey correlation coefficient and can be understood as a system for implementing the above method. This system includes an index data acquisition and matrix construction module, a grey correlation coefficient calculation module, an ideal solution model construction and closeness degree calculation module, and a comprehensive road maintenance evaluation module that are connected in sequence. Specifically,

[0077] The index data acquisition and matrix construction module constructs a multi-level comprehensive maintenance evaluation index system including business assessment, management assessment, financial assessment, and economic and social benefit indicators. Subsequently, instructions are sent to different maintenance units, and then the road maintenance evaluation index data corresponding to each maintenance unit and the multi-level comprehensive maintenance evaluation index system are extracted. Then, an initial matrix is constructed based on different maintenance units and their corresponding road maintenance evaluation index data, and the initial matrix is standardized to obtain a standardized matrix;

[0078] The grey correlation coefficient calculation module calculates the weight of each road maintenance evaluation index in the initial matrix by using the entropy weight method, and calculates a weighted standardized evaluation matrix based on the standardized matrix and the weight; then, according to the weighted standardized evaluation matrix and the most ideal value in each evaluation index, a grey correlation analysis method is used to calculate multiple grey correlation coefficients, and then a grey correlation coefficient matrix is constructed;

[0079] The ideal solution model construction and closeness degree calculation module constructs an improved positive ideal solution model and a negative ideal solution model respectively by introducing a grey correlation coefficient matrix on the basis of the TOPSIS model, calculates the first Euclidean distance between each maintenance unit and the positive ideal solution model according to the grey correlation coefficient and the improved positive ideal solution model, and calculates the second Euclidean distance between each maintenance unit and the negative ideal solution model according to the grey correlation coefficient and the improved negative ideal solution model; calculates the closeness degree of each maintenance unit according to the first Euclidean distance and the second Euclidean distance;

[0080] The comprehensive road maintenance evaluation module sorts the closeness degrees of each maintenance unit according to their magnitudes to obtain the comprehensive evaluation results of each maintenance unit, and completes the comprehensive evaluation of road maintenance.

[0081] Preferably, in the grey correlation coefficient calculation module, calculating the weight of each evaluation index in the initial matrix by using the entropy weight method specifically includes: standardizing the evaluation indexes of each maintenance unit in the initial matrix by using the range variation method, then normalizing the standardized evaluation indexes of the maintenance unit, calculating the entropy value according to the normalized evaluation indexes of the maintenance unit, and then calculating the weight of each evaluation index in the initial matrix according to the entropy value.

[0082] Preferably, in the ideal solution model construction and closeness degree calculation module, constructing an improved positive ideal solution model and a negative ideal solution model respectively by introducing a grey correlation coefficient matrix on the basis of the TOPSIS model includes: constructing an improved positive ideal solution model based on the maximum value of each column composed of multiple evaluation indexes in the grey correlation coefficient matrix; and constructing an improved negative ideal solution model based on the minimum value of each column composed of evaluation indexes in the grey correlation coefficient matrix.

[0083] Preferably, in the index data acquisition and matrix construction module, the constructed multi-level comprehensive maintenance evaluation index system includes two evaluation index levels: primary road maintenance evaluation indexes and secondary road maintenance evaluation indexes. The primary road maintenance evaluation indexes include business evaluation, management evaluation, financial evaluation, and economic and social benefit indexes. The secondary road maintenance evaluation indexes include the road technical condition index, pavement condition index, international roughness index, number of demonstration roads, and bridge number change index covered under the business evaluation index dimension; the pavement automatic acquisition coverage rate, popularization rate of scientific decision-making technology, real-time monitoring coverage rate, proportion of annual mileage of preventive maintenance implemented, and daily assessment score index covered under the management evaluation index dimension; the fund implementation rate, fund utilization rate, and planned audit index covered under the financial evaluation index dimension; and the road asset value change, complaint settlement rate, media exposure rate, and number of work safety accidents index covered under the economic and social benefit index dimension.

[0084] Preferably, in the index data acquisition and matrix construction module, the constructed multi-level comprehensive maintenance evaluation index system includes, in addition to the two evaluation index levels, evaluation index types, which include positive evaluation indexes and negative evaluation indexes. All the secondary road maintenance evaluation indexes covered under the business assessment, management assessment, and financial assessment index dimensions are positive evaluation indexes. Among the secondary road maintenance evaluation indexes covered under the economic and social benefit index dimension, the change in road asset value and the complaint settlement rate are both positive evaluation indexes. The media exposure rate and the number of work safety accidents are both negative evaluation indexes;

[0085] In the grey correlation coefficient calculation module, the maximum value of the positive evaluation index or the minimum value of the negative evaluation index is used as the ideal value of each corresponding evaluation index.

[0086] The present invention provides an objective and scientific road maintenance comprehensive evaluation method and system based on grey correlation coefficients, which can quantitatively evaluate the degree of closeness between the performance of each maintenance unit and the ideal state, effectively improve the objectivity and authenticity of the evaluation, and can effectively conduct a comprehensive performance evaluation of the work of the maintenance unit, thus providing support for promoting marketization.

[0087] It should be noted that the above specific implementation manners can enable those skilled in the art to understand the present invention more comprehensively, but do not limit the present invention in any way. Therefore, although this specification has described the present invention in detail with reference to the drawings and embodiments, those skilled in the art should understand that the present invention can still be modified or equivalently replaced. In short, all technical solutions and their improvements that do not depart from the spirit and scope of the present invention should be covered by the protection scope of the patent of the present invention.

Claims

1. A road maintenance comprehensive evaluation method based on grey correlation coefficient, characterized in that: The following steps are involved: Indicator data acquisition and matrix construction steps: construct a multi-level comprehensive maintenance evaluation indicator system including business evaluation, management evaluation, financial evaluation, economic and social benefit indicators, then issue instructions to different maintenance units, and then extract the road maintenance evaluation indicator data of each maintenance unit corresponding to the multi-level comprehensive maintenance evaluation indicator system, and then construct an initial matrix based on different maintenance units and their corresponding road maintenance evaluation indicator data, and standardize the initial matrix to obtain a standardized matrix; Grey correlation coefficient calculation steps: the entropy weight method is used to calculate the weight of each road maintenance evaluation index in the initial matrix, and the weighted standardized evaluation matrix is ​​obtained according to the standardized matrix and the weight calculation; then, the grey correlation analysis method is used to calculate multiple grey correlation coefficients according to the weighted standardized evaluation matrix and the optimal value of each evaluation index, and then the grey correlation coefficient matrix is ​​constructed; Ideal solution model construction and closeness calculation steps: Based on the TOPSIS model, the grey correlation coefficient matrix is ​​introduced to respectively construct the improved positive ideal solution model and the negative ideal solution model, and the first Euclidean distance between each maintenance unit and the positive ideal solution model is calculated according to the grey correlation coefficient and the improved positive ideal solution model, and the second Euclidean distance between each maintenance unit and the negative ideal solution model is calculated according to the grey correlation coefficient and the improved negative ideal solution model; the closeness of each maintenance unit is calculated according to the first Euclidean distance and the second Euclidean distance; Comprehensive evaluation steps for road maintenance: sort the closeness of each maintenance unit according to size, obtain the comprehensive evaluation results of each maintenance unit, and complete the comprehensive evaluation of road maintenance.

2. The road maintenance comprehensive evaluation method based on grey correlation coefficient according to claim 1 is characterized in that: In the grey correlation coefficient calculation step, the entropy weight method is used to calculate the weight of each evaluation index in the initial matrix, which specifically includes: using the extreme difference change method to standardize the evaluation index of each maintenance unit in the initial matrix, and then normalizing the evaluation index of the standardized maintenance unit, and calculating the entropy value according to the evaluation index of the normalized maintenance unit, and then calculating the weight of each evaluation index in the initial matrix according to the entropy value.

3. The road maintenance comprehensive evaluation method based on grey correlation coefficient according to claim 1 is characterized in that: In the ideal solution model construction and closeness calculation steps, a grey correlation coefficient matrix is ​​introduced on the basis of the TOPSIS model to respectively construct an improved positive ideal solution model and a negative ideal solution model, including: constructing an improved positive ideal solution model based on the maximum value of each column composed of multiple evaluation indicators in the grey correlation coefficient matrix; and constructing an improved negative ideal solution model based on the minimum value of each column composed of evaluation indicators in the grey correlation coefficient matrix.

4. The road maintenance comprehensive evaluation method based on grey correlation coefficient according to any one of claims 1 to 3, characterized in that: In the indicator data acquisition and matrix construction steps, the constructed multi-level comprehensive maintenance evaluation indicator system includes two evaluation indicator levels: primary road maintenance evaluation indicators and secondary road maintenance evaluation indicators. The primary road maintenance evaluation indicators include business evaluation, management evaluation, financial evaluation and economic and social benefit indicators. The secondary road maintenance evaluation indicators include road technical condition index, road surface condition index, international flatness index, number of demonstration roads, and change indicators of the number of bridges covered under the business evaluation indicator dimension; the road surface automation collection coverage rate, scientific decision-making technology popularization rate, real-time monitoring coverage rate, annual mileage proportion of preventive maintenance, and daily assessment score indicators covered under the management evaluation indicator dimension; the funding implementation rate, funding utilization rate, and planned audit indicators covered under the financial evaluation indicator dimension; and the road asset value change, complaint settlement rate, media exposure rate, and number of production safety accidents covered under the economic and social benefit indicator dimension.

5. The road maintenance comprehensive evaluation method based on grey correlation coefficient according to claim 4 is characterized in that: In the indicator data acquisition and matrix construction steps, the constructed multi-level comprehensive maintenance evaluation indicator system includes evaluation indicator types in addition to the two evaluation indicator levels. The evaluation indicator types include positive evaluation indicators and negative evaluation indicators. The secondary road maintenance evaluation indicators covered under the business evaluation, management evaluation, and financial evaluation indicator dimensions are all positive evaluation indicators. The road asset value change and complaint settlement rate in the secondary road maintenance evaluation indicators covered under the economic and social benefit indicator dimension are both positive evaluation indicators. The media exposure rate and the number of production safety accidents are both negative evaluation indicators. In the grey correlation coefficient calculation step, the maximum value of the positive evaluation index or the minimum value of the negative evaluation index is taken as the optimal value of each corresponding evaluation index.

6. A road maintenance comprehensive evaluation system based on grey correlation coefficient, characterized in that: It includes the index data acquisition and matrix construction module, the grey correlation coefficient calculation module, the ideal solution model construction and closeness calculation module and the road maintenance comprehensive evaluation module. The indicator data acquisition and matrix construction module constructs a multi-level comprehensive maintenance evaluation indicator system including business evaluation, management evaluation, financial evaluation, economic and social benefit indicators, and then issues instructions to different maintenance units to extract the road maintenance evaluation indicator data corresponding to the multi-level comprehensive maintenance evaluation indicator system of each maintenance unit, and then constructs an initial matrix according to different maintenance units and their corresponding road maintenance evaluation indicator data, and standardizes the initial matrix to obtain a standardized matrix; The grey correlation coefficient calculation module uses the entropy weight method to calculate the weight of each road maintenance evaluation index in the initial matrix, and obtains a weighted standardized evaluation matrix based on the standardized matrix and the weight calculation; then, a plurality of grey correlation coefficients are calculated using the grey correlation analysis method based on the weighted standardized evaluation matrix and the optimal value of each evaluation index, thereby constructing a grey correlation coefficient matrix; The ideal solution model construction and closeness calculation module introduces a grey correlation coefficient matrix on the basis of the TOPSIS model to respectively construct an improved positive ideal solution model and a negative ideal solution model, and calculates the first Euclidean distance between each maintenance unit and the positive ideal solution model according to the grey correlation coefficient and the improved positive ideal solution model, and calculates the second Euclidean distance between each maintenance unit and the negative ideal solution model according to the grey correlation coefficient and the improved negative ideal solution model; calculates the closeness of each maintenance unit according to the first Euclidean distance and the second Euclidean distance; The road maintenance comprehensive evaluation module sorts the closeness of each maintenance unit according to size, obtains the comprehensive evaluation results of each maintenance unit, and completes the comprehensive evaluation of road maintenance.

7. The road maintenance comprehensive evaluation system based on grey correlation coefficient according to claim 6 is characterized in that: In the grey correlation coefficient calculation module, the entropy weight method is used to calculate the weight of each evaluation index in the initial matrix, specifically including: using the extreme difference change method to standardize the evaluation index of each maintenance unit in the initial matrix, and then normalizing the evaluation index of the standardized maintenance unit, and calculating the entropy value according to the evaluation index of the normalized maintenance unit, and then calculating the weight of each evaluation index in the initial matrix according to the entropy value.

8. The road maintenance comprehensive evaluation system based on grey correlation coefficient according to claim 6 is characterized in that: In the ideal solution model construction and closeness calculation module, a grey correlation coefficient matrix is ​​introduced on the basis of the TOPSIS model to respectively construct an improved positive ideal solution model and a negative ideal solution model, including: constructing an improved positive ideal solution model based on the maximum value of each column composed of multiple evaluation indicators in the grey correlation coefficient matrix; and constructing an improved negative ideal solution model based on the minimum value of each column composed of evaluation indicators in the grey correlation coefficient matrix.

9. The road maintenance comprehensive evaluation system based on grey correlation coefficient according to any one of claims 6 to 8, characterized in that: In the indicator data acquisition and matrix construction module, the constructed multi-level comprehensive maintenance evaluation indicator system includes two evaluation indicator levels: primary road maintenance evaluation indicators and secondary road maintenance evaluation indicators. The primary road maintenance evaluation indicators include business evaluation, management evaluation, financial evaluation and economic and social benefit indicators. The secondary road maintenance evaluation indicators include the road technical condition index, road surface condition index, international flatness index, number of demonstration roads, and bridge number change indicators covered under the business evaluation indicator dimension; the road surface automation collection coverage rate, scientific decision-making technology popularization rate, real-time monitoring coverage rate, annual preventive maintenance mileage ratio, and daily assessment score indicators covered under the management evaluation indicator dimension; the funding implementation rate, funding utilization rate, and plan audit indicators covered under the financial evaluation indicator dimension; and the road asset value change, complaint settlement rate, media exposure rate, and production safety accident number indicators covered under the economic and social benefit indicator dimension.

10. The road maintenance comprehensive evaluation system based on grey correlation coefficient according to claim 9 is characterized in that: In the indicator data acquisition and matrix construction module, the constructed multi-level comprehensive maintenance evaluation indicator system includes not only the two evaluation indicator levels, but also evaluation indicator types, and the evaluation indicator types include positive evaluation indicators and negative evaluation indicators. The secondary road maintenance evaluation indicators covered under the business evaluation, management evaluation, and financial evaluation indicator dimensions are all positive evaluation indicators. The road asset value change and complaint settlement rate in the secondary road maintenance evaluation indicators covered under the economic and social benefit indicator dimension are both positive evaluation indicators. The media exposure rate and the number of production safety accidents are both negative evaluation indicators. In the grey correlation coefficient calculation module, the maximum value of the positive evaluation index or the minimum value of the negative evaluation index is taken as the optimal value of each corresponding evaluation index.

Images