CN116662466A - Land full life cycle maintenance system through big data - Google Patents
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Abstract
The invention provides a land full life cycle maintenance system through big data, which comprises the following steps: s1, acquiring land planning construction basic data through a big data platform, collecting and classifying the basic data, and carrying out land data clustering correction through a clustering algorithm; s2, performing abnormal adjustment on the land data clustering data after performing standardization according to the clustering correction of the land data set, and improving the data quality; s3, improving the difference degree score of the actual change and the expected change of the land data through adjustment of the deviation coefficient in the land data scoring function; and S4, evaluating the accuracy and the recall rate of the land data after the score is calculated, and performing corresponding land data maintenance operation according to the evaluation result.
Description
Technical Field
The invention relates to the field of map information analysis, in particular to a land full life cycle maintenance system through big data.
Background
For the maintenance and management of the full life cycle of the land, the full-scale supervision operation is required to be carried out on the land in a reserved state, the land planning, the land application, the land construction and the full-scale data of the land maintenance are obtained in the supervision process, the later optimization and management are carried out, the management of the land refinement is carried out on massive data, and the data maintenance is carried out on the full life cycle of the land, so that the technical problems corresponding to the full life cycle of the land are needed to be solved by the technicians in the field.
Disclosure of Invention
The invention aims at least solving the technical problems in the prior art, and particularly creatively provides a land full life cycle maintenance system through big data.
In order to achieve the above object of the present invention, the present invention provides a land full life cycle maintenance system by big data, comprising the steps of:
s1, acquiring land planning construction basic data through a big data platform, collecting and classifying the basic data, and carrying out land data clustering correction through a clustering algorithm;
s2, performing abnormal adjustment on the land data clustering data after performing standardization according to the clustering correction of the land data set, and improving the data quality;
s3, improving the difference degree score of the actual change and the expected change of the land data through adjustment of the deviation coefficient in the land data scoring function;
and S4, evaluating the accuracy and the recall rate of the land data after the score is calculated, and performing corresponding land data maintenance operation according to the evaluation result.
Preferably, in the above technical solution, the S1 includes:
s1-1, preprocessing land planning construction data, classifying according to land supply time and supply use, and forming an initial data set for the land planning construction data;
s1-2, clustering operation is carried out according to the content of the initial data set, and the clustering algorithm of the land data in the historical time stage is that
Where i is the number of elements in the land dataset, k is the random point of the dataset, a i Is a cluster in the dataset, b i Is cluster a i μ is an abnormal adjustment coefficient, and F is a clustering algorithm expression character.
Preferably, in the above technical solution, the S1 further includes:
s1-3, for the elements with the number of i in the land data set, the abnormal adjustment coefficient mu E [0,1] is calculated by the following formula:
wherein the method comprises the steps ofSuperscript D for feature vector corresponding to ith element in land dataset I i Superscript D for feature vector number of actual land data int As the number of feature vectors of the initial land data,superscript T The transpose is represented by the number,for sigmoid activation function, the neural network learns to obtain actual land data abnormal weightAnd initial land data anomaly weight +.>Is a linear transformation of feature vectors in the land dataset, initial land data bias term +.>Actual land data bias term A B E I is used for correcting abnormal quantity of land data; d (D) i ×D int Is phi i Weight dimension of (2); the output value of each abnormal adjustment coefficient is an input feature vector C i Abnormal weight phi of actual land data i And correcting the clustering algorithm through an adjustment coefficient by adjusting the adjustment characteristic calculated element by element of the initial land data abnormal weight B.
Preferably, in the above technical solution, the S2 includes:
s2-1, determining a feature vector analysis target in the full life cycle maintenance of the land, selecting a scoring function for analysis according to different targets for monitoring land utilization change,
s2-2, carrying out corresponding operation on initial change characteristics of the land data by using a clustering algorithm, and carrying out score function calculation on feature vectors in the newly acquired land data set;
because the land data relates to geographic coordinates, the real-time transition of the land data and the expected land data form corresponding offset in the corresponding land block, and the change score of the obtained land data is obtained through a score function;
score function of
Wherein sigma is the actual land change data weight,to anticipate land change data weight, S F The actual land data change area after the clustering algorithm F is calculated is S' F To anticipate land data change area, alpha i Actual offset coefficient, beta, for the ith element in the land dataset i Is the expected offset coefficient for the i-th element in the land dataset.
Preferably, in the above technical solution, the S3 includes:
s3-1, gradually adjusting parameters until optimal parameters are obtained, and determining parameter ranges: verification is performed and a land dataset is applied to verify the generalization performance of the model,
s3-2, according to the discrete error formed in the land data set of the actual land data change, the actual deviation coefficient alpha of the ith element in the land data set i The calculation is performed such that,
wherein c is the actual change value of the ith element in the land data set, S c And delta T is the actual offset of the land data for the actual construction value of the land data.
Preferably, in the above technical solution, the S3 includes:
s3-3, according to the discrete error formed in the land data set of the expected land data change, the expected deviation coefficient alpha of the ith element in the land data set i The calculation is performed such that,
wherein c is the expected change value of the ith element in the land data set, S' c For the expected construction value of the land data, Δt' is the expected offset of the land data.
Preferably, in the above technical solution, the S4 includes:
the integral acquired e of the clustering algorithm for acquiring the soil data to be monitored is the total cluster number, e J Is the aggregation after the calculation of the scoring functionThe class sample is within the recommended threshold value, and d is outside the recommended threshold value after being calculated by a scoring function J The definition of the accuracy and recall is set as follows:
in summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
the optimal use of the land can be evaluated through maintenance and management of the full life cycle of the land, the land resource is managed by analyzing the resource utilization condition of the land by utilizing the big data, the land resource is ensured to be reasonably utilized, and the land utilization efficiency is ensured to be maximized by formulating a land protection scheme in the process of analyzing the full life cycle of the land by utilizing the big data, and meanwhile, the land resource is ensured to be fully protected. By optimizing the land use plan by utilizing the big data technology, a decision maker can be helped to make a scientific and reasonable land use scheme, the land use efficiency is improved, and the sustainable development of economy, society and environment is promoted.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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The foregoing and/or additional aspects and advantages of the invention will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
fig. 1 is a general schematic of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
As shown in fig. 1, the invention discloses a land full life cycle maintenance system through big data, comprising the following steps:
s1, acquiring land planning construction basic data through a big data platform, collecting and classifying the basic data, and carrying out land data clustering correction through a clustering algorithm;
s2, performing abnormal adjustment on the land data clustering data after performing standardization according to the clustering correction of the land data set, and improving the data quality;
s3, improving the difference degree score of the actual change and the expected change of the land data through adjustment of the deviation coefficient in the land data scoring function;
and S4, evaluating the accuracy and the recall rate of the land data after the score is calculated, and performing corresponding land data maintenance operation according to the evaluation result.
The evaluation method can evaluate the expected use scene of the land more accurately. In practice, the performance of the model may be evaluated in combination with a number of parameters to ensure the accuracy and reliability of the evaluation results.
Based on big data technology, a land use environment early warning system is established, land use behaviors which can have adverse effects on the environment are monitored and early warned in real time, and countermeasures are taken in time.
By adopting a proper data analysis model, the influence of land utilization on the environment can be more comprehensively and deeply known, and the environmental risk of land utilization can be effectively monitored and controlled, so that the sustainable development of land utilization is realized.
Classifying land construction land areas, merging elements according to corresponding land use specifications, deleting land planning construction land to obtain planning construction land data; and carrying out classification index management according to the classified content of the planned construction land, and analyzing the corresponding relation between the vacant planned construction land area and the planned construction land feature classification.
Preferably, in the above technical solution, the S1 includes:
s1-1, preprocessing land planning construction data, classifying according to land supply time and supply use, and forming an initial data set for the land planning construction data; each land data element contains content of the land data at a respective time period, including: according to the land prediction rule, the method comprises the steps of planning construction land and public green land, and according to the actual use condition of the land, dividing into: idle land, actual construction land and unbatched first construction land;
s1-2, clustering operation is carried out according to the content of the initial data set, and the clustering algorithm of the land data in the historical time stage is that
Where i is the number of elements in the land dataset, k is the random point of the dataset, a i Is a cluster in the dataset, b i Is cluster a i μ is an abnormal adjustment coefficient, and F is a clustering algorithm expression character;
carrying out clustering evaluation on land data in a historical time stage through a clustering algorithm, and carrying out correction operation on the land data by using an abnormal adjustment coefficient; and the abnormal adjustment processing is completed, so that the quality of land data is improved, the problems of inaccurate classification and clustering result caused by poor quality of data are avoided, and the accuracy and stability of clustering are improved.
According to the land data change formed by the time continuation, preliminary land data calculation is needed through a clustering algorithm, so that data deviation and data abnormality can be found from the real-time change of the land data;
parameter selection: for the clustering algorithm, the abnormal adjustment coefficient of a proper interval needs to be selected, and the effect of optimizing and clustering the land data is achieved.
S1-3, for the elements with the number of i in the land data set, the abnormal adjustment coefficient mu E [0,1] is calculated by the following formula:
wherein the method comprises the steps ofSuperscript D for feature vector corresponding to ith element in land dataset I i Superscript D for feature vector number of actual land data int Superscript. For feature vector number of initial land data T The transpose is represented by the number,for sigmoid activation function, the neural network learns to obtain actual land data abnormal weightAnd initial land data anomaly weight +.>Is a linear transformation of feature vectors in the land dataset, initial land data bias term +.>Actual land data bias term A B E I is used for correcting abnormal quantity of land data; d (D) i ×D int Is phi i Weight dimension of (2); the output value of each abnormal adjustment coefficient is an input feature vector C i Abnormal weight phi of actual land data i And correcting the clustering algorithm by adjusting the adjusting characteristics calculated element by element of the abnormal weight B of the initial land data through adjusting coefficients;
the accuracy and stability of clustering are effectively improved through the optimization methods of data preprocessing, clustering feature extraction and parameter adjustment, and powerful support and guidance are provided for simulation and contrast analysis of land use schemes.
Preferably, in the above technical solution, the S2 includes:
s2-1, determining a feature vector analysis target in the full life cycle maintenance of the land, selecting a scoring function for analysis according to different targets for monitoring land utilization change,
s2-2, carrying out corresponding operation on initial change characteristics of the land data by using a clustering algorithm, and carrying out score function calculation on feature vectors in the newly acquired land data set;
because the land data relates to geographic coordinates, the real-time transition of the land data and the expected land data form corresponding offset in the corresponding land block, and the change score of the obtained land data is obtained through a score function;
score function of
Wherein sigma is the actual land change data weight,to anticipate land change data weight, S F The actual land data change area after the clustering algorithm F is calculated is S' F To anticipate land data change area, alpha i Actual offset coefficient, beta, for the ith element in the land dataset i For the expected offset coefficient of the i-th element in the land dataset,
since the assignment of parameters has important influence on the accuracy and stability of results when the calculation of the land data scores is carried out, the method is guided according to practical land data experience: when the parameters are selected, the importance of the corresponding weights and coefficients to the land score is known by the experience and knowledge of the field expert. According to the invention, the data preprocessing is performed according to the clustering algorithm of the initial land data, and the change condition of the full life cycle of the land is identified by performing score calculation on the actual planning data and the expected planning data, so that the comprehensive maintenance effect is realized on the land planning and supervision in the later stage.
It can be seen that the calculation of the scoring function requires analysis based on specific land data and expected land data characteristics. Better data analysis results are obtained through a parameter configuration method based on a model.
Preferably, in the above technical solution, the S3 includes:
s3-1, gradually adjusting parameters until optimal parameters are obtained, and determining parameter ranges: verification is performed and a land dataset is applied to verify the generalization performance of the model,
s3-2, according to the discrete error formed in the land data set of the actual land data change, the actual deviation coefficient alpha of the ith element in the land data set i The calculation is performed such that,
wherein c is the actual change value of the ith element in the land data set, S c For the actual construction value of the land data, deltat is the actual offset of the land data,
s3-3, according to the discrete error formed in the land data set of the expected land data change, the expected deviation coefficient alpha of the ith element in the land data set i The calculation is performed such that,
wherein c is the expected change value of the ith element in the land data set, S' c For the expected construction value of the land data, Δt' is the expected offset of the land data,
preferably, in the above technical solution, the S4 includes:
the evaluation of the model performance is a very important step in data analysis, and the data junction model performance is verified by calculating the accuracy and recall rate (recovery) of the evaluation index.
Because of a certain deviation between the actual change value and the expected change value of the land data, the section value detection needs to be carried out through the evaluation index, and the evaluation result is not considered beyond the set section value range.
Table The describe of city land data
Clustering algorithm for land data acquisition to be monitoredThe total acquired c of the method is the total cluster number, c J The clustering samples after the score function calculation are in the recommended threshold value, and d is outside the recommended threshold value after the score function calculation J The definition of the accuracy and recall is set as follows:
for the coincidence of recommended thresholds set by a scoring algorithm in clustered land data, detecting the coincidence of actual construction land data and expected land change data detected by a scoring function, wherein a calculation result is c in the recommended thresholds J The target detection evaluation index for land data maintenance is usually given a recommended threshold value of 65% or more, and if the calculated overlap ratio is smaller than the given threshold value, the detection result is considered to be that the land planning data is risky, and the clustered land data needs to be monitored with emphasis.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the spirit and scope of the invention as defined by the appended claims and their equivalents.
Claims (7)
1. A land full life cycle maintenance system through big data, comprising the steps of:
s1, acquiring land planning construction basic data through a big data platform, collecting and classifying the basic data, and carrying out land data clustering correction through a clustering algorithm;
s2, performing abnormal adjustment on the land data clustering data after performing standardization according to the clustering correction of the land data set, and improving the data quality;
s3, improving the difference degree score of the actual change and the expected change of the land data through adjustment of the deviation coefficient in the land data scoring function;
and S4, evaluating the accuracy and the recall rate of the land data after the score is calculated, and performing corresponding land data maintenance operation according to the evaluation result.
2. The land full life cycle maintenance system of claim 1, wherein said S1 comprises:
s1-1, preprocessing land planning construction data, classifying according to land supply time and supply use, and forming an initial data set for the land planning construction data;
s1-2, clustering operation is carried out according to the content of the initial data set, and the clustering algorithm of the land data in the historical time stage is that
Where i is the number of elements in the land dataset, k is the random point of the dataset, a i Is a cluster in the dataset, b i Is cluster a i μ is an abnormal adjustment coefficient, and F is a clustering algorithm expression character.
3. The land full life cycle maintenance system of claim 2, wherein S1 further comprises:
s1-3, for the elements with the number of i in the land data set, the abnormal adjustment coefficient mu E [0,1] is calculated by the following formula:
wherein the method comprises the steps ofSuperscript D for feature vector corresponding to ith element in land dataset I i Superscript D for feature vector number of actual land data int For the number of eigenvectors of the original land data, superscript. T indicates transpose, ++>For sigmoid activation function, neural network learns to obtain actual land data abnormal weight +.>And initial land data anomaly weight +.>Is a linear transformation of the eigenvectors in the land dataset,
initial land data bias termActual land data bias term A B E I is used for correcting abnormal quantity of land data; d (D) i ×D int Is phi i Weight dimension of (2);
the output value of each abnormal adjustment coefficient is an input feature vector C i Abnormal weight phi of actual land data i And correcting the clustering algorithm through an adjustment coefficient by adjusting the adjustment characteristic calculated element by element of the initial land data abnormal weight B.
4. The land full life cycle maintenance system of claim 1, wherein S2 comprises:
s2-1, determining a feature vector analysis target in the full life cycle maintenance of the land, selecting a scoring function for analysis according to different targets for monitoring land utilization change,
s2-2, carrying out corresponding operation on initial change characteristics of the land data by using a clustering algorithm, and carrying out score function calculation on feature vectors in the newly acquired land data set;
because the land data relates to geographic coordinates, the real-time transition of the land data and the expected land data form corresponding offset in the corresponding land block, and the change score of the obtained land data is obtained through a score function;
score function of
Wherein sigma is the actual land change data weight,to anticipate land change data weight, S F The actual land data change area after the clustering algorithm F is calculated is S' F To anticipate land data change area, alpha i Actual offset coefficient, beta, for the ith element in the land dataset i Is the expected offset coefficient for the i-th element in the land dataset.
5. The land full life cycle maintenance system of claim 1, wherein S3 comprises:
s3-1, gradually adjusting parameters until optimal parameters are obtained, and determining parameter ranges: verification is performed and a land dataset is applied to verify the generalization performance of the model,
s3-2, according to the discrete error formed in the land data set of the actual land data change, the actual deviation coefficient alpha of the ith element in the land data set i The calculation is performed such that,
wherein c is the actual change value of the ith element in the land data set, S c And delta T is the actual offset of the land data for the actual construction value of the land data.
6. The land full life cycle maintenance system of claim 1, wherein S3 comprises:
s3-3, according to the discrete error formed in the land data set of the expected land data change, the expected deviation coefficient alpha of the ith element in the land data set i The calculation is performed such that,
wherein c is the expected change value of the ith element in the land data set, S' c For the expected construction value of the land data, Δt' is the expected offset of the land data.
7. The land full life cycle maintenance system of claim 1, wherein S4 comprises:
the integral acquired e of the clustering algorithm for acquiring the soil data to be monitored is the total cluster number, e J The clustering samples after the score function calculation are in the recommended threshold value, and d is outside the recommended threshold value after the score function calculation J The definition of the accuracy and recall is set as follows:
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