A data quality assessment method for infrastructure ERP system

Abstract

A data quality evaluation method for infrastructure ERP system, the method first constructs data quality evaluation indicators for infrastructure ERP system, then determines the weight of each evaluation indicator, and determines the data quality evaluation value of each stage of infrastructure project based on fuzzy comprehensive evaluation, and then based on the data quality evaluation value of each stage, respectively uses the constructed bottom note scoring model and bottom note integral model to conduct differentiated comprehensive evaluation on the data quality of each stage. This design is not only conducive to a more comprehensive analysis of the data use value of infrastructure ERP system, but also can effectively mine infrastructure ERP system data with potential use value.

Description

Technical Field

[0001] The present invention belongs to the field of data evaluation, and specifically relates to a data quality evaluation method for an infrastructure ERP system, so as to directly reflect the authenticity and accuracy of data in the infrastructure ERP system. Background Art

[0002] With the advancement and development of Internet technology, Enterprise Resource Planning (ERP) provides an effective management platform for complex infrastructure business. ERP is an advanced management system that integrates organizational models, business processes, information technology, enterprise specifications and other modules. Power infrastructure ERP is to apply the ERP system management ideas to power infrastructure business. However, the infrastructure ERP system records a huge amount of project data, the authenticity and accuracy of which cannot be directly seen, and the processing and analysis of the data requires a lot of manpower and financial resources. Therefore, it is urgent to build a scientific, complete and accurate evaluation system to evaluate the data quality of the infrastructure ERP system to help enterprises quickly obtain infrastructure data with good use value. Summary of the invention

[0003] The purpose of the present invention is to provide a data quality assessment method for an infrastructure ERP system that can highlight the stage-by-stage quality changes of data in response to the above-mentioned problems existing in the prior art.

[0004] To achieve the above objectives, the technical solution of the present invention is as follows:

[0005] A data quality assessment method for an infrastructure ERP system comprises the following steps in sequence:

[0006] Step A, constructing data quality evaluation indicators for the infrastructure ERP system, wherein the evaluation indicators include the completeness of infrastructure project data, the accuracy of infrastructure project data, the consistency of infrastructure project data, the scarcity of infrastructure project data, the dimension of infrastructure project data, and the timeliness of infrastructure project data;

[0007] Step B: first determine the weight of each evaluation indicator, and then determine the data quality evaluation value of each stage of the infrastructure project based on fuzzy comprehensive evaluation;

[0008] Step C: For the evaluation value of each stage, if it is greater than or equal to the corresponding threshold, it is determined that the data quality of this stage meets the requirements; otherwise, it is determined that the data quality of this stage does not meet the requirements and needs to be corrected.

[0009] The step C comprises the following steps in sequence:

[0010] Step C1: For the kth stage, if M k If the data quality of this stage meets the requirements, it is judged that it is greater than or equal to the corresponding threshold, and then enter step C3; if Mk If the value is less than the corresponding threshold, it is determined that the data quality at this stage does not meet the requirements, and then the process goes to step C2, where k≥2;

[0011] Step C2: Based on data envelopment analysis, evaluate whether the data of this stage is DEA strong and effective. If so, determine the comprehensive evaluation value of this stage through the bottom bet integral model and use it as the bottom score of the next stage; if not, proceed to step C3;

[0012] Step C3: Determine the comprehensive evaluation value of this stage through the base score model and use it as the base score for the next stage.

[0013] In step C2, the bottom bet integral model is:

[0014]

[0015] In the above formula, Λ k is the ante score of the kth stage, M k , S k are the evaluation value and comprehensive evaluation value of the kth stage, respectively. ΔM is the difference between the evaluation value of the kth stage and the comprehensive evaluation value of the k-1th stage. pass is the threshold value;

[0016] In step C3, the bottom note scoring model is:

[0017]

[0018] In the above formula, Γ k Score the bet at the kth stage, M k , S k are the evaluation value and comprehensive evaluation value of the kth stage respectively.

[0019] In step C2, the data envelopment analysis refers to: performing data envelopment analysis on the project input data and output data of the kth stage in the infrastructure ERP system to determine whether the data of this stage is DEA-strong and effective.

[0020] Step B uses the following formula to determine the data quality assessment value of each stage of the infrastructure project:

[0021]

[0022] In the above formula, M k is the data quality evaluation value of the kth stage, C is the evaluation index weight set, γ is the membership matrix of the evaluation index on the evaluation level set V, γ ij is the jth evaluation level V in the evaluation level set V corresponding to the i-th evaluation indicator jThe membership degree is determined based on the evaluation index value by fuzzy comprehensive evaluation, where m and n are the number of evaluation indexes and evaluation levels, respectively.

[0023] The evaluation level set V = {V1, V2, V3, V4, V5} = {high, relatively high, average, relatively low, low}.

[0024] In step B, the weight of each evaluation indicator is determined by the following formula:

[0025] C={c1,…,c i ,…c m}

[0026]

[0027]

[0028] In the above formula, C is the weight set of evaluation indicators, m is the number of evaluation indicators, and H i is the importance score of the i-th evaluation indicator, h ij is the importance of the i-th evaluation indicator relative to the j-th evaluation indicator. If the i-th evaluation indicator is more important than the j-th evaluation indicator, then h ij is 1, if the jth evaluation index is more important than the ith evaluation index, then h ij is 0. If both are equally important, then h ij is 0.5.

[0029] In step B, the division criteria of the project stages are: the project preparation stage is the first stage, and every two months after the project implementation is a stage.

[0030] In step A, the completeness η1 of the infrastructure project data is calculated using the following formula:

[0031]

[0032] In the above formula, N c is the missing amount of infrastructure project data, N total The total amount of infrastructure project data;

[0033] The accuracy rate η2 of the infrastructure project data is calculated using the following formula:

[0034]

[0035] In the above formula, M1 and M2 are the number of abnormal and erroneous infrastructure project data, respectively. total The total amount of infrastructure project data;

[0036] The consistency of the infrastructure project data is characterized by the intraclass correlation coefficient ICC. If ICC < 0.4, it indicates poor consistency; if 0.4 ≤ ICC ≤ 0.75, it indicates good consistency; if ICC > 0.75, it indicates high consistency;

[0037] The scarcity η4 of the infrastructure project data is calculated using the following formula:

[0038]

[0039] In the above formula, x1 is the amount of scarce data in the power market in the infrastructure project data, x total The total amount of infrastructure project data;

[0040] The dimensions of the infrastructure project data are calculated using the following formula:

[0041] η5=(y1 y2…y i ) i

[0042] In the above formula, η5 is the business dimension of a single set of data, y i is the ith business of a single set of data;

[0043] The timeliness of the infrastructure project data is the interval between the time when the data was last updated and the current time.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. The data quality evaluation method of a infrastructure ERP system of the present invention first constructs the data quality evaluation index of the infrastructure ERP system, then determines the weight of each evaluation index, and determines the data quality evaluation value of each stage of the infrastructure project based on fuzzy comprehensive evaluation. Finally, by comparing the evaluation value of each stage with the size of the corresponding threshold, it is determined whether the data quality meets the requirements. This method aims to directly reflect the authenticity and accuracy of the data of the infrastructure ERP system. The data quality is evaluated according to the project stage. On the one hand, it can directly highlight the staged quality changes of the data, which is conducive to a more comprehensive analysis of the data use value of the infrastructure ERP system. On the other hand, it is also convenient for targeted data correction in the later stage. Therefore, the present invention is conducive to a more comprehensive analysis of the data use value of the infrastructure ERP system and later data correction.

[0046] 2. The data quality evaluation method of a capital construction ERP system of the present invention introduces a dual-system difference evaluation model composed of a bottom note scoring model and a bottom note integral model for the data evaluation of each stage. The model focuses on the evaluation of the differences in the stages of the capital construction ERP system. When comprehensively evaluating the data of each stage, the evaluation results of the previous stage are considered. For the stage where the data quality does not meet the requirements, the data envelopment analysis is used to determine whether the data of this stage is DEA-strong and effective. If so, it means that the data conversion efficiency of this stage cannot meet the system data conversion requirements, and its comprehensive evaluation value adopts the actual evaluation value. If not, it means that the data conversion efficiency of this stage can meet the system data conversion requirements and has potential use value. At this time, the comprehensive evaluation value is improved by the bottom note integral method, so that the capital construction ERP system data with potential use value can be effectively mined. Therefore, the present invention can effectively mine the capital construction ERP system data with potential use value. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a comprehensive evaluation flow chart of each stage in the present invention.

[0048] Figure 2 Schematic diagram of comprehensive evaluation results of the infrastructure ERP1 system at each stage in Example 1.

[0049] Figure 3 This is a schematic diagram of the comprehensive evaluation results of the infrastructure ERP2 system at each stage in Example 1. DETAILED DESCRIPTION

[0050] The present invention is further described in detail below in conjunction with specific implementations and drawings.

[0051] See also Figure 1 , a data quality evaluation method for an infrastructure ERP system, comprising the following steps in sequence:

[0052] Step A, constructing data quality evaluation indicators for the infrastructure ERP system, wherein the evaluation indicators include the completeness of infrastructure project data, the accuracy of infrastructure project data, the consistency of infrastructure project data, the scarcity of infrastructure project data, the dimension of infrastructure project data, and the timeliness of infrastructure project data;

[0053] Step B: first determine the weight of each evaluation indicator, and then determine the data quality evaluation value of each stage of the infrastructure project based on fuzzy comprehensive evaluation;

[0054] Step C: For the evaluation value of each stage, if it is greater than or equal to the corresponding threshold, it is determined that the data quality of this stage meets the requirements; otherwise, it is determined that the data quality of this stage does not meet the requirements and needs to be corrected.

[0055] The step C comprises the following steps in sequence:

[0056] Step C1: For the kth stage, if M k If the data quality of this stage meets the requirements, it is judged that it is greater than or equal to the corresponding threshold, and then enter step C3; if M k If the value is less than the corresponding threshold, it is determined that the data quality at this stage does not meet the requirements, and then the process goes to step C2, where k≥2;

[0057] Step C2: Based on data envelopment analysis, evaluate whether the data of this stage is DEA strong and effective. If so, determine the comprehensive evaluation value of this stage through the bottom bet integral model and use it as the bottom score of the next stage; if not, proceed to step C3;

[0058] Step C3: Determine the comprehensive evaluation value of this stage through the base score model and use it as the base score for the next stage.

[0059] In step C2, the bottom bet integral model is:

[0060]

[0061] In the above formula, Λ k is the ante score of the kth stage, M k , S k are the evaluation value and comprehensive evaluation value of the kth stage, respectively. ΔM is the difference between the evaluation value of the kth stage and the comprehensive evaluation value of the k-1th stage. pass is the threshold value;

[0062] In step C3, the bottom note scoring model is:

[0063]

[0064] In the above formula, Γ k Score the bet at the kth stage, M k , S k are the evaluation value and comprehensive evaluation value of the kth stage respectively.

[0065] In step C2, the data envelopment analysis refers to: performing data envelopment analysis on the project input data and output data of the kth stage in the infrastructure ERP system to determine whether the data of this stage is DEA-strong and effective.

[0066] Step B uses the following formula to determine the data quality assessment value of each stage of the infrastructure project:

[0067]

[0068] In the above formula, M k is the data quality evaluation value of the kth stage, C is the evaluation index weight set, γ is the membership matrix of the evaluation index on the evaluation level set V, γij is the jth evaluation level V in the evaluation level set V corresponding to the i-th evaluation indicator j The membership degree is determined based on the evaluation index value by fuzzy comprehensive evaluation, where m and n are the number of evaluation indexes and evaluation levels, respectively.

[0069] The evaluation level set V = {V1, V2, V3, V4, V5} = {high, relatively high, average, relatively low, low}.

[0070] In step B, the weight of each evaluation indicator is determined by the following formula:

[0071] C={c1,…,c i ,…c m}

[0072]

[0073]

[0074] In the above formula, C is the weight set of evaluation indicators, m is the number of evaluation indicators, and H i is the importance score of the i-th evaluation indicator, h ij is the importance of the i-th evaluation indicator relative to the j-th evaluation indicator. If the i-th evaluation indicator is more important than the j-th evaluation indicator, then h ij is 1, if the jth evaluation index is more important than the ith evaluation index, then h ij is 0. If both are equally important, then h ij is 0.5.

[0075] In step B, the division criteria of the project stages are: the project preparation stage is the first stage, and every two months after the project implementation is a stage.

[0076] In step A, the completeness η1 of the infrastructure project data is calculated using the following formula:

[0077]

[0078] In the above formula, N c is the missing amount of infrastructure project data, N total The total amount of infrastructure project data;

[0079] The accuracy rate η2 of the infrastructure project data is calculated using the following formula:

[0080]

[0081] In the above formula, M1 and M2 are the number of abnormal and erroneous infrastructure project data, respectively. total The total amount of infrastructure project data;

[0082] The consistency of the infrastructure project data is characterized by the intraclass correlation coefficient ICC. If ICC < 0.4, it indicates poor consistency; if 0.4 ≤ ICC ≤ 0.75, it indicates good consistency; if ICC > 0.75, it indicates high consistency;

[0083] The scarcity η4 of the infrastructure project data is calculated using the following formula:

[0084]

[0085] In the above formula, x1 is the amount of scarce data in the power market in the infrastructure project data, x total The total amount of infrastructure project data;

[0086] The dimensions of the infrastructure project data are calculated using the following formula:

[0087] η5=(y1 y2…y i ) i

[0088] In the above formula, η5 is the business dimension of a single set of data, y i is the ith business of a single set of data;

[0089] The timeliness of the infrastructure project data is the interval between the time when the data was last updated and the current time.

[0090] The principle of the present invention is described as follows:

[0091] The present invention provides a data quality evaluation method for an infrastructure ERP system. The method constructs a dual-system difference evaluation model, which distinguishes the differences in data value at different stages of the infrastructure ERP system. The phased data differences of the infrastructure ERP system are taken into account, and the phased difference scoring is fair. At the same time, the bottom bet scoring model and the bottom bet points model can horizontally evaluate the relative position of the infrastructure ERP system in their respective evaluation systems, and vertically evaluate the data quality changes of the infrastructure ERP system at different stages. The evaluation result is a comprehensive expression of the authenticity and accuracy of the data contained in the infrastructure ERP system, which can directly reflect the phased changes in data quality and the growth of the use value of project data, and help enterprises using the ERP system quickly obtain infrastructure data with good use value.

[0092] Embodiment 1:

[0093] See also Figure 1 A data quality assessment method for infrastructure ERP system is proposed. The method takes two infrastructure ERP systems with different project difficulties as the research objects and proceeds in the following steps:

[0094] 1. Construct data quality evaluation indicators for the infrastructure ERP system. The evaluation indicators include the completeness of infrastructure project data, the accuracy of infrastructure project data, the consistency of infrastructure project data, the scarcity of infrastructure project data, the dimension of infrastructure project data, and the timeliness of infrastructure project data. The completeness of the infrastructure project data η1 represents the completeness of data records and information in the infrastructure ERP system, and whether there are any missing conditions. It is calculated using the following formula:

[0095]

[0096] In the above formula, N c is the missing amount of infrastructure project data, N total The total amount of infrastructure project data;

[0097] The accuracy rate η2 of the infrastructure project data indicates whether the information and data recorded in the infrastructure ERP system data summary are accurate, and whether there are abnormal or erroneous information. It is calculated by the following formula:

[0098]

[0099] In the above formula, M1 and M2 are the number of abnormal and erroneous infrastructure project data, respectively. total The total amount of infrastructure project data;

[0100] The consistency of the infrastructure project data indicates the ability of the data contained in the infrastructure ERP system to maintain the original attributes when called by different users, which is characterized by the intra-group correlation coefficient ICC. If ICC < 0.4, it indicates poor consistency; if 0.4 ≤ ICC ≤ 0.75, it indicates good consistency; if ICC > 0.75, it indicates high consistency. The ICC coefficient is determined by analyzing the intra-group correlation coefficient of the infrastructure ERP system data using SPSS software;

[0101] The scarcity of infrastructure project data η4 represents the scarcity of this type of data in the infrastructure ERP system in the power market, and is calculated using the following formula:

[0102]

[0103] In the above formula, x1 is the amount of scarce data in the power market in the infrastructure project data, x total The total amount of infrastructure project data;

[0104] The dimensions of the infrastructure project data are the different business angles of the data contained in a single set of data in the infrastructure ERP system, which are calculated using the following formula:

[0105] η5=(y1 y2…y i ) i

[0106] In the above formula, η5 is the business dimension of a single set of data, y i is the ith business of a single set of data;

[0107] The timeliness of the infrastructure project data refers to the timeliness and effectiveness of the data contained in the infrastructure ERP system data under different demand scenarios, which is reflected by the interval between the last update time of the data and the current time;

[0108] 2. Use the priority diagram method to determine the weight of each evaluation indicator:

[0109] C={c1,…,c i ,…c m}

[0110]

[0111]

[0112] In the above formula, C is the weight set of evaluation indicators, m is the number of evaluation indicators, and H i is the importance score of the i-th evaluation indicator, h ij is the importance of the i-th evaluation indicator relative to the j-th evaluation indicator. If the i-th evaluation indicator is more important than the j-th evaluation indicator, then h ij is 1, if the jth evaluation index is more important than the ith evaluation index, then h ij is 0. If both are equally important, then h ij is 0.5;

[0113] The weights of the evaluation indicators calculated in this embodiment are shown in Table 1:

[0114] Table 1 Weights of each evaluation index

[0115] index Weight Completeness of infrastructure project data 0.247 Accuracy of infrastructure project data 0.214 Data consistency of infrastructure projects 0.136 Scarcity of infrastructure project data 0.095 Dimensions of infrastructure project data 0.136 Timeliness of infrastructure project data 0.172

[0116] 3. First, the infrastructure project is divided into four stages, with the project preparation stage as the first stage and every two months after the project implementation as a stage. Then the following formula is used to determine the data quality assessment value of each stage of the infrastructure project:

[0117]

[0118] In the above formula, M k is the data quality evaluation value of the kth stage, C is the evaluation index weight set, γ is the membership matrix of the evaluation index on the evaluation level set V, γ ij is the jth evaluation level V in the evaluation level set V corresponding to the i-th evaluation indicator jThe membership degree is determined by fuzzy comprehensive evaluation based on the evaluation index value, V = {V1, V2, V3, V4, V5} = {high, relatively high, general, relatively low, low}, and the five evaluation levels are assigned values ​​of 100, 80, 60, 40, and 20 respectively, and m and n are the number of evaluation indicators and evaluation levels respectively;

[0119] The data quality evaluation values ​​of the two infrastructure ERP systems at each stage calculated in this embodiment are shown in Table 2:

[0120] Table 2 Data quality assessment values ​​of infrastructure ERP1 and ERP2 systems at each stage

[0121]

[0122]

[0123] 4. Construct an ante scoring model and an ante score model respectively, wherein the ante scoring model is:

[0124]

[0125] In the above formula, Γ k Score the bet at the kth stage, M k , S k are the evaluation value and comprehensive evaluation value of the kth stage respectively;

[0126] This model uses M1 as the base score of the second stage, and the calculated second stage evaluation value M2 as the comprehensive evaluation value S2 of this stage. S2 is then used as the base score of the third stage, and so on. The comprehensive evaluation value obtained in each stage is used as the base score of the next stage.

[0127] The ante integral model is:

[0128]

[0129] In the above formula, Λ k is the ante score of the kth stage, M k , S k are the evaluation value and comprehensive evaluation value of the kth stage, respectively. ΔM is the difference between the evaluation value of the kth stage and the comprehensive evaluation value of the k-1th stage. pass is the threshold value;

[0130] 5. For the infrastructure ERP1 system, M2=81, which is greater than the set threshold of 50. Therefore, it is determined that the data quality of this stage meets the requirements. The comprehensive evaluation value S2 of this stage is determined to be 81 through the bottom note scoring model. Similarly, the bottom note scoring model is used in the third and fourth stages to determine S3 and S4 as 86 and 88 respectively. For the infrastructure ERP2 system, M2=48, which is less than the set threshold of 50. Therefore, it is determined that the data quality of this stage does not meet the requirements. Data envelopment analysis is performed on the project input data and output data of the second stage in the infrastructure ERP2 system. It is determined that DEA is strongly effective, indicating that the data conversion efficiency of this stage can meet the system data conversion requirements and has potential use value. The bottom note integral model is used for this stage. Its bottom score is 41, the evaluation value of the first stage, and the calculated comprehensive evaluation value of the second stage is 57. The evaluation values ​​of the third and fourth stages are both greater than the threshold of 50. Therefore, the bottom note scoring model is used in the third and fourth stages. The calculated comprehensive evaluation values ​​are 61 and 67 respectively. The comprehensive evaluation results of the infrastructure ERP1 system and the infrastructure ERP1 system at each stage are as follows: Figure 2 , Figure 3 shown.

Claims

1. A data quality assessment method for an infrastructure ERP system, characterized by: The evaluation method comprises the following steps in sequence: Step A, constructing data quality evaluation indicators for the infrastructure ERP system, wherein the evaluation indicators include the completeness of infrastructure project data, the accuracy of infrastructure project data, the consistency of infrastructure project data, the scarcity of infrastructure project data, the dimension of infrastructure project data, and the timeliness of infrastructure project data; Step B: first determine the weight of each evaluation indicator, and then determine the data quality evaluation value of each stage of the infrastructure project based on fuzzy comprehensive evaluation; Step C: For the evaluation value of each stage, if it is greater than or equal to the corresponding threshold, it is determined that the data quality of this stage meets the requirements; otherwise, it is determined that the data quality of this stage does not meet the requirements and needs to be corrected, which specifically includes the following steps: Step C1: For the kth stage, if M k If the data quality of this stage meets the requirements, it is judged that it is greater than or equal to the corresponding threshold, and then enter step C3; if M k If the value is less than the corresponding threshold, it is determined that the data quality at this stage does not meet the requirements, and then the process goes to step C2, where k≥2; Step C2: Based on data envelopment analysis, evaluate whether the data of this stage is DEA strong and effective. If so, determine the comprehensive evaluation value of this stage through the bottom bet integral model and use it as the bottom score of the next stage; if not, proceed to step C3; wherein the bottom bet integral model is: In the above formula, Λ k is the ante score of the kth stage, M k , S k are the evaluation value and comprehensive evaluation value of the kth stage, respectively. ΔM is the difference between the evaluation value of the kth stage and the comprehensive evaluation value of the k-1th stage. pass is the threshold value; Step C3: Determine the comprehensive evaluation value of this stage through the base score model, and use it as the base score of the next stage, wherein the base score model is: In the above formula, Γ k Score the bet at the kth stage, M k , S k are the evaluation value and comprehensive evaluation value of the kth stage respectively.

2. The data quality assessment method of an infrastructure ERP system according to claim 1, characterized in that: In step C2, the data envelopment analysis refers to: performing data envelopment analysis on the project input data and output data of the kth stage in the infrastructure ERP system to determine whether the data of this stage is DEA-strong and effective.

3. The data quality assessment method of a capital construction ERP system according to claim 1, characterized in that: Step B uses the following formula to determine the data quality assessment value of each stage of the infrastructure project: In the above formula, M k is the data quality evaluation value of the kth stage, C is the evaluation index weight set, γ is the membership matrix of the evaluation index on the evaluation level set V, γ ij is the jth evaluation level V in the evaluation level set V corresponding to the i-th evaluation indicator j The membership degree is determined based on the evaluation index value by fuzzy comprehensive evaluation, where m and n are the number of evaluation indexes and evaluation levels, respectively.

4. The data quality assessment method of a capital construction ERP system according to claim 3 is characterized by: The evaluation level set V = {V1, V2, V3, V4, V5} = {high, relatively high, average, relatively low, low}.

5. The data quality assessment method of a capital construction ERP system according to claim 1, characterized in that: In step B, the weight of each evaluation indicator is determined by the following formula: C={c1,…,c i ,…c m } In the above formula, C is the weight set of evaluation indicators, m is the number of evaluation indicators, and H i is the importance score of the i-th evaluation indicator, h ij is the importance of the i-th evaluation indicator relative to the j-th evaluation indicator. If the i-th evaluation indicator is more important than the j-th evaluation indicator, then h ij is 1, if the jth evaluation index is more important than the ith evaluation index, then h ij is 0, if both are equally important, then h ij is 0.

5.

6. The data quality assessment method of a capital construction ERP system according to claim 1, characterized in that: In step B, the division criteria of the project stages are: the project preparation stage is the first stage, and every two months after the project implementation is a stage.

7. The data quality assessment method of a capital construction ERP system according to claim 1, characterized in that: In step A, the completeness η1 of the infrastructure project data is calculated using the following formula: In the above formula, N c is the missing amount of infrastructure project data, N total The total amount of infrastructure project data; The accuracy rate η2 of the infrastructure project data is calculated using the following formula: In the above formula, M1 and M2 are the number of abnormal and erroneous infrastructure project data, respectively. total The total amount of infrastructure project data; The consistency of the infrastructure project data is characterized by the intraclass correlation coefficient ICC. If ICC < 0.4, it indicates poor consistency; if 0.4 ≤ ICC ≤ 0.75, it indicates good consistency; if ICC > 0.75, it indicates high consistency; The scarcity η4 of the infrastructure project data is calculated using the following formula: In the above formula, x1 is the amount of scarce data in the power market in the infrastructure project data, x total The total amount of infrastructure project data; The dimensions of the infrastructure project data are calculated using the following formula: η5=(y1 y2…y i ) i In the above formula, η5 is the business dimension of a single set of data, y i is the ith business of a single set of data; The timeliness of the infrastructure project data is the interval between the time when the data was last updated and the current time.

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