CORS network data quality evaluation method based on entropy weight TOPSIS

Abstract

The invention provides a CORS network data quality evaluation method based on entropy weight TOPSIS. The method comprises the following steps: S-1, forming an original matrix; s-2, standardizing the xij to obtain a standardized Zij (Zij); s-3, calculating the proportion of the index j corresponding to the object i in the column sum; s-4, calculating an entropy value of an index j corresponding to the object i; s-5, calculating a difference coefficient of the jth index; s-6, calculating the weight of the jth index; s-7, multiplying the weight point calculated in the step S6 by the standardized matrix to obtain a weighted standardized matrix; s-8, positive and negative ideal solutions are determined; s-9, calculating an Euclidean distance; s-10, finally calculating a comprehensive score Si of the base station in the ith CORS network; and S-11, judging the final advantages and disadvantages of all the reference stations of the current CORS station network according to the score. The GNSS and CORS data quality evaluation method combines observation data acquired by a CORS station network in real time or after the event and entropy weight TOPSIS with objectivity, comprehensiveness and high efficiency as core advantages, and provides quality evaluation for GNSS and CORS data.

Description

Technical Field

[0001] The present invention relates to the field of global satellite positioning technology, and in particular to a method for comprehensively evaluating the data quality of a satellite navigation positioning continuous operational reference station (CORS) network. Background Art

[0002] The Continuous Operational Reference System (CORS) is a continuously operating satellite positioning service system built on a multi-base station network using RTK (real-time kinematic positioning) technology. Its core components include a base station network, a data center, and user application systems. By integrating multiple technologies, including satellite navigation (such as Beidou and GPS), computer networks, digital communications, and modern geodesy, a dynamic positioning reference framework covering a specific region is formed. This framework not only monitors transient and long-term changes in regional and even global crustal movement and meteorological conditions, but also provides a variety of high-precision spatial positioning services and diversified information services. CORS is not only a core component of modern spatial data infrastructure but also a key technology for promoting the development of a smart society. Currently, CORS networks are being established across countries and provinces and cities. The development of various industries, such as natural resources, traffic control, major infrastructure construction, geological disasters, and emergency rescue, has become indispensable to surveying and mapping information services. Therefore, as a system that empowers applications across various industries, its stable operation is the foundation of surveying and mapping information services and crucial for ensuring the robust operation of many infrastructures. However, high-quality data is a prerequisite for the long-term and stable operation of the CORS system. Data quality indicators are mainly used to evaluate the reliability and accuracy of data provided by satellite navigation systems. They are also an important part of modern surveying and mapping standards and have important reference value for judging whether the CORS station network is operating properly.

[0003] In modern society, with the ever-increasing amount of information and increasing data complexity, effective multi-metric comprehensive evaluation has become a crucial research topic. Whether in economic management, social development, or scientific research, comprehensive evaluation and comparison of complex systems are essential. For example, in economic management, enterprises need to assess their overall competitiveness; in social development, governments need to evaluate the impact of different policies; and in scientific research, scholars need to compare the advantages and disadvantages of different experimental methods. Therefore, establishing a scientific and objective comprehensive evaluation model is crucial. The entropy-weighted TOPSIS method is a combined evaluation method, a combination of the entropy-weighted method and the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. The entropy-weighted method objectively assigns weights to indicators. The greater the degree of variation of an indicator in the system being measured, the greater the amount of information it contains, and accordingly, a higher weight should be assigned. The TOPSIS method is a multi-criteria decision analysis technique. Its basic concept is to rank and evaluate each evaluation target by comparing its distance from the optimal and worst-case scenarios. Specifically, the TOPSIS method treats evaluation objects as points in a multidimensional space and determines the quality of each evaluation object by calculating the distance between these points and the ideal best point and the ideal worst point. The closer the evaluation object is to the ideal best point and the farther it is from the ideal worst point, the better the overall evaluation result. This method fully utilizes raw data information to quantitatively reflect the quality of different evaluation objects. Therefore, the TOPSIS method has been widely used in various fields, such as hospital management, medical quality control, benefit evaluation, health decision-making, and health service management. The introduction of the entropy weight method overcomes the TOPSIS method's inability to highlight important indicators and enhances the scientific nature of the evaluation results.

[0004] Currently, CORS network data quality evaluation metrics include signal-to-noise ratio (SNR), multipath (MP), data complete rate (DCR), cycle slip rate (CSR), number of clock jumps (Njump), data utilization rate (DUR), and dilution of precision (DOP). However, a unified, scientific, and objective evaluation system for each of these local metrics has yet to be established. This makes it impossible to quantitatively reflect the quality of the CORS network and provide a comprehensive, systematic assessment for regional operations and maintenance, as well as for decision-makers. Summary of the Invention

[0005] The present invention aims to at least solve the technical problems existing in the prior art. In particular, it innovatively proposes a CORS network data quality evaluation method based on entropy weight TOPSIS to address the limitations of traditional methods when processing multi-type data. In addition to the data and local levels, it provides a comprehensive and scientific evaluation method for the CORS station network from a comprehensive level to improve the accuracy and reliability of the evaluation. The entropy weight TOPSIS evaluation model has no strict restrictions on the distribution type and sample size of the data. By objectively assigning weights, comprehensively analyzing and integrating information from multiple indicators, and making full use of the original data information, it can well characterize the combined impact of multiple influencing indicators and avoid the deviation caused by human factors.

[0006] In order to achieve the above-mentioned purpose of the present invention, the present invention provides a method for achieving the above-mentioned purpose by the following technical steps:

[0007] A. Calculate the data quality indicators corresponding to all reference stations in the CORS network and construct the original matrix X (where x ij is the jth index corresponding to the i-th reference station, i = 1, 2, 3, ..., n, j = 1, 2, 3, ..., m, n is the total number of reference stations, m is the total number of data quality indicators), and its form is shown in formula (1). ij Here, i represents the serial number of the reference station in the CORS network, and j represents the serial number of the indicator corresponding to each reference station.

[0008]

[0009] B. For x ij Normalize to get the standardized Z ij :

[0010] Z ij is the normalized value of the jth indicator corresponding to the i-th object, i = 1, 2, 3, ..., n; j = 1, 2, 3, ..., m;

[0011] Evaluation indicators generally include positive indicators, negative indicators, and appropriate indicators. The formula for standardizing each of them using the range method is as follows:

[0012] Positive indicators:

[0013]

[0014] Negative indicators:

[0015]

[0016] Moderation indicators:

[0017]

[0018] Where: is the minimum value of the index in the jth column of X;

[0019] is the maximum value of the index in the jth column of X;

[0020] x ij is the jth indicator corresponding to the i-th object (reference station), where i = 1, 2, 3, ..., n; j = 1, 2, 3, ..., m;

[0021] a and b are the two boundary values ​​of the corresponding interval endpoints in the fitness index;

[0022] Z ij is the normalized value of the jth indicator corresponding to the i-th object (reference station);

[0023] max(,) means taking the larger value;

[0024] Since different evaluation indicators usually have different dimensions, the original matrix needs to be standardized to eliminate the differences in magnitude or trend caused by different dimensions. Common standardization methods include averaging, normalization, and intervalization. In order to facilitate program writing and unify indicator trends, negative indicators are subjected to the same trend processing to ensure that all indicators have the same trend during the evaluation process, thereby avoiding evaluation bias caused by different indicator directions. The specific process is to first take out the jth indicator x n×j The maximum value in Then all indicators are subtracted from this value and the absolute value is taken. The calculation formula is the same as shown in formula (3).

[0025] C. 0 influence elimination

[0026] After obtaining the normalized matrix Z, since the normalized data will fall between 0 and 1, in order to prevent the influence of eliminating 0 in subsequent steps such as natural logarithm calculation, it is set to 0.001.

[0027] D. Calculate the proportion of index j corresponding to object i in the column sum:

[0028]

[0029] Where: Σ is the summation symbol;

[0030] P ij is the proportion of the jth indicator corresponding to the i-th object;

[0031] n is the total number of reference stations;

[0032] m is the total number of data quality indicators;

[0033] Z ijis the normalized value of the jth indicator corresponding to the i-th object;

[0034] E. Calculate the entropy value of index j corresponding to object i:

[0035]

[0036] Where: E j is the entropy value of the jth indicator corresponding to the i-th object;

[0037] ln is the logarithm sign based on the natural base e;

[0038] P ij is the proportion of the jth indicator corresponding to the i-th object;

[0039] n is the total number of reference stations;

[0040] m is the total number of data quality indicators;

[0041] F. Calculate the coefficient of variation of the j-th indicator:

[0042] G j =1-E j (8)

[0043] Where: G j represents the coefficient of variation of the j-th indicator;

[0044] E j is the entropy value of the jth indicator corresponding to the i-th object;

[0045] G. Calculate the weight of the j-th indicator:

[0046]

[0047] Where: ω j represents the weight of the j-th indicator;

[0048] G j represents the coefficient of variation of the j-th indicator;

[0049] m is the total number of data quality indicators;

[0050] H. Multiply the weights calculated in step G by the normalized matrix to obtain the weighted normalized matrix F:

[0051] F ij =Z ij ω j (10)

[0052] Where: F ij represents the weighted normalized value;

[0053] Z ijis the normalized value of the jth indicator corresponding to the i-th object;

[0054] ω j represents the weight of the j-th indicator;

[0055] Its matrix expression is as follows:

[0056]

[0057] I. Determine positive and negative ideal solutions The positive ideal solution is the maximum value of the i-th indicator among the n evaluation objects, and the negative ideal solution is the minimum value of the i-th indicator among the n evaluation objects. The ideal solution of each indicator is selected according to the following formula.

[0058]

[0059] In the formula: max{} means taking the maximum value;

[0060] min{} means taking the minimum value;

[0061] represents the maximum value among the j-th index;

[0062] represents the minimum value of the j-th index;

[0063] n is the total number of reference stations;

[0064] m is the total number of data quality indicators;

[0065] J. Calculate Euclidean distance Taking the positive and negative ideal solutions of each indicator as the evaluation criteria, the Euclidean distance between each indicator and the positive and negative ideal solutions in each evaluation scheme is calculated.

[0066] For the reference station in the i-th CORS network, calculate its distance to the optimal solution:

[0067]

[0068] Where: represents the distance to the i-th optimal solution;

[0069] represents the maximum value among the j-th index;

[0070] Z ij is the standardized value of the jth indicator corresponding to the i-th object;

[0071] m is the total number of data quality indicators;

[0072] Distance to the worst solution:

[0073]

[0074] Where: To find the square root sign;

[0075] represents the distance of the i-th worst solution;

[0076] represents the minimum value of the j-th index;

[0077] Z ij is the standardized value of the jth indicator corresponding to the i-th object;

[0078] m is the total number of data quality indicators;

[0079] K, finally calculate the comprehensive score S of the reference station in the i-th CORS network i :

[0080]

[0081] Where: S i (0≤S i ≤1) is the comprehensive score calculated for the i-th benchmark station; and The smaller it is, that is, the smaller the distance between the solution and the optimal solution, S i The bigger; accordingly, The larger the value, the greater the distance between the solution and the worst solution. i That is, the current benchmark station has the highest score.

[0082] L. Perform a comprehensive evaluation based on the score calculated in step K, and determine the final quality of all benchmark sites in the current CORS network according to the score size.

[0083] In summary, due to the adoption of the above technical solution, the present invention has no strict restrictions on the distribution type and sample size of the data based on the entropy weight TOPSIS evaluation model. By objectively assigning weights, comprehensively analyzing and integrating the information of multiple indicators, and making full use of the original data information, it can well characterize the comprehensive impact of multiple influencing indicators and avoid the deviation caused by human factors.

[0084] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments with reference to the accompanying drawings, in which:

[0086] Figure 1 It is a schematic flow chart of the present invention.

[0087] Figure 2 It is a schematic diagram of the indicator calculation flow of the present invention. DETAILED DESCRIPTION

[0088] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0089] The present invention discloses a CORS network data quality evaluation method based on entropy weight TOPSIS. Figures 1-2 As shown in the figure, all evaluation indicators of different satellite constellations in the CORS station network are calculated, including signal-to-noise ratio (SNR), multipath value (MP), data integrity rate (DCR), cycle slip ratio (CSR), number of clock jumps (Njump), data utilization rate (DUR), and dilution of precision (DOP). The entropy weight method is then used to calculate the indicator weights, including constructing an original matrix for all stations in the base station network, determining the data type of each indicator, unifying the trend of the data indicators, non-negative shifting, and calculating the entropy value, difference coefficient, and weight of each indicator. Finally, the weighted normalization matrix is ​​calculated to determine the positive and negative ideal solution vectors in each indicator, and the Euclidean distance of each indicator from the positive and negative ideal solutions is calculated. The degree of closeness of each object to the ideal solution is calculated, and a comprehensive evaluation of the quality of all base stations in the CORS network is achieved.

[0090] The specific steps include:

[0091] Step 1: Traverse all the reference stations in the CORS network one by one. For any station, calculate the corresponding multipath, cycle slip ratio and other indicators. The calculation formulas for some indicators are as follows:

[0092] (1) Multipath calculation formula

[0093]

[0094] Where: P1 and P2 are pseudorange observation values, L1 and L2 are carrier phase observation values;

[0095] f1 and f2 are the frequencies corresponding to the frequency points (their values ​​are the frequency points designed for each constellation).

[0096] N1 and N2 are the corresponding ambiguities; λ1 and λ2 are the corresponding carrier wavelengths;

[0097] Iron1 is the ionospheric delay at the first frequency point;

[0098] B1 is the observation noise at the first frequency point;

[0099] α is the proportional coefficient,

[0100] Similarly, for frequency 2 or other frequency points, the formula is as follows:

[0101]

[0102] Where: P1 and P2 are pseudorange observation values, L1 and L2 are carrier phase observation values;

[0103] f1 and f2 are the frequencies corresponding to the frequency points (their values ​​are the frequency points designed for each constellation).

[0104] N1 and N2 are the corresponding ambiguities; λ1 and λ2 are the corresponding carrier wavelengths;

[0105] MP1 and MP2 are the pseudo-range multipath values ​​at each frequency point; α is the proportional coefficient,

[0106] Iron2 is the ionospheric delay at the second frequency point;

[0107] B2 is the observation noise at the first frequency point;

[0108] For the multi-path calculation of Beidou three-frequency to five-frequency, the above formula can be used.

[0109] (2) Cycle slip ratio

[0110] The ionospheric residual combination and MW combination are used to detect data quality. The specific formula is as follows:

[0111] Ionospheric residual combination:

[0112]

[0113] MW combined observation:

[0114]

[0115] Where: P1, P2 are pseudorange observation values, φ1, φ2 are carrier distances;

[0116] N1 and N2 are the corresponding ambiguities; λ1 and λ2 are the corresponding carrier wavelengths;

[0117] δρ I,1 ,δρ I,2 are pseudorange and residual values ​​respectively;

[0118] Step 2: Construct the original matrix X based on the indicators of all reference stations calculated in the CORS network (where x ijwhere i = 1, 2, 3, ..., n; j = 1, 2, 3, ..., m, n is the total number of reference stations, and m is the total number of data quality indicators). ij Here, i represents the serial number of the reference station in the CORS network, and j represents the serial number of the indicator corresponding to each reference station.

[0119]

[0120] Step 3: Use the range method to normalize the data. The purpose of normalization is to eliminate the influence of different indicator dimensions so that each indicator can be compared and analyzed under the same framework. The specific formula is as follows:

[0121] Positive indicators:

[0122]

[0123] Negative indicators:

[0124]

[0125] Moderation indicators:

[0126]

[0127] Where: is the minimum value of the index in the jth column of X;

[0128] is the maximum value of the index in the jth column of X;

[0129] x ij is the jth index corresponding to the i-th object, where i = 1, 2, 3, ..., n; j = 1, 2, 3, ..., m;

[0130] a and b are the two boundary values ​​of the corresponding interval endpoints in the fitness index;

[0131] Z ij is the normalized value of the jth indicator corresponding to the i-th object;

[0132] max(,) means taking the larger value;

[0133]

[0134] Z represents the normalized matrix obtained by normalization;

[0135] Z ij is the normalized value of the jth indicator corresponding to the i-th object;

[0136] i=1,2,3,…,n; j=1,2,3,…,m;

[0137] Step 4: Trending: Determine the data type of each indicator and convert negative and moderate indicators into positive ones. This is because evaluation indicators may contain both positive and negative indicators. The larger the value of a positive indicator, the better, while the smaller the value of a negative indicator, the better. To ensure that all indicators have the same trend, it is usually necessary to convert negative indicators into positive ones.

[0138]

[0139] Where: | | is the absolute value symbol, and the other symbols have the same meanings as above;

[0140] Step 5: After obtaining the normalized matrix Z, since the normalized data will fall between 0 and 1, in order to prevent the influence of eliminating 0 in subsequent steps such as natural logarithm calculation, let 0 be ζ, and ζ is the preset clearing influence threshold, which can be 0.001 here, to obtain the matrix that eliminates the influence.

[0141] Step 6: Calculate the proportion of the index j corresponding to object i in the column sum. The specific operation is the proportion of the current index to the sum of the index of all reference stations, which is used to calculate the entropy value of the current index.

[0142]

[0143] Where: ∑ is the summation symbol;

[0144] P ij is the proportion of the jth indicator corresponding to the i-th object;

[0145] n is the total number of reference stations;

[0146] m is the total number of data quality indicators;

[0147] Z ij is the normalized value of the jth indicator corresponding to the i-th object;

[0148] Step 7: To ensure the (independent) additivity of information, calculate the entropy value of index j corresponding to object i:

[0149]

[0150] Where: ln is the sign of the logarithm with the natural base e as the base;

[0151] E j is the entropy value of the jth indicator corresponding to the i-th object;

[0152] Step 8: After step 7, the same weight is guaranteed for the same indicator of all reference stations to ensure the scientificity of the calculated results of the reference stations. Then calculate the difference coefficient of j indicators:

[0153] Gj =1-E j

[0154] Where: G j represents the coefficient of variation of the j-th indicator;

[0155] E j is the entropy value of the jth indicator corresponding to the i-th object;

[0156] Step 9: Calculate the weight of the j-th indicator:

[0157]

[0158] Where: ω j represents the weight of the j-th indicator;

[0159] G j represents the coefficient of variation of the j-th indicator;

[0160] m is the total number of data quality indicators;

[0161] Step 10: Multiply the weights calculated in step G by the normalized matrix to obtain the weighted normalized matrix F ij :

[0162] F ij =Z ij ω j

[0163] Where: F ij represents the weighted normalized value;

[0164] Z ij is the normalized value of the jth indicator corresponding to the i-th object;

[0165] ω j represents the weight of the j-th indicator;

[0166] Its matrix expression is as follows:

[0167]

[0168] Step 11: Determine the positive and negative ideal solutions Positive ideal solution That is, the maximum value of the i-th indicator among n evaluation objects, negative ideal solution That is, the minimum value of the i-th indicator among the n evaluation objects. In actual processing, it is obtained from the weighted normalization matrix F ij The maximum and minimum values ​​of each indicator in Size is the number of indicator types m. The specific calculation and explanation are shown in the following formula:

[0169]

[0170] In the formula: max{} means taking the maximum value;

[0171] min{} means taking the minimum value;

[0172] represents the maximum value among the j-th index;

[0173] represents the minimum value of the j-th index;

[0174] n is the total number of reference stations;

[0175] m is the total number of data quality indicators;

[0176] Step 12: Determine the closeness of each indicator to the ideal solution and calculate the Euclidean distance Taking the positive and negative ideal solutions of each indicator as the evaluation criteria, the Euclidean distance between each indicator and the positive and negative ideal solutions in each evaluation scheme is calculated. The specific implementation process is to calculate the sum of the squares of the differences between the current indicator value of each object and the ideal solution, and then calculate the square root. The size of is the number n of reference stations in the CORS station network.

[0177] For the reference station in the i-th CORS network, calculate its distance to the optimal solution:

[0178]

[0179] Distance to the worst solution:

[0180]

[0181] Where: To find the square root sign;

[0182] Z ij is the standardized value of the jth indicator corresponding to the i-th object;

[0183] represents the distance of the i-th worst solution;

[0184] represents the distance to the i-th optimal solution;

[0185] represents the maximum value among the j-th index;

[0186] represents the minimum value of the j-th index;

[0187] m is the total number of data quality indicators;

[0188] Step 13: Finally calculate the comprehensive score S of the reference station in the i-th CORS networki :

[0189]

[0190] Where: S i (0≤S i ≤1) is the comprehensive score calculated for the i-th benchmark station; and The smaller it is, that is, the smaller the distance between the solution and the optimal solution, S i The bigger; accordingly, The larger the value, the greater the distance between the solution and the worst solution. i That is, the current benchmark station has the highest score.

[0191] Step 14: Based on the scores calculated in Step 13, a comprehensive evaluation is performed to determine the final quality of all reference stations in the current CORS network. The daily comprehensive scores are used to assess the CORS network's short-term anomalies (such as ionospheric disturbances) and long-term performance (such as the stability of the reference station coordinate time series). This helps guide equipment upgrades, optimize site selection, and prioritize maintenance for low-scoring stations, thereby reducing the cost of reference station operation and maintenance.

[0192] If S i >ξ1, the i-th reference station is a first-level reference station;

[0193] If ξ2<S i ≤ξ1, the i-th reference station is a secondary reference station;

[0194] If ξ3<S i ≤ξ2, the i-th reference station is a third-level reference station;

[0195] If S i ≤ξ3, the i-th reference station is a fourth-level reference station;

[0196] ξ1, ξ2, and ξ3 are the preset judgment thresholds (ξ1>ξ2>ξ3), ξ1 is the first judgment threshold, ξ2 is the second judgment threshold, and ξ3 is the third judgment threshold. Then, a visual score display is implemented for each base station:

[0197]

[0198] Where: R i 、B i , G i They are visual display values ​​respectively;

[0199] S i The comprehensive score calculated for the i-th benchmark station; S Bi 、S Gi For common display values, the present invention takes 0, which is B i=G i =0.

[0200] b is the number of visualization bits, which is 8 here. At this time:

[0201]

[0202] is the floor function, S max The preset maximum score threshold.

[0203] The present invention combines the observation data collected in real time or after the CORS station network. The entropy-weighted TOPSIS method takes objectivity, comprehensiveness and efficiency as its core advantages, providing a scientific tool for GNSS and CORS data quality evaluation. It is particularly suitable for large-scale station network operation and maintenance, equipment selection optimization and service performance improvement. It is an effective means of new PNT (positioning, navigation and timing) infrastructure management. Multi-dimensional comprehensive evaluation achieves objective weighting and reduces subjective bias. By calculating the discrete degree of GNSS data indicators (such as multipath effect, cycle slip rate, signal-to-noise ratio), the weights are automatically and scientifically assigned to reflect the information value of the data itself, avoiding the subjectivity of traditional methods that rely on manual experience. At the same time, it can evaluate the short-term anomalies (such as ionospheric disturbances) and long-term performance (such as the stability of the reference station coordinate time series) of the CORS station network. Based on this method, complex data can be processed efficiently, which is suitable for large-scale CORS station networks (such as provincial / national reference station networks, generally with more than 200 provincial reference stations), quickly identifying low-quality sites (such as data interruption rate > 5% or data noise > 1cm), and adapting to the characteristics of GNSS data changing over time and space. In addition, the CORS network data quality evaluation method based on entropy-weighted TOPSIS is highly portable and can intuitively reflect the quality of the data at the base station. Through the output comprehensive site score value, the site performance degradation trend can be predicted (such as time series modeling based on historical entropy-weighted TOPSIS results). At the same time, it can guide equipment upgrades, site optimization, and guide maintenance personnel to prioritize maintenance of low-scoring sites, reducing the cost of base station operation and maintenance, ensuring the long-term stable operation of the base station, and providing a reasonable evaluation method for the quality stability of the surveying and mapping benchmark framework.

[0204] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. A CORS network data quality evaluation method based on entropy weight TOPSIS, characterized in that: The following steps are involved: S-1, calculate the data quality indicators corresponding to all reference stations in the CORS network and form the original matrix X: Among them, x ij Indicates the jth index corresponding to the i-th reference station; i = 1, 2, 3, ..., n, j = 1, 2, 3, ..., m; n is the total number of reference stations; m is the total number of data quality indicators; S-2, for x ij Normalize to get the standardized Z ij : Where Z represents the normalized matrix; Z ij is the normalized value of the jth indicator corresponding to the i-th object; i = 1, 2, 3, ..., n; j = 1, 2, 3, ..., m; S-3, calculate the proportion of indicator j corresponding to object i in the sum of the column; S-4, calculate the entropy value of index j corresponding to object i; S-5, calculate the coefficient of variation of the jth indicator; S-6, calculate the weight of the jth indicator; S-7, multiply the weights calculated in step S6 by the normalized matrix to obtain a weighted normalized matrix F: Among them, F ij represents the weighted normalized value; i = 1, 2, 3, ..., n, j = 1, 2, 3, ..., m; n is the total number of reference stations; m is the total number of data quality indicators; Z ij is the normalized value of the jth indicator corresponding to the i-th object; ω j represents the weight of the j-th indicator; S-8, determine positive and negative ideal solutions S-9, calculate Euclidean distance S-10, finally calculate the comprehensive score S of the reference station in the i-th CORS network i ; S-11, based on the comprehensive evaluation score calculated in step S-10, the final quality of all benchmark sites in the current CORS site network is determined according to the score size.

2. The CORS network data quality evaluation method based on entropy weight TOPSIS according to claim 1 is characterized in that, After normalization in step S-2, Z ij The calculation method is: Positive indicators: Negative indicators: Moderation indicators: Where: is the minimum value of the index in the jth column of X; is the maximum value of the index in the jth column of X; x ij is the jth index corresponding to the i-th object, where i = 1, 2, 3, ..., n; j = 1, 2, 3, ..., m; a and b are the two boundary values ​​of the corresponding interval endpoints in the fitness index; Z ij is the normalized value of the jth indicator corresponding to the i-th object; max(,) means taking the larger value.

3. The CORS network data quality evaluation method based on entropy weight TOPSIS according to claim 1 is characterized in that, In step S-3, the proportion of the index j corresponding to the object i in the column sum is calculated as follows: Where: ∑ is the summation symbol; P ij is the proportion of the jth indicator corresponding to the i-th object; n is the total number of reference stations; m is the total number of data quality indicators; Z ij The normalized value of the jth indicator corresponding to the i-th object.

4. The CORS network data quality evaluation method based on entropy weight TOPSIS according to claim 1 is characterized in that, In step S-4, the entropy value of the index j corresponding to the object i is calculated as follows: Where: E j is the entropy value of the jth indicator corresponding to the i-th object; ln is the logarithm sign based on the natural base e; P ij is the proportion of the jth indicator corresponding to the i-th object; n is the total number of reference stations; m is the total number of data quality indicators.

5. The CORS network data quality evaluation method based on entropy weight TOPSIS according to claim 1 is characterized in that, The calculation method of the coefficient of variation of the j-th indicator in step S-5 is: G j =1-E j (8) Where: G j represents the coefficient of variation of the j-th indicator; E j is the entropy value of the jth indicator corresponding to the i-th object.

6. The CORS network data quality evaluation method based on entropy weight TOPSIS according to claim 1 is characterized in that: The weight of the j-th indicator in step S-6 is calculated as follows: Where: ω j represents the weight of the j-th indicator; G j represents the coefficient of variation of the j-th indicator; m is the total number of data quality indicators.

7. The CORS network data quality evaluation method based on entropy weight TOPSIS according to claim 1 is characterized in that: The weighted normalized value in step S-7 is calculated as follows: F ij =Z ij ω j (10) Where: F ij represents the weighted normalized value; Z ij is the normalized value of the jth indicator corresponding to the i-th object; ω j Represents the weight of the j-th indicator.

8. The CORS network data quality evaluation method based on entropy weight TOPSIS according to claim 1 is characterized in that: The calculation method of the positive and negative ideal solutions in step S-8 is: In the formula: max{} means taking the maximum value; min{} means taking the minimum value; represents the maximum value among the j-th indicators; represents the minimum value of the j-th index; n is the total number of reference stations; m is the total number of data quality indicators; Or / and the Euclidean distance calculation method in step S-9 is: Where: represents the distance to the i-th optimal solution; represents the maximum value among the j-th indicators; Z ij is the standardized value of the jth indicator corresponding to the i-th object; m is the total number of data quality indicators; Where: To find the square root sign; represents the distance of the i-th worst solution; represents the minimum value of the j-th index; Z ij is the standardized value of the jth indicator corresponding to the i-th object; m is the total number of data quality indicators; Or / and the calculation method of the comprehensive score in step S-11 is: Where: S i The comprehensive score calculated for the i-th benchmark station; represents the distance of the i-th worst solution; represents the distance to the i-th optimal solution.

9. A computer system, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to implement the CORS network data quality evaluation method based on entropy weight TOPSIS according to one of claims 1 to 8 when executing the executable instructions.

10. A computer-readable storage medium, characterized in that include: a memory having a computer program stored thereon; A processor is used to execute the program in the memory to implement the CORS network data quality evaluation method based on entropy weight TOPSIS according to any one of claims 1 to 8.

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